Predicting the effect of cytochrome P450 inhibitors on substrate drugs: analysis of physiologically based pharmacokinetic modeling submissions to the US Food and Drug Administration.

PubMed

Wagner, Christian; Pan, Yuzhuo; Hsu, Vicky; Grillo, Joseph A; Zhang, Lei; Reynolds, Kellie S; Sinha, Vikram; Zhao, Ping

2015-01-01

The US Food and Drug Administration (FDA) has seen a recent increase in the application of physiologically based pharmacokinetic (PBPK) modeling towards assessing the potential of drug-drug interactions (DDI) in clinically relevant scenarios. To continue our assessment of such approaches, we evaluated the predictive performance of PBPK modeling in predicting cytochrome P450 (CYP)-mediated DDI. This evaluation was based on 15 substrate PBPK models submitted by nine sponsors between 2009 and 2013. For these 15 models, a total of 26 DDI studies (cases) with various CYP inhibitors were available. Sponsors developed the PBPK models, reportedly without considering clinical DDI data. Inhibitor models were either developed by sponsors or provided by PBPK software developers and applied with minimal or no modification. The metric for assessing predictive performance of the sponsors' PBPK approach was the R predicted/observed value (R predicted/observed = [predicted mean exposure ratio]/[observed mean exposure ratio], with the exposure ratio defined as [C max (maximum plasma concentration) or AUC (area under the plasma concentration-time curve) in the presence of CYP inhibition]/[C max or AUC in the absence of CYP inhibition]). In 81 % (21/26) and 77 % (20/26) of cases, respectively, the R predicted/observed values for AUC and C max ratios were within a pre-defined threshold of 1.25-fold of the observed data. For all cases, the R predicted/observed values for AUC and C max were within a 2-fold range. These results suggest that, based on the submissions to the FDA to date, there is a high degree of concordance between PBPK-predicted and observed effects of CYP inhibition, especially CYP3A-based, on the exposure of drug substrates.

From laptop to benchtop to bedside: Structure-based Drug Design on Protein Targets

PubMed Central

Chen, Lu; Morrow, John K.; Tran, Hoang T.; Phatak, Sharangdhar S.; Du-Cuny, Lei; Zhang, Shuxing

2013-01-01

As an important aspect of computer-aided drug design, structure-based drug design brought a new horizon to pharmaceutical development. This in silico method permeates all aspects of drug discovery today, including lead identification, lead optimization, ADMET prediction and drug repurposing. Structure-based drug design has resulted in fruitful successes drug discovery targeting protein-ligand and protein-protein interactions. Meanwhile, challenges, noted by low accuracy and combinatoric issues, may also cause failures. In this review, state-of-the-art techniques for protein modeling (e.g. structure prediction, modeling protein flexibility, etc.), hit identification/optimization (e.g. molecular docking, focused library design, fragment-based design, molecular dynamic, etc.), and polypharmacology design will be discussed. We will explore how structure-based techniques can facilitate the drug discovery process and interplay with other experimental approaches. PMID:22316152

Applications of physiologically based pharmacokinetic modeling for the optimization of anti-infective therapies.

PubMed

Moss, Darren Michael; Marzolini, Catia; Rajoli, Rajith K R; Siccardi, Marco

2015-01-01

The pharmacokinetic properties of anti-infective drugs are a determinant part of treatment success. Pathogen replication is inhibited if adequate drug levels are achieved in target sites, whereas excessive drug concentrations linked to toxicity are to be avoided. Anti-infective distribution can be predicted by integrating in vitro drug properties and mathematical descriptions of human anatomy in physiologically based pharmacokinetic models. This method reduces the need for animal and human studies and is used increasingly in drug development and simulation of clinical scenario such as, for instance, drug-drug interactions, dose optimization, novel formulations and pharmacokinetics in special populations. We have assessed the relevance of physiologically based pharmacokinetic modeling in the anti-infective research field, giving an overview of mechanisms involved in model design and have suggested strategies for future applications of physiologically based pharmacokinetic models. Physiologically based pharmacokinetic modeling provides a powerful tool in anti-infective optimization, and there is now no doubt that both industry and regulatory bodies have recognized the importance of this technology. It should be acknowledged, however, that major challenges remain to be addressed and that information detailing disease group physiology and anti-infective pharmacodynamics is required if a personalized medicine approach is to be achieved.

A reaction limited in vivo dissolution model for the study of drug absorption: Towards a new paradigm for the biopharmaceutic classification of drugs.

PubMed

Macheras, Panos; Iliadis, Athanassios; Melagraki, Georgia

2018-05-30

The aim of this work is to develop a gastrointestinal (GI) drug absorption model based on a reaction limited model of dissolution and consider its impact on the biopharmaceutic classification of drugs. Estimates for the fraction of dose absorbed as a function of dose, solubility, reaction/dissolution rate constant and the stoichiometry of drug-GI fluids reaction/dissolution were derived by numerical solution of the model equations. The undissolved drug dose and the reaction/dissolution rate constant drive the dissolution rate and determine the extent of absorption when high-constant drug permeability throughout the gastrointestinal tract is assumed. Dose is an important element of drug-GI fluids reaction/dissolution while solubility exclusively acts as an upper limit for drug concentrations in the lumen. The 3D plots of fraction of dose absorbed as a function of dose and reaction/dissolution rate constant for highly soluble and low soluble drugs for different "stoichiometries" (0.7, 1.0, 2.0) of the drug-reaction/dissolution with the GI fluids revealed that high extent of absorption was found assuming high drug- reaction/dissolution rate constant and high drug solubility. The model equations were used to simulate in vivo supersaturation and precipitation phenomena. The model developed provides the theoretical basis for the interpretation of the extent of drug's absorption on the basis of the parameters associated with the drug-GI fluids reaction/dissolution. A new paradigm emerges for the biopharmaceutic classification of drugs, namely, a model independent biopharmaceutic classification scheme of four drug categories based on either the fulfillment or not of the current dissolution criteria and the high or low % drug metabolism. Copyright © 2018. Published by Elsevier B.V.

Pharmacokinetic-Pharmacodynamic Modeling in Pediatric Drug Development, and the Importance of Standardized Scaling of Clearance.

PubMed

Germovsek, Eva; Barker, Charlotte I S; Sharland, Mike; Standing, Joseph F

2018-04-19

Pharmacokinetic/pharmacodynamic (PKPD) modeling is important in the design and conduct of clinical pharmacology research in children. During drug development, PKPD modeling and simulation should underpin rational trial design and facilitate extrapolation to investigate efficacy and safety. The application of PKPD modeling to optimize dosing recommendations and therapeutic drug monitoring is also increasing, and PKPD model-based dose individualization will become a core feature of personalized medicine. Following extensive progress on pediatric PK modeling, a greater emphasis now needs to be placed on PD modeling to understand age-related changes in drug effects. This paper discusses the principles of PKPD modeling in the context of pediatric drug development, summarizing how important PK parameters, such as clearance (CL), are scaled with size and age, and highlights a standardized method for CL scaling in children. One standard scaling method would facilitate comparison of PK parameters across multiple studies, thus increasing the utility of existing PK models and facilitating optimal design of new studies.

In silico and in vitro methods to optimize the performance of experimental gastroretentive floating mini-tablets.

PubMed

Eberle, Veronika A; Häring, Armella; Schoelkopf, Joachim; Gane, Patrick A C; Huwyler, Jörg; Puchkov, Maxim

2016-01-01

Development of floating drug delivery systems (FDDS) is challenging. To facilitate this task, an evaluation method was proposed, which allows for a combined investigation of drug release and flotation. It was the aim of the study to use functionalized calcium carbonate (FCC)-based lipophilic mini-tablet formulations as a model system to design FDDS with a floating behavior characterized by no floating lag time, prolonged flotation and loss of floating capability after complete drug release. Release of the model drug caffeine from the mini-tablets was assessed in vitro by a custom-built stomach model. A cellular automata-based model was used to simulate tablet dissolution. Based on the in silico data, floating forces were calculated and analyzed as a function of caffeine release. Two floating behaviors were identified for mini-tablets: linear decrease of the floating force and maintaining of the floating capability until complete caffeine release. An optimal mini-tablet formulation with desired drug release time and floating behavior was developed and tested. A classification system for a range of varied floating behavior of FDDS was proposed. The FCC-based mini-tablets had an ideal floating behavior: duration of flotation is defined and floating capability decreases after completion of drug release.

Probabilistic modeling of percutaneous absorption for risk-based exposure assessments and transdermal drug delivery.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ho, Clifford Kuofei

Chemical transport through human skin can play a significant role in human exposure to toxic chemicals in the workplace, as well as to chemical/biological warfare agents in the battlefield. The viability of transdermal drug delivery also relies on chemical transport processes through the skin. Models of percutaneous absorption are needed for risk-based exposure assessments and drug-delivery analyses, but previous mechanistic models have been largely deterministic. A probabilistic, transient, three-phase model of percutaneous absorption of chemicals has been developed to assess the relative importance of uncertain parameters and processes that may be important to risk-based assessments. Penetration routes through the skinmore » that were modeled include the following: (1) intercellular diffusion through the multiphase stratum corneum; (2) aqueous-phase diffusion through sweat ducts; and (3) oil-phase diffusion through hair follicles. Uncertainty distributions were developed for the model parameters, and a Monte Carlo analysis was performed to simulate probability distributions of mass fluxes through each of the routes. Sensitivity analyses using stepwise linear regression were also performed to identify model parameters that were most important to the simulated mass fluxes at different times. This probabilistic analysis of percutaneous absorption (PAPA) method has been developed to improve risk-based exposure assessments and transdermal drug-delivery analyses, where parameters and processes can be highly uncertain.« less

In silico modeling to predict drug-induced phospholipidosis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Choi, Sydney S.; Kim, Jae S.; Valerio, Luis G., E-mail: luis.valerio@fda.hhs.gov

2013-06-01

Drug-induced phospholipidosis (DIPL) is a preclinical finding during pharmaceutical drug development that has implications on the course of drug development and regulatory safety review. A principal characteristic of drugs inducing DIPL is known to be a cationic amphiphilic structure. This provides evidence for a structure-based explanation and opportunity to analyze properties and structures of drugs with the histopathologic findings for DIPL. In previous work from the FDA, in silico quantitative structure–activity relationship (QSAR) modeling using machine learning approaches has shown promise with a large dataset of drugs but included unconfirmed data as well. In this study, we report the constructionmore » and validation of a battery of complementary in silico QSAR models using the FDA's updated database on phospholipidosis, new algorithms and predictive technologies, and in particular, we address high performance with a high-confidence dataset. The results of our modeling for DIPL include rigorous external validation tests showing 80–81% concordance. Furthermore, the predictive performance characteristics include models with high sensitivity and specificity, in most cases above ≥ 80% leading to desired high negative and positive predictivity. These models are intended to be utilized for regulatory toxicology applied science needs in screening new drugs for DIPL. - Highlights: • New in silico models for predicting drug-induced phospholipidosis (DIPL) are described. • The training set data in the models is derived from the FDA's phospholipidosis database. • We find excellent predictivity values of the models based on external validation. • The models can support drug screening and regulatory decision-making on DIPL.« less

Physiologically-Based Pharmacokinetic Modeling of Macitentan: Prediction of Drug-Drug Interactions.

PubMed

de Kanter, Ruben; Sidharta, Patricia N; Delahaye, Stéphane; Gnerre, Carmela; Segrestaa, Jerome; Buchmann, Stephan; Kohl, Christopher; Treiber, Alexander

2016-03-01

Macitentan is a novel dual endothelin receptor antagonist for the treatment of pulmonary arterial hypertension (PAH). It is metabolized by cytochrome P450 (CYP) enzymes, mainly CYP3A4, to its active metabolite ACT-132577. A physiological-based pharmacokinetic (PBPK) model was developed by combining observations from clinical studies and physicochemical parameters as well as absorption, distribution, metabolism and excretion parameters determined in vitro. The model predicted the observed pharmacokinetics of macitentan and its active metabolite ACT-132577 after single and multiple dosing. It performed well in recovering the observed effect of the CYP3A4 inhibitors ketoconazole and cyclosporine, and the CYP3A4 inducer rifampicin, as well as in predicting interactions with S-warfarin and sildenafil. The model was robust enough to allow prospective predictions of macitentan-drug combinations not studied, including an alternative dosing regimen of ketoconazole and nine other CYP3A4-interacting drugs. Among these were the HIV drugs ritonavir and saquinavir, which were included because HIV infection is a known risk factor for the development of PAH. This example of the application of PBPK modeling to predict drug-drug interactions was used to support the labeling of macitentan (Opsumit).

In Silico Augmentation of the Drug Development Pipeline: Examples from the study of Acute Inflammation.

PubMed

An, Gary; Bartels, John; Vodovotz, Yoram

2011-03-01

The clinical translation of promising basic biomedical findings, whether derived from reductionist studies in academic laboratories or as the product of extensive high-throughput and -content screens in the biotechnology and pharmaceutical industries, has reached a period of stagnation in which ever higher research and development costs are yielding ever fewer new drugs. Systems biology and computational modeling have been touted as potential avenues by which to break through this logjam. However, few mechanistic computational approaches are utilized in a manner that is fully cognizant of the inherent clinical realities in which the drugs developed through this ostensibly rational process will be ultimately used. In this article, we present a Translational Systems Biology approach to inflammation. This approach is based on the use of mechanistic computational modeling centered on inherent clinical applicability, namely that a unified suite of models can be applied to generate in silico clinical trials, individualized computational models as tools for personalized medicine, and rational drug and device design based on disease mechanism.

Using the [beta][subscript 2]-Adrenoceptor for Structure-Based Drug Design

ERIC Educational Resources Information Center

Manallack, David T.; Chalmers, David K.; Yuriev, Elizabeth

2010-01-01

The topics of molecular modeling and drug design are studied in a medicinal chemistry course. The recently reported structures of several G protein-coupled receptors (GPCR) with bound ligands have been used to develop a simple computer-based experiment employing molecular-modeling software. Knowledge of the specific interactions between a ligand…

Discovery of Boolean metabolic networks: integer linear programming based approach.

PubMed

Qiu, Yushan; Jiang, Hao; Ching, Wai-Ki; Cheng, Xiaoqing

2018-04-11

Traditional drug discovery methods focused on the efficacy of drugs rather than their toxicity. However, toxicity and/or lack of efficacy are produced when unintended targets are affected in metabolic networks. Thus, identification of biological targets which can be manipulated to produce the desired effect with minimum side-effects has become an important and challenging topic. Efficient computational methods are required to identify the drug targets while incurring minimal side-effects. In this paper, we propose a graph-based computational damage model that summarizes the impact of enzymes on compounds in metabolic networks. An efficient method based on Integer Linear Programming formalism is then developed to identify the optimal enzyme-combination so as to minimize the side-effects. The identified target enzymes for known successful drugs are then verified by comparing the results with those in the existing literature. Side-effects reduction plays a crucial role in the study of drug development. A graph-based computational damage model is proposed and the theoretical analysis states the captured problem is NP-completeness. The proposed approaches can therefore contribute to the discovery of drug targets. Our developed software is available at " http://hkumath.hku.hk/~wkc/APBC2018-metabolic-network.zip ".

Design of an expert system for the development and formulation of push-pull osmotic pump tablets containing poorly water-soluble drugs.

PubMed

Zhang, Zhi-hong; Dong, Hong-ye; Peng, Bo; Liu, Hong-fei; Li, Chun-lei; Liang, Min; Pan, Wei-san

2011-05-30

The purpose of this article was to build an expert system for the development and formulation of push-pull osmotic pump tablets (PPOP). Hundreds of PPOP formulations were studied according to different poorly water-soluble drugs and pharmaceutical acceptable excipients. The knowledge base including database and rule base was built based on the reported results of hundreds of PPOP formulations containing different poorly water-soluble drugs and pharmaceutical excipients and the experiences available from other researchers. The prediction model of release behavior was built using back propagation (BP) neural network, which is good at nonlinear mapping and learning function. Formulation design model was established based on the prediction model of release behavior, which was the nucleus of the inference engine. Finally, the expert system program was constructed by VB.NET associating with SQL Server. Expert system is one of the most popular aspects in artificial intelligence. To date there is no expert system available for the formulation of controlled release dosage forms yet. Moreover, osmotic pump technology (OPT) is gradually getting consummate all over the world. It is meaningful to apply expert system on OPT. Famotidine, a water insoluble drug was chosen as the model drug to validate the applicability of the developed expert system. Copyright © 2011 Elsevier B.V. All rights reserved.

Mechanism - based translational pharmacokinetic - pharmacodynamic model to predict intraocular pressure lowering effect of drugs in patients with glaucoma or ocular hypertension.

PubMed

Durairaj, Chandrasekar; Shen, Jie; Cherukury, Madhu

2014-08-01

To develop a mechanism based translational pharmacokinetic-pharmacodynamic (PKPD) model in preclinical species and to predict the intraocular pressure (IOP) following drug treatment in patients with glaucoma or ocular hypertension (OHT). Baseline diurnal IOP of normotensive albino rabbits, beagle dogs and patients with glaucoma or OHT was collected from literature. In addition, diurnal IOP of patients treated with brimonidine or Xalatan® were also obtained from literature. Healthy normotensive New Zealand rabbits were topically treated with a single drop of 0.15% brimonidine tartrate and normotensive beagle dogs were treated with a single drop of Xalatan®. At pre-determined time intervals, IOP was measured and aqueous humor samples were obtained from a satellite group of animals. Population based PKPD modeling was performed to describe the IOP data and the chosen model was extended to predict the IOP in patients. Baseline IOP clearly depicts a distinctive circadian rhythm in rabbits versus human. An aqueous humor dynamics based physiological model was developed to describe the baseline diurnal IOP across species. Model was extended to incorporate the effect of drug administration on baseline IOP in rabbits and dogs. The translational model with substituted human aqueous humor dynamic parameters predicted IOP in patients following drug treatment. A physiology based mechanistic PKPD model was developed to describe the baseline and post-treatment IOP in animals. The preclinical PKPD model was successfully translated to predict IOP in patients with glaucoma or OHT and can be applied in assisting dose and treatment selection and predicting outcome of glaucoma clinical trials.

Model-Informed Drug Development for Ixazomib, an Oral Proteasome Inhibitor.

PubMed

Gupta, Neeraj; Hanley, Michael J; Diderichsen, Paul M; Yang, Huyuan; Ke, Alice; Teng, Zhaoyang; Labotka, Richard; Berg, Deborah; Patel, Chirag; Liu, Guohui; van de Velde, Helgi; Venkatakrishnan, Karthik

2018-02-15

Model-informed drug development (MIDD) was central to the development of the oral proteasome inhibitor ixazomib, facilitating internal decisions (switch from body surface area (BSA)-based to fixed dosing, inclusive phase III trials, portfolio prioritization of ixazomib-based combinations, phase III dose for maintenance treatment), regulatory review (model-informed QT analysis, benefit-risk of 4 mg dose), and product labeling (absolute bioavailability and intrinsic/extrinsic factors). This review discusses the impact of MIDD in enabling patient-centric therapeutic optimization during the development of ixazomib. © 2017 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

Effect of ingested lipids on drug dissolution and release with concurrent digestion: a modeling approach

PubMed Central

Buyukozturk, Fulden; Di Maio, Selena; Budil, David E.; Carrier, Rebecca L.

2014-01-01

Purpose To mechanistically study and model the effect of lipids, either from food or self-emulsifying drug delivery systems (SEDDS), on drug transport in the intestinal lumen. Methods Simultaneous lipid digestion, dissolution/release, and drug partitioning were experimentally studied and modeled for two dosing scenarios: solid drug with a food-associated lipid (soybean oil) and drug solubilized in a model SEDDS (soybean oil and Tween 80 at 1:1 ratio). Rate constants for digestion, permeability of emulsion droplets, and partition coefficients in micellar and oil phases were measured, and used to numerically solve the developed model. Results Strong influence of lipid digestion on drug release from SEDDS and solid drug dissolution into food-associated lipid emulsion were observed and predicted by the developed model. 90 minutes after introduction of SEDDS, there was 9% and 70% drug release in the absence and presence of digestion, respectively. However, overall drug dissolution in the presence of food-associated lipids occurred over a longer period than without digestion. Conclusion A systems-based mechanistic model incorporating simultaneous dynamic processes occurring upon dosing of drug with lipids enabled prediction of aqueous drug concentration profile. This model, once incorporated with a pharmacokinetic model considering processes of drug absorption and drug lymphatic transport in the presence of lipids, could be highly useful for quantitative prediction of impact of lipids on bioavailability of drugs. PMID:24234918

Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome

PubMed Central

Low, Yen S; Caster, Ola; Bergvall, Tomas; Fourches, Denis; Zang, Xiaoling; Norén, G Niklas; Rusyn, Ivan; Edwards, Ralph

2016-01-01

Objective Quantitative Structure-Activity Relationship (QSAR) models can predict adverse drug reactions (ADRs), and thus provide early warnings of potential hazards. Timely identification of potential safety concerns could protect patients and aid early diagnosis of ADRs among the exposed. Our objective was to determine whether global spontaneous reporting patterns might allow chemical substructures associated with Stevens-Johnson Syndrome (SJS) to be identified and utilized for ADR prediction by QSAR models. Materials and Methods Using a reference set of 364 drugs having positive or negative reporting correlations with SJS in the VigiBase global repository of individual case safety reports (Uppsala Monitoring Center, Uppsala, Sweden), chemical descriptors were computed from drug molecular structures. Random Forest and Support Vector Machines methods were used to develop QSAR models, which were validated by external 5-fold cross validation. Models were employed for virtual screening of DrugBank to predict SJS actives and inactives, which were corroborated using knowledge bases like VigiBase, ChemoText, and MicroMedex (Truven Health Analytics Inc, Ann Arbor, Michigan). Results We developed QSAR models that could accurately predict if drugs were associated with SJS (area under the curve of 75%–81%). Our 10 most active and inactive predictions were substantiated by SJS reports (or lack thereof) in the literature. Discussion Interpretation of QSAR models in terms of significant chemical descriptors suggested novel SJS structural alerts. Conclusions We have demonstrated that QSAR models can accurately identify SJS active and inactive drugs. Requiring chemical structures only, QSAR models provide effective computational means to flag potentially harmful drugs for subsequent targeted surveillance and pharmacoepidemiologic investigations. PMID:26499102

General Pharmacokinetic Model for Topically Administered Ocular Drug Dosage Forms.

PubMed

Deng, Feng; Ranta, Veli-Pekka; Kidron, Heidi; Urtti, Arto

2016-11-01

In ocular drug development, an early estimate of drug behavior before any in vivo experiments is important. The pharmacokinetics (PK) and bioavailability depend not only on active compound and excipients but also on physicochemical properties of the ocular drug formulation. We propose to utilize PK modelling to predict how drug and formulational properties affect drug bioavailability and pharmacokinetics. A physiologically relevant PK model based on the rabbit eye was built to simulate the effect of formulation and physicochemical properties on PK of pilocarpine solutions and fluorometholone suspensions. The model consists of four compartments: solid and dissolved drug in tear fluid, drug in corneal epithelium and aqueous humor. Parameter values and in vivo PK data in rabbits were taken from published literature. The model predicted the pilocarpine and fluorometholone concentrations in the corneal epithelium and aqueous humor with a reasonable accuracy for many different formulations. The model includes a graphical user interface that enables the user to modify parameters easily and thus simulate various formulations. The model is suitable for the development of ophthalmic formulations and the planning of bioequivalence studies.

Scientific white paper on concentration-QTc modeling.

PubMed

Garnett, Christine; Bonate, Peter L; Dang, Qianyu; Ferber, Georg; Huang, Dalong; Liu, Jiang; Mehrotra, Devan; Riley, Steve; Sager, Philip; Tornoe, Christoffer; Wang, Yaning

2018-06-01

The International Council for Harmonisation revised the E14 guideline through the questions and answers process to allow concentration-QTc (C-QTc) modeling to be used as the primary analysis for assessing the QTc interval prolongation risk of new drugs. A well-designed and conducted QTc assessment based on C-QTc modeling in early phase 1 studies can be an alternative approach to a thorough QT study for some drugs to reliably exclude clinically relevant QTc effects. This white paper provides recommendations on how to plan and conduct a definitive QTc assessment of a drug using C-QTc modeling in early phase clinical pharmacology and thorough QT studies. Topics included are: important study design features in a phase 1 study; modeling objectives and approach; exploratory plots; the pre-specified linear mixed effects model; general principles for model development and evaluation; and expectations for modeling analysis plans and reports. The recommendations are based on current best modeling practices, scientific literature and personal experiences of the authors. These recommendations are expected to evolve as their implementation during drug development provides additional data and with advances in analytical methodology.

Exposure–response model for sibutramine and placebo: suggestion for application to long-term weight-control drug development

PubMed Central

Han, Seunghoon; Jeon, Sangil; Hong, Taegon; Lee, Jongtae; Bae, Soo Hyeon; Park, Wan-su; Park, Gab-jin; Youn, Sunil; Jang, Doo Yeon; Kim, Kyung-Soo; Yim, Dong-Seok

2015-01-01

No wholly successful weight-control drugs have been developed to date, despite the tremendous demand. We present an exposure–response model of sibutramine mesylate that can be applied during clinical development of other weight-control drugs. Additionally, we provide a model-based evaluation of sibutramine efficacy. Data from a double-blind, randomized, placebo-controlled, multicenter study were used (N=120). Subjects in the treatment arm were initially given 8.37 mg sibutramine base daily, and those who lost <2 kg after 4 weeks’ treatment were escalated to 12.55 mg. The duration of treatment was 24 weeks. Drug concentration and body weight were measured predose and at 4 weeks, 8 weeks, and 24 weeks after treatment initiation. Exposure and response to sibutramine, including the placebo effect, were modeled using NONMEM 7.2. An asymptotic model approaching the final body weight was chosen to describe the time course of weight loss. Extent of weight loss was described successfully using a sigmoidal exposure–response relationship of the drug with a constant placebo effect in each individual. The placebo effect was influenced by subjects’ sex and baseline body mass index. Maximal weight loss was predicted to occur around 1 year after treatment initiation. The difference in mean weight loss between the sibutramine (daily 12.55 mg) and placebo groups was predicted to be 4.5% in a simulation of 1 year of treatment, with considerable overlap of prediction intervals. Our exposure–response model, which included the placebo effect, is the first example of a quantitative model that can be used to predict the efficacy of weight-control drugs. Similar approaches can help decision-making during clinical development of novel weight-loss drugs. PMID:26392753

Exposure-response model for sibutramine and placebo: suggestion for application to long-term weight-control drug development.

PubMed

Han, Seunghoon; Jeon, Sangil; Hong, Taegon; Lee, Jongtae; Bae, Soo Hyeon; Park, Wan-su; Park, Gab-jin; Youn, Sunil; Jang, Doo Yeon; Kim, Kyung-Soo; Yim, Dong-Seok

2015-01-01

No wholly successful weight-control drugs have been developed to date, despite the tremendous demand. We present an exposure-response model of sibutramine mesylate that can be applied during clinical development of other weight-control drugs. Additionally, we provide a model-based evaluation of sibutramine efficacy. Data from a double-blind, randomized, placebo-controlled, multicenter study were used (N=120). Subjects in the treatment arm were initially given 8.37 mg sibutramine base daily, and those who lost <2 kg after 4 weeks' treatment were escalated to 12.55 mg. The duration of treatment was 24 weeks. Drug concentration and body weight were measured predose and at 4 weeks, 8 weeks, and 24 weeks after treatment initiation. Exposure and response to sibutramine, including the placebo effect, were modeled using NONMEM 7.2. An asymptotic model approaching the final body weight was chosen to describe the time course of weight loss. Extent of weight loss was described successfully using a sigmoidal exposure-response relationship of the drug with a constant placebo effect in each individual. The placebo effect was influenced by subjects' sex and baseline body mass index. Maximal weight loss was predicted to occur around 1 year after treatment initiation. The difference in mean weight loss between the sibutramine (daily 12.55 mg) and placebo groups was predicted to be 4.5% in a simulation of 1 year of treatment, with considerable overlap of prediction intervals. Our exposure-response model, which included the placebo effect, is the first example of a quantitative model that can be used to predict the efficacy of weight-control drugs. Similar approaches can help decision-making during clinical development of novel weight-loss drugs.

Rapid analysis of pharmaceutical drugs using LIBS coupled with multivariate analysis.

PubMed

Tiwari, P K; Awasthi, S; Kumar, R; Anand, R K; Rai, P K; Rai, A K

2018-02-01

Type 2 diabetes drug tablets containing voglibose having dose strengths of 0.2 and 0.3 mg of various brands have been examined, using laser-induced breakdown spectroscopy (LIBS) technique. The statistical methods such as the principal component analysis (PCA) and the partial least square regression analysis (PLSR) have been employed on LIBS spectral data for classifying and developing the calibration models of drug samples. We have developed the ratio-based calibration model applying PLSR in which relative spectral intensity ratios H/C, H/N and O/N are used. Further, the developed model has been employed to predict the relative concentration of element in unknown drug samples. The experiment has been performed in air and argon atmosphere, respectively, and the obtained results have been compared. The present model provides rapid spectroscopic method for drug analysis with high statistical significance for online control and measurement process in a wide variety of pharmaceutical industrial applications.

Diverse ways of perturbing the human arachidonic acid metabolic network to control inflammation.

PubMed

Meng, Hu; Liu, Ying; Lai, Luhua

2015-08-18

Inflammation and other common disorders including diabetes, cardiovascular disease, and cancer are often the result of several molecular abnormalities and are not likely to be resolved by a traditional single-target drug discovery approach. Though inflammation is a normal bodily reaction, uncontrolled and misdirected inflammation can cause inflammatory diseases such as rheumatoid arthritis and asthma. Nonsteroidal anti-inflammatory drugs including aspirin, ibuprofen, naproxen, or celecoxib are commonly used to relieve aches and pains, but often these drugs have undesirable and sometimes even fatal side effects. To facilitate safer and more effective anti-inflammatory drug discovery, a balanced treatment strategy should be developed at the biological network level. In this Account, we focus on our recent progress in modeling the inflammation-related arachidonic acid (AA) metabolic network and subsequent multiple drug design. We first constructed a mathematical model of inflammation based on experimental data and then applied the model to simulate the effects of commonly used anti-inflammatory drugs. Our results indicated that the model correctly reproduced the established bleeding and cardiovascular side effects. Multitarget optimal intervention (MTOI), a Monte Carlo simulated annealing based computational scheme, was then developed to identify key targets and optimal solutions for controlling inflammation. A number of optimal multitarget strategies were discovered that were both effective and safe and had minimal associated side effects. Experimental studies were performed to evaluate these multitarget control solutions further using different combinations of inhibitors to perturb the network. Consequently, simultaneous control of cyclooxygenase-1 and -2 and leukotriene A4 hydrolase, as well as 5-lipoxygenase and prostaglandin E2 synthase were found to be among the best solutions. A single compound that can bind multiple targets presents advantages including low risk of drug-drug interactions and robustness regarding concentration fluctuations. Thus, we developed strategies for multiple-target drug design and successfully discovered several series of multiple-target inhibitors. Optimal solutions for a disease network often involve mild but simultaneous interventions of multiple targets, which is in accord with the philosophy of traditional Chinese medicine (TCM). To this end, our AA network model can aptly explain TCM anti-inflammatory herbs and formulas at the molecular level. We also aimed to identify activators for several enzymes that appeared to have increased activity based on MTOI outcomes. Strategies were then developed to predict potential allosteric sites and to discover enzyme activators based on our hypothesis that combined treatment with the projected activators and inhibitors could balance different AA network pathways, control inflammation, and reduce associated adverse effects. Our work demonstrates that the integration of network modeling and drug discovery can provide novel solutions for disease control, which also calls for new developments in drug design concepts and methodologies. With the rapid accumulation of quantitative data and knowledge of the molecular networks of disease, we can expect an increase in the development and use of quantitative disease models to facilitate efficient and safe drug discovery.

A physiome interoperability roadmap for personalized drug development

PubMed Central

2016-01-01

The goal of developing therapies and dosage regimes for characterized subgroups of the general population can be facilitated by the use of simulation models able to incorporate information about inter-individual variability in drug disposition (pharmacokinetics), toxicity and response effect (pharmacodynamics). Such observed variability can have multiple causes at various scales, ranging from gross anatomical differences to differences in genome sequence. Relevant data for many of these aspects, particularly related to molecular assays (known as ‘-omics’), are available in online resources, but identification and assignment to appropriate model variables and parameters is a significant bottleneck in the model development process. Through its efforts to standardize annotation with consequent increase in data usability, the human physiome project has a vital role in improving productivity in model development and, thus, the development of personalized therapy regimes. Here, we review the current status of personalized medicine in clinical practice, outline some of the challenges that must be overcome in order to expand its applicability, and discuss the relevance of personalized medicine to the more widespread challenges being faced in drug discovery and development. We then review some of (i) the key data resources available for use in model development and (ii) the potential areas where advances made within the physiome modelling community could contribute to physiologically based pharmacokinetic and physiologically based pharmacokinetic/pharmacodynamic modelling in support of personalized drug development. We conclude by proposing a roadmap to further guide the physiome community in its on-going efforts to improve data usability, and integration with modelling efforts in the support of personalized medicine development. PMID:27051513

Physiologically Based Absorption Modeling to Explore the Impact of Food and Gastric pH Changes on the Pharmacokinetics of Alectinib.

PubMed

Parrott, Neil J; Yu, Li J; Takano, Ryusuke; Nakamura, Mikiko; Morcos, Peter N

2016-11-01

Alectinib, a lipophilic, basic, anaplastic lymphoma kinase (ALK) inhibitor with very low aqueous solubility, has received Food and Drug Administration-accelerated approval for the treatment of patients with ALK+ non-small-cell lung cancer. This paper describes the application of physiologically based absorption modeling during clinical development to predict and understand the impact of food and gastric pH changes on alectinib absorption. The GastroPlus ™ software was used to develop an absorption model integrating in vitro and in silico data on drug substance properties. Oral pharmacokinetics was simulated by linking the absorption model to a disposition model fit to pharmacokinetic data obtained after an intravenous infusion. Simulations were compared to clinical data from a food effect study and a drug-drug interaction study with esomeprazole, a gastric acid-reducing agent. Prospective predictions of a positive food effect and negligible impact of gastric pH elevation were confirmed with clinical data, although the exact magnitude of the food effect could not be predicted with confidence. After optimization of the absorption model with clinical food effect data, a refined model was further applied to derive recommendations on the timing of dose administration with respect to a meal. The application of biopharmaceutical absorption modeling is an area with great potential to further streamline late stage drug development and with impact on regulatory questions.

In silico prediction of drug-induced myelotoxicity by using Naïve Bayes method.

PubMed

Zhang, Hui; Yu, Peng; Zhang, Teng-Guo; Kang, Yan-Li; Zhao, Xiao; Li, Yuan-Yuan; He, Jia-Hui; Zhang, Ji

2015-11-01

Drug-induced myelotoxicity usually leads to decrease the production of platelets, red cells, and white cells. Thus, early identification and characterization of myelotoxicity hazard in drug development is very necessary. The purpose of this investigation was to develop a prediction model of drug-induced myelotoxicity by using a Naïve Bayes classifier. For comparison, other prediction models based on support vector machine and single-hidden-layer feed-forward neural network  methods were also established. Among all the prediction models, the Naïve Bayes classification model showed the best prediction performance, which offered an average overall prediction accuracy of [Formula: see text] for the training set and [Formula: see text] for the external test set. The significant contributions of this study are that we first developed a Naïve Bayes classification model of drug-induced myelotoxicity adverse effect using a larger scale dataset, which could be employed for the prediction of drug-induced myelotoxicity. In addition, several important molecular descriptors and substructures of myelotoxic compounds have been identified, which should be taken into consideration in the design of new candidate compounds to produce safer and more effective drugs, ultimately reducing the attrition rate in later stages of drug development.

Computer-aided design of liposomal drugs: In silico prediction and experimental validation of drug candidates for liposomal remote loading.

PubMed

Cern, Ahuva; Barenholz, Yechezkel; Tropsha, Alexander; Goldblum, Amiram

2014-01-10

Previously we have developed and statistically validated Quantitative Structure Property Relationship (QSPR) models that correlate drugs' structural, physical and chemical properties as well as experimental conditions with the relative efficiency of remote loading of drugs into liposomes (Cern et al., J. Control. Release 160 (2012) 147-157). Herein, these models have been used to virtually screen a large drug database to identify novel candidate molecules for liposomal drug delivery. Computational hits were considered for experimental validation based on their predicted remote loading efficiency as well as additional considerations such as availability, recommended dose and relevance to the disease. Three compounds were selected for experimental testing which were confirmed to be correctly classified by our previously reported QSPR models developed with Iterative Stochastic Elimination (ISE) and k-Nearest Neighbors (kNN) approaches. In addition, 10 new molecules with known liposome remote loading efficiency that were not used by us in QSPR model development were identified in the published literature and employed as an additional model validation set. The external accuracy of the models was found to be as high as 82% or 92%, depending on the model. This study presents the first successful application of QSPR models for the computer-model-driven design of liposomal drugs. © 2013.

DNAPrint Genomics, Inc.: better drugs for segmented markets.

PubMed

Frudakis, Tony

2008-02-01

The postgenome era promises more efficient drug-development cycles and medications targeted to compatible populations, resulting in improved outcomes, fewer drug-company failures, less litigation, fewer recalls and a refurbished image of 'pharma' in the mind of the customer. DNAPrint was founded to help precipitate these changes. Since 1999, we have developed and optimized novel methods for assessing patient response proclivities as individuals but also as constituents of populations, and we have introduced a computational platform for modeling drug biology. We expect these tools will allow us to maximize the efficiency of our clinical trials and, more importantly, ensure better postmarket performance parameters. With these tools, we are now carefully engineering select drug-development projects in an attempt to illustrate the viability of a novel drug-development model - one based on the application of intelligence and new technologies for superior drug performance in segmented markets.

How Preclinical Models Evolved to Resemble the Diagnostic Criteria of Drug Addiction.

PubMed

Belin-Rauscent, Aude; Fouyssac, Maxime; Bonci, Antonello; Belin, David

2016-01-01

Drug addiction is a complex neuropsychiatric disorder that affects a subset of the individuals who take drugs. It is characterized by maladaptive drug-seeking habits that are maintained despite adverse consequences and intense drug craving. The pathophysiology and etiology of addiction is only partially understood despite extensive research because of the gap between current preclinical models of addiction and the clinical criteria of the disorder. This review presents a brief overview, based on selected methodologies, of how behavioral models have evolved over the last 50 years to the development of recent preclinical models of addiction that more closely mimic diagnostic criteria of addiction. It is hoped that these new models will increase our understanding of the complex neurobiological mechanisms whereby some individuals switch from controlled drug use to compulsive drug-seeking habits and relapse to these maladaptive habits. Additionally, by paving the way to bridge the gap that exists between biobehavioral research on addiction and the human situation, these models may provide new perspectives for the development of novel and effective therapeutic strategies for drug addiction. Published by Elsevier Inc.

How preclinical models evolved to resemble the diagnostic criteria of drug addiction

PubMed Central

Belin-Rauscent, Aude; Fouyssac, Maxime; Bonci, Antonello; Belin, David

2015-01-01

Drug addiction is a complex neuropsychiatric disorder that affects a subset of the individuals who take drugs. It is characterized by maladaptive drug-seeking habits that are maintained despite adverse consequences and intense drug craving. Despite extensive research, the pathophysiology and aetiology of addiction is only partially understood, due to the gap between current preclinical models of addiction and the clinical criteria of the disorder. Here we give a brief overview, based on selected methodologies, of how behavioral models have evolved over the last fifty years to the development of recent preclinical models of addiction that more closely mimic diagnostic criteria of addiction. These new models will hopefully increase our understanding of the complex neurobiological mechanisms whereby some individuals switch from controlled drug use to compulsive drug-seeking habits and relapse to these maladaptive habits. Additional, by paving the way to bridge the gap that exists between biobehavioral research on addiction and the human situation, these models may provide new perspectives for the development of novel and effective therapeutic strategies for drug addiction. PMID:25747744

Slammin' and Snortin': A Community Based Model of Intervention in Drug Cases.

ERIC Educational Resources Information Center

Boggess, Patricia A.; Reiman, Dean

This report describes a service model developed for the Division of Children and Family Services in Wenatchee, Washington that attempts to address the needs of families impacted by substance abuse. The service area is rural and has an overwhelming drug problem. Caseload trends provide evidence for a large number of drug-related referrals and an…

Zero-inflated Poisson model based likelihood ratio test for drug safety signal detection.

PubMed

Huang, Lan; Zheng, Dan; Zalkikar, Jyoti; Tiwari, Ram

2017-02-01

In recent decades, numerous methods have been developed for data mining of large drug safety databases, such as Food and Drug Administration's (FDA's) Adverse Event Reporting System, where data matrices are formed by drugs such as columns and adverse events as rows. Often, a large number of cells in these data matrices have zero cell counts and some of them are "true zeros" indicating that the drug-adverse event pairs cannot occur, and these zero counts are distinguished from the other zero counts that are modeled zero counts and simply indicate that the drug-adverse event pairs have not occurred yet or have not been reported yet. In this paper, a zero-inflated Poisson model based likelihood ratio test method is proposed to identify drug-adverse event pairs that have disproportionately high reporting rates, which are also called signals. The maximum likelihood estimates of the model parameters of zero-inflated Poisson model based likelihood ratio test are obtained using the expectation and maximization algorithm. The zero-inflated Poisson model based likelihood ratio test is also modified to handle the stratified analyses for binary and categorical covariates (e.g. gender and age) in the data. The proposed zero-inflated Poisson model based likelihood ratio test method is shown to asymptotically control the type I error and false discovery rate, and its finite sample performance for signal detection is evaluated through a simulation study. The simulation results show that the zero-inflated Poisson model based likelihood ratio test method performs similar to Poisson model based likelihood ratio test method when the estimated percentage of true zeros in the database is small. Both the zero-inflated Poisson model based likelihood ratio test and likelihood ratio test methods are applied to six selected drugs, from the 2006 to 2011 Adverse Event Reporting System database, with varying percentages of observed zero-count cells.

Animal models of drug addiction.

PubMed

García Pardo, María Pilar; Roger Sánchez, Concepción; De la Rubia Ortí, José Enrique; Aguilar Calpe, María Asunción

2017-09-29

The development of animal models of drug reward and addiction is an essential factor for progress in understanding the biological basis of this disorder and for the identification of new therapeutic targets. Depending on the component of reward to be studied, one type of animal model or another may be used. There are models of reinforcement based on the primary hedonic effect produced by the consumption of the addictive substance, such as the self-administration (SA) and intracranial self-stimulation (ICSS) paradigms, and there are models based on the component of reward related to associative learning and cognitive ability to make predictions about obtaining reward in the future, such as the conditioned place preference (CPP) paradigm. In recent years these models have incorporated methodological modifications to study extinction, reinstatement and reconsolidation processes, or to model specific aspects of addictive behavior such as motivation to consume drugs, compulsive consumption or drug seeking under punishment situations. There are also models that link different reinforcement components or model voluntary motivation to consume (two-bottle choice, or drinking in the dark tests). In short, innovations in these models allow progress in scientific knowledge regarding the different aspects that lead individuals to consume a drug and develop compulsive consumption, providing a target for future treatments of addiction.

A Physiologically Based Pharmacokinetic Model for Pregnant Women to Predict the Pharmacokinetics of Drugs Metabolized Via Several Enzymatic Pathways.

PubMed

Dallmann, André; Ince, Ibrahim; Coboeken, Katrin; Eissing, Thomas; Hempel, Georg

2017-09-18

Physiologically based pharmacokinetic modeling is considered a valuable tool for predicting pharmacokinetic changes in pregnancy to subsequently guide in-vivo pharmacokinetic trials in pregnant women. The objective of this study was to extend and verify a previously developed physiologically based pharmacokinetic model for pregnant women for the prediction of pharmacokinetics of drugs metabolized via several cytochrome P450 enzymes. Quantitative information on gestation-specific changes in enzyme activity available in the literature was incorporated in a pregnancy physiologically based pharmacokinetic model and the pharmacokinetics of eight drugs metabolized via one or multiple cytochrome P450 enzymes was predicted. The tested drugs were caffeine, midazolam, nifedipine, metoprolol, ondansetron, granisetron, diazepam, and metronidazole. Pharmacokinetic predictions were evaluated by comparison with in-vivo pharmacokinetic data obtained from the literature. The pregnancy physiologically based pharmacokinetic model successfully predicted the pharmacokinetics of all tested drugs. The observed pregnancy-induced pharmacokinetic changes were qualitatively and quantitatively reasonably well predicted for all drugs. Ninety-seven percent of the mean plasma concentrations predicted in pregnant women fell within a twofold error range and 63% within a 1.25-fold error range. For all drugs, the predicted area under the concentration-time curve was within a 1.25-fold error range. The presented pregnancy physiologically based pharmacokinetic model can quantitatively predict the pharmacokinetics of drugs that are metabolized via one or multiple cytochrome P450 enzymes by integrating prior knowledge of the pregnancy-related effect on these enzymes. This pregnancy physiologically based pharmacokinetic model may thus be used to identify potential exposure changes in pregnant women a priori and to eventually support informed decision making when clinical trials are designed in this special population.

Biophysical interactions with model lipid membranes: applications in drug discovery and drug delivery

PubMed Central

Peetla, Chiranjeevi; Stine, Andrew; Labhasetwar, Vinod

2009-01-01

The transport of drugs or drug delivery systems across the cell membrane is a complex biological process, often difficult to understand because of its dynamic nature. In this regard, model lipid membranes, which mimic many aspects of cell-membrane lipids, have been very useful in helping investigators to discern the roles of lipids in cellular interactions. One can use drug-lipid interactions to predict pharmacokinetic properties of drugs, such as their transport, biodistribution, accumulation, and hence efficacy. These interactions can also be used to study the mechanisms of transport, based on the structure and hydrophilicity/hydrophobicity of drug molecules. In recent years, model lipid membranes have also been explored to understand their mechanisms of interactions with peptides, polymers, and nanocarriers. These interaction studies can be used to design and develop efficient drug delivery systems. Changes in the lipid composition of cells and tissue in certain disease conditions may alter biophysical interactions, which could be explored to develop target-specific drugs and drug delivery systems. In this review, we discuss different model membranes, drug-lipid interactions and their significance, studies of model membrane interactions with nanocarriers, and how biophysical interaction studies with lipid model membranes could play an important role in drug discovery and drug delivery. PMID:19432455

Food, gastrointestinal pH, and models of oral drug absorption.

PubMed

Abuhelwa, Ahmad Y; Williams, Desmond B; Upton, Richard N; Foster, David J R

2017-03-01

This article reviews the major physiological and physicochemical principles of the effect of food and gastrointestinal (GI) pH on the absorption and bioavailability of oral drugs, and the various absorption models that are used to describe/predict oral drug absorption. The rate and extent of oral drug absorption is determined by a complex interaction between a drug's physicochemical properties, GI physiologic factors, and the nature of the formulation administered. GI pH is an important factor that can markedly affect oral drug absorption and bioavailability as it may have significant influence on drug dissolution & solubility, drug release, drug stability, and intestinal permeability. Different regions of the GI tract have different drug absorptive properties. Thus, the transit time in each GI region and its variability between subjects may contribute to the variability in the rate and/or extent of drug absorption. Food-drug interactions can result in delayed, decreased, increased, and sometimes un-altered drug absorption. Food effects on oral absorption can be achieved by direct and indirect mechanisms. Various models have been proposed to describe oral absorption ranging from empirical models to the more sophisticated "mechanism-based" models. Through understanding of the physicochemical and physiological rate-limiting factors affecting oral absorption, modellers can implement simplified population-based modelling approaches that are less complex than whole-body physiologically-based models but still capture the essential elements in a physiological way and hence will be more suited for population modelling of large clinical data sets. It will also help formulation scientists to better predict formulation performance and to develop formulations that maximize oral bioavailability. Copyright © 2016 Elsevier B.V. All rights reserved.

Time-programmable drug dosing allows the manipulation, suppression and reversal of antibiotic drug resistance in vitro

NASA Astrophysics Data System (ADS)

Yoshida, Mari; Reyes, Sabrina Galiñanes; Tsuda, Soichiro; Horinouchi, Takaaki; Furusawa, Chikara; Cronin, Leroy

2017-06-01

Multi-drug strategies have been attempted to prolong the efficacy of existing antibiotics, but with limited success. Here we show that the evolution of multi-drug-resistant Escherichia coli can be manipulated in vitro by administering pairs of antibiotics and switching between them in ON/OFF manner. Using a multiplexed cell culture system, we find that switching between certain combinations of antibiotics completely suppresses the development of resistance to one of the antibiotics. Using this data, we develop a simple deterministic model, which allows us to predict the fate of multi-drug evolution in this system. Furthermore, we are able to reverse established drug resistance based on the model prediction by modulating antibiotic selection stresses. Our results support the idea that the development of antibiotic resistance may be potentially controlled via continuous switching of drugs.

Systematic drug safety evaluation based on public genomic expression (Connectivity Map) data: Myocardial and infectious adverse reactions as application cases

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Kejian, E-mail: kejian.wang.bio@gmail.com; Weng, Zuquan; Sun, Liya

Adverse drug reaction (ADR) is of great importance to both regulatory agencies and the pharmaceutical industry. Various techniques, such as quantitative structure–activity relationship (QSAR) and animal toxicology, are widely used to identify potential risks during the preclinical stage of drug development. Despite these efforts, drugs with safety liabilities can still pass through safety checkpoints and enter the market. This situation raises the concern that conventional chemical structure analysis and phenotypic screening are not sufficient to avoid all clinical adverse events. Genomic expression data following in vitro drug treatments characterize drug actions and thus have become widely used in drug repositioning. Inmore » the present study, we explored prediction of ADRs based on the drug-induced gene-expression profiles from cultured human cells in the Connectivity Map (CMap) database. The results showed that drugs inducing comparable ADRs generally lead to similar CMap expression profiles. Based on such ADR-gene expression association, we established prediction models for various ADRs, including severe myocardial and infectious events. Drugs with FDA boxed warnings of safety liability were effectively identified. We therefore suggest that drug-induced gene expression change, in combination with effective computational methods, may provide a new dimension of information to facilitate systematic drug safety evaluation. - Highlights: • Drugs causing common toxicity lead to similar in vitro gene expression changes. • We built a model to predict drug toxicity with drug-specific expression profiles. • Drugs with FDA black box warnings were effectively identified by our model. • In vitro assay can detect severe toxicity in the early stage of drug development.« less

Emerging In Vitro Liver Technologies for Drug Metabolism and Inter-Organ Interactions

PubMed Central

Bale, Shyam Sundhar; Moore, Laura

2016-01-01

In vitro liver models provide essential information for evaluating drug metabolism, metabolite formation, and hepatotoxicity. Interfacing liver models with other organ models could provide insights into the desirable as well as unintended systemic side effects of therapeutic agents and their metabolites. Such information is invaluable for drug screening processes particularly in the context of secondary organ toxicity. While interfacing of liver models with other organ models has been achieved, platforms that effectively provide human-relevant precise information are needed. In this concise review, we discuss the current state-of-the-art of liver-based multiorgan cell culture platforms primarily from a drug and metabolite perspective, and highlight the importance of media-to-cell ratio in interfacing liver models with other organ models. In addition, we briefly discuss issues related to development of optimal liver models that include recent advances in hepatic cell lines, stem cells, and challenges associated with primary hepatocyte-based liver models. Liver-based multiorgan models that achieve physiologically relevant coupling of different organ models can have a broad impact in evaluating drug efficacy and toxicity, as well as mechanistic investigation of human-relevant disease conditions. PMID:27049038

In Silico Augmentation of the Drug Development Pipeline: Examples from the study of Acute Inflammation

PubMed Central

An, Gary; Bartels, John; Vodovotz, Yoram

2011-01-01

The clinical translation of promising basic biomedical findings, whether derived from reductionist studies in academic laboratories or as the product of extensive high-throughput and –content screens in the biotechnology and pharmaceutical industries, has reached a period of stagnation in which ever higher research and development costs are yielding ever fewer new drugs. Systems biology and computational modeling have been touted as potential avenues by which to break through this logjam. However, few mechanistic computational approaches are utilized in a manner that is fully cognizant of the inherent clinical realities in which the drugs developed through this ostensibly rational process will be ultimately used. In this article, we present a Translational Systems Biology approach to inflammation. This approach is based on the use of mechanistic computational modeling centered on inherent clinical applicability, namely that a unified suite of models can be applied to generate in silico clinical trials, individualized computational models as tools for personalized medicine, and rational drug and device design based on disease mechanism. PMID:21552346

Quantitative structure-activity relationship: promising advances in drug discovery platforms.

PubMed

Wang, Tao; Wu, Mian-Bin; Lin, Jian-Ping; Yang, Li-Rong

2015-12-01

Quantitative structure-activity relationship (QSAR) modeling is one of the most popular computer-aided tools employed in medicinal chemistry for drug discovery and lead optimization. It is especially powerful in the absence of 3D structures of specific drug targets. QSAR methods have been shown to draw public attention since they were first introduced. In this review, the authors provide a brief discussion of the basic principles of QSAR, model development and model validation. They also highlight the current applications of QSAR in different fields, particularly in virtual screening, rational drug design and multi-target QSAR. Finally, in view of recent controversies, the authors detail the challenges faced by QSAR modeling and the relevant solutions. The aim of this review is to show how QSAR modeling can be applied in novel drug discovery, design and lead optimization. QSAR should intentionally be used as a powerful tool for fragment-based drug design platforms in the field of drug discovery and design. Although there have been an increasing number of experimentally determined protein structures in recent years, a great number of protein structures cannot be easily obtained (i.e., membrane transport proteins and G-protein coupled receptors). Fragment-based drug discovery, such as QSAR, could be applied further and have a significant role in dealing with these problems. Moreover, along with the development of computer software and hardware, it is believed that QSAR will be increasingly important.

Network-based drug discovery by integrating systems biology and computational technologies

PubMed Central

Leung, Elaine L.; Cao, Zhi-Wei; Jiang, Zhi-Hong; Zhou, Hua

2013-01-01

Network-based intervention has been a trend of curing systemic diseases, but it relies on regimen optimization and valid multi-target actions of the drugs. The complex multi-component nature of medicinal herbs may serve as valuable resources for network-based multi-target drug discovery due to its potential treatment effects by synergy. Recently, robustness of multiple systems biology platforms shows powerful to uncover molecular mechanisms and connections between the drugs and their targeting dynamic network. However, optimization methods of drug combination are insufficient, owning to lacking of tighter integration across multiple ‘-omics’ databases. The newly developed algorithm- or network-based computational models can tightly integrate ‘-omics’ databases and optimize combinational regimens of drug development, which encourage using medicinal herbs to develop into new wave of network-based multi-target drugs. However, challenges on further integration across the databases of medicinal herbs with multiple system biology platforms for multi-target drug optimization remain to the uncertain reliability of individual data sets, width and depth and degree of standardization of herbal medicine. Standardization of the methodology and terminology of multiple system biology and herbal database would facilitate the integration. Enhance public accessible databases and the number of research using system biology platform on herbal medicine would be helpful. Further integration across various ‘-omics’ platforms and computational tools would accelerate development of network-based drug discovery and network medicine. PMID:22877768

Revisiting lab-on-a-chip technology for drug discovery.

PubMed

Neuži, Pavel; Giselbrecht, Stefan; Länge, Kerstin; Huang, Tony Jun; Manz, Andreas

2012-08-01

The field of microfluidics or lab-on-a-chip technology aims to improve and extend the possibilities of bioassays, cell biology and biomedical research based on the idea of miniaturization. Microfluidic systems allow more accurate modelling of physiological situations for both fundamental research and drug development, and enable systematic high-volume testing for various aspects of drug discovery. Microfluidic systems are in development that not only model biological environments but also physically mimic biological tissues and organs; such 'organs on a chip' could have an important role in expediting early stages of drug discovery and help reduce reliance on animal testing. This Review highlights the latest lab-on-a-chip technologies for drug discovery and discusses the potential for future developments in this field.

Absorption and Clearance of Pharmaceutical Aerosols in the Human Nose: Development of a CFD Model.

PubMed

Rygg, Alex; Longest, P Worth

2016-10-01

The objective of this study was to develop a computational fluid dynamics (CFD) model to predict the deposition, dissolution, clearance, and absorption of pharmaceutical particles in the human nasal cavity. A three-dimensional nasal cavity geometry was converted to a surface-based model, providing an anatomically-accurate domain for the simulations. Particle deposition data from a commercial nasal spray product was mapped onto the surface model, and a mucus velocity field was calculated and validated with in vivo nasal clearance rates. A submodel for the dissolution of deposited particles was developed and validated based on comparisons to existing in vitro data for multiple pharmaceutical products. A parametric study was then performed to assess sensitivity of epithelial drug uptake to model conditions and assumptions. The particle displacement distance (depth) in the mucus layer had a modest effect on overall drug absorption, while the mucociliary clearance rate was found to be primarily responsible for drug uptake over the timescale of nasal clearance for the corticosteroid mometasone furoate (MF). The model revealed that drug deposition in the nasal vestibule (NV) could slowly be transported into the main passage (MP) and then absorbed through connection of the liquid layer in the NV and MP regions. As a result, high intersubject variability in cumulative uptake was predicted, depending on the length of time the NV dose was left undisturbed without blowing or wiping the nose. This study has developed, for the first time, a complete CFD model of nasal aerosol delivery from the point of spray formation through absorption at the respiratory epithelial surface. For the development and assessment of nasal aerosol products, this CFD-based in silico model provides a new option to complement existing in vitro nasal cast studies of deposition and in vivo imaging experiments of clearance.

Development of soy lecithin based novel self-assembled emulsion hydrogels.

PubMed

Singh, Vinay K; Pandey, Preeti M; Agarwal, Tarun; Kumar, Dilip; Banerjee, Indranil; Anis, Arfat; Pal, Kunal

2015-03-01

The current study reports the development and characterization of soy lecithin based novel self-assembled emulsion hydrogels. Sesame oil was used as the representative oil phase. Emulsion gels were formed when the concentration of soy lecithin was >40% w/w. Metronidazole was used as the model drug for the drug release and the antimicrobial tests. Microscopic study showed the apolar dispersed phase in an aqueous continuum phase, suggesting the formation of emulsion hydrogels. FTIR study indicated the formation of intermolecular hydrogen bonding, whereas, the XRD study indicated predominantly amorphous nature of the emulsion gels. Composition dependent mechanical and drug release properties of the emulsion gels were observed. In-depth analyses of the mechanical studies were done using Ostwald-de Waele power-law, Kohlrausch and Weichert models, whereas, the drug release profiles were modeled using Korsmeyer-Peppas and Peppas-Sahlin models. The mechanical analyses indicated viscoelastic nature of the emulsion gels. The release of the drug from the emulsion gels was diffusion mediated. The drug loaded emulsion gels showed good antimicrobial activity. The biocompatibility test using HaCaT cells (human keratinocytes) suggested biocompatibility of the emulsion gels. Copyright © 2015 Elsevier Ltd. All rights reserved.

Development of a Drug-Response Modeling Framework to Identify Cell Line Derived Translational Biomarkers That Can Predict Treatment Outcome to Erlotinib or Sorafenib.

PubMed

Li, Bin; Shin, Hyunjin; Gulbekyan, Georgy; Pustovalova, Olga; Nikolsky, Yuri; Hope, Andrew; Bessarabova, Marina; Schu, Matthew; Kolpakova-Hart, Elona; Merberg, David; Dorner, Andrew; Trepicchio, William L

2015-01-01

Development of drug responsive biomarkers from pre-clinical data is a critical step in drug discovery, as it enables patient stratification in clinical trial design. Such translational biomarkers can be validated in early clinical trial phases and utilized as a patient inclusion parameter in later stage trials. Here we present a study on building accurate and selective drug sensitivity models for Erlotinib or Sorafenib from pre-clinical in vitro data, followed by validation of individual models on corresponding treatment arms from patient data generated in the BATTLE clinical trial. A Partial Least Squares Regression (PLSR) based modeling framework was designed and implemented, using a special splitting strategy and canonical pathways to capture robust information for model building. Erlotinib and Sorafenib predictive models could be used to identify a sub-group of patients that respond better to the corresponding treatment, and these models are specific to the corresponding drugs. The model derived signature genes reflect each drug's known mechanism of action. Also, the models predict each drug's potential cancer indications consistent with clinical trial results from a selection of globally normalized GEO expression datasets.

Quantitative systems toxicology

PubMed Central

Bloomingdale, Peter; Housand, Conrad; Apgar, Joshua F.; Millard, Bjorn L.; Mager, Donald E.; Burke, John M.; Shah, Dhaval K.

2017-01-01

The overarching goal of modern drug development is to optimize therapeutic benefits while minimizing adverse effects. However, inadequate efficacy and safety concerns remain to be the major causes of drug attrition in clinical development. For the past 80 years, toxicity testing has consisted of evaluating the adverse effects of drugs in animals to predict human health risks. The U.S. Environmental Protection Agency recognized the need to develop innovative toxicity testing strategies and asked the National Research Council to develop a long-range vision and strategy for toxicity testing in the 21st century. The vision aims to reduce the use of animals and drug development costs through the integration of computational modeling and in vitro experimental methods that evaluates the perturbation of toxicity-related pathways. Towards this vision, collaborative quantitative systems pharmacology and toxicology modeling endeavors (QSP/QST) have been initiated amongst numerous organizations worldwide. In this article, we discuss how quantitative structure-activity relationship (QSAR), network-based, and pharmacokinetic/pharmacodynamic modeling approaches can be integrated into the framework of QST models. Additionally, we review the application of QST models to predict cardiotoxicity and hepatotoxicity of drugs throughout their development. Cell and organ specific QST models are likely to become an essential component of modern toxicity testing, and provides a solid foundation towards determining individualized therapeutic windows to improve patient safety. PMID:29308440

Cost-effectiveness analysis of microdose clinical trials in drug development.

PubMed

Yamane, Naoe; Igarashi, Ataru; Kusama, Makiko; Maeda, Kazuya; Ikeda, Toshihiko; Sugiyama, Yuichi

2013-01-01

Microdose (MD) clinical trials have been introduced to obtain human pharmacokinetic data early in drug development. Here we assessed the cost-effectiveness of microdose integrated drug development in a hypothetical model, as there was no such quantitative research that weighed the additional effectiveness against the additional time and/or cost. First, we calculated the cost and effectiveness (i.e., success rate) of 3 types of MD integrated drug development strategies: liquid chromatography-tandem mass spectrometry, accelerator mass spectrometry, and positron emission tomography. Then, we analyzed the cost-effectiveness of 9 hypothetical scenarios where 100 drug candidates entering into a non-clinical toxicity study were selected by different methods as the conventional scenario without MD. In the base-case, where 70 drug candidates were selected without MD and 30 selected evenly by one of the three MD methods, incremental cost-effectiveness ratio per one additional drug approved was JPY 12.7 billion (US$ 0.159 billion), whereas the average cost-effectiveness ratio of the conventional strategy was JPY 24.4 billion, which we set as a threshold. Integrating MD in the conventional drug development was cost-effective in this model. This quantitative analytical model which allows various modifications according to each company's conditions, would be helpful for guiding decisions early in clinical development.

Animal models of addiction

PubMed Central

Spanagel, Rainer

2017-01-01

In recent years, animal models in psychiatric research have been criticized for their limited translational value to the clinical situation. Failures in clinical trials have thus often been attributed to the lack of predictive power of preclinical animal models. Here, I argue that animal models of voluntary drug intake—under nonoperant and operant conditions—and addiction models based on the Diagnostic and Statistical Manual of Mental Disorders are crucial and informative tools for the identification of pathological mechanisms, target identification, and drug development. These models provide excellent face validity, and it is assumed that the neurochemical and neuroanatomical substrates involved in drug-intake behavior are similar in laboratory rodents and humans. Consequently, animal models of drug consumption and addiction provide predictive validity. This predictive power is best illustrated in alcohol research, in which three approved medications—acamprosate, naltrexone, and nalmefene—were developed by means of animal models and then successfully translated into the clinical situation. PMID:29302222

The relationship between perceived discrimination and high-risk social ties among illicit drug users in New York City, 2006-2009.

PubMed

Crawford, Natalie D; Ford, Chandra; Galea, Sandro; Latkin, Carl; Jones, Kandice C; Fuller, Crystal M

2013-01-01

Discrimination can influence risk of disease by promoting unhealthy behaviors (e.g., smoking, alcohol use). Whether it influences the formation of high-risk social ties that facilitate HIV transmission is unclear. Using cross-sectional data from a cohort of illicit drug users, this study examined the association between discrimination based on race, drug use and prior incarceration and risky sex and drug ties. Negative binomial regression models were performed. Participants who reported discrimination based on race and drug use had significantly more sex and drug-using ties. But, after accounting for both racial and drug use discrimination, only racial discrimination was associated with increased sex, drug-using, and injecting ties. Drug users who experience discrimination and subsequently develop more sex and drug-using ties, increase their risk of contracting HIV. Future longitudinal studies illuminating the pathways linking discrimination and social network development may guide intervention development and identify drug-using subpopulations at high risk for disease transmission.

Development of a Physiologically-Based Pharmacokinetic Model of the Rat Central Nervous System

PubMed Central

Badhan, Raj K. Singh; Chenel, Marylore; Penny, Jeffrey I.

2014-01-01

Central nervous system (CNS) drug disposition is dictated by a drug’s physicochemical properties and its ability to permeate physiological barriers. The blood–brain barrier (BBB), blood-cerebrospinal fluid barrier and centrally located drug transporter proteins influence drug disposition within the central nervous system. Attainment of adequate brain-to-plasma and cerebrospinal fluid-to-plasma partitioning is important in determining the efficacy of centrally acting therapeutics. We have developed a physiologically-based pharmacokinetic model of the rat CNS which incorporates brain interstitial fluid (ISF), choroidal epithelial and total cerebrospinal fluid (CSF) compartments and accurately predicts CNS pharmacokinetics. The model yielded reasonable predictions of unbound brain-to-plasma partition ratio (Kpuu,brain) and CSF:plasma ratio (CSF:Plasmau) using a series of in vitro permeability and unbound fraction parameters. When using in vitro permeability data obtained from L-mdr1a cells to estimate rat in vivo permeability, the model successfully predicted, to within 4-fold, Kpuu,brain and CSF:Plasmau for 81.5% of compounds simulated. The model presented allows for simultaneous simulation and analysis of both brain biophase and CSF to accurately predict CNS pharmacokinetics from preclinical drug parameters routinely available during discovery and development pathways. PMID:24647103

Computer-aided design of liposomal drugs: in silico prediction and experimental validation of drug candidates for liposomal remote loading

PubMed Central

Cern, Ahuva; Barenholz, Yechezkel; Tropsha, Alexander; Goldblum, Amiram

2014-01-01

Previously we have developed and statistically validated Quantitative Structure Property Relationship (QSPR) models that correlate drugs’ structural, physical and chemical properties as well as experimental conditions with the relative efficiency of remote loading of drugs into liposomes (Cern et al, Journal of Controlled Release, 160(2012) 14–157). Herein, these models have been used to virtually screen a large drug database to identify novel candidate molecules for liposomal drug delivery. Computational hits were considered for experimental validation based on their predicted remote loading efficiency as well as additional considerations such as availability, recommended dose and relevance to the disease. Three compounds were selected for experimental testing which were confirmed to be correctly classified by our previously reported QSPR models developed with Iterative Stochastic Elimination (ISE) and k-nearest neighbors (kNN) approaches. In addition, 10 new molecules with known liposome remote loading efficiency that were not used in QSPR model development were identified in the published literature and employed as an additional model validation set. The external accuracy of the models was found to be as high as 82% or 92%, depending on the model. This study presents the first successful application of QSPR models for the computer-model-driven design of liposomal drugs. PMID:24184343

Evaluation of the whole body physiologically based pharmacokinetic (WB-PBPK) modeling of drugs.

PubMed

Munir, Anum; Azam, Shumaila; Fazal, Sahar; Bhatti, A I

2018-08-14

The Physiologically based pharmacokinetic (PBPK) modeling is a supporting tool in drug discovery and improvement. Simulations produced by these models help to save time and aids in examining the effects of different variables on the pharmacokinetics of drugs. For this purpose, Sheila and Peters suggested a PBPK model capable of performing simulations to study a given drug absorption. There is a need to extend this model to the whole body entailing all another process like distribution, metabolism, and elimination, besides absorption. The aim of this scientific study is to hypothesize a WB-PBPK model through integrating absorption, distribution, metabolism, and elimination processes with the existing PBPK model.Absorption, distribution, metabolism, and elimination models are designed, integrated with PBPK model and validated. For validation purposes, clinical records of few drugs are collected from the literature. The developed WB-PBPK model is affirmed by comparing the simulations produced by the model against the searched clinical data. . It is proposed that the WB-PBPK model may be used in pharmaceutical industries to create of the pharmacokinetic profiles of drug candidates for better outcomes, as it is advance PBPK model and creates comprehensive PK profiles for drug ADME in concentration-time plots. Copyright © 2018 Elsevier Ltd. All rights reserved.

Cochlear pharmacokinetics with local inner ear drug delivery using a three-dimensional finite-element computer model.

PubMed

Plontke, Stefan K; Siedow, Norbert; Wegener, Raimund; Zenner, Hans-Peter; Salt, Alec N

2007-01-01

Cochlear fluid pharmacokinetics can be better represented by three-dimensional (3D) finite-element simulations of drug dispersal. Local drug deliveries to the round window membrane are increasingly being used to treat inner ear disorders. Crucial to the development of safe therapies is knowledge of drug distribution in the inner ear with different delivery methods. Computer simulations allow application protocols and drug delivery systems to be evaluated, and may permit animal studies to be extrapolated to the larger cochlea of the human. A finite-element 3D model of the cochlea was constructed based on geometric dimensions of the guinea pig cochlea. Drug propagation along and between compartments was described by passive diffusion. To demonstrate the potential value of the model, methylprednisolone distribution in the cochlea was calculated for two clinically relevant application protocols using pharmacokinetic parameters derived from a prior one-dimensional (1D) model. In addition, a simplified geometry was used to compare results from 3D with 1D simulations. For the simplified geometry, calculated concentration profiles with distance were in excellent agreement between the 1D and the 3D models. Different drug delivery strategies produce very different concentration time courses, peak concentrations and basal-apical concentration gradients of drug. In addition, 3D computations demonstrate the existence of substantial gradients across the scalae in the basal turn. The 3D model clearly shows the presence of drug gradients across the basal scalae of guinea pigs, demonstrating the necessity of a 3D approach to predict drug movements across and between scalae with larger cross-sectional areas, such as the human, with accuracy. This is the first model to incorporate the volume of the spiral ligament and to calculate diffusion through this structure. Further development of the 3D model will have to incorporate a more accurate geometry of the entire inner ear and incorporate more of the specific processes that contribute to drug removal from the inner ear fluids. Appropriate computer models may assist in both drug and drug delivery system design and can thus accelerate the development of a rationale-based local drug delivery to the inner ear and its successful establishment in clinical practice. Copyright 2007 S. Karger AG, Basel.

Cochlear Pharmacokinetics with Local Inner Ear Drug Delivery Using a Three-Dimensional Finite-Element Computer Model

PubMed Central

Plontke, Stefan K.; Siedow, Norbert; Wegener, Raimund; Zenner, Hans-Peter; Salt, Alec N.

2006-01-01

Hypothesis: Cochlear fluid pharmacokinetics can be better represented by three-dimensional (3D) finite-element simulations of drug dispersal. Background: Local drug deliveries to the round window membrane are increasingly being used to treat inner ear disorders. Crucial to the development of safe therapies is knowledge of drug distribution in the inner ear with different delivery methods. Computer simulations allow application protocols and drug delivery systems to be evaluated, and may permit animal studies to be extrapolated to the larger cochlea of the human. Methods: A finite-element 3D model of the cochlea was constructed based on geometric dimensions of the guinea pig cochlea. Drug propagation along and between compartments was described by passive diffusion. To demonstrate the potential value of the model, methylprednisolone distribution in the cochlea was calculated for two clinically relevant application protocols using pharmacokinetic parameters derived from a prior one-dimensional (1D) model. In addition, a simplified geometry was used to compare results from 3D with 1D simulations. Results: For the simplified geometry, calculated concentration profiles with distance were in excellent agreement between the 1D and the 3D models. Different drug delivery strategies produce very different concentration time courses, peak concentrations and basal-apical concentration gradients of drug. In addition, 3D computations demonstrate the existence of substantial gradients across the scalae in the basal turn. Conclusion: The 3D model clearly shows the presence of drug gradients across the basal scalae of guinea pigs, demonstrating the necessity of a 3D approach to predict drug movements across and between scalae with larger cross-sectional areas, such as the human, with accuracy. This is the first model to incorporate the volume of the spiral ligament and to calculate diffusion through this structure. Further development of the 3D model will have to incorporate a more accurate geometry of the entire inner ear and incorporate more of the specific processes that contribute to drug removal from the inner ear fluids. Appropriate computer models may assist in both drug and drug delivery system design and can thus accelerate the development of a rationale-based local drug delivery to the inner ear and its successful establishment in clinical practice. PMID:17119332

A combined ligand-based and target-based drug design approach for G-protein coupled receptors: application to salvinorin A, a selective kappa opioid receptor agonist

NASA Astrophysics Data System (ADS)

Singh, Nidhi; Chevé, Gwénaël; Ferguson, David M.; McCurdy, Christopher R.

2006-08-01

Combined ligand-based and target-based drug design approaches provide a synergistic advantage over either method individually. Therefore, we set out to develop a powerful virtual screening model to identify novel molecular scaffolds as potential leads for the human KOP (hKOP) receptor employing a combined approach. Utilizing a set of recently reported derivatives of salvinorin A, a structurally unique KOP receptor agonist, a pharmacophore model was developed that consisted of two hydrogen bond acceptor and three hydrophobic features. The model was cross-validated by randomizing the data using the CatScramble technique. Further validation was carried out using a test set that performed well in classifying active and inactive molecules correctly. Simultaneously, a bovine rhodopsin based "agonist-bound" hKOP receptor model was also generated. The model provided more accurate information about the putative binding site of salvinorin A based ligands. Several protein structure-checking programs were used to validate the model. In addition, this model was in agreement with the mutation experiments carried out on KOP receptor. The predictive ability of the model was evaluated by docking a set of known KOP receptor agonists into the active site of this model. The docked scores correlated reasonably well with experimental p K i values. It is hypothesized that the integration of these two independently generated models would enable a swift and reliable identification of new lead compounds that could reduce time and cost of hit finding within the drug discovery and development process, particularly in the case of GPCRs.

Professional ideologies and the development of syringe exchange: Wales as a case study.

PubMed

Keene, J M; Stimson, G V

1997-12-01

This paper is derived from an evaluative study of HIV prevention programs for drug injectors across Wales. It considers how different professional territories and ideologies, concepts of drug misuse and models of HIV prevention may influence policy development. The research involved monitoring the introduction and development of agency and community based syringe exchange schemes and initiatives taken by community pharmacists. Interviews with staff, managers and administrators, and descriptions of service history, development and delivery inform the discussion. HIV prevention varied in different areas of Wales depending on the particular professional group involved, local ideologies regarding drug use treatment, and the extent to which HIV prevention was seen either as a specialist area of expertise and specific remit of drug workers or a generic health care task. Drug agencies with an abstinence policy rejected syringe exchange; instead, prevention in those areas developed in ad hoc ways as health care workers and pharmacists attempted to develop a community based service. Drug agencies with a pre-existing harm minimisation model easily integrated syringe exchange into their work and played the major part in establishing the service, but there was difficulty in extending it beyond their professional caseloads. As there were disincentives to use treatment agencies, and their catchment areas were limited, these factors influenced effective service provision.

Drug Target Mining and Analysis of the Chinese Tree Shrew for Pharmacological Testing

PubMed Central

Liu, Jie; Lee, Wen-hui; Zhang, Yun

2014-01-01

The discovery of new drugs requires the development of improved animal models for drug testing. The Chinese tree shrew is considered to be a realistic candidate model. To assess the potential of the Chinese tree shrew for pharmacological testing, we performed drug target prediction and analysis on genomic and transcriptomic scales. Using our pipeline, 3,482 proteins were predicted to be drug targets. Of these predicted targets, 446 and 1,049 proteins with the highest rank and total scores, respectively, included homologs of targets for cancer chemotherapy, depression, age-related decline and cardiovascular disease. Based on comparative analyses, more than half of drug target proteins identified from the tree shrew genome were shown to be higher similarity to human targets than in the mouse. Target validation also demonstrated that the constitutive expression of the proteinase-activated receptors of tree shrew platelets is similar to that of human platelets but differs from that of mouse platelets. We developed an effective pipeline and search strategy for drug target prediction and the evaluation of model-based target identification for drug testing. This work provides useful information for future studies of the Chinese tree shrew as a source of novel targets for drug discovery research. PMID:25105297

Medicines as a Service

PubMed Central

Mattke, Soeren; Klautzer, Lisa; Mengistu, Tewodaj

2012-01-01

Abstract The past decade has not been kind to large pharmaceutical companies. Their share prices have been lagging the market after many years of outperforming it. Many had to undergo painful restructuring and workforce reductions because their traditional blockbuster model is becoming extinct. More and more top-selling drugs are being replaced by cheap generics, and developing new drugs is more difficult because fewer opportunities exist and more-costly research and development (R&D) productivity has declined. Although this diagnosis is not disputed, the best course of treatment is not clear. Companies have tried to stop the bleeding with the help of mergers and reorganizations and infused new blood by acquiring biotech companies or their innovative products or by diversifying into products other than prescription drugs. In this article, the authors propose that the pharmaceutical industry can reconfigure its considerable resources to develop innovative and meaningful business models that are based on services that improve access and adherence to prescription drugs for chronic conditions. They argue that such innovation beyond drug development is consistent with the core capabilities of large pharmaceutical companies and has the potential to achieve profit levels similar to those of its traditional models. Their argument is based on the fact that, although effective medicines for most chronic conditions exist, access and adherence to medicines is far from what would be needed to achieve full treatment efficacy. Therefore, value can be created by getting and keeping more patients on their drugs, and innovative business models would allow pharmaceutical companies to capture that value. PMID:28083254

[In silico, in vitro, in omic experimental models and drug safety evaluation].

PubMed

Claude, Nancy; Goldfain-Blanc, Françoise; Guillouzo, André

2009-01-01

Over the last few decades, toxicology has benefited from scientific, technical, and bioinformatic developments relating to patient safety assessment during clinical and drug marketing studies. Based on this knowledge, new in silico, in vitro, and "omic" experimental models are emerging. Although these models cannot currently replace classic safety evaluations performed on laboratory animals, they allow compounds with unacceptable toxicity to be rejected in the early stages of drug development, thereby reducing the number of laboratory animals needed. In addition, because these models are particularly adapted to mechanistic studies, they can help to improve the relevance of the data obtained, thus enabling better prevention and screening of the adverse effects that may occur in humans. Much progress remains to be done, especially in the field of validation. Nevertheless, current efforts by industrial, academic laboratories, and regulatory agencies should, in coming years, significantly improve preclinical drug safety evaluation thanks to the integration of these new methods into the drug research and development process.

Kinetics of drug release from ointments: Role of transient-boundary layer.

PubMed

Xu, Xiaoming; Al-Ghabeish, Manar; Krishnaiah, Yellela S R; Rahman, Ziyaur; Khan, Mansoor A

2015-10-15

In the current work, an in vitro release testing method suitable for ointment formulations was developed using acyclovir as a model drug. Release studies were carried out using enhancer cells on acyclovir ointments prepared with oleaginous, absorption, and water-soluble bases. Kinetics and mechanism of drug release was found to be highly dependent on the type of ointment bases. In oleaginous bases, drug release followed a unique logarithmic-time dependent profile; in both absorption and water-soluble bases, drug release exhibited linearity with respect to square root of time (Higuchi model) albeit differences in the overall release profile. To help understand the underlying cause of logarithmic-time dependency of drug release, a novel transient-boundary hypothesis was proposed, verified, and compared to Higuchi theory. Furthermore, impact of drug solubility (under various pH conditions) and temperature on drug release were assessed. Additionally, conditions under which deviations from logarithmic-time drug release kinetics occur were determined using in situ UV fiber-optics. Overall, the results suggest that for oleaginous ointments containing dispersed drug particles, kinetics and mechanism of drug release is controlled by expansion of transient boundary layer, and drug release increases linearly with respect to logarithmic time. Published by Elsevier B.V.

Application of Pharmacokinetic and Pharmacodynamic Analysis to the Development of Liposomal Formulations for Oncology

PubMed Central

Ait-Oudhia, Sihem; Mager, Donald E.; Straubinger, Robert M.

2014-01-01

Liposomal formulations of anticancer agents have been developed to prolong drug circulating lifetime, enhance anti-tumor efficacy by increasing tumor drug deposition, and reduce drug toxicity by avoiding critical normal tissues. Despite the clinical approval of numerous liposome-based chemotherapeutics, challenges remain in the development and clinical deployment of micro- and nano-particulate formulations, as well as combining these novel agents with conventional drugs and standard-of-care therapies. Factors requiring optimization include control of drug biodistribution, release rates of the encapsulated drug, and uptake by target cells. Quantitative mathematical modeling of formulation performance can provide an important tool for understanding drug transport, uptake, and disposition processes, as well as their role in therapeutic outcomes. This review identifies several relevant pharmacokinetic/pharmacodynamic models that incorporate key physical, biochemical, and physiological processes involved in delivery of oncology drugs by liposomal formulations. They capture observed data, lend insight into factors determining overall antitumor response, and in some cases, predict conditions for optimizing chemotherapy combinations that include nanoparticulate drug carriers. PMID:24647104

Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity.

PubMed

Passini, Elisa; Britton, Oliver J; Lu, Hua Rong; Rohrbacher, Jutta; Hermans, An N; Gallacher, David J; Greig, Robert J H; Bueno-Orovio, Alfonso; Rodriguez, Blanca

2017-01-01

Early prediction of cardiotoxicity is critical for drug development. Current animal models raise ethical and translational questions, and have limited accuracy in clinical risk prediction. Human-based computer models constitute a fast, cheap and potentially effective alternative to experimental assays, also facilitating translation to human. Key challenges include consideration of inter-cellular variability in drug responses and integration of computational and experimental methods in safety pharmacology. Our aim is to evaluate the ability of in silico drug trials in populations of human action potential (AP) models to predict clinical risk of drug-induced arrhythmias based on ion channel information, and to compare simulation results against experimental assays commonly used for drug testing. A control population of 1,213 human ventricular AP models in agreement with experimental recordings was constructed. In silico drug trials were performed for 62 reference compounds at multiple concentrations, using pore-block drug models (IC 50 /Hill coefficient). Drug-induced changes in AP biomarkers were quantified, together with occurrence of repolarization/depolarization abnormalities. Simulation results were used to predict clinical risk based on reports of Torsade de Pointes arrhythmias, and further evaluated in a subset of compounds through comparison with electrocardiograms from rabbit wedge preparations and Ca 2+ -transient recordings in human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs). Drug-induced changes in silico vary in magnitude depending on the specific ionic profile of each model in the population, thus allowing to identify cell sub-populations at higher risk of developing abnormal AP phenotypes. Models with low repolarization reserve (increased Ca 2+ /late Na + currents and Na + /Ca 2+ -exchanger, reduced Na + /K + -pump) are highly vulnerable to drug-induced repolarization abnormalities, while those with reduced inward current density (fast/late Na + and Ca 2+ currents) exhibit high susceptibility to depolarization abnormalities. Repolarization abnormalities in silico predict clinical risk for all compounds with 89% accuracy. Drug-induced changes in biomarkers are in overall agreement across different assays: in silico AP duration changes reflect the ones observed in rabbit QT interval and hiPS-CMs Ca 2+ -transient, and simulated upstroke velocity captures variations in rabbit QRS complex. Our results demonstrate that human in silico drug trials constitute a powerful methodology for prediction of clinical pro-arrhythmic cardiotoxicity, ready for integration in the existing drug safety assessment pipelines.

Computational 3D structures of drug-targeting proteins in the 2009-H1N1 influenza A virus

NASA Astrophysics Data System (ADS)

Du, Qi-Shi; Wang, Shu-Qing; Huang, Ri-Bo; Chou, Kuo-Chen

2010-01-01

The neuraminidase (NA) and M2 proton channel of influenza virus are the drug-targeting proteins, based on which several drugs were developed. However these once powerful drugs encountered drug-resistant problem to the H5N1 and H1N1 flu. To address this problem, the computational 3D structures of NA and M2 proteins of 2009-H1N1 influenza virus were built using the molecular modeling technique and computational chemistry method. Based on the models the structure features of NA and M2 proteins were analyzed, the docking structures of drug-protein complexes were computed, and the residue mutations were annotated. The results may help to solve the drug-resistant problem and stimulate designing more effective drugs against 2009-H1N1 influenza pandemic.

An Ensemble Approach for Drug Side Effect Prediction

PubMed Central

Jahid, Md Jamiul; Ruan, Jianhua

2014-01-01

In silico prediction of drug side-effects in early stage of drug development is becoming more popular now days, which not only reduces the time for drug design but also reduces the drug development costs. In this article we propose an ensemble approach to predict drug side-effects of drug molecules based on their chemical structure. Our idea originates from the observation that similar drugs have similar side-effects. Based on this observation we design an ensemble approach that combine the results from different classification models where each model is generated by a different set of similar drugs. We applied our approach to 1385 side-effects in the SIDER database for 888 drugs. Results show that our approach outperformed previously published approaches and standard classifiers. Furthermore, we applied our method to a number of uncharacterized drug molecules in DrugBank database and predict their side-effect profiles for future usage. Results from various sources confirm that our method is able to predict the side-effects for uncharacterized drugs and more importantly able to predict rare side-effects which are often ignored by other approaches. The method described in this article can be useful to predict side-effects in drug design in an early stage to reduce experimental cost and time. PMID:25327524

Data-intensive drug development in the information age: applications of Systems Biology/Pharmacology/Toxicology.

PubMed

Kiyosawa, Naoki; Manabe, Sunao

2016-01-01

Pharmaceutical companies continuously face challenges to deliver new drugs with true medical value. R&D productivity of drug development projects depends on 1) the value of the drug concept and 2) data and in-depth knowledge that are used rationally to evaluate the drug concept's validity. A model-based data-intensive drug development approach is a key competitive factor used by innovative pharmaceutical companies to reduce information bias and rationally demonstrate the value of drug concepts. Owing to the accumulation of publicly available biomedical information, our understanding of the pathophysiological mechanisms of diseases has developed considerably; it is the basis for identifying the right drug target and creating a drug concept with true medical value. Our understanding of the pathophysiological mechanisms of disease animal models can also be improved; it can thus support rational extrapolation of animal experiment results to clinical settings. The Systems Biology approach, which leverages publicly available transcriptome data, is useful for these purposes. Furthermore, applying Systems Pharmacology enables dynamic simulation of drug responses, from which key research questions to be addressed in the subsequent studies can be adequately informed. Application of Systems Biology/Pharmacology to toxicology research, namely Systems Toxicology, should considerably improve the predictability of drug-induced toxicities in clinical situations that are difficult to predict from conventional preclinical toxicology studies. Systems Biology/Pharmacology/Toxicology models can be continuously improved using iterative learn-confirm processes throughout preclinical and clinical drug discovery and development processes. Successful implementation of data-intensive drug development approaches requires cultivation of an adequate R&D culture to appreciate this approach.

3D printing of high drug loaded dosage forms using thermoplastic polyurethanes.

PubMed

Verstraete, G; Samaro, A; Grymonpré, W; Vanhoorne, V; Van Snick, B; Boone, M N; Hellemans, T; Van Hoorebeke, L; Remon, J P; Vervaet, C

2018-01-30

It was the aim of this study to develop high drug loaded (>30%, w/w), thermoplastic polyurethane (TPU)-based dosage forms via fused deposition modelling (FDM). Model drugs with different particle size and aqueous solubility were pre-processed in combination with diverse TPU grades via hot melt extrusion (HME) into filaments with a diameter of 1.75 ± 0.05 mm. Subsequently, TPU-based filaments which featured acceptable quality attributes (i.e. consistent filament diameter, smooth surface morphology and good mechanical properties) were printed into tablets. The sustained release potential of the 3D printed dosage forms was tested in vitro. Moreover, the impact of printing parameters on the in vitro drug release was investigated. TPU-based filaments could be loaded with 60% (w/w) fine drug powder without observing severe shark skinning or inconsistent filament diameter. During 3D printing experiments, HME filaments based on hard TPU grades were successfully converted into personalized dosage forms containing a high concentration of crystalline drug (up to 60%, w/w). In vitro release kinetics were mainly affected by the matrix composition and tablet infill degree. Therefore, this study clearly demonstrated that TPU-based FDM feedstock material offers a lot of formulation freedom for the development of personalized dosage forms. Copyright © 2017 Elsevier B.V. All rights reserved.

Quantitative self-assembly prediction yields targeted nanomedicines

NASA Astrophysics Data System (ADS)

Shamay, Yosi; Shah, Janki; Işık, Mehtap; Mizrachi, Aviram; Leibold, Josef; Tschaharganeh, Darjus F.; Roxbury, Daniel; Budhathoki-Uprety, Januka; Nawaly, Karla; Sugarman, James L.; Baut, Emily; Neiman, Michelle R.; Dacek, Megan; Ganesh, Kripa S.; Johnson, Darren C.; Sridharan, Ramya; Chu, Karen L.; Rajasekhar, Vinagolu K.; Lowe, Scott W.; Chodera, John D.; Heller, Daniel A.

2018-02-01

Development of targeted nanoparticle drug carriers often requires complex synthetic schemes involving both supramolecular self-assembly and chemical modification. These processes are generally difficult to predict, execute, and control. We describe herein a targeted drug delivery system that is accurately and quantitatively predicted to self-assemble into nanoparticles based on the molecular structures of precursor molecules, which are the drugs themselves. The drugs assemble with the aid of sulfated indocyanines into particles with ultrahigh drug loadings of up to 90%. We devised quantitative structure-nanoparticle assembly prediction (QSNAP) models to identify and validate electrotopological molecular descriptors as highly predictive indicators of nano-assembly and nanoparticle size. The resulting nanoparticles selectively targeted kinase inhibitors to caveolin-1-expressing human colon cancer and autochthonous liver cancer models to yield striking therapeutic effects while avoiding pERK inhibition in healthy skin. This finding enables the computational design of nanomedicines based on quantitative models for drug payload selection.

Role of Statistical Random-Effects Linear Models in Personalized Medicine.

PubMed

Diaz, Francisco J; Yeh, Hung-Wen; de Leon, Jose

2012-03-01

Some empirical studies and recent developments in pharmacokinetic theory suggest that statistical random-effects linear models are valuable tools that allow describing simultaneously patient populations as a whole and patients as individuals. This remarkable characteristic indicates that these models may be useful in the development of personalized medicine, which aims at finding treatment regimes that are appropriate for particular patients, not just appropriate for the average patient. In fact, published developments show that random-effects linear models may provide a solid theoretical framework for drug dosage individualization in chronic diseases. In particular, individualized dosages computed with these models by means of an empirical Bayesian approach may produce better results than dosages computed with some methods routinely used in therapeutic drug monitoring. This is further supported by published empirical and theoretical findings that show that random effects linear models may provide accurate representations of phase III and IV steady-state pharmacokinetic data, and may be useful for dosage computations. These models have applications in the design of clinical algorithms for drug dosage individualization in chronic diseases; in the computation of dose correction factors; computation of the minimum number of blood samples from a patient that are necessary for calculating an optimal individualized drug dosage in therapeutic drug monitoring; measure of the clinical importance of clinical, demographic, environmental or genetic covariates; study of drug-drug interactions in clinical settings; the implementation of computational tools for web-site-based evidence farming; design of pharmacogenomic studies; and in the development of a pharmacological theory of dosage individualization.

Open source drug discovery--a new paradigm of collaborative research in tuberculosis drug development.

PubMed

Bhardwaj, Anshu; Scaria, Vinod; Raghava, Gajendra Pal Singh; Lynn, Andrew Michael; Chandra, Nagasuma; Banerjee, Sulagna; Raghunandanan, Muthukurussi V; Pandey, Vikas; Taneja, Bhupesh; Yadav, Jyoti; Dash, Debasis; Bhattacharya, Jaijit; Misra, Amit; Kumar, Anil; Ramachandran, Srinivasan; Thomas, Zakir; Brahmachari, Samir K

2011-09-01

It is being realized that the traditional closed-door and market driven approaches for drug discovery may not be the best suited model for the diseases of the developing world such as tuberculosis and malaria, because most patients suffering from these diseases have poor paying capacity. To ensure that new drugs are created for patients suffering from these diseases, it is necessary to formulate an alternate paradigm of drug discovery process. The current model constrained by limitations for collaboration and for sharing of resources with confidentiality hampers the opportunities for bringing expertise from diverse fields. These limitations hinder the possibilities of lowering the cost of drug discovery. The Open Source Drug Discovery project initiated by Council of Scientific and Industrial Research, India has adopted an open source model to power wide participation across geographical borders. Open Source Drug Discovery emphasizes integrative science through collaboration, open-sharing, taking up multi-faceted approaches and accruing benefits from advances on different fronts of new drug discovery. Because the open source model is based on community participation, it has the potential to self-sustain continuous development by generating a storehouse of alternatives towards continued pursuit for new drug discovery. Since the inventions are community generated, the new chemical entities developed by Open Source Drug Discovery will be taken up for clinical trial in a non-exclusive manner by participation of multiple companies with majority funding from Open Source Drug Discovery. This will ensure availability of drugs through a lower cost community driven drug discovery process for diseases afflicting people with poor paying capacity. Hopefully what LINUX the World Wide Web have done for the information technology, Open Source Drug Discovery will do for drug discovery. Copyright © 2011 Elsevier Ltd. All rights reserved.

Drug target ontology to classify and integrate drug discovery data.

PubMed

Lin, Yu; Mehta, Saurabh; Küçük-McGinty, Hande; Turner, John Paul; Vidovic, Dusica; Forlin, Michele; Koleti, Amar; Nguyen, Dac-Trung; Jensen, Lars Juhl; Guha, Rajarshi; Mathias, Stephen L; Ursu, Oleg; Stathias, Vasileios; Duan, Jianbin; Nabizadeh, Nooshin; Chung, Caty; Mader, Christopher; Visser, Ubbo; Yang, Jeremy J; Bologa, Cristian G; Oprea, Tudor I; Schürer, Stephan C

2017-11-09

One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome. As part of that effort, we have developed a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships. DTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the website http://drugtargetontology.org/ , Github ( http://github.com/DrugTargetOntology/DTO ), and the NCBO Bioportal ( http://bioportal.bioontology.org/ontologies/DTO ). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource.

Three-dimensional prostate tumor model based on a hyaluronic acid-alginate hydrogel for evaluation of anti-cancer drug efficacy.

PubMed

Tang, Yadong; Huang, Boxin; Dong, Yuqin; Wang, Wenlong; Zheng, Xi; Zhou, Wei; Zhang, Kun; Du, Zhiyun

2017-10-01

In vitro cell-based assays are widely applied to evaluate anti-cancer drug efficacy. However, the conventional approaches are mostly based on two-dimensional (2D) culture systems, making it difficult to recapitulate the in vivo tumor scenario because of spatial limitations. Here, we develop an in vitro three-dimensional (3D) prostate tumor model based on a hyaluronic acid (HA)-alginate hybrid hydrogel to bridge the gap between in vitro and in vivo anticancer drug evaluations. In situ encapsulation of PCa cells was achieved by mixing HA and alginate aqueous solutions in the presence of cells and then crosslinking with calcium ions. Unlike in 2D culture, cells were found to aggregate into spheroids in a 3D matrix. The expression of epithelial to mesenchyme transition (EMT) biomarkers was found to be largely enhanced, indicating an increased invasion and metastasis potential in the hydrogel matrix. A significant up-regulation of proangiogenic growth factors (IL-8, VEGF) and matrix metalloproteinases (MMPs) was observed in 3D-cultured PCa cells. The results of anti-cancer drug evaluation suggested a higher drug tolerance within the 3D tumor model compared to conventional 2D-cultured cells. Finally, we found that the drug effect within the in vitro 3D cancer model based on HA-alginate matrix exhibited better predictability for in vivo drug efficacy.

Development and in vitro evaluation of potential electromodulated transdermal drug delivery systems based on carbon nanotube buckypapers.

PubMed

Schwengber, Alex; Prado, Héctor J; Bonelli, Pablo R; Cukierman, Ana L

2017-07-01

Buckypapers based on different types of carbon nanotubes with and without the addition of four model drugs, two of basic nature (clonidine hydrochloride, selegiline hydrochloride) and the others of acidic character (flurbiprofen, ketorolac tromethamine) were prepared and characterized. The influence of the conditions employed in the preparation of the buckypapers (dispersion time and solvents used in the preparation, as well as the type of carbon nanotubes used and the characteristics of the drug involved) on their conductivity was especially examined. The in vitro performance of the drug loaded buckypapers as passive and active transdermal drug release systems, the latter being modulated by means of the application of electric voltages, was studied. Passive drug loaded buckypapers presented characteristic release profiles, also depending on the drug used, which indicate differences in the drug-carbon nanotubes non-covalent interactions. Application of electrical biases of appropriate polarities enabled the modulation of the drug release profiles in any desired direction. Different mathematical models were fitted to passive and electromodulated experimental release data for the four model drugs. Among these models, the most appropriate for data description was a two-compartment pseudo-second-order one. Copyright © 2017 Elsevier B.V. All rights reserved.

Hybrid Drug Delivery Patches Based on Spherical Cellulose Nanocrystals and Colloid Titania—Synthesis and Antibacterial Properties

PubMed Central

Svensson, Fredric G.; Agafonov, Alexander V.; Håkansson, Sebastian; Seisenbaeva, Gulaim A.

2018-01-01

Spherical cellulose nanocrystal-based hybrids grafted with titania nanoparticles were successfully produced for topical drug delivery. The conventional analytical filter paper was used as a precursor material for cellulose nanocrystals (CNC) production. Cellulose nanocrystals were extracted via a simple and quick two-step process based on first the complexation with Cu(II) solution in aqueous ammonia followed by acid hydrolysis with diluted H2SO4. Triclosan was selected as a model drug for complexation with titania and further introduction into the nanocellulose based composite. Obtained materials were characterized by a broad variety of microscopic, spectroscopic, and thermal analysis methods. The drug release studies showed long-term release profiles of triclosan from the titania based nanocomposite that agreed with Higuchi model. The bacterial susceptibility tests demonstrated that released triclosan retained its antibacterial activity against Escherichia coli and Staphylococcus aureus. It was found that a small amount of titania significantly improved the antibacterial activity of obtained nanocomposites, even without immobilization of model drug. Thus, the developed hybrid patches are highly promising candidates for potential application as antibacterial agents. PMID:29642486

Hybrid Drug Delivery Patches Based on Spherical Cellulose Nanocrystals and Colloid Titania-Synthesis and Antibacterial Properties.

PubMed

Evdokimova, Olga L; Svensson, Fredric G; Agafonov, Alexander V; Håkansson, Sebastian; Seisenbaeva, Gulaim A; Kessler, Vadim G

2018-04-08

Spherical cellulose nanocrystal-based hybrids grafted with titania nanoparticles were successfully produced for topical drug delivery. The conventional analytical filter paper was used as a precursor material for cellulose nanocrystals (CNC) production. Cellulose nanocrystals were extracted via a simple and quick two-step process based on first the complexation with Cu(II) solution in aqueous ammonia followed by acid hydrolysis with diluted H₂SO₄. Triclosan was selected as a model drug for complexation with titania and further introduction into the nanocellulose based composite. Obtained materials were characterized by a broad variety of microscopic, spectroscopic, and thermal analysis methods. The drug release studies showed long-term release profiles of triclosan from the titania based nanocomposite that agreed with Higuchi model. The bacterial susceptibility tests demonstrated that released triclosan retained its antibacterial activity against Escherichia coli and Staphylococcus aureus . It was found that a small amount of titania significantly improved the antibacterial activity of obtained nanocomposites, even without immobilization of model drug. Thus, the developed hybrid patches are highly promising candidates for potential application as antibacterial agents.

A novel integrated framework and improved methodology of computer-aided drug design.

PubMed

Chen, Calvin Yu-Chian

2013-01-01

Computer-aided drug design (CADD) is a critical initiating step of drug development, but a single model capable of covering all designing aspects remains to be elucidated. Hence, we developed a drug design modeling framework that integrates multiple approaches, including machine learning based quantitative structure-activity relationship (QSAR) analysis, 3D-QSAR, Bayesian network, pharmacophore modeling, and structure-based docking algorithm. Restrictions for each model were defined for improved individual and overall accuracy. An integration method was applied to join the results from each model to minimize bias and errors. In addition, the integrated model adopts both static and dynamic analysis to validate the intermolecular stabilities of the receptor-ligand conformation. The proposed protocol was applied to identifying HER2 inhibitors from traditional Chinese medicine (TCM) as an example for validating our new protocol. Eight potent leads were identified from six TCM sources. A joint validation system comprised of comparative molecular field analysis, comparative molecular similarity indices analysis, and molecular dynamics simulation further characterized the candidates into three potential binding conformations and validated the binding stability of each protein-ligand complex. The ligand pathway was also performed to predict the ligand "in" and "exit" from the binding site. In summary, we propose a novel systematic CADD methodology for the identification, analysis, and characterization of drug-like candidates.

Computational predictive models for P-glycoprotein inhibition of in-house chalcone derivatives and drug-bank compounds.

PubMed

Ngo, Trieu-Du; Tran, Thanh-Dao; Le, Minh-Tri; Thai, Khac-Minh

2016-11-01

The human P-glycoprotein (P-gp) efflux pump is of great interest for medicinal chemists because of its important role in multidrug resistance (MDR). Because of the high polyspecificity as well as the unavailability of high-resolution X-ray crystal structures of this transmembrane protein, ligand-based, and structure-based approaches which were machine learning, homology modeling, and molecular docking were combined for this study. In ligand-based approach, individual two-dimensional quantitative structure-activity relationship models were developed using different machine learning algorithms and subsequently combined into the Ensemble model which showed good performance on both the diverse training set and the validation sets. The applicability domain and the prediction quality of the developed models were also judged using the state-of-the-art methods and tools. In our structure-based approach, the P-gp structure and its binding region were predicted for a docking study to determine possible interactions between the ligands and the receptor. Based on these in silico tools, hit compounds for reversing MDR were discovered from the in-house and DrugBank databases through virtual screening using prediction models and molecular docking in an attempt to restore cancer cell sensitivity to cytotoxic drugs.

Identification of the mechanism of action of a glucokinase activator from oral glucose tolerance test data in type 2 diabetic patients based on an integrated glucose-insulin model.

PubMed

Jauslin, Petra M; Karlsson, Mats O; Frey, Nicolas

2012-12-01

A mechanistic drug-disease model was developed on the basis of a previously published integrated glucose-insulin model by Jauslin et al. A glucokinase activator was used as a test compound to evaluate the model's ability to identify a drug's mechanism of action and estimate its effects on glucose and insulin profiles following oral glucose tolerance tests. A kinetic-pharmacodynamic approach was chosen to describe the drug's pharmacodynamic effects in a dose-response-time model. Four possible mechanisms of action of antidiabetic drugs were evaluated, and the corresponding affected model parameters were identified: insulin secretion, glucose production, insulin effect on glucose elimination, and insulin-independent glucose elimination. Inclusion of drug effects in the model at these sites of action was first tested one-by-one and then in combination. The results demonstrate the ability of this model to identify the dual mechanism of action of a glucokinase activator and describe and predict its effects: Estimating a stimulating drug effect on insulin secretion and an inhibiting effect on glucose output resulted in a significantly better model fit than any other combination of effect sites. The model may be used for dose finding in early clinical drug development and for gaining more insight into a drug candidate's mechanism of action.

Application of Fused Deposition Modelling (FDM) Method of 3D Printing in Drug Delivery.

PubMed

Long, Jingjunjiao; Gholizadeh, Hamideh; Lu, Jun; Bunt, Craig; Seyfoddin, Ali

2017-01-01

Three-dimensional (3D) printing is an emerging manufacturing technology for biomedical and pharmaceutical applications. Fused deposition modelling (FDM) is a low cost extrusion-based 3D printing technique that can deposit materials layer-by-layer to create solid geometries. This review article aims to provide an overview of FDM based 3D printing application in developing new drug delivery systems. The principle methodology, suitable polymers and important parameters in FDM technology and its applications in fabrication of personalised tablets and drug delivery devices are discussed in this review. FDM based 3D printing is a novel and versatile manufacturing technique for creating customised drug delivery devices that contain accurate dose of medicine( s) and provide controlled drug released profiles. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

3D-Lab: a collaborative web-based platform for molecular modeling.

PubMed

Grebner, Christoph; Norrby, Magnus; Enström, Jonatan; Nilsson, Ingemar; Hogner, Anders; Henriksson, Jonas; Westin, Johan; Faramarzi, Farzad; Werner, Philip; Boström, Jonas

2016-09-01

The use of 3D information has shown impact in numerous applications in drug design. However, it is often under-utilized and traditionally limited to specialists. We want to change that, and present an approach making 3D information and molecular modeling accessible and easy-to-use 'for the people'. A user-friendly and collaborative web-based platform (3D-Lab) for 3D modeling, including a blazingly fast virtual screening capability, was developed. 3D-Lab provides an interface to automatic molecular modeling, like conformer generation, ligand alignments, molecular dockings and simple quantum chemistry protocols. 3D-Lab is designed to be modular, and to facilitate sharing of 3D-information to promote interactions between drug designers. Recent enhancements to our open-source virtual reality tool Molecular Rift are described. The integrated drug-design platform allows drug designers to instantaneously access 3D information and readily apply advanced and automated 3D molecular modeling tasks, with the aim to improve decision-making in drug design projects.

Predicting the impact of combined therapies on myeloma cell growth using a hybrid multi-scale agent-based model.

PubMed

Ji, Zhiwei; Su, Jing; Wu, Dan; Peng, Huiming; Zhao, Weiling; Nlong Zhao, Brian; Zhou, Xiaobo

2017-01-31

Multiple myeloma is a malignant still incurable plasma cell disorder. This is due to refractory disease relapse, immune impairment, and development of multi-drug resistance. The growth of malignant plasma cells is dependent on the bone marrow (BM) microenvironment and evasion of the host's anti-tumor immune response. Hence, we hypothesized that targeting tumor-stromal cell interaction and endogenous immune system in BM will potentially improve the response of multiple myeloma (MM). Therefore, we proposed a computational simulation of the myeloma development in the complicated microenvironment which includes immune cell components and bone marrow stromal cells and predicted the effects of combined treatment with multi-drugs on myeloma cell growth. We constructed a hybrid multi-scale agent-based model (HABM) that combines an ODE system and Agent-based model (ABM). The ODEs was used for modeling the dynamic changes of intracellular signal transductions and ABM for modeling the cell-cell interactions between stromal cells, tumor, and immune components in the BM. This model simulated myeloma growth in the bone marrow microenvironment and revealed the important role of immune system in this process. The predicted outcomes were consistent with the experimental observations from previous studies. Moreover, we applied this model to predict the treatment effects of three key therapeutic drugs used for MM, and found that the combination of these three drugs potentially suppress the growth of myeloma cells and reactivate the immune response. In summary, the proposed model may serve as a novel computational platform for simulating the formation of MM and evaluating the treatment response of MM to multiple drugs.

TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models.

PubMed

Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

2016-05-01

Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com .

TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models

NASA Astrophysics Data System (ADS)

Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

2016-05-01

Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com.

gPKPDSim: a SimBiology®-based GUI application for PKPD modeling in drug development.

PubMed

Hosseini, Iraj; Gajjala, Anita; Bumbaca Yadav, Daniela; Sukumaran, Siddharth; Ramanujan, Saroja; Paxson, Ricardo; Gadkar, Kapil

2018-04-01

Modeling and simulation (M&S) is increasingly used in drug development to characterize pharmacokinetic-pharmacodynamic (PKPD) relationships and support various efforts such as target feasibility assessment, molecule selection, human PK projection, and preclinical and clinical dose and schedule determination. While model development typically require mathematical modeling expertise, model exploration and simulations could in many cases be performed by scientists in various disciplines to support the design, analysis and interpretation of experimental studies. To this end, we have developed a versatile graphical user interface (GUI) application to enable easy use of any model constructed in SimBiology ® to execute various common PKPD analyses. The MATLAB ® -based GUI application, called gPKPDSim, has a single screen interface and provides functionalities including simulation, data fitting (parameter estimation), population simulation (exploring the impact of parameter variability on the outputs of interest), and non-compartmental PK analysis. Further, gPKPDSim is a user-friendly tool with capabilities including interactive visualization, exporting of results and generation of presentation-ready figures. gPKPDSim was designed primarily for use in preclinical and translational drug development, although broader applications exist. gPKPDSim is a MATLAB ® -based open-source application and is publicly available to download from MATLAB ® Central™. We illustrate the use and features of gPKPDSim using multiple PKPD models to demonstrate the wide applications of this tool in pharmaceutical sciences. Overall, gPKPDSim provides an integrated, multi-purpose user-friendly GUI application to enable efficient use of PKPD models by scientists from various disciplines, regardless of their modeling expertise.

Development of drug-loaded polymer microcapsules for treatment of epilepsy.

PubMed

Chen, Yu; Gu, Qi; Yue, Zhilian; Crook, Jeremy M; Moulton, Simon E; Cook, Mark J; Wallace, Gordon G

2017-09-26

Despite significant progress in developing new drugs for seizure control, epilepsy still affects 1% of the global population and is drug-resistant in more than 30% of cases. To improve the therapeutic efficacy of epilepsy medication, a promising approach is to deliver anti-epilepsy drugs directly to affected brain areas using local drug delivery systems. The drug delivery systems must meet a number of criteria, including high drug loading efficiency, biodegradability, neuro-cytocompatibility and predictable drug release profiles. Here we report the development of fibre- and sphere-based microcapsules that exhibit controllable uniform morphologies and drug release profiles as predicted by mathematical modelling. Importantly, both forms of fabricated microcapsules are compatible with human brain derived neural stem cells and differentiated neurons and neuroglia, indicating clinical compliance for neural implantation and therapeutic drug delivery.

Target specific delivery of anticancer drug in silk fibroin based 3D distribution model of bone-breast cancer cells.

PubMed

Subia, Bano; Dey, Tuli; Sharma, Shaily; Kundu, Subhas C

2015-02-04

To avoid the indiscriminating action of anticancer drugs, the cancer cell specific targeting of drug molecule becomes a preferred choice for the treatment. The successful screening of the drug molecules in 2D culture system requires further validation. The failure of target specific drug in animal model raises the issue of creating a platform in between the in vitro (2D) and in vivo animal testing. The metastatic breast cancer cells migrate and settle at different sites such as bone tissue. This work evaluates the in vitro 3D model of the breast cancer and bone cells to understand the cellular interactions in the presence of a targeted anticancer drug delivery system. The silk fibroin based cytocompatible 3D scaffold is used as in vitro 3D distribution model. Human breast adenocarcinoma and osteoblast like cells are cocultured to evaluate the efficiency of doxorubicin loaded folic acid conjugated silk fibroin nanoparticle as drug delivery system. Decreasing population of the cancer cells, which lower the levels of vascular endothelial growth factors, glucose consumption, and lactate production are observed in the drug treated coculture constructs. The drug treated constructs do not show any major impact on bone mineralization. The diminished expression of osteogenic markers such as osteocalcein and alkaline phosphatase are recorded. The result indicates that this type of silk based 3D in vitro coculture model may be utilized as a bridge between the traditional 2D and animal model system to evaluate the new drug molecule (s) or to reassay the known drug molecules or to develop target specific drug in cancer research.

Pre-clinical methods for detecting the hypersensitivity potential of pharmaceuticals: regulatory considerations.

PubMed

Hastings, K L

2001-02-02

Immune-based systemic hypersensitivities account for a significant number of adverse drug reactions. There appear to be no adequate nonclinical models to predict systemic hypersensitivity to small molecular weight drugs. Although there are very good methods for detecting drugs that can induce contact sensitization, these have not been successfully adapted for prediction of systemic hypersensitivity. Several factors have made the development of adequate models difficult. The term systemic hypersensitivity encompases many discrete immunopathologies. Each type of immunopathology presumably is the result of a specific cluster of immunologic and biochemical phenomena. Certainly other factors, such as genetic predisposition, metabolic idiosyncrasies, and concomitant diseases, further complicate the problem. Therefore, it may be difficult to find common mechanisms upon which to construct adequate models to predict specific types of systemic hypersensitivity reactions. There is some reason to hope, however, that adequate methods could be developed for at least identifying drugs that have the potential to produce signs indicative of a general hazard for immune-based reactions.

What is a new drug worth? An innovative model for performance-based pricing.

PubMed

Dranitsaris, G; Dorward, K; Owens, R C; Schipper, H

2015-05-01

This article focuses on a novel method to derive prices for new pharmaceuticals by making price a function of drug performance. We briefly review current models for determining price for a new product and discuss alternatives that have historically been favoured by various funding bodies. The progressive approach to drug pricing, proposed herein, may better address the views and concerns of multiple stakeholders in a developed healthcare system by acknowledging and incorporating input from disparate parties via comprehensive and successive negotiation stages. In proposing a valid construct for performance-based pricing, the following model seeks to achieve several crucial objectives: earlier and wider access to new treatments; improved transparency in drug pricing; multi-stakeholder involvement through phased pricing negotiations; recognition of innovative product performance and latent changes in value; an earlier and more predictable return for developers without sacrificing total return on investment (ROI); more involved and informed risk sharing by the end-user. © 2014 John Wiley & Sons Ltd.

Classification of toxicity effects of biotransformed hepatic drugs using whale optimized support vector machines.

PubMed

Tharwat, Alaa; Moemen, Yasmine S; Hassanien, Aboul Ella

2017-04-01

Measuring toxicity is an important step in drug development. Nevertheless, the current experimental methods used to estimate the drug toxicity are expensive and time-consuming, indicating that they are not suitable for large-scale evaluation of drug toxicity in the early stage of drug development. Hence, there is a high demand to develop computational models that can predict the drug toxicity risks. In this study, we used a dataset that consists of 553 drugs that biotransformed in liver. The toxic effects were calculated for the current data, namely, mutagenic, tumorigenic, irritant and reproductive effect. Each drug is represented by 31 chemical descriptors (features). The proposed model consists of three phases. In the first phase, the most discriminative subset of features is selected using rough set-based methods to reduce the classification time while improving the classification performance. In the second phase, different sampling methods such as Random Under-Sampling, Random Over-Sampling and Synthetic Minority Oversampling Technique (SMOTE), BorderLine SMOTE and Safe Level SMOTE are used to solve the problem of imbalanced dataset. In the third phase, the Support Vector Machines (SVM) classifier is used to classify an unknown drug into toxic or non-toxic. SVM parameters such as the penalty parameter and kernel parameter have a great impact on the classification accuracy of the model. In this paper, Whale Optimization Algorithm (WOA) has been proposed to optimize the parameters of SVM, so that the classification error can be reduced. The experimental results proved that the proposed model achieved high sensitivity to all toxic effects. Overall, the high sensitivity of the WOA+SVM model indicates that it could be used for the prediction of drug toxicity in the early stage of drug development. Copyright © 2017 Elsevier Inc. All rights reserved.

Multidimensional family therapy HIV/STD risk-reduction intervention: an integrative family-based model for drug-involved juvenile offenders.

PubMed

Marvel, Francoise; Rowe, Cynthia L; Colon-Perez, Lissette; DiClemente, Ralph J; Liddle, Howard A

2009-03-01

Drug and juvenile justice involved youths show remarkably high rates of human immunodeficiency virus (HIV)/sexually transmitted disease (STD) risk behaviors. However, existing interventions aimed at reducing adolescent HIV risk behavior have rarely targeted these vulnerable young adolescents, and many approaches focus on individual-level change without attention to family or contextual influences. We describe a new, family-based HIV/ STD prevention model that embeds HIV/STD focused multifamily groups within an adolescent drug abuse and delinquency evidence-based treatment, Multidimensional Family Therapy (MDFT). The approach has been evaluated in a multisite randomized clinical trial with juvenile justice involved youths in the National Institute on Drug Abuse Criminal Justice Drug Abuse Treatment Studies (www.cjdats.org). Preliminary baseline to 6-month outcomes are promising. We describe research on family risk and protective factors for adolescent problem behaviors, and offer a rationale for family-based approaches to reduce HIV/STD risk in this population. We describe the development and implementation of the Multidimensional Family Therapy HIV/STD risk-reduction intervention (MDFT-HIV/ STD) in terms of using multifamily groups and their integration in standard MDFT and also offers a clinical vignette. The potential significance of this empirically based intervention development work is high; MDFT-HIV/STD is the first model to address largely unmet HIV/STD prevention and sexual health needs of substance abusing juvenile offenders within the context of a family-oriented evidence-based intervention.

An AIDS risk reduction program for Dutch drug users: an intervention mapping approach to planning.

PubMed

van Empelen, Pepijn; Kok, Gerjo; Schaalma, Herman P; Bartholomew, L Kay

2003-10-01

This article presents the development of a theory- and evidence-based AIDS prevention program targeting Dutch drug users and aimed at promoting condom use. The emphasis is on the development of the program using a five-step intervention development protocol called intervention mapping (IM). Preceding Step 1 of the IM process, an assessment of the HIV problem among drug users was conducted. The product of IM Step 1 was a series of program objectives specifying what drug users should learn in order to use condoms consistently. In Step 2, theoretical methods for influencing the most important determinants were chosen and translated into practical strategies that fit the program objectives. The main strategy chosen was behavioral journalism. In Step 3, leaflets with role-model stories based on authentic interviews with drug users were developed and pilot tested. Finally, the need for cooperation with program users is discussed in IM Steps 4 and 5.

Genome-Scale Screening of Drug-Target Associations Relevant to Ki Using a Chemogenomics Approach

PubMed Central

Cao, Dong-Sheng; Liang, Yi-Zeng; Deng, Zhe; Hu, Qian-Nan; He, Min; Xu, Qing-Song; Zhou, Guang-Hua; Zhang, Liu-Xia; Deng, Zi-xin; Liu, Shao

2013-01-01

The identification of interactions between drugs and target proteins plays a key role in genomic drug discovery. In the present study, the quantitative binding affinities of drug-target pairs are differentiated as a measurement to define whether a drug interacts with a protein or not, and then a chemogenomics framework using an unbiased set of general integrated features and random forest (RF) is employed to construct a predictive model which can accurately classify drug-target pairs. The predictability of the model is further investigated and validated by several independent validation sets. The built model is used to predict drug-target associations, some of which were confirmed by comparing experimental data from public biological resources. A drug-target interaction network with high confidence drug-target pairs was also reconstructed. This network provides further insight for the action of drugs and targets. Finally, a web-based server called PreDPI-Ki was developed to predict drug-target interactions for drug discovery. In addition to providing a high-confidence list of drug-target associations for subsequent experimental investigation guidance, these results also contribute to the understanding of drug-target interactions. We can also see that quantitative information of drug-target associations could greatly promote the development of more accurate models. The PreDPI-Ki server is freely available via: http://sdd.whu.edu.cn/dpiki. PMID:23577055

A prediction model-based algorithm for computer-assisted database screening of adverse drug reactions in the Netherlands.

PubMed

Scholl, Joep H G; van Hunsel, Florence P A M; Hak, Eelko; van Puijenbroek, Eugène P

2018-02-01

The statistical screening of pharmacovigilance databases containing spontaneously reported adverse drug reactions (ADRs) is mainly based on disproportionality analysis. The aim of this study was to improve the efficiency of full database screening using a prediction model-based approach. A logistic regression-based prediction model containing 5 candidate predictors was developed and internally validated using the Summary of Product Characteristics as the gold standard for the outcome. All drug-ADR associations, with the exception of those related to vaccines, with a minimum of 3 reports formed the training data for the model. Performance was based on the area under the receiver operating characteristic curve (AUC). Results were compared with the current method of database screening based on the number of previously analyzed associations. A total of 25 026 unique drug-ADR associations formed the training data for the model. The final model contained all 5 candidate predictors (number of reports, disproportionality, reports from healthcare professionals, reports from marketing authorization holders, Naranjo score). The AUC for the full model was 0.740 (95% CI; 0.734-0.747). The internal validity was good based on the calibration curve and bootstrapping analysis (AUC after bootstrapping = 0.739). Compared with the old method, the AUC increased from 0.649 to 0.740, and the proportion of potential signals increased by approximately 50% (from 12.3% to 19.4%). A prediction model-based approach can be a useful tool to create priority-based listings for signal detection in databases consisting of spontaneous ADRs. © 2017 The Authors. Pharmacoepidemiology & Drug Safety Published by John Wiley & Sons Ltd.

School-Based Practices and Programs That Promote Safe and Drug-Free Schools. CASE/CCBD Mini-Library Series on Safe, Drug-Free, and Effective Schools.

ERIC Educational Resources Information Center

Guthrie, Patricia M.

This monograph focuses on school-based practices and programs that promote safe and drug-free schools. It begins with a description of the key characteristics of schools with effective programs and provides a model for school-wide support. Necessary steps for developing an effective system of universal prevention are listed and include: (1)…

An empirical approach to estimate near-infra-red photon propagation and optically induced drug release in brain tissues

NASA Astrophysics Data System (ADS)

Prabhu Verleker, Akshay; Fang, Qianqian; Choi, Mi-Ran; Clare, Susan; Stantz, Keith M.

2015-03-01

The purpose of this study is to develop an alternate empirical approach to estimate near-infra-red (NIR) photon propagation and quantify optically induced drug release in brain metastasis, without relying on computationally expensive Monte Carlo techniques (gold standard). Targeted drug delivery with optically induced drug release is a noninvasive means to treat cancers and metastasis. This study is part of a larger project to treat brain metastasis by delivering lapatinib-drug-nanocomplexes and activating NIR-induced drug release. The empirical model was developed using a weighted approach to estimate photon scattering in tissues and calibrated using a GPU based 3D Monte Carlo. The empirical model was developed and tested against Monte Carlo in optical brain phantoms for pencil beams (width 1mm) and broad beams (width 10mm). The empirical algorithm was tested against the Monte Carlo for different albedos along with diffusion equation and in simulated brain phantoms resembling white-matter (μs'=8.25mm-1, μa=0.005mm-1) and gray-matter (μs'=2.45mm-1, μa=0.035mm-1) at wavelength 800nm. The goodness of fit between the two models was determined using coefficient of determination (R-squared analysis). Preliminary results show the Empirical algorithm matches Monte Carlo simulated fluence over a wide range of albedo (0.7 to 0.99), while the diffusion equation fails for lower albedo. The photon fluence generated by empirical code matched the Monte Carlo in homogeneous phantoms (R2=0.99). While GPU based Monte Carlo achieved 300X acceleration compared to earlier CPU based models, the empirical code is 700X faster than the Monte Carlo for a typical super-Gaussian laser beam.

Using Media Resources to Explore Drug Use in Society: A Special 5th Grade Drug Education Lesson That Makes Use of Library Media Resources.

ERIC Educational Resources Information Center

Packer, Kenneth L.; And Others

This teaching guide, written for elementary school teachers and librarians, combines drug education for fifth grade students with library skill development. Following a preface to the guide, the affective model upon which the program is based (development of positive self-image and self-concept, and communication and coping skills) is presented.…

The story of artesunate–mefloquine (ASMQ), innovative partnerships in drug development: case study

PubMed Central

2013-01-01

Background The Drugs for Neglected Diseases initiative (DNDi) is a not-for profit organization committed to providing affordable medicines and access to treatments in resource-poor settings. Traditionally drug development has happened “in house” within pharmaceutical companies, with research and development costs ultimately recuperated through drug sales. The development of drugs for the treatment of neglected tropical diseases requires a completely different model that goes beyond the scope of market-driven research and development. Artesunate and mefloquine are well-established drugs for the treatment of uncomplicated malaria, with a strong safety record based on many years of field-based studies and use. The administration of such artemisinin-based combination therapy in a fixed-dose combination is expected to improve patient compliance and to reduce the risk of emerging drug resistance. Case description DNDi developed an innovative approach to drug development, reliant on strong collaborations with a wide range of partners from the commercial world, academia, government institutions and NGOs, each of which had a specific role to play in the development of a fixed dose combination of artesunate and mefloquine. Discussion and evaluation DNDi undertook the development of a fixed-dose combination of artesunate with mefloquine. Partnerships were formed across five continents, addressing formulation, control and production through to clinical trials and product registration, resulting in a safe and efficacious fixed dose combination treatment which is now available to treat patients in resource-poor settings. The south-south technology transfer of production from Farmanguinhos/Fiocruz in Brazil to Cipla Ltd in India was the first of its kind. Of additional benefit was the increased capacity within the knowledge base and infrastructure in developing countries. Conclusions This collaborative approach to drug development involving international partnerships and independent funding mechanisms is a powerful new way to develop drugs for tropical diseases. PMID:23433060

A knowledge-based approach for identification of drugs against vivapain-2 protein of Plasmodium vivax through pharmacophore-based virtual screening with comparative modelling.

PubMed

Yadav, Manoj Kumar; Singh, Amisha; Swati, D

2014-08-01

Malaria is one of the most infectious diseases in the world. Plasmodium vivax, the pathogen causing endemic malaria in humans worldwide, is responsible for extensive disease morbidity. Due to the emergence of resistance to common anti-malarial drugs, there is a continuous need to develop a new class of drugs for this pathogen. P. vivax cysteine protease, also known as vivapain-2, plays an important role in haemoglobin hydrolysis and is considered essential for the survival of the parasite. The three-dimensional (3D) structure of vivapain-2 is not predicted experimentally, so its structure is modelled by using comparative modelling approach and further validated by Qualitative Model Energy Analysis (QMEAN) and RAMPAGE tools. The potential binding site of selected vivapain-2 structure has been detected by grid-based function prediction method. Drug targets and their respective drugs similar to vivapain-2 have been identified using three publicly available databases: STITCH 3.1, DrugBank and Therapeutic Target Database (TTD). The second approach of this work focuses on docking study of selected drug E-64 against vivapain-2 protein. Docking reveals crucial information about key residues (Asn281, Cys283, Val396 and Asp398) that are responsible for holding the ligand in the active site. The similarity-search criterion is used for the preparation of our in-house database of drugs, obtained from filtering the drugs from the DrugBank database. A five-point 3D pharmacophore model is generated for the docked complex of vivapain-2 with E-64. This study of 3D pharmacophore-based virtual screening results in identifying three new drugs, amongst which one is approved and the other two are experimentally proved. The ADMET properties of these drugs are found to be in the desired range. These drugs with novel scaffolds may act as potent drugs for treating malaria caused by P. vivax.

Mathematical modeling and simulation in animal health - Part II: principles, methods, applications, and value of physiologically based pharmacokinetic modeling in veterinary medicine and food safety assessment.

PubMed

Lin, Z; Gehring, R; Mochel, J P; Lavé, T; Riviere, J E

2016-10-01

This review provides a tutorial for individuals interested in quantitative veterinary pharmacology and toxicology and offers a basis for establishing guidelines for physiologically based pharmacokinetic (PBPK) model development and application in veterinary medicine. This is important as the application of PBPK modeling in veterinary medicine has evolved over the past two decades. PBPK models can be used to predict drug tissue residues and withdrawal times in food-producing animals, to estimate chemical concentrations at the site of action and target organ toxicity to aid risk assessment of environmental contaminants and/or drugs in both domestic animals and wildlife, as well as to help design therapeutic regimens for veterinary drugs. This review provides a comprehensive summary of PBPK modeling principles, model development methodology, and the current applications in veterinary medicine, with a focus on predictions of drug tissue residues and withdrawal times in food-producing animals. The advantages and disadvantages of PBPK modeling compared to other pharmacokinetic modeling approaches (i.e., classical compartmental/noncompartmental modeling, nonlinear mixed-effects modeling, and interspecies allometric scaling) are further presented. The review finally discusses contemporary challenges and our perspectives on model documentation, evaluation criteria, quality improvement, and offers solutions to increase model acceptance and applications in veterinary pharmacology and toxicology. © 2016 John Wiley & Sons Ltd.

Role of Statistical Random-Effects Linear Models in Personalized Medicine

PubMed Central

Diaz, Francisco J; Yeh, Hung-Wen; de Leon, Jose

2012-01-01

Some empirical studies and recent developments in pharmacokinetic theory suggest that statistical random-effects linear models are valuable tools that allow describing simultaneously patient populations as a whole and patients as individuals. This remarkable characteristic indicates that these models may be useful in the development of personalized medicine, which aims at finding treatment regimes that are appropriate for particular patients, not just appropriate for the average patient. In fact, published developments show that random-effects linear models may provide a solid theoretical framework for drug dosage individualization in chronic diseases. In particular, individualized dosages computed with these models by means of an empirical Bayesian approach may produce better results than dosages computed with some methods routinely used in therapeutic drug monitoring. This is further supported by published empirical and theoretical findings that show that random effects linear models may provide accurate representations of phase III and IV steady-state pharmacokinetic data, and may be useful for dosage computations. These models have applications in the design of clinical algorithms for drug dosage individualization in chronic diseases; in the computation of dose correction factors; computation of the minimum number of blood samples from a patient that are necessary for calculating an optimal individualized drug dosage in therapeutic drug monitoring; measure of the clinical importance of clinical, demographic, environmental or genetic covariates; study of drug-drug interactions in clinical settings; the implementation of computational tools for web-site-based evidence farming; design of pharmacogenomic studies; and in the development of a pharmacological theory of dosage individualization. PMID:23467392

Hit Generation in TB Drug Discovery: From Genome to Granuloma

PubMed Central

2018-01-01

Current tuberculosis (TB) drug development efforts are not sufficient to end the global TB epidemic. Recent efforts have focused on the development of whole-cell screening assays because biochemical, target-based inhibitor screens during the last two decades have not delivered new TB drugs. Mycobacterium tuberculosis (Mtb), the causative agent of TB, encounters diverse microenvironments and can be found in a variety of metabolic states in the human host. Due to the complexity and heterogeneity of Mtb infection, no single model can fully recapitulate the in vivo conditions in which Mtb is found in TB patients, and there is no single “standard” screening condition to generate hit compounds for TB drug development. However, current screening assays have become more sophisticated as researchers attempt to mirror the complexity of TB disease in the laboratory. In this review, we describe efforts using surrogates and engineered strains of Mtb to focus screens on specific targets. We explain model culture systems ranging from carbon starvation to hypoxia, and combinations thereof, designed to represent the microenvironment which Mtb encounters in the human body. We outline ongoing efforts to model Mtb infection in the lung granuloma. We assess these different models, their ability to generate hit compounds, and needs for further TB drug development, to provide direction for future TB drug discovery. PMID:29384369

Paving the critical path: how can clinical pharmacology help achieve the vision?

PubMed

Lesko, L J

2007-02-01

It has been almost 3 years since the launch of the FDA critical path initiative following the publication of the paper "Innovation or Stagnation: Challenges and Opportunities on the Critical Path of New Medical Product Development." The initiative was intended to create an urgency with the drug development enterprise to address the so-called "productivity problem" in modern drug development. Clinical pharmacologists are strategically aligned with solutions designed to reduce late phase clinical trial failures to show adequate efficacy and/or safety. This article reviews some of the ways that clinical pharmacologists can lead and implement change in the drug development process. It includes a discussion of model-based, semi-mechanistic drug development, drug/disease models that facilitate informed clinical trial designs and optimal dosing, the qualification process and criteria for new biomarkers and surrogate endpoints, approaches to streamlining clinical trials and new types of interaction between industry and FDA such as the end-of-phase 2A and voluntary genomic data submission meetings respectively.

Development and Validation of a Computational Model Ensemble for the Early Detection of BCRP/ABCG2 Substrates during the Drug Design Stage.

PubMed

Gantner, Melisa E; Peroni, Roxana N; Morales, Juan F; Villalba, María L; Ruiz, María E; Talevi, Alan

2017-08-28

Breast Cancer Resistance Protein (BCRP) is an ATP-dependent efflux transporter linked to the multidrug resistance phenomenon in many diseases such as epilepsy and cancer and a potential source of drug interactions. For these reasons, the early identification of substrates and nonsubstrates of this transporter during the drug discovery stage is of great interest. We have developed a computational nonlinear model ensemble based on conformational independent molecular descriptors using a combined strategy of genetic algorithms, J48 decision tree classifiers, and data fusion. The best model ensemble consists in averaging the ranking of the 12 decision trees that showed the best performance on the training set, which also demonstrated a good performance for the test set. It was experimentally validated using the ex vivo everted rat intestinal sac model. Five anticonvulsant drugs classified as nonsubstrates for BRCP by the model ensemble were experimentally evaluated, and none of them proved to be a BCRP substrate under the experimental conditions used, thus confirming the predictive ability of the model ensemble. The model ensemble reported here is a potentially valuable tool to be used as an in silico ADME filter in computer-aided drug discovery campaigns intended to overcome BCRP-mediated multidrug resistance issues and to prevent drug-drug interactions.

Modeling the modified drug release from curved shape drug delivery systems - Dome Matrix®.

PubMed

Caccavo, D; Barba, A A; d'Amore, M; De Piano, R; Lamberti, G; Rossi, A; Colombo, P

2017-12-01

The controlled drug release from hydrogel-based drug delivery systems is a topic of large interest for research in pharmacology. The mathematical modeling of the behavior of these systems is a tool of emerging relevance, since the simulations can be of use in the design of novel systems, in particular for complex shaped tablets. In this work a model, previously developed, was applied to complex-shaped oral drug delivery systems based on hydrogels (Dome Matrix®). Furthermore, the model was successfully adopted in the description of drug release from partially accessible Dome Matrix® systems (systems with some surfaces coated). In these simulations, the erosion rate was used asa fitting parameter, and its dependence upon the surface area/volume ratio and upon the local fluid dynamics was discussed. The model parameters were determined by comparison with the drug release profile from a cylindrical tablet, then the model was successfully used for the prediction of the drug release from a Dome Matrix® system, for simple module configuration and for module assembled (void and piled) configurations. It was also demonstrated that, given the same initial S/V ratio, the drug release is independent upon the shape of the tablets but it is only influenced by the S/V evolution. The model reveals itself able to describe the observed phenomena, and thus it can be of use for the design of oral drug delivery systems, even if complex shaped. Copyright © 2017 Elsevier B.V. All rights reserved.

Drug delivery with microsecond laser pulses into gelatin

NASA Astrophysics Data System (ADS)

Shangguan, Hanqun; Casperson, Lee W.; Shearin, Alan; Gregory, Kenton W.; Prahl, Scott A.

1996-07-01

Photoacoustic drug delivery is a technique for localized drug delivery by laser-induced hydrodynamic pressure following cavitation bubble expansion and collapse. Photoacoustic drug delivery was investigated on gelatin-based thrombus models with planar and cylindrical geometries by use of one microsecond laser pulses. Solutions of a hydrophobic dye in mineral oil permitted monitoring of delivered colored oil into clear gelatin-based thrombus models. Cavitation bubble development and photoacoustic drug delivery were visualized with flash photography. This study demonstrated that cavitation is the governing mechanism for photoacoustic drug delivery, and the deepest penetration of colored oil in gels followed the bubble collapse. Spatial distribution measurements revealed that colored oil could be driven a few millimeters into the gels in both axial and radial directions, and the penetration was less than 500 mu m when the gelatin structure was not fractured. localized drug delivery, cavitation bubble, laser thrombolysis.

Biorelevant Dissolution Models for a Weak Base To Facilitate Formulation Development and Overcome Reduced Bioavailability Caused by Hypochlordyria or Achlorhydria.

PubMed

Kou, Dawen; Dwaraknath, Sudharsan; Fischer, Yannick; Nguyen, Daniel; Kim, Myeonghui; Yiu, Hiuwing; Patel, Preeti; Ng, Tania; Mao, Chen; Durk, Matthew; Chinn, Leslie; Winter, Helen; Wigman, Larry; Yehl, Peter

2017-10-02

In this study, two dissolution models were developed to achieve in vitro-in vivo relationship for immediate release formulations of Compound-A, a poorly soluble weak base with pH-dependent solubility and low bioavailability in hypochlorhydric and achlorhydric patients. The dissolution models were designed to approximate the hypo-/achlorhydric and normal fasted stomach conditions after a glass of water was ingested with the drug. The dissolution data from the two models were predictive of the relative in vivo bioavailability of various formulations under the same gastric condition, hypo-/achlorhydric or normal. Furthermore, the dissolution data were able to estimate the relative performance under hypo-/achlorhydric and normal fasted conditions for the same formulation. Together, these biorelevant dissolution models facilitated formulation development for Compound-A by identifying the right type and amount of key excipient to enhance bioavailability and mitigate the negative effect of hypo-/achlorhydria due to drug-drug interaction with acid-reducing agents. The dissolution models use readily available USP apparatus 2, and their broader utility can be evaluated on other BCS 2B compounds with reduced bioavailability caused by hypo-/achlorhydria.

Structure-based discovery and binding site analysis of histamine receptor ligands.

PubMed

Kiss, Róbert; Keserű, György M

2016-12-01

The application of structure-based drug discovery in histamine receptor projects was previously hampered by the lack of experimental structures. The publication of the first X-ray structure of the histamine H1 receptor has been followed by several successful virtual screens and binding site analysis studies of H1-antihistamines. This structure together with several other recently solved aminergic G-protein coupled receptors (GPCRs) enabled the development of more realistic homology models for H2, H3 and H4 receptors. Areas covered: In this paper, the authors review the development of histamine receptor models and their application in drug discovery. Expert opinion: In the authors' opinion, the application of atomistic histamine receptor models has played a significant role in understanding key ligand-receptor interactions as well as in the discovery of novel chemical starting points. The recently solved H1 receptor structure is a major milestone in structure-based drug discovery; however, our analysis also demonstrates that for building H3 and H4 receptor homology models, other GPCRs may be more suitable as templates. For these receptors, the authors envisage that the development of higher quality homology models will significantly contribute to the discovery and optimization of novel H3 and H4 ligands.

Modeling Drug- and Chemical-Induced Hepatotoxicity with Systems Biology Approaches

PubMed Central

Bhattacharya, Sudin; Shoda, Lisl K.M.; Zhang, Qiang; Woods, Courtney G.; Howell, Brett A.; Siler, Scott Q.; Woodhead, Jeffrey L.; Yang, Yuching; McMullen, Patrick; Watkins, Paul B.; Andersen, Melvin E.

2012-01-01

We provide an overview of computational systems biology approaches as applied to the study of chemical- and drug-induced toxicity. The concept of “toxicity pathways” is described in the context of the 2007 US National Academies of Science report, “Toxicity testing in the 21st Century: A Vision and A Strategy.” Pathway mapping and modeling based on network biology concepts are a key component of the vision laid out in this report for a more biologically based analysis of dose-response behavior and the safety of chemicals and drugs. We focus on toxicity of the liver (hepatotoxicity) – a complex phenotypic response with contributions from a number of different cell types and biological processes. We describe three case studies of complementary multi-scale computational modeling approaches to understand perturbation of toxicity pathways in the human liver as a result of exposure to environmental contaminants and specific drugs. One approach involves development of a spatial, multicellular “virtual tissue” model of the liver lobule that combines molecular circuits in individual hepatocytes with cell–cell interactions and blood-mediated transport of toxicants through hepatic sinusoids, to enable quantitative, mechanistic prediction of hepatic dose-response for activation of the aryl hydrocarbon receptor toxicity pathway. Simultaneously, methods are being developing to extract quantitative maps of intracellular signaling and transcriptional regulatory networks perturbed by environmental contaminants, using a combination of gene expression and genome-wide protein-DNA interaction data. A predictive physiological model (DILIsym™) to understand drug-induced liver injury (DILI), the most common adverse event leading to termination of clinical development programs and regulatory actions on drugs, is also described. The model initially focuses on reactive metabolite-induced DILI in response to administration of acetaminophen, and spans multiple biological scales. PMID:23248599

Drug delivery with microsecond laser pulses into gelatin.

PubMed

Shangguan, H; Casperson, L W; Shearin, A; Gregory, K W; Prahl, S A

1996-07-01

Photo acoustic drug delivery is a technique for localized drug delivery by laser-induced hydrodynamic pressure following cavitation bubble expansion and collapse. Photoacoustic drug delivery was investigated on gelatin-based thrombus models with planar and cylindrical geometries by use of one microsecond laser pulses. Solutions of a hydrophobic dye in mineral oil permitted monitoring of delivered colored oil into clear gelatin-based thrombus models. Cavitation bubble development and photoacoustic drug delivery were visualized with flash photography. This study demonstrated that cavitation is the governing mechanism for photoacoustic drug delivery, and the deepest penetration of colored oil in gels followed the bubble collapse. Spatial distribution measurements revealed that colored oil could be driven a few millimeters into the gels in both axial and radial directions, and the penetration was less than 500 µm when the gelatin structure was not fractured.

Assessing the Financial Benefits of Faster Development Times: The Case of Single-source Versus Multi-vendor Outsourced Biopharmaceutical Manufacturing.

PubMed

DiMasi, Joseph A; Smith, Zachary; Getz, Kenneth A

2018-05-10

The extent to which new drug developers can benefit financially from shorter development times has implications for development efficiency and innovation incentives. We provided a real-world example of such gains by using recent estimates of drug development costs and returns. Time and fee data were obtained on 5 single-source manufacturing projects. Time and fees were modeled for these projects as if the drug substance and drug product processes had been contracted separately from 2 vendors. The multi-vendor model was taken as the base case, and financial impacts from single-source contracting were determined relative to the base case. The mean and median after-tax financial benefits of shorter development times from single-source contracting were $44.7 million and $34.9 million, respectively (2016 dollars). The after-tax increases in sponsor fees from single-source contracting were small in comparison (mean and median of $0.65 million and $0.25 million). For the data we examined, single-source contracting yielded substantial financial benefits over multi-source contracting, even after accounting for somewhat higher sponsor fees. Copyright © 2018 Elsevier HS Journals, Inc. All rights reserved.

Response surface methodology for the determination of the design space of enantiomeric separations on cinchona-based zwitterionic chiral stationary phases by high performance liquid chromatography.

PubMed

Hanafi, Rasha Sayed; Lämmerhofer, Michael

2018-01-26

Quality-by-Design approach for enantioselective HPLC method development surpasses Quality-by-Testing in offering the optimal separation conditions with the least number of experiments and in its ability to describe the method's Design Space visually which helps to determine enantiorecognition to a significant extent. Although some schemes exist for enantiomeric separations on Cinchona-based zwitterionic stationary phases, the exact design space and the weights by which each of the chromatographic parameters influences the separation have not yet been statistically studied. In the current work, a screening design followed by a Response Surface Methodology optimization design were adopted for enantioseparation optimization of 3 model drugs namely the acidic Fmoc leucine, the amphoteric tryptophan and the basic salbutamol. The screening design proved that the acid/base additives are of utmost importance for the 3 chiral drugs, and that among 3 different pairs of acids and bases, acetic acid and diethylamine is the couple able to provide acceptable resolution at variable conditions. Visualization of the response surface of the retention factor, separation factor and resolution helped describe accurately the magnitude by which each chromatographic factor (% MeOH, concentration and ratio of acid base modifiers) affects the separation while interacting with other parameters. The global optima compromising highest enantioresolution with the least run time for the 3 chiral model drugs varied extremely, where it was best to set low % methanol with equal ratio of acid-base modifiers for the acidic drug, very high % methanol and 10-fold higher concentration of the acid for the amphoteric drug while 20 folds of the base modifier with moderate %methanol were needed for the basic drug. Considering the selected drugs as models for many series of structurally related compounds, the design space defined and the optimum conditions computed are the key for method development on cinchona-based chiral stationary phases. Copyright © 2017 Elsevier B.V. All rights reserved.

Microengineering methods for cell-based microarrays and high-throughput drug-screening applications.

PubMed

Xu, Feng; Wu, JinHui; Wang, ShuQi; Durmus, Naside Gozde; Gurkan, Umut Atakan; Demirci, Utkan

2011-09-01

Screening for effective therapeutic agents from millions of drug candidates is costly, time consuming, and often faces concerns due to the extensive use of animals. To improve cost effectiveness, and to minimize animal testing in pharmaceutical research, in vitro monolayer cell microarrays with multiwell plate assays have been developed. Integration of cell microarrays with microfluidic systems has facilitated automated and controlled component loading, significantly reducing the consumption of the candidate compounds and the target cells. Even though these methods significantly increased the throughput compared to conventional in vitro testing systems and in vivo animal models, the cost associated with these platforms remains prohibitively high. Besides, there is a need for three-dimensional (3D) cell-based drug-screening models which can mimic the in vivo microenvironment and the functionality of the native tissues. Here, we present the state-of-the-art microengineering approaches that can be used to develop 3D cell-based drug-screening assays. We highlight the 3D in vitro cell culture systems with live cell-based arrays, microfluidic cell culture systems, and their application to high-throughput drug screening. We conclude that among the emerging microengineering approaches, bioprinting holds great potential to provide repeatable 3D cell-based constructs with high temporal, spatial control and versatility.

Microengineering Methods for Cell Based Microarrays and High-Throughput Drug Screening Applications

PubMed Central

Xu, Feng; Wu, JinHui; Wang, ShuQi; Durmus, Naside Gozde; Gurkan, Umut Atakan; Demirci, Utkan

2011-01-01

Screening for effective therapeutic agents from millions of drug candidates is costly, time-consuming and often face ethical concerns due to extensive use of animals. To improve cost-effectiveness, and to minimize animal testing in pharmaceutical research, in vitro monolayer cell microarrays with multiwell plate assays have been developed. Integration of cell microarrays with microfluidic systems have facilitated automated and controlled component loading, significantly reducing the consumption of the candidate compounds and the target cells. Even though these methods significantly increased the throughput compared to conventional in vitro testing systems and in vivo animal models, the cost associated with these platforms remains prohibitively high. Besides, there is a need for three-dimensional (3D) cell based drug-screening models, which can mimic the in vivo microenvironment and the functionality of the native tissues. Here, we present the state-of-the-art microengineering approaches that can be used to develop 3D cell based drug screening assays. We highlight the 3D in vitro cell culture systems with live cell-based arrays, microfluidic cell culture systems, and their application to high-throughput drug screening. We conclude that among the emerging microengineering approaches, bioprinting holds a great potential to provide repeatable 3D cell based constructs with high temporal, spatial control and versatility. PMID:21725152

pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures

PubMed Central

2015-01-01

Drug development has a high attrition rate, with poor pharmacokinetic and safety properties a significant hurdle. Computational approaches may help minimize these risks. We have developed a novel approach (pkCSM) which uses graph-based signatures to develop predictive models of central ADMET properties for drug development. pkCSM performs as well or better than current methods. A freely accessible web server (http://structure.bioc.cam.ac.uk/pkcsm), which retains no information submitted to it, provides an integrated platform to rapidly evaluate pharmacokinetic and toxicity properties. PMID:25860834

Investigating the Impact of Drug Crystallinity in Amorphous Tacrolimus Capsules on Pharmacokinetics and Bioequivalence Using Discriminatory In Vitro Dissolution Testing and Physiologically Based Pharmacokinetic Modeling and Simulation.

PubMed

Purohit, Hitesh S; Trasi, Niraj S; Sun, Dajun D; Chow, Edwin C Y; Wen, Hong; Zhang, Xinyuan; Gao, Yi; Taylor, Lynne S

2018-05-01

Delivering a drug in amorphous form in a formulated product is a strategy used to enhance the apparent solubility of a drug substance and its oral bioavailability. Drug crystallization in such products may occur during the manufacturing process or on storage, reducing the solubility advantage of the amorphous drug. However, the impact of partial drug crystallization in the drug product on the resulting bioavailability and pharmacokinetics is unknown. In this study, dissolution testing of commercial tacrolimus capsules (which are formulated to contain amorphous drug), both fresh and those containing different amounts of crystalline drug, was conducted using both United States Pharmacopeia and noncompendial dissolution tests with different dissolution media and volumes. A physiologically based pharmacokinetic (PBPK) absorption model was developed to predict the impact of crystallinity extent on the oral absorption of the products and to evaluate the discriminatory ability of the different dissolution methods. Virtual bioequivalence simulations between partially crystallized tacrolimus capsules versus fresh Prograf or generic tacrolimus capsules were performed using the PBPK model and in vitro dissolution data of the various fresh and partially crystallized capsules under United States Pharmacopeia and noncompendial dissolution conditions. The results suggest that compendial dissolution tests may not be sufficiently discriminatory with respect to the presence of crystallinity in an amorphous formulation. Nonsink dissolution tests using lower dissolution volumes generate more discriminatory profiles that predict different pharmacokinetics of tacrolimus capsules containing different extents of drug crystallinity. In conclusion, the PBPK modeling approach can be used to assess the impact of partial drug crystallinity in the formulated product and to guide the development of appropriate dissolution methods. Copyright © 2018 American Pharmacists Association®. All rights reserved.

Budget Impact Analysis of Biosimilar Trastuzumab for the Treatment of Breast Cancer in Croatia.

PubMed

Cesarec, August; Likić, Robert

2017-04-01

Breast cancer is the most common cancer in women and has considerable impact on healthcare budgets and patients' quality of life. Trastuzumab (Herceptin ® ) is a monoclonal antibody directed against the human epidermal growth factor receptor (HER2) for the treatment of breast cancer. Several trastuzumab biosimilars are currently in development. In 2015, trastuzumab was the drug with the highest financial consumption among all drugs in Croatia. This model estimates the 1-year budget impact of the introduction of biosimilar trastuzumab in Croatia. A budget impact model, based on approvals for trastuzumab treatment in 2015, was developed for the introduction of biosimilars. Two biosimilar scenarios were developed: biosimilar scenario 1, based on all approvals in 2015, and biosimilar scenario 2, based on approvals after February 2015 and the reimbursement of the subcutaneous formulation of trastuzumab in Croatia. Only trastuzumab-naïve patients and drug-acquisition costs were used in the model. Uptake of biosimilar was assumed at 50 %. Scenarios were calculated with price discounts of 15, 25 and 35 %. The robustness of the model was tested by extensive sensitivity analyses. The projected drug cost savings from the introduction of biosimilar trastuzumab range from €0.26 million (scenario 2, 15 % price discount) to €0.69 million (scenario 1, 35 % price discount). If budget savings were reinvested to treat additional patients with trastuzumab, 14 (scenario 2, 15 % price discount) to 47 (scenario 1, 35 % price discount) additional patients could be treated. Sensitivity analyses showed that the incidence of breast cancer had the highest impact on the model, with a 10 % decrease in incidence leading to an 11.3 % decrease in projected savings. The introduction of biosimilar trastuzumab could lead to significant drug cost savings in Croatia.

Molecular design of anticancer drug leads based on three-dimensional quantitative structure-activity relationship.

PubMed

Huang, Xiao Yan; Shan, Zhi Jie; Zhai, Hong Lin; Li, Li Na; Zhang, Xiao Yun

2011-08-22

Heat shock protein 90 (Hsp90) takes part in the developments of several cancers. Novobiocin, a typically C-terminal inhibitor for Hsp90, will probably used as an important anticancer drug in the future. In this work, we explored the valuable information and designed new novobiocin derivatives based on a three-dimensional quantitative structure-activity relationship (3D QSAR). The comparative molecular field analysis and comparative molecular similarity indices analysis models with high predictive capability were established, and their reliabilities are supported by the statistical parameters. Based on the several important influence factors obtained from these models, six new novobiocin derivatives with higher inhibitory activities were designed and confirmed by the molecular simulation with our models, which provide the potential anticancer drug leads for further research.

GPCR homomers and heteromers: a better choice as targets for drug development than GPCR monomers?

PubMed

Casadó, Vicent; Cortés, Antoni; Mallol, Josefa; Pérez-Capote, Kamil; Ferré, Sergi; Lluis, Carmen; Franco, Rafael; Canela, Enric I

2009-11-01

G protein-coupled receptors (GPCR) are targeted by many therapeutic drugs marketed to fight against a variety of diseases. Selection of novel lead compounds are based on pharmacological parameters obtained assuming that GPCR are monomers. However, many GPCR are expressed as dimers/oligomers. Therefore, drug development may consider GPCR as homo- and hetero-oligomers. A two-state dimer receptor model is now available to understand GPCR operation and to interpret data obtained from drugs interacting with dimers, and even from mixtures of monomers and dimers. Heteromers are distinct entities and therefore a given drug is expected to have different affinities and different efficacies depending on the heteromer. All these concepts would lead to broaden the therapeutic potential of drugs targeting GPCRs, including receptor heteromer-selective drugs with a lower incidence of side effects, or to identify novel pharmacological profiles using cell models expressing receptor heteromers.

PRIORITIZING FUTURE RESEACH ON OFF-LABEL PRESCRIBING: RESULTS OF A QUANTITATIVE EVALUATION

PubMed Central

Walton, Surrey M.; Schumock, Glen T.; Lee, Ky-Van; Alexander, G. Caleb; Meltzer, David; Stafford, Randall S.

2015-01-01

Background Drug use for indications not approved by the Food and Drug Administration exceeds 20% of prescribing. Available compendia indicate that a minority of off-label uses are well supported by evidence. Policy makers, however, lack information to identify where systematic reviews of the evidence or other research would be most valuable. Methods We developed a quantitative model for prioritizing individual drugs for future research on off-label uses. The base model incorporated three key factors, 1) the volume of off-label use with inadequate evidence, 2) safety, and 3) cost and market considerations. Nationally representative prescribing data were used to estimate the number of off-label drug uses by indication from 1/2005 through 6/2007 in the United States, and these indications were then categorized according to the adequacy of scientific support. Black box warnings and safety alerts were used to quantify drug safety. Drug cost, date of market entry, and marketing expenditures were used to quantify cost and market considerations. Each drug was assigned a relative value for each factor, and the factors were then weighted in the final model to produce a priority score. Sensitivity analyses were conducted by varying the weightings and model parameters. Results Drugs that were consistently ranked highly in both our base model and sensitivity analyses included quetiapine, warfarin, escitalopram, risperidone, montelukast, bupropion, sertraline, venlafaxine, celecoxib, lisinopril, duloxetine, trazodone, olanzapine, and epoetin alfa. Conclusion Future research into off-label drug use should focus on drugs used frequently with inadequate supporting evidence, particularly if further concerns are raised by known safety issues, high drug cost, recent market entry, and extensive marketing. Based on quantitative measures of these factors, we have prioritized drugs where targeted research and policy activities have high potential value. PMID:19025425

Physiologically-based pharmacokinetic models: approaches for enabling personalized medicine.

PubMed

Hartmanshenn, Clara; Scherholz, Megerle; Androulakis, Ioannis P

2016-10-01

Personalized medicine strives to deliver the 'right drug at the right dose' by considering inter-person variability, one of the causes for therapeutic failure in specialized populations of patients. Physiologically-based pharmacokinetic (PBPK) modeling is a key tool in the advancement of personalized medicine to evaluate complex clinical scenarios, making use of physiological information as well as physicochemical data to simulate various physiological states to predict the distribution of pharmacokinetic responses. The increased dependency on PBPK models to address regulatory questions is aligned with the ability of PBPK models to minimize ethical and technical difficulties associated with pharmacokinetic and toxicology experiments for special patient populations. Subpopulation modeling can be achieved through an iterative and integrative approach using an adopt, adapt, develop, assess, amend, and deliver methodology. PBPK modeling has two valuable applications in personalized medicine: (1) determining the importance of certain subpopulations within a distribution of pharmacokinetic responses for a given drug formulation and (2) establishing the formulation design space needed to attain a targeted drug plasma concentration profile. This review article focuses on model development for physiological differences associated with sex (male vs. female), age (pediatric vs. young adults vs. elderly), disease state (healthy vs. unhealthy), and temporal variation (influence of biological rhythms), connecting them to drug product formulation development within the quality by design framework. Although PBPK modeling has come a long way, there is still a lengthy road before it can be fully accepted by pharmacologists, clinicians, and the broader industry.

The Best Prevention: Model Alcohol and Drug Education Program. NHTSA Prevention Guide.

ERIC Educational Resources Information Center

National Highway Traffic Safety Administration (DOT), Washington, DC.

This guide was created for school administrators, parents, teachers, and community groups interested in developing effective alcohol and drug abuse prevention programs for elementary and secondary schools. A comprehensive approach to school-based alcohol and drug prevention is described and various prevention activities which have been selected by…

Engineering a functional three-dimensional human cardiac tissue model for drug toxicity screening.

PubMed

Lu, Hong Fang; Leong, Meng Fatt; Lim, Tze Chiun; Chua, Ying Ping; Lim, Jia Kai; Du, Chan; Wan, Andrew C A

2017-05-11

Cardiotoxicity is one of the major reasons for clinical drug attrition. In vitro tissue models that can provide efficient and accurate drug toxicity screening are highly desired for preclinical drug development and personalized therapy. Here, we report the fabrication and characterization of a human cardiac tissue model for high throughput drug toxicity studies. Cardiac tissues were fabricated via cellular self-assembly of human transgene-free induced pluripotent stem cells-derived cardiomyocytes in pre-fabricated polydimethylsiloxane molds. The formed tissue constructs expressed cardiomyocyte-specific proteins, exhibited robust production of extracellular matrix components such as laminin, collagen and fibronectin, aligned sarcomeric organization, and stable spontaneous contractions for up to 2 months. Functional characterization revealed that the cardiac cells cultured in 3D tissues exhibited higher contraction speed and rate, and displayed a significantly different drug response compared to cells cultured in age-matched 2D monolayer. A panel of clinically relevant compounds including antibiotic, antidiabetic and anticancer drugs were tested in this study. Compared to conventional viability assays, our functional contractility-based assays were more sensitive in predicting drug-induced cardiotoxic effects, demonstrating good concordance with clinical observations. Thus, our 3D cardiac tissue model shows great potential to be used for early safety evaluation in drug development and drug efficiency testing for personalized therapy.

Development of a Drug-Response Modeling Framework to Identify Cell Line Derived Translational Biomarkers That Can Predict Treatment Outcome to Erlotinib or Sorafenib

PubMed Central

Li, Bin; Shin, Hyunjin; Gulbekyan, Georgy; Pustovalova, Olga; Nikolsky, Yuri; Hope, Andrew; Bessarabova, Marina; Schu, Matthew; Kolpakova-Hart, Elona; Merberg, David; Dorner, Andrew; Trepicchio, William L.

2015-01-01

Development of drug responsive biomarkers from pre-clinical data is a critical step in drug discovery, as it enables patient stratification in clinical trial design. Such translational biomarkers can be validated in early clinical trial phases and utilized as a patient inclusion parameter in later stage trials. Here we present a study on building accurate and selective drug sensitivity models for Erlotinib or Sorafenib from pre-clinical in vitro data, followed by validation of individual models on corresponding treatment arms from patient data generated in the BATTLE clinical trial. A Partial Least Squares Regression (PLSR) based modeling framework was designed and implemented, using a special splitting strategy and canonical pathways to capture robust information for model building. Erlotinib and Sorafenib predictive models could be used to identify a sub-group of patients that respond better to the corresponding treatment, and these models are specific to the corresponding drugs. The model derived signature genes reflect each drug’s known mechanism of action. Also, the models predict each drug’s potential cancer indications consistent with clinical trial results from a selection of globally normalized GEO expression datasets. PMID:26107615

A Dynamic Stress Model Explains the Delayed Drug Effect in Artemisinin Treatment of Plasmodium falciparum

PubMed Central

Cao, Pengxing; Klonis, Nectarios; Zaloumis, Sophie; Dogovski, Con; Xie, Stanley C.; Saralamba, Sompob; White, Lisa J.; Fowkes, Freya J. I.; Tilley, Leann; Simpson, Julie A.

2017-01-01

ABSTRACT Artemisinin resistance constitutes a major threat to the continued success of control programs for malaria, particularly in light of developing resistance to partner drugs. Improving our understanding of how artemisinin-based drugs act and how resistance manifests is essential for the optimization of dosing regimens and the development of strategies to prolong the life span of current first-line treatment options. Recent short-drug-pulse in vitro experiments have shown that the parasite killing rate depends not only on drug concentration but also the exposure time, challenging the standard pharmacokinetic-pharmacodynamic (PK-PD) paradigm in which the killing rate depends only on drug concentration. Here, we introduce a dynamic stress model of parasite killing and show through application to 3D7 laboratory strain viability data that the inclusion of a time-dependent parasite stress response dramatically improves the model's explanatory power compared to that of a traditional PK-PD model. Our model demonstrates that the previously reported hypersensitivity of early-ring-stage parasites of the 3D7 strain to dihydroartemisinin compared to other parasite stages is due primarily to a faster development of stress rather than a higher maximum achievable killing rate. We also perform in vivo simulations using the dynamic stress model and demonstrate that the complex temporal features of artemisinin action observed in vitro have a significant impact on predictions for in vivo parasite clearance. Given the important role that PK-PD models play in the design of clinical trials for the evaluation of alternative drug dosing regimens, our novel model will contribute to the further development and improvement of antimalarial therapies. PMID:28993326

Porous starch-based drug delivery systems processed by a microwave route.

PubMed

Malafaya, P B; Elvira, C; Gallardo, A; San Román, J; Reis, R L

2001-01-01

Abstract-A new simple processing route to produce starch-based porous materials was developed based on a microwave baking methodology. This innovative processing route was used to obtain non-loaded controls and loaded drug delivery carriers, incorporating a non-steroid anti-inflammatory agent. This bioactive agent was selected as model drug with expectations that the developed methodology might be used for other drugs and growth factors. The prepared systems were characterized by 1H and 13C NMR spectroscopy which allow the study of the interactions between the starch-based materials and the processing components, i.e, the blowing agents. The porosity of the prepared materials was estimated by measuring their apparent density and studied by comparing drug-loaded and non-loaded carriers. The behaviour of the porous structures, while immersed in aqueous media, was studied in terms of swelling and degradation, being intimately related to their porosity. Finally, in vitro drug release studies were performed showing a clear burst effect, followed by a slow controlled release of the drug over several days (up to 10 days).

SemaTyP: a knowledge graph based literature mining method for drug discovery.

PubMed

Sang, Shengtian; Yang, Zhihao; Wang, Lei; Liu, Xiaoxia; Lin, Hongfei; Wang, Jian

2018-05-30

Drug discovery is the process through which potential new medicines are identified. High-throughput screening and computer-aided drug discovery/design are the two main drug discovery methods for now, which have successfully discovered a series of drugs. However, development of new drugs is still an extremely time-consuming and expensive process. Biomedical literature contains important clues for the identification of potential treatments. It could support experts in biomedicine on their way towards new discoveries. Here, we propose a biomedical knowledge graph-based drug discovery method called SemaTyP, which discovers candidate drugs for diseases by mining published biomedical literature. We first construct a biomedical knowledge graph with the relations extracted from biomedical abstracts, then a logistic regression model is trained by learning the semantic types of paths of known drug therapies' existing in the biomedical knowledge graph, finally the learned model is used to discover drug therapies for new diseases. The experimental results show that our method could not only effectively discover new drug therapies for new diseases, but also could provide the potential mechanism of action of the candidate drugs. In this paper we propose a novel knowledge graph based literature mining method for drug discovery. It could be a supplementary method for current drug discovery methods.

Semimechanistic Bone Marrow Exhaustion Pharmacokinetic/Pharmacodynamic Model for Chemotherapy-Induced Cumulative Neutropenia.

PubMed

Henrich, Andrea; Joerger, Markus; Kraff, Stefanie; Jaehde, Ulrich; Huisinga, Wilhelm; Kloft, Charlotte; Parra-Guillen, Zinnia Patricia

2017-08-01

Paclitaxel is a commonly used cytotoxic anticancer drug with potentially life-threatening toxicity at therapeutic doses and high interindividual pharmacokinetic variability. Thus, drug and effect monitoring is indicated to control dose-limiting neutropenia. Joerger et al. (2016) developed a dose individualization algorithm based on a pharmacokinetic (PK)/pharmacodynamic (PD) model describing paclitaxel and neutrophil concentrations. Furthermore, the algorithm was prospectively compared in a clinical trial against standard dosing (Central European Society for Anticancer Drug Research Study of Paclitaxel Therapeutic Drug Monitoring; 365 patients, 720 cycles) but did not substantially improve neutropenia. This might be caused by misspecifications in the PK/PD model underlying the algorithm, especially without consideration of the observed cumulative pattern of neutropenia or the platinum-based combination therapy, both impacting neutropenia. This work aimed to externally evaluate the original PK/PD model for potential misspecifications and to refine the PK/PD model while considering the cumulative neutropenia pattern and the combination therapy. An underprediction was observed for the PK (658 samples), the PK parameters, and these parameters were re-estimated using the original estimates as prior information. Neutrophil concentrations (3274 samples) were overpredicted by the PK/PD model, especially for later treatment cycles when the cumulative pattern aggravated neutropenia. Three different modeling approaches (two from the literature and one newly developed) were investigated. The newly developed model, which implemented the bone marrow hypothesis semiphysiologically, was superior. This model further included an additive effect for toxicity of carboplatin combination therapy. Overall, a physiologically plausible PK/PD model was developed that can be used for dose adaptation simulations and prospective studies to further improve paclitaxel/carboplatin combination therapy. Copyright © 2017 by The American Society for Pharmacology and Experimental Therapeutics.

PBPK models for the prediction of in vivo performance of oral dosage forms.

PubMed

Kostewicz, Edmund S; Aarons, Leon; Bergstrand, Martin; Bolger, Michael B; Galetin, Aleksandra; Hatley, Oliver; Jamei, Masoud; Lloyd, Richard; Pepin, Xavier; Rostami-Hodjegan, Amin; Sjögren, Erik; Tannergren, Christer; Turner, David B; Wagner, Christian; Weitschies, Werner; Dressman, Jennifer

2014-06-16

Drug absorption from the gastrointestinal (GI) tract is a highly complex process dependent upon numerous factors including the physicochemical properties of the drug, characteristics of the formulation and interplay with the underlying physiological properties of the GI tract. The ability to accurately predict oral drug absorption during drug product development is becoming more relevant given the current challenges facing the pharmaceutical industry. Physiologically-based pharmacokinetic (PBPK) modeling provides an approach that enables the plasma concentration-time profiles to be predicted from preclinical in vitro and in vivo data and can thus provide a valuable resource to support decisions at various stages of the drug development process. Whilst there have been quite a few successes with PBPK models identifying key issues in the development of new drugs in vivo, there are still many aspects that need to be addressed in order to maximize the utility of the PBPK models to predict drug absorption, including improving our understanding of conditions in the lower small intestine and colon, taking the influence of disease on GI physiology into account and further exploring the reasons behind population variability. Importantly, there is also a need to create more appropriate in vitro models for testing dosage form performance and to streamline data input from these into the PBPK models. As part of the Oral Biopharmaceutical Tools (OrBiTo) project, this review provides a summary of the current status of PBPK models available. The current challenges in PBPK set-ups for oral drug absorption including the composition of GI luminal contents, transit and hydrodynamics, permeability and intestinal wall metabolism are discussed in detail. Further, the challenges regarding the appropriate integration of results from in vitro models, such as consideration of appropriate integration/estimation of solubility and the complexity of the in vitro release and precipitation data, are also highlighted as important steps to advancing the application of PBPK models in drug development. It is expected that the "innovative" integration of in vitro data from more appropriate in vitro models and the enhancement of the GI physiology component of PBPK models, arising from the OrBiTo project, will lead to a significant enhancement in the ability of PBPK models to successfully predict oral drug absorption and advance their role in preclinical and clinical development, as well as for regulatory applications. Copyright © 2013 Elsevier B.V. All rights reserved.

Drugs for Neglected Diseases initiative model of drug development for neglected diseases: current status and future challenges.

PubMed

Ioset, Jean-Robert; Chang, Shing

2011-09-01

The Drugs for Neglected Diseases initiative (DNDi) is a patients' needs-driven organization committed to the development of new treatments for neglected diseases. Created in 2003, DNDi has delivered four improved treatments for malaria, sleeping sickness and visceral leishmaniasis. A main DNDi challenge is to build a solid R&D portfolio for neglected diseases and to deliver preclinical candidates in a timely manner using an original model based on partnership. To address this challenge DNDi has remodeled its discovery activities from a project-based academic-bound network to a fully integrated process-oriented platform in close collaboration with pharmaceutical companies. This discovery platform relies on dedicated screening capacity and lead-optimization consortia supported by a pragmatic, structured and pharmaceutical-focused compound sourcing strategy.

Computational methods in drug discovery

PubMed Central

Leelananda, Sumudu P

2016-01-01

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein–ligand docking, pharmacophore modeling and QSAR techniques are reviewed. PMID:28144341

Computational methods in drug discovery.

PubMed

Leelananda, Sumudu P; Lindert, Steffen

2016-01-01

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

On the Road to Translation for PTSD Treatment: Theoretical and Practical Considerations of the Use of Human Models of Conditioned Fear for Drug Development.

PubMed

Risbrough, Victoria B; Glenn, Daniel E; Baker, Dewleen G

The use of quantitative, laboratory-based measures of threat in humans for proof-of-concept studies and target development for novel drug discovery has grown tremendously in the last 2 decades. In particular, in the field of posttraumatic stress disorder (PTSD), human models of fear conditioning have been critical in shaping our theoretical understanding of fear processes and importantly, validating findings from animal models of the neural substrates and signaling pathways required for these complex processes. Here, we will review the use of laboratory-based measures of fear processes in humans including cued and contextual conditioning, generalization, extinction, reconsolidation, and reinstatement to develop novel drug treatments for PTSD. We will primarily focus on recent advances in using behavioral and physiological measures of fear, discussing their sensitivity as biobehavioral markers of PTSD symptoms, their response to known and novel PTSD treatments, and in the case of d-cycloserine, how well these findings have translated to outcomes in clinical trials. We will highlight some gaps in the literature and needs for future research, discuss benefits and limitations of these outcome measures in designing proof-of-concept trials, and offer practical guidelines on design and interpretation when using these fear models for drug discovery.

Mathematical modeling of coupled drug and drug-encapsulated nanoparticle transport in patient-specific coronary artery walls

NASA Astrophysics Data System (ADS)

Hossain, Shaolie S.; Hossainy, Syed F. A.; Bazilevs, Yuri; Calo, Victor M.; Hughes, Thomas J. R.

2012-02-01

The majority of heart attacks occur when there is a sudden rupture of atherosclerotic plaque, exposing prothrombotic emboli to coronary blood flow, forming clots that can cause blockages of the arterial lumen. Diseased arteries can be treated with drugs delivered locally to vulnerable plaques. The objective of this work was to develop a computational tool-set to support the design and analysis of a catheter-based nanoparticulate drug delivery system to treat vulnerable plaques and diffuse atherosclerosis. A three-dimensional mathematical model of coupled mass transport of drug and drug-encapsulated nanoparticles was developed and solved numerically utilizing isogeometric finite element analysis. Simulations were run on a patient-specific multilayered coronary artery wall segment with a vulnerable plaque and the effect of artery and plaque inhomogeneity was analyzed. The method captured trends observed in local drug delivery and demonstrated potential for optimizing drug design parameters, including delivery location, nanoparticle surface properties, and drug release rate.

Mucoadhesive ocular insert based on thiolated poly(acrylic acid): development and in vivo evaluation in humans.

PubMed

Hornof, Margit; Weyenberg, Wim; Ludwig, Annick; Bernkop-Schnürch, Andreas

2003-05-20

The aim of the study was to develop a mucoadhesive ocular insert for the controlled delivery of ophthalmic drugs and to evaluate its efficacy in vivo. The inserts tested were based either on unmodified or thiolated poly(acrylic acid). Water uptake and swelling behavior of the inserts as well as the drug release rates of the model drugs fluorescein and two diclofenac salts with different solubility properties were evaluated in vitro. Fluorescein was used as fluorescent tracer to study the drug release from the insert in humans. The mean fluorescein concentration in the cornea/tearfilm compartment as a function of time was determined after application of aqueous eye drops and inserts composed of unmodified and of thiolated poly(acrylic acid). The acceptability of the inserts by the volunteers was also evaluated. Inserts based on thiolated poly(acrylic acid) were not soluble and had good cohesive properties. A controlled release was achieved for the incorporated model drugs. The in vivo study showed that inserts based on thiolated poly(acrylic acid) provide a fluorescein concentration on the eye surface for more than 8 h, whereas the fluorescein concentration rapidly decreased after application of aqueous eye drops or inserts based on unmodified poly(acrylic acid). Moreover, these inserts were well accepted by the volunteers. The present study indicates that ocular inserts based on thiolated poly(acrylic acid) are promising new solid devices for ocular drug delivery.

Mechanistic Oral Absorption Modeling and Simulation for Formulation Development and Bioequivalence Evaluation: Report of an FDA Public Workshop

PubMed Central

Duan, J; Kesisoglou, F; Novakovic, J; Amidon, GL; Jamei, M; Lukacova, V; Eissing, T; Tsakalozou, E; Zhao, L; Lionberger, R

2017-01-01

On May 19, 2016, the US Food and Drug Administration (FDA) hosted a public workshop, entitled “Mechanistic Oral Absorption Modeling and Simulation for Formulation Development and Bioequivalence Evaluation.”1 The topic of mechanistic oral absorption modeling, which is one of the major applications of physiologically based pharmacokinetic (PBPK) modeling and simulation, focuses on predicting oral absorption by mechanistically integrating gastrointestinal transit, dissolution, and permeation processes, incorporating systems, active pharmaceutical ingredient (API), and the drug product information, into a systemic mathematical whole‐body framework.2 PMID:28571121

Multiwell capillarity-based microfluidic device for the study of 3D tumour tissue-2D endothelium interactions and drug screening in co-culture models.

PubMed

Virumbrales-Muñoz, María; Ayuso, José María; Olave, Marta; Monge, Rosa; de Miguel, Diego; Martínez-Lostao, Luis; Le Gac, Séverine; Doblare, Manuel; Ochoa, Ignacio; Fernandez, Luis J

2017-09-20

The tumour microenvironment is very complex, and essential in tumour development and drug resistance. The endothelium is critical in the tumour microenvironment: it provides nutrients and oxygen to the tumour and is essential for systemic drug delivery. Therefore, we report a simple, user-friendly microfluidic device for co-culture of a 3D breast tumour model and a 2D endothelium model for cross-talk and drug delivery studies. First, we demonstrated the endothelium was functional, whereas the tumour model exhibited in vivo features, e.g., oxygen gradients and preferential proliferation of cells with better access to nutrients and oxygen. Next, we observed the endothelium structure lost its integrity in the co-culture. Following this, we evaluated two drug formulations of TRAIL (TNF-related apoptosis inducing ligand): soluble and anchored to a LUV (large unilamellar vesicle). Both diffused through the endothelium, LUV-TRAIL being more efficient in killing tumour cells, showing no effect on the integrity of endothelium. Overall, we have developed a simple capillary force-based microfluidic device for 2D and 3D cell co-cultures. Our device allows high-throughput approaches, patterning different cell types and generating gradients without specialised equipment. We anticipate this microfluidic device will facilitate drug screening in a relevant microenvironment thanks to its simple, effective and user-friendly operation.

A Critical Assessment of Combined Ligand-based and Structure-based Approaches to hERG Channel Blocker Modeling

PubMed Central

Du-Cuny, Lei; Chen, Lu; Zhang, Shuxing

2014-01-01

Blockade of hERG channel prolongs the duration of the cardiac action potential and is a common reason for drug failure in preclinical safety trials. Therefore, it is of great importance to develop robust in silico tools to predict potential hERG blockers in the early stages of drug discovery and development. Herein we described comprehensive approaches to assess the discrimination of hERG-active and -inactive compounds by combining QSAR modeling, pharmacophore analysis, and molecular docking. Our consensus models demonstrated high predictive capacity and improved enrichment, and they could correctly classify 91.8% of 147 hERG blockers from 351 inactives. To further enhance our modeling effort, hERG homology models were constructed and molecular docking studies were conducted, resulting in high correlations (R2=0.81) between predicted and experimental binding affinities. We expect our unique models can be applied to efficient screening for hERG blockades, and our extensive understanding of the hERG-inhibitor interactions will facilitate the rational design of drugs devoid of hERG channel activity and hence with reduced cardiac toxicities. PMID:21902220

Similarity-based prediction for Anatomical Therapeutic Chemical classification of drugs by integrating multiple data sources.

PubMed

Liu, Zhongyang; Guo, Feifei; Gu, Jiangyong; Wang, Yong; Li, Yang; Wang, Dan; Lu, Liang; Li, Dong; He, Fuchu

2015-06-01

Anatomical Therapeutic Chemical (ATC) classification system, widely applied in almost all drug utilization studies, is currently the most widely recognized classification system for drugs. Currently, new drug entries are added into the system only on users' requests, which leads to seriously incomplete drug coverage of the system, and bioinformatics prediction is helpful during this process. Here we propose a novel prediction model of drug-ATC code associations, using logistic regression to integrate multiple heterogeneous data sources including chemical structures, target proteins, gene expression, side-effects and chemical-chemical associations. The model obtains good performance for the prediction not only on ATC codes of unclassified drugs but also on new ATC codes of classified drugs assessed by cross-validation and independent test sets, and its efficacy exceeds previous methods. Further to facilitate the use, the model is developed into a user-friendly web service SPACE ( S: imilarity-based P: redictor of A: TC C: od E: ), which for each submitted compound, will give candidate ATC codes (ranked according to the decreasing probability_score predicted by the model) together with corresponding supporting evidence. This work not only contributes to knowing drugs' therapeutic, pharmacological and chemical properties, but also provides clues for drug repositioning and side-effect discovery. In addition, the construction of the prediction model also provides a general framework for similarity-based data integration which is suitable for other drug-related studies such as target, side-effect prediction etc. The web service SPACE is available at http://www.bprc.ac.cn/space. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Development, Verification, and Prediction of Osimertinib Drug-Drug Interactions Using PBPK Modeling Approach to Inform Drug Label.

PubMed

Pilla Reddy, Venkatesh; Walker, Michael; Sharma, Pradeep; Ballard, Peter; Vishwanathan, Karthick

2018-02-22

Osimertinib is a potent, highly selective, irreversible inhibitor of epidermal growth factor receptor (EGFR) and T790M resistance mutation. In vitro metabolism data suggested osimertinib is a substrate of cytochrome P450 (CYP)3A4/5, a weak inducer of CYP3A, and an inhibitor of breast cancer resistance protein (BCRP). A combination of in vitro data, clinical pharmacokinetic data, and drug-drug interaction (DDI) data of osimertinib in oncology patients were used to develop the physiologically based pharmacokinetic (PBPK) model and verify the DDI data of osimertinib. The model predicted the observed monotherapy concentration profile of osimertinib within 1.1-fold, and showed good predictability (within 1.7-fold) to the observed peak plasma concentration (C max ) and area under the curve (AUC) DDI ratio changes, when co-administered with rifampicin, itraconazole, and simvastatin, but not with rosuvastatin. Based on observed clinical data and PBPK simulations, the recommended dose of osimertinib when dosed with strong CYP3A inducers is 160 mg once daily. PBPK modeling suggested no dose adjustment with moderate and weak CYP3A inducers. © 2018 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for CPT: Pharmacometrics & Systems Pharmacology.

Understanding Attitude Change in Developing Effective Substance Abuse Prevention Programs for Adolescents.

ERIC Educational Resources Information Center

Scott, Cynthia G.

1996-01-01

Alcohol and drug use may be a significant part of the adolescent, high school experience. Programs should be based on an understanding of attitudes and patterns of use, and how change occurs. Elaboration Likelihood Model of Persuasion is a framework with which to examine attitude change and provide a base for building sound drug prevention…

A network-based approach for resistance transmission in bacterial populations.

PubMed

Gehring, Ronette; Schumm, Phillip; Youssef, Mina; Scoglio, Caterina

2010-01-07

Horizontal transfer of mobile genetic elements (conjugation) is an important mechanism whereby resistance is spread through bacterial populations. The aim of our work is to develop a mathematical model that quantitatively describes this process, and to use this model to optimize antimicrobial dosage regimens to minimize resistance development. The bacterial population is conceptualized as a compartmental mathematical model to describe changes in susceptible, resistant, and transconjugant bacteria over time. This model is combined with a compartmental pharmacokinetic model to explore the effect of different plasma drug concentration profiles. An agent-based simulation tool is used to account for resistance transfer occurring when two bacteria are adjacent or in close proximity. In addition, a non-linear programming optimal control problem is introduced to minimize bacterial populations as well as the drug dose. Simulation and optimization results suggest that the rapid death of susceptible individuals in the population is pivotal in minimizing the number of transconjugants in a population. This supports the use of potent antimicrobials that rapidly kill susceptible individuals and development of dosage regimens that maintain effective antimicrobial drug concentrations for as long as needed to kill off the susceptible population. Suggestions are made for experiments to test the hypotheses generated by these simulations.

How can attrition rates be reduced in cancer drug discovery?

PubMed

Moreno, Lucas; Pearson, Andrew D J

2013-04-01

Attrition is a major issue in anticancer drug development with up to 95% of drugs tested in Phase I trials not reaching a marketing authorisation making the drug development process enormously costly and inefficient. It is essential that this problem is addressed throughout the whole drug development process to improve efficiency which will ultimately result in increased patient benefit with more profitable drugs. The approach to reduce cancer drug attrition rates must be based on three pillars. The first of these is that there is a need for new pre-clinical models which can act as better predictors of success in clinical trials. Furthermore, clinical trials driven by tumour biology with the incorporation of predictive and pharmacodynamic biomarkers would be beneficial in drug development. Finally, there is a need for increased collaboration to combine the unique strengths between industry, academia and regulators to ensure that the needs of all stakeholders are met.

Funding innovation for treatment for rare diseases: adopting a cost-based yardstick approach

PubMed Central

2013-01-01

Background Manufacturers justify the high prices for orphan drugs on the basis that the associated R&D costs must be spread over few patients. The proliferation of these drugs in the last three decades, combined with high prices commonly in excess of $100,000 per patient per year are placing a substantial strain on the budgets of drug plans in many countries. Do insurers spend a growing portion of their budgets on small patient populations, or leave vulnerable patients without coverage for valuable treatments? We suggest that a third option is present in the form of a cost-based regulatory mechanism. Methods This article explores the use of a cost-based price control mechanism for orphan drugs, adapted from the standard models applied in utilities regulation. Results and conclusions A rate-of-return style model, employing yardsticked cost allocations and a modified two-stage rate of return calculation could be effective in setting a new standard for orphan drugs pricing. This type of cost-based pricing would limit the costs faced by insurers while continuing to provide an efficient incentive for new drug development. PMID:24237605

Droplet-based microtumor model to assess cell-ECM interactions and drug resistance of gastric cancer cells.

PubMed

Jang, Minjeong; Koh, Ilkyoo; Lee, Seok Jae; Cheong, Jae-Ho; Kim, Pilnam

2017-01-27

Gastric cancer (GC) is a common aggressive malignant tumor with high incidence and mortality worldwide. GC is classified into intestinal and diffuse types according to the histo-morphological features. Because of distinctly different clinico-pathological features, new cancer therapy strategies and in vitro preclinical models for the two pathological variants of GC is necessary. Since extracellular matrix (ECM) influence the biological behavior of tumor cells, we hypothesized that GC might be more similarly modeled in 3D with matrix rather than in 2D. Herein, we developed a microfluidic-based a three-dimensional (3D) in vitro gastric cancer model, with subsequent drug resistance assay. AGS (intestinal type) and Hs746T (diffuse type) gastric cancer cell lines were encapsulated in collagen beads with high cellular viability. AGS exhibited an aggregation pattern with expansive growth, whereas Hs746T showed single-cell-level infiltration. Importantly, in microtumor models, epithelial-mesenchymal transition (EMT) and metastatic genes were upregulated, whereas E-cadherin was downregulated. Expression of ß-catenin was decreased in drug-resistant cells, and chemosensitivity toward the anticancer drug (5-FU) was observed in microtumors. These results suggest that in vitro microtumor models may represent a biologically relevant platform for studying gastric cancer cell biology and tumorigenesis, and for accelerating the development of novel therapeutic targets.

MATLAB-Based Teaching Modules in Biochemical Engineering

ERIC Educational Resources Information Center

Lee, Kilho; Comolli, Noelle K.; Kelly, William J.; Huang, Zuyi

2015-01-01

Mathematical models play an important role in biochemical engineering. For example, the models developed in the field of systems biology have been used to identify drug targets to treat pathogens such as Pseudomonas aeruginosa in biofilms. In addition, competitive binding models for chromatography processes have been developed to predict expanded…

Rational design of liposomal drug delivery systems, a review: Combined experimental and computational studies of lipid membranes, liposomes and their PEGylation.

PubMed

Bunker, Alex; Magarkar, Aniket; Viitala, Tapani

2016-10-01

Combined experimental and computational studies of lipid membranes and liposomes, with the aim to attain mechanistic understanding, result in a synergy that makes possible the rational design of liposomal drug delivery system (LDS) based therapies. The LDS is the leading form of nanoscale drug delivery platform, an avenue in drug research, known as "nanomedicine", that holds the promise to transcend the current paradigm of drug development that has led to diminishing returns. Unfortunately this field of research has, so far, been far more successful in generating publications than new drug therapies. This partly results from the trial and error based methodologies used. We discuss experimental techniques capable of obtaining mechanistic insight into LDS structure and behavior. Insight obtained purely experimentally is, however, limited; computational modeling using molecular dynamics simulation can provide insight not otherwise available. We review computational research, that makes use of the multiscale modeling paradigm, simulating the phospholipid membrane with all atom resolution and the entire liposome with coarse grained models. We discuss in greater detail the computational modeling of liposome PEGylation. Overall, we wish to convey the power that lies in the combined use of experimental and computational methodologies; we hope to provide a roadmap for the rational design of LDS based therapies. Computational modeling is able to provide mechanistic insight that explains the context of experimental results and can also take the lead and inspire new directions for experimental research into LDS development. This article is part of a Special Issue entitled: Biosimulations edited by Ilpo Vattulainen and Tomasz Róg. Copyright © 2016 Elsevier B.V. All rights reserved.

In vitro cell culture models to study the corneal drug absorption.

PubMed

Reichl, Stephan; Kölln, Christian; Hahne, Matthias; Verstraelen, Jessica

2011-05-01

Many diseases of the anterior eye segment are treated using topically applied ophthalmic drugs. For these drugs, the cornea is the main barrier to reaching the interior of the eye. In vitro studies regarding transcorneal drug absorption are commonly performed using excised corneas from experimental animals. Due to several disadvantages and limitations of these animal experiments, establishing corneal cell culture models has been attempted as an alternative. This review summarizes the development of in vitro models based on corneal cell cultures for permeation studies during the last 20 years, starting with simple epithelial models and moving toward complex organotypical 3D corneal equivalents. Current human 3D corneal cell culture models have the potential to replace excised animal corneas in drug absorption studies. However, for widespread use, the contemporary validation of existent systems is required.

Model‐Based Approach to Predict Adherence to Protocol During Antiobesity Trials

PubMed Central

Sharma, Vishnu D.; Combes, François P.; Vakilynejad, Majid; Lahu, Gezim; Lesko, Lawrence J.

2017-01-01

Abstract Development of antiobesity drugs is continuously challenged by high dropout rates during clinical trials. The objective was to develop a population pharmacodynamic model that describes the temporal changes in body weight, considering disease progression, lifestyle intervention, and drug effects. Markov modeling (MM) was applied for quantification and characterization of responder and nonresponder as key drivers of dropout rates, to ultimately support the clinical trial simulations and the outcome in terms of trial adherence. Subjects (n = 4591) from 6 Contrave® trials were included in this analysis. An indirect‐response model developed by van Wart et al was used as a starting point. Inclusion of drug effect was dose driven using a population dose‐ and time‐dependent pharmacodynamic (DTPD) model. Additionally, a population‐pharmacokinetic parameter‐ and data (PPPD)‐driven model was developed using the final DTPD model structure and final parameter estimates from a previously developed population pharmacokinetic model based on available Contrave® pharmacokinetic concentrations. Last, MM was developed to predict transition rate probabilities among responder, nonresponder, and dropout states driven by the pharmacodynamic effect resulting from the DTPD or PPPD model. Covariates included in the models and parameters were diabetes mellitus and race. The linked DTPD‐MM and PPPD‐MM was able to predict transition rates among responder, nonresponder, and dropout states well. The analysis concluded that body‐weight change is an important factor influencing dropout rates, and the MM depicted that overall a DTPD model‐driven approach provides a reasonable prediction of clinical trial outcome probabilities similar to a pharmacokinetic‐driven approach. PMID:28858397

Integrating risk minimization planning throughout the clinical development and commercialization lifecycle: an opinion on how drug development could be improved

PubMed Central

Morrato, Elaine H; Smith, Meredith Y

2015-01-01

Pharmaceutical risk minimization programs are now an established requirement in the regulatory landscape. However, pharmaceutical companies have been slow to recognize and embrace the significant potential these programs offer in terms of enhancing trust with health care professionals and patients, and for providing a mechanism for bringing products to the market that might not otherwise have been approved. Pitfalls of the current drug development process include risk minimization programs that are not data driven; missed opportunities to incorporate pragmatic methods and market-based insights, outmoded tools and data sources, lack of rapid evaluative learning to support timely adaption, lack of systematic approaches for patient engagement, and questions on staffing and organizational infrastructure. We propose better integration of risk minimization with clinical drug development and commercialization work streams throughout the product lifecycle. We articulate a vision and propose broad adoption of organizational models for incorporating risk minimization expertise into the drug development process. Three organizational models are discussed and compared: outsource/external vendor, embedded risk management specialist model, and Center of Excellence. PMID:25750537

Performance of Machine Learning Algorithms for Qualitative and Quantitative Prediction Drug Blockade of hERG1 channel.

PubMed

Wacker, Soren; Noskov, Sergei Yu

2018-05-01

Drug-induced abnormal heart rhythm known as Torsades de Pointes (TdP) is a potential lethal ventricular tachycardia found in many patients. Even newly released anti-arrhythmic drugs, like ivabradine with HCN channel as a primary target, block the hERG potassium current in overlapping concentration interval. Promiscuous drug block to hERG channel may potentially lead to perturbation of the action potential duration (APD) and TdP, especially when with combined with polypharmacy and/or electrolyte disturbances. The example of novel anti-arrhythmic ivabradine illustrates clinically important and ongoing deficit in drug design and warrants for better screening methods. There is an urgent need to develop new approaches for rapid and accurate assessment of how drugs with complex interactions and multiple subcellular targets can predispose or protect from drug-induced TdP. One of the unexpected outcomes of compulsory hERG screening implemented in USA and European Union resulted in large datasets of IC 50 values for various molecules entering the market. The abundant data allows now to construct predictive machine-learning (ML) models. Novel ML algorithms and techniques promise better accuracy in determining IC 50 values of hERG blockade that is comparable or surpassing that of the earlier QSAR or molecular modeling technique. To test the performance of modern ML techniques, we have developed a computational platform integrating various workflows for quantitative structure activity relationship (QSAR) models using data from the ChEMBL database. To establish predictive powers of ML-based algorithms we computed IC 50 values for large dataset of molecules and compared it to automated patch clamp system for a large dataset of hERG blocking and non-blocking drugs, an industry gold standard in studies of cardiotoxicity. The optimal protocol with high sensitivity and predictive power is based on the novel eXtreme gradient boosting (XGBoost) algorithm. The ML-platform with XGBoost displays excellent performance with a coefficient of determination of up to R 2 ~0.8 for pIC 50 values in evaluation datasets, surpassing other metrics and approaches available in literature. Ultimately, the ML-based platform developed in our work is a scalable framework with automation potential to interact with other developing technologies in cardiotoxicity field, including high-throughput electrophysiology measurements delivering large datasets of profiled drugs, rapid synthesis and drug development via progress in synthetic biology.

Dashboard systems: implementing pharmacometrics from bench to bedside.

PubMed

Mould, Diane R; Upton, Richard N; Wojciechowski, Jessica

2014-09-01

In recent years, there has been increasing interest in the development of medical decision-support tools, including dashboard systems. Dashboard systems are software packages that integrate information and calculations about therapeutics from multiple components into a single interface for use in the clinical environment. Given the high cost of medical care, and the increasing need to demonstrate positive clinical outcomes for reimbursement, dashboard systems may become an important tool for improving patient outcome, improving clinical efficiency and containing healthcare costs. Similarly the costs associated with drug development are also rising. The use of model-based drug development (MBDD) has been proposed as a tool to streamline this process, facilitating the selection of appropriate doses and making informed go/no-go decisions. However, complete implementation of MBDD has not always been successful owing to a variety of factors, including the resources required to provide timely modeling and simulation updates. The application of dashboard systems in drug development reduces the resource requirement and may expedite updating models as new data are collected, allowing modeling results to be available in a timely fashion. In this paper, we present some background information on dashboard systems and propose the use of these systems both in the clinic and during drug development.

Induced Pluripotent Stem Cell Models to Enable In Vitro Models for Screening in the Central Nervous System.

PubMed

Hunsberger, Joshua G; Efthymiou, Anastasia G; Malik, Nasir; Behl, Mamta; Mead, Ivy L; Zeng, Xianmin; Simeonov, Anton; Rao, Mahendra

2015-08-15

There is great need to develop more predictive drug discovery tools to identify new therapies to treat diseases of the central nervous system (CNS). Current nonpluripotent stem cell-based models often utilize non-CNS immortalized cell lines and do not enable the development of personalized models of disease. In this review, we discuss why in vitro models are necessary for translational research and outline the unique advantages of induced pluripotent stem cell (iPSC)-based models over those of current systems. We suggest that iPSC-based models can be patient specific and isogenic lines can be differentiated into many neural cell types for detailed comparisons. iPSC-derived cells can be combined to form small organoids, or large panels of lines can be developed that enable new forms of analysis. iPSC and embryonic stem cell-derived cells can be readily engineered to develop reporters for lineage studies or mechanism of action experiments further extending the utility of iPSC-based systems. We conclude by describing novel technologies that include strategies for the development of diversity panels, novel genomic engineering tools, new three-dimensional organoid systems, and modified high-content screens that may bring toxicology into the 21st century. The strategic integration of these technologies with the advantages of iPSC-derived cell technology, we believe, will be a paradigm shift for toxicology and drug discovery efforts.

From Metabonomics to Pharmacometabonomics: The Role of Metabolic Profiling in Personalized Medicine

PubMed Central

Everett, Jeremy R.

2016-01-01

Variable patient responses to drugs are a key issue for medicine and for drug discovery and development. Personalized medicine, that is the selection of medicines for subgroups of patients so as to maximize drug efficacy and minimize toxicity, is a key goal of twenty-first century healthcare. Currently, most personalized medicine paradigms rely on clinical judgment based on the patient's history, and on the analysis of the patients' genome to predict drug effects i.e., pharmacogenomics. However, variability in patient responses to drugs is dependent upon many environmental factors to which human genomics is essentially blind. A new paradigm for predicting drug responses based on individual pre-dose metabolite profiles has emerged in the past decade: pharmacometabonomics, which is defined as “the prediction of the outcome (for example, efficacy or toxicity) of a drug or xenobiotic intervention in an individual based on a mathematical model of pre-intervention metabolite signatures.” The new pharmacometabonomics paradigm is complementary to pharmacogenomics but has the advantage of being sensitive to environmental as well as genomic factors. This review will chart the discovery and development of pharmacometabonomics, and provide examples of its current utility and possible future developments. PMID:27660611

Drug Education Curriculum, Junior High. Health Education: Substance Abuse Prevention. Revised 1982.

ERIC Educational Resources Information Center

New York State Education Dept., Albany. Bureau of Health and Drug Education and Services.

This junior high school level curriculum guide on drug education is a revision of a 1981 guide. It is one of nine sequential guides for elementary and secondary teachers and administrators designed to prevent drug misuse and abuse through combined cognitive and affective development. The affective model upon which this curriculum is based has…

Adolescent Neurocognitive Development, Self-Regulation, and School-Based Drug Use Prevention

PubMed Central

Herzog, Thaddeus A.; Black, David S.; Zaman, Adnin; Riggs, Nathaniel R.; Sussman, Steve

2014-01-01

Adolescence is marked by several key development-related changes, including neurocognitive changes. Cognitive abilities associated with self-regulation are not fully developed until late adolescence or early adulthood whereas tendencies to take risks and seek thrilling and novel experience seem to increase significantly throughout this phase, resulting in a discrepancy between increased susceptibility to poor regulation and lower ability to exercise self-control. Increased vulnerability to drug use initiation, maintenance, and dependence during adolescence may be explained based on this imbalance in the self-regulation system. In this paper, we highlight the relevance of schools as a setting for delivering adolescent drug use prevention programs that are based on recent findings from neuroscience concerning adolescent brain development. We discuss evidence from school-based as well as laboratory research that suggests that suitable training may improve adolescents’ executive brain functions that underlie self-regulation abilities and, as a result, help prevent drug use and abuse. We note that considerable further research is needed in order (1) to determine that self-regulation training has effects at the neurocognitive level and (2) to effectively incorporate self-regulation training based on neuropsychological models into school-based programming. PMID:23408284

Adolescent neurocognitive development, self-regulation, and school-based drug use prevention.

PubMed

Pokhrel, Pallav; Herzog, Thaddeus A; Black, David S; Zaman, Adnin; Riggs, Nathaniel R; Sussman, Steve

2013-06-01

Adolescence is marked by several key development-related changes, including neurocognitive changes. Cognitive abilities associated with self-regulation are not fully developed until late adolescence or early adulthood whereas tendencies to take risks and seek thrilling and novel experience seem to increase significantly throughout this phase, resulting in a discrepancy between increased susceptibility to poor regulation and lower ability to exercise self-control. Increased vulnerability to drug use initiation, maintenance, and dependence during adolescence may be explained based on this imbalance in the self-regulation system. In this paper, we highlight the relevance of schools as a setting for delivering adolescent drug use prevention programs that are based on recent findings from neuroscience concerning adolescent brain development. We discuss evidence from school-based as well as laboratory research that suggests that suitable training may improve adolescents' executive brain functions that underlie self-regulation abilities and, as a result, help prevent drug use and abuse. We note that considerable further research is needed in order (1) to determine that self-regulation training has effects at the neurocognitive level and (2) to effectively incorporate self-regulation training based on neuropsychological models into school-based programming.

Discovering Synergistic Drug Combination from a Computational Perspective.

PubMed

Ding, Pingjian; Luo, Jiawei; Liang, Cheng; Xiao, Qiu; Cao, Buwen; Li, Guanghui

2018-03-30

Synergistic drug combinations play an important role in the treatment of complex diseases. The identification of effective drug combination is vital to further reduce the side effects and improve therapeutic efficiency. In previous years, in vitro method has been the main route to discover synergistic drug combinations. However, many limitations of time and resource consumption lie within the in vitro method. Therefore, with the rapid development of computational models and the explosive growth of large and phenotypic data, computational methods for discovering synergistic drug combinations are an efficient and promising tool and contribute to precision medicine. It is the key of computational methods how to construct the computational model. Different computational strategies generate different performance. In this review, the recent advancements in computational methods for predicting effective drug combination are concluded from multiple aspects. First, various datasets utilized to discover synergistic drug combinations are summarized. Second, we discussed feature-based approaches and partitioned these methods into two classes including feature-based methods in terms of similarity measure, and feature-based methods in terms of machine learning. Third, we discussed network-based approaches for uncovering synergistic drug combinations. Finally, we analyzed and prospected computational methods for predicting effective drug combinations. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Advantages of Structure-Based Drug Design Approaches in Neurological Disorders

PubMed Central

Aarthy, Murali; Panwar, Umesh; Selvaraj, Chandrabose; Singh, Sanjeev Kumar

2017-01-01

Objective: The purpose of the review is to portray the theoretical concept on neurological disorders from research data. Background: The freak changes in chemical response of nerve impulse causes neurological disorders. The research evidence of the effort done in the older history suggests that the biological drug targets and their effective feature with responsive drugs could be valuable in promoting the future development of health statistics structure for improved treatment for curing the nervous disorders. Methods: In this review, we summarized the most iterative theoretical concept of structure based drug design approaches in various neurological disorders to unfathomable understanding of reported information for future drug design and development. Results: On the premise of reported information we analyzed the model of theoretical drug designing process for understanding the mechanism and pathology of the neurological diseases which covers the development of potentially effective inhibitors against the biological drug targets. Finally, it also suggests the management and implementation of the current treatment in improving the human health system behaviors. Conclusion: With the survey of reported information we concluded the development strategies of diagnosis and treatment against neurological diseases which leads to supportive progress in the drug discovery. PMID:28042767

AutoDrug: fully automated macromolecular crystallography workflows for fragment-based drug discovery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tsai, Yingssu; Stanford University, 333 Campus Drive, Mudd Building, Stanford, CA 94305-5080; McPhillips, Scott E.

New software has been developed for automating the experimental and data-processing stages of fragment-based drug discovery at a macromolecular crystallography beamline. A new workflow-automation framework orchestrates beamline-control and data-analysis software while organizing results from multiple samples. AutoDrug is software based upon the scientific workflow paradigm that integrates the Stanford Synchrotron Radiation Lightsource macromolecular crystallography beamlines and third-party processing software to automate the crystallography steps of the fragment-based drug-discovery process. AutoDrug screens a cassette of fragment-soaked crystals, selects crystals for data collection based on screening results and user-specified criteria and determines optimal data-collection strategies. It then collects and processes diffraction data,more » performs molecular replacement using provided models and detects electron density that is likely to arise from bound fragments. All processes are fully automated, i.e. are performed without user interaction or supervision. Samples can be screened in groups corresponding to particular proteins, crystal forms and/or soaking conditions. A single AutoDrug run is only limited by the capacity of the sample-storage dewar at the beamline: currently 288 samples. AutoDrug was developed in conjunction with RestFlow, a new scientific workflow-automation framework. RestFlow simplifies the design of AutoDrug by managing the flow of data and the organization of results and by orchestrating the execution of computational pipeline steps. It also simplifies the execution and interaction of third-party programs and the beamline-control system. Modeling AutoDrug as a scientific workflow enables multiple variants that meet the requirements of different user groups to be developed and supported. A workflow tailored to mimic the crystallography stages comprising the drug-discovery pipeline of CoCrystal Discovery Inc. has been deployed and successfully demonstrated. This workflow was run once on the same 96 samples that the group had examined manually and the workflow cycled successfully through all of the samples, collected data from the same samples that were selected manually and located the same peaks of unmodeled density in the resulting difference Fourier maps.« less

An appraisal of drug development timelines in the Era of precision oncology

PubMed Central

Jardim, Denis Leonardo; Schwaederle, Maria; Hong, David S.; Kurzrock, Razelle

2016-01-01

The effects of incorporating a biomarker-based (personalized or precision) selection strategy on drug development timelines for new oncology drugs merit investigation. Here we accessed documents from the Food and Drug Administration (FDA) database for anticancer agents approved between 09/1998 and 07/2014 to compare drugs developed with and without a personalized strategy. Sixty-three drugs were included (28 [44%] personalized and 35 [56%] non-personalized). No differences in access to FDA-expedited programs were observed between personalized and non-personalized drugs. A personalized approach for drug development was associated with faster clinical development (Investigational New Drug [IND] to New Drug Application [NDA] submission; median = 58.8 months [95% CI 53.8–81.8] vs. 93.5 months [95% CI 73.9–112.9], P =.001), but a similar approval time (NDA submission to approval; median=6.0 months [95% CI 5.5–8.4] vs. 6.1 months [95% CI 5.9–8.3], P = .756) compared to a non-personalized strategy. In the multivariate model, class of drug stratified by personalized status (targeted personalized vs. targeted non-personalized vs. cytotoxic) was the only independent factor associated with faster total time of clinical drug development (clinical plus approval phase, median = 64.6 vs 87.1 vs. 112.7 months [cytotoxic], P = .038). Response rates (RR) in early trials were positively correlated with RR in registration trials (r = 0.63, P = <.001), and inversely associated with total time of drug development (r = −0.29, P = .049). In conclusion, targeted agents were developed faster than cytotoxic agents. Shorter times to approval were associated, in multivariate analysis, with a biomarker-based clinical development strategy. PMID:27419632

Bioreactor Technologies to Support Liver Function In Vitro

PubMed Central

Ebrahimkhani, Mohammad R; Neiman, Jaclyn A Shepard; Raredon, Micah Sam B; Hughes, David J; Griffith, Linda G

2014-01-01

Liver is a central nexus integrating metabolic and immunologic homeostasis in the human body, and the direct or indirect target of most molecular therapeutics. A wide spectrum of therapeutic and technological needs drive efforts to capture liver physiology and pathophysiology in vitro, ranging from prediction of metabolism and toxicity of small molecule drugs, to understanding off-target effects of proteins, nucleic acid therapies, and targeted therapeutics, to serving as disease models for drug development. Here we provide perspective on the evolving landscape of bioreactor-based models to meet old and new challenges in drug discovery and development, emphasizing design challenges in maintaining long-term liver-specific function and how emerging technologies in biomaterials and microdevices are providing new experimental models. PMID:24607703

Physiologically Based Pharmacokinetic and Absorption Modeling for Osmotic Pump Products.

PubMed

Ni, Zhanglin; Talattof, Arjang; Fan, Jianghong; Tsakalozou, Eleftheria; Sharan, Satish; Sun, Dajun; Wen, Hong; Zhao, Liang; Zhang, Xinyuan

2017-07-01

Physiologically based pharmacokinetic (PBPK) and absorption modeling approaches were employed for oral extended-release (ER) drug products based on an osmotic drug delivery system (osmotic pumps). The purpose was to systemically evaluate the in vivo relevance of in vitro dissolution for this type of formulation. As expected, in vitro dissolution appeared to be generally predictive of in vivo PK profiles, because of the unique feature of this delivery system that the in vitro and in vivo release of osmotic pump drug products is less susceptible to surrounding environment in the gastrointestinal (GI) tract such as pH, hydrodynamic, and food effects. The present study considered BCS (Biopharmaceutics Classification System) class 1, 2, and 3 drug products with half-lives ranging from 2 to greater than 24 h. In some cases, the colonic absorption models needed to be adjusted to account for absorption in the colon. C max (maximum plasma concentration) and AUCt (area under the concentration curve) of the studied drug products were sensitive to changes in colon permeability and segmental GI transit times in a drug product-dependent manner. While improvement of the methodology is still warranted for more precise prediction (e.g., colonic absorption and dynamic movement in the GI tract), the results from the present study further emphasized the advantage of using PBPK modeling in addressing product-specific questions arising from regulatory review and drug development.

Homology modeling and docking analyses of M. leprae Mur ligases reveals the common binding residues for structure based drug designing to eradicate leprosy.

PubMed

Shanmugam, Anusuya; Natarajan, Jeyakumar

2012-06-01

Multi drug resistance capacity for Mycobacterium leprae (MDR-Mle) demands the profound need for developing new anti-leprosy drugs. Since most of the drugs target a single enzyme, mutation in the active site renders the antibiotic ineffective. However, structural and mechanistic information on essential bacterial enzymes in a pathway could lead to the development of antibiotics that targets multiple enzymes. Peptidoglycan is an important component of the cell wall of M. leprae. The biosynthesis of bacterial peptidoglycan represents important targets for the development of new antibacterial drugs. Biosynthesis of peptidoglycan is a multi-step process that involves four key Mur ligase enzymes: MurC (EC:6.3.2.8), MurD (EC:6.3.2.9), MurE (EC:6.3.2.13) and MurF (EC:6.3.2.10). Hence in our work, we modeled the three-dimensional structure of the above Mur ligases using homology modeling method and analyzed its common binding features. The residues playing an important role in the catalytic activity of each of the Mur enzymes were predicted by docking these Mur ligases with their substrates and ATP. The conserved sequence motifs significant for ATP binding were predicted as the probable residues for structure based drug designing. Overall, the study was successful in listing significant and common binding residues of Mur enzymes in peptidoglycan pathway for multi targeted therapy.

Cocaine addiction and personality: a mathematical model.

PubMed

Caselles, Antonio; Micó, Joan C; Amigó, Salvador

2010-05-01

The existence of a close relation between personality and drug consumption is recognized, but the corresponding causal connection is not well known. Neither is it well known whether personality exercises an influence predominantly at the beginning and development of addiction, nor whether drug consumption produces changes in personality. This paper presents a dynamic mathematical model of personality and addiction based on the unique personality trait theory (UPTT) and the general modelling methodology. This model attempts to integrate personality, the acute effect of drugs, and addiction. The UPTT states the existence of a unique trait of personality called extraversion, understood as a dimension that ranges from impulsive behaviour and sensation-seeking (extravert pole) to fearful and anxious behaviour (introvert pole). As a consequence of drug consumption, the model provides the main patterns of extraversion dynamics through a system of five coupled differential equations. It combines genetic extraversion, as a steady state, and dynamic extraversion in a unique variable measured on the hedonic scale. The dynamics of this variable describes the effects of stimulant drugs on a short-term time scale (typical of the acute effect); while its mean time value describes the effects of stimulant drugs on a long-term time scale (typical of the addiction effect). This understanding may help to develop programmes of prevention and intervention in drug misuse.

Prediction of Drug-Plasma Protein Binding Using Artificial Intelligence Based Algorithms.

PubMed

Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

2018-01-01

Plasma protein binding (PPB) has vital importance in the characterization of drug distribution in the systemic circulation. Unfavorable PPB can pose a negative effect on clinical development of promising drug candidates. The drug distribution properties should be considered at the initial phases of the drug design and development. Therefore, PPB prediction models are receiving an increased attention. In the current study, we present a systematic approach using Support vector machine, Artificial neural network, k- nearest neighbor, Probabilistic neural network, Partial least square and Linear discriminant analysis to relate various in vitro and in silico molecular descriptors to a diverse dataset of 736 drugs/drug-like compounds. The overall accuracy of Support vector machine with Radial basis function kernel came out to be comparatively better than the rest of the applied algorithms. The training set accuracy, validation set accuracy, precision, sensitivity, specificity and F1 score for the Suprort vector machine was found to be 89.73%, 89.97%, 92.56%, 87.26%, 91.97% and 0.898, respectively. This model can potentially be useful in screening of relevant drug candidates at the preliminary stages of drug design and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Open challenges in structure-based virtual screening: Receptor modeling, target flexibility consideration and active site water molecules description.

PubMed

Spyrakis, Francesca; Cavasotto, Claudio N

2015-10-01

Structure-based virtual screening is currently an established tool in drug lead discovery projects. Although in the last years the field saw an impressive progress in terms of algorithm development, computational performance, and retrospective and prospective applications in ligand identification, there are still long-standing challenges where further improvement is needed. In this review, we consider the conceptual frame, state-of-the-art and recent developments of three critical "structural" issues in structure-based drug lead discovery: the use of homology modeling to accurately model the binding site when no experimental structures are available, the necessity of accounting for the dynamics of intrinsically flexible systems as proteins, and the importance of considering active site water molecules in lead identification and optimization campaigns. Copyright © 2015 Elsevier Inc. All rights reserved.

The Devil Is in the Details: Examining the Evidence for "Proven" School-Based Drug Abuse Prevention Programs

ERIC Educational Resources Information Center

Gandhi, Allison Gruner; Murphy-Graham, Erin; Petrosino, Anthony; Chrismer, Sara Schwartz; Weiss, Carol H.

2007-01-01

In an effort to promote evidence-based practice, government officials, researchers, and program developers have developed lists of model programs in the prevention field. This article reviews the evidence used by seven best-practice lists to select five model prevention programs. The authors' examination of this research raises questions about the…

Model-Based Approach to Predict Adherence to Protocol During Antiobesity Trials.

PubMed

Sharma, Vishnu D; Combes, François P; Vakilynejad, Majid; Lahu, Gezim; Lesko, Lawrence J; Trame, Mirjam N

2018-02-01

Development of antiobesity drugs is continuously challenged by high dropout rates during clinical trials. The objective was to develop a population pharmacodynamic model that describes the temporal changes in body weight, considering disease progression, lifestyle intervention, and drug effects. Markov modeling (MM) was applied for quantification and characterization of responder and nonresponder as key drivers of dropout rates, to ultimately support the clinical trial simulations and the outcome in terms of trial adherence. Subjects (n = 4591) from 6 Contrave ® trials were included in this analysis. An indirect-response model developed by van Wart et al was used as a starting point. Inclusion of drug effect was dose driven using a population dose- and time-dependent pharmacodynamic (DTPD) model. Additionally, a population-pharmacokinetic parameter- and data (PPPD)-driven model was developed using the final DTPD model structure and final parameter estimates from a previously developed population pharmacokinetic model based on available Contrave ® pharmacokinetic concentrations. Last, MM was developed to predict transition rate probabilities among responder, nonresponder, and dropout states driven by the pharmacodynamic effect resulting from the DTPD or PPPD model. Covariates included in the models and parameters were diabetes mellitus and race. The linked DTPD-MM and PPPD-MM was able to predict transition rates among responder, nonresponder, and dropout states well. The analysis concluded that body-weight change is an important factor influencing dropout rates, and the MM depicted that overall a DTPD model-driven approach provides a reasonable prediction of clinical trial outcome probabilities similar to a pharmacokinetic-driven approach. © 2017, The Authors. The Journal of Clinical Pharmacology published by Wiley Periodicals, Inc. on behalf of American College of Clinical Pharmacology.

Relative contributions of the major human CYP450 to the metabolism of icotinib and its implication in prediction of drug-drug interaction between icotinib and CYP3A4 inhibitors/inducers using physiologically based pharmacokinetic modeling.

PubMed

Chen, Jia; Liu, Dongyang; Zheng, Xin; Zhao, Qian; Jiang, Ji; Hu, Pei

2015-06-01

Icotinib is an anticancer drug, but relative contributions of CYP450 have not been identified. This study was carried out to identify the contribution percentage of CYP450 to icotinib and use the results to develop a physiologically based pharmacokinetic (PBPK) model, which can help to predict drug-drug interaction (DDI). Human liver microsome (HLM) and supersome using relative activity factor (RAF) were employed to determine the relative contributions of the major human P450 to the net hepatic metabolism of icotinib. These values were introduced to develop a PBPK model using SimCYP. The model was validated by the observed data in a Phase I clinical trial in Chinese healthy subjects. Finally, the model was used to simulate the DDI with ketoconazole or rifampin. Final contribution of CYP450 isoforms determined by HLM showed that CYP3A4 provided major contributions to the metabolism of icotinib. The percentage contributions of the P450 to the net hepatic metabolism of icotinib were determined by HLM inhibition assay and RAF. The AUC ratio under concomitant use of ketoconazole and rifampin was 3.22 and 0.55, respectively. Percentage of contribution of CYP450 to icotinib metabolism was calculated by RAF. The model has been proven to fit the observed data and is used in predicting icotinib-ketoconazole/rifampin interaction.

Animal model for hepatitis C virus infection.

PubMed

Tsukiyama-Kohara, Kyoko; Kohara, Michinori

2015-01-01

Hepatitis C virus (HCV) infects more than 170 million people in the world and chronic HCV infection develops into cirrhosis and hepatocellular carcinoma (HCC). Recently, the effective compounds have been approved for HCV treatment, the protease inhibitor and polymerase inhibitor (direct acting antivirals; DAA). DAA-based therapy enabled to cure from HCV infection. However, development of new drug and vaccine is still required because of the generation of HCV escape mutants from DAA, development of HCC after treatment of DAA, and the high cost of DAA. In order to develop new anti-HCV drug and vaccine, animal infection model of HCV is essential. In this manuscript, we would like to introduce the history and the current status of the development of HCV animal infection model.

Separation of antibody drug conjugate species by RPLC: A generic method development approach.

PubMed

Fekete, Szabolcs; Molnár, Imre; Guillarme, Davy

2017-04-15

This study reports the use of modelling software for the successful method development of IgG1 cysteine conjugated antibody drug conjugate (ADC) in RPLC. The goal of such a method is to be able to calculate the average drug to antibody ratio (DAR) of and ADC product. A generic method development strategy was proposed including the optimization of mobile phase temperature, gradient profile and mobile phase ternary composition. For the first time, a 3D retention modelling was presented for large therapeutic protein. Based on a limited number of preliminary experiments, a fast and efficient separation of the DAR species of a commercial ADC sample, namely brentuximab vedotin, was achieved. The prediction offered by the retention model was found to be highly reliable, with an average error of retention time prediction always lower than 0.5% using a 2D or 3D retention models. For routine purpose, four to six initial experiments were required to build the 2D retention models, while 12 experiments were recommended to create the 3D model. At the end, RPLC can therefore be considered as a good method for estimating the average DAR of an ADC, based on the observed peak area ratios of RPLC chromatogram of the reduced ADC sample. Copyright © 2017 Elsevier B.V. All rights reserved.

Alginate based 3D hydrogels as an in vitro co-culture model platform for the toxicity screening of new chemical entities.

PubMed

Lan, Shih-Feng; Starly, Binil

2011-10-01

Prediction of human response to potential therapeutic drugs is through conventional methods of in vitro cell culture assays and expensive in vivo animal testing. Alternatives to animal testing require sophisticated in vitro model systems that must replicate in vivo like function for reliable testing applications. Advancements in biomaterials have enabled the development of three-dimensional (3D) cell encapsulated hydrogels as in vitro drug screening tissue model systems. In this study, we have developed an in vitro platform to enable high density 3D culture of liver cells combined with a monolayer growth of target breast cancer cell line (MCF-7) in a static environment as a representative example of screening drug compounds for hepatotoxicity and drug efficacy. Alginate hydrogels encapsulated with serial cell densities of HepG2 cells (10(5)-10(8) cells/ml) are supported by a porous poly-carbonate disc platform and co-cultured with MCF-7 cells within standard cell culture plates during a 3 day study period. The clearance rates of drug transformation by HepG2 cells are measured using a coumarin based pro-drug. The platform was used to test for HepG2 cytotoxicity 50% (CT(50)) using commercially available drugs which further correlated well with published in vivo LD(50) values. The developed test platform allowed us to evaluate drug dose concentrations to predict hepatotoxicity and its effect on the target cells. The in vitro 3D co-culture platform provides a scalable and flexible approach to test multiple-cell types in a hybrid setting within standard cell culture plates which may open up novel 3D in vitro culture techniques to screen new chemical entity compounds. Copyright © 2011 Elsevier Inc. All rights reserved.

Clustering drug-drug interaction networks with energy model layouts: community analysis and drug repurposing.

PubMed

Udrescu, Lucreţia; Sbârcea, Laura; Topîrceanu, Alexandru; Iovanovici, Alexandru; Kurunczi, Ludovic; Bogdan, Paul; Udrescu, Mihai

2016-09-07

Analyzing drug-drug interactions may unravel previously unknown drug action patterns, leading to the development of new drug discovery tools. We present a new approach to analyzing drug-drug interaction networks, based on clustering and topological community detection techniques that are specific to complex network science. Our methodology uncovers functional drug categories along with the intricate relationships between them. Using modularity-based and energy-model layout community detection algorithms, we link the network clusters to 9 relevant pharmacological properties. Out of the 1141 drugs from the DrugBank 4.1 database, our extensive literature survey and cross-checking with other databases such as Drugs.com, RxList, and DrugBank 4.3 confirm the predicted properties for 85% of the drugs. As such, we argue that network analysis offers a high-level grasp on a wide area of pharmacological aspects, indicating possible unaccounted interactions and missing pharmacological properties that can lead to drug repositioning for the 15% drugs which seem to be inconsistent with the predicted property. Also, by using network centralities, we can rank drugs according to their interaction potential for both simple and complex multi-pathology therapies. Moreover, our clustering approach can be extended for applications such as analyzing drug-target interactions or phenotyping patients in personalized medicine applications.

Clustering drug-drug interaction networks with energy model layouts: community analysis and drug repurposing

PubMed Central

Udrescu, Lucreţia; Sbârcea, Laura; Topîrceanu, Alexandru; Iovanovici, Alexandru; Kurunczi, Ludovic; Bogdan, Paul; Udrescu, Mihai

2016-01-01

Analyzing drug-drug interactions may unravel previously unknown drug action patterns, leading to the development of new drug discovery tools. We present a new approach to analyzing drug-drug interaction networks, based on clustering and topological community detection techniques that are specific to complex network science. Our methodology uncovers functional drug categories along with the intricate relationships between them. Using modularity-based and energy-model layout community detection algorithms, we link the network clusters to 9 relevant pharmacological properties. Out of the 1141 drugs from the DrugBank 4.1 database, our extensive literature survey and cross-checking with other databases such as Drugs.com, RxList, and DrugBank 4.3 confirm the predicted properties for 85% of the drugs. As such, we argue that network analysis offers a high-level grasp on a wide area of pharmacological aspects, indicating possible unaccounted interactions and missing pharmacological properties that can lead to drug repositioning for the 15% drugs which seem to be inconsistent with the predicted property. Also, by using network centralities, we can rank drugs according to their interaction potential for both simple and complex multi-pathology therapies. Moreover, our clustering approach can be extended for applications such as analyzing drug-target interactions or phenotyping patients in personalized medicine applications. PMID:27599720

Recent advancements in nanoparticle based drug delivery for gastrointestinal disorders.

PubMed

Mittal, Rahul; Patel, Amit P; Jhaveri, Vasanti M; Kay, Sae-In S; Debs, Luca H; Parrish, James M; Pan, Debbie R; Nguyen, Desiree; Mittal, Jeenu; Jayant, Rahul Dev

2018-03-01

The emergent field of nanoparticles has presented a wealth of opportunities for improving the treatment of human diseases. Recent advances have allowed for promising developments in drug delivery, diagnostics, and therapeutics. Modified delivery systems allow improved drug delivery over traditional pH, microbe, or receptor dependent models, while antibody association allows for more advanced imaging modalities. Nanoparticles have potential clinical application in the field of gastroenterology as they offer several advantages compared to the conventional treatment systems including target drug delivery, enhanced treatment efficacy, and reduced side effects. Areas covered: The aim of this review article is to summarize the recent advancements in developing nanoparticle technologies to treat gastrointestinal diseases. We have covered the application of nanoparticles in various gastrointestinal disorders including inflammatory bowel disease and colorectal cancer. We also have discussed how the gut microbiota affects the nanoparticle based drug delivery in the gastrointestinal tract. Expert opinion: Nanoparticles based drug delivery offers a great platform for targeted drug delivery for gastrointestinal disorders. However, it is influenced by the presence of microbiota, drug interaction with nanoparticles, and cytotoxicity of nanoparticles. With the advancements in nanoparticle technology, it may be possible to overcome these barriers leading to efficient drug delivery for gastrointestinal disorders based on nanoparticle platform.

In silico prediction of potential chemical reactions mediated by human enzymes.

PubMed

Yu, Myeong-Sang; Lee, Hyang-Mi; Park, Aaron; Park, Chungoo; Ceong, Hyithaek; Rhee, Ki-Hyeong; Na, Dokyun

2018-06-13

Administered drugs are often converted into an ineffective or activated form by enzymes in our body. Conventional in silico prediction approaches focused on therapeutically important enzymes such as CYP450. However, there are more than thousands of different cellular enzymes that potentially convert administered drug into other forms. We developed an in silico model to predict which of human enzymes including metabolic enzymes as well as CYP450 family can catalyze a given chemical compound. The prediction is based on the chemical and physical similarity between known enzyme substrates and a query chemical compound. Our in silico model was developed using multiple linear regression and the model showed high performance (AUC = 0.896) despite of the large number of enzymes. When evaluated on a test dataset, it also showed significantly high performance (AUC = 0.746). Interestingly, evaluation with literature data showed that our model can be used to predict not only enzymatic reactions but also drug conversion and enzyme inhibition. Our model was able to predict enzymatic reactions of a query molecule with a high accuracy. This may foster to discover new metabolic routes and to accelerate the computational development of drug candidates by enabling the prediction of the potential conversion of administered drugs into active or inactive forms.

Discovery of Novel HIV-1 Integrase Inhibitors Using QSAR-Based Virtual Screening of the NCI Open Database.

PubMed

Ko, Gene M; Garg, Rajni; Bailey, Barbara A; Kumar, Sunil

2016-01-01

Quantitative structure-activity relationship (QSAR) models can be used as a predictive tool for virtual screening of chemical libraries to identify novel drug candidates. The aims of this paper were to report the results of a study performed for descriptor selection, QSAR model development, and virtual screening for identifying novel HIV-1 integrase inhibitor drug candidates. First, three evolutionary algorithms were compared for descriptor selection: differential evolution-binary particle swarm optimization (DE-BPSO), binary particle swarm optimization, and genetic algorithms. Next, three QSAR models were developed from an ensemble of multiple linear regression, partial least squares, and extremely randomized trees models. A comparison of the performances of three evolutionary algorithms showed that DE-BPSO has a significant improvement over the other two algorithms. QSAR models developed in this study were used in consensus as a predictive tool for virtual screening of the NCI Open Database containing 265,242 compounds to identify potential novel HIV-1 integrase inhibitors. Six compounds were predicted to be highly active (plC50 > 6) by each of the three models. The use of a hybrid evolutionary algorithm (DE-BPSO) for descriptor selection and QSAR model development in drug design is a novel approach. Consensus modeling may provide better predictivity by taking into account a broader range of chemical properties within the data set conducive for inhibition that may be missed by an individual model. The six compounds identified provide novel drug candidate leads in the design of next generation HIV- 1 integrase inhibitors targeting drug resistant mutant viruses.

Costs of providing infusion therapy for patients with inflammatory bowel disease in a hospital-based infusion center setting.

PubMed

Afzali, Anita; Ogden, Kristine; Friedman, Michael L; Chao, Jingdong; Wang, Anthony

2017-04-01

Inflammatory bowel disease (IBD) (e.g. ulcerative colitis [UC] and Crohn's disease [CD]) severely impacts patient quality-of-life. Moderate-to-severe disease is often treated with biologics requiring infusion therapy, adding incremental costs beyond drug costs. This study evaluates US hospital-based infusion services costs for treatment of UC or CD patients receiving infliximab or vedolizumab therapy. A model was developed, estimating annual costs of providing monitored infusions using an activity-based costing framework approach. Multiple sources (published literature, treatment product inserts) informed base-case model input estimates. The total modeled per patient infusion therapy costs in Year 1 with infliximab and vedolizumab was $38,782 and $41,320, respectively, and Year 2+, $49,897 and $36,197, respectively. Drug acquisition cost was the largest total costs driver (90-93%), followed by costs associated with hospital-based infusion provision: labor (53-56%, non-drug costs), allocated overhead (23%, non-drug costs), non-labor (23%, non-drug costs), and laboratory (7-10%, non-drug costs). Limitations included reliance on published estimates, base-case cost estimates infusion drug, and supplies, not accounting for volume pricing, assumption of a small hospital infusion center, and that, given the model adopts the hospital perspective, costs to the patient were not included in infusion administration cost base-case estimates. This model is an early step towards a framework to fully analyze infusion therapies' associated costs. Given the lack of published data, it would be beneficial for hospital administrators to assess total costs and trade-offs with alternative means of providing biologic therapies. This analysis highlights the value to hospital administrators of assessing cost associated with infusion patient mix to make more informed resource allocation decisions. As the landscape for reimbursement changes, tools for evaluating the costs of infusion therapy may help hospital administrators make informed choices and weigh trade-offs associated with providing infusion services for IBD patients.

Computational drug discovery

PubMed Central

Ou-Yang, Si-sheng; Lu, Jun-yan; Kong, Xiang-qian; Liang, Zhong-jie; Luo, Cheng; Jiang, Hualiang

2012-01-01

Computational drug discovery is an effective strategy for accelerating and economizing drug discovery and development process. Because of the dramatic increase in the availability of biological macromolecule and small molecule information, the applicability of computational drug discovery has been extended and broadly applied to nearly every stage in the drug discovery and development workflow, including target identification and validation, lead discovery and optimization and preclinical tests. Over the past decades, computational drug discovery methods such as molecular docking, pharmacophore modeling and mapping, de novo design, molecular similarity calculation and sequence-based virtual screening have been greatly improved. In this review, we present an overview of these important computational methods, platforms and successful applications in this field. PMID:22922346

Research and Development of Hepatitis B Drugs: An Analysis Based on Technology Flows Measured by Patent Citations

PubMed Central

Wan, Jian-bo; He, Chengwei; Hu, Yuanjia

2016-01-01

Despite the existence of available therapies, the Hepatitis B virus infection continues to be one of the most serious threats to human health, especially in developing countries such as China and India. To shed light on the improvement of current therapies and development of novel anti-HBV drugs, we thoroughly investigated 212 US patents of anti-HBV drugs and analyzed the technology flow in research and development of anti-HBV drugs based on data from IMS LifeCycle databases. Moreover, utilizing the patent citation method, which is an effective indicator of technology flow, we constructed patent citation network models and performed network analysis in order to reveal the features of different technology clusters. As a result, we identified the stagnant status of anti-HBV drug development and pointed the way for development of domestic pharmaceuticals in developing countries. We also discussed about therapeutic vaccines as the potential next generation therapy for HBV infection. Lastly, we depicted the cooperation between entities and found that novel forms of cooperation added diversity to the conventional form of cooperation within the pharmaceutical industry. In summary, our study provides inspiring insights for investors, policy makers, researchers, and other readers interested in anti-HBV drug development. PMID:27727319

Implications of formulation design on lipid-based nanostructured carrier system for drug delivery to brain.

PubMed

Salunkhe, Sachin S; Bhatia, Neela M; Bhatia, Manish S

2016-05-01

The aim of present investigation was to formulate and develop lipid-based nanostructured carriers (NLCs) containing Idebenone (IDE) for delivery to brain. Attempts have been made to evaluate IDE NLCs for its pharmacokinetic and pharmacodynamic profile through the objective of enhancement in bioavailability and effectivity of drug. Nanoprecipitation technique was used for development of drug loaded NLCs. The components solid lipid Precirol ATO 5, oil Miglyol 840, surfactants Tween 80 and Labrasol have been screened out for formulation development by consideration of preformulation parameters including solubility, Required Hydrophilic lipophilic balance (HLB) of lipids and stability study. Developed IDE NLCs were subjected for particle size, zeta potential, entrapment efficiency (%EE), crystallographic investigation, transmission electron microscopy, in vitro drug release, pharmacokinetics, in vivo and stability study. Formulation under investigation has particle size 174.1 ± 2.6 nm, zeta potential -18.65 ± 1.13 mV and% EE 90.68 ± 2.90. Crystallographic studies exemplified for partial amorphization of IDE by molecularly dispersion within lipid crust. IDE NLCs showed drug release 93.56 ± 0.39% at end of 24 h by following Higuchi model which necessitates for appropriate drug delivery with enhancement in bioavailability of drug by 4.6-fold in plasma and 2.8-fold in brain over plain drug loaded aqueous dispersions. In vivo studies revealed that effect of drug was enhanced by prepared lipid nanocarriers. IDE lipid-based nanostructured carriers could have potential for efficient drug delivery to brain with enhancement in bioavailability of drug over the conventional formulations.

Ontological approach for safe and effective polypharmacy prescription

PubMed Central

Grando, Adela; Farrish, Susan; Boyd, Cynthia; Boxwala, Aziz

2012-01-01

The intake of multiple medications in patients with various medical conditions challenges the delivery of medical care. Initial empirical studies and pilot implementations seem to indicate that generic safe and effective multi-drug prescription principles could be defined and reused to reduce adverse drug events and to support compliance with medical guidelines and drug formularies. Given that ontologies are known to provide well-principled, sharable, setting-independent and machine-interpretable declarative specification frameworks for modeling and reasoning on biomedical problems, we explore here their use in the context of multi-drug prescription. We propose an ontology for modeling drug-related knowledge and a repository of safe and effective generic prescription principles. To test the usability and the level of granularity of the developed ontology-based specification models and heuristic we implemented a tool that computes the complexity of multi-drug treatments, and a decision aid to check the safeness and effectiveness of prescribed multi-drug treatments. PMID:23304299

Molecular models of NS3 protease variants of the Hepatitis C virus.

PubMed

da Silveira, Nelson J F; Arcuri, Helen A; Bonalumi, Carlos E; de Souza, Fátima P; Mello, Isabel M V G C; Rahal, Paula; Pinho, João R R; de Azevedo, Walter F

2005-01-21

Hepatitis C virus (HCV) currently infects approximately three percent of the world population. In view of the lack of vaccines against HCV, there is an urgent need for an efficient treatment of the disease by an effective antiviral drug. Rational drug design has not been the primary way for discovering major therapeutics. Nevertheless, there are reports of success in the development of inhibitor using a structure-based approach. One of the possible targets for drug development against HCV is the NS3 protease variants. Based on the three-dimensional structure of these variants we expect to identify new NS3 protease inhibitors. In order to speed up the modeling process all NS3 protease variant models were generated in a Beowulf cluster. The potential of the structural bioinformatics for development of new antiviral drugs is discussed. The atomic coordinates of crystallographic structure 1CU1 and 1DY9 were used as starting model for modeling of the NS3 protease variant structures. The NS3 protease variant structures are composed of six subdomains, which occur in sequence along the polypeptide chain. The protease domain exhibits the dual beta-barrel fold that is common among members of the chymotrypsin serine protease family. The helicase domain contains two structurally related beta-alpha-beta subdomains and a third subdomain of seven helices and three short beta strands. The latter domain is usually referred to as the helicase alpha-helical subdomain. The rmsd value of bond lengths and bond angles, the average G-factor and Verify 3D values are presented for NS3 protease variant structures. This project increases the certainty that homology modeling is an useful tool in structural biology and that it can be very valuable in annotating genome sequence information and contributing to structural and functional genomics from virus. The structural models will be used to guide future efforts in the structure-based drug design of a new generation of NS3 protease variants inhibitors. All models in the database are publicly accessible via our interactive website, providing us with large amount of structural models for use in protein-ligand docking analysis.

Cell based assays for anti-Plasmodium activity evaluation.

PubMed

Mokgethi-Morule, Thabang; N'Da, David D

2016-03-10

Malaria remains one of the most common and deadly infectious diseases worldwide. The severity of this global public health challenge is reflected by the approximately 198 million people, who were reportedly infected in 2013 and by the more than 584,000 related deaths in that same year. The rising emergence of drug resistance towards the once effective artemisinin combination therapies (ACTs) has become a serious concern and warrants more robust drug development strategies, with the objective of eradicating malaria infections. The intricate biology and life cycle of Plasmodium parasites complicate the understanding of the disease in such a way that would enhance the development of more effective chemotherapies that would achieve radical clinical cure and that would prevent disease relapse. Phenotypic cell based assays have for long been a valuable approach and involve the screening and analysis of diverse compounds with regards to their activities towards whole Plasmodium parasites in vitro. To achieve the Millennium Development Goal (MDG) of malaria eradication by 2020, new generation drugs that are active against all parasite stages (erythrocytic (blood), exo-erythrocytic (liver stages and gametocytes)) are needed. Significant advances are being made in assay development to overcome some of the practical challenges of assessing drug efficacy, particularly in the liver and transmission stage Plasmodium models. This review discusses primary screening models and the fundamental progress being made in whole cell based efficacy screens of anti-malarial activity. Ongoing challenges and some opportunities for improvements in assay development that would assist in the discovery of effective, safe and affordable drugs for malaria treatments are also discussed. Copyright © 2016 Elsevier B.V. All rights reserved.

Physiologically Based Pharmacokinetic Modeling of Therapeutic Proteins.

PubMed

Wong, Harvey; Chow, Timothy W

2017-09-01

Biologics or therapeutic proteins are becoming increasingly important as treatments for disease. The most common class of biologics are monoclonal antibodies (mAbs). Recently, there has been an increase in the use of physiologically based pharmacokinetic (PBPK) modeling in the pharmaceutical industry in drug development. We review PBPK models for therapeutic proteins with an emphasis on mAbs. Due to their size and similarity to endogenous antibodies, there are distinct differences between PBPK models for small molecules and mAbs. The high-level organization of a typical mAb PBPK model consists of a whole-body PBPK model with organ compartments interconnected by both blood and lymph flows. The whole-body PBPK model is coupled with tissue-level submodels used to describe key mechanisms governing mAb disposition including tissue efflux via the lymphatic system, elimination by catabolism, protection from catabolism binding to the neonatal Fc (FcRn) receptor, and nonlinear binding to specific pharmacological targets of interest. The use of PBPK modeling in the development of therapeutic proteins is still in its infancy. Further application of PBPK modeling for therapeutic proteins will help to define its developing role in drug discovery and development. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Drugp-Induced Rhabdomyolysis Atlas (DIRA) for idiosyncratic adverse drug reaction management.

PubMed

Wen, Zhining; Liang, Yu; Hao, Yingyi; Delavan, Brian; Huang, Ruili; Mikailov, Mike; Tong, Weida; Li, Menglong; Liu, Zhichao

2018-06-11

Drug-induced rhabdomyolysis (DIR) is an idiosyncratic and fatal adverse drug reaction (ADR) characterized in severe muscle injuries accompanied by multiple-organ failure. Limited knowledge regarding the pathophysiology of rhabdomyolysis is the main obstacle to developing early biomarkers and prevention strategies. Given the lack of a centralized data resource to curate, organize, and standardize widespread DIR information, here we present a Drug-Induced Rhabdomyolysis Atlas (DIRA) that provides DIR-related information, including: a classification scheme for DIR based on drug labeling information; postmarketing surveillance data of DIR; and DIR drug property information. To elucidate the utility of DIRA, we used precision dosing, concomitant use of DIR drugs, and predictive modeling development to exemplify strategies for idiosyncratic ADR (IADR) management. Published by Elsevier Ltd.

From bench to patient: model systems in drug discovery

PubMed Central

Breyer, Matthew D.; Look, A. Thomas; Cifra, Alessandra

2015-01-01

ABSTRACT Model systems, including laboratory animals, microorganisms, and cell- and tissue-based systems, are central to the discovery and development of new and better drugs for the treatment of human disease. In this issue, Disease Models & Mechanisms launches a Special Collection that illustrates the contribution of model systems to drug discovery and optimisation across multiple disease areas. This collection includes reviews, Editorials, interviews with leading scientists with a foot in both academia and industry, and original research articles reporting new and important insights into disease therapeutics. This Editorial provides a summary of the collection's current contents, highlighting the impact of multiple model systems in moving new discoveries from the laboratory bench to the patients' bedsides. PMID:26438689

Alzheimer's Therapeutics: Translation of Preclinical Science to Clinical Drug Development

PubMed Central

Savonenko, Alena V; Melnikova, Tatiana; Hiatt, Andrew; Li, Tong; Worley, Paul F; Troncoso, Juan C; Wong, Phil C; Price, Don L

2012-01-01

Over the past three decades, significant progress has been made in understanding the neurobiology of Alzheimer's disease. In recent years, the first attempts to implement novel mechanism-based treatments brought rather disappointing results, with low, if any, drug efficacy and significant side effects. A discrepancy between our expectations based on preclinical models and the results of clinical trials calls for a revision of our theoretical views and questions every stage of translation—from how we model the disease to how we run clinical trials. In the following sections, we will use some specific examples of the therapeutics from acetylcholinesterase inhibitors to recent anti-Aβ immunization and γ-secretase inhibition to discuss whether preclinical studies could predict the limitations in efficacy and side effects that we were so disappointed to observe in recent clinical trials. We discuss ways to improve both the predictive validity of mouse models and the translation of knowledge between preclinical and clinical stages of drug development. PMID:21937983

Biocompatible and biodegradable dual-drug release system based on silk hydrogel containing silk nanoparticles.

PubMed

Numata, Keiji; Yamazaki, Shoya; Naga, Naofumi

2012-05-14

We developed a facile and quick ethanol-based method for preparing silk nanoparticles and then fabricated a biodegradable and biocompatible dual-drug release system based on silk nanoparticles and the molecular networks of silk hydrogels. Model drugs incorporated in the silk nanoparticles and silk hydrogels showed fast and constant release, respectively, indicating successful dual-drug release from silk hydrogel containing silk nanoparticles. The release behaviors achieved by this dual-drug release system suggest to be regulated by physical properties (e.g., β-sheet contents and size of the silk nanoparticles and network size of the silk hydrogels), which is an important advantage for biomedical applications. The present silk-based system for dual-drug release also demonstrated no significant cytotoxicity against human mesenchymal stem cells (hMSCs), and thus, this silk-based dual-drug release system has potential as a versatile and useful new platform of polymeric materials for various types of dual delivery of bioactive molecules.

QSAR-Based Models for Designing Quinazoline/Imidazothiazoles/Pyrazolopyrimidines Based Inhibitors against Wild and Mutant EGFR

PubMed Central

Chauhan, Jagat Singh; Dhanda, Sandeep Kumar; Singla, Deepak; Agarwal, Subhash M.; Raghava, Gajendra P. S.

2014-01-01

Overexpression of EGFR is responsible for causing a number of cancers, including lung cancer as it activates various downstream signaling pathways. Thus, it is important to control EGFR function in order to treat the cancer patients. It is well established that inhibiting ATP binding within the EGFR kinase domain regulates its function. The existing quinazoline derivative based drugs used for treating lung cancer that inhibits the wild type of EGFR. In this study, we have made a systematic attempt to develop QSAR models for designing quinazoline derivatives that could inhibit wild EGFR and imidazothiazoles/pyrazolopyrimidines derivatives against mutant EGFR. In this study, three types of prediction methods have been developed to design inhibitors against EGFR (wild, mutant and both). First, we developed models for predicting inhibitors against wild type EGFR by training and testing on dataset containing 128 quinazoline based inhibitors. This dataset was divided into two subsets called wild_train and wild_valid containing 103 and 25 inhibitors respectively. The models were trained and tested on wild_train dataset while performance was evaluated on the wild_valid called validation dataset. We achieved a maximum correlation between predicted and experimentally determined inhibition (IC50) of 0.90 on validation dataset. Secondly, we developed models for predicting inhibitors against mutant EGFR (L858R) on mutant_train, and mutant_valid dataset and achieved a maximum correlation between 0.834 to 0.850 on these datasets. Finally, an integrated hybrid model has been developed on a dataset containing wild and mutant inhibitors and got maximum correlation between 0.761 to 0.850 on different datasets. In order to promote open source drug discovery, we developed a webserver for designing inhibitors against wild and mutant EGFR along with providing standalone (http://osddlinux.osdd.net/) and Galaxy (http://osddlinux.osdd.net:8001) version of software. We hope our webserver (http://crdd.osdd.net/oscadd/ntegfr/) will play a vital role in designing new anticancer drugs. PMID:24992720

Blinded Prospective Evaluation of Computer-Based Mechanistic Schizophrenia Disease Model for Predicting Drug Response

PubMed Central

Geerts, Hugo; Spiros, Athan; Roberts, Patrick; Twyman, Roy; Alphs, Larry; Grace, Anthony A.

2012-01-01

The tremendous advances in understanding the neurobiological circuits involved in schizophrenia have not translated into more effective treatments. An alternative strategy is to use a recently published ‘Quantitative Systems Pharmacology’ computer-based mechanistic disease model of cortical/subcortical and striatal circuits based upon preclinical physiology, human pathology and pharmacology. The physiology of 27 relevant dopamine, serotonin, acetylcholine, norepinephrine, gamma-aminobutyric acid (GABA) and glutamate-mediated targets is calibrated using retrospective clinical data on 24 different antipsychotics. The model was challenged to predict quantitatively the clinical outcome in a blinded fashion of two experimental antipsychotic drugs; JNJ37822681, a highly selective low-affinity dopamine D2 antagonist and ocaperidone, a very high affinity dopamine D2 antagonist, using only pharmacology and human positron emission tomography (PET) imaging data. The model correctly predicted the lower performance of JNJ37822681 on the positive and negative syndrome scale (PANSS) total score and the higher extra-pyramidal symptom (EPS) liability compared to olanzapine and the relative performance of ocaperidone against olanzapine, but did not predict the absolute PANSS total score outcome and EPS liability for ocaperidone, possibly due to placebo responses and EPS assessment methods. Because of its virtual nature, this modeling approach can support central nervous system research and development by accounting for unique human drug properties, such as human metabolites, exposure, genotypes and off-target effects and can be a helpful tool for drug discovery and development. PMID:23251349

The age of anxiety: role of animal models of anxiolytic action in drug discovery

PubMed Central

Cryan, John F; Sweeney, Fabian F

2011-01-01

Anxiety disorders are common, serious and a growing health problem worldwide. However, the causative factors, aetiology and underlying mechanisms of anxiety disorders, as for most psychiatric disorders, remain relatively poorly understood. Animal models are an important aid in giving insight into the aetiology, neurobiology and, ultimately, the therapy of human anxiety disorders. The approach, however, is challenged with a number of complexities. In particular, the heterogeneous nature of anxiety disorders in humans coupled with the associated multifaceted and descriptive diagnostic criteria, creates challenges in both animal modelling and in clinical research. In this paper, we describe some of the more widely used approaches for assessing the anxiolytic activity of known and potential therapeutic agents. These include ethological, conflict-based, hyponeophagia, vocalization-based, physiological and cognitive-based paradigms. Developments in the characterization of translational models are also summarized, as are the challenges facing researchers in their drug discovery efforts in developing new anxiolytic drugs, not least the ever-shifting clinical conceptualization of anxiety disorders. In conclusion, to date, although animal models of anxiety have relatively good validity, anxiolytic drugs with novel mechanisms have been slow to emerge. It is clear that a better alignment of the interactions between basic and clinical scientists is needed if this is to change. LINKED ARTICLES This article is part of a themed issue on Translational Neuropharmacology. To view the other articles in this issue visit http://dx.doi.org/10.1111/bph.2011.164.issue-4 PMID:21545412

A role for two-stage pharmacoeconomic appraisal? Is there a role for interim approval of a drug for reimbursement based on modelling studies with subsequent full approval using phase III data?

PubMed

Hill, Suzanne; Freemantle, Nick

2003-01-01

Healthcare decision makers and pharmaceutical companies are increasingly using techniques of economic evaluation, particularly modelling, to assist them in their decisions about drug purchasing and drug development. The use of models in other types of policy decisions is also well established. One option, to shorten the time to a purchasing decision, would be for an interim decision for approval for reimbursement to be based on an economic model. Such a system would mainly benefit the drug development process and thus the pharmaceutical industry; however the approach could also lead to poor decision making, unethical marketing and withdrawal of drugs from the consumer. In this article, we consider the option of a two-stage economic appraisal process from the point of view of the seller, the purchaser and the patient and public. Although a two-stage process may offer some advantages in terms of early return on investment and access, there are significant disadvantages in terms of certainty about effects and public policy and expenditure. Until there are better methods of predicting the effectiveness of a new product, it is unlikely that interim decisions can be seen as a reasonable health policy alternative, although it seems likely that industry may continue to lobby for such an approach.

Systems pharmacology - Towards the modeling of network interactions.

PubMed

Danhof, Meindert

2016-10-30

Mechanism-based pharmacokinetic and pharmacodynamics (PKPD) and disease system (DS) models have been introduced in drug discovery and development research, to predict in a quantitative manner the effect of drug treatment in vivo in health and disease. This requires consideration of several fundamental properties of biological systems behavior including: hysteresis, non-linearity, variability, interdependency, convergence, resilience, and multi-stationarity. Classical physiology-based PKPD models consider linear transduction pathways, connecting processes on the causal path between drug administration and effect, as the basis of drug action. Depending on the drug and its biological target, such models may contain expressions to characterize i) the disposition and the target site distribution kinetics of the drug under investigation, ii) the kinetics of target binding and activation and iii) the kinetics of transduction. When connected to physiology-based DS models, PKPD models can characterize the effect on disease progression in a mechanistic manner. These models have been found useful to characterize hysteresis and non-linearity, yet they fail to explain the effects of the other fundamental properties of biological systems behavior. Recently systems pharmacology has been introduced as novel approach to predict in vivo drug effects, in which biological networks rather than single transduction pathways are considered as the basis of drug action and disease progression. These models contain expressions to characterize the functional interactions within a biological network. Such interactions are relevant when drugs act at multiple targets in the network or when homeostatic feedback mechanisms are operative. As a result systems pharmacology models are particularly useful to describe complex patterns of drug action (i.e. synergy, oscillatory behavior) and disease progression (i.e. episodic disorders). In this contribution it is shown how physiology-based PKPD and disease models can be extended to account for internal systems interactions. It is demonstrated how SP models can be used to predict the effects of multi-target interactions and of homeostatic feedback on the pharmacological response. In addition it is shown how DS models may be used to distinguish symptomatic from disease modifying effects and to predict the long term effects on disease progression, from short term biomarker responses. It is concluded that incorporation of expressions to describe the interactions in biological network analysis opens new avenues to the understanding of the effects of drug treatment on the fundamental aspects of biological systems behavior. Copyright © 2016 The Author. Published by Elsevier B.V. All rights reserved.

Prediction and Factor Extraction of Drug Function by Analyzing Medical Records in Developing Countries.

PubMed

Hu, Min; Nohara, Yasunobu; Nakamura, Masafumi; Nakashima, Naoki

2017-01-01

The World Health Organization has declared Bangladesh one of 58 countries facing acute Human Resources for Health (HRH) crisis. Artificial intelligence in healthcare has been shown to be successful for diagnostics. Using machine learning to predict pharmaceutical prescriptions may solve HRH crises. In this study, we investigate a predictive model by analyzing prescription data of 4,543 subjects in Bangladesh. We predict the function of prescribed drugs, comparing three machine-learning approaches. The approaches compare whether a subject shall be prescribed medicine from the 21 most frequently prescribed drug functions. Receiver Operating Characteristics (ROC) were selected as a way to evaluate and assess prediction models. The results show the drug function with the best prediction performance was oral hypoglycemic drugs, which has an average AUC of 0.962. To understand how the variables affect prediction, we conducted factor analysis based on tree-based algorithms and natural language processing techniques.

AlgiMatrix™ Based 3D Cell Culture System as an In-Vitro Tumor Model for Anticancer Studies

PubMed Central

Godugu, Chandraiah; Patel, Apurva R.; Desai, Utkarsh; Andey, Terrick; Sams, Alexandria; Singh, Mandip

2013-01-01

Background Three-dimensional (3D) in-vitro cultures are recognized for recapitulating the physiological microenvironment and exhibiting high concordance with in-vivo conditions. Taking the advantages of 3D culture, we have developed the in-vitro tumor model for anticancer drug screening. Methods Cancer cells grown in 6 and 96 well AlgiMatrix™ scaffolds resulted in the formation of multicellular spheroids in the size range of 100–300 µm. Spheroids were grown in two weeks in cultures without compromising the growth characteristics. Different marketed anticancer drugs were screened by incubating them for 24 h at 7, 9 and 11 days in 3D cultures and cytotoxicity was measured by AlamarBlue® assay. Effectiveness of anticancer drug treatments were measured based on spheroid number and size distribution. Evaluation of apoptotic and anti-apoptotic markers was done by immunohistochemistry and RT-PCR. The 3D results were compared with the conventional 2D monolayer cultures. Cellular uptake studies for drug (Doxorubicin) and nanoparticle (NLC) were done using spheroids. Results IC50 values for anticancer drugs were significantly higher in AlgiMatrix™ systems compared to 2D culture models. The cleaved caspase-3 expression was significantly decreased (2.09 and 2.47 folds respectively for 5-Fluorouracil and Camptothecin) in H460 spheroid cultures compared to 2D culture system. The cytotoxicity, spheroid size distribution, immunohistochemistry, RT-PCR and nanoparticle penetration data suggested that in vitro tumor models show higher resistance to anticancer drugs and supporting the fact that 3D culture is a better model for the cytotoxic evaluation of anticancer drugs in vitro. Conclusion The results from our studies are useful to develop a high throughput in vitro tumor model to study the effect of various anticancer agents and various molecular pathways affected by the anticancer drugs and formulations. PMID:23349734

Multiple target drug cocktail design for attacking the core network markers of four cancers using ligand-based and structure-based virtual screening methods

PubMed Central

2015-01-01

Background Computer-aided drug design has a long history of being applied to discover new molecules to treat various cancers, but it has always been focused on single targets. The development of systems biology has let scientists reveal more hidden mechanisms of cancers, but attempts to apply systems biology to cancer therapies remain at preliminary stages. Our lab has successfully developed various systems biology models for several cancers. Based on these achievements, we present the first attempt to combine multiple-target therapy with systems biology. Methods In our previous study, we identified 28 significant proteins--i.e., common core network markers--of four types of cancers as house-keeping proteins of these cancers. In this study, we ranked these proteins by summing their carcinogenesis relevance values (CRVs) across the four cancers, and then performed docking and pharmacophore modeling to do virtual screening on the NCI database for anti-cancer drugs. We also performed pathway analysis on these proteins using Panther and MetaCore to reveal more mechanisms of these cancer house-keeping proteins. Results We designed several approaches to discover targets for multiple-target cocktail therapies. In the first one, we identified the top 20 drugs for each of the 28 cancer house-keeping proteins, and analyzed the docking pose to further understand the interaction mechanisms of these drugs. After screening for duplicates, we found that 13 of these drugs could target 11 proteins simultaneously. In the second approach, we chose the top 5 proteins with the highest summed CRVs and used them as the drug targets. We built a pharmacophore and applied it to do virtual screening against the Life-Chemical library for anti-cancer drugs. Based on these results, wet-lab bio-scientists could freely investigate combinations of these drugs for multiple-target therapy for cancers, in contrast to the traditional single target therapy. Conclusions Combination of systems biology with computer-aided drug design could help us develop novel drug cocktails with multiple targets. We believe this will enhance the efficiency of therapeutic practice and lead to new directions for cancer therapy. PMID:26680552

Potential of agricultural fungicides for antifungal drug discovery.

PubMed

Jampilek, Josef

2016-01-01

While it is true that only a small fraction of fungal species are responsible for human mycoses, the increasing prevalence of fungal diseases has highlighted an urgent need to develop new antifungal drugs, especially for systemic administration. This contribution focuses on the similarities between agricultural fungicides and drugs. Inorganic, organometallic and organic compounds can be found amongst agricultural fungicides. Furthermore, fungicides are designed and developed in a similar fashion to drugs based on similar rules and guidelines, with fungicides also having to meet similar criteria of lead-likeness and/or drug-likeness. Modern approved specific-target fungicides are well-characterized entities with a proposed structure-activity relationships hypothesis and a defined mode of action. Extensive toxicological evaluation, including mammalian toxicology assays, is performed during the whole discovery and development process. Thus modern agrochemical research (design of modern agrochemicals) comes close to drug design, discovery and development. Therefore, modern specific-target fungicides represent excellent lead-like structures/models for novel drug design and development.

Drug Delivery and Transport into the Central Circulation: An Example of Zero-Order In vivo Absorption of Rotigotine from a Transdermal Patch Formulation.

PubMed

Cawello, Willi; Braun, Marina; Andreas, Jens-Otto

2018-01-13

Pharmacokinetic studies using deconvolution methods and non-compartmental analysis to model clinical absorption of drugs are not well represented in the literature. The purpose of this research was (1) to define the system of equations for description of rotigotine (a dopamine receptor agonist delivered via a transdermal patch) absorption based on a pharmacokinetic model and (2) to describe the kinetics of rotigotine disposition after single and multiple dosing. The kinetics of drug disposition was evaluated based on rotigotine plasma concentration data from three phase 1 trials. In two trials, rotigotine was administered via a single patch over 24 h in healthy subjects. In a third trial, rotigotine was administered once daily over 1 month in subjects with early-stage Parkinson's disease (PD). A pharmacokinetic model utilizing deconvolution methods was developed to describe the relationship between drug release from the patch and plasma concentrations. Plasma-concentration over time profiles were modeled based on a one-compartment model with a time lag, a zero-order input (describing a constant absorption via skin into central circulation) and first-order elimination. Corresponding mathematical models for single- and multiple-dose administration were developed. After single-dose administration of rotigotine patches (using 2, 4 or 8 mg/day) in healthy subjects, a constant in vivo absorption was present after a minor time lag (2-3 h). On days 27 and 30 of the multiple-dose study in patients with PD, absorption was constant during patch-on periods and resembled zero-order kinetics. Deconvolution based on rotigotine pharmacokinetic profiles after single- or multiple-dose administration of the once-daily patch demonstrated that in vivo absorption of rotigotine showed constant input through the skin into the central circulation (resembling zero-order kinetics). Continuous absorption through the skin is a basis for stable drug exposure.

Cancer biomarker discovery is improved by accounting for variability in general levels of drug sensitivity in pre-clinical models.

PubMed

Geeleher, Paul; Cox, Nancy J; Huang, R Stephanie

2016-09-21

We show that variability in general levels of drug sensitivity in pre-clinical cancer models confounds biomarker discovery. However, using a very large panel of cell lines, each treated with many drugs, we could estimate a general level of sensitivity to all drugs in each cell line. By conditioning on this variable, biomarkers were identified that were more likely to be effective in clinical trials than those identified using a conventional uncorrected approach. We find that differences in general levels of drug sensitivity are driven by biologically relevant processes. We developed a gene expression based method that can be used to correct for this confounder in future studies.

Mechanistic modeling to predict the transporter- and enzyme-mediated drug-drug interactions of repaglinide.

PubMed

Varma, Manthena V S; Lai, Yurong; Kimoto, Emi; Goosen, Theunis C; El-Kattan, Ayman F; Kumar, Vikas

2013-04-01

Quantitative prediction of complex drug-drug interactions (DDIs) is challenging. Repaglinide is mainly metabolized by cytochrome-P-450 (CYP)2C8 and CYP3A4, and is also a substrate of organic anion transporting polypeptide (OATP)1B1. The purpose is to develop a physiologically based pharmacokinetic (PBPK) model to predict the pharmacokinetics and DDIs of repaglinide. In vitro hepatic transport of repaglinide, gemfibrozil and gemfibrozil 1-O-β-glucuronide was characterized using sandwich-culture human hepatocytes. A PBPK model, implemented in Simcyp (Sheffield, UK), was developed utilizing in vitro transport and metabolic clearance data. In vitro studies suggested significant active hepatic uptake of repaglinide. Mechanistic model adequately described repaglinide pharmacokinetics, and successfully predicted DDIs with several OATP1B1 and CYP3A4 inhibitors (<10% error). Furthermore, repaglinide-gemfibrozil interaction at therapeutic dose was closely predicted using in vitro fraction metabolism for CYP2C8 (0.71), when primarily considering reversible inhibition of OATP1B1 and mechanism-based inactivation of CYP2C8 by gemfibrozil and gemfibrozil 1-O-β-glucuronide. This study demonstrated that hepatic uptake is rate-determining in the systemic clearance of repaglinide. The model quantitatively predicted several repaglinide DDIs, including the complex interactions with gemfibrozil. Both OATP1B1 and CYP2C8 inhibition contribute significantly to repaglinide-gemfibrozil interaction, and need to be considered for quantitative rationalization of DDIs with either drug.

Cheminformatic models based on machine learning for pyruvate kinase inhibitors of Leishmania mexicana.

PubMed

Jamal, Salma; Scaria, Vinod

2013-11-19

Leishmaniasis is a neglected tropical disease which affects approx. 12 million individuals worldwide and caused by parasite Leishmania. The current drugs used in the treatment of Leishmaniasis are highly toxic and has seen widespread emergence of drug resistant strains which necessitates the need for the development of new therapeutic options. The high throughput screen data available has made it possible to generate computational predictive models which have the ability to assess the active scaffolds in a chemical library followed by its ADME/toxicity properties in the biological trials. In the present study, we have used publicly available, high-throughput screen datasets of chemical moieties which have been adjudged to target the pyruvate kinase enzyme of L. mexicana (LmPK). The machine learning approach was used to create computational models capable of predicting the biological activity of novel antileishmanial compounds. Further, we evaluated the molecules using the substructure based approach to identify the common substructures contributing to their activity. We generated computational models based on machine learning methods and evaluated the performance of these models based on various statistical figures of merit. Random forest based approach was determined to be the most sensitive, better accuracy as well as ROC. We further added a substructure based approach to analyze the molecules to identify potentially enriched substructures in the active dataset. We believe that the models developed in the present study would lead to reduction in cost and length of clinical studies and hence newer drugs would appear faster in the market providing better healthcare options to the patients.

Prediction on the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase based on gene expression programming.

PubMed

Li, Yuqin; You, Guirong; Jia, Baoxiu; Si, Hongzong; Yao, Xiaojun

2014-01-01

Quantitative structure-activity relationships (QSAR) were developed to predict the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase via heuristic method (HM) and gene expression programming (GEP). The descriptors of 33 pyrrolidine derivatives were calculated by the software CODESSA, which can calculate quantum chemical, topological, geometrical, constitutional, and electrostatic descriptors. HM was also used for the preselection of 5 appropriate molecular descriptors. Linear and nonlinear QSAR models were developed based on the HM and GEP separately and two prediction models lead to a good correlation coefficient (R (2)) of 0.93 and 0.94. The two QSAR models are useful in predicting the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase during the discovery of new anticancer drugs and providing theory information for studying the new drugs.

Accessing external innovation in drug discovery and development.

PubMed

Tufféry, Pierre

2015-06-01

A decline in the productivity of the pharmaceutical industry research and development (R&D) pipeline has highlighted the need to reconsider the classical strategies of drug discovery and development, which are based on internal resources, and to identify new means to improve the drug discovery process. Accepting that the combination of internal and external ideas can improve innovation, ways to access external innovation, that is, opening projects to external contributions, have recently been sought. In this review, the authors look at a number of external innovation opportunities. These include increased interactions with academia via academic centers of excellence/innovation centers, better communication on projects using crowdsourcing or social media and new models centered on external providers such as built-to-buy startups or virtual pharmaceutical companies. The buzz for accessing external innovation relies on the pharmaceutical industry's major challenge to improve R&D productivity, a conjuncture favorable to increase interactions with academia and new business models supporting access to external innovation. So far, access to external innovation has mostly been considered during early stages of drug development, and there is room for enhancement. First outcomes suggest that external innovation should become part of drug development in the long term. However, the balance between internal and external developments in drug discovery can vary largely depending on the company strategies.

Antiepileptic Drugs in Clinical Development: Differentiate or Die?

PubMed

Zaccara, Gaetano; Schmidt, D

2017-01-01

Animal models when carefully selected, designed and conducted, are important parts of any translational drug development strategy. However, research of new compounds for patients with drugresistant epilepsies is still based on animal experiments, mostly in rodents, which are far from being a model of chronic human epilepsy and have failed to differentiate the efficacy of new compounds versus standard drug treatment. The objective was identification and description of compounds in clinical development in 2016. Search was conducted from the website of the U.S. National Institutes of Health and from literature. Identified compounds have been divided in two groups: 1) compounds initially developed for the treatment of diseases other than epilepsy: biperiden, bumetanide, everolimus, fenfluramine, melatonin, minocycline, verapamil. 2) Compounds specifically developed for the treatment of epilepsy: allopregnanolone, cannabidiol, cannabidivarin, ganaxolone, nalutozan, PF-06372865, UCB0942, and cenobamate. Everolimus, and perhaps, fenfluramine are effective in specific epileptic diseases and may be considered as true disease modifying antiepileptic drugs. These are tuberous sclerosis complex for everolimus and Dravet syndrome for fenfluramine. With the exception of a few other compounds such as cannabinidiol, cannabidivarin and minocycline, the vast majority of other compounds had mechanisms of action which are similar to the mechanism of action of the anti-seizure drugs already in the market. Substantial improvements in the efficacy, specifically as pharmacological treatment of drug-resistant epilepsy is regarded, are not expected. New drugs should be developed to specifically target the biochemical alteration which characterizes the underlying disease and also include targets that contribute to epileptogenesis in relevant epilepsy models. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Targeting Tumor Associated Phosphatidylserine with New Zinc Dipicolylamine-Based Drug Conjugates.

PubMed

Liu, Yu-Wei; Shia, Kak-Shan; Wu, Chien-Huang; Liu, Kuan-Liang; Yeh, Yu-Cheng; Lo, Chen-Fu; Chen, Chiung-Tong; Chen, Yun-Yu; Yeh, Teng-Kuang; Chen, Wei-Han; Jan, Jiing-Jyh; Huang, Yu-Chen; Huang, Chen-Lung; Fang, Ming-Yu; Gray, Brian D; Pak, Koon Y; Hsu, Tsu-An; Huang, Kuan-Hsun; Tsou, Lun K

2017-07-19

A series of zinc(II) dipicolylamine (ZnDPA)-based drug conjugates have been synthesized to probe the potential of phosphatidylserine (PS) as a new antigen for small molecule drug conjugate (SMDC) development. Using in vitro cytotoxicity and plasma stability studies, PS-binding assay, in vivo pharmacokinetic studies, and maximum tolerated dose profiles, we provided a roadmap and the key parameters required for the development of the ZnDPA based drug conjugate. In particular, conjugate 24 induced tumor regression in the COLO 205 xenograft model and exhibited a more potent antitumor effect with a 70% reduction of cytotoxic payload compared to that of the marketed irinotecan when dosed at the same regimen. In addition to the validation of PS as an effective pharmacodelivery target for SMDC, our work also provided the foundation that, if applicable, a variety of therapeutic agents could be conjugated in the same manner to treat other PS-associated diseases.

Factorial Design Based Multivariate Modeling and Optimization of Tunable Bioresponsive Arginine Grafted Poly(cystaminebis(acrylamide)-diaminohexane) Polymeric Matrix Based Nanocarriers.

PubMed

Yang, Rongbing; Nam, Kihoon; Kim, Sung Wan; Turkson, James; Zou, Ye; Zuo, Yi Y; Haware, Rahul V; Chougule, Mahavir B

2017-01-03

Desired characteristics of nanocarriers are crucial to explore its therapeutic potential. This investigation aimed to develop tunable bioresponsive newly synthesized unique arginine grafted poly(cystaminebis(acrylamide)-diaminohexane) [ABP] polymeric matrix based nanocarriers by using L9 Taguchi factorial design, desirability function, and multivariate method. The selected formulation and process parameters were ABP concentration, acetone concentration, the volume ratio of acetone to ABP solution, and drug concentration. The measured nanocarrier characteristics were particle size, polydispersity index, zeta potential, and percentage drug loading. Experimental validation of nanocarrier characteristics computed from initially developed predictive model showed nonsignificant differences (p > 0.05). The multivariate modeling based optimized cationic nanocarrier formulation of <100 nm loaded with hydrophilic acetaminophen was readapted for a hydrophobic etoposide loading without significant changes (p > 0.05) except for improved loading percentage. This is the first study focusing on ABP polymeric matrix based nanocarrier development. Nanocarrier particle size was stable in PBS 7.4 for 48 h. The increase of zeta potential at lower pH 6.4, compared to the physiological pH, showed possible endosomal escape capability. The glutathione triggered release at the physiological conditions indicated the competence of cytosolic targeting delivery of the loaded drug from bioresponsive nanocarriers. In conclusion, this unique systematic approach provides rational evaluation and prediction of a tunable bioresponsive ABP based matrix nanocarrier, which was built on selected limited number of smart experimentation.

Human iPSC-derived cardiomyocytes and tissue engineering strategies for disease modeling and drug screening

PubMed Central

Smith, Alec S.T.; Macadangdang, Jesse; Leung, Winnie; Laflamme, Michael A.; Kim, Deok-Ho

2016-01-01

Improved methodologies for modeling cardiac disease phenotypes and accurately screening the efficacy and toxicity of potential therapeutic compounds are actively being sought to advance drug development and improve disease modeling capabilities. To that end, much recent effort has been devoted to the development of novel engineered biomimetic cardiac tissue platforms that accurately recapitulate the structure and function of the human myocardium. Within the field of cardiac engineering, induced pluripotent stem cells (iPSCs) are an exciting tool that offer the potential to advance the current state of the art, as they are derived from somatic cells, enabling the development of personalized medical strategies and patient specific disease models. Here we review different aspects of iPSC-based cardiac engineering technologies. We highlight methods for producing iPSC-derived cardiomyocytes (iPSC-CMs) and discuss their application to compound efficacy/toxicity screening and in vitro modeling of prevalent cardiac diseases. Special attention is paid to the application of micro- and nano-engineering techniques for the development of novel iPSC-CM based platforms and their potential to advance current preclinical screening modalities. PMID:28007615

Accelerating drug development for neuroblastoma - New Drug Development Strategy: an Innovative Therapies for Children with Cancer, European Network for Cancer Research in Children and Adolescents and International Society of Paediatric Oncology Europe Neuroblastoma project.

PubMed

Moreno, Lucas; Caron, Hubert; Geoerger, Birgit; Eggert, Angelika; Schleiermacher, Gudrun; Brock, Penelope; Valteau-Couanet, Dominique; Chesler, Louis; Schulte, Johannes H; De Preter, Katleen; Molenaar, Jan; Schramm, Alexander; Eilers, Martin; Van Maerken, Tom; Johnsen, John Inge; Garrett, Michelle; George, Sally L; Tweddle, Deborah A; Kogner, Per; Berthold, Frank; Koster, Jan; Barone, Giuseppe; Tucker, Elizabeth R; Marshall, Lynley; Herold, Ralf; Sterba, Jaroslav; Norga, Koen; Vassal, Gilles; Pearson, Andrew Dj

2017-08-01

Neuroblastoma, the commonest paediatric extra-cranial tumour, remains a leading cause of death from cancer in children. There is an urgent need to develop new drugs to improve cure rates and reduce long-term toxicity and to incorporate molecularly targeted therapies into treatment. Many potential drugs are becoming available, but have to be prioritised for clinical trials due to the relatively small numbers of patients. Areas covered: The current drug development model has been slow, associated with significant attrition, and few new drugs have been developed for neuroblastoma. The Neuroblastoma New Drug Development Strategy (NDDS) has: 1) established a group with expertise in drug development; 2) prioritised targets and drugs according to tumour biology (target expression, dependency, pre-clinical data; potential combinations; biomarkers), identifying as priority targets ALK, MEK, CDK4/6, MDM2, MYCN (druggable by BET bromodomain, aurora kinase, mTORC1/2) BIRC5 and checkpoint kinase 1; 3) promoted clinical trials with target-prioritised drugs. Drugs showing activity can be rapidly transitioned via parallel randomised trials into front-line studies. Expert opinion: The Neuroblastoma NDDS is based on the premise that optimal drug development is reliant on knowledge of tumour biology and prioritisation. This approach will accelerate neuroblastoma drug development and other poor prognosis childhood malignancies.

Network-Based Approaches in Drug Discovery and Early Development

PubMed Central

Harrold, JM; Ramanathan, M; Mager, DE

2015-01-01

Identification of novel targets is a critical first step in the drug discovery and development process. Most diseases such as cancer, metabolic disorders, and neurological disorders are complex, and their pathogenesis involves multiple genetic and environmental factors. Finding a viable drug target–drug combination with high potential for yielding clinical success within the efficacy–toxicity spectrum is extremely challenging. Many examples are now available in which network-based approaches show potential for the identification of novel targets and for the repositioning of established targets. The objective of this article is to highlight network approaches for identifying novel targets with greater chances of gaining approved drugs with maximal efficacy and minimal side effects. Further enhancement of these approaches may emerge from effectively integrating computational systems biology with pharmacodynamic systems analysis. Coupling genomics, proteomics, and metabolomics databases with systems pharmacology modeling may aid in the development of disease-specific networks that can be further used to build confidence in target identification. PMID:24025802

Embracing model-based designs for dose-finding trials

PubMed Central

Love, Sharon B; Brown, Sarah; Weir, Christopher J; Harbron, Chris; Yap, Christina; Gaschler-Markefski, Birgit; Matcham, James; Caffrey, Louise; McKevitt, Christopher; Clive, Sally; Craddock, Charlie; Spicer, James; Cornelius, Victoria

2017-01-01

Background: Dose-finding trials are essential to drug development as they establish recommended doses for later-phase testing. We aim to motivate wider use of model-based designs for dose finding, such as the continual reassessment method (CRM). Methods: We carried out a literature review of dose-finding designs and conducted a survey to identify perceived barriers to their implementation. Results: We describe the benefits of model-based designs (flexibility, superior operating characteristics, extended scope), their current uptake, and existing resources. The most prominent barriers to implementation of a model-based design were lack of suitable training, chief investigators’ preference for algorithm-based designs (e.g., 3+3), and limited resources for study design before funding. We use a real-world example to illustrate how these barriers can be overcome. Conclusions: There is overwhelming evidence for the benefits of CRM. Many leading pharmaceutical companies routinely implement model-based designs. Our analysis identified barriers for academic statisticians and clinical academics in mirroring the progress industry has made in trial design. Unified support from funders, regulators, and journal editors could result in more accurate doses for later-phase testing, and increase the efficiency and success of clinical drug development. We give recommendations for increasing the uptake of model-based designs for dose-finding trials in academia. PMID:28664918

A Detailed Physiologically Based Model to Simulate the Pharmacokinetics and Hormonal Pharmacodynamics of Enalapril on the Circulating Endocrine Renin-Angiotensin-Aldosterone System

PubMed Central

Claassen, Karina; Willmann, Stefan; Eissing, Thomas; Preusser, Tobias; Block, Michael

2013-01-01

The renin-angiotensin-aldosterone system (RAAS) plays a key role in the pathogenesis of cardiovascular disorders including hypertension and is one of the most important targets for drugs. A whole body physiologically based pharmacokinetic (wb PBPK) model integrating this hormone circulation system and its inhibition can be used to explore the influence of drugs that interfere with this system, and thus to improve the understanding of interactions between drugs and the target system. In this study, we describe the development of a mechanistic RAAS model and exemplify drug action by a simulation of enalapril administration. Enalapril and its metabolite enalaprilat are potent inhibitors of the angiotensin-converting-enzyme (ACE). To this end, a coupled dynamic parent-metabolite PBPK model was developed and linked with the RAAS model that consists of seven coupled PBPK models for aldosterone, ACE, angiotensin 1, angiotensin 2, angiotensin 2 receptor type 1, renin, and prorenin. The results indicate that the model represents the interactions in the RAAS in response to the pharmacokinetics (PK) and pharmacodynamics (PD) of enalapril and enalaprilat in an accurate manner. The full set of RAAS-hormone profiles and interactions are consistently described at pre- and post-administration steady state as well as during their dynamic transition and show a good agreement with literature data. The model allows a simultaneous representation of the parent-metabolite conversion to the active form as well as the effect of the drug on the hormone levels, offering a detailed mechanistic insight into the hormone cascade and its inhibition. This model constitutes a first major step to establish a PBPK-PD-model including the PK and the mode of action (MoA) of a drug acting on a dynamic RAAS that can be further used to link to clinical endpoints such as blood pressure. PMID:23404365

Drug discovery and development for rare genetic disorders.

PubMed

Sun, Wei; Zheng, Wei; Simeonov, Anton

2017-09-01

Approximately 7,000 rare diseases affect millions of individuals in the United States. Although rare diseases taken together have an enormous impact, there is a significant gap between basic research and clinical interventions. Opportunities now exist to accelerate drug development for the treatment of rare diseases. Disease foundations and research centers worldwide focus on better understanding rare disorders. Here, the state-of-the-art drug discovery strategies for small molecules and biological approaches for orphan diseases are reviewed. Rare diseases are usually genetic diseases; hence, employing pharmacogenetics to develop treatments and using whole genome sequencing to identify the etiologies for such diseases are appropriate strategies to exploit. Beginning with high throughput screening of small molecules, the benefits and challenges of target-based and phenotypic screens are discussed. Explanations and examples of drug repurposing are given; drug repurposing as an approach to quickly move programs to clinical trials is evaluated. Consideration is given to the category of biologics which include gene therapy, recombinant proteins, and autologous transplants. Disease models, including animal models and induced pluripotent stem cells (iPSCs) derived from patients, are surveyed. Finally, the role of biomarkers in drug discovery and development, as well as clinical trials, is elucidated. © 2017 Wiley Periodicals, Inc.

Encapsulated microbubbles and echogenic liposomes for contrast ultrasound imaging and targeted drug delivery

PubMed Central

Paul, Shirshendu; Nahire, Rahul; Mallik, Sanku; Sarkar, Kausik

2014-01-01

Micron- to nanometer-sized ultrasound agents, like encapsulated microbubbles and echogenic liposomes, are being developed for diagnostic imaging and ultrasound mediated drug/gene delivery. This review provides an overview of the current state of the art of the mathematical models of the acoustic behavior of ultrasound contrast microbubbles. We also present a review of the in vitro experimental characterization of the acoustic properties of microbubble based contrast agents undertaken in our laboratory. The hierarchical two-pronged approach of modeling contrast agents we developed is demonstrated for a lipid coated (Sonazoid™) and a polymer shelled (poly D-L-lactic acid) contrast microbubbles. The acoustic and drug release properties of the newly developed echogenic liposomes are discussed for their use as simultaneous imaging and drug/gene delivery agents. Although echogenicity is conclusively demonstrated in experiments, its physical mechanisms remain uncertain. Addressing questions raised here will accelerate further development and eventual clinical approval of these novel technologies. PMID:26097272

Design of pharmaceutical products to meet future patient needs requires modification of current development paradigms and business models.

PubMed

Stegemann, S; Baeyens, J-P; Becker, R; Maio, M; Bresciani, M; Shreeves, T; Ecker, F; Gogol, M

2014-06-01

Drugs represent the most common intervention strategy for managing acute and chronic medical conditions. In light of demographic change and the increasing age of patients, the classic model of drug research and development by the pharmaceutical industry and drug prescription by physicians is reaching its limits. Different stakeholders, e.g. industry, regulatory authorities, health insurance systems, physicians etc., have at least partially differing interests regarding the process of healthcare provision. The primary responsibility for the correct handling of medication and adherence to treatment schedules lies with the recipient of a drug-based therapy, i.e. the patient. It is thus necessary to interactively involve elderly patients, as well as the other stakeholders, in the development of medication and medication application devices, and in clinical trials. This approach will provide the basis for developing a strategy that better meets patients' needs, thus resulting in improved adherence to treatment schedules and better therapeutic outcomes.

Mathematical model to analyze the dissolution behavior of metastable crystals or amorphous drug accompanied with a solid-liquid interface reaction.

PubMed

Hirai, Daiki; Iwao, Yasunori; Kimura, Shin-Ichiro; Noguchi, Shuji; Itai, Shigeru

2017-04-30

Metastable crystals and the amorphous state of poorly water-soluble drugs in solid dispersions (SDs), are subject to a solid-liquid interface reaction upon exposure to a solvent. The dissolution behavior during the solid-liquid interface reaction often shows that the concentration of drugs is supersaturated, with a high initial drug concentration compared with the solubility of stable crystals but finally approaching the latter solubility with time. However, a method for measuring the precipitation rate of stable crystals and/or the potential solubility of metastable crystals or amorphous drugs has not been established. In this study, a novel mathematical model that can represent the dissolution behavior of the solid-liquid interface reaction for metastable crystals or amorphous drug was developed and its validity was evaluated. The theory for this model was based on the Noyes-Whitney equation and assumes that the precipitation of stable crystals at the solid-liquid interface occurs through a first-order reaction. Moreover, two models were developed, one assuming that the surface area of the drug remains constant because of the presence of excess drug in the bulk and the other that the surface area changes in time-dependency because of agglomeration of the drug. SDs of Ibuprofen (IB)/polyvinylpyrrolidone (PVP) were prepared and their dissolution behaviors under non-sink conditions were fitted by the models to evaluate improvements in solubility. The model assuming time-dependent surface area showed good agreement with experimental values. Furthermore, by applying the model to the dissolution profile, parameters such as the precipitation rate and the potential solubility of the amorphous drug were successfully calculated. In addition, it was shown that the improvement in solubility with supersaturation was able to be evaluated quantitatively using this model. Therefore, this mathematical model would be a useful tool to quantitatively determine the supersaturation concentration of a metastable drug from solid dispersions. Copyright © 2017 Elsevier B.V. All rights reserved.

Development of a Predictive Model for the Stabilizer Concentration Estimation in Microreservoir Transdermal Drug Delivery Systems Using Lipophilic Pressure-Sensitive Adhesives as Matrix/Carrier.

PubMed

Chenevas-Paule, Clémence; Wolff, Hans-Michael; Ashton, Mark; Schubert, Martin; Dodou, Kalliopi

2017-05-01

Microreservoir-type transdermal drug delivery systems (MTDDS) can prevent drug crystallization; however, no current predictive model considers the impact of drug load and hydration on their physical stability. We investigated MTDDS films containing polyvinylpyrrolidone (PVP) as polymeric drug stabilizer in lipophilic pressure-sensitive adhesive (silicone). Medicated and unmedicated silicone films with different molar N-vinylpyrrolidone:drug ratios were prepared and characterized by Fourier transform infrared spectroscopy, differential scanning calorimetry, scanning electron microscopy, microscopy, dynamic vapor sorption (DVS), and stability testing for 4 months at different storage conditions. Homogeneously distributed drug-PVP associates were observed when nonaqueous emulsions, containing drug-PVP (inner phase) and silicone adhesive (outer phase), were dried to films. DVS data were essential to predict physical stability at different humidities. A predictive thermodynamic model was developed based on drug-polymer hydrogen-bonding interactions, using the Hoffman equation, to estimate the drug-PVP ratio needed to obtain stable MTDDS and to evaluate the impact of humidity on their physical stability. This new approach considers the impact of polymorphism on drug solubility by using easily accessible experimental data (T m and DVS) and avoids uncertainties associated with the solubility parameter approach. In conclusion, a good fit of predicted and experimental data was observed. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Role of Quantitative Clinical Pharmacology in Pediatric Approval and Labeling.

PubMed

Mehrotra, Nitin; Bhattaram, Atul; Earp, Justin C; Florian, Jeffry; Krudys, Kevin; Lee, Jee Eun; Lee, Joo Yeon; Liu, Jiang; Mulugeta, Yeruk; Yu, Jingyu; Zhao, Ping; Sinha, Vikram

2016-07-01

Dose selection is one of the key decisions made during drug development in pediatrics. There are regulatory initiatives that promote the use of model-based drug development in pediatrics. Pharmacometrics or quantitative clinical pharmacology enables development of models that can describe factors affecting pharmacokinetics and/or pharmacodynamics in pediatric patients. This manuscript describes some examples in which pharmacometric analysis was used to support approval and labeling in pediatrics. In particular, the role of pharmacokinetic (PK) comparison of pediatric PK to adults and utilization of dose/exposure-response analysis for dose selection are highlighted. Dose selection for esomeprazole in pediatrics was based on PK matching to adults, whereas for adalimumab, exposure-response, PK, efficacy, and safety data together were useful to recommend doses for pediatric Crohn's disease. For vigabatrin, demonstration of similar dose-response between pediatrics and adults allowed for selection of a pediatric dose. Based on model-based pharmacokinetic simulations and safety data from darunavir pediatric clinical studies with a twice-daily regimen, different once-daily dosing regimens for treatment-naïve human immunodeficiency virus 1-infected pediatric subjects 3 to <12 years of age were evaluated. The role of physiologically based pharmacokinetic modeling (PBPK) in predicting pediatric PK is rapidly evolving. However, regulatory review experiences and an understanding of the state of science indicate that there is a lack of established predictive performance of PBPK in pediatric PK prediction. Moving forward, pharmacometrics will continue to play a key role in pediatric drug development contributing toward decisions pertaining to dose selection, trial designs, and assessing disease similarity to adults to support extrapolation of efficacy. Copyright © 2016 U.S. Government work not protected by U.S. copyright.

Injection molding as a one-step process for the direct production of pharmaceutical dosage forms from primary powders.

PubMed

Eggenreich, K; Windhab, S; Schrank, S; Treffer, D; Juster, H; Steinbichler, G; Laske, S; Koscher, G; Roblegg, E; Khinast, J G

2016-05-30

The objective of the present study was to develop a one-step process for the production of tablets directly from primary powder by means of injection molding (IM), to create solid-dispersion based tablets. Fenofibrate was used as the model API, a polyvinyl caprolactame-polyvinyl acetate-polyethylene glycol graft co-polymer served as a matrix system. Formulations were injection-molded into tablets using state-of-the-art IM equipment. The resulting tablets were physico-chemically characterized and the drug release kinetics and mechanism were determined. Comparison tablets were produced, either directly from powder or from pre-processed pellets prepared via hot melt extrusion (HME). The content of the model drug in the formulations was 10% (w/w), 20% (w/w) and 30% (w/w), respectively. After 120min, both powder-based and pellet-based injection-molded tablets exhibited a drug release of 60% independent of the processing route. Content uniformity analysis demonstrated that the model drug was homogeneously distributed. Moreover, analysis of single dose uniformity also revealed geometric drug homogeneity between tablets of one shot. Copyright © 2016 Elsevier B.V. All rights reserved.

Prediction of Drug-Drug Interactions with Crizotinib as the CYP3A Substrate Using a Physiologically Based Pharmacokinetic Model.

PubMed

Yamazaki, Shinji; Johnson, Theodore R; Smith, Bill J

2015-10-01

An orally available multiple tyrosine kinase inhibitor, crizotinib (Xalkori), is a CYP3A substrate, moderate time-dependent inhibitor, and weak inducer. The main objectives of the present study were to: 1) develop and refine a physiologically based pharmacokinetic (PBPK) model of crizotinib on the basis of clinical single- and multiple-dose results, 2) verify the crizotinib PBPK model from crizotinib single-dose drug-drug interaction (DDI) results with multiple-dose coadministration of ketoconazole or rifampin, and 3) apply the crizotinib PBPK model to predict crizotinib multiple-dose DDI outcomes. We also focused on gaining insights into the underlying mechanisms mediating crizotinib DDIs using a dynamic PBPK model, the Simcyp population-based simulator. First, PBPK model-predicted crizotinib exposures adequately matched clinically observed results in the single- and multiple-dose studies. Second, the model-predicted crizotinib exposures sufficiently matched clinically observed results in the crizotinib single-dose DDI studies with ketoconazole or rifampin, resulting in the reasonably predicted fold-increases in crizotinib exposures. Finally, the predicted fold-increases in crizotinib exposures in the multiple-dose DDI studies were roughly comparable to those in the single-dose DDI studies, suggesting that the effects of crizotinib CYP3A time-dependent inhibition (net inhibition) on the multiple-dose DDI outcomes would be negligible. Therefore, crizotinib dose-adjustment in the multiple-dose DDI studies could be made on the basis of currently available single-dose results. Overall, we believe that the crizotinib PBPK model developed, refined, and verified in the present study would adequately predict crizotinib oral exposures in other clinical studies, such as DDIs with weak/moderate CYP3A inhibitors/inducers and drug-disease interactions in patients with hepatic or renal impairment. Copyright © 2015 by The American Society for Pharmacology and Experimental Therapeutics.

Making Transporter Models for Drug-Drug Interaction Prediction Mobile.

PubMed

Ekins, Sean; Clark, Alex M; Wright, Stephen H

2015-10-01

The past decade has seen increased numbers of studies publishing ligand-based computational models for drug transporters. Although they generally use small experimental data sets, these models can provide insights into structure-activity relationships for the transporter. In addition, such models have helped to identify new compounds as substrates or inhibitors of transporters of interest. We recently proposed that many transporters are promiscuous and may require profiling of new chemical entities against multiple substrates for a specific transporter. Furthermore, it should be noted that virtually all of the published ligand-based transporter models are only accessible to those involved in creating them and, consequently, are rarely shared effectively. One way to surmount this is to make models shareable or more accessible. The development of mobile apps that can access such models is highlighted here. These apps can be used to predict ligand interactions with transporters using Bayesian algorithms. We used recently published transporter data sets (MATE1, MATE2K, OCT2, OCTN2, ASBT, and NTCP) to build preliminary models in a commercial tool and in open software that can deliver the model in a mobile app. In addition, several transporter data sets extracted from the ChEMBL database were used to illustrate how such public data and models can be shared. Predicting drug-drug interactions for various transporters using computational models is potentially within reach of anyone with an iPhone or iPad. Such tools could help prioritize which substrates should be used for in vivo drug-drug interaction testing and enable open sharing of models. Copyright © 2015 by The American Society for Pharmacology and Experimental Therapeutics.

Microemulsion-Based Topical Hydrogels of Tenoxicam for Treatment of Arthritis.

PubMed

Goindi, Shishu; Narula, Manleen; Kalra, Atin

2016-06-01

Tenoxicam (TNX) is a non-steroidal anti-inflammatory drug (NSAID) used for the treatment of rheumatoid arthritis, osteoarthritis, ankylosing spondylitis, backache and pain. However, prolonged oral use of this drug is associated with gastrointestinal adverse events like peptic ulceration, thus necessitating its development as topical formulation that could obviate the adverse effects and improve patient compliance. The present study was aimed at development of microemulsion-based formulations of TNX for topical delivery at the affected site. The pseudoternary phase diagrams were developed and microemulsion formulations were prepared using Captex 300/oleic acid as oil, Tween 80 as surfactant and n-butanol/ethanol as co-surfactant. Optimized microemulsions were characterized for drug content, droplet size, viscosity, pH and zeta potential. The ex vivo permeation studies through Laca mice skin were performed using Franz diffusion cell assembly, and the permeation profile of the microemulsion formulation was compared with aqueous suspension of drug and drug incorporated in conventional cream. Microemulsion formulations of TNX showed significantly higher (p < 0.001) mean cumulative percent permeation values in comparison to conventional cream and suspension of drug. In vivo anti-arthritic and anti-inflammatory activity of the developed TNX formulations was evaluated using various inflammatory models such as air pouch model, xylene-induced ear edema, cotton pellet granuloma and carrageenan-induced inflammation. Microemulsion formulations were found to be superior in controlling inflammation as compared to conventional topical dosage forms and showed efficacy equivalent to oral formulation. Results suggest that the developed microemulsion formulations may be used for effective topical delivery of TNX to treat various inflammatory conditions.

Comprehensive summary--Predict-IV: A systems toxicology approach to improve pharmaceutical drug safety testing.

PubMed

Mueller, Stefan O; Dekant, Wolfgang; Jennings, Paul; Testai, Emanuela; Bois, Frederic

2015-12-25

This special issue of Toxicology in Vitro is dedicated to disseminating the results of the EU-funded collaborative project "Profiling the toxicity of new drugs: a non animal-based approach integrating toxicodynamics and biokinetics" (Predict-IV; Grant 202222). The project's overall aim was to develop strategies to improve the assessment of drug safety in the early stage of development and late discovery phase, by an intelligent combination of non animal-based test systems, cell biology, mechanistic toxicology and in silico modeling, in a rapid and cost effective manner. This overview introduces the scope and overall achievements of Predict-IV. Copyright © 2014 Elsevier Ltd. All rights reserved.

Zebrafish as tools for drug discovery.

PubMed

MacRae, Calum A; Peterson, Randall T

2015-10-01

The zebrafish has become a prominent vertebrate model for disease and has already contributed to several examples of successful phenotype-based drug discovery. For the zebrafish to become useful in drug development more broadly, key hurdles must be overcome, including a more comprehensive elucidation of the similarities and differences between human and zebrafish biology. Recent studies have begun to establish the capabilities and limitations of zebrafish for disease modelling, drug screening, target identification, pharmacology, and toxicology. As our understanding increases and as the technologies for manipulating zebrafish improve, it is hoped that the zebrafish will have a key role in accelerating the emergence of precision medicine.

Antiprotozoan lead discovery by aligning dry and wet screening: prediction, synthesis, and biological assay of novel quinoxalinones.

PubMed

Martins Alho, Miriam A; Marrero-Ponce, Yovani; Barigye, Stephen J; Meneses-Marcel, Alfredo; Machado Tugores, Yanetsy; Montero-Torres, Alina; Gómez-Barrio, Alicia; Nogal, Juan J; García-Sánchez, Rory N; Vega, María Celeste; Rolón, Miriam; Martínez-Fernández, Antonio R; Escario, José A; Pérez-Giménez, Facundo; Garcia-Domenech, Ramón; Rivera, Norma; Mondragón, Ricardo; Mondragón, Mónica; Ibarra-Velarde, Froylán; Lopez-Arencibia, Atteneri; Martín-Navarro, Carmen; Lorenzo-Morales, Jacob; Cabrera-Serra, Maria Gabriela; Piñero, Jose; Tytgat, Jan; Chicharro, Roberto; Arán, Vicente J

2014-03-01

Protozoan parasites have been one of the most significant public health problems for centuries and several human infections caused by them have massive global impact. Most of the current drugs used to treat these illnesses have been used for decades and have many limitations such as the emergence of drug resistance, severe side-effects, low-to-medium drug efficacy, administration routes, cost, etc. These drugs have been largely neglected as models for drug development because they are majorly used in countries with limited resources and as a consequence with scarce marketing possibilities. Nowadays, there is a pressing need to identify and develop new drug-based antiprotozoan therapies. In an effort to overcome this problem, the main purpose of this study is to develop a QSARs-based ensemble classifier for antiprotozoan drug-like entities from a heterogeneous compounds collection. Here, we use some of the TOMOCOMD-CARDD molecular descriptors and linear discriminant analysis (LDA) to derive individual linear classification functions in order to discriminate between antiprotozoan and non-antiprotozoan compounds as a way to enable the computational screening of virtual combinatorial datasets and/or drugs already approved. Firstly, we construct a wide-spectrum benchmark database comprising of 680 organic chemicals with great structural variability (254 of them antiprotozoan agents and 426 to drugs having other clinical uses). This series of compounds was processed by a k-means cluster analysis in order to design training and predicting sets. In total, seven discriminant functions were obtained, by using the whole set of atom-based linear indices. All the LDA-based QSAR models show accuracies above 85% in the training set and values of Matthews correlation coefficients (C) vary from 0.70 to 0.86. The external validation set shows rather-good global classifications of around 80% (92.05% for best equation). Later, we developed a multi-agent QSAR classification system, in which the individual QSAR outputs are the inputs of the aforementioned fusion approach. Finally, the fusion model was used for the identification of a novel generation of lead-like antiprotozoan compounds by using ligand-based virtual screening of 'available' small molecules (with synthetic feasibility) in our 'in-house' library. A new molecular subsystem (quinoxalinones) was then theoretically selected as a promising lead series, and its derivatives subsequently synthesized, structurally characterized, and experimentally assayed by using in vitro screening that took into consideration a battery of five parasite-based assays. The chemicals 11(12) and 16 are the most active (hits) against apicomplexa (sporozoa) and mastigophora (flagellata) subphylum parasites, respectively. Both compounds depicted good activity in every protozoan in vitro panel and they did not show unspecific cytotoxicity on the host cells. The described technical framework seems to be a promising QSAR-classifier tool for the molecular discovery and development of novel classes of broad-antiprotozoan-spectrum drugs, which may meet the dual challenges posed by drug-resistant parasites and the rapid progression of protozoan illnesses. Copyright © 2014 Elsevier Ltd. All rights reserved.

Population pharmacokinetics of busulfan in pediatric and young adult patients undergoing hematopoietic cell transplant: a model-based dosing algorithm for personalized therapy and implementation into routine clinical use.

PubMed

Long-Boyle, Janel R; Savic, Rada; Yan, Shirley; Bartelink, Imke; Musick, Lisa; French, Deborah; Law, Jason; Horn, Biljana; Cowan, Morton J; Dvorak, Christopher C

2015-04-01

Population pharmacokinetic (PK) studies of busulfan in children have shown that individualized model-based algorithms provide improved targeted busulfan therapy when compared with conventional dose guidelines. The adoption of population PK models into routine clinical practice has been hampered by the tendency of pharmacologists to develop complex models too impractical for clinicians to use. The authors aimed to develop a population PK model for busulfan in children that can reliably achieve therapeutic exposure (concentration at steady state) and implement a simple model-based tool for the initial dosing of busulfan in children undergoing hematopoietic cell transplantation. Model development was conducted using retrospective data available in 90 pediatric and young adult patients who had undergone hematopoietic cell transplantation with busulfan conditioning. Busulfan drug levels and potential covariates influencing drug exposure were analyzed using the nonlinear mixed effects modeling software, NONMEM. The final population PK model was implemented into a clinician-friendly Microsoft Excel-based tool and used to recommend initial doses of busulfan in a group of 21 pediatric patients prospectively dosed based on the population PK model. Modeling of busulfan time-concentration data indicates that busulfan clearance displays nonlinearity in children, decreasing up to approximately 20% between the concentrations of 250-2000 ng/mL. Important patient-specific covariates found to significantly impact busulfan clearance were actual body weight and age. The percentage of individuals achieving a therapeutic concentration at steady state was significantly higher in subjects receiving initial doses based on the population PK model (81%) than in historical controls dosed on conventional guidelines (52%) (P = 0.02). When compared with the conventional dosing guidelines, the model-based algorithm demonstrates significant improvement for providing targeted busulfan therapy in children and young adults.

DOTAM derivatives as active cartilage-targeting drug carriers for the treatment of osteoarthritis.

PubMed

Hu, Hai-Yu; Lim, Ngee-Han; Ding-Pfennigdorff, Danping; Saas, Joachim; Wendt, K Ulrich; Ritzeler, Olaf; Nagase, Hideaki; Plettenburg, Oliver; Schultz, Carsten; Nazare, Marc

2015-03-18

Targeted drug-delivery methods are crucial for effective treatment of degenerative joint diseases such as osteoarthritis (OA). Toward this goal, we developed a small multivalent structure as a model drug for the attenuation of cartilage degradation. The DOTAM (1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid amide)-based model structure is equipped with the cathepsin D protease inhibitor pepstatin A, a fluorophore, and peptide moieties targeting collagen II. In vivo injection of these soluble probes into the knee joints of mice resulted in 7-day-long local retention, while the drug carrier equipped with a scrambled peptide sequence was washed away within 6-8 h. The model drug conjugate successfully reduced the cathepsin D protease activity as measured by release of GAG peptide. Therefore, these conjugates represent a promising first drug conjugate for the targeted treatment of degenerative joint diseases.

Development of immunopharmacotherapy against drugs of abuse.

PubMed

Meijler, Michael M; Matsushita, Masayuki; Wirsching, Peter; Janda, Kim D

2004-01-01

Drug addiction is a major worldwide medical and social problem that continues to escalate. The addiction syndrome is remarkably similar between different drugs of abuse, and can be characterized as a chronic relapsing brain disorder with neurobiological changes that lead to a compulsion to take a drug with loss of control over drug intake. Presently used medications for the treatment of dependence disorders are based on drugs that are either agonists or antagonists of drugs of abuse, and have yielded only limited success. Immunopharmacotherapy is based on the generation or administration of antibodies that are capable of binding the targeted drug before it can reach the brain, whereas replacement strategies based on agonists or antagonists of these drugs generally cause many undesired side effects. A large amount of data has been gathered in recent years on the effects of active and passive immunization against cocaine, nicotine, PCP and methamphetamine in animal models, suggesting potential efficacy of these treatments in humans; and clinical trials are currently underway for vaccines against cocaine and nicotine.

Hepatocyte-based in vitro model for assessment of drug-induced cholestasis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chatterjee, Sagnik, E-mail: Sagnik.Chatterjee@pharm.kuleuven.be; Richert, Lysiane, E-mail: l.richert@kaly-cell.com; Augustijns, Patrick, E-mail: Patrick.Augustijns@pharm.kuleuven.be

Early detection of drug-induced cholestasis remains a challenge during drug development. We have developed and validated a biorelevant sandwich-cultured hepatocytes- (SCH) based model that can identify compounds causing cholestasis by altering bile acid disposition. Human and rat SCH were exposed (24–48 h) to known cholestatic and/or hepatotoxic compounds, in the presence or in the absence of a concentrated mixture of bile acids (BAs). Urea assay was used to assess (compromised) hepatocyte functionality at the end of the incubations. The cholestatic potential of the compounds was expressed by calculating a drug-induced cholestasis index (DICI), reflecting the relative residual urea formation bymore » hepatocytes co-incubated with BAs and test compound as compared to hepatocytes treated with test compound alone. Compounds with clinical reports of cholestasis, including cyclosporin A, troglitazone, chlorpromazine, bosentan, ticlopidine, ritonavir, and midecamycin showed enhanced toxicity in the presence of BAs (DICI ≤ 0.8) for at least one of the tested concentrations. In contrast, the in vitro toxicity of compounds causing hepatotoxicity by other mechanisms (including diclofenac, valproic acid, amiodarone and acetaminophen), remained unchanged in the presence of BAs. A safety margin (SM) for drug-induced cholestasis was calculated as the ratio of lowest in vitro concentration for which was DICI ≤ 0.8, to the reported mean peak therapeutic plasma concentration. SM values obtained in human SCH correlated well with reported % incidence of clinical drug-induced cholestasis, while no correlation was observed in rat SCH. This in vitro model enables early identification of drug candidates causing cholestasis by disturbed BA handling. - Highlights: • Novel in vitro assay to detect drug-induced cholestasis • Rat and human sandwich-cultured hepatocytes (SCH) as in vitro models • Cholestatic compounds sensitize SCH to toxic effects of accumulating bile acids • Drug-induced cholestasis index (DICI) as measure of a drug's cholestatic signature • In vitro findings correlate well with clinical reports on cholestasis.« less

Incorporation of lysosomal sequestration in the mechanistic model for prediction of tissue distribution of basic drugs.

PubMed

Assmus, Frauke; Houston, J Brian; Galetin, Aleksandra

2017-11-15

The prediction of tissue-to-plasma water partition coefficients (Kpu) from in vitro and in silico data using the tissue-composition based model (Rodgers & Rowland, J Pharm Sci. 2005, 94(6):1237-48.) is well established. However, distribution of basic drugs, in particular into lysosome-rich lung tissue, tends to be under-predicted by this approach. The aim of this study was to develop an extended mechanistic model for the prediction of Kpu which accounts for lysosomal sequestration and the contribution of different cell types in the tissue of interest. The extended model is based on compound-specific physicochemical properties and tissue composition data to describe drug ionization, distribution into tissue water and drug binding to neutral lipids, neutral phospholipids and acidic phospholipids in tissues, including lysosomes. Physiological data on the types of cells contributing to lung, kidney and liver, their lysosomal content and lysosomal pH were collated from the literature. The predictive power of the extended mechanistic model was evaluated using a dataset of 28 basic drugs (pK a ≥7.8, 17 β-blockers, 11 structurally diverse drugs) for which experimentally determined Kpu data in rat tissue have been reported. Accounting for the lysosomal sequestration in the extended mechanistic model improved the accuracy of Kpu predictions in lung compared to the original Rodgers model (56% drugs within 2-fold or 88% within 3-fold of observed values). Reduction in the extent of Kpu under-prediction was also evident in liver and kidney. However, consideration of lysosomal sequestration increased the occurrence of over-predictions, yielding overall comparable model performances for kidney and liver, with 68% and 54% of Kpu values within 2-fold error, respectively. High lysosomal concentration ratios relative to cytosol (>1000-fold) were predicted for the drugs investigated; the extent differed depending on the lysosomal pH and concentration of acidic phospholipids among cell types. Despite this extensive lysosomal sequestration in the individual cells types, the maximal change in the overall predicted tissue Kpu was <3-fold for lysosome-rich tissues investigated here. Accounting for the variability in cellular physiological model input parameters, in particular lysosomal pH and fraction of the cellular volume occupied by the lysosomes, only partially explained discrepancies between observed and predicted Kpu data in the lung. Improved understanding of the system properties, e.g., cell/organelle composition is required to support further development of mechanistic equations for the prediction of drug tissue distribution. Application of this revised mechanistic model is recommended for prediction of Kpu in lysosome-rich tissue to facilitate the advancement of physiologically-based prediction of volume of distribution and drug exposure in the tissues. Copyright © 2017 Elsevier B.V. All rights reserved.

Logic-Based and Cellular Pharmacodynamic Modeling of Bortezomib Responses in U266 Human Myeloma Cells

PubMed Central

Chudasama, Vaishali L.; Ovacik, Meric A.; Abernethy, Darrell R.

2015-01-01

Systems models of biological networks show promise for informing drug target selection/qualification, identifying lead compounds and factors regulating disease progression, rationalizing combinatorial regimens, and explaining sources of intersubject variability and adverse drug reactions. However, most models of biological systems are qualitative and are not easily coupled with dynamical models of drug exposure-response relationships. In this proof-of-concept study, logic-based modeling of signal transduction pathways in U266 multiple myeloma (MM) cells is used to guide the development of a simple dynamical model linking bortezomib exposure to cellular outcomes. Bortezomib is a commonly used first-line agent in MM treatment; however, knowledge of the signal transduction pathways regulating bortezomib-mediated cell cytotoxicity is incomplete. A Boolean network model of 66 nodes was constructed that includes major survival and apoptotic pathways and was updated using responses to several chemical probes. Simulated responses to bortezomib were in good agreement with experimental data, and a reduction algorithm was used to identify key signaling proteins. Bortezomib-mediated apoptosis was not associated with suppression of nuclear factor κB (NFκB) protein inhibition in this cell line, which contradicts a major hypothesis of bortezomib pharmacodynamics. A pharmacodynamic model was developed that included three critical proteins (phospho-NFκB, BclxL, and cleaved poly (ADP ribose) polymerase). Model-fitted protein dynamics and cell proliferation profiles agreed with experimental data, and the model-predicted IC50 (3.5 nM) is comparable to the experimental value (1.5 nM). The cell-based pharmacodynamic model successfully links bortezomib exposure to MM cellular proliferation via protein dynamics, and this model may show utility in exploring bortezomib-based combination regimens. PMID:26163548

Dabigatran - a continuing exemplar case history demonstrating the need for comprehensive models to optimize the utilization of new drugs.

PubMed

Godman, Brian; Malmström, Rickard E; Diogene, Eduardo; Jayathissa, Sisira; McTaggart, Stuart; Cars, Thomas; Alvarez-Madrazo, Samantha; Baumgärtel, Christoph; Brzezinska, Anna; Bucsics, Anna; Campbell, Stephen; Eriksson, Irene; Finlayson, Alexander; Fürst, Jurij; Garuoliene, Kristina; Gutiérrez-Ibarluzea, Iñaki; Hviding, Krystyna; Herholz, Harald; Joppi, Roberta; Kalaba, Marija; Laius, Ott; Malinowska, Kamila; Pedersen, Hanne B; Markovic-Pekovic, Vanda; Piessnegger, Jutta; Selke, Gisbert; Sermet, Catherine; Spillane, Susan; Tomek, Dominik; Vončina, Luka; Vlahović-Palčevski, Vera; Wale, Janet; Wladysiuk, Magdalena; van Woerkom, Menno; Zara, Corinne; Gustafsson, Lars L

2014-01-01

There are potential conflicts between authorities and companies to fund new premium priced drugs especially where there are effectiveness, safety and/or budget concerns. Dabigatran, a new oral anticoagulant for the prevention of stroke in patients with non-valvular atrial fibrillation (AF), exemplifies this issue. Whilst new effective treatments are needed, there are issues in the elderly with dabigatran due to variable drug concentrations, no known antidote and dependence on renal elimination. Published studies showed dabigatran to be cost-effective but there are budget concerns given the prevalence of AF. These concerns resulted in extensive activities pre- to post-launch to manage its introduction. To (i) review authority activities across countries, (ii) use the findings to develop new models to better manage the entry of new drugs, and (iii) review the implications based on post-launch activities. (i) Descriptive review and appraisal of activities regarding dabigatran, (ii) development of guidance for key stakeholder groups through an iterative process, (iii) refining guidance following post launch studies. Plethora of activities to manage dabigatran including extensive pre-launch activities, risk sharing arrangements, prescribing restrictions and monitoring of prescribing post launch. Reimbursement has been denied in some countries due to concerns with its budget impact and/or excessive bleeding. Development of a new model and future guidance is proposed to better manage the entry of new drugs, centering on three pillars of pre-, peri-, and post-launch activities. Post-launch activities include increasing use of patient registries to monitor the safety and effectiveness of new drugs in clinical practice. Models for introducing new drugs are essential to optimize their prescribing especially where concerns. Without such models, new drugs may be withdrawn prematurely and/or struggle for funding.

Dabigatran - a continuing exemplar case history demonstrating the need for comprehensive models to optimize the utilization of new drugs

PubMed Central

Godman, Brian; Malmström, Rickard E.; Diogene, Eduardo; Jayathissa, Sisira; McTaggart, Stuart; Cars, Thomas; Alvarez-Madrazo, Samantha; Baumgärtel, Christoph; Brzezinska, Anna; Bucsics, Anna; Campbell, Stephen; Eriksson, Irene; Finlayson, Alexander; Fürst, Jurij; Garuoliene, Kristina; Gutiérrez-Ibarluzea, Iñaki; Hviding, Krystyna; Herholz, Harald; Joppi, Roberta; Kalaba, Marija; Laius, Ott; Malinowska, Kamila; Pedersen, Hanne B.; Markovic-Pekovic, Vanda; Piessnegger, Jutta; Selke, Gisbert; Sermet, Catherine; Spillane, Susan; Tomek, Dominik; Vončina, Luka; Vlahović-Palčevski, Vera; Wale, Janet; Wladysiuk, Magdalena; van Woerkom, Menno; Zara, Corinne; Gustafsson, Lars L.

2014-01-01

Background: There are potential conflicts between authorities and companies to fund new premium priced drugs especially where there are effectiveness, safety and/or budget concerns. Dabigatran, a new oral anticoagulant for the prevention of stroke in patients with non-valvular atrial fibrillation (AF), exemplifies this issue. Whilst new effective treatments are needed, there are issues in the elderly with dabigatran due to variable drug concentrations, no known antidote and dependence on renal elimination. Published studies showed dabigatran to be cost-effective but there are budget concerns given the prevalence of AF. These concerns resulted in extensive activities pre- to post-launch to manage its introduction. Objective: To (i) review authority activities across countries, (ii) use the findings to develop new models to better manage the entry of new drugs, and (iii) review the implications based on post-launch activities. Methodology: (i) Descriptive review and appraisal of activities regarding dabigatran, (ii) development of guidance for key stakeholder groups through an iterative process, (iii) refining guidance following post launch studies. Results: Plethora of activities to manage dabigatran including extensive pre-launch activities, risk sharing arrangements, prescribing restrictions and monitoring of prescribing post launch. Reimbursement has been denied in some countries due to concerns with its budget impact and/or excessive bleeding. Development of a new model and future guidance is proposed to better manage the entry of new drugs, centering on three pillars of pre-, peri-, and post-launch activities. Post-launch activities include increasing use of patient registries to monitor the safety and effectiveness of new drugs in clinical practice. Conclusion: Models for introducing new drugs are essential to optimize their prescribing especially where concerns. Without such models, new drugs may be withdrawn prematurely and/or struggle for funding. PMID:24959145

Mechanistic Oral Absorption Modeling and Simulation for Formulation Development and Bioequivalence Evaluation: Report of an FDA Public Workshop.

PubMed

Zhang, X; Duan, J; Kesisoglou, F; Novakovic, J; Amidon, G L; Jamei, M; Lukacova, V; Eissing, T; Tsakalozou, E; Zhao, L; Lionberger, R

2017-08-01

On May 19, 2016, the US Food and Drug Administration (FDA) hosted a public workshop, entitled "Mechanistic Oral Absorption Modeling and Simulation for Formulation Development and Bioequivalence Evaluation." The topic of mechanistic oral absorption modeling, which is one of the major applications of physiologically based pharmacokinetic (PBPK) modeling and simulation, focuses on predicting oral absorption by mechanistically integrating gastrointestinal transit, dissolution, and permeation processes, incorporating systems, active pharmaceutical ingredient (API), and the drug product information, into a systemic mathematical whole-body framework. © 2017 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

Novel Polysaccharide Based Polymers and Nanoparticles for Controlled Drug Delivery and Biomedical Imaging

NASA Astrophysics Data System (ADS)

Shalviri, Alireza

The use of polysaccharides as building blocks in the development of drugs and contrast agents delivery systems is rapidly growing. This can be attributed to the outstanding virtues of polysaccharides such as biocompatibility, biodegradability, upgradability, multiple reacting groups and low cost. The focus of this thesis was to develop and characterize novel starch based hydrogels and nanoparticles for delivery of drugs and imaging agents. To this end, two different systems were developed. The first system includes polymer and nanoparticles prepared by graft polymerization of polymethacrylic acid and polysorbate 80 onto starch. This starch based platform nanotechnology was developed using the design principles based on the pathophysiology of breast cancer, with applications in both medical imaging and breast cancer chemotherapy. The nanoparticles exhibited a high degree of doxorubicin loading as well as sustained pH dependent release of the drug. The drug loaded nanoparticles were significantly more effective against multidrug resistant human breast cancer cells compared to free doxorubicin. Systemic administration of the starch based nanoparticles co-loaded with doxorubicin and a near infrared fluorescent probe allowed for non-invasive real time monitoring of the nanoparticles biodistribution, tumor accumulation, and clearance. Systemic administration of the clinically relevant doses of the drug loaded particles to a mouse model of breast cancer significantly enhanced therapeutic efficacy while minimizing side effects compared to free doxorubicin. A novel, starch based magnetic resonance imaging (MRI) contrast agent with good in vitro and in vivo tolerability was formulated which exhibited superior signal enhancement in tumor and vasculature. The second system is a co-polymeric hydrogel of starch and xanthan gum with adjustable swelling and permeation properties. The hydrogels exhibited excellent film forming capability, and appeared to be particularly useful in controlled delivery applications of larger molecular size compounds. The starch based hydrogels, polymers and nanoparticles developed in this work have shown great potentials for controlled drug delivery and biomedical imaging applications.

Tools for Early Prediction of Drug Loading in Lipid-Based Formulations

PubMed Central

2015-01-01

Identification of the usefulness of lipid-based formulations (LBFs) for delivery of poorly water-soluble drugs is at date mainly experimentally based. In this work we used a diverse drug data set, and more than 2,000 solubility measurements to develop experimental and computational tools to predict the loading capacity of LBFs. Computational models were developed to enable in silico prediction of solubility, and hence drug loading capacity, in the LBFs. Drug solubility in mixed mono-, di-, triglycerides (Maisine 35-1 and Capmul MCM EP) correlated (R2 0.89) as well as the drug solubility in Carbitol and other ethoxylated excipients (PEG400, R2 0.85; Polysorbate 80, R2 0.90; Cremophor EL, R2 0.93). A melting point below 150 °C was observed to result in a reasonable solubility in the glycerides. The loading capacity in LBFs was accurately calculated from solubility data in single excipients (R2 0.91). In silico models, without the demand of experimentally determined solubility, also gave good predictions of the loading capacity in these complex formulations (R2 0.79). The framework established here gives a better understanding of drug solubility in single excipients and of LBF loading capacity. The large data set studied revealed that experimental screening efforts can be rationalized by solubility measurements in key excipients or from solid state information. For the first time it was shown that loading capacity in complex formulations can be accurately predicted using molecular information extracted from calculated descriptors and thermal properties of the crystalline drug. PMID:26568134

Tools for Early Prediction of Drug Loading in Lipid-Based Formulations.

PubMed

Alskär, Linda C; Porter, Christopher J H; Bergström, Christel A S

2016-01-04

Identification of the usefulness of lipid-based formulations (LBFs) for delivery of poorly water-soluble drugs is at date mainly experimentally based. In this work we used a diverse drug data set, and more than 2,000 solubility measurements to develop experimental and computational tools to predict the loading capacity of LBFs. Computational models were developed to enable in silico prediction of solubility, and hence drug loading capacity, in the LBFs. Drug solubility in mixed mono-, di-, triglycerides (Maisine 35-1 and Capmul MCM EP) correlated (R(2) 0.89) as well as the drug solubility in Carbitol and other ethoxylated excipients (PEG400, R(2) 0.85; Polysorbate 80, R(2) 0.90; Cremophor EL, R(2) 0.93). A melting point below 150 °C was observed to result in a reasonable solubility in the glycerides. The loading capacity in LBFs was accurately calculated from solubility data in single excipients (R(2) 0.91). In silico models, without the demand of experimentally determined solubility, also gave good predictions of the loading capacity in these complex formulations (R(2) 0.79). The framework established here gives a better understanding of drug solubility in single excipients and of LBF loading capacity. The large data set studied revealed that experimental screening efforts can be rationalized by solubility measurements in key excipients or from solid state information. For the first time it was shown that loading capacity in complex formulations can be accurately predicted using molecular information extracted from calculated descriptors and thermal properties of the crystalline drug.

Floating gastroretentive drug delivery systems: Comparison of experimental and simulated dissolution profiles and floatation behavior.

PubMed

Eberle, Veronika A; Schoelkopf, Joachim; Gane, Patrick A C; Alles, Rainer; Huwyler, Jörg; Puchkov, Maxim

2014-07-16

Gastroretentive drug delivery systems (GRDDS) play an important role in the delivery of drug substances to the upper part of the gastrointestinal tract; they offer a possibility to overcome the limited gastric residence time of conventional dosage forms. The aim of the study was to understand drug-release and floatation mechanisms of a floating GRDDS based on functionalized calcium carbonate (FCC). The inherently low apparent density of the excipient (approx. 0.6 g/cm(3)) enabled a mechanism of floatation. The higher specific surface of FCC (approx. 70 m(2)) allowed sufficient hardness of resulting compacts. The floating mechanism of GRDDS was simulated in silico under simulated acidic and neutral conditions, and the results were compared to those obtained in vitro. United States Pharmacopeia (USP) dissolution methods are of limited usefulness for evaluating floating behavior and drug release of floating dosage forms. Therefore, we developed a custom-built stomach model to simultaneously analyze floating characteristics and drug release. In silico dissolution and floatation profiles of the FCC-based tablet were simulated using a three-dimensional cellular automata-based model. In simulated gastric fluid, the FCC-based tablets showed instant floatation. The compacts stayed afloat during the measurement in 0.1 N HCl and eroded completely while releasing the model drug substance. When water was used as dissolution medium, the tablets had no floating lag time and sank down during the measurement, resulting in a change of release kinetics. Floating dosage forms based on FCC appear promising. It was possible to manufacture floating tablets featuring a density of less than unity and sufficient hardness for further processing. In silico dissolution simulation offered a possibility to understand floating behavior and drug-release mechanism. Copyright © 2014 Elsevier B.V. All rights reserved.

Development and application of a population physiologically based pharmacokinetic model for penicillin G in swine and cattle for food safety assessment.

PubMed

Li, Miao; Gehring, Ronette; Riviere, Jim E; Lin, Zhoumeng

2017-09-01

Penicillin G is a widely used antimicrobial in food-producing animals, and one of the most predominant drug residues in animal-derived food products. Due to reduced sensitivity of bacteria to penicillin, extralabel use of penicillin G is common, which may lead to violative residues in edible tissues and cause adverse reactions in consumers. This study aimed to develop a physiologically based pharmacokinetic (PBPK) model to predict drug residues in edible tissues and estimate extended withdrawal intervals for penicillin G in swine and cattle. A flow-limited PBPK model was developed with data from Food Animal Residue Avoidance Databank using Berkeley Madonna. The model predicted observed drug concentrations in edible tissues, including liver, muscle, and kidney for penicillin G both in swine and cattle well, including data not used in model calibration. For extralabel use (5× and 10× label dose) of penicillin G, Monte Carlo sampling technique was applied to predict times needed for tissue concentrations to fall below established tolerances for the 99th percentile of the population. This model provides a useful tool to predict tissue residues of penicillin G in swine and cattle to aid food safety assessment, and also provide a framework for extrapolation to other food animal species. Copyright © 2017 Elsevier Ltd. All rights reserved.

Rational design on controlled release ion-exchange polymeric microspheres and polymer-lipid hybrid nanoparticles for the delivery of water-soluble drugs through a multidisciplinary approach

NASA Astrophysics Data System (ADS)

Li, Yongqiang

Sulfopropyl dextran sulfate (SP-DS) microspheres and polymer-lipid hybrid nanoparticles (PLN) for the delivery of water-soluble anticancer drugs and P-glycoprotein inhibitors were developed by our group recently and demonstrated effectiveness in local chemotherapy. To optimize the delivery performance of these particulate systems, particularly PLN, an integrated multidisciplinary approach was developed, based on an in-depth understanding of drug-excipient interactions, internal structure, drug loading and release mechanisms, and application of advanced modeling/optimization techniques. An artificial neural networks (ANN) simulator capable of formulation optimization and drug release prediction was developed. In vitro drug release kinetics of SP-DS microspheres, with various drug loading and in different release media, were predicted by ANN. The effects of independent variables on drug release were evaluated. Good modeling performance suggested that ANN is a useful tool to predict drug release from ion-exchange microspheres. To further improve the performance of PLN, drug-polymer-lipid interactions were characterized theoretically and experimentally using verapamil hydrochloride (VRP) as a model drug and dextran sulfate sodium (DS) as a counter-ion polymer. VRP-DS complexation followed a stoichiometric rule and solid-state transformation of VRP were observed. Dodecanoic acid (DA) was identified as the lead lipid carrier material. Based upon the optimized drug-polymer-lipid interactions, PLN with high drug loading capacity (36%, w/w) and sustained release without initial burst release were achieved. VRP remained amorphous and was molecularly dispersed within PLN. H-bonding contributed to the miscibility between the VRP-DS complex and DA. Drug release from PLN was mainly controlled by diffusion and ion-exchange processes. Drug loading capacity and particle size of PLN depend on the formulation factors of the weight ratio of drug to lipid and concentrations of surfactants applied. A three-factor spherical composite experimental design was used to map the cause-and-effect relationship. PLN with high drug loading efficiency (92%) and small particle size (100 nm) were predicted by ANN and confirmed by experiment. The roles of various factors on the properties of PLN were also investigated. In summary, this thesis demonstrates that an integrated multidisciplinary strategy ranging from preformulation to formulation to optimization is suitable for the rational design of SP-DS microspheres and PLN with desired properties.

From bench to patient: model systems in drug discovery.

PubMed

Breyer, Matthew D; Look, A Thomas; Cifra, Alessandra

2015-10-01

Model systems, including laboratory animals, microorganisms, and cell- and tissue-based systems, are central to the discovery and development of new and better drugs for the treatment of human disease. In this issue, Disease Models & Mechanisms launches a Special Collection that illustrates the contribution of model systems to drug discovery and optimisation across multiple disease areas. This collection includes reviews, Editorials, interviews with leading scientists with a foot in both academia and industry, and original research articles reporting new and important insights into disease therapeutics. This Editorial provides a summary of the collection's current contents, highlighting the impact of multiple model systems in moving new discoveries from the laboratory bench to the patients' bedsides. © 2015. Published by The Company of Biologists Ltd.

Multiscale Modeling in the Clinic: Drug Design and Development

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clancy, Colleen E.; An, Gary; Cannon, William R.

A wide range of length and time scales are relevant to pharmacology, especially in drug development, drug design and drug delivery. Therefore, multi-scale computational modeling and simulation methods and paradigms that advance the linkage of phenomena occurring at these multiple scales have become increasingly important. Multi-scale approaches present in silico opportunities to advance laboratory research to bedside clinical applications in pharmaceuticals research. This is achievable through the capability of modeling to reveal phenomena occurring across multiple spatial and temporal scales, which are not otherwise readily accessible to experimentation. The resultant models, when validated, are capable of making testable predictions tomore » guide drug design and delivery. In this review we describe the goals, methods, and opportunities of multi-scale modeling in drug design and development. We demonstrate the impact of multiple scales of modeling in this field. We indicate the common mathematical techniques employed for multi-scale modeling approaches used in pharmacology and present several examples illustrating the current state-of-the-art regarding drug development for: Excitable Systems (Heart); Cancer (Metastasis and Differentiation); Cancer (Angiogenesis and Drug Targeting); Metabolic Disorders; and Inflammation and Sepsis. We conclude with a focus on barriers to successful clinical translation of drug development, drug design and drug delivery multi-scale models.« less

Reducing the Complexity of an Agent-Based Local Heroin Market Model

PubMed Central

Heard, Daniel; Bobashev, Georgiy V.; Morris, Robert J.

2014-01-01

This project explores techniques for reducing the complexity of an agent-based model (ABM). The analysis involved a model developed from the ethnographic research of Dr. Lee Hoffer in the Larimer area heroin market, which involved drug users, drug sellers, homeless individuals and police. The authors used statistical techniques to create a reduced version of the original model which maintained simulation fidelity while reducing computational complexity. This involved identifying key summary quantities of individual customer behavior as well as overall market activity and replacing some agents with probability distributions and regressions. The model was then extended to allow external market interventions in the form of police busts. Extensions of this research perspective, as well as its strengths and limitations, are discussed. PMID:25025132

3D Printing Factors Important for the Fabrication of Polyvinylalcohol Filament-Based Tablets.

PubMed

Tagami, Tatsuaki; Fukushige, Kaori; Ogawa, Emi; Hayashi, Naomi; Ozeki, Tetsuya

2017-01-01

Three-dimensional (3D) printers have been applied in many fields, including engineering and the medical sciences. In the pharmaceutical field, approval of the first 3D-printed tablet by the U.S. Food and Drug Administration in 2015 has attracted interest in the manufacture of tablets and drugs by 3D printing techniques as a means of delivering tailor-made drugs in the future. In current study, polyvinylalcohol (PVA)-based tablets were prepared using a fused-deposition-modeling-type 3D printer and the effect of 3D printing conditions on tablet production was investigated. Curcumin, a model drug/fluorescent marker, was loaded into PVA-filament. We found that several printing parameters, such as the rate of extruding PVA (flow rate), can affect the formability of the resulting PVA-tablets. The 3D-printing temperature is controlled by heating the print nozzle and was shown to affect the color of the tablets and their curcumin content. PVA-based infilled tablets with different densities were prepared by changing the fill density as a printing parameter. Tablets with lower fill density floated in an aqueous solution and their curcumin content tended to dissolve faster. These findings will be useful in developing drug-loaded PVA-based 3D objects and other polymer-based articles prepared using fused-deposition-modeling-type 3D printers.

[Validity evidence of the Health-Related Quality of Life for Drug Abusers Test based on the Biaxial Model of Addiction].

PubMed

Lozano, Oscar M; Rojas, Antonio J; Pérez, Cristino; González-Sáiz, Francisco; Ballesta, Rosario; Izaskun, Bilbao

2008-05-01

The aim of this work is to show evidence of the validity of the Health-Related Quality of Life for Drug Abusers Test (HRQoLDA Test). This test was developed to measure specific HRQoL for drugs abusers, within the theoretical addiction framework of the biaxial model. The sample comprised 138 patients diagnosed with opiate drug dependence. In this study, the following constructs and variables of the biaxial model were measured: severity of dependence, physical health status, psychological adjustment and substance consumption. Results indicate that the HRQoLDA Test scores are related to dependency and consumption-related problems. Multiple regression analysis reveals that HRQoL can be predicted from drug dependence, physical health status and psychological adjustment. These results contribute empirical evidence of the theoretical relationships established between HRQoL and the biaxial model, and they support the interpretation of the HRQoLDA Test to measure HRQoL in drug abusers, thus providing a test to measure this specific construct in this population.

Probability Based hERG Blocker Classifiers.

PubMed

Wang, Zhi; Mussa, Hamse Y; Lowe, Robert; Glen, Robert C; Yan, Aixia

2012-09-01

The US Food and Drug Administration (FDA) require in vitro human ether-a-go-go related (hERG) ion channel affinity tests for all drug candidates prior to clinical trials. In this study, probabilistic-based methods were employed to develop prediction models on hERG inhibition prediction, which are different from traditional QSAR models that are mainly based on supervised 'hard point' (HP) classification approaches giving 'yes/no' answers. The obtained models can 'ascertain' whether or not a given set of compounds can block hERG ion channels. The results presented indicate that the proposed probabilistic-based method can be a valuable tool for ranking compounds with respect to their potential cardio-toxicity and will be promising for other toxic property predictions. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A large-scale initiative to disseminate an evidence-based drug abuse prevention program in Italy: Lessons learned for practitioners and researchers.

PubMed

Velasco, Veronica; Griffin, Kenneth W; Antichi, Mariella; Celata, Corrado

2015-10-01

Across developed countries, experimentation with alcohol, tobacco, and other drugs often begins in the early adolescent years. Several evidence-based programs have been developed to prevent adolescent substance use. Many of the most rigorously tested and empirically supported prevention programs were initially developed and tested in the United States. Increasingly, these interventions are being adopted for use in Europe and throughout the world. This paper reports on a large-scale comprehensive initiative designed to select, adapt, implement, and sustain an evidence-based drug abuse prevention program in Italy. As part of a large-scale regionally funded collaboration in the Lombardy region of Italy, we report on processes through which a team of stakeholders selected, translated and culturally adapted, planned, implemented and evaluated the Life Skills Training (LST) school-based drug abuse prevention program, an evidence-based intervention developed in the United States. We discuss several challenges and lessons learned and implications for prevention practitioners and researchers attempting to undertake similar international dissemination projects. We review several published conceptual models designed to promote the replication and widespread dissemination of effective programs, and discuss their strengths and limitations in the context of planning and implementing a complex, large-scale real-world dissemination effort. Copyright © 2015 Elsevier Ltd. All rights reserved.

RFDT: A Rotation Forest-based Predictor for Predicting Drug-Target Interactions Using Drug Structure and Protein Sequence Information.

PubMed

Wang, Lei; You, Zhu-Hong; Chen, Xing; Yan, Xin; Liu, Gang; Zhang, Wei

2018-01-01

Identification of interaction between drugs and target proteins plays an important role in discovering new drug candidates. However, through the experimental method to identify the drug-target interactions remain to be extremely time-consuming, expensive and challenging even nowadays. Therefore, it is urgent to develop new computational methods to predict potential drugtarget interactions (DTI). In this article, a novel computational model is developed for predicting potential drug-target interactions under the theory that each drug-target interaction pair can be represented by the structural properties from drugs and evolutionary information derived from proteins. Specifically, the protein sequences are encoded as Position-Specific Scoring Matrix (PSSM) descriptor which contains information of biological evolutionary and the drug molecules are encoded as fingerprint feature vector which represents the existence of certain functional groups or fragments. Four benchmark datasets involving enzymes, ion channels, GPCRs and nuclear receptors, are independently used for establishing predictive models with Rotation Forest (RF) model. The proposed method achieved the prediction accuracy of 91.3%, 89.1%, 84.1% and 71.1% for four datasets respectively. In order to make our method more persuasive, we compared our classifier with the state-of-theart Support Vector Machine (SVM) classifier. We also compared the proposed method with other excellent methods. Experimental results demonstrate that the proposed method is effective in the prediction of DTI, and can provide assistance for new drug research and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Advances in visual representation of molecular potentials.

PubMed

Du, Qi-Shi; Huang, Ri-Bo; Chou, Kuo-Chen

2010-06-01

The recent advances in visual representations of molecular properties in 3D space are summarized, and their applications in molecular modeling study and rational drug design are introduced. The visual representation methods provide us with detailed insights into protein-ligand interactions, and hence can play a major role in elucidating the structure or reactivity of a biomolecular system. Three newly developed computation and visualization methods for studying the physical and chemical properties of molecules are introduced, including their electrostatic potential, lipophilicity potential and excess chemical potential. The newest application examples of visual representations in structure-based rational drug are presented. The 3D electrostatic potentials, calculated using the empirical method (EM-ESP), in which the classical Coulomb equation and traditional atomic partial changes are discarded, are highly consistent with the results by the higher level quantum chemical method. The 3D lipophilicity potentials, computed by the heuristic molecular lipophilicity potential method based on the principles of quantum mechanics and statistical mechanics, are more accurate and reliable than those by using the traditional empirical methods. The 3D excess chemical potentials, derived by the reference interaction site model-hypernetted chain theory, provide a new tool for computational chemistry and molecular modeling. For structure-based drug design, the visual representations of molecular properties will play a significant role in practical applications. It is anticipated that the new advances in computational chemistry will stimulate the development of molecular modeling methods, further enriching the visual representation techniques for rational drug design, as well as other relevant fields in life science.

The role of academic institutions in the development of drugs for rare and neglected diseases.

PubMed

Coles, L D; Cloyd, J C

2012-08-01

There are approximately 7,000 rare disorders, many of which are life-threatening. Diagnosis is often problematic, and therapies are few. Before the passage of the Orphan Drug Act in 1983, neither the pharmaceutical industry nor universities devoted much effort to research on rare diseases. Important changes have occurred within and outside universities that position them to play a significant role in developing orphan drugs. Several models are being employed to promote drug-related research, including disease-focused, discovery-focused, development-focused, and industry-partnership-focused approaches. However, significant barriers challenge universities' ability to fully contribute to orphan drug development. Academic institutions, along with industry, government, and not-for-profit organizations, must address these issues in order to advance the field. New initiatives designed to increase university-based orphan drug research include creating mechanisms to ensure program continuity, building research and regulatory support infrastructure, facilitating commercialization, expanding government support, and developing mutually beneficial partnerships among academe, industry, and government.

Ontology-based literature mining and class effect analysis of adverse drug reactions associated with neuropathy-inducing drugs.

PubMed

Hur, Junguk; Özgür, Arzucan; He, Yongqun

2018-06-07

Adverse drug reactions (ADRs), also called as drug adverse events (AEs), are reported in the FDA drug labels; however, it is a big challenge to properly retrieve and analyze the ADRs and their potential relationships from textual data. Previously, we identified and ontologically modeled over 240 drugs that can induce peripheral neuropathy through mining public drug-related databases and drug labels. However, the ADR mechanisms of these drugs are still unclear. In this study, we aimed to develop an ontology-based literature mining system to identify ADRs from drug labels and to elucidate potential mechanisms of the neuropathy-inducing drugs (NIDs). We developed and applied an ontology-based SciMiner literature mining strategy to mine ADRs from the drug labels provided in the Text Analysis Conference (TAC) 2017, which included drug labels for 53 neuropathy-inducing drugs (NIDs). We identified an average of 243 ADRs per NID and constructed an ADR-ADR network, which consists of 29 ADR nodes and 149 edges, including only those ADR-ADR pairs found in at least 50% of NIDs. Comparison to the ADR-ADR network of non-NIDs revealed that the ADRs such as pruritus, pyrexia, thrombocytopenia, nervousness, asthenia, acute lymphocytic leukaemia were highly enriched in the NID network. Our ChEBI-based ontology analysis identified three benzimidazole NIDs (i.e., lansoprazole, omeprazole, and pantoprazole), which were associated with 43 ADRs. Based on ontology-based drug class effect definition, the benzimidazole drug group has a drug class effect on all of these 43 ADRs. Many of these 43 ADRs also exist in the enriched NID ADR network. Our Ontology of Adverse Events (OAE) classification further found that these 43 benzimidazole-related ADRs were distributed in many systems, primarily in behavioral and neurological, digestive, skin, and immune systems. Our study demonstrates that ontology-based literature mining and network analysis can efficiently identify and study specific group of drugs and their associated ADRs. Furthermore, our analysis of drug class effects identified 3 benzimidazole drugs sharing 43 ADRs, leading to new hypothesis generation and possible mechanism understanding of drug-induced peripheral neuropathy.

Physiologically Based Absorption Modeling to Design Extended-Release Clinical Products for an Ester Prodrug.

PubMed

Ding, Xuan; Day, Jeffrey S; Sperry, David C

2016-11-01

Absorption modeling has demonstrated its great value in modern drug product development due to its utility in understanding and predicting in vivo performance. In this case, we integrated physiologically based modeling in the development processes to effectively design extended-release (ER) clinical products for an ester prodrug LY545694. By simulating the trial results of immediate-release products, we delineated complex pharmacokinetics due to prodrug conversion and established an absorption model to describe the clinical observations. This model suggested the prodrug has optimal biopharmaceutical properties to warrant developing an ER product. Subsequently, we incorporated release profiles of prototype ER tablets into the absorption model to simulate the in vivo performance of these products observed in an exploratory trial. The models suggested that the absorption of these ER tablets was lower than the IR products because the extended release from the formulations prevented the drug from taking advantage of the optimal absorption window. Using these models, we formed a strategy to optimize the ER product to minimize the impact of the absorption window limitation. Accurate prediction of the performance of these optimized products by modeling was confirmed in a third clinical trial.

A Liver-centric Multiscale Modeling Framework for Xenobiotics ...

EPA Pesticide Factsheets

We describe a multi-scale framework for modeling acetaminophen-induced liver toxicity. Acetaminophen is a widely used analgesic. Overdose of acetaminophen can result in liver injury via its biotransformation into toxic product, which further induce massive necrosis. Our study focuses on developing a multi-scale computational model to characterize both phase I and phase II metabolism of acetaminophen, by bridging Physiologically Based Pharmacokinetic (PBPK) modeling at the whole body level, cell movement and blood flow at the tissue level and cell signaling and drug metabolism at the sub-cellular level. To validate the model, we estimated our model parameters by fi?tting serum concentrations of acetaminophen and its glucuronide and sulfate metabolites to experiments, and carried out sensitivity analysis on 35 parameters selected from three modules. Our study focuses on developing a multi-scale computational model to characterize both phase I and phase II metabolism of acetaminophen, by bridging Physiologically Based Pharmacokinetic (PBPK) modeling at the whole body level, cell movement and blood flow at the tissue level and cell signaling and drug metabolism at the sub-cellular level. This multiscale model bridges the CompuCell3D tool used by the Virtual Tissue project with the httk tool developed by the Rapid Exposure and Dosimetry project.

Drug development costs when financial risk is measured using the Fama-French three-factor model.

PubMed

Vernon, John A; Golec, Joseph H; Dimasi, Joseph A

2010-08-01

In a widely cited article, DiMasi, Hansen, and Grabowski (2003) estimate the average pre-tax cost of bringing a new molecular entity to market. Their base case estimate, excluding post-marketing studies, was $802 million (in $US 2000). Strikingly, almost half of this cost (or $399 million) is the cost of capital (COC) used to fund clinical development expenses to the point of FDA marketing approval. The authors used an 11% real COC computed using the capital asset pricing model (CAPM). But the CAPM is a single factor risk model, and multi-factor risk models are the current state of the art in finance. Using the Fama-French three factor model we find that the cost of drug development to be higher than the earlier estimate. Copyright (c) 2009 John Wiley & Sons, Ltd.

Homology Modeling of Dopamine D2 and D3 Receptors: Molecular Dynamics Refinement and Docking Evaluation

PubMed Central

Platania, Chiara Bianca Maria; Salomone, Salvatore; Leggio, Gian Marco; Drago, Filippo; Bucolo, Claudio

2012-01-01

Dopamine (DA) receptors, a class of G-protein coupled receptors (GPCRs), have been targeted for drug development for the treatment of neurological, psychiatric and ocular disorders. The lack of structural information about GPCRs and their ligand complexes has prompted the development of homology models of these proteins aimed at structure-based drug design. Crystal structure of human dopamine D3 (hD3) receptor has been recently solved. Based on the hD3 receptor crystal structure we generated dopamine D2 and D3 receptor models and refined them with molecular dynamics (MD) protocol. Refined structures, obtained from the MD simulations in membrane environment, were subsequently used in molecular docking studies in order to investigate potential sites of interaction. The structure of hD3 and hD2L receptors was differentiated by means of MD simulations and D3 selective ligands were discriminated, in terms of binding energy, by docking calculation. Robust correlation of computed and experimental Ki was obtained for hD3 and hD2L receptor ligands. In conclusion, the present computational approach seems suitable to build and refine structure models of homologous dopamine receptors that may be of value for structure-based drug discovery of selective dopaminergic ligands. PMID:22970199

Drug target inference through pathway analysis of genomics data

PubMed Central

Ma, Haisu; Zhao, Hongyu

2013-01-01

Statistical modeling coupled with bioinformatics is commonly used for drug discovery. Although there exist many approaches for single target based drug design and target inference, recent years have seen a paradigm shift to system-level pharmacological research. Pathway analysis of genomics data represents one promising direction for computational inference of drug targets. This article aims at providing a comprehensive review on the evolving issues is this field, covering methodological developments, their pros and cons, as well as future research directions. PMID:23369829

Acid-activatable oxidative stress-inducing polysaccharide nanoparticles for anticancer therapy.

PubMed

Yoo, Wooyoung; Yoo, Donghyuck; Hong, Eunmi; Jung, Eunkyeong; Go, Yebin; Singh, S V Berwin; Khang, Gilson; Lee, Dongwon

2018-01-10

Drug delivery systems have been extensively developed to enhance the therapeutic efficacy of drugs by altering their pharmacokinetics and biodistribution. However, the use of high quantities of drug delivery systems can cause toxicity due to their poor metabolism and elimination. In this study, we developed polysaccharide-based drug delivery systems which exert potent therapeutic effects and could display synergistic therapeutic effects with drug payloads, leading to dose reduction. Cinnamaldehyde, a major component of cinnamon is known to induce anticancer activity by generating ROS (reactive oxygen species). We developed cinnamaldehyde-conjugated maltodextrin (CMD) as a polymeric prodrug of cinnamaldehyde and a drug carrier. Cinnamaldehyde was conjugated to the hydroxyl groups of maltodextrin via acid-cleavable acetal linkages, allowing facile formulation of nanoparticles and drug encapsulation. CMD nanoparticles induced acid-triggered ROS generation to induce apoptotic cell death. Camptothecin (CPT) was used as a model drug to investigate the potential of CMD nanoparticles as a drug carrier and also evaluate the synergistic anticancer effects with CMD nanoparticles. CPT-loaded CMD nanoparticles exhibited significantly higher anticancer activity than empty CMD nanoparticles and CPT alone in the study of mouse xenograft models, demonstrating the synergistic therapeutic effects of CMD with CPT. Taken together, we believe that CMD nanoparticles hold tremendous potential as a polymeric prodrug of cinnamaldehyde and a drug carrier in anticancer therapy. Copyright © 2017 Elsevier B.V. All rights reserved.

Minimum Transendothelial Electrical Resistance Thresholds for the Study of Small and Large Molecule Drug Transport in a Human in Vitro Blood-Brain Barrier Model.

PubMed

Mantle, Jennifer L; Min, Lie; Lee, Kelvin H

2016-12-05

A human cell-based in vitro model that can accurately predict drug penetration into the brain as well as metrics to assess these in vitro models are valuable for the development of new therapeutics. Here, human induced pluripotent stem cells (hPSCs) are differentiated into a polarized monolayer that express blood-brain barrier (BBB)-specific proteins and have transendothelial electrical resistance (TEER) values greater than 2500 Ω·cm 2 . By assessing the permeabilities of several known drugs, a benchmarking system to evaluate brain permeability of drugs was established. Furthermore, relationships between TEER and permeability to both small and large molecules were established, demonstrating that different minimum TEER thresholds must be achieved to study the brain transport of these two classes of drugs. This work demonstrates that this hPSC-derived BBB model exhibits an in vivo-like phenotype, and the benchmarks established here are useful for assessing functionality of other in vitro BBB models.

Computational Fragment-Based Drug Design: Current Trends, Strategies, and Applications.

PubMed

Bian, Yuemin; Xie, Xiang-Qun Sean

2018-04-09

Fragment-based drug design (FBDD) has become an effective methodology for drug development for decades. Successful applications of this strategy brought both opportunities and challenges to the field of Pharmaceutical Science. Recent progress in the computational fragment-based drug design provide an additional approach for future research in a time- and labor-efficient manner. Combining multiple in silico methodologies, computational FBDD possesses flexibilities on fragment library selection, protein model generation, and fragments/compounds docking mode prediction. These characteristics provide computational FBDD superiority in designing novel and potential compounds for a certain target. The purpose of this review is to discuss the latest advances, ranging from commonly used strategies to novel concepts and technologies in computational fragment-based drug design. Particularly, in this review, specifications and advantages are compared between experimental and computational FBDD, and additionally, limitations and future prospective are discussed and emphasized.

Analysis of the Mechanism of Prolonged Persistence of Drug Interaction between Terbinafine and Amitriptyline or Nortriptyline.

PubMed

Mikami, Akiko; Hori, Satoko; Ohtani, Hisakazu; Sawada, Yasufumi

2017-01-01

The purpose of the study was to quantitatively estimate and predict drug interactions between terbinafine and tricyclic antidepressants (TCAs), amitriptyline or nortriptyline, based on in vitro studies. Inhibition of TCA-metabolizing activity by terbinafine was investigated using human liver microsomes. Based on the unbound K i values obtained in vitro and reported pharmacokinetic parameters, a pharmacokinetic model of drug interaction was fitted to the reported plasma concentration profiles of TCAs administered concomitantly with terbinafine to obtain the drug-drug interaction parameters. Then, the model was used to predict nortriptyline plasma concentration with concomitant administration of terbinafine and changes of area under the curve (AUC) of nortriptyline after cessation of terbinafine. The CYP2D6 inhibitory potency of terbinafine was unaffected by preincubation, so the inhibition seems to be reversible. Terbinafine competitively inhibited amitriptyline or nortriptyline E-10-hydroxylation, with unbound K i values of 13.7 and 12.4 nM, respectively. Observed plasma concentrations of TCAs administered concomitantly with terbinafine were successfully simulated with the drug interaction model using the in vitro parameters. Model-predicted nortriptyline plasma concentration after concomitant nortriptylene/terbinafine administration for two weeks exceeded the toxic level, and drug interaction was predicted to be prolonged; the AUC of nortriptyline was predicted to be increased by 2.5- or 2.0- and 1.5-fold at 0, 3 and 6 months after cessation of terbinafine, respectively. The developed model enables us to quantitatively predict the prolonged drug interaction between terbinafine and TCAs. The model should be helpful for clinical management of terbinafine-CYP2D6 substrate drug interactions, which are difficult to predict due to their time-dependency.

Drug-disease modeling in the pharmaceutical industry - where mechanistic systems pharmacology and statistical pharmacometrics meet.

PubMed

Helmlinger, Gabriel; Al-Huniti, Nidal; Aksenov, Sergey; Peskov, Kirill; Hallow, Karen M; Chu, Lulu; Boulton, David; Eriksson, Ulf; Hamrén, Bengt; Lambert, Craig; Masson, Eric; Tomkinson, Helen; Stanski, Donald

2017-11-15

Modeling & simulation (M&S) methodologies are established quantitative tools, which have proven to be useful in supporting the research, development (R&D), regulatory approval, and marketing of novel therapeutics. Applications of M&S help design efficient studies and interpret their results in context of all available data and knowledge to enable effective decision-making during the R&D process. In this mini-review, we focus on two sets of modeling approaches: population-based models, which are well-established within the pharmaceutical industry today, and fall under the discipline of clinical pharmacometrics (PMX); and systems dynamics models, which encompass a range of models of (patho-)physiology amenable to pharmacological intervention, of signaling pathways in biology, and of substance distribution in the body (today known as physiologically-based pharmacokinetic models) - which today may be collectively referred to as quantitative systems pharmacology models (QSP). We next describe the convergence - or rather selected integration - of PMX and QSP approaches into 'middle-out' drug-disease models, which retain selected mechanistic aspects, while remaining parsimonious, fit-for-purpose, and able to address variability and the testing of covariates. We further propose development opportunities for drug-disease systems models, to increase their utility and applicability throughout the preclinical and clinical spectrum of pharmaceutical R&D. Copyright © 2017 Elsevier B.V. All rights reserved.

The development of a value based pricing index for new drugs in metastatic colorectal cancer.

PubMed

Dranitsaris, George; Truter, Ilse; Lubbe, Martie S

2011-06-01

Worldwide, prices for cancer drugs have been under downward pressure where several governments have mandated price cuts of branded products. A better alternative to government mandated price cuts would be to estimate a final price based on drug performance, cost effectiveness and a country's ability to pay. We developed a global pricing index for new cancer drugs in patients with metastatic colorectal cancer (mCRC) that encompasses all of these attributes. A pharmacoeconomic model was developed to simulate mCRC patients receiving chemotherapy plus a 'new drug' that improves survival by 1.4, 3 and 6months, respectively. Cost and utility data were obtained from cancer centres and oncology nurses (n=112) in Canada, Spain, India, South Africa and Malaysia. Multivariable analysis was then used to develop the pricing index, which considers survival benefit, per capita GDP and income dispersion (as measured by the Gini coefficient) as predictor variables. Higher survival benefits were associated with elevated drug prices, especially in higher income countries such as Canada. For Argentina with a per capita GDP of $15,000 and a Gini coefficient of 51, the index estimated that for a drug which provides a 4month survival benefit in mCRC, the value based price would be $US 630 per dose. In contrast, the same drug in a wealthier country like Norway (per capita GDP=$50,000) could command a price of $US 2,775 per dose. The application of this index to estimate a price based on cost effectiveness and the wealth of a nation would be important for opening dialogue between the key stakeholders and a better alternative to government mandated price cuts. Copyright © 2011 Elsevier Ltd. All rights reserved.

Development of a gastrointestinal tract microscale cell culture analog to predict drug transport

USDA-ARS?s Scientific Manuscript database

Microscale cell culture analogs (uCCAs) are used to study the metabolism and toxicity of a chemical or drug. These in vitro devices are physical replicas of physiologically based pharmacokinetic models that combine microfabrication and cell culture. The goal of this project is to add an independent ...

Agreement between PRE2DUP register data modeling method and comprehensive drug use interview among older persons

PubMed Central

Taipale, Heidi; Tanskanen, Antti; Koponen, Marjaana; Tolppanen, Anna-Maija; Tiihonen, Jari; Hartikainen, Sirpa

2016-01-01

Background PRE2DUP is a modeling method that generates drug use periods (ie, when drug use started and ended) from drug purchases recorded in dispensing-based register data. It is based on the evaluation of personal drug purchasing patterns and considers hospital stays, possible stockpiling of drugs, and package information. Objective The objective of this study was to investigate person-level agreement between self-reported drug use in the interview and drug use modeled from dispensing data with PRE2DUP method for various drug classes used by older persons. Methods Self-reported drug use was assessed from the GeMS Study including a random sample of persons aged ≥75 years from the city of Kuopio, Finland, in 2006. Drug purchases recorded in the Prescription register data of these persons were modeled to determine drug use periods with PRE2DUP modeling method. Agreement between self-reported drug use on the interview date and drug use calculated from register-based data was compared in order to find the frequently used drugs and drug classes, which was evaluated by Cohen’s kappa. Kappa values 0.61–0.80 were considered to represent good and 0.81–1.00 as very good agreement. Results Among 569 participants with mean age of 82 years, the agreement between interview and register data was very good for 75% and very good or good for 93% of the studied drugs or drug classes. Good or very good agreement was observed for drugs that are typically used on regular bases, whereas “as needed” drugs represented poorer results. Conclusion PRE2DUP modeling method validly describes regular drug use among older persons. For most of drug classes investigated, PRE2DUP-modeled register data described drug use as well as interview-based data which are more time-consuming to collect. Further studies should be conducted by comparing it with other methods and in different drug user populations. PMID:27785101

Engineering and evaluating drug delivery particles in microfluidic devices.

PubMed

Björnmalm, Mattias; Yan, Yan; Caruso, Frank

2014-09-28

The development of new and improved particle-based drug delivery is underpinned by an enhanced ability to engineer particles with high fidelity and integrity, as well as increased knowledge of their biological performance. Microfluidics can facilitate these processes through the engineering of spatiotemporally highly controlled environments using designed microstructures in combination with physical phenomena present at the microscale. In this review, we discuss microfluidics in the context of addressing key challenges in particle-based drug delivery. We provide an overview of how microfluidic devices can: (i) be employed to engineer particles, by providing highly controlled interfaces, and (ii) be used to establish dynamic in vitro models that mimic in vivo environments for studying the biological behavior of engineered particles. Finally, we discuss how the flexible and modular nature of microfluidic devices provides opportunities to create increasingly realistic models of the in vivo milieu (including multi-cell, multi-tissue and even multi-organ devices), and how ongoing developments toward commercialization of microfluidic tools are opening up new opportunities for the engineering and evaluation of drug delivery particles. Copyright © 2014 Elsevier B.V. All rights reserved.

The Development of CK2 Inhibitors: From Traditional Pharmacology to in Silico Rational Drug Design

PubMed Central

Cozza, Giorgio

2017-01-01

Casein kinase II (CK2) is an ubiquitous and pleiotropic serine/threonine protein kinase able to phosphorylate hundreds of substrates. Being implicated in several human diseases, from neurodegeneration to cancer, the biological roles of CK2 have been intensively studied. Upregulation of CK2 has been shown to be critical to tumor progression, making this kinase an attractive target for cancer therapy. Several CK2 inhibitors have been developed so far, the first being discovered by “trial and error testing”. In the last decade, the development of in silico rational drug design has prompted the discovery, de novo design and optimization of several CK2 inhibitors, active in the low nanomolar range. The screening of big chemical libraries and the optimization of hit compounds by Structure Based Drug Design (SBDD) provide telling examples of a fruitful application of rational drug design to the development of CK2 inhibitors. Ligand Based Drug Design (LBDD) models have been also applied to CK2 drug discovery, however they were mainly focused on methodology improvements rather than being critical for de novo design and optimization. This manuscript provides detailed description of in silico methodologies whose applications to the design and development of CK2 inhibitors proved successful and promising. PMID:28230762

Cardiotoxicity screening: a review of rapid-throughput in vitro approaches.

PubMed

Li, Xichun; Zhang, Rui; Zhao, Bin; Lossin, Christoph; Cao, Zhengyu

2016-08-01

Cardiac toxicity represents one of the leading causes of drug failure along different stages of drug development. Multiple very successful pharmaceuticals had to be pulled from the market or labeled with strict usage warnings due to adverse cardiac effects. In order to protect clinical trial participants and patients, the International Conference on Harmonization published guidelines to recommend that all new drugs to be tested preclinically for hERG (Kv11.1) channel sensitivity before submitting for regulatory reviews. However, extensive studies have demonstrated that measurement of hERG activity has limitations due to the multiple molecular targets of drug compound through which it may mitigate or abolish a potential arrhythmia, and therefore, a model measuring multiple ion channel effects is likely to be more predictive. Several phenotypic rapid-throughput methods have been developed to predict the potential cardiac toxic compounds in the early stages of drug development using embryonic stem cells- or human induced pluripotent stem cell-derived cardiomyocytes. These rapid-throughput methods include microelectrode array-based field potential assay, impedance-based or Ca(2+) dynamics-based cardiomyocytes contractility assays. This review aims to discuss advantages and limitations of these phenotypic assays for cardiac toxicity assessment.

Predicting human developmental toxicity of pharmaceuticals using human embryonic stem cells and metabolomics

DOE Office of Scientific and Technical Information (OSTI.GOV)

West, Paul R., E-mail: pwest@stemina.co; Weir, April M.; Smith, Alan M.

2010-08-15

Teratogens, substances that may cause fetal abnormalities during development, are responsible for a significant number of birth defects. Animal models used to predict teratogenicity often do not faithfully correlate to human response. Here, we seek to develop a more predictive developmental toxicity model based on an in vitro method that utilizes both human embryonic stem (hES) cells and metabolomics to discover biomarkers of developmental toxicity. We developed a method where hES cells were dosed with several drugs of known teratogenicity then LC-MS analysis was performed to measure changes in abundance levels of small molecules in response to drug dosing. Statisticalmore » analysis was employed to select for specific mass features that can provide a prediction of the developmental toxicity of a substance. These molecules can serve as biomarkers of developmental toxicity, leading to better prediction of teratogenicity. In particular, our work shows a correlation between teratogenicity and changes of greater than 10% in the ratio of arginine to asymmetric dimethylarginine levels. In addition, this study resulted in the establishment of a predictive model based on the most informative mass features. This model was subsequently tested for its predictive accuracy in two blinded studies using eight drugs of known teratogenicity, where it correctly predicted the teratogenicity for seven of the eight drugs. Thus, our initial data shows that this platform is a robust alternative to animal and other in vitro models for the prediction of the developmental toxicity of chemicals that may also provide invaluable information about the underlying biochemical pathways.« less

Population Pharmacokinetic and Pharmacodynamic Model-Based Comparability Assessment of a Recombinant Human Epoetin Alfa and the Biosimilar HX575

PubMed Central

Yan, Xiaoyu; Lowe, Philip J.; Fink, Martin; Berghout, Alexander; Balser, Sigrid; Krzyzanski, Wojciech

2012-01-01

The aim of this study was to develop an integrated pharmacokinetic and pharmacodynamic (PK/PD) model and assess the comparability between epoetin alfa HEXAL/Binocrit (HX575) and a comparator epoetin alfa by a model-based approach. PK/PD data—including serum drug concentrations, reticulocyte counts, red blood cells, and hemoglobin levels—were obtained from 2 clinical studies. In sum, 149 healthy men received multiple intravenous or subcutaneous doses of HX575 (100 IU/kg) and the comparator 3 times a week for 4 weeks. A population model based on pharmacodynamics-mediated drug disposition and cell maturation processes was used to characterize the PK/PD data for the 2 drugs. Simulations showed that due to target amount changes, total clearance may increase up to 2.4-fold as compared with the baseline. Further simulations suggested that once-weekly and thrice-weekly subcutaneous dosing regimens would result in similar efficacy. The findings from the model-based analysis were consistent with previous results using the standard noncompartmental approach demonstrating PK/PD comparability between HX575 and comparator. However, due to complexity of the PK/PD model, control of random effects was not straightforward. Whereas population PK/PD model-based analyses are suited for studying complex biological systems, such models have their limitations (statistical), and their comparability results should be interpreted carefully. PMID:22162538

Enabling personalized cancer medicine decisions: The challenging pharmacological approach of PBPK models for nanomedicine and pharmacogenomics (Review).

PubMed

Vizirianakis, Ioannis S; Mystridis, George A; Avgoustakis, Konstantinos; Fatouros, Dimitrios G; Spanakis, Marios

2016-04-01

The existing tumor heterogeneity and the complexity of cancer cell biology critically demand powerful translational tools with which to support interdisciplinary efforts aiming to advance personalized cancer medicine decisions in drug development and clinical practice. The development of physiologically based pharmacokinetic (PBPK) models to predict the effects of drugs in the body facilitates the clinical translation of genomic knowledge and the implementation of in vivo pharmacology experience with pharmacogenomics. Such a direction unequivocally empowers our capacity to also make personalized drug dosage scheme decisions for drugs, including molecularly targeted agents and innovative nanoformulations, i.e. in establishing pharmacotyping in prescription. In this way, the applicability of PBPK models to guide individualized cancer therapeutic decisions of broad clinical utility in nanomedicine in real-time and in a cost-affordable manner will be discussed. The latter will be presented by emphasizing the need for combined efforts within the scientific borderlines of genomics with nanotechnology to ensure major benefits and productivity for nanomedicine and personalized medicine interventions.

How modeling and simulation have enhanced decision making in new drug development.

PubMed

Miller, Raymond; Ewy, Wayne; Corrigan, Brian W; Ouellet, Daniele; Hermann, David; Kowalski, Kenneth G; Lockwood, Peter; Koup, Jeffrey R; Donevan, Sean; El-Kattan, Ayman; Li, Cheryl S W; Werth, John L; Feltner, Douglas E; Lalonde, Richard L

2005-04-01

The idea of model-based drug development championed by Lewis Sheiner, in which pharmacostatistical models of drug efficacy and safety are developed from preclinical and available clinical data, offers a quantitative approach to improving drug development and development decision-making. Examples are presented that support this paradigm. The first example describes a preclinical model of behavioral activity to predict potency and time-course of response in humans and assess the potential for differentiation between compounds. This example illustrates how modeling procedures expounded by Lewis Sheiner provided the means to differentiate potency and the lag time between drug exposure and response and allow for rapid decision making and dose selection. The second example involves planning a Phase 2a dose-ranging and proof of concept trial in Alzheimer's disease (AD). The issue was how to proceed with the study and what criteria to use for a go/no go decision. The combined knowledge of AD disease progression, and preclinical and clinical information about the drug were used to simulate various clinical trial scenarios to identify an efficient and effective Phase 2 study. A design was selected and carried out resulting in a number of important learning experiences as well as extensive financial savings. The motivation for this case in point was the "Learn-Confirm" paradigm described by Lewis Sheiner. The final example describes the use of Pharmacokinetic and Pharmacodynamic (PK/PD) modeling and simulation to confirm efficacy across doses. In the New Drug Application for gabapentin, data from two adequate and well-controlled clinical trials was submitted to the Food and Drug Administration (FDA) in support of the approval of the indication for the treatment of post-herpetic neuralgia. The clinical trial data was not replicated for each of the sought dose levels in the drug application presenting a regulatory dilemma. Exposure response analysis submitted in the New Drug Application was applied to confirm the evidence of efficacy across these dose levels. Modeling and simulation analyses showed that the two studies corroborate each other with respect to the pain relief profiles. The use of PK/PD information confirmed evidence of efficacy across the three studied doses, eliminating the need for additional clinical trials and thus supporting the approval of the product. It can be speculated that the work by Lewis Sheiner reflected in the FDA document titled "Innovation or Stagnation: Challenge and Opportunity on the Critical Path to New Medical Products" made this scientific approach to the drug approval process possible.

Dependency-based long short term memory network for drug-drug interaction extraction.

PubMed

Wang, Wei; Yang, Xi; Yang, Canqun; Guo, Xiaowei; Zhang, Xiang; Wu, Chengkun

2017-12-28

Drug-drug interaction extraction (DDI) needs assistance from automated methods to address the explosively increasing biomedical texts. In recent years, deep neural network based models have been developed to address such needs and they have made significant progress in relation identification. We propose a dependency-based deep neural network model for DDI extraction. By introducing the dependency-based technique to a bi-directional long short term memory network (Bi-LSTM), we build three channels, namely, Linear channel, DFS channel and BFS channel. All of these channels are constructed with three network layers, including embedding layer, LSTM layer and max pooling layer from bottom up. In the embedding layer, we extract two types of features, one is distance-based feature and another is dependency-based feature. In the LSTM layer, a Bi-LSTM is instituted in each channel to better capture relation information. Then max pooling is used to get optimal features from the entire encoding sequential data. At last, we concatenate the outputs of all channels and then link it to the softmax layer for relation identification. To the best of our knowledge, our model achieves new state-of-the-art performance with the F-score of 72.0% on the DDIExtraction 2013 corpus. Moreover, our approach obtains much higher Recall value compared to the existing methods. The dependency-based Bi-LSTM model can learn effective relation information with less feature engineering in the task of DDI extraction. Besides, the experimental results show that our model excels at balancing the Precision and Recall values.

Physiologically based pharmacokinetic modeling for predicting irinotecan exposure in human body.

PubMed

Fan, Yingfang; Mansoor, Najia; Ahmad, Tasneem; Khan, Rafeeq Alam; Czejka, Martin; Sharib, Syed; Yang, Dong-Hua; Ahmed, Mansoor

2017-07-18

Colorectal cancer is the third leading cause of cancer-related deaths in the United States. Treatment of colorectal cancer remains a challenge to clinicians as well as drug developers. Irinotecan, a Camptothecin derivative, is successfully used for the treatment of this rapidly progressing malignancy and finds its place in the first line of therapeutic agents. Irinotecan is also effective in treating SCLC, malignant glioma and pancreatic adenocarcinoma. However, its adverse effects limit its clinical application. Mainly metabolized by hepatic route, and excreted through biliary tract, this dug has been found to possess high variation in patients in its pharmacokinetic (PK) profile. Physiologically based pharmacokinetic (PBPK) models using compartmental approach have attained their position to foresee the possible PK behavior of different drugs before their administration to patients and such models have been proposed for several anticancer agents. In this work, we used WB-PBPK technology to develop a model in a population of tumor patients who used IV irinotecan therapy. This model depicted the concentration of drug and its pharmacologically active metabolite in human body over a specific period of time. Knowledge about pharmacokinetic parameters is extracted from this profile and the model is evaluated by the observed results of clinical study presented in literature. The predicted behavior of the drug by this approach is in good agreement with the observed results and can aid in further exploration of PK of irinotecan in cancer patients, especially in those concomitantly suffer from other morbidity.

Physiologically based pharmacokinetic modeling for predicting irinotecan exposure in human body

PubMed Central

Ahmad, Tasneem; Khan, Rafeeq Alam; Czejka, Martin; Sharib, Syed; Yang, Dong-Hua; Ahmed, Mansoor

2017-01-01

Colorectal cancer is the third leading cause of cancer-related deaths in the United States. Treatment of colorectal cancer remains a challenge to clinicians as well as drug developers. Irinotecan, a Camptothecin derivative, is successfully used for the treatment of this rapidly progressing malignancy and finds its place in the first line of therapeutic agents. Irinotecan is also effective in treating SCLC, malignant glioma and pancreatic adenocarcinoma. However, its adverse effects limit its clinical application. Mainly metabolized by hepatic route, and excreted through biliary tract, this dug has been found to possess high variation in patients in its pharmacokinetic (PK) profile. Physiologically based pharmacokinetic (PBPK) models using compartmental approach have attained their position to foresee the possible PK behavior of different drugs before their administration to patients and such models have been proposed for several anticancer agents. In this work, we used WB-PBPK technology to develop a model in a population of tumor patients who used IV irinotecan therapy. This model depicted the concentration of drug and its pharmacologically active metabolite in human body over a specific period of time. Knowledge about pharmacokinetic parameters is extracted from this profile and the model is evaluated by the observed results of clinical study presented in literature. The predicted behavior of the drug by this approach is in good agreement with the observed results and can aid in further exploration of PK of irinotecan in cancer patients, especially in those concomitantly suffer from other morbidity. PMID:28636998

Targeted intervention: Computational approaches to elucidate and predict relapse in alcoholism.

PubMed

Heinz, Andreas; Deserno, Lorenz; Zimmermann, Ulrich S; Smolka, Michael N; Beck, Anne; Schlagenhauf, Florian

2017-05-01

Alcohol use disorder (AUD) and addiction in general is characterized by failures of choice resulting in repeated drug intake despite severe negative consequences. Behavioral change is hard to accomplish and relapse after detoxification is common and can be promoted by consumption of small amounts of alcohol as well as exposure to alcohol-associated cues or stress. While those environmental factors contributing to relapse have long been identified, the underlying psychological and neurobiological mechanism on which those factors act are to date incompletely understood. Based on the reinforcing effects of drugs of abuse, animal experiments showed that drug, cue and stress exposure affect Pavlovian and instrumental learning processes, which can increase salience of drug cues and promote habitual drug intake. In humans, computational approaches can help to quantify changes in key learning mechanisms during the development and maintenance of alcohol dependence, e.g. by using sequential decision making in combination with computational modeling to elucidate individual differences in model-free versus more complex, model-based learning strategies and their neurobiological correlates such as prediction error signaling in fronto-striatal circuits. Computational models can also help to explain how alcohol-associated cues trigger relapse: mechanisms such as Pavlovian-to-Instrumental Transfer can quantify to which degree Pavlovian conditioned stimuli can facilitate approach behavior including alcohol seeking and intake. By using generative models of behavioral and neural data, computational approaches can help to quantify individual differences in psychophysiological mechanisms that underlie the development and maintenance of AUD and thus promote targeted intervention. Copyright © 2016 Elsevier Inc. All rights reserved.

SVM Based Descriptor Selection and Classification of Neurodegenerative Disease Drugs for Pharmacological Modeling.

PubMed

Shahid, Mohammad; Shahzad Cheema, Muhammad; Klenner, Alexander; Younesi, Erfan; Hofmann-Apitius, Martin

2013-03-01

Systems pharmacological modeling of drug mode of action for the next generation of multitarget drugs may open new routes for drug design and discovery. Computational methods are widely used in this context amongst which support vector machines (SVM) have proven successful in addressing the challenge of classifying drugs with similar features. We have applied a variety of such SVM-based approaches, namely SVM-based recursive feature elimination (SVM-RFE). We use the approach to predict the pharmacological properties of drugs widely used against complex neurodegenerative disorders (NDD) and to build an in-silico computational model for the binary classification of NDD drugs from other drugs. Application of an SVM-RFE model to a set of drugs successfully classified NDD drugs from non-NDD drugs and resulted in overall accuracy of ∼80 % with 10 fold cross validation using 40 top ranked molecular descriptors selected out of total 314 descriptors. Moreover, SVM-RFE method outperformed linear discriminant analysis (LDA) based feature selection and classification. The model reduced the multidimensional descriptors space of drugs dramatically and predicted NDD drugs with high accuracy, while avoiding over fitting. Based on these results, NDD-specific focused libraries of drug-like compounds can be designed and existing NDD-specific drugs can be characterized by a well-characterized set of molecular descriptors. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Antiviral Information Management System (AIMS): a prototype for operational innovation in drug development.

PubMed

Jadhav, Pravin R; Neal, Lauren; Florian, Jeff; Chen, Ying; Naeger, Lisa; Robertson, Sarah; Soon, Guoxing; Birnkrant, Debra

2010-09-01

This article presents a prototype for an operational innovation in knowledge management (KM). These operational innovations are geared toward managing knowledge efficiently and accessing all available information by embracing advances in bioinformatics and allied fields. The specific components of the proposed KM system are (1) a database to archive hepatitis C virus (HCV) treatment data in a structured format and retrieve information in a query-capable manner and (2) an automated analysis tool to inform trial design elements for HCV drug development. The proposed framework is intended to benefit drug development by increasing efficiency of dose selection and improving the consistency of advice from US Food and Drug Administration (FDA). It is also hoped that the framework will encourage collaboration among FDA, industry, and academic scientists to guide the HCV drug development process using model-based quantitative analysis techniques.

The development of a healing model of care for an Indigenous drug and alcohol residential rehabilitation service: a community-based participatory research approach.

PubMed

Munro, Alice; Shakeshaft, Anthony; Clifford, Anton

2017-12-04

Given the well-established evidence of disproportionately high rates of substance-related morbidity and mortality after release from incarceration for Indigenous Australians, access to comprehensive, effective and culturally safe residential rehabilitation treatment will likely assist in reducing recidivism to both prison and substance dependence for this population. In the absence of methodologically rigorous evidence, the delivery of Indigenous drug and alcohol residential rehabilitation services vary widely, and divergent views exist regarding the appropriateness and efficacy of different potential treatment components. One way to increase the methodological quality of evaluations of Indigenous residential rehabilitation services is to develop partnerships with researchers to better align models of care with the client's, and the community's, needs. An emerging research paradigm to guide the development of high quality evidence through a number of sequential steps that equitably involves services, stakeholders and researchers is community-based participatory research (CBPR). The purpose of this study is to articulate an Indigenous drug and alcohol residential rehabilitation service model of care, developed in collaboration between clients, service providers and researchers using a CBPR approach. This research adopted a mixed methods CBPR approach to triangulate collected data to inform the development of a model of care for a remote Indigenous drug and alcohol residential rehabilitation service. Four iterative CBPR steps of research activity were recorded during the 3-year research partnership. As a direct outcome of the CBPR framework, the service and researchers co-designed a Healing Model of Care that comprises six core treatment components, three core organisational components and is articulated in two program logics. The program logics were designed to specifically align each component and outcome with the mechanism of change for the client or organisation to improve data collection and program evaluation. The description of the CBPR process and the Healing Model of Care provides one possible solution about how to provide better care for the large and growing population of Indigenous people with substance.

[A model list of high risk drugs].

PubMed

Cotrina Luque, J; Guerrero Aznar, M D; Alvarez del Vayo Benito, C; Jimenez Mesa, E; Guzman Laura, K P; Fernández Fernández, L

2013-12-01

«High-risk drugs» are those that have a very high «risk» of causing death or serious injury if an error occurs during its use. The Institute for Safe Medication Practices (ISMP) has prepared a high-risk drugs list applicable to the general population (with no differences between the pediatric and adult population). Thus, there is a lack of information for the pediatric population. The main objective of this work is to develop a high-risk drug list adapted to the neonatal or pediatric population as a reference model for the pediatric hospital health workforce. We made a literature search in May 2012 to identify any published lists or references in relation to pediatric and/or neonatal high-risk drugs. A total of 15 studies were found, from which 9 were selected. A model list was developed mainly based on the ISMP one, adding strongly perceived pediatric risk drugs and removing those where the pediatric use was anecdotal. There is no published list that suits pediatric risk management. The list of pediatric and neonatal high-risk drugs presented here could be a «reference list of high-risk drugs » for pediatric hospitals. Using this list and training will help to prevent medication errors in each drug supply chain (prescribing, transcribing, dispensing and administration). Copyright © 2013 Asociación Española de Pediatría. Published by Elsevier Espana. All rights reserved.

Machine learning for classifying tuberculosis drug-resistance from DNA sequencing data.

PubMed

Yang, Yang; Niehaus, Katherine E; Walker, Timothy M; Iqbal, Zamin; Walker, A Sarah; Wilson, Daniel J; Peto, Tim E A; Crook, Derrick W; Smith, E Grace; Zhu, Tingting; Clifton, David A

2018-05-15

Correct and rapid determination of Mycobacterium tuberculosis (MTB) resistance against available tuberculosis (TB) drugs is essential for the control and management of TB. Conventional molecular diagnostic test assumes that the presence of any well-studied single nucleotide polymorphisms is sufficient to cause resistance, which yields low sensitivity for resistance classification. Given the availability of DNA sequencing data from MTB, we developed machine learning models for a cohort of 1839 UK bacterial isolates to classify MTB resistance against eight anti-TB drugs (isoniazid, rifampicin, ethambutol, pyrazinamide, ciprofloxacin, moxifloxacin, ofloxacin, streptomycin) and to classify multi-drug resistance. Compared to previous rules-based approach, the sensitivities from the best-performing models increased by 2-4% for isoniazid, rifampicin and ethambutol to 97% (P < 0.01), respectively; for ciprofloxacin and multi-drug resistant TB, they increased to 96%. For moxifloxacin and ofloxacin, sensitivities increased by 12 and 15% from 83 and 81% based on existing known resistance alleles to 95% and 96% (P < 0.01), respectively. Particularly, our models improved sensitivities compared to the previous rules-based approach by 15 and 24% to 84 and 87% for pyrazinamide and streptomycin (P < 0.01), respectively. The best-performing models increase the area-under-the-ROC curve by 10% for pyrazinamide and streptomycin (P < 0.01), and 4-8% for other drugs (P < 0.01). The details of source code are provided at http://www.robots.ox.ac.uk/~davidc/code.php. david.clifton@eng.ox.ac.uk. Supplementary data are available at Bioinformatics online.

Experimental and computational prediction of glass transition temperature of drugs.

PubMed

Alzghoul, Ahmad; Alhalaweh, Amjad; Mahlin, Denny; Bergström, Christel A S

2014-12-22

Glass transition temperature (Tg) is an important inherent property of an amorphous solid material which is usually determined experimentally. In this study, the relation between Tg and melting temperature (Tm) was evaluated using a data set of 71 structurally diverse druglike compounds. Further, in silico models for prediction of Tg were developed based on calculated molecular descriptors and linear (multilinear regression, partial least-squares, principal component regression) and nonlinear (neural network, support vector regression) modeling techniques. The models based on Tm predicted Tg with an RMSE of 19.5 K for the test set. Among the five computational models developed herein the support vector regression gave the best result with RMSE of 18.7 K for the test set using only four chemical descriptors. Hence, two different models that predict Tg of drug-like molecules with high accuracy were developed. If Tm is available, a simple linear regression can be used to predict Tg. However, the results also suggest that support vector regression and calculated molecular descriptors can predict Tg with equal accuracy, already before compound synthesis.

26th National Medicinal Chemistry Symposium--Developments in chemokines, carbohydrates, p53 and drug metabolism. 14-18 June 1998, Richmond, Virginia, USA.

PubMed

Swords, B

1998-08-01

This symposium, organized by the American Chemical Society, is held every two years. This year's meeting, sponsored by the ACS and The Virginia Commonwealth University, was attended by approximately 300 delegates and covered developments in chemokines, carbohydrates, p53, drug metabolism, prodrugs, structure-based design and molecular modeling. At the opening ceremony, John Topliss began by paying tribute to the distinguished medicinal chemistry career of Alfred Burger (University of Virginia, USA). He then reviewed the application of physicochemical principles to drug design, including the development and application of quantitative structure-activity relationship methodology.

Predicting the F(ab)-mediated effect of monoclonal antibodies in vivo by combining cell-level kinetic and pharmacokinetic modelling.

PubMed

Krippendorff, Ben-Fillippo; Oyarzún, Diego A; Huisinga, Wilhelm

2012-04-01

Cell-level kinetic models for therapeutically relevant processes increasingly benefit the early stages of drug development. Later stages of the drug development processes, however, rely on pharmacokinetic compartment models while cell-level dynamics are typically neglected. We here present a systematic approach to integrate cell-level kinetic models and pharmacokinetic compartment models. Incorporating target dynamics into pharmacokinetic models is especially useful for the development of therapeutic antibodies because their effect and pharmacokinetics are inherently interdependent. The approach is illustrated by analysing the F(ab)-mediated inhibitory effect of therapeutic antibodies targeting the epidermal growth factor receptor. We build a multi-level model for anti-EGFR antibodies by combining a systems biology model with in vitro determined parameters and a pharmacokinetic model based on in vivo pharmacokinetic data. Using this model, we investigated in silico the impact of biochemical properties of anti-EGFR antibodies on their F(ab)-mediated inhibitory effect. The multi-level model suggests that the F(ab)-mediated inhibitory effect saturates with increasing drug-receptor affinity, thereby limiting the impact of increasing antibody affinity on improving the effect. This indicates that observed differences in the therapeutic effects of high affinity antibodies in the market and in clinical development may result mainly from Fc-mediated indirect mechanisms such as antibody-dependent cell cytotoxicity.

A technology platform to assess multiple cancer agents simultaneously within a patient's tumor

PubMed Central

Klinghoffer, Richard A.; Frazier, Jason P.; Moreno-Gonzalez, Alicia; Strand, Andrew D.; Kerwin, William S.; Casalini, Joseph R.; Thirstrup, Derek J.; You, Sheng; Morris, Shelli M.; Watts, Korashon L.; Veiseh, Mandana; Grenley, Marc O.; Tretyak, Ilona; Dey, Joyoti; Carleton, Michael; Beirne, Emily; Pedro, Kyle D.; Ditzler, Sally H.; Girard, Emily J.; Deckwerth, Thomas L.; Bertout, Jessica A.; Meleo, Karri A.; Filvaroff, Ellen H.; Chopra, Rajesh; Press, Oliver W.; Olson, James M.

2016-01-01

A fundamental problem in cancer drug development is that antitumor efficacy in preclinical cancer models does not translate faithfully to patient outcomes. Much of early cancer drug discovery is performed under in vitro conditions in cell-based models that poorly represent actual malignancies. To address this inconsistency, we have developed a technology platform called CIVO, which enables simultaneous assessment of up to eight drugs or drug combinations within a single solid tumor in vivo. The platform is currently designed for use in animal models of cancer and patients with superficial tumors but can be modified for investigation of deeper-seated malignancies. In xenograft lymphoma models, CIVO microinjection of well-characterized anticancer agents (vincristine, doxorubicin, mafosfamide, and prednisolone) induced spatially defined cellular changes around sites of drug exposure, specific to the known mechanisms of action of each drug. The observed localized responses predicted responses to systemically delivered drugs in animals. In pair-matched lymphoma models, CIVO correctly demonstrated tumor resistance to doxorubicin and vincristine and an unexpected enhanced sensitivity to mafosfamide in multidrug-resistant lymphomas compared with chemotherapy-naïve lymphomas. A CIVO-enabled in vivo screen of 97 approved oncology agents revealed a novel mTOR (mammalian target of rapamycin) pathway inhibitor that exhibits significantly increased tumor-killing activity in the drug-resistant setting compared with chemotherapy-naïve tumors. Finally, feasibility studies to assess the use of CIVO in human and canine patients demonstrated that microinjection of drugs is toxicity-sparing while inducing robust, easily tracked, drug-specific responses in autochthonous tumors, setting the stage for further application of this technology in clinical trials. PMID:25904742

A Role for Fragment-Based Drug Design in Developing Novel Lead Compounds for Central Nervous System Targets.

PubMed

Wasko, Michael J; Pellegrene, Kendy A; Madura, Jeffry D; Surratt, Christopher K

2015-01-01

Hundreds of millions of U.S. dollars are invested in the research and development of a single drug. Lead compound development is an area ripe for new design strategies. Therapeutic lead candidates have been traditionally found using high-throughput in vitro pharmacological screening, a costly method for assaying thousands of compounds. This approach has recently been augmented by virtual screening (VS), which employs computer models of the target protein to narrow the search for possible leads. A variant of VS is fragment-based drug design (FBDD), an emerging in silico lead discovery method that introduces low-molecular weight fragments, rather than intact compounds, into the binding pocket of the receptor model. These fragments serve as starting points for "growing" the lead candidate. Current efforts in virtual FBDD within central nervous system (CNS) targets are reviewed, as is a recent rule-based optimization strategy in which new molecules are generated within a 3D receptor-binding pocket using the fragment as a scaffold. This process not only places special emphasis on creating synthesizable molecules but also exposes computational questions worth addressing. Fragment-based methods provide a viable, relatively low-cost alternative for therapeutic lead discovery and optimization that can be applied to CNS targets to augment current design strategies.

A Role for Fragment-Based Drug Design in Developing Novel Lead Compounds for Central Nervous System Targets

PubMed Central

Wasko, Michael J.; Pellegrene, Kendy A.; Madura, Jeffry D.; Surratt, Christopher K.

2015-01-01

Hundreds of millions of U.S. dollars are invested in the research and development of a single drug. Lead compound development is an area ripe for new design strategies. Therapeutic lead candidates have been traditionally found using high-throughput in vitro pharmacological screening, a costly method for assaying thousands of compounds. This approach has recently been augmented by virtual screening (VS), which employs computer models of the target protein to narrow the search for possible leads. A variant of VS is fragment-based drug design (FBDD), an emerging in silico lead discovery method that introduces low-molecular weight fragments, rather than intact compounds, into the binding pocket of the receptor model. These fragments serve as starting points for “growing” the lead candidate. Current efforts in virtual FBDD within central nervous system (CNS) targets are reviewed, as is a recent rule-based optimization strategy in which new molecules are generated within a 3D receptor-binding pocket using the fragment as a scaffold. This process not only places special emphasis on creating synthesizable molecules but also exposes computational questions worth addressing. Fragment-based methods provide a viable, relatively low-cost alternative for therapeutic lead discovery and optimization that can be applied to CNS targets to augment current design strategies. PMID:26441817

Predicting drug-induced liver injury using ensemble learning methods and molecular fingerprints.

PubMed

Ai, Haixin; Chen, Wen; Zhang, Li; Huang, Liangchao; Yin, Zimo; Hu, Huan; Zhao, Qi; Zhao, Jian; Liu, Hongsheng

2018-05-21

Drug-induced liver injury (DILI) is a major safety concern in the drug-development process, and various methods have been proposed to predict the hepatotoxicity of compounds during the early stages of drug trials. In this study, we developed an ensemble model using three machine learning algorithms and 12 molecular fingerprints from a dataset containing 1,241 diverse compounds. The ensemble model achieved an average accuracy of 71.1±2.6%, sensitivity of 79.9±3.6%, specificity of 60.3±4.8%, and area under the receiver operating characteristic curve (AUC) of 0.764±0.026 in five-fold cross-validation and an accuracy of 84.3%, sensitivity of 86.9%, specificity of 75.4%, and AUC of 0.904 in an external validation dataset of 286 compounds collected from the Liver Toxicity Knowledge Base (LTKB). Compared with previous methods, the ensemble model achieved relatively high accuracy and sensitivity. We also identified several substructures related to DILI. In addition, we provide a web server offering access to our models (http://ccsipb.lnu.edu.cn/toxicity/HepatoPred-EL/).

Predicting Drug Combination Index and Simulating the Network-Regulation Dynamics by Mathematical Modeling of Drug-Targeted EGFR-ERK Signaling Pathway

NASA Astrophysics Data System (ADS)

Huang, Lu; Jiang, Yuyang; Chen, Yuzong

2017-01-01

Synergistic drug combinations enable enhanced therapeutics. Their discovery typically involves the measurement and assessment of drug combination index (CI), which can be facilitated by the development and applications of in-silico CI predictive tools. In this work, we developed and tested the ability of a mathematical model of drug-targeted EGFR-ERK pathway in predicting CIs and in analyzing multiple synergistic drug combinations against observations. Our mathematical model was validated against the literature reported signaling, drug response dynamics, and EGFR-MEK drug combination effect. The predicted CIs and combination therapeutic effects of the EGFR-BRaf, BRaf-MEK, FTI-MEK, and FTI-BRaf inhibitor combinations showed consistent synergism. Our results suggest that existing pathway models may be potentially extended for developing drug-targeted pathway models to predict drug combination CI values, isobolograms, and drug-response surfaces as well as to analyze the dynamics of individual and combinations of drugs. With our model, the efficacy of potential drug combinations can be predicted. Our method complements the developed in-silico methods (e.g. the chemogenomic profile and the statistically-inferenced network models) by predicting drug combination effects from the perspectives of pathway dynamics using experimental or validated molecular kinetic constants, thereby facilitating the collective prediction of drug combination effects in diverse ranges of disease systems.

Animal models of Parkinson's disease: a source of novel treatments and clues to the cause of the disease.

PubMed

Duty, Susan; Jenner, Peter

2011-10-01

Animal models of Parkinson's disease (PD) have proved highly effective in the discovery of novel treatments for motor symptoms of PD and in the search for clues to the underlying cause of the illness. Models based on specific pathogenic mechanisms may subsequently lead to the development of neuroprotective agents for PD that stop or slow disease progression. The array of available rodent models is large and ranges from acute pharmacological models, such as the reserpine- or haloperidol-treated rats that display one or more parkinsonian signs, to models exhibiting destruction of the dopaminergic nigro-striatal pathway, such as the classical 6-hydroxydopamine (6-OHDA) rat and 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) mouse models. All of these have provided test beds in which new molecules for treating the motor symptoms of PD can be assessed. In addition, the emergence of abnormal involuntary movements (AIMs) with repeated treatment of 6-OHDA-lesioned rats with L-DOPA has allowed for examination of the mechanisms responsible for treatment-related dyskinesia in PD, and the detection of molecules able to prevent or reverse their appearance. Other toxin-based models of nigro-striatal tract degeneration include the systemic administration of the pesticides rotenone and paraquat, but whilst providing clues to disease pathogenesis, these are not so commonly used for drug development. The MPTP-treated primate model of PD, which closely mimics the clinical features of PD and in which all currently used anti-parkinsonian medications have been shown to be effective, is undoubtedly the most clinically-relevant of all available models. The MPTP-treated primate develops clear dyskinesia when repeatedly exposed to L-DOPA, and these parkinsonian animals have shown responses to novel dopaminergic agents that are highly predictive of their effect in man. Whether non-dopaminergic drugs show the same degree of predictability of response is a matter of debate. As our understanding of the pathogenesis of PD has improved, so new rodent models produced by agents mimicking these mechanisms, including proteasome inhibitors such as PSI, lactacystin and epoximycin or inflammogens like lipopolysaccharide (LPS) have been developed. A further generation of models aimed at mimicking the genetic causes of PD has also sprung up. Whilst these newer models have provided further clues to the disease pathology, they have so far been less commonly used for drug development. There is little doubt that the availability of experimental animal models of PD has dramatically altered dopaminergic drug treatment of the illness and the prevention and reversal of drug-related side effects that emerge with disease progression and chronic medication. However, so far, we have made little progress in moving into other pharmacological areas for the treatment of PD, and we have not developed models that reflect the progressive nature of the illness and its complexity in terms of the extent of pathology and biochemical change. Only when this occurs are we likely to make progress in developing agents to stop or slow the disease progression. The overarching question that draws all of these models together in the quest for better drug treatments for PD is how well do they recapitulate the human condition and how predictive are they of successful translation of drugs into the clinic? This article aims to clarify the current position and highlight the strengths and weaknesses of available models. © 2011 The Authors. British Journal of Pharmacology © 2011 The British Pharmacological Society.

Functional analysis and transcriptional output of the Göttingen minipig genome.

PubMed

Heckel, Tobias; Schmucki, Roland; Berrera, Marco; Ringshandl, Stephan; Badi, Laura; Steiner, Guido; Ravon, Morgane; Küng, Erich; Kuhn, Bernd; Kratochwil, Nicole A; Schmitt, Georg; Kiialainen, Anna; Nowaczyk, Corinne; Daff, Hamina; Khan, Azinwi Phina; Lekolool, Isaac; Pelle, Roger; Okoth, Edward; Bishop, Richard; Daubenberger, Claudia; Ebeling, Martin; Certa, Ulrich

2015-11-14

In the past decade the Göttingen minipig has gained increasing recognition as animal model in pharmaceutical and safety research because it recapitulates many aspects of human physiology and metabolism. Genome-based comparison of drug targets together with quantitative tissue expression analysis allows rational prediction of pharmacology and cross-reactivity of human drugs in animal models thereby improving drug attrition which is an important challenge in the process of drug development. Here we present a new chromosome level based version of the Göttingen minipig genome together with a comparative transcriptional analysis of tissues with pharmaceutical relevance as basis for translational research. We relied on mapping and assembly of WGS (whole-genome-shotgun sequencing) derived reads to the reference genome of the Duroc pig and predict 19,228 human orthologous protein-coding genes. Genome-based prediction of the sequence of human drug targets enables the prediction of drug cross-reactivity based on conservation of binding sites. We further support the finding that the genome of Sus scrofa contains about ten-times less pseudogenized genes compared to other vertebrates. Among the functional human orthologs of these minipig pseudogenes we found HEPN1, a putative tumor suppressor gene. The genomes of Sus scrofa, the Tibetan boar, the African Bushpig, and the Warthog show sequence conservation of all inactivating HEPN1 mutations suggesting disruption before the evolutionary split of these pig species. We identify 133 Sus scrofa specific, conserved long non-coding RNAs (lncRNAs) in the minipig genome and show that these transcripts are highly conserved in the African pigs and the Tibetan boar suggesting functional significance. Using a new minipig specific microarray we show high conservation of gene expression signatures in 13 tissues with biomedical relevance between humans and adult minipigs. We underline this relationship for minipig and human liver where we could demonstrate similar expression levels for most phase I drug-metabolizing enzymes. Higher expression levels and metabolic activities were found for FMO1, AKR/CRs and for phase II drug metabolizing enzymes in minipig as compared to human. The variability of gene expression in equivalent human and minipig tissues is considerably higher in minipig organs, which is important for study design in case a human target belongs to this variable category in the minipig. The first analysis of gene expression in multiple tissues during development from young to adult shows that the majority of transcriptional programs are concluded four weeks after birth. This finding is in line with the advanced state of human postnatal organ development at comparative age categories and further supports the minipig as model for pediatric drug safety studies. Genome based assessment of sequence conservation combined with gene expression data in several tissues improves the translational value of the minipig for human drug development. The genome and gene expression data presented here are important resources for researchers using the minipig as model for biomedical research or commercial breeding. Potential impact of our data for comparative genomics, translational research, and experimental medicine are discussed.

Dynamic Structure-Based Pharmacophore Model Development: A New and Effective Addition in the Histone Deacetylase 8 (HDAC8) Inhibitor Discovery

PubMed Central

Thangapandian, Sundarapandian; John, Shalini; Lee, Yuno; Kim, Songmi; Lee, Keun Woo

2011-01-01

Histone deacetylase 8 (HDAC8) is an enzyme involved in deacetylating the amino groups of terminal lysine residues, thereby repressing the transcription of various genes including tumor suppressor gene. The over expression of HDAC8 was observed in many cancers and thus inhibition of this enzyme has emerged as an efficient cancer therapeutic strategy. In an effort to facilitate the future discovery of HDAC8 inhibitors, we developed two pharmacophore models containing six and five pharmacophoric features, respectively, using the representative structures from two molecular dynamic (MD) simulations performed in Gromacs 4.0.5 package. Various analyses of trajectories obtained from MD simulations have displayed the changes upon inhibitor binding. Thus utilization of the dynamically-responded protein structures in pharmacophore development has the added advantage of considering the conformational flexibility of protein. The MD trajectories were clustered based on single-linkage method and representative structures were taken to be used in the pharmacophore model development. Active site complimenting structure-based pharmacophore models were developed using Discovery Studio 2.5 program and validated using a dataset of known HDAC8 inhibitors. Virtual screening of chemical database coupled with drug-like filter has identified drug-like hit compounds that match the pharmacophore models. Molecular docking of these hits reduced the false positives and identified two potential compounds to be used in future HDAC8 inhibitor design. PMID:22272142

A semi-mechanistic model of CP-690,550-induced reduction in neutrophil counts in patients with rheumatoid arthritis.

PubMed

Gupta, Pankaj; Friberg, Lena E; Karlsson, Mats O; Krishnaswami, Sriram; French, Jonathan

2010-06-01

CP-690,550, a selective inhibitor of the Janus kinase family, is being developed as an oral disease-modifying antirheumatic drug for the treatment of rheumatoid arthritis (RA). A semi-mechanistic model was developed to characterize the time course of drug-induced absolute neutrophil count (ANC) reduction in a phase 2a study. Data from 264 RA patients receiving 6-week treatment (placebo, 5, 15, 30 mg bid) followed by a 6-week off-treatment period were analyzed. The model included a progenitor cell pool, a maturation chain comprising transit compartments, a circulation pool, and a feedback mechanism. The model was adequately described by system parameters (BASE(h), ktr(h), gamma, and k(circ)), disease effect parameters (DIS), and drug effect parameters (k(off) and k(D)). The disease manifested as an increase in baseline ANC and reduced maturation time due to increased demand from the inflammation site. The drug restored the perturbed system parameters to their normal values via an indirect mechanism. ANC reduction due to a direct myelosuppressive drug effect was not supported. The final model successfully described the dose- and time-dependent changes in ANC and predicted the incidence of neutropenia at different doses reasonably well.

An update on the potential role of intestinal first-pass metabolism for the prediction of drug-drug interactions: the role of PBPK modeling.

PubMed

Alqahtani, Saeed; Bukhari, Ishfaq; Albassam, Ahmed; Alenazi, Maha

2018-05-28

The intestinal absorption process is a combination of several events that are governed by various factors. Several transport mechanisms are involved in drug absorption through enterocytes via active and/or passive processes. The transported molecules then undergo intestinal metabolism, which together with intestinal transport may affect the systemic availability of drugs. Many studies have provided clear evidence on the significant role of intestinal first-pass metabolism on drug bioavailability and degree of drug-drug interactions (DDIs). Areas covered: This review provides an update on the role of intestinal first-pass metabolism in the oral bioavailability of drugs and prediction of drug-drug interactions. It also provides a comprehensive overview and summary of the latest update in the role of PBPK modeling in prediction of intestinal metabolism and DDIs in humans. Expert opinion: The contribution of intestinal first-pass metabolism in the oral bioavailability of drugs and prediction of DDIs has become more evident over the last few years. Several in vitro, in situ, and in vivo models have been developed to evaluate the role of first-pass metabolism and to predict DDIs. Currently, physiologically based pharmacokinetic modeling is considered the most valuable tool for the prediction of intestinal first-pass metabolism and DDIs.

Role of Electrostatic Interactions on the Transport of Druglike Molecules in Hydrogel-Based Articular Cartilage Mimics: Implications for Drug Delivery.

PubMed

Ye, Fengbin; Baldursdottir, Stefania; Hvidt, Søren; Jensen, Henrik; Larsen, Susan W; Yaghmur, Anan; Larsen, Claus; Østergaard, Jesper

2016-03-07

In the field of drug delivery to the articular cartilage, it is advantageous to apply artificial tissue models as surrogates of cartilage for investigating drug transport and release properties. In this study, artificial cartilage models consisting of 0.5% (w/v) agarose gel containing 0.5% (w/v) chondroitin sulfate or 0.5% (w/v) hyaluronic acid were developed, and their rheological and morphological properties were characterized. UV imaging was utilized to quantify the transport properties of the following four model compounds in the agarose gel and in the developed artificial cartilage models: H-Ala-β-naphthylamide, H-Lys-Lys-β-naphthylamide, lysozyme, and α-lactalbumin. The obtained results showed that the incorporation of the polyelectrolytes chondroitin sulfate or hyaluronic acid into agarose gel induced a significant reduction in the apparent diffusivities of the cationic model compounds as compared to the pure agarose gel. The decrease in apparent diffusivity of the cationic compounds was not caused by a change in the gel structure since a similar reduction in apparent diffusivity was not observed for the net negatively charged protein α-lactalbumin. The apparent diffusivity of the cationic compounds in the negatively charged hydrogels was highly dependent on the ionic strength, pointing out the importance of electrostatic interactions between the diffusant and the polyelectrolytes. Solution based affinity studies between the model compounds and the two investigated polyelectrolytes further confirmed the electrostatic nature of their interactions. The results obtained from the UV imaging diffusion studies are important for understanding the effect of drug physicochemical properties on the transport in articular cartilage. The extracted information may be useful in the development of hydrogels for in vitro release testing having features resembling the articular cartilage.

Novel Non-Peptide Inhibitors against SmCL1 of Schistosoma mansoni: In Silico Elucidation, Implications and Evaluation via Knowledge Based Drug Discovery

PubMed Central

Zafar, Atif; Ahmad, Sabahuddin; Rizvi, Asim; Ahmad, Masood

2015-01-01

Schistosomiasis is a major endemic disease known for excessive mortality and morbidity in developing countries. Because praziquantel is the only drug available for its treatment, the risk of drug resistance emphasizes the need to discover new drugs for this disease. Cathepsin SmCL1 is the critical target for drug design due to its essential role in the digestion of host proteins for growth and development of Schistosoma mansoni. Inhibiting the function of SmCL1 could control the wide spread of infections caused by S. mansoni in humans. With this objective, a homology modeling approach was used to obtain theoretical three-dimensional (3D) structure of SmCL1. In order to find the potential inhibitors of SmCL1, a plethora of in silico techniques were employed to screen non-peptide inhibitors against SmCL1 via structure-based drug discovery protocol. Receiver operating characteristic (ROC) curve analysis and molecular dynamics (MD) simulation were performed on the results of docked protein-ligand complexes to identify top ranking molecules against the modelled 3D structure of SmCL1. MD simulation results suggest the phytochemical Simalikalactone-D as a potential lead against SmCL1, whose pharmacophore model may be useful for future screening of potential drug molecules. To conclude, this is the first report to discuss the virtual screening of non-peptide inhibitors against SmCL1 of S. mansoni, with significant therapeutic potential. Results presented herein provide a valuable contribution to identify the significant leads and further derivatize them to suitable drug candidates for antischistosomal therapy. PMID:25933436

Meaningful engagement of people living with HIV who use drugs: methodology for the design of a Peer Research Associate (PRA) hiring model.

PubMed

Closson, K; McNeil, R; McDougall, P; Fernando, S; Collins, A B; Baltzer Turje, R; Howard, T; Parashar, S

2016-10-07

Community-based HIV, harm reduction, and addiction research increasingly involve members of affected communities as Peer Research Associates (PRAs)-individuals with common experiences to the participant population (e.g. people who use drugs, people living with HIV [PLHIV]). However, there is a paucity of literature detailing the operationalization of PRA hiring and thus limited understanding regarding how affected communities can be meaningfully involved through low-barrier engagement in paid positions within community-based participatory research (CBPR) projects. We aim to address this gap by describing a low-threshold PRA hiring process. In 2012, the BC Centre for Excellence in HIV/AIDS and the Dr. Peter AIDS Foundation collaborated to develop a mixed-method CBPR project evaluating the effectiveness of the Dr. Peter Centre (DPC)-an integrative HIV care facility in Vancouver, Canada. A primary objective of the study was to assess the impact of DPC services among clients who have a history of illicit drug use. In keeping with CBPR principles, affected populations, community-based organizations, and key stakeholders guided the development and dissemination of a low-barrier PRA hiring process to meaningfully engage affected communities (e.g. PLHIV who have a history of illicit drug use) in all aspects of the research project. The hiring model was implemented in a number of stages, including (1) the establishment of a hiring team; (2) the development and dissemination of the job posting; (3) interviewing applicants; and (4) the selection of participants. The hiring model presented in this paper demonstrates the benefits of hiring vulnerable PLHIV who use drugs as PRAs in community-based research. The provision of low-barrier access to meaningful research employment described herein attempts to engage affected communities beyond tokenistic involvement in research. Our hiring model was successful at engaging five PRAs over a 2-year period and fostered opportunities for future paid employment or volunteer opportunities through ongoing collaboration between PRAs and a diverse range of stakeholders working in HIV/AIDS and addictions. Additionally, this model has the potential to be used across a range of studies and community-based settings interested in meaningfully engaging communities in all stages of the research process.

High-Throughput Screening for Identification of Blood-Brain Barrier Integrity Enhancers: A Drug Repurposing Opportunity to Rectify Vascular Amyloid Toxicity.

PubMed

Qosa, Hisham; Mohamed, Loqman A; Al Rihani, Sweilem B; Batarseh, Yazan S; Duong, Quoc-Viet; Keller, Jeffrey N; Kaddoumi, Amal

2016-07-06

The blood-brain barrier (BBB) is a dynamic interface that maintains brain homeostasis and protects it from free entry of chemicals, toxins, and drugs. The barrier function of the BBB is maintained mainly by capillary endothelial cells that physically separate brain from blood. Several neurological diseases, such as Alzheimer's disease (AD), are known to disrupt BBB integrity. In this study, a high-throughput screening (HTS) was developed to identify drugs that rectify/protect BBB integrity from vascular amyloid toxicity associated with AD progression. Assessing Lucifer Yellow permeation across in-vitro BBB model composed from mouse brain endothelial cells (bEnd3) grown on 96-well plate inserts was used to screen 1280 compounds of Sigma LOPAC®1280 library for modulators of bEnd3 monolayer integrity. HTS identified 62 compounds as disruptors, and 50 compounds as enhancers of the endothelial barrier integrity. From these 50 enhancers, 7 FDA approved drugs were identified with EC50 values ranging from 0.76-4.56 μM. Of these 7 drugs, 5 were able to protect bEnd3-based BBB model integrity against amyloid toxicity. Furthermore, to test the translational potential to humans, the 7 drugs were tested for their ability to rectify the disruptive effect of Aβ in the human endothelial cell line hCMEC/D3. Only 3 (etodolac, granisetron, and beclomethasone) out of the 5 effective drugs in the bEnd3-based BBB model demonstrated a promising effect to protect the hCMEC/D3-based BBB model integrity. These drugs are compelling candidates for repurposing as therapeutic agents that could rectify dysfunctional BBB associated with AD.

High-throughput screening for identification of blood-brain barrier integrity enhancers: a drug repurposing opportunity to rectify vascular amyloid toxicity

PubMed Central

Qosa, Hisham; Mohamed, Loqman A.; Al Rihani, Sweilem B.; Batarseh, Yazan S.; Duong, Quoc-Viet; Keller, Jeffrey N.; Kaddoumi, Amal

2016-01-01

The blood-brain barrier (BBB) is a dynamic interface that maintains brain homeostasis and protects it from free entry of chemicals, toxins and drugs. The barrier function of the BBB is maintained mainly by capillary endothelial cells that physically separate brain from blood. Several neurological diseases, such as Alzheimer’s disease (AD), are known to disrupt BBB integrity. In this study, a high-throughput screening (HTS) was developed to identify drugs that rectify/protect BBB integrity from vascular amyloid toxicity associated with AD progression. Assessing Lucifer Yellow permeation across in-vitro BBB model composed from mouse brain endothelial cells (bEnd3) grown on 96-well plate inserts was used to screen 1280 compounds of Sigma LOPAC®1280 library for modulators of bEnd3 monolayer integrity. HTS identified 62 compounds as disruptors, and 50 compounds as enhancers of the endothelial barrier integrity. From these 50 enhancers, 7 FDA approved drugs were identified with EC50 values ranging from 0.76–4.56 μM. Of these 7 drugs, five were able to protect bEnd3-based BBB model integrity against amyloid toxicity. Furthermore, to test the translational potential to humans, the 7 drugs were tested for their ability to rectify the disruptive effect of Aβ in the human endothelial cell line hCMEC/D3. Only 3 (etodolac, granisetron and beclomethasone) out of the 5 effective drugs in the bEnd3-based BBB model demonstrated a promising effect to protect the hCMEC/D3-based BBB model integrity. These drugs are compelling candidates for repurposing as therapeutic agents that could rectify dysfunctional BBB associated with AD. PMID:27392852

Pharmacometric Models for Characterizing the Pharmacokinetics of Orally Inhaled Drugs.

PubMed

Borghardt, Jens Markus; Weber, Benjamin; Staab, Alexander; Kloft, Charlotte

2015-07-01

During the last decades, the importance of modeling and simulation in clinical drug development, with the goal to qualitatively and quantitatively assess and understand mechanisms of pharmacokinetic processes, has strongly increased. However, this increase could not equally be observed for orally inhaled drugs. The objectives of this review are to understand the reasons for this gap and to demonstrate the opportunities that mathematical modeling of pharmacokinetics of orally inhaled drugs offers. To achieve these objectives, this review (i) discusses pulmonary physiological processes and their impact on the pharmacokinetics after drug inhalation, (ii) provides a comprehensive overview of published pharmacokinetic models, (iii) categorizes these models into physiologically based pharmacokinetic (PBPK) and (clinical data-derived) empirical models, (iv) explores both their (mechanistic) plausibility, and (v) addresses critical aspects of different pharmacometric approaches pertinent for drug inhalation. In summary, pulmonary deposition, dissolution, and absorption are highly complex processes and may represent the major challenge for modeling and simulation of PK after oral drug inhalation. Challenges in relating systemic pharmacokinetics with pulmonary efficacy may be another factor contributing to the limited number of existing pharmacokinetic models for orally inhaled drugs. Investigations comprising in vitro experiments, clinical studies, and more sophisticated mathematical approaches are considered to be necessary for elucidating these highly complex pulmonary processes. With this additional knowledge, the PBPK approach might gain additional attractiveness. Currently, (semi-)mechanistic modeling offers an alternative to generate and investigate hypotheses and to more mechanistically understand the pulmonary and systemic pharmacokinetics after oral drug inhalation including the impact of pulmonary diseases.

A theory of drug tolerance and dependence I: a conceptual analysis.

PubMed

Peper, Abraham

2004-08-21

A mathematical model of drug tolerance and its underlying theory is presented. The model extends a first approach, published previously. The model is essentially more complex than the generally used model of homeostasis, which is demonstrated to fail in describing tolerance development to repeated drug administrations. The model assumes the development of tolerance to a repeatedly administered drug to be the result of a regulated adaptive process. The oral detection and analysis of exogenous substances is proposed to be the primary stimulus for the mechanism of drug tolerance. Anticipation and environmental cues are in the model considered secondary stimuli, becoming primary only in dependence and addiction or when the drug administration bypasses the natural-oral-route, as is the case when drugs are administered intravenously. The model considers adaptation to the effect of a drug and adaptation to the interval between drug taking autonomous tolerance processes. Simulations with the mathematical model demonstrate the model's behavior to be consistent with important characteristics of the development of tolerance to repeatedly administered drugs: the gradual decrease in drug effect when tolerance develops, the high sensitivity to small changes in drug dose, the rebound phenomenon and the large reactions following withdrawal in dependence. The mathematical model verifies the proposed theory and provides a basis for the implementation of mathematical models of specific physiological processes. In addition, it establishes a relation between the drug dose at any moment, and the resulting drug effect and relates the magnitude of the reactions following withdrawal to the rate of tolerance and other parameters involved in the tolerance process. The present paper analyses the concept behind the model. The next paper discusses the mathematical model.

The clinical development of p53-reactivating drugs in sarcomas - charting future therapeutic approaches and understanding the clinical molecular toxicology of Nutlins.

PubMed

Biswas, Swethajit; Killick, Emma; Jochemsen, Aart G; Lunec, John

2014-05-01

The majority of human sarcomas, particularly soft tissue sarcomas, are relatively resistant to traditional cytotoxic therapies. The proof-of-concept study by Ray-Coquard et al., using the Nutlin human double minute (HDM)2-binding antagonist RG7112, has recently opened a new chapter in the molecular targeting of human sarcomas. In this review, the authors discuss the challenges and prospective remedies for minimizing the significant haematological toxicities of the cis-imidazole Nutlin HDM2-binding antagonists. Furthermore, they also chart the future direction of the development of p53-reactivating (p53-RA) drugs in 12q13-15 amplicon sarcomas and as potential chemopreventative therapies against sarcomagenesis in germ line mutated TP53 carriers. Drawing lessons from the therapeutic use of Imatinib in gastrointestinal tumours, the authors predict the potential pitfalls, which may lie in ahead for the future clinical development of p53-RA agents, as well as discussing potential non-invasive methods to identify the development of drug resistance. Medicinal chemistry strategies, based on structure-based drug design, are required to re-engineer cis-imidazoline Nutlin HDM2-binding antagonists into less haematologically toxic drugs. In silico modelling is also required to predict toxicities of other p53-RA drugs at a much earlier stage in drug development. Whether p53-RA drugs will be therapeutically effective as a monotherapy remains to be determined.

Bioerodible System for Sequential Release of Multiple Drugs

PubMed Central

Sundararaj, Sharath C.; Thomas, Mark V.; Dziubla, Thomas D.; Puleo, David A.

2013-01-01

Because many complex physiological processes are controlled by multiple biomolecules, comprehensive treatment of certain disease conditions may be more effectively achieved by administration of more than one type of drug. Thus, the objective of the present research was to develop a multilayered, polymer-based system for sequential delivery of multiple drugs. The polymers used were cellulose acetate phthalate (CAP) complexed with Pluronic F-127 (P). After evaluating morphology of the resulting CAPP system, in vitro release of small molecule drugs and a model protein was studied from both single and multilayered devices. Drug release from single-layered CAPP films followed zero-order kinetics related to surface erosion of the association polymer. Release studies from multilayered CAPP devices showed the possibility of achieving intermittent release of one type of drug as well as sequential release of more than one type of drug. Mathematical modeling accurately predicted the release profiles for both single layer and multilayered devices. The present CAPP association polymer-based multilayer devices can be used for localized, sequential delivery of multiple drugs for the possible treatment of complex disease conditions, and perhaps for tissue engineering applications, that require delivery of more than one type of biomolecule. PMID:24096151

Human iPSC-derived cardiomyocytes and tissue engineering strategies for disease modeling and drug screening.

PubMed

Smith, Alec S T; Macadangdang, Jesse; Leung, Winnie; Laflamme, Michael A; Kim, Deok-Ho

Improved methodologies for modeling cardiac disease phenotypes and accurately screening the efficacy and toxicity of potential therapeutic compounds are actively being sought to advance drug development and improve disease modeling capabilities. To that end, much recent effort has been devoted to the development of novel engineered biomimetic cardiac tissue platforms that accurately recapitulate the structure and function of the human myocardium. Within the field of cardiac engineering, induced pluripotent stem cells (iPSCs) are an exciting tool that offer the potential to advance the current state of the art, as they are derived from somatic cells, enabling the development of personalized medical strategies and patient specific disease models. Here we review different aspects of iPSC-based cardiac engineering technologies. We highlight methods for producing iPSC-derived cardiomyocytes (iPSC-CMs) and discuss their application to compound efficacy/toxicity screening and in vitro modeling of prevalent cardiac diseases. Special attention is paid to the application of micro- and nano-engineering techniques for the development of novel iPSC-CM based platforms and their potential to advance current preclinical screening modalities. Published by Elsevier Inc.

Microfluidic cell culture systems for drug research.

PubMed

Wu, Min-Hsien; Huang, Song-Bin; Lee, Gwo-Bin

2010-04-21

In pharmaceutical research, an adequate cell-based assay scheme to efficiently screen and to validate potential drug candidates in the initial stage of drug discovery is crucial. In order to better predict the clinical response to drug compounds, a cell culture model that is faithful to in vivo behavior is required. With the recent advances in microfluidic technology, the utilization of a microfluidic-based cell culture has several advantages, making it a promising alternative to the conventional cell culture methods. This review starts with a comprehensive discussion on the general process for drug discovery and development, the role of cell culture in drug research, and the characteristics of the cell culture formats commonly used in current microfluidic-based, cell-culture practices. Due to the significant differences in several physical phenomena between microscale and macroscale devices, microfluidic technology provides unique functionality, which is not previously possible by using traditional techniques. In a subsequent section, the niches for using microfluidic-based cell culture systems for drug research are discussed. Moreover, some critical issues such as cell immobilization, medium pumping or gradient generation in microfluidic-based, cell-culture systems are also reviewed. Finally, some practical applications of microfluidic-based, cell-culture systems in drug research particularly those pertaining to drug toxicity testing and those with a high-throughput capability are highlighted.

A systems approach for tumor pharmacokinetics.

PubMed

Thurber, Greg Michael; Weissleder, Ralph

2011-01-01

Recent advances in genome inspired target discovery, small molecule screens, development of biological and nanotechnology have led to the introduction of a myriad of new differently sized agents into the clinic. The differences in small and large molecule delivery are becoming increasingly important in combination therapies as well as the use of drugs that modify the physiology of tumors such as anti-angiogenic treatment. The complexity of targeting has led to the development of mathematical models to facilitate understanding, but unfortunately, these studies are often only applicable to a particular molecule, making pharmacokinetic comparisons difficult. Here we develop and describe a framework for categorizing primary pharmacokinetics of drugs in tumors. For modeling purposes, we define drugs not by their mechanism of action but rather their rate-limiting step of delivery. Our simulations account for variations in perfusion, vascularization, interstitial transport, and non-linear local binding and metabolism. Based on a comparison of the fundamental rates determining uptake, drugs were classified into four categories depending on whether uptake is limited by blood flow, extravasation, interstitial diffusion, or local binding and metabolism. Simulations comparing small molecule versus macromolecular drugs show a sharp difference in distribution, which has implications for multi-drug therapies. The tissue-level distribution differs widely in tumors for small molecules versus macromolecular biologic drugs, and this should be considered in the design of agents and treatments. An example using antibodies in mouse xenografts illustrates the different in vivo behavior. This type of transport analysis can be used to aid in model development, experimental data analysis, and imaging and therapeutic agent design.

A liposomal steroid nano-drug for treating systemic lupus erythematosus.

PubMed

Moallem, E; Koren, E; Ulmansky, R; Pizov, G; Barlev, M; Barenholz, Y; Naparstek, Y

2016-10-01

Glucocorticoids have been known for years to be the most effective therapy in systemic lupus erythematosus. Their use, however, is limited by the need for high doses due to their unfavorable pharmacokinetics and biodistribution. We have previously developed a novel liposome-based steroidal (methylprednisolone hemisuccinate (MPS)) nano-drug and demonstrated its specific accumulation in inflamed tissues, as well as its superior therapeutic efficacy over that of free glucocorticoids (non-liposomal) in the autoimmune diseases, including the adjuvant arthritis rat model and the experimental autoimmune encephalomyelitis mouse model. In the present work we have evaluated the therapeutic effect of the above liposome-based steroidal (MPS) nano-drug in the MRL-lpr/lpr murine model of SLE and compared it with similar doses of the free MPS. MRL-lpr/lpr mice were treated with daily injections of free MPS or weekly injections of 10% dextrose, empty nano-liposomes or the steroidal nano-drug and the course of their disease was followed up to the age of 24 weeks. Treatment with the steroidal nano-drug was found to be significantly superior to the free MPS in suppressing anti-dsDNA antibody levels, proliferation of lymphoid tissue and renal damage, and in prolonging survival of animals. This significant superiority of our liposome based steroidal nano-drug administered weekly compared with daily injections of free methylprednisolone hemisuccinate in suppressing murine lupus indicates this glucocorticoid nano-drug formulation may be a good candidate for the treatment of human SLE. © The Author(s) 2016.

Comparative protein modeling of methionine S-adenosyltransferase (MAT) enzyme from Mycobacterium tuberculosis: a potential target for antituberculosis drug discovery.

PubMed

Khedkar, Santosh A; Malde, Alpeshkumar K; Coutinho, Evans C

2005-01-01

Mycobacterium tuberculosis (Mtb) is a successful pathogen that overcomes the numerous challenges presented by the immune system of the host. In the last 40 years few anti-TB drugs have been developed, while the drug-resistance problem is increasing; there is thus a pressing need to develop new anti-TB drugs active against both the acute and chronic growth phases of the mycobacterium. Methionine S-adenosyltransferase (MAT) is an enzyme involved in the synthesis of S-adenosylmethionine (SAM), a methyl donor essential for mycolipid biosynthesis. As an anti-TB drug target, Mtb-MAT has been well validated. A homology model of MAT has been constructed using the X-ray structures of E. coli MAT (PDB code: 1MXA) and rat MAT (PDB code: 1QM4) as templates, by comparative protein modeling principles. The resulting model has the correct stereochemistry as gauged from the Ramachandran plot and good three-dimensional (3D) structure compatibility as assessed by the Profiles-3D score. The structurally and functionally important residues (active site) of Mtb-MAT have been identified using the E. coli and rat MAT crystal structures and the reported point mutation data. The homology model conserves the topological and active site features of the MAT family of proteins. The differences in the molecular electrostatic potentials (MEP) of Mtb and human MAT provide evidences that selective and specific Mtb-MAT inhibitors can be designed using the homology model, by the structure-based drug design approaches.

In Vitro and in Silico Tools To Assess Extent of Cellular Uptake and Lysosomal Sequestration of Respiratory Drugs in Human Alveolar Macrophages.

PubMed

Ufuk, Ayşe; Assmus, Frauke; Francis, Laura; Plumb, Jonathan; Damian, Valeriu; Gertz, Michael; Houston, J Brian; Galetin, Aleksandra

2017-04-03

Accumulation of respiratory drugs in human alveolar macrophages (AMs) has not been extensively studied in vitro and in silico despite its potential impact on therapeutic efficacy and/or occurrence of phospholipidosis. The current study aims to characterize the accumulation and subcellular distribution of drugs with respiratory indication in human AMs and to develop an in silico mechanistic AM model to predict lysosomal accumulation of investigated drugs. The data set included 9 drugs previously investigated in rat AM cell line NR8383. Cell-to-unbound medium concentration ratio (K p,cell ) of all drugs (5 μM) was determined to assess the magnitude of intracellular accumulation. The extent of lysosomal sequestration in freshly isolated human AMs from multiple donors (n = 5) was investigated for clarithromycin and imipramine (positive control) using an indirect in vitro method (±20 mM ammonium chloride, NH 4 Cl). The AM cell parameters and drug physicochemical data were collated to develop an in silico mechanistic AM model. Three in silico models differing in their description of drug membrane partitioning were evaluated; model (1) relied on octanol-water partitioning of drugs, model (2) used in vitro data to account for this process, and model (3) predicted membrane partitioning by incorporating AM phospholipid fractions. In vitro K p,cell ranged >200-fold for respiratory drugs, with the highest accumulation seen for clarithromycin. A good agreement in K p,cell was observed between human AMs and NR8383 (2.45-fold bias), highlighting NR8383 as a potentially useful in vitro surrogate tool to characterize drug accumulation in AMs. The mean K p,cell of clarithromycin (81, CV = 51%) and imipramine (963, CV = 54%) were reduced in the presence of NH 4 Cl by up to 67% and 81%, respectively, suggesting substantial contribution of lysosomal sequestration and intracellular binding in the accumulation of these drugs in human AMs. The in vitro data showed variability in drug accumulation between individual human AM donors due to possible differences in lysosomal abundance, volume, and phospholipid content, which may have important clinical implications. Consideration of drug-acidic phospholipid interactions significantly improved the performance of the in silico models; use of in vitro K p,cell obtained in the presence of NH 4 Cl as a surrogate for membrane partitioning (model (2)) captured the variability in clarithromycin and imipramine K p,cell observed in vitro and showed the best ability to predict correctly positive and negative lysosomotropic properties. The developed mechanistic AM model represents a useful in silico tool to predict lysosomal and cellular drug concentrations based on drug physicochemical data and system specific properties, with potential application to other cell types.

Elasticity-based development of functionally enhanced multicellular 3D liver encapsulated in hybrid hydrogel.

PubMed

Lee, Ho-Joon; Son, Myung Jin; Ahn, Jiwon; Oh, Soo Jin; Lee, Mihee; Kim, Ansoon; Jeung, Yun-Ji; Kim, Han-Gyeul; Won, Misun; Lim, Jung Hwa; Kim, Nam-Soon; Jung, Cho-Rock; Chung, Kyung-Sook

2017-12-01

Current in vitro liver models provide three-dimensional (3-D) microenvironments in combination with tissue engineering technology and can perform more accurate in vivo mimicry than two-dimensional models. However, a human cell-based, functionally mature liver model is still desired, which would provide an alternative to animal experiments and resolve low-prediction issues on species differences. Here, we prepared hybrid hydrogels of varying elasticity and compared them with a normal liver, to develop a more mature liver model that preserves liver properties in vitro. We encapsulated HepaRG cells, either alone or with supporting cells, in a biodegradable hybrid hydrogel. The elastic modulus of the 3D liver dynamically changed during culture due to the combined effects of prolonged degradation of hydrogel and extracellular matrix formation provided by the supporting cells. As a result, when the elastic modulus of the 3D liver model converges close to that of the in vivo liver (≅ 2.3 to 5.9 kPa), both phenotypic and functional maturation of the 3D liver were realized, while hepatic gene expression, albumin secretion, cytochrome p450-3A4 activity, and drug metabolism were enhanced. Finally, the 3D liver model was expanded to applications with embryonic stem cell-derived hepatocytes and primary human hepatocytes, and it supported prolonged hepatocyte survival and functionality in long-term culture. Our model represents critical progress in developing a biomimetic liver system to simulate liver tissue remodeling, and provides a versatile platform in drug development and disease modeling, ranging from physiology to pathology. We provide a functionally improved 3D liver model that recapitulates in vivo liver stiffness. We have experimentally addressed the issues of orchestrated effects of mechanical compliance, controlled matrix formation by stromal cells in conjunction with hepatic differentiation, and functional maturation of hepatocytes in a dynamic 3D microenvironment. Our model represents critical progress in developing a biomimetic liver system to simulate liver tissue remodeling, and provides a versatile platform in drug development and disease modeling, ranging from physiology to pathology. Additionally, recent advances in the stem-cell technologies have made the development of 3D organoid possible, and thus, our study also provides further contribution to the development of physiologically relevant stem-cell-based 3D tissues that provide an elasticity-based predefined biomimetic 3D microenvironment. Copyright © 2017 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Prevalidation of a human cornea construct as an alternative to animal corneas for in vitro drug absorption studies.

PubMed

Hahne, Matthias; Zorn-Kruppa, Michaela; Guzman, Gustavo; Brandner, Johanna M; Haltner-Ukomado, Eleonore; Wätzig, Hermann; Reichl, Stephan

2012-08-01

The use of ophthalmic drugs has increased consistently over the past few decades. Currently, most research is conducted using in vivo and ex vivo animal experiments; however, they have many disadvantages, including ethical concerns, high costs, the questionable extension of animal results to humans, and poor standardization. Although several cell culture-based cornea models have been developed, none have been validated and accepted for general use. In this study, a standardized, three-dimensional model of the human cornea (Hemicornea, HC) based on immortalized human corneal cells and cultivated in serum-free conditions was developed for drug absorption studies and prevalidated using compounds with a wide range of molecular characteristics (sodium fluorescein, rhodamine B, fluorescein isothiocyanate-labeled dextran, aciclovir, bimatoprost, dexamethasone, and timolol maleate). The HC model was independently cultured in three different laboratories, and the intralaboratory and interlaboratory reproducibility was analyzed and compared with the rabbit cornea. This analysis showed that the HC has a barrier in the same range as excised animal corneas, although with a higher reproducibility and lower variability. Because of the demonstrated transferability, the HC represents a promising in vitro alternative to the use of ex vivo tissue and offers a well-defined and standardized system for drug absorption studies. Copyright © 2012 Wiley Periodicals, Inc.

A Drug-Centric View of Drug Development: How Drugs Spread from Disease to Disease.

PubMed

Rodriguez-Esteban, Raul

2016-04-01

Drugs are often seen as ancillary to the purpose of fighting diseases. Here an alternative view is proposed in which they occupy a spearheading role. In this view, drugs are technologies with an inherent therapeutic potential. Once created, they can spread from disease to disease independently of the drug creator's original intentions. Through the analysis of extensive literature and clinical trial records, it can be observed that successful drugs follow a life cycle in which they are studied at an increasing rate, and for the treatment of an increasing number of diseases, leading to clinical advancement. Such initial growth, following a power law on average, has a degree of momentum, but eventually decelerates, leading to stagnation and decay. A network model can describe the propagation of drugs from disease to disease in which diseases communicate with each other by receiving and sending drugs. Within this model, some diseases appear more prone to influence other diseases than be influenced, and vice versa. Diseases can also be organized into a drug-centric disease taxonomy based on the drugs that each adopts. This taxonomy reflects not only biological similarities across diseases, but also the level of differentiation of existing therapies. In sum, this study shows that drugs can become contagious technologies playing a driving role in the fight against disease. By better understanding such dynamics, pharmaceutical developers may be able to manage drug projects more effectively.

A new approach to identify, classify and count drugrelated events

PubMed Central

Bürkle, Thomas; Müller, Fabian; Patapovas, Andrius; Sonst, Anja; Pfistermeister, Barbara; Plank-Kiegele, Bettina; Dormann, Harald; Maas, Renke

2013-01-01

Aims The incidence of clinical events related to medication errors and/or adverse drug reactions reported in the literature varies by a degree that cannot solely be explained by the clinical setting, the varying scrutiny of investigators or varying definitions of drug-related events. Our hypothesis was that the individual complexity of many clinical cases may pose relevant limitations for current definitions and algorithms used to identify, classify and count adverse drug-related events. Methods Based on clinical cases derived from an observational study we identified and classified common clinical problems that cannot be adequately characterized by the currently used definitions and algorithms. Results It appears that some key models currently used to describe the relation of medication errors (MEs), adverse drug reactions (ADRs) and adverse drug events (ADEs) can easily be misinterpreted or contain logical inconsistencies that limit their accurate use to all but the simplest clinical cases. A key limitation of current models is the inability to deal with complex interactions such as one drug causing two clinically distinct side effects or multiple drugs contributing to a single clinical event. Using a large set of clinical cases we developed a revised model of the interdependence between MEs, ADEs and ADRs and extended current event definitions when multiple medications cause multiple types of problems. We propose algorithms that may help to improve the identification, classification and counting of drug-related events. Conclusions The new model may help to overcome some of the limitations that complex clinical cases pose to current paper- or software-based drug therapy safety. PMID:24007453

Innovative polymeric system (IPS) for solvent-free lipophilic drug transdermal delivery via dissolving microneedles.

PubMed

Dangol, Manita; Yang, Huisuk; Li, Cheng Guo; Lahiji, Shayan Fakhraei; Kim, Suyong; Ma, Yonghao; Jung, Hyungil

2016-02-10

Lipophilic drugs are potential drug candidates during drug development. However, due to the need for hazardous organic solvents for their solubilization, these drugs often fail to reach the pharmaceutical market, and in doing so highlight the importance of solvent free systems. Although transdermal drug delivery systems (TDDSs) are considered prospective safe drug delivery routes, a system involving lipophilic drugs in solvent free or powder form has not yet been described. Here, we report, for the first time, a novel approach for the delivery of every kind of lipophilic drug in powder form based on an innovative polymeric system (IPS). The phase transition of powder form of lipophilic drugs due to interior chemical bonds between drugs and biodegradable polymers and formation of nano-sized colloidal structures allowed the fabrication of dissolving microneedles (DMNs) to generate a powerful TDDS. We showed that IPS based DMN with powder capsaicin enhances the therapeutic effect for treatment of the rheumatic arthritis in a DBA/1 mouse model compared to a solvent-based system, indicating the promising potential of this new solvent-free platform for lipophilic drug delivery. Copyright © 2016 Elsevier B.V. All rights reserved.

A probabilistic approach to identify putative drug targets in biochemical networks.

PubMed

Murabito, Ettore; Smallbone, Kieran; Swinton, Jonathan; Westerhoff, Hans V; Steuer, Ralf

2011-06-06

Network-based drug design holds great promise in clinical research as a way to overcome the limitations of traditional approaches in the development of drugs with high efficacy and low toxicity. This novel strategy aims to study how a biochemical network as a whole, rather than its individual components, responds to specific perturbations in different physiological conditions. Proteins exerting little control over normal cells and larger control over altered cells may be considered as good candidates for drug targets. The application of network-based drug design would greatly benefit from using an explicit computational model describing the dynamics of the system under investigation. However, creating a fully characterized kinetic model is not an easy task, even for relatively small networks, as it is still significantly hampered by the lack of data about kinetic mechanisms and parameters values. Here, we propose a Monte Carlo approach to identify the differences between flux control profiles of a metabolic network in different physiological states, when information about the kinetics of the system is partially or totally missing. Based on experimentally accessible information on metabolic phenotypes, we develop a novel method to determine probabilistic differences in the flux control coefficients between the two observable phenotypes. Knowledge of how differences in flux control are distributed among the different enzymatic steps is exploited to identify points of fragility in one of the phenotypes. Using a prototypical cancerous phenotype as an example, we demonstrate how our approach can assist researchers in developing compounds with high efficacy and low toxicity. © 2010 The Royal Society

Rapid formation of size-controllable multicellular spheroids via 3D acoustic tweezers.

PubMed

Chen, Kejie; Wu, Mengxi; Guo, Feng; Li, Peng; Chan, Chung Yu; Mao, Zhangming; Li, Sixing; Ren, Liqiang; Zhang, Rui; Huang, Tony Jun

2016-07-05

The multicellular spheroid is an important 3D cell culture model for drug screening, tissue engineering, and fundamental biological research. Although several spheroid formation methods have been reported, the field still lacks high-throughput and simple fabrication methods to accelerate its adoption in drug development industry. Surface acoustic wave (SAW) based cell manipulation methods, which are known to be non-invasive, flexible, and high-throughput, have not been successfully developed for fabricating 3D cell assemblies or spheroids, due to the limited understanding on SAW-based vertical levitation. In this work, we demonstrated the capability of fabricating multicellular spheroids in the 3D acoustic tweezers platform. Our method used drag force from microstreaming to levitate cells in the vertical direction, and used radiation force from Gor'kov potential to aggregate cells in the horizontal plane. After optimizing the device geometry and input power, we demonstrated the rapid and high-throughput nature of our method by continuously fabricating more than 150 size-controllable spheroids and transferring them to Petri dishes every 30 minutes. The spheroids fabricated by our 3D acoustic tweezers can be cultured for a week with good cell viability. We further demonstrated that spheroids fabricated by this method could be used for drug testing. Unlike the 2D monolayer model, HepG2 spheroids fabricated by the 3D acoustic tweezers manifested distinct drug resistance, which matched existing reports. The 3D acoustic tweezers based method can serve as a novel bio-manufacturing tool to fabricate complex 3D cell assembles for biological research, tissue engineering, and drug development.

Predicting Drug-Target Interactions With Multi-Information Fusion.

PubMed

Peng, Lihong; Liao, Bo; Zhu, Wen; Li, Zejun; Li, Keqin

2017-03-01

Identifying potential associations between drugs and targets is a critical prerequisite for modern drug discovery and repurposing. However, predicting these associations is difficult because of the limitations of existing computational methods. Most models only consider chemical structures and protein sequences, and other models are oversimplified. Moreover, datasets used for analysis contain only true-positive interactions, and experimentally validated negative samples are unavailable. To overcome these limitations, we developed a semi-supervised based learning framework called NormMulInf through collaborative filtering theory by using labeled and unlabeled interaction information. The proposed method initially determines similarity measures, such as similarities among samples and local correlations among the labels of the samples, by integrating biological information. The similarity information is then integrated into a robust principal component analysis model, which is solved using augmented Lagrange multipliers. Experimental results on four classes of drug-target interaction networks suggest that the proposed approach can accurately classify and predict drug-target interactions. Part of the predicted interactions are reported in public databases. The proposed method can also predict possible targets for new drugs and can be used to determine whether atropine may interact with alpha1B- and beta1- adrenergic receptors. Furthermore, the developed technique identifies potential drugs for new targets and can be used to assess whether olanzapine and propiomazine may target 5HT2B. Finally, the proposed method can potentially address limitations on studies of multitarget drugs and multidrug targets.

A multi-model approach to nucleic acid-based drug development.

PubMed

Gautherot, Isabelle; Sodoyer, Regís

2004-01-01

With the advent of functional genomics and the shift of interest towards sequence-based therapeutics, the past decades have witnessed intense research efforts on nucleic acid-mediated gene regulation technologies. Today, RNA interference is emerging as a groundbreaking discovery, holding promise for development of genetic modulators of unprecedented potency. Twenty-five years after the discovery of antisense RNA and ribozymes, gene control therapeutics are still facing developmental difficulties, with only one US FDA-approved antisense drug currently available in the clinic. Limited predictability of target site selection models is recognized as one major stumbling block that is shared by all of the so-called complementary technologies, slowing the progress towards a commercial product. Currently employed in vitro systems for target site selection include RNAse H-based mapping, antisense oligonucleotide microarrays, and functional screening approaches using libraries of catalysts with randomized target-binding arms to identify optimal ribozyme/DNAzyme cleavage sites. Individually, each strategy has its drawbacks from a drug development perspective. Utilization of message-modulating sequences as therapeutic agents requires that their action on a given target transcript meets criteria of potency and selectivity in the natural physiological environment. In addition to sequence-dependent characteristics, other factors will influence annealing reactions and duplex stability, as well as nucleic acid-mediated catalysis. Parallel consideration of physiological selection systems thus appears essential for screening for nucleic acid compounds proposed for therapeutic applications. Cellular message-targeting studies face issues relating to efficient nucleic acid delivery and appropriate analysis of response. For reliability and simplicity, prokaryotic systems can provide a rapid and cost-effective means of studying message targeting under pseudo-cellular conditions, but such approaches also have limitations. To streamline nucleic acid drug discovery, we propose a multi-model strategy integrating high-throughput-adapted bacterial screening, followed by reporter-based and/or natural cellular models and potentially also in vitro assays for characterization of the most promising candidate sequences, before final in vivo testing.

Evidence-based selection of training compounds for use in the mechanism-based integrated prediction of drug-induced liver injury in man.

PubMed

Dragovic, Sanja; Vermeulen, Nico P E; Gerets, Helga H; Hewitt, Philip G; Ingelman-Sundberg, Magnus; Park, B Kevin; Juhila, Satu; Snoeys, Jan; Weaver, Richard J

2016-12-01

The current test systems employed by pharmaceutical industry are poorly predictive for drug-induced liver injury (DILI). The 'MIP-DILI' project addresses this situation by the development of innovative preclinical test systems which are both mechanism-based and of physiological, pharmacological and pathological relevance to DILI in humans. An iterative, tiered approach with respect to test compounds, test systems, bioanalysis and systems analysis is adopted to evaluate existing models and develop new models that can provide validated test systems with respect to the prediction of specific forms of DILI and further elucidation of mechanisms. An essential component of this effort is the choice of compound training set that will be used to inform refinement and/or development of new model systems that allow prediction based on knowledge of mechanisms, in a tiered fashion. In this review, we focus on the selection of MIP-DILI training compounds for mechanism-based evaluation of non-clinical prediction of DILI. The selected compounds address both hepatocellular and cholestatic DILI patterns in man, covering a broad range of pharmacologies and chemistries, and taking into account available data on potential DILI mechanisms (e.g. mitochondrial injury, reactive metabolites, biliary transport inhibition, and immune responses). Known mechanisms by which these compounds are believed to cause liver injury have been described, where many if not all drugs in this review appear to exhibit multiple toxicological mechanisms. Thus, the training compounds selection offered a valuable tool to profile DILI mechanisms and to interrogate existing and novel in vitro systems for the prediction of human DILI.

MRI-Based Computational Model of Heterogeneous Tracer Transport following Local Infusion into a Mouse Hind Limb Tumor

PubMed Central

Magdoom, Kulam Najmudeen; Pishko, Gregory L.; Rice, Lori; Pampo, Chris; Siemann, Dietmar W.; Sarntinoranont, Malisa

2014-01-01

Systemic drug delivery to solid tumors involving macromolecular therapeutic agents is challenging for many reasons. Amongst them is their chaotic microvasculature which often leads to inadequate and uneven uptake of the drug. Localized drug delivery can circumvent such obstacles and convection-enhanced delivery (CED) - controlled infusion of the drug directly into the tissue - has emerged as a promising delivery method for distributing macromolecules over larger tissue volumes. In this study, a three-dimensional MR image-based computational porous media transport model accounting for realistic anatomical geometry and tumor leakiness was developed for predicting the interstitial flow field and distribution of albumin tracer following CED into the hind-limb tumor (KHT sarcoma) in a mouse. Sensitivity of the model to changes in infusion flow rate, catheter placement and tissue hydraulic conductivity were investigated. The model predictions suggest that 1) tracer distribution is asymmetric due to heterogeneous porosity; 2) tracer distribution volume varies linearly with infusion volume within the whole leg, and exponentially within the tumor reaching a maximum steady-state value; 3) infusion at the center of the tumor with high flow rates leads to maximum tracer coverage in the tumor with minimal leakage outside; and 4) increasing the tissue hydraulic conductivity lowers the tumor interstitial fluid pressure and decreases the tracer distribution volume within the whole leg and tumor. The model thus predicts that the interstitial fluid flow and drug transport is sensitive to porosity and changes in extracellular space. This image-based model thus serves as a potential tool for exploring the effects of transport heterogeneity in tumors. PMID:24619021

Development and Translational Application of a Minimal Physiologically Based Pharmacokinetic Model for a Monoclonal Antibody against Interleukin 23 (IL-23) in IL-23-Induced Psoriasis-Like Mice.

PubMed

Chen, Xi; Jiang, Xiling; Doddareddy, Rajitha; Geist, Brian; McIntosh, Thomas; Jusko, William J; Zhou, Honghui; Wang, Weirong

2018-04-01

The interleukin (IL)-23/T h 17/IL-17 immune pathway has been identified to play an important role in the pathogenesis of psoriasis. Many therapeutic proteins targeting IL-23 or IL-17 are currently under development for the treatment of psoriasis. In the present study, a mechanistic pharmacokinetics (PK)/pharmacodynamics (PD) study was conducted to assess the target-binding and disposition kinetics of a monoclonal antibody (mAb), CNTO 3723, and its soluble target, mouse IL-23, in an IL-23-induced psoriasis-like mouse model. A minimal physiologically based pharmacokinetic model with target-mediated drug disposition features was developed to quantitatively assess the kinetics and interrelationship between CNTO 3723 and exogenously administered, recombinant mouse IL-23 in both serum and lesional skin site. Furthermore, translational applications of the developed model were evaluated with incorporation of human PK for ustekinumab, an anti-human IL-23/IL-12 mAb developed for treatment of psoriasis, and human disease pathophysiology information in psoriatic patients. The results agreed well with the observed clinical data for ustekinumab. Our work provides an example on how mechanism-based PK/PD modeling can be applied during early drug discovery and how preclinical data can be used for human efficacious dose projection and guide decision making during early clinical development of therapeutic proteins. Copyright © 2018 by The Author(s).

Economic and developmental considerations for pharmacogenomic technology.

PubMed

Vernon, John A; Johnson, Scott J; Hughen, W Keener; Trujillo, Antonio

2006-01-01

The pharmaceutical industry's core business is the innovation, development and marketing of new drugs. Pharmacogenetic (PG) testing and technology has the potential to increase a drug's value in many ways. A critical issue for the industry is whether products in development should be teamed with genetic tests that could segment the total population into responders and non-responders. In this paper we use a cost-effectiveness framework to model the strategic decision-making considerations by pharmaceutical manufacturers as they relate to drug development and the new technology of PG (the science of using genetic markers to predict drug response). In a simple, static, one-period model we consider three drug development strategies: a drug is exclusively developed and marketed to patients with a particular genetic marker; no distinguishing among patients based on the expression of a genetic marker is made (traditional approach); and a strategy whereby a drug is marketed to patients both with and without the genetic marker but there is price discrimination between the two subpopulations. We developed three main principles: revenues under a strategy targeting only the responder subpopulation will never generate more revenue than that which could have been obtained under a traditional approach; total revenues under a targeted PG strategy will be less than that under a traditional approach but higher than a naive [corrected] view would believe them to be; and a traditional [corrected] approach will earn the same total revenues as a price discrimination strategy, assuming no intermarket arbitrage. While these principles relate to the singular (and quite narrow) consideration of drug revenues, they may nevertheless partially explain why PG is not being used as widely as was predicted several years ago when the technology first became available, especially in terms of pharmaceutical manufacturer-developed tests.

Moving evidence-based drug abuse prevention programs from basic science to practice: "bridging the efficacy-effectiveness interface".

PubMed

August, Gerald J; Winters, Ken C; Realmuto, George M; Tarter, Ralph; Perry, Cheryl; Hektner, Joel M

2004-01-01

This article examines the challenges faced by developers of youth drug abuse prevention programs in transporting scientifically proven or evidence-based programs into natural community practice systems. Models for research on the transfer of prevention technology are described with specific emphasis given to the relationship between efficacy and effectiveness studies. Barriers that impede the successful integration of efficacy methods within effectiveness studies (e.g., client factors, practitioner factors, intervention structure characteristics, and environmental and organizational factors) are discussed. We present a modified model for program development and evaluation that includes a new type of research design, the hybrid efficacy-effectiveness study that addresses program transportability. The utility of the hybrid study is illustrated in the evaluation of the Early Risers "Skills for Success" prevention program.

Pharmacokinetic/pharmacodynamic modelling approaches in paediatric infectious diseases and immunology☆

PubMed Central

Barker, Charlotte I.S.; Germovsek, Eva; Hoare, Rollo L.; Lestner, Jodi M.; Lewis, Joanna; Standing, Joseph F.

2014-01-01

Pharmacokinetic/pharmacodynamic (PKPD) modelling is used to describe and quantify dose–concentration–effect relationships. Within paediatric studies in infectious diseases and immunology these methods are often applied to developing guidance on appropriate dosing. In this paper, an introduction to the field of PKPD modelling is given, followed by a review of the PKPD studies that have been undertaken in paediatric infectious diseases and immunology. The main focus is on identifying the methodological approaches used to define the PKPD relationship in these studies. The major findings were that most studies of infectious diseases have developed a PK model and then used simulations to define a dose recommendation based on a pre-defined PD target, which may have been defined in adults or in vitro. For immunological studies much of the modelling has focused on either PK or PD, and since multiple drugs are usually used, delineating the relative contributions of each is challenging. The use of dynamical modelling of in vitro antibacterial studies, and paediatric HIV mechanistic PD models linked with the PK of all drugs, are emerging methods that should enhance PKPD-based recommendations in the future. PMID:24440429

West Nile Virus Drug Discovery

PubMed Central

Lim, Siew Pheng; Shi, Pei-Yong

2013-01-01

The outbreak of West Nile virus (WNV) in 1999 in the USA, and its continued spread throughout the Americas, parts of Europe, the Middle East and Africa, underscored the need for WNV antiviral development. Here, we review the current status of WNV drug discovery. A number of approaches have been used to search for inhibitors of WNV, including viral infection-based screening, enzyme-based screening, structure-based virtual screening, structure-based rationale design, and antibody-based therapy. These efforts have yielded inhibitors of viral or cellular factors that are critical for viral replication. For small molecule inhibitors, no promising preclinical candidate has been developed; most of the inhibitors could not even be advanced to the stage of hit-to-lead optimization due to their poor drug-like properties. However, several inhibitors developed for related members of the family Flaviviridae, such as dengue virus and hepatitis C virus, exhibited cross-inhibition of WNV, suggesting the possibility to re-purpose these antivirals for WNV treatment. Most promisingly, therapeutic antibodies have shown excellent efficacy in mouse model; one of such antibodies has been advanced into clinical trial. The knowledge accumulated during the past fifteen years has provided better rationale for the ongoing WNV and other flavivirus antiviral development. PMID:24300672

West Nile virus drug discovery.

PubMed

Lim, Siew Pheng; Shi, Pei-Yong

2013-12-03

The outbreak of West Nile virus (WNV) in 1999 in the USA, and its continued spread throughout the Americas, parts of Europe, the Middle East and Africa, underscored the need for WNV antiviral development. Here, we review the current status of WNV drug discovery. A number of approaches have been used to search for inhibitors of WNV, including viral infection-based screening, enzyme-based screening, structure-based virtual screening, structure-based rationale design, and antibody-based therapy. These efforts have yielded inhibitors of viral or cellular factors that are critical for viral replication. For small molecule inhibitors, no promising preclinical candidate has been developed; most of the inhibitors could not even be advanced to the stage of hit-to-lead optimization due to their poor drug-like properties. However, several inhibitors developed for related members of the family Flaviviridae, such as dengue virus and hepatitis C virus, exhibited cross-inhibition of WNV, suggesting the possibility to re-purpose these antivirals for WNV treatment. Most promisingly, therapeutic antibodies have shown excellent efficacy in mouse model; one of such antibodies has been advanced into clinical trial. The knowledge accumulated during the past fifteen years has provided better rationale for the ongoing WNV and other flavivirus antiviral development.

Identifying Medication Targets for Psychostimulant Addiction: Unraveling the Dopamine D3 Receptor Hypothesis

PubMed Central

2016-01-01

The dopamine D3 receptor (D3R) is a target for developing medications to treat substance use disorders. D3R-selective compounds with high affinity and varying efficacies have been discovered, providing critical research tools for cell-based studies that have been translated to in vivo models of drug abuse. D3R antagonists and partial agonists have shown especially promising results in rodent models of relapse-like behavior, including stress-, drug-, and cue-induced reinstatement of drug seeking. However, to date, translation to human studies has been limited. Herein, we present an overview and illustrate some of the pitfalls and challenges of developing novel D3R-selective compounds toward clinical utility, especially for treatment of cocaine abuse. Future research and development of D3R-selective antagonists and partial agonists for substance abuse remains critically important but will also require further evaluation and development of translational animal models to determine the best time in the addiction cycle to target D3Rs for optimal therapeutic efficacy. PMID:25826710

Stem cells in pharmaceutical biotechnology.

PubMed

Zuba-Surma, Ewa K; Józkowicz, Alicja; Dulak, Józef

2011-11-01

Multiple populations of stem cells have been indicated to potentially participate in regeneration of injured organs. Especially, embryonic stem cells (ESC) and recently inducible pluripotent stem cells (iPS) receive a marked attention from scientists and clinicians for regenerative medicine because of their high proliferative and differentiation capacities. Despite that ESC and iPS cells are expected to give rise into multiple regenerative applications when their side effects are overcame during appropriate preparation procedures, in fact their most recent application of human ESC may, however, reside in their use as a tool in drug development and disease modeling. This review focuses on the applications of stem cells in pharmaceutical biotechnology. We discuss possible relevance of pluripotent cell stem populations in developing physiological models for any human tissue cell type useful for pharmacological, metabolic and toxicity evaluation necessary in the earliest steps of drug development. The present models applied for preclinical drug testing consist of primary cells or immortalized cell lines that show limitations in terms of accessibility or relevance to their in vivo counterparts. The availability of renewable human cells with functional similarities to their in vivo counterparts is the first landmark for a new generation of cell-based assays. We discuss the approaches for using stem cells as valuable physiological targets of drug activity which may increase the strength of target validation and efficacy potentially resulting in introducing new safer remedies into clinical trials and the marketplace. Moreover, we discuss the possible applications of stem cells for elucidating mechanisms of disease pathogenesis. The knowledge about the mechanisms governing the development and progression of multitude disorders which would come from the cellular models established based on stem cells, may give rise to new therapeutical strategies for such diseases. All together, the applications of various cell types derived from patient specific pluripotent stem cells may lead to targeted drug and cellular therapies for certain individuals.

Computational Methods in Drug Discovery

PubMed Central

Sliwoski, Gregory; Kothiwale, Sandeepkumar; Meiler, Jens

2014-01-01

Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature. PMID:24381236

Repaglinide-loaded solid lipid nanoparticles: effect of using different surfactants/stabilizers on physicochemical properties of nanoparticles.

PubMed

Ebrahimi, Hossein Ali; Javadzadeh, Yousef; Hamidi, Mehrdad; Jalali, Mohammad Barzegar

2015-09-21

Repaglinide is an efficient anti-diabetic drug which is prescribed widely as multi-dosage oral daily regimens. Due to the low compliance inherent to each multi-dosage regimen, development of prolonged-release formulations could enhance the overall drug efficacy in patient populations. Repaglinide-loaded solid lipid nanoparticles (SLNs) were developed and characterized in vitro. Various surfactants were used in this study during the nanocarrier preparation procedure and their corresponding effects on some physicochemical properties of SLNs such as size, zeta potential; drug loading parameters and drug release profiles was investigated. Stearic acid and glyceryl mono stearate (GMS) were used as lipid phase and phosphatidylcholin, Tween80, Pluronic F127, poly vinyl alcohol (PVA) and polyvinyl pyrrolidone (PVP) were used as surfactant/stabilizer. The results showed some variations between formulations; where the Tween80-based SLNs showed smallest size, the phosphatidylcholin-based SLNs indicated most prolonged drug release time and the highest loading capacity. SEM images of these formulations showed morphological variations and also confirmed the nanoscale size of these particles. The FTIR and DSC results demonstrated no interaction between drug and excipients. The invitro release profiles of different formulations were studied and observed slow release of drug from all formulations. However significant differences were found among them in terms of their initial burst release as well as the whole drug release profile. From fitting these data to various statistical models, the Peppas model was proposed as the best model to describe the statistical indices and, therefore, mechanism of drug release. The results of this study confirmed the effect of surfactant type on SLNs physicochemical properties such as morphological features, loading parameters, particle sizes and drug release kinetic. With respect to the outcome data, the mixture of phosphatidylcholin/Pluronic F127 was selected as the best surfactant/stabilizer to coat the lipid core comprising stearic acid and GMS.

Induced Pluripotent Stem Cells for Disease Modeling and Evaluation of Therapeutics for Niemann-Pick Disease Type A.

PubMed

Long, Yan; Xu, Miao; Li, Rong; Dai, Sheng; Beers, Jeanette; Chen, Guokai; Soheilian, Ferri; Baxa, Ulrich; Wang, Mengqiao; Marugan, Juan J; Muro, Silvia; Li, Zhiyuan; Brady, Roscoe; Zheng, Wei

2016-12-01

: Niemann-Pick disease type A (NPA) is a lysosomal storage disease caused by mutations in the SMPD1 gene that encodes acid sphingomyelinase (ASM). Deficiency in ASM function results in lysosomal accumulation of sphingomyelin and neurodegeneration. Currently, there is no effective treatment for NPA. To accelerate drug discovery for treatment of NPA, we generated induced pluripotent stem cells from two patient dermal fibroblast lines and differentiated them into neural stem cells. The NPA neural stem cells exhibit a disease phenotype of lysosomal sphingomyelin accumulation and enlarged lysosomes. By using this disease model, we also evaluated three compounds that reportedly reduced lysosomal lipid accumulation in Niemann-Pick disease type C as well as enzyme replacement therapy with ASM. We found that α-tocopherol, δ-tocopherol, hydroxypropyl-β-cyclodextrin, and ASM reduced sphingomyelin accumulation and enlarged lysosomes in NPA neural stem cells. Therefore, the NPA neural stem cells possess the characteristic NPA disease phenotype that can be ameliorated by tocopherols, cyclodextrin, and ASM. Our results demonstrate the efficacies of cyclodextrin and tocopherols in the NPA cell-based model. Our data also indicate that the NPA neural stem cells can be used as a new cell-based disease model for further study of disease pathophysiology and for high-throughput screening to identify new lead compounds for drug development. Currently, there is no effective treatment for Niemann-Pick disease type A (NPA). To accelerate drug discovery for treatment of NPA, NPA-induced pluripotent stem cells were generated from patient dermal fibroblasts and differentiated into neural stem cells. By using the differentiated NPA neuronal cells as a cell-based disease model system, α-tocopherol, δ-tocopherol, and hydroxypropyl-β-cyclodextrin significantly reduced sphingomyelin accumulation in these NPA neuronal cells. Therefore, this cell-based NPA model can be used for further study of disease pathophysiology and for high-throughput screening of compound libraries to identify lead compounds for drug development. ©AlphaMed Press.

Generic Raman-based calibration models enabling real-time monitoring of cell culture bioreactors.

PubMed

Mehdizadeh, Hamidreza; Lauri, David; Karry, Krizia M; Moshgbar, Mojgan; Procopio-Melino, Renee; Drapeau, Denis

2015-01-01

Raman-based multivariate calibration models have been developed for real-time in situ monitoring of multiple process parameters within cell culture bioreactors. Developed models are generic, in the sense that they are applicable to various products, media, and cell lines based on Chinese Hamster Ovarian (CHO) host cells, and are scalable to large pilot and manufacturing scales. Several batches using different CHO-based cell lines and corresponding proprietary media and process conditions have been used to generate calibration datasets, and models have been validated using independent datasets from separate batch runs. All models have been validated to be generic and capable of predicting process parameters with acceptable accuracy. The developed models allow monitoring multiple key bioprocess metabolic variables, and hence can be utilized as an important enabling tool for Quality by Design approaches which are strongly supported by the U.S. Food and Drug Administration. © 2015 American Institute of Chemical Engineers.

Pharmacodynamic Modeling of Cell Cycle Effects for Gemcitabine and Trabectedin Combinations in Pancreatic Cancer Cells

PubMed Central

Miao, Xin; Koch, Gilbert; Ait-Oudhia, Sihem; Straubinger, Robert M.; Jusko, William J.

2016-01-01

Combinations of gemcitabine and trabectedin exert modest synergistic cytotoxic effects on two pancreatic cancer cell lines. Here, systems pharmacodynamic (PD) models that integrate cellular response data and extend a prototype model framework were developed to characterize dynamic changes in cell cycle phases of cancer cell subpopulations in response to gemcitabine and trabectedin as single agents and in combination. Extensive experimental data were obtained for two pancreatic cancer cell lines (MiaPaCa-2 and BxPC-3), including cell proliferation rates over 0–120 h of drug exposure, and the fraction of cells in different cell cycle phases or apoptosis. Cell cycle analysis demonstrated that gemcitabine induced cell cycle arrest in S phase, and trabectedin induced transient cell cycle arrest in S phase that progressed to G2/M phase. Over time, cells in the control group accumulated in G0/G1 phase. Systems cell cycle models were developed based on observed mechanisms and were used to characterize both cell proliferation and cell numbers in the sub G1, G0/G1, S, and G2/M phases in the control and drug-treated groups. The proposed mathematical models captured well both single and joint effects of gemcitabine and trabectedin. Interaction parameters were applied to quantify unexplainable drug-drug interaction effects on cell cycle arrest in S phase and in inducing apoptosis. The developed models were able to identify and quantify the different underlying interactions between gemcitabine and trabectedin, and captured well our large datasets in the dimensions of time, drug concentrations, and cellular subpopulations. PMID:27895579

A Decade of Experience in Developing Preclinical Models of Advanced- or Early-Stage Spontaneous Metastasis to Study Antiangiogenic Drugs, Metronomic Chemotherapy, and the Tumor Microenvironment.

PubMed

Kerbel, Robert S

2015-01-01

The clinical circumstance of treating spontaneous metastatic disease, after resection of primary tumors, whether advanced/overt or microscopic in nature, is seldom modeled in mice and may be a major factor in explaining the frequent discordance between preclinical and clinical therapeutic outcomes where the trend is "overprediction" of positive results in preclinical mouse model studies. To evaluate this hypothesis, a research program was initiated a decade ago to develop multiple models of metastasis in mice, using variants of human tumor cell lines selected in vivo for enhanced spontaneous metastatic aggressiveness after surgical resection of established orthotopic primary tumors. These models have included breast, renal, and colorectal carcinomas; ovarian cancer (but without prior surgery); and malignant melanoma. They have been used primarily for experimental therapeutic investigations involving various antiangiogenic drugs alone or with chemotherapy, especially "metronomic" low-dose chemotherapy. The various translational studies undertaken have revealed a number of clinically relevant findings. These include the following: (i) the potential of metronomic chemotherapy, especially when combined with a vascular endothelial growth factor pathway targeting drug to successfully treat advanced metastatic disease; (ii) the development of relapsed spontaneous brain metastases in mice with melanoma or breast cancer whose systemic metastatic disease is successfully controlled for a period with a given therapy; (iii) foreshadowing the failure of adjuvant antiangiogenic drug-based phase III trials; (iv) recapitulating the failure of oral antiangiogenic tyrosine kinase inhibitors plus standard chemotherapy in contrast to the modest successes of antiangiogenic antibodies plus chemotherapy in metastatic breast cancer; and (v) revealing "vessel co-option" and absence of angiogenesis as a determinant of intrinsic resistance or minimal responsiveness to antiangiogenic therapy in lung metastases. Developing similar models of metastatic disease but involving mouse tumors grown in syngeneic immunocompetent mice may also prove useful for future translational studies of immune therapy-based treatments.

Analysis of pharmacology data and the prediction of adverse drug reactions and off-target effects from chemical structure.

PubMed

Bender, Andreas; Scheiber, Josef; Glick, Meir; Davies, John W; Azzaoui, Kamal; Hamon, Jacques; Urban, Laszlo; Whitebread, Steven; Jenkins, Jeremy L

2007-06-01

Preclinical Safety Pharmacology (PSP) attempts to anticipate adverse drug reactions (ADRs) during early phases of drug discovery by testing compounds in simple, in vitro binding assays (that is, preclinical profiling). The selection of PSP targets is based largely on circumstantial evidence of their contribution to known clinical ADRs, inferred from findings in clinical trials, animal experiments, and molecular studies going back more than forty years. In this work we explore PSP chemical space and its relevance for the prediction of adverse drug reactions. Firstly, in silico (computational) Bayesian models for 70 PSP-related targets were built, which are able to detect 93% of the ligands binding at IC(50) < or = 10 microM at an overall correct classification rate of about 94%. Secondly, employing the World Drug Index (WDI), a model for adverse drug reactions was built directly based on normalized side-effect annotations in the WDI, which does not require any underlying functional knowledge. This is, to our knowledge, the first attempt to predict adverse drug reactions across hundreds of categories from chemical structure alone. On average 90% of the adverse drug reactions observed with known, clinically used compounds were detected, an overall correct classification rate of 92%. Drugs withdrawn from the market (Rapacuronium, Suprofen) were tested in the model and their predicted ADRs align well with known ADRs. The analysis was repeated for acetylsalicylic acid and Benperidol which are still on the market. Importantly, features of the models are interpretable and back-projectable to chemical structure, raising the possibility of rationally engineering out adverse effects. By combining PSP and ADR models new hypotheses linking targets and adverse effects can be proposed and examples for the opioid mu and the muscarinic M2 receptors, as well as for cyclooxygenase-1 are presented. It is hoped that the generation of predictive models for adverse drug reactions is able to help support early SAR to accelerate drug discovery and decrease late stage attrition in drug discovery projects. In addition, models such as the ones presented here can be used for compound profiling in all development stages.

Multiple response optimization of processing and formulation parameters of Eudragit RL/RS-based matrix tablets for sustained delivery of diclofenac.

PubMed

Elzayat, Ehab M; Abdel-Rahman, Ali A; Ahmed, Sayed M; Alanazi, Fars K; Habib, Walid A; Sakr, Adel

2017-11-01

Multiple response optimization is an efficient technique to develop sustained release formulation while decreasing the number of experiments based on trial and error approach. Diclofenac matrix tablets were optimized to achieve a release profile conforming to USP monograph, matching Voltaren ® SR and withstand formulation variables. The percent of drug released at predetermined multiple time points were the response variables in the design. Statistical models were obtained with relative contour diagrams being overlaid to predict process and formulation parameters expected to produce the target release profile. Tablets were prepared by wet granulation using mixture of equivalent quantities of Eudragit RL/RS at overall polymer concentration of 10-30%w/w and compressed at 5-15KN. Drug release from the optimized formulation E4 (15%w/w, 15KN) was similar to Voltaren, conformed to USP monograph and found to be stable. Substituting lactose with mannitol, reversing the ratio between lactose and microcrystalline cellulose or increasing drug load showed no significant difference in drug release. Using dextromethorphan hydrobromide as a model soluble drug showed burst release due to higher solubility and formation of micro cavities. A numerical optimization technique was employed to develop a stable consistent promising formulation for sustained delivery of diclofenac.

Predicting Rat and Human Pregnane X Receptor Activators Using Bayesian Classification Models.

PubMed

AbdulHameed, Mohamed Diwan M; Ippolito, Danielle L; Wallqvist, Anders

2016-10-17

The pregnane X receptor (PXR) is a ligand-activated transcription factor that acts as a master regulator of metabolizing enzymes and transporters. To avoid adverse drug-drug interactions and diseases such as steatosis and cancers associated with PXR activation, identifying drugs and chemicals that activate PXR is of crucial importance. In this work, we developed ligand-based predictive computational models for both rat and human PXR activation, which allowed us to identify potentially harmful chemicals and evaluate species-specific effects of a given compound. We utilized a large publicly available data set of nearly 2000 compounds screened in cell-based reporter gene assays to develop Bayesian quantitative structure-activity relationship models using physicochemical properties and structural descriptors. Our analysis showed that PXR activators tend to be hydrophobic and significantly different from nonactivators in terms of their physicochemical properties such as molecular weight, logP, number of rings, and solubility. Our Bayesian models, evaluated by using 5-fold cross-validation, displayed a sensitivity of 75% (76%), specificity of 76% (75%), and accuracy of 89% (89%) for human (rat) PXR activation. We identified structural features shared by rat and human PXR activators as well as those unique to each species. We compared rat in vitro PXR activation data to in vivo data by using DrugMatrix, a large toxicogenomics database with gene expression data obtained from rats after exposure to diverse chemicals. Although in vivo gene expression data pointed to cross-talk between nuclear receptor activators that is captured only by in vivo assays, overall we found broad agreement between in vitro and in vivo PXR activation. Thus, the models developed here serve primarily as efficient initial high-throughput in silico screens of in vitro activity.

Boosting drug named entity recognition using an aggregate classifier.

PubMed

Korkontzelos, Ioannis; Piliouras, Dimitrios; Dowsey, Andrew W; Ananiadou, Sophia

2015-10-01

Drug named entity recognition (NER) is a critical step for complex biomedical NLP tasks such as the extraction of pharmacogenomic, pharmacodynamic and pharmacokinetic parameters. Large quantities of high quality training data are almost always a prerequisite for employing supervised machine-learning techniques to achieve high classification performance. However, the human labour needed to produce and maintain such resources is a significant limitation. In this study, we improve the performance of drug NER without relying exclusively on manual annotations. We perform drug NER using either a small gold-standard corpus (120 abstracts) or no corpus at all. In our approach, we develop a voting system to combine a number of heterogeneous models, based on dictionary knowledge, gold-standard corpora and silver annotations, to enhance performance. To improve recall, we employed genetic programming to evolve 11 regular-expression patterns that capture common drug suffixes and used them as an extra means for recognition. Our approach uses a dictionary of drug names, i.e. DrugBank, a small manually annotated corpus, i.e. the pharmacokinetic corpus, and a part of the UKPMC database, as raw biomedical text. Gold-standard and silver annotated data are used to train maximum entropy and multinomial logistic regression classifiers. Aggregating drug NER methods, based on gold-standard annotations, dictionary knowledge and patterns, improved the performance on models trained on gold-standard annotations, only, achieving a maximum F-score of 95%. In addition, combining models trained on silver annotations, dictionary knowledge and patterns are shown to achieve comparable performance to models trained exclusively on gold-standard data. The main reason appears to be the morphological similarities shared among drug names. We conclude that gold-standard data are not a hard requirement for drug NER. Combining heterogeneous models build on dictionary knowledge can achieve similar or comparable classification performance with that of the best performing model trained on gold-standard annotations. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

CNS Anticancer Drug Discovery and Development: 2016 conference insights

PubMed Central

Levin, Victor A; Abrey, Lauren E; Heffron, Timothy P; Tonge, Peter J; Dar, Arvin C; Weiss, William A; Gallo, James M

2017-01-01

CNS Anticancer Drug Discovery and Development, 16-17 November 2016, Scottsdale, AZ, USA The 2016 second CNS Anticancer Drug Discovery and Development Conference addressed diverse viewpoints about why new drug discovery/development focused on CNS cancers has been sorely lacking. Despite more than 70,000 individuals in the USA being diagnosed with a primary brain malignancy and 151,669–286,486 suffering from metastatic CNS cancer, in 1999, temozolomide was the last drug approved by the US FDA as an anticancer agent for high-grade gliomas. Among the topics discussed were economic factors and pharmaceutical risk assessments, regulatory constraints and perceptions and the need for improved imaging surrogates of drug activity. Included were modeling tumor growth and drug effects in a medical environment in which direct tumor sampling for biological effects can be problematic, potential new drugs under investigation and targets for drug discovery and development. The long trajectory and diverse impediments to novel drug discovery, and expectation that more than one drug will be needed to adequately inhibit critical intracellular tumor pathways were viewed as major disincentives for most pharmaceutical/biotechnology companies. While there were a few unanimities, one consensus is the need for continued and focused discussion among academic and industry scientists and clinicians to address tumor targets, new drug chemistry, and more time- and cost-efficient clinical trials based on surrogate end points. PMID:28718326

Anticancer drug sensitivity prediction in cell lines from baseline gene expression through recursive feature selection.

PubMed

Dong, Zuoli; Zhang, Naiqian; Li, Chun; Wang, Haiyun; Fang, Yun; Wang, Jun; Zheng, Xiaoqi

2015-06-30

An enduring challenge in personalized medicine is to select right drug for individual patients. Testing drugs on patients in large clinical trials is one way to assess their efficacy and toxicity, but it is impractical to test hundreds of drugs currently under development. Therefore the preclinical prediction model is highly expected as it enables prediction of drug response to hundreds of cell lines in parallel. Recently, two large-scale pharmacogenomic studies screened multiple anticancer drugs on over 1000 cell lines in an effort to elucidate the response mechanism of anticancer drugs. To this aim, we here used gene expression features and drug sensitivity data in Cancer Cell Line Encyclopedia (CCLE) to build a predictor based on Support Vector Machine (SVM) and a recursive feature selection tool. Robustness of our model was validated by cross-validation and an independent dataset, the Cancer Genome Project (CGP). Our model achieved good cross validation performance for most drugs in the Cancer Cell Line Encyclopedia (≥80% accuracy for 10 drugs, ≥75% accuracy for 19 drugs). Independent tests on eleven common drugs between CCLE and CGP achieved satisfactory performance for three of them, i.e., AZD6244, Erlotinib and PD-0325901, using expression levels of only twelve, six and seven genes, respectively. These results suggest that drug response could be effectively predicted from genomic features. Our model could be applied to predict drug response for some certain drugs and potentially play a complementary role in personalized medicine.

HIVprotI: an integrated web based platform for prediction and design of HIV proteins inhibitors.

PubMed

Qureshi, Abid; Rajput, Akanksha; Kaur, Gazaldeep; Kumar, Manoj

2018-03-09

A number of anti-retroviral drugs are being used for treating Human Immunodeficiency Virus (HIV) infection. Due to emergence of drug resistant strains, there is a constant quest to discover more effective anti-HIV compounds. In this endeavor, computational tools have proven useful in accelerating drug discovery. Although methods were published to design a class of compounds against a specific HIV protein, but an integrated web server for the same is lacking. Therefore, we have developed support vector machine based regression models using experimentally validated data from ChEMBL repository. Quantitative structure activity relationship based features were selected for predicting inhibition activity of a compound against HIV proteins namely protease (PR), reverse transcriptase (RT) and integrase (IN). The models presented a maximum Pearson correlation coefficient of 0.78, 0.76, 0.74 and 0.76, 0.68, 0.72 during tenfold cross-validation on IC 50 and percent inhibition datasets of PR, RT, IN respectively. These models performed equally well on the independent datasets. Chemical space mapping, applicability domain analyses and other statistical tests further support robustness of the predictive models. Currently, we have identified a number of chemical descriptors that are imperative in predicting the compound inhibition potential. HIVprotI platform ( http://bioinfo.imtech.res.in/manojk/hivproti ) would be useful in virtual screening of inhibitors as well as designing of new molecules against the important HIV proteins for therapeutics development.

Modelling and multi-parametric control for delivery of anaesthetic agents.

PubMed

Dua, Pinky; Dua, Vivek; Pistikopoulos, Efstratios N

2010-06-01

This article presents model predictive controllers (MPCs) and multi-parametric model-based controllers for delivery of anaesthetic agents. The MPC can take into account constraints on drug delivery rates and state of the patient but requires solving an optimization problem at regular time intervals. The multi-parametric controller has all the advantages of the MPC and does not require repetitive solution of optimization problem for its implementation. This is achieved by obtaining the optimal drug delivery rates as a set of explicit functions of the state of the patient. The derivation of the controllers relies on using detailed models of the system. A compartmental model for the delivery of three drugs for anaesthesia is developed. The key feature of this model is that mean arterial pressure, cardiac output and unconsciousness of the patient can be simultaneously regulated. This is achieved by using three drugs: dopamine (DP), sodium nitroprusside (SNP) and isoflurane. A number of dynamic simulation experiments are carried out for the validation of the model. The model is then used for the design of model predictive and multi-parametric controllers, and the performance of the controllers is analyzed.

Human neuron-astrocyte 3D co-culture-based assay for evaluation of neuroprotective compounds.

PubMed

Terrasso, Ana Paula; Silva, Ana Carina; Filipe, Augusto; Pedroso, Pedro; Ferreira, Ana Lúcia; Alves, Paula Marques; Brito, Catarina

Central nervous system drug development has registered high attrition rates, mainly due to the lack of efficacy of drug candidates, highlighting the low reliability of the models used in early-stage drug development and the need for new in vitro human cell-based models and assays to accurately identify and validate drug candidates. 3D human cell models can include different tissue cell types and represent the spatiotemporal context of the original tissue (co-cultures), allowing the establishment of biologically-relevant cell-cell and cell-extracellular matrix interactions. Nevertheless, exploitation of these 3D models for neuroprotection assessment has been limited due to the lack of data to validate such 3D co-culture approaches. In this work we combined a 3D human neuron-astrocyte co-culture with a cell viability endpoint for the implementation of a novel in vitro neuroprotection assay, over an oxidative insult. Neuroprotection assay robustness and specificity, and the applicability of Presto Blue, MTT and CytoTox-Glo viability assays to the 3D co-culture were evaluated. Presto Blue was the adequate endpoint as it is non-destructive and is a simpler and reliable assay. Semi-automation of the cell viability endpoint was performed, indicating that the assay setup is amenable to be transferred to automated screening platforms. Finally, the neuroprotection assay setup was applied to a series of 36 test compounds and several candidates with higher neuroprotective effect than the positive control, Idebenone, were identified. The robustness and simplicity of the implemented neuroprotection assay with the cell viability endpoint enables the use of more complex and reliable 3D in vitro cell models to identify and validate drug candidates. Copyright © 2016 Elsevier Inc. All rights reserved.

The Development and Validation of an In Vitro Airway Model to Assess Realistic Airway Deposition and Drug Permeation Behavior of Orally Inhaled Products Across Synthetic Membranes.

PubMed

Huynh, Bao K; Traini, Daniela; Farkas, Dale R; Longest, P Worth; Hindle, Michael; Young, Paul M

2018-04-01

Current in vitro approaches to assess lung deposition, dissolution, and cellular transport behavior of orally inhaled products (OIPs) have relied on compendial impactors to collect drug particles that are likely to deposit in the airway; however, the main drawback with this approach is that these impactors do not reflect the airway and may not necessarily represent drug deposition behavior in vivo. The aim of this article is to describe the development and method validation of a novel hybrid in vitro approach to assess drug deposition and permeation behavior in a more representative airway model. The medium-sized Virginia Commonwealth University (VCU) mouth-throat (MT) and tracheal-bronchial (TB) realistic upper airway models were used in this study as representative models of the upper airway. The TB model was modified to accommodate two Snapwell ® inserts above the first TB airway bifurcation region to collect deposited nebulized ciprofloxacin-hydrochloride (CIP-HCL) droplets as a model drug aerosol system. Permeation characteristics of deposited nebulized CIP-HCL droplets were assessed across different synthetic membranes using the Snapwell test system. The Snapwell test system demonstrated reproducible and discriminatory drug permeation profiles for already dissolved and nebulized CIP-HCL droplets through a range of synthetic permeable membranes under different test conditions. The rate and extent of drug permeation depended on the permeable membrane material used, presence of a stirrer in the receptor compartment, and, most importantly, the drug collection method. This novel hybrid in vitro approach, which incorporates a modified version of a realistic upper airway model, coupled with the Snapwell test system holds great potential to evaluate postairway deposition characteristics, such as drug permeation and particle dissolution behavior of OIPs. Future studies will expand this approach using a cell culture-based setup instead of synthetic membranes, within a humidified chamber, to assess airway epithelia transport behavior in a more representative manner.

Current State of Animal (Mouse) Modeling in Melanoma Research.

PubMed

Kuzu, Omer F; Nguyen, Felix D; Noory, Mohammad A; Sharma, Arati

2015-01-01

Despite the considerable progress in understanding the biology of human cancer and technological advancement in drug discovery, treatment failure remains an inevitable outcome for most cancer patients with advanced diseases, including melanoma. Despite FDA-approved BRAF-targeted therapies for advanced stage melanoma showed a great deal of promise, development of rapid resistance limits the success. Hence, the overall success rate of melanoma therapy still remains to be one of the worst compared to other malignancies. Advancement of next-generation sequencing technology allowed better identification of alterations that trigger melanoma development. As development of successful therapies strongly depends on clinically relevant preclinical models, together with the new findings, more advanced melanoma models have been generated. In this article, besides traditional mouse models of melanoma, we will discuss recent ones, such as patient-derived tumor xenografts, topically inducible BRAF mouse model and RCAS/TVA-based model, and their advantages as well as limitations. Although mouse models of melanoma are often criticized as poor predictors of whether an experimental drug would be an effective treatment, development of new and more relevant models could circumvent this problem in the near future.

Cell cultures in drug discovery and development: The need of reliable in vitro-in vivo extrapolation for pharmacodynamics and pharmacokinetics assessment.

PubMed

Jaroch, Karol; Jaroch, Alina; Bojko, Barbara

2018-01-05

For ethical and cost-related reasons, use of animals for the assessment of mode of action, metabolism and/or toxicity of new drug candidates has been increasingly scrutinized in research and industrial applications. Implementation of the 3 "Rs" 1 ; rule (Reduction, Replacement, Refinement) through development of in silico or in vitro assays has become an essential element of risk assessment. Physiologically based pharmacokinetic (PBPK 2 ) modeling is the most potent in silico tool used for extrapolation of pharmacokinetic parameters to animal or human models from results obtained in vitro. Although, many types of in vitro assays are conducted during drug development, use of cell cultures is the most reliable one. Two-dimensional (2D) cell cultures have been a part of drug development for many years. Nowadays, their role is decreasing in favor of three-dimensional (3D) cell cultures and co-cultures. 3D cultures exhibit protein expression patterns and intercellular junctions that are closer to in vivo states in comparison to classical monolayer cultures. Co-cultures allow for examinations of the mutual influence of different cell lines. However, the complexity and high costs of co-cultures and 3D equipment exclude such methods from high-throughput screening (HTS). 3 In vitro absorption, distribution, metabolism, and excretion assessment, as well as drug-drug interaction (DDI), are usually performed with the use of various cell culture based assays. Progress in in silico and in vitro methods can lead to better in vitro-in vivo extrapolation (IVIVE 4 ) outcomes and have a potential to contribute towards a significant reduction in the number of laboratory animals needed for drug research. As such, concentrated efforts need to be spent towards the development of an HTS in vitro platform with satisfactory IVIVE features. Copyright © 2017 Elsevier B.V. All rights reserved.

Guidelines for conducting pharmaceutical budget impact analyses for submission to public drug plans in Canada.

PubMed

Marshall, Deborah A; Douglas, Patrick R; Drummond, Michael F; Torrance, George W; Macleod, Stuart; Manti, Orlando; Cheruvu, Lokanadha; Corvari, Ron

2008-01-01

Until now, there has been no standardized method of performing and presenting budget impact analyses (BIAs) in Canada. Nevertheless, most drug plan managers have been requiring this economic data to inform drug reimbursement decisions. This paper describes the process used to develop the Canadian BIA Guidelines; describes the Guidelines themselves, including the model template; and compares this guidance with other guidance on BIAs. The intended audience includes those who develop, submit or use BIA models, and drug plan managers who evaluate BIA submissions. The Patented Medicine Prices Review Board (PMPRB) initiated the development of the Canadian BIA Guidelines on behalf of the National Prescription Drug Utilisation Information System (NPDUIS). The findings and recommendations from a needs assessment with respect to BIA submissions were reviewed to inform guideline development. In addition, a literature review was performed to identify existing BIA guidance. The detailed guidance was developed on this basis, and with the input of the NPDUIS Advisory Committee, including drug plan managers from multiple provinces in Canada and a representative from the Canadian Agency for Drugs and Technologies in Health. A Microsoft Excel-based interactive model template was designed to support BIA model development. Input regarding the guidelines and model template was sought from each NPDUIS Advisory Committee member to ensure compatibility with existing drug plan needs. Decisions were made by consensus through multiple rounds of review and discussion. Finally, BIA guidance in Canadian provinces and other countries were compared on the basis of multiple criteria. The BIA guidelines consist of three major sections: Analytic Framework, Inputs and Data Sources, and Reporting Format. The Analytic Framework section contains a discussion of nine general issues surrounding BIAs (model design, analytic perspective, time horizon, target population, costing, scenarios to be compared, the characterisation of uncertainty, discounting, and validation methods). The Inputs and Data Sources section addresses methods for market size estimation, comparator selection, scenario forecasting and drug price estimation. The Reporting Format section describes methods for BIA reporting. The new Canadian BIA Guidelines represent a significant departure from the limited guidance that was previously available from some of the provinces, because they include specific details of the methods of performing BIAs. The Canadian BIA Guidelines differ from the Principles of Good Research Practice for BIAs developed by the International Society for Pharmacoeconomic and Outcomes Research (ISPOR), which provide more general guidance. The Canadian BIA Guidelines and template build upon existing guidance to address the specific requirements of each of the participating drug plans in Canada. Both have been endorsed by the NPDUIS Steering Committee and the PMPRB for the standardization of BIA submissions.

Optimal Therapy Scheduling Based on a Pair of Collaterally Sensitive Drugs.

PubMed

Yoon, Nara; Vander Velde, Robert; Marusyk, Andriy; Scott, Jacob G

2018-05-07

Despite major strides in the treatment of cancer, the development of drug resistance remains a major hurdle. One strategy which has been proposed to address this is the sequential application of drug therapies where resistance to one drug induces sensitivity to another drug, a concept called collateral sensitivity. The optimal timing of drug switching in these situations, however, remains unknown. To study this, we developed a dynamical model of sequential therapy on heterogeneous tumors comprised of resistant and sensitive cells. A pair of drugs (DrugA, DrugB) are utilized and are periodically switched during therapy. Assuming resistant cells to one drug are collaterally sensitive to the opposing drug, we classified cancer cells into two groups, [Formula: see text] and [Formula: see text], each of which is a subpopulation of cells resistant to the indicated drug and concurrently sensitive to the other, and we subsequently explored the resulting population dynamics. Specifically, based on a system of ordinary differential equations for [Formula: see text] and [Formula: see text], we determined that the optimal treatment strategy consists of two stages: an initial stage in which a chosen effective drug is utilized until a specific time point, T, and a second stage in which drugs are switched repeatedly, during which each drug is used for a relative duration (i.e., [Formula: see text]-long for DrugA and [Formula: see text]-long for DrugB with [Formula: see text] and [Formula: see text]). We prove that the optimal duration of the initial stage, in which the first drug is administered, T, is shorter than the period in which it remains effective in decreasing the total population, contrary to current clinical intuition. We further analyzed the relationship between population makeup, [Formula: see text], and the effect of each drug. We determine a critical ratio, which we term [Formula: see text], at which the two drugs are equally effective. As the first stage of the optimal strategy is applied, [Formula: see text] changes monotonically to [Formula: see text] and then, during the second stage, remains at [Formula: see text] thereafter. Beyond our analytic results, we explored an individual-based stochastic model and presented the distribution of extinction times for the classes of solutions found. Taken together, our results suggest opportunities to improve therapy scheduling in clinical oncology.

Machine learning for classifying tuberculosis drug-resistance from DNA sequencing data

PubMed Central

Yang, Yang; Niehaus, Katherine E; Walker, Timothy M; Iqbal, Zamin; Walker, A Sarah; Wilson, Daniel J; Peto, Tim E A; Crook, Derrick W; Smith, E Grace; Zhu, Tingting; Clifton, David A

2018-01-01

Abstract Motivation Correct and rapid determination of Mycobacterium tuberculosis (MTB) resistance against available tuberculosis (TB) drugs is essential for the control and management of TB. Conventional molecular diagnostic test assumes that the presence of any well-studied single nucleotide polymorphisms is sufficient to cause resistance, which yields low sensitivity for resistance classification. Summary Given the availability of DNA sequencing data from MTB, we developed machine learning models for a cohort of 1839 UK bacterial isolates to classify MTB resistance against eight anti-TB drugs (isoniazid, rifampicin, ethambutol, pyrazinamide, ciprofloxacin, moxifloxacin, ofloxacin, streptomycin) and to classify multi-drug resistance. Results Compared to previous rules-based approach, the sensitivities from the best-performing models increased by 2-4% for isoniazid, rifampicin and ethambutol to 97% (P < 0.01), respectively; for ciprofloxacin and multi-drug resistant TB, they increased to 96%. For moxifloxacin and ofloxacin, sensitivities increased by 12 and 15% from 83 and 81% based on existing known resistance alleles to 95% and 96% (P < 0.01), respectively. Particularly, our models improved sensitivities compared to the previous rules-based approach by 15 and 24% to 84 and 87% for pyrazinamide and streptomycin (P < 0.01), respectively. The best-performing models increase the area-under-the-ROC curve by 10% for pyrazinamide and streptomycin (P < 0.01), and 4–8% for other drugs (P < 0.01). Availability and implementation The details of source code are provided at http://www.robots.ox.ac.uk/~davidc/code.php. Contact david.clifton@eng.ox.ac.uk Supplementary information Supplementary data are available at Bioinformatics online. PMID:29240876

Development and application of hybrid structure based method for efficient screening of ligands binding to G-protein coupled receptors

NASA Astrophysics Data System (ADS)

Kortagere, Sandhya; Welsh, William J.

2006-12-01

G-protein coupled receptors (GPCRs) comprise a large superfamily of proteins that are targets for nearly 50% of drugs in clinical use today. In the past, the use of structure-based drug design strategies to develop better drug candidates has been severely hampered due to the absence of the receptor's three-dimensional structure. However, with recent advances in molecular modeling techniques and better computing power, atomic level details of these receptors can be derived from computationally derived molecular models. Using information from these models coupled with experimental evidence, it has become feasible to build receptor pharmacophores. In this study, we demonstrate the use of the Hybrid Structure Based (HSB) method that can be used effectively to screen and identify prospective ligands that bind to GPCRs. Essentially; this multi-step method combines ligand-based methods for building enriched libraries of small molecules and structure-based methods for screening molecules against the GPCR target. The HSB method was validated to identify retinal and its analogues from a random dataset of ˜300,000 molecules. The results from this study showed that the 9 top-ranking molecules are indeed analogues of retinal. The method was also tested to identify analogues of dopamine binding to the dopamine D2 receptor. Six of the ten top-ranking molecules are known analogues of dopamine including a prodrug, while the other thirty-four molecules are currently being tested for their activity against all dopamine receptors. The results from both these test cases have proved that the HSB method provides a realistic solution to bridge the gap between the ever-increasing demand for new drugs to treat psychiatric disorders and the lack of efficient screening methods for GPCRs.

Drugs for solid cancer: the productivity crisis prompts a rethink

PubMed Central

Rösel, Daniel; Brábek, Jan; Veselý, Pavel; Fernandes, Michael

2013-01-01

Despite remarkable progress in cancer-drug discovery, the delivery of novel, safe, and sustainably effective products to the clinic has stalled. Using Src as a model, we examine key steps in drug development. The preclinical evidence on the relationship between Src and solid cancer is in sharp contrast with the modest anticancer effect noted in conventional clinical trials. Here, we consider Src inhibitors as an example of a promising drug class directed to invasion and metastasis and identify roadblocks in translation. We question the assumption that a drug-induced tumor shrinkage in preclinical and clinical studies predicts a successful outcome. Our analysis indicates that the key areas requiring attention are related, and include preclinical models (in vitro and mouse models), meaningful clinical trial end points, and an appreciation of the role of metastasis in morbidity and mortality. Current regulations do not reflect the natural history of the disease, and may be unrelated to the key complications: local invasion, metastasis, and the development of resistance. Alignment of preclinical and clinical studies and regulations based on mechanistic trial end points and platforms may help in overcoming these roadblocks. Viewed kaleidoscopically, most elements necessary and sufficient for a novel translational paradigm are in place. PMID:23836990

In vitro models to estimate drug penetration through the compromised stratum corneum barrier.

PubMed

Engesland, André; Škalko-Basnet, Nataša; Flaten, Gøril Eide

2016-11-01

The phospholipid vesicle-based permeation assay (PVPA) is a recently established in vitro stratum corneum model to estimate the permeability of intact and healthy skin. The aim here was to further evolve this model to mimic the stratum corneum in a compromised skin barrier by reducing the barrier functions in a controlled manner. To mimic compromised skin barriers, PVPA barriers were prepared with explicitly defined reduced barrier function and compared with literature data from both human and animal skin with compromised barrier properties. Caffeine, diclofenac sodium, chloramphenicol and the hydrophilic marker calcein were tested to compare the PVPA models with established models. The established PVPA models mimicking the stratum corneum in healthy skin showed good correlation with biological barriers by ranking drugs similar to those ranked by the pig ear skin model and were comparable to literature data on permeation through healthy human skin. The PVPA models provided reproducible and consistent results with a distinction between the barriers mimicking compromised and healthy skin. The trends in increasing drug permeation with an increasing degree of compromised barriers for the model drugs were similar to the literature data from other in vivo and in vitro models. The PVPA models have the potential to provide permeation predictions when investigating drugs or cosmeceuticals intended for various compromised skin conditions and can thus possibly reduce the time and cost of testing as well as the use of animal testing in the early development of drug candidates, drugs and cosmeceuticals.

A theory of drug tolerance and dependence II: the mathematical model.

PubMed

Peper, Abraham

2004-08-21

The preceding paper presented a model of drug tolerance and dependence. The model assumes the development of tolerance to a repeatedly administered drug to be the result of a regulated adaptive process. The oral detection and analysis of exogenous substances is proposed to be the primary stimulus for the mechanism of drug tolerance. Anticipation and environmental cues are in the model considered secondary stimuli, becoming primary in dependence and addiction or when the drug administration bypasses the natural-oral-route, as is the case when drugs are administered intravenously. The model considers adaptation to the effect of a drug and adaptation to the interval between drug taking autonomous tolerance processes. Simulations with the mathematical model demonstrate the model's behaviour to be consistent with important characteristics of the development of tolerance to repeatedly administered drugs: the gradual decrease in drug effect when tolerance develops, the high sensitivity to small changes in drug dose, the rebound phenomenon and the large reactions following withdrawal in dependence. The present paper discusses the mathematical model in terms of its design. The model is a nonlinear, learning feedback system, fully satisfying control theoretical principles. It accepts any form of the stimulus-the drug intake-and describes how the physiological processes involved affect the distribution of the drug through the body and the stability of the regulation loop. The mathematical model verifies the proposed theory and provides a basis for the implementation of mathematical models of specific physiological processes.

PockDrug: A Model for Predicting Pocket Druggability That Overcomes Pocket Estimation Uncertainties.

PubMed

Borrel, Alexandre; Regad, Leslie; Xhaard, Henri; Petitjean, Michel; Camproux, Anne-Claude

2015-04-27

Predicting protein druggability is a key interest in the target identification phase of drug discovery. Here, we assess the pocket estimation methods' influence on druggability predictions by comparing statistical models constructed from pockets estimated using different pocket estimation methods: a proximity of either 4 or 5.5 Å to a cocrystallized ligand or DoGSite and fpocket estimation methods. We developed PockDrug, a robust pocket druggability model that copes with uncertainties in pocket boundaries. It is based on a linear discriminant analysis from a pool of 52 descriptors combined with a selection of the most stable and efficient models using different pocket estimation methods. PockDrug retains the best combinations of three pocket properties which impact druggability: geometry, hydrophobicity, and aromaticity. It results in an average accuracy of 87.9% ± 4.7% using a test set and exhibits higher accuracy (∼5-10%) than previous studies that used an identical apo set. In conclusion, this study confirms the influence of pocket estimation on pocket druggability prediction and proposes PockDrug as a new model that overcomes pocket estimation variability.

Population Pharmacokinetics of Busulfan in Pediatric and Young Adult Patients Undergoing Hematopoietic Cell Transplant: A Model-Based Dosing Algorithm for Personalized Therapy and Implementation into Routine Clinical Use

PubMed Central

Long-Boyle, Janel; Savic, Rada; Yan, Shirley; Bartelink, Imke; Musick, Lisa; French, Deborah; Law, Jason; Horn, Biljana; Cowan, Morton J.; Dvorak, Christopher C.

2014-01-01

Background Population pharmacokinetic (PK) studies of busulfan in children have shown that individualized model-based algorithms provide improved targeted busulfan therapy when compared to conventional dosing. The adoption of population PK models into routine clinical practice has been hampered by the tendency of pharmacologists to develop complex models too impractical for clinicians to use. The authors aimed to develop a population PK model for busulfan in children that can reliably achieve therapeutic exposure (concentration-at-steady-state, Css) and implement a simple, model-based tool for the initial dosing of busulfan in children undergoing HCT. Patients and Methods Model development was conducted using retrospective data available in 90 pediatric and young adult patients who had undergone HCT with busulfan conditioning. Busulfan drug levels and potential covariates influencing drug exposure were analyzed using the non-linear mixed effects modeling software, NONMEM. The final population PK model was implemented into a clinician-friendly, Microsoft Excel-based tool and used to recommend initial doses of busulfan in a group of 21 pediatric patients prospectively dosed based on the population PK model. Results Modeling of busulfan time-concentration data indicates busulfan CL displays non-linearity in children, decreasing up to approximately 20% between the concentrations of 250–2000 ng/mL. Important patient-specific covariates found to significantly impact busulfan CL were actual body weight and age. The percentage of individuals achieving a therapeutic Css was significantly higher in subjects receiving initial doses based on the population PK model (81%) versus historical controls dosed on conventional guidelines (52%) (p = 0.02). Conclusion When compared to the conventional dosing guidelines, the model-based algorithm demonstrates significant improvement for providing targeted busulfan therapy in children and young adults. PMID:25162216

Clinical Trials in a Dish: A Perspective on the Coming Revolution in Drug Development.

PubMed

Fermini, Bernard; Coyne, Shawn T; Coyne, Kevin P

2018-05-01

The pharmaceutical industry is facing unprecedented challenges as the cost of developing new drugs has reached unsustainable levels, fueled in large parts by a high attrition rate in clinical development. Strategies to bridge studies between preclinical testing and clinical trials are needed to reduce the knowledge gap and allow earlier decisions to be made on the continuation or discontinuation of further development of drugs. The discovery and development of human induced pluripotent stem cells (hiPSCs) have opened up new avenues that support the concept of screening for cell-based safety and toxicity at the level of a population. This approach, termed "Clinical Trials in a Dish" (CTiD), allows testing medical therapies for safety or efficacy on cells collected from a representative sample of human patients, before moving into actual clinical trials. It can be applied to the development of drugs for specific populations, and it allows predicting not only the magnitude of effects but also the incidence of patients in a population who will benefit or be harmed by these drugs. This, in turn, can lead to the selection of safer drugs to move into clinical development, resulting in a reduction in attrition. The current article offers a perspective of this new model for "humanized" preclinical drug development.

SELF-BLM: Prediction of drug-target interactions via self-training SVM.

PubMed

Keum, Jongsoo; Nam, Hojung

2017-01-01

Predicting drug-target interactions is important for the development of novel drugs and the repositioning of drugs. To predict such interactions, there are a number of methods based on drug and target protein similarity. Although these methods, such as the bipartite local model (BLM), show promise, they often categorize unknown interactions as negative interaction. Therefore, these methods are not ideal for finding potential drug-target interactions that have not yet been validated as positive interactions. Thus, here we propose a method that integrates machine learning techniques, such as self-training support vector machine (SVM) and BLM, to develop a self-training bipartite local model (SELF-BLM) that facilitates the identification of potential interactions. The method first categorizes unlabeled interactions and negative interactions among unknown interactions using a clustering method. Then, using the BLM method and self-training SVM, the unlabeled interactions are self-trained and final local classification models are constructed. When applied to four classes of proteins that include enzymes, G-protein coupled receptors (GPCRs), ion channels, and nuclear receptors, SELF-BLM showed the best performance for predicting not only known interactions but also potential interactions in three protein classes compare to other related studies. The implemented software and supporting data are available at https://github.com/GIST-CSBL/SELF-BLM.

Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs.

PubMed

Liu, Mei; Wu, Yonghui; Chen, Yukun; Sun, Jingchun; Zhao, Zhongming; Chen, Xue-wen; Matheny, Michael Edwin; Xu, Hua

2012-06-01

Adverse drug reaction (ADR) is one of the major causes of failure in drug development. Severe ADRs that go undetected until the post-marketing phase of a drug often lead to patient morbidity. Accurate prediction of potential ADRs is required in the entire life cycle of a drug, including early stages of drug design, different phases of clinical trials, and post-marketing surveillance. Many studies have utilized either chemical structures or molecular pathways of the drugs to predict ADRs. Here, the authors propose a machine-learning-based approach for ADR prediction by integrating the phenotypic characteristics of a drug, including indications and other known ADRs, with the drug's chemical structures and biological properties, including protein targets and pathway information. A large-scale study was conducted to predict 1385 known ADRs of 832 approved drugs, and five machine-learning algorithms for this task were compared. This evaluation, based on a fivefold cross-validation, showed that the support vector machine algorithm outperformed the others. Of the three types of information, phenotypic data were the most informative for ADR prediction. When biological and phenotypic features were added to the baseline chemical information, the ADR prediction model achieved significant improvements in area under the curve (from 0.9054 to 0.9524), precision (from 43.37% to 66.17%), and recall (from 49.25% to 63.06%). Most importantly, the proposed model successfully predicted the ADRs associated with withdrawal of rofecoxib and cerivastatin. The results suggest that phenotypic information on drugs is valuable for ADR prediction. Moreover, they demonstrate that different models that combine chemical, biological, or phenotypic information can be built from approved drugs, and they have the potential to detect clinically important ADRs in both preclinical and post-marketing phases.

Computational modeling-based discovery of novel classes of anti-inflammatory drugs that target lanthionine synthetase C-like protein 2.

PubMed

Lu, Pinyi; Hontecillas, Raquel; Horne, William T; Carbo, Adria; Viladomiu, Monica; Pedragosa, Mireia; Bevan, David R; Lewis, Stephanie N; Bassaganya-Riera, Josep

2012-01-01

Lanthionine synthetase component C-like protein 2 (LANCL2) is a member of the eukaryotic lanthionine synthetase component C-Like protein family involved in signal transduction and insulin sensitization. Recently, LANCL2 is a target for the binding and signaling of abscisic acid (ABA), a plant hormone with anti-diabetic and anti-inflammatory effects. The goal of this study was to determine the role of LANCL2 as a potential therapeutic target for developing novel drugs and nutraceuticals against inflammatory diseases. Previously, we performed homology modeling to construct a three-dimensional structure of LANCL2 using the crystal structure of lanthionine synthetase component C-like protein 1 (LANCL1) as a template. Using this model, structure-based virtual screening was performed using compounds from NCI (National Cancer Institute) Diversity Set II, ChemBridge, ZINC natural products, and FDA-approved drugs databases. Several potential ligands were identified using molecular docking. In order to validate the anti-inflammatory efficacy of the top ranked compound (NSC61610) in the NCI Diversity Set II, a series of in vitro and pre-clinical efficacy studies were performed using a mouse model of dextran sodium sulfate (DSS)-induced colitis. Our findings showed that the lead compound, NSC61610, activated peroxisome proliferator-activated receptor gamma in a LANCL2- and adenylate cyclase/cAMP dependent manner in vitro and ameliorated experimental colitis by down-modulating colonic inflammatory gene expression and favoring regulatory T cell responses. LANCL2 is a novel therapeutic target for inflammatory diseases. High-throughput, structure-based virtual screening is an effective computational-based drug design method for discovering anti-inflammatory LANCL2-based drug candidates.

Computational Modeling-Based Discovery of Novel Classes of Anti-Inflammatory Drugs That Target Lanthionine Synthetase C-Like Protein 2

PubMed Central

Lu, Pinyi; Hontecillas, Raquel; Horne, William T.; Carbo, Adria; Viladomiu, Monica; Pedragosa, Mireia; Bevan, David R.; Lewis, Stephanie N.; Bassaganya-Riera, Josep

2012-01-01

Background Lanthionine synthetase component C-like protein 2 (LANCL2) is a member of the eukaryotic lanthionine synthetase component C-Like protein family involved in signal transduction and insulin sensitization. Recently, LANCL2 is a target for the binding and signaling of abscisic acid (ABA), a plant hormone with anti-diabetic and anti-inflammatory effects. Methodology/Principal Findings The goal of this study was to determine the role of LANCL2 as a potential therapeutic target for developing novel drugs and nutraceuticals against inflammatory diseases. Previously, we performed homology modeling to construct a three-dimensional structure of LANCL2 using the crystal structure of lanthionine synthetase component C-like protein 1 (LANCL1) as a template. Using this model, structure-based virtual screening was performed using compounds from NCI (National Cancer Institute) Diversity Set II, ChemBridge, ZINC natural products, and FDA-approved drugs databases. Several potential ligands were identified using molecular docking. In order to validate the anti-inflammatory efficacy of the top ranked compound (NSC61610) in the NCI Diversity Set II, a series of in vitro and pre-clinical efficacy studies were performed using a mouse model of dextran sodium sulfate (DSS)-induced colitis. Our findings showed that the lead compound, NSC61610, activated peroxisome proliferator-activated receptor gamma in a LANCL2- and adenylate cyclase/cAMP dependent manner in vitro and ameliorated experimental colitis by down-modulating colonic inflammatory gene expression and favoring regulatory T cell responses. Conclusions/Significance LANCL2 is a novel therapeutic target for inflammatory diseases. High-throughput, structure-based virtual screening is an effective computational-based drug design method for discovering anti-inflammatory LANCL2-based drug candidates. PMID:22509338

Model-Based Individualized Treatment of Chemotherapeutics: Bayesian Population Modeling and Dose Optimization

PubMed Central

Jayachandran, Devaraj; Laínez-Aguirre, José; Rundell, Ann; Vik, Terry; Hannemann, Robert; Reklaitis, Gintaras; Ramkrishna, Doraiswami

2015-01-01

6-Mercaptopurine (6-MP) is one of the key drugs in the treatment of many pediatric cancers, auto immune diseases and inflammatory bowel disease. 6-MP is a prodrug, converted to an active metabolite 6-thioguanine nucleotide (6-TGN) through enzymatic reaction involving thiopurine methyltransferase (TPMT). Pharmacogenomic variation observed in the TPMT enzyme produces a significant variation in drug response among the patient population. Despite 6-MP’s widespread use and observed variation in treatment response, efforts at quantitative optimization of dose regimens for individual patients are limited. In addition, research efforts devoted on pharmacogenomics to predict clinical responses are proving far from ideal. In this work, we present a Bayesian population modeling approach to develop a pharmacological model for 6-MP metabolism in humans. In the face of scarcity of data in clinical settings, a global sensitivity analysis based model reduction approach is used to minimize the parameter space. For accurate estimation of sensitive parameters, robust optimal experimental design based on D-optimality criteria was exploited. With the patient-specific model, a model predictive control algorithm is used to optimize the dose scheduling with the objective of maintaining the 6-TGN concentration within its therapeutic window. More importantly, for the first time, we show how the incorporation of information from different levels of biological chain-of response (i.e. gene expression-enzyme phenotype-drug phenotype) plays a critical role in determining the uncertainty in predicting therapeutic target. The model and the control approach can be utilized in the clinical setting to individualize 6-MP dosing based on the patient’s ability to metabolize the drug instead of the traditional standard-dose-for-all approach. PMID:26226448

Application of Physiologically Based Pharmacokinetic Modeling in Understanding Bosutinib Drug-Drug Interactions: Importance of Intestinal P-Glycoprotein.

PubMed

Yamazaki, Shinji; Loi, Cho-Ming; Kimoto, Emi; Costales, Chester; Varma, Manthena V

2018-05-08

Bosutinib is an orally available Src/Abl tyrosine kinase inhibitor indicated for the treatment of patients with Ph+ chronic myelogenous leukemia at a clinically recommended dose of 500 mg once daily. Clinical results indicated that increases in bosutinib oral exposures were supra-proportional at the lower doses (50 to 200 mg) and approximately dose-proportional at the higher doses (200 to 600 mg). Bosutinib is a substrate of CYP3A4 and P-glycoprotein and exhibits pH-dependent solubility with moderate intestinal permeability. These findings led us to investigate the factors influencing the underlying pharmacokinetic mechanisms of bosutinib with physiologically-based pharmacokinetic (PBPK) models. Our primary objectives were to: 1) refine the previously developed bosutinib PBPK model based on the latest oral bioavailability data and 2) verify the refined PBPK model with P-glycoprotein kinetics based on the bosutinib drug-drug interaction (DDI) results with ketoconazole and rifampin. Additionally, the verified PBPK model was applied to predict bosutinib DDIs with dual CYP3A/P-glycoprotein inhibitors. The results indicated that 1) the refined PBPK model adequately described the observed plasma concentration-time profiles of bosutinib and 2) the verified PBPK model reasonably predicted the effects of ketoconazole and rifampin on bosutinib exposures by accounting for intestinal P-gp inhibition/induction. These results suggested that bosutinib DDI mechanism could involve not only CYP3A4-mediated metabolism but also P-glycoprotein-mediated efflux on absorption. In summary, P-glycoprotein kinetics could constitute a critical element in the PBPK models to understand the pharmacokinetic mechanism of dual CYP3A/P-glycoprotein substrates such as bosutinib exhibiting nonlinear pharmacokinetics due largely to a saturation of intestinal P-glycoprotein-mediated efflux. The American Society for Pharmacology and Experimental Therapeutics.

Cognitive processing of drug-related stimuli: the role of memory and attention.

PubMed

Weinstein, Aviv; Cox, W Miles

2006-11-01

Recent studies have investigated the role of attentional biases and memory in alcohol and other drugs of dependence and the relationship between the motivation to use alcohol or other drugs and vigilance for relevant stimuli in alcohol and drug dependence. Based on this research, we describe relationships among motivation, memory, and attentional biases in order to enable better understanding of their multiple and interacting roles in the maintenance and development of alcohol and other drug dependence. We argue that memory and attentional processes are critical in the development and maintenance of addiction processes. Furthermore, we assume that attentional bias is not simply a by-product of an addiction disorder but plays a vital role in its development and maintenance, and it serves to enhance actual drug use. Finally, we predict that the motivation to use alcohol or other drugs will increase vigilance for substance-related stimuli, which in turn can lead to actual use. Future research is needed to ll gaps in our knowledge and lead to a more defined and articulated cognitive-behavioural model of drug dependence.

Rational Design of Iron Oxide Nanoparticles as Targeted Nanomedicines for Cancer Therapy

NASA Astrophysics Data System (ADS)

Kievit, Forrest M.

2011-07-01

Nanotechnology provides a flexible platform for the development of effective therapeutic nanomaterials that can interact specifically with a target in a biological system and provoke a desired biological response. Of the nanomaterials studied, superparamagnetic iron oxide nanoparticles (SPIONs) have emerged as one of top candidates for cancer therapy due to their intrinsic superparamagnetism that enables non-invasive magnetic resonance imaging (MRI) and biodegradability favorable for in vivo application. This dissertation is aimed at development of SPION-based nanomedicines to overcome the current limitations in cancer therapy. These limitations include non-specificity of therapy which can harm healthy tissue, the difficulty in delivering nucleic acids for gene therapy, the formation of drug resistance, and the inability to detect and treat micrometastases. First, a SPION-based non-viral gene delivery vehicle was developed through functionalization of the SPION core with a co-polymer designed to provide stable binding of DNA and low toxicity which showed excellent gene delivery in vitro and in vivo. This SPION-based non-viral gene delivery vehicle was then activated with a targeting agent to improve gene delivery throughout a xenograft tumor of brain cancer. It was found that targeting did not promote the accumulation of SPIONs at the tumor site, but rather improved the distribution of SPIONs throughout the tumor so a higher proportion of cells received treatment. Next, the high surface area of SPIONs was utilized for loading large amounts of drug which was shown to overcome the multidrug resistance acquired by many cancer cells. Drug bound to SPIONs showed significantly higher multidrug resistant cell uptake as compared to free drug which translated into improved cell kill. Also, an antibody activated SPION was developed and was shown to be able to target micrometastases in a transgenic animal model of metastatic breast cancer. These SPION-based nanomedicines provide a platform for the future development of therapies that are hoped to overcome the current limitations in cancer therapy. Finally, a three-dimensional in vitro tumor tissue culture model was developed for mimicking the tumor microenvironment in which cultured cells showed higher malignancy than traditional two-dimensional and three-dimensional models. This in vitro model should provided researches with a better tool for testing novel nanomedicines in vitro before moving to the more costly in vivo experiments.

Fractional derivatives in the diffusion process in heterogeneous systems: The case of transdermal patches.

PubMed

Caputo, Michele; Cametti, Cesare

2017-09-01

In this note, we present a simple mathematical model of drug delivery through transdermal patches by introducing a memory formalism in the classical Fick diffusion equation based on the fractional derivative. This approach is developed in the case of a medicated adhesive patch placed on the skin to deliver a time released dose of medication through the skin towards the bloodstream.The main resistance to drug transport across the skin resides in the diffusion through its outermost layer (the stratum corneum). Due to the complicated architecture of this region, a model based on a constant diffusivity in a steady-state condition results in too simplistic assumptions and more refined models are required.The introduction of a memory formalism in the diffusion process, where diffusion parameters depend at a certain time or position on what happens at preceeding times, meets this requirement and allows a significantly better description of the experimental results.The present model may be useful not only for analyzing the rate of skin permeation but also for predicting the drug concentration after transdermal drug delivery depending on the diffusion characteristics of the patch (its thickness and pseudo-diffusion coefficient). Copyright © 2017 Elsevier Inc. All rights reserved.

Molecular Docking for Prediction and Interpretation of Adverse Drug Reactions.

PubMed

Luo, Heng; Fokoue-Nkoutche, Achille; Singh, Nalini; Yang, Lun; Hu, Jianying; Zhang, Ping

2018-05-23

Adverse drug reactions (ADRs) present a major burden for patients and the healthcare industry. Various computational methods have been developed to predict ADRs for drug molecules. However, many of these methods require experimental or surveillance data and cannot be used when only structural information is available. We collected 1,231 small molecule drugs and 600 human proteins and utilized molecular docking to generate binding features among them. We developed machine learning models that use these docking features to make predictions for 1,533 ADRs. These models obtain an overall area under the receiver operating characteristic curve (AUROC) of 0.843 and an overall area under the precision-recall curve (AUPR) of 0.395, outperforming seven structural fingerprint-based prediction models. Using the method, we predicted skin striae for fluticasone propionate, dermatitis acneiform for mometasone, and decreased libido for irinotecan, as demonstrations. Furthermore, we analyzed the top binding proteins associated with some of the ADRs, which can help to understand and/or generate hypotheses for underlying mechanisms of ADRs. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

The role of intracochlear drug delivery devices in the management of inner ear disease.

PubMed

Ayoob, Andrew M; Borenstein, Jeffrey T

2015-03-01

Diseases of the inner ear include those of the auditory and vestibular systems, and frequently result in disabling hearing loss or vertigo. Despite a rapidly expanding pipeline of potential cochlear therapeutics, the inner ear remains a challenging organ for targeted drug delivery, and new technologies are required to deliver these therapies in a safe and efficacious manner. In addition to traditional approaches for direct inner ear drug delivery, novel microfluidics-based systems are under development, promising improved control over pharmacokinetics over longer periods of delivery, ultimately with application towards hair cell regeneration in humans. Advances in the development of intracochlear drug delivery systems are reviewed, including passive systems, active microfluidic technologies and cochlear prosthesis-mediated delivery. This article provides a description of novel delivery systems and their potential future clinical applications in treating inner ear disease. Recent progresses in microfluidics and miniaturization technologies are enabling the development of wearable and ultimately implantable drug delivery microsystems. Progress in this field is being spurred by the convergence of advances in molecular biology, microfluidic flow control systems and models for drug transport in the inner ear. These advances will herald a new generation of devices, with near-term applications in preclinical models, and ultimately with human clinical use for a range of diseases of the inner ear.

Structure and ligand-based design of P-glycoprotein inhibitors: a historical perspective.

PubMed

Palmeira, Andreia; Sousa, Emilia; Vasconcelos, M Helena; Pinto, Madalena; Fernandes, Miguel X

2012-01-01

Computer-assisted drug design (CADD) is a valuable approach for the discovery of new chemical entities in the field of cancer therapy. There is a pressing need to design and develop new, selective, and safe drugs for the treatment of multidrug resistance (MDR) cancer forms, specifically active against P-glycoprotein (P-gp). Recently, a crystallographic structure for mouse P-gp was obtained. However, for decades the design of new P-gp inhibitors employed mainly ligand-based approaches (SAR, QSAR, 3D-QSAR and pharmacophore studies), and structure-based studies used P-gp homology models. However, some of those results are still the pillars used as a starting point for the design of potential P-gp inhibitors. Here, pharmacophore mapping, (Q)SAR, 3D-QSAR and homology modeling, for the discovery of P-gp inhibitors are reviewed. The importance of these methods for understanding mechanisms of drug resistance at a molecular level, and design P-gp inhibitors drug candidates are discussed. The examples mentioned in the review could provide insights into the wide range of possibilities of using CADD methodologies for the discovery of efficient P-gp inhibitors.

Prediction of drug indications based on chemical interactions and chemical similarities.

PubMed

Huang, Guohua; Lu, Yin; Lu, Changhong; Zheng, Mingyue; Cai, Yu-Dong

2015-01-01

Discovering potential indications of novel or approved drugs is a key step in drug development. Previous computational approaches could be categorized into disease-centric and drug-centric based on the starting point of the issues or small-scaled application and large-scale application according to the diversity of the datasets. Here, a classifier has been constructed to predict the indications of a drug based on the assumption that interactive/associated drugs or drugs with similar structures are more likely to target the same diseases using a large drug indication dataset. To examine the classifier, it was conducted on a dataset with 1,573 drugs retrieved from Comprehensive Medicinal Chemistry database for five times, evaluated by 5-fold cross-validation, yielding five 1st order prediction accuracies that were all approximately 51.48%. Meanwhile, the model yielded an accuracy rate of 50.00% for the 1st order prediction by independent test on a dataset with 32 other drugs in which drug repositioning has been confirmed. Interestingly, some clinically repurposed drug indications that were not included in the datasets are successfully identified by our method. These results suggest that our method may become a useful tool to associate novel molecules with new indications or alternative indications with existing drugs.

Prediction of Drug Indications Based on Chemical Interactions and Chemical Similarities

PubMed Central

Huang, Guohua; Lu, Yin; Lu, Changhong; Cai, Yu-Dong

2015-01-01

Discovering potential indications of novel or approved drugs is a key step in drug development. Previous computational approaches could be categorized into disease-centric and drug-centric based on the starting point of the issues or small-scaled application and large-scale application according to the diversity of the datasets. Here, a classifier has been constructed to predict the indications of a drug based on the assumption that interactive/associated drugs or drugs with similar structures are more likely to target the same diseases using a large drug indication dataset. To examine the classifier, it was conducted on a dataset with 1,573 drugs retrieved from Comprehensive Medicinal Chemistry database for five times, evaluated by 5-fold cross-validation, yielding five 1st order prediction accuracies that were all approximately 51.48%. Meanwhile, the model yielded an accuracy rate of 50.00% for the 1st order prediction by independent test on a dataset with 32 other drugs in which drug repositioning has been confirmed. Interestingly, some clinically repurposed drug indications that were not included in the datasets are successfully identified by our method. These results suggest that our method may become a useful tool to associate novel molecules with new indications or alternative indications with existing drugs. PMID:25821813

Combined Recirculatory-compartmental Population Pharmacokinetic Modeling of Arterial and Venous Plasma S(+) and R(-) Ketamine Concentrations.

PubMed

Henthorn, Thomas K; Avram, Michael J; Dahan, Albert; Gustafsson, Lars L; Persson, Jan; Krejcie, Tom C; Olofsen, Erik

2018-05-16

The pharmacokinetics of infused drugs have been modeled without regard for recirculatory or mixing kinetics. We used a unique ketamine dataset with simultaneous arterial and venous blood sampling, during and after separate S(+) and R(-) ketamine infusions, to develop a simplified recirculatory model of arterial and venous plasma drug concentrations. S(+) or R(-) ketamine was infused over 30 min on two occasions to 10 healthy male volunteers. Frequent, simultaneous arterial and forearm venous blood samples were obtained for up to 11 h. A multicompartmental pharmacokinetic model with front-end arterial mixing and venous blood components was developed using nonlinear mixed effects analyses. A three-compartment base pharmacokinetic model with additional arterial mixing and arm venous compartments and with shared S(+)/R(-) distribution kinetics proved superior to standard compartmental modeling approaches. Total pharmacokinetic flow was estimated to be 7.59 ± 0.36 l/min (mean ± standard error of the estimate), and S(+) and R(-) elimination clearances were 1.23 ± 0.04 and 1.06 ± 0.03 l/min, respectively. The arm-tissue link rate constant was 0.18 ± 0.01 min and the fraction of arm blood flow estimated to exchange with arm tissue was 0.04 ± 0.01. Arterial drug concentrations measured during drug infusion have two kinetically distinct components: partially or lung-mixed drug and fully mixed-recirculated drug. Front-end kinetics suggest the partially mixed concentration is proportional to the ratio of infusion rate and total pharmacokinetic flow. This simplified modeling approach could lead to more generalizable models for target-controlled infusions and improved methods for analyzing pharmacokinetic-pharmacodynamic data.

Establishment of a Human Blood-Brain Barrier Co-culture Model Mimicking the Neurovascular Unit Using Induced Pluri- and Multipotent Stem Cells.

PubMed

Appelt-Menzel, Antje; Cubukova, Alevtina; Günther, Katharina; Edenhofer, Frank; Piontek, Jörg; Krause, Gerd; Stüber, Tanja; Walles, Heike; Neuhaus, Winfried; Metzger, Marco

2017-04-11

In vitro models of the human blood-brain barrier (BBB) are highly desirable for drug development. This study aims to analyze a set of ten different BBB culture models based on primary cells, human induced pluripotent stem cells (hiPSCs), and multipotent fetal neural stem cells (fNSCs). We systematically investigated the impact of astrocytes, pericytes, and NSCs on hiPSC-derived BBB endothelial cell function and gene expression. The quadruple culture models, based on these four cell types, achieved BBB characteristics including transendothelial electrical resistance (TEER) up to 2,500 Ω cm 2 and distinct upregulation of typical BBB genes. A complex in vivo-like tight junction (TJ) network was detected by freeze-fracture and transmission electron microscopy. Treatment with claudin-specific TJ modulators caused TEER decrease, confirming the relevant role of claudin subtypes for paracellular tightness. Drug permeability tests with reference substances were performed and confirmed the suitability of the models for drug transport studies. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

Exploring the status of retail private drug shops in Bangladesh and action points for developing an accredited drug shop model: a facility based cross-sectional study.

PubMed

Ahmed, Syed Masud; Naher, Nahitun; Hossain, Tarek; Rawal, Lal Bahadur

2017-01-01

The private retail drug shops market in Bangladesh is largely unregulated and unaccountable, giving rise to irrational use of drugs and high Out-of-pocket expenditure on health. These shops are served by salespersons with meagre or no formal training in dispensing. This facility-based cross-sectional study was undertaken to investigate how the drug shops currently operate vis-a-vis the regulatory regime including dispensing practices of the salespersons, for identifying key action points to develop an accredited model for Bangladesh. About 90 rural and 21 urban retail drug shops from seven divisions were included in the survey. The salespersons were interviewed for relevant information, supplemented by qualitative data on perceptions of the catchment community as well as structured observation of client-provider interactions from a sub-sample. In 76% of the shops, the owner and the salesperson was the same person, and >90% of these were located within 30 min walking distance from a public sector health facility. The licensing process was perceived to be a cumbersome, lengthy, and costly process. Shop visit by drug inspectors were brief, wasn't structured, and not problem solving. Only 9% shops maintained a stock register and 10% a drug sales record. Overall, 65% clients visited drug shops without a prescription. Forty-nine percent of the salespersons had no formal training in dispensing and learned the trade through apprenticeship with fellow drug retailers (42%), relatives (18%), and village doctors (16%) etc. The catchment population of the drug shops mostly did not bother about dispensing training, drug shop licensing and buying drugs without prescription. Observed client-dispenser interactions were found to concentrate mainly on financial transaction, unless, the client pro-actively sought advice regarding the use of the drug. Majority of the drug shops studied are run by salespersons who have informal 'training' through apprenticeship. Visiting drug shops without a prescription, and dispensing without counseling unless pro-actively sought by the client, was very common. The existing process is discouraging for the shop owners to seek license, and the shop inspection visits are irregular, unstructured and punitive. These facts should be considered while designing an accredited model of drug shop for Bangladesh.

Risk of Acute Liver Failure in Patients With Drug-Induced Liver Injury: Evaluation of Hy's Law and a New Prognostic Model.

PubMed

Lo Re, Vincent; Haynes, Kevin; Forde, Kimberly A; Goldberg, David S; Lewis, James D; Carbonari, Dena M; Leidl, Kimberly B F; Reddy, K Rajender; Nezamzadeh, Melissa S; Roy, Jason; Sha, Daohang; Marks, Amy R; De Boer, Jolanda; Schneider, Jennifer L; Strom, Brian L; Corley, Douglas A

2015-12-01

Few studies have evaluated the ability of laboratory tests to predict risk of acute liver failure (ALF) among patients with drug-induced liver injury (DILI). We aimed to develop a highly sensitive model to identify DILI patients at increased risk of ALF. We compared its performance with that of Hy's Law, which predicts severity of DILI based on levels of alanine aminotransferase or aspartate aminotransferase and total bilirubin, and validated the model in a separate sample. We conducted a retrospective cohort study of 15,353 Kaiser Permanente Northern California members diagnosed with DILI from 2004 through 2010, liver aminotransferase levels above the upper limit of normal, and no pre-existing liver disease. Thirty ALF events were confirmed by medical record review. Logistic regression was used to develop prognostic models for ALF based on laboratory results measured at DILI diagnosis. External validation was performed in a sample of 76 patients with DILI at the University of Pennsylvania. Hy's Law identified patients that developed ALF with a high level of specificity (0.92) and negative predictive value (0.99), but low level of sensitivity (0.68) and positive predictive value (0.02). The model we developed, comprising data on platelet count and total bilirubin level, identified patients with ALF with a C statistic of 0.87 (95% confidence interval [CI], 0.76-0.96) and enabled calculation of a risk score (Drug-Induced Liver Toxicity ALF Score). We found a cut-off score that identified patients at high risk patients for ALF with a sensitivity value of 0.91 (95% CI, 0.71-0.99) and a specificity value of 0.76 (95% CI, 0.75-0.77). This cut-off score identified patients at high risk for ALF with a high level of sensitivity (0.89; 95% CI, 0.52-1.00) in the validation analysis. Hy's Law identifies patients with DILI at high risk for ALF with low sensitivity but high specificity. We developed a model (the Drug-Induced Liver Toxicity ALF Score) based on platelet count and total bilirubin level that identifies patients at increased risk for ALF with high sensitivity. Copyright © 2015 AGA Institute. Published by Elsevier Inc. All rights reserved.

In vitro pharmacokinetic/pharmacodynamic models in anti-infective drug development: focus on TB

PubMed Central

Vaddady, Pavan K; Lee, Richard E; Meibohm, Bernd

2011-01-01

For rapid anti-tuberculosis (TB) drug development in vitro pharmacokinetic/pharmacodynamic (PK/PD) models are useful in evaluating the direct interaction between the drug and the bacteria, thereby guiding the selection of candidate compounds and the optimization of their dosing regimens. Utilizing in vivo drug-clearance profiles from animal and/or human studies and simulating them in an in vitro PK/PD model allows the in-depth characterization of antibiotic activity of new and existing antibacterials by generating time–kill data. These data capture the dynamic interplay between mycobacterial growth and changing drug concentration as encountered during prolonged drug therapy. This review focuses on important PK/PD parameters relevant to anti-TB drug development, provides an overview of in vitro PK/PD models used to evaluate the efficacy of agents against mycobacteria and discusses the related mathematical modeling approaches of time–kill data. Overall, it provides an introduction to in vitro PK/PD models and their application as critical tools in evaluating anti-TB drugs. PMID:21359155

Introduction to biological complexity as a missing link in drug discovery.

PubMed

Gintant, Gary A; George, Christopher H

2018-06-06

Despite a burgeoning knowledge of the intricacies and mechanisms responsible for human disease, technological advances in medicinal chemistry, and more efficient assays used for drug screening, it remains difficult to discover novel and effective pharmacologic therapies. Areas covered: By reference to the primary literature and concepts emerging from academic and industrial drug screening landscapes, the authors propose that this disconnect arises from the inability to scale and integrate responses from simpler model systems to outcomes from more complex and human-based biological systems. Expert opinion: Further collaborative efforts combining target-based and phenotypic-based screening along with systems-based pharmacology and informatics will be necessary to harness the technological breakthroughs of today to derive the novel drug candidates of tomorrow. New questions must be asked of enabling technologies-while recognizing inherent limitations-in a way that moves drug development forward. Attempts to integrate mechanistic and observational information acquired across multiple scales frequently expose the gap between our knowledge and our understanding as the level of complexity increases. We hope that the thoughts and actionable items highlighted will help to inform the directed evolution of the drug discovery process.

Using exosomes, naturally-equipped nanocarriers, for drug delivery.

PubMed

Batrakova, Elena V; Kim, Myung Soo

2015-12-10

Exosomes offer distinct advantages that uniquely position them as highly effective drug carriers. Comprised of cellular membranes with multiple adhesive proteins on their surface, exosomes are known to specialize in cell-cell communications and provide an exclusive approach for the delivery of various therapeutic agents to target cells. In addition, exosomes can be amended through their parental cells to express a targeting moiety on their surface, or supplemented with desired biological activity. Development and validation of exosome-based drug delivery systems are the focus of this review. Different techniques of exosome isolation, characterization, drug loading, and applications in experimental disease models and clinic are discussed. Exosome-based drug formulations may be applied to a wide variety of disorders such as cancer, various infectious, cardiovascular, and neurodegenerative disorders. Overall, exosomes combine benefits of both synthetic nanocarriers and cell-mediated drug delivery systems while avoiding their limitations. Published by Elsevier B.V.

Mechanistic Physiologically Based Pharmacokinetic Modeling of the Dissolution and Food Effect of a Biopharmaceutics Classification System IV Compound-The Venetoclax Story.

PubMed

Emami Riedmaier, Arian; Lindley, David J; Hall, Jeffrey A; Castleberry, Steven; Slade, Russell T; Stuart, Patricia; Carr, Robert A; Borchardt, Thomas B; Bow, Daniel A J; Nijsen, Marjoleen

2018-01-01

Venetoclax, a selective B-cell lymphoma-2 inhibitor, is a biopharmaceutics classification system class IV compound. The aim of this study was to develop a physiologically based pharmacokinetic (PBPK) model to mechanistically describe absorption and disposition of an amorphous solid dispersion formulation of venetoclax in humans. A mechanistic PBPK model was developed incorporating measured amorphous solubility, dissolution, metabolism, and plasma protein binding. A middle-out approach was used to define permeability. Model predictions of oral venetoclax pharmacokinetics were verified against clinical studies of fed and fasted healthy volunteers, and clinical drug interaction studies with strong CYP3A inhibitor (ketoconazole) and inducer (rifampicin). Model verification demonstrated accurate prediction of the observed food effect following a low-fat diet. Ratios of predicted versus observed C max and area under the curve of venetoclax were within 0.8- to 1.25-fold of observed ratios for strong CYP3A inhibitor and inducer interactions, indicating that the venetoclax elimination pathway was correctly specified. The verified venetoclax PBPK model is one of the first examples mechanistically capturing absorption, food effect, and exposure of an amorphous solid dispersion formulated compound. This model allows evaluation of untested drug-drug interactions, especially those primarily occurring in the intestine, and paves the way for future modeling of biopharmaceutics classification system IV compounds. Copyright © 2018 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

A Drug-Centric View of Drug Development: How Drugs Spread from Disease to Disease

PubMed Central

Rodriguez-Esteban, Raul

2016-01-01

Drugs are often seen as ancillary to the purpose of fighting diseases. Here an alternative view is proposed in which they occupy a spearheading role. In this view, drugs are technologies with an inherent therapeutic potential. Once created, they can spread from disease to disease independently of the drug creator’s original intentions. Through the analysis of extensive literature and clinical trial records, it can be observed that successful drugs follow a life cycle in which they are studied at an increasing rate, and for the treatment of an increasing number of diseases, leading to clinical advancement. Such initial growth, following a power law on average, has a degree of momentum, but eventually decelerates, leading to stagnation and decay. A network model can describe the propagation of drugs from disease to disease in which diseases communicate with each other by receiving and sending drugs. Within this model, some diseases appear more prone to influence other diseases than be influenced, and vice versa. Diseases can also be organized into a drug-centric disease taxonomy based on the drugs that each adopts. This taxonomy reflects not only biological similarities across diseases, but also the level of differentiation of existing therapies. In sum, this study shows that drugs can become contagious technologies playing a driving role in the fight against disease. By better understanding such dynamics, pharmaceutical developers may be able to manage drug projects more effectively. PMID:27124390

Predicting targets of compounds against neurological diseases using cheminformatic methodology

NASA Astrophysics Data System (ADS)

Nikolic, Katarina; Mavridis, Lazaros; Bautista-Aguilera, Oscar M.; Marco-Contelles, José; Stark, Holger; do Carmo Carreiras, Maria; Rossi, Ilaria; Massarelli, Paola; Agbaba, Danica; Ramsay, Rona R.; Mitchell, John B. O.

2015-02-01

Recently developed multi-targeted ligands are novel drug candidates able to interact with monoamine oxidase A and B; acetylcholinesterase and butyrylcholinesterase; or with histamine N-methyltransferase and histamine H3-receptor (H3R). These proteins are drug targets in the treatment of depression, Alzheimer's disease, obsessive disorders, and Parkinson's disease. A probabilistic method, the Parzen-Rosenblatt window approach, was used to build a "predictor" model using data collected from the ChEMBL database. The model can be used to predict both the primary pharmaceutical target and off-targets of a compound based on its structure. Molecular structures were represented based on the circular fingerprint methodology. The same approach was used to build a "predictor" model from the DrugBank dataset to determine the main pharmacological groups of the compound. The study of off-target interactions is now recognised as crucial to the understanding of both drug action and toxicology. Primary pharmaceutical targets and off-targets for the novel multi-target ligands were examined by use of the developed cheminformatic method. Several multi-target ligands were selected for further study, as compounds with possible additional beneficial pharmacological activities. The cheminformatic targets identifications were in agreement with four 3D-QSAR (H3R/D1R/D2R/5-HT2aR) models and by in vitro assays for serotonin 5-HT1a and 5-HT2a receptor binding of the most promising ligand ( 71/MBA-VEG8).

Computational modeling in melanoma for novel drug discovery.

PubMed

Pennisi, Marzio; Russo, Giulia; Di Salvatore, Valentina; Candido, Saverio; Libra, Massimo; Pappalardo, Francesco

2016-06-01

There is a growing body of evidence highlighting the applications of computational modeling in the field of biomedicine. It has recently been applied to the in silico analysis of cancer dynamics. In the era of precision medicine, this analysis may allow the discovery of new molecular targets useful for the design of novel therapies and for overcoming resistance to anticancer drugs. According to its molecular behavior, melanoma represents an interesting tumor model in which computational modeling can be applied. Melanoma is an aggressive tumor of the skin with a poor prognosis for patients with advanced disease as it is resistant to current therapeutic approaches. This review discusses the basics of computational modeling in melanoma drug discovery and development. Discussion includes the in silico discovery of novel molecular drug targets, the optimization of immunotherapies and personalized medicine trials. Mathematical and computational models are gradually being used to help understand biomedical data produced by high-throughput analysis. The use of advanced computer models allowing the simulation of complex biological processes provides hypotheses and supports experimental design. The research in fighting aggressive cancers, such as melanoma, is making great strides. Computational models represent the key component to complement these efforts. Due to the combinatorial complexity of new drug discovery, a systematic approach based only on experimentation is not possible. Computational and mathematical models are necessary for bringing cancer drug discovery into the era of omics, big data and personalized medicine.

Deep-Learning-Based Drug-Target Interaction Prediction.

PubMed

Wen, Ming; Zhang, Zhimin; Niu, Shaoyu; Sha, Haozhi; Yang, Ruihan; Yun, Yonghuan; Lu, Hongmei

2017-04-07

Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug-target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs. In silico prediction of DTI can also provide insights about the potential drug-drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represent the drugs and the target proteins. In this paper, to accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, we developed a deep-learning-based algorithmic framework named DeepDTIs. It first abstracts representations from raw input descriptors using unsupervised pretraining and then applies known label pairs of interaction to build a classification model. Compared with other methods, it is found that DeepDTIs reaches or outperforms other state-of-the-art methods. The DeepDTIs can be further used to predict whether a new drug targets to some existing targets or whether a new target interacts with some existing drugs.

The paradigm shift to an "open" model in drug development.

PubMed

Au, Regina

2014-12-01

The rising cost of healthcare, the rising cost for drug development, the patent cliff for Big pharma, shorter patent protection, decrease reimbursement, and the recession have made it more difficult for the pharmaceutical and biotechnology industry to develop drugs. Due to the unsustainable amount of time and money in developing a drug that will have a significant return on investment (ROI) it has become hard to sustain a robust pipeline. The industry is transforming its business model to meet these challenges. In essence a paradigm shift is occurring; the old "closed" model is giving way to a new "open" business model.

A physiologically based pharmacokinetic (PBPK) model for predicting the efficacy of drug overdose treatment with liposomes in man.

PubMed

Howell, Brett A; Chauhan, Anuj

2010-08-01

Physiologically based pharmacokinetic (PBPK) models were developed for design and optimization of liposome therapy for treatment of overdoses of tricyclic antidepressants and local anesthetics. In vitro drug-binding data for pegylated, anionic liposomes and published mechanistic equations for partition coefficients were used to develop the models. The models were proven reliable through comparisons to intravenous data. The liposomes were predicted to be highly effective at treating amitriptyline overdoses, with reductions in the area under the concentration versus time curves (AUC) of 64% for the heart and brain. Peak heart and brain drug concentrations were predicted to drop by 20%. Bupivacaine AUC and peak concentration reductions were lower at 15.4% and 17.3%, respectively, for the heart and brain. The predicted pharmacokinetic profiles following liposome administration agreed well with data from clinical studies where protein fragments were administered to patients for overdose treatment. Published data on local cardiac function were used to relate the predicted concentrations in the body to local pharmacodynamic effects in the heart. While the results offer encouragement for future liposome therapies geared toward overdose, it is imperative to point out that animal experiments and phase I clinical trials are the next steps to ensuring the efficacy of the treatment. (c) 2010 Wiley-Liss, Inc. and the American Pharmacists Association

Evaluating Intra-Articular Drug Delivery for the Treatment of Osteoarthritis in a Rat Model

PubMed Central

Allen, Kyle D.; Adams, Samuel B.

2010-01-01

Osteoarthritis (OA) is a degenerative joint disease that can result in joint pain, loss of joint function, and deleterious effects on activity levels and lifestyle habits. Current therapies for OA are largely aimed at symptomatic relief and may have limited effects on the underlying cascade of joint degradation. Local drug delivery strategies may provide for the development of more successful OA treatment outcomes that have potential to reduce local joint inflammation, reduce joint destruction, offer pain relief, and restore patient activity levels and joint function. As increasing interest turns toward intra-articular drug delivery routes, parallel interest has emerged in evaluating drug biodistribution, safety, and efficacy in preclinical models. Rodent models provide major advantages for the development of drug delivery strategies, chiefly because of lower cost, successful replication of human OA-like characteristics, rapid disease development, and small joint volumes that enable use of lower total drug amounts during protocol development. These models, however, also offer the potential to investigate the therapeutic effects of local drug therapy on animal behavior, including pain sensitivity thresholds and locomotion characteristics. Herein, we describe a translational paradigm for the evaluation of an intra-articular drug delivery strategy in a rat OA model. This model, a rat interleukin-1β overexpression model, offers the ability to evaluate anti-interleukin-1 therapeutics for drug biodistribution, activity, and safety as well as the therapeutic relief of disease symptoms. Once the action against interleukin-1 is confirmed in vivo, the newly developed anti-inflammatory drug can be evaluated for evidence of disease-modifying effects in more complex preclinical models. PMID:19943805

3D bioprinting for drug discovery and development in pharmaceutics.

PubMed

Peng, Weijie; Datta, Pallab; Ayan, Bugra; Ozbolat, Veli; Sosnoski, Donna; Ozbolat, Ibrahim T

2017-07-15

Successful launch of a commercial drug requires significant investment of time and financial resources wherein late-stage failures become a reason for catastrophic failures in drug discovery. This calls for infusing constant innovations in technologies, which can give reliable prediction of efficacy, and more importantly, toxicology of the compound early in the drug discovery process before clinical trials. Though computational advances have resulted in more rationale in silico designing, in vitro experimental studies still require gaining industry confidence and improving in vitro-in vivo correlations. In this quest, due to their ability to mimic the spatial and chemical attributes of native tissues, three-dimensional (3D) tissue models have now proven to provide better results for drug screening compared to traditional two-dimensional (2D) models. However, in vitro fabrication of living tissues has remained a bottleneck in realizing the full potential of 3D models. Recent advances in bioprinting provide a valuable tool to fabricate biomimetic constructs, which can be applied in different stages of drug discovery research. This paper presents the first comprehensive review of bioprinting techniques applied for fabrication of 3D tissue models for pharmaceutical studies. A comparative evaluation of different bioprinting modalities is performed to assess the performance and ability of fabricating 3D tissue models for pharmaceutical use as the critical selection of bioprinting modalities indeed plays a crucial role in efficacy and toxicology testing of drugs and accelerates the drug development cycle. In addition, limitations with current tissue models are discussed thoroughly and future prospects of the role of bioprinting in pharmaceutics are provided to the reader. Present advances in tissue biofabrication have crucial role to play in aiding the pharmaceutical development process achieve its objectives. Advent of three-dimensional (3D) models, in particular, is viewed with immense interest by the community due to their ability to mimic in vivo hierarchical tissue architecture and heterogeneous composition. Successful realization of 3D models will not only provide greater in vitro-in vivo correlation compared to the two-dimensional (2D) models, but also eventually replace pre-clinical animal testing, which has their own shortcomings. Amongst all fabrication techniques, bioprinting- comprising all the different modalities (extrusion-, droplet- and laser-based bioprinting), is emerging as the most viable fabrication technique to create the biomimetic tissue constructs. Notwithstanding the interest in bioprinting by the pharmaceutical development researchers, it can be seen that there is a limited availability of comparative literature which can guide the proper selection of bioprinting processes and associated considerations, such as the bioink selection for a particular pharmaceutical study. Thus, this work emphasizes these aspects of bioprinting and presents them in perspective of differential requirements of different pharmaceutical studies like in vitro predictive toxicology, high-throughput screening, drug delivery and tissue-specific efficacies. Moreover, since bioprinting techniques are mostly applied in regenerative medicine and tissue engineering, a comparative analysis of similarities and differences are also expounded to help researchers make informed decisions based on contemporary literature. Copyright © 2017 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Popping the balloon effect: assessing drug law enforcement in terms of displacement, diffusion, and the containment hypothesis.

PubMed

Windle, James; Farrell, Graham

2012-01-01

The "balloon effect" is an often used but rather dismissive representation of the effects of drug law enforcement. It implies a hydraulic displacement model and an impervious illicit drug trade. This paper reviews theoretical and empirical developments in policing and crime prevention. Based on this, 10 types of displacement are identified and four arguments developed: (1) Displacement is less extensive and harmful than often contended; (2) Where displacement may occur it preferably should be exploited as a policy tool to delay the illicit drug industry and deflect it to less harmful locations and forms; (3) The opposite of displacement occurs, termed a diffusion of drug control benefits, wherein law enforcement has benefits that extend further than envisaged, and has 10 types mirroring those of displacement; (4) The net impact of drug law enforcement is often underestimated, and a containment hypothesis may offer a more accurate framework for evaluation.

Integrated nanotechnology platform for tumor-targeted multimodal imaging and therapeutic cargo release

PubMed Central

Hosoya, Hitomi; Dobroff, Andrey S.; Driessen, Wouter H. P.; Cristini, Vittorio; Brinker, Lina M.; Staquicini, Fernanda I.; Cardó-Vila, Marina; D’Angelo, Sara; Ferrara, Fortunato; Proneth, Bettina; Lin, Yu-Shen; Dunphy, Darren R.; Dogra, Prashant; Melancon, Marites P.; Stafford, R. Jason; Miyazono, Kohei; Gelovani, Juri G.; Kataoka, Kazunori; Brinker, C. Jeffrey; Sidman, Richard L.; Arap, Wadih; Pasqualini, Renata

2016-01-01

A major challenge of targeted molecular imaging and drug delivery in cancer is establishing a functional combination of ligand-directed cargo with a triggered release system. Here we develop a hydrogel-based nanotechnology platform that integrates tumor targeting, photon-to-heat conversion, and triggered drug delivery within a single nanostructure to enable multimodal imaging and controlled release of therapeutic cargo. In proof-of-concept experiments, we show a broad range of ligand peptide-based applications with phage particles, heat-sensitive liposomes, or mesoporous silica nanoparticles that self-assemble into a hydrogel for tumor-targeted drug delivery. Because nanoparticles pack densely within the nanocarrier, their surface plasmon resonance shifts to near-infrared, thereby enabling a laser-mediated photothermal mechanism of cargo release. We demonstrate both noninvasive imaging and targeted drug delivery in preclinical mouse models of breast and prostate cancer. Finally, we applied mathematical modeling to predict and confirm tumor targeting and drug delivery. These results are meaningful steps toward the design and initial translation of an enabling nanotechnology platform with potential for broad clinical applications. PMID:26839407

Integrated nanotechnology platform for tumor-targeted multimodal imaging and therapeutic cargo release.

PubMed

Hosoya, Hitomi; Dobroff, Andrey S; Driessen, Wouter H P; Cristini, Vittorio; Brinker, Lina M; Staquicini, Fernanda I; Cardó-Vila, Marina; D'Angelo, Sara; Ferrara, Fortunato; Proneth, Bettina; Lin, Yu-Shen; Dunphy, Darren R; Dogra, Prashant; Melancon, Marites P; Stafford, R Jason; Miyazono, Kohei; Gelovani, Juri G; Kataoka, Kazunori; Brinker, C Jeffrey; Sidman, Richard L; Arap, Wadih; Pasqualini, Renata

2016-02-16

A major challenge of targeted molecular imaging and drug delivery in cancer is establishing a functional combination of ligand-directed cargo with a triggered release system. Here we develop a hydrogel-based nanotechnology platform that integrates tumor targeting, photon-to-heat conversion, and triggered drug delivery within a single nanostructure to enable multimodal imaging and controlled release of therapeutic cargo. In proof-of-concept experiments, we show a broad range of ligand peptide-based applications with phage particles, heat-sensitive liposomes, or mesoporous silica nanoparticles that self-assemble into a hydrogel for tumor-targeted drug delivery. Because nanoparticles pack densely within the nanocarrier, their surface plasmon resonance shifts to near-infrared, thereby enabling a laser-mediated photothermal mechanism of cargo release. We demonstrate both noninvasive imaging and targeted drug delivery in preclinical mouse models of breast and prostate cancer. Finally, we applied mathematical modeling to predict and confirm tumor targeting and drug delivery. These results are meaningful steps toward the design and initial translation of an enabling nanotechnology platform with potential for broad clinical applications.

Integrated nanotechnology platform for tumor-targeted multimodal imaging and therapeutic cargo release

DOE PAGES

Hosoya, Hitomi; Dobroff, Andrey S.; Driessen, Wouter H. P.; ...

2016-02-02

A major challenge of targeted molecular imaging and drug delivery in cancer is establishing a functional combination of ligand-directed cargo with a triggered release system. Here we develop a hydrogel-based nanotechnology platform that integrates tumor targeting, photon-to-heat conversion, and triggered drug delivery within a single nanostructure to enable multimodal imaging and controlled release of therapeutic cargo. In proof-of-concept experiments, we show a broad range of ligand peptide-based applications with phage particles, heat-sensitive liposomes, or mesoporous silica nanoparticles that self-assemble into a hydrogel for tumor-targeted drug delivery. Because nanoparticles pack densely within the nanocarrier, their surface plasmon resonance shifts to near-infrared,more » thereby enabling a laser-mediated photothermal mechanism of cargo release. We demonstrate both noninvasive imaging and targeted drug delivery in preclinical mouse models of breast and prostate cancer. Finally, we applied mathematical modeling to predict and confirm tumor targeting and drug delivery. We conclude that these results are meaningful steps toward the design and initial translation of an enabling nanotechnology platform with potential for broad clinical applications.« less

Integrated nanotechnology platform for tumor-targeted multimodal imaging and therapeutic cargo release

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hosoya, Hitomi; Dobroff, Andrey S.; Driessen, Wouter H. P.

A major challenge of targeted molecular imaging and drug delivery in cancer is establishing a functional combination of ligand-directed cargo with a triggered release system. Here we develop a hydrogel-based nanotechnology platform that integrates tumor targeting, photon-to-heat conversion, and triggered drug delivery within a single nanostructure to enable multimodal imaging and controlled release of therapeutic cargo. In proof-of-concept experiments, we show a broad range of ligand peptide-based applications with phage particles, heat-sensitive liposomes, or mesoporous silica nanoparticles that self-assemble into a hydrogel for tumor-targeted drug delivery. Because nanoparticles pack densely within the nanocarrier, their surface plasmon resonance shifts to near-infrared,more » thereby enabling a laser-mediated photothermal mechanism of cargo release. We demonstrate both noninvasive imaging and targeted drug delivery in preclinical mouse models of breast and prostate cancer. Finally, we applied mathematical modeling to predict and confirm tumor targeting and drug delivery. We conclude that these results are meaningful steps toward the design and initial translation of an enabling nanotechnology platform with potential for broad clinical applications.« less

A cross-species analysis method to analyze animal models' similarity to human's disease state

PubMed Central

2012-01-01

Background Animal models are indispensable tools in studying the cause of human diseases and searching for the treatments. The scientific value of an animal model depends on the accurate mimicry of human diseases. The primary goal of the current study was to develop a cross-species method by using the animal models' expression data to evaluate the similarity to human diseases' and assess drug molecules' efficiency in drug research. Therefore, we hoped to reveal that it is feasible and useful to compare gene expression profiles across species in the studies of pathology, toxicology, drug repositioning, and drug action mechanism. Results We developed a cross-species analysis method to analyze animal models' similarity to human diseases and effectiveness in drug research by utilizing the existing animal gene expression data in the public database, and mined some meaningful information to help drug research, such as potential drug candidates, possible drug repositioning, side effects and analysis in pharmacology. New animal models could be evaluated by our method before they are used in drug discovery. We applied the method to several cases of known animal model expression profiles and obtained some useful information to help drug research. We found that trichostatin A and some other HDACs could have very similar response across cell lines and species at gene expression level. Mouse hypoxia model could accurately mimic the human hypoxia, while mouse diabetes drug model might have some limitation. The transgenic mouse of Alzheimer was a useful model and we deeply analyzed the biological mechanisms of some drugs in this case. In addition, all the cases could provide some ideas for drug discovery and drug repositioning. Conclusions We developed a new cross-species gene expression module comparison method to use animal models' expression data to analyse the effectiveness of animal models in drug research. Moreover, through data integration, our method could be applied for drug research, such as potential drug candidates, possible drug repositioning, side effects and information about pharmacology. PMID:23282076

A cross-species analysis method to analyze animal models' similarity to human's disease state.

PubMed

Yu, Shuhao; Zheng, Lulu; Li, Yun; Li, Chunyan; Ma, Chenchen; Li, Yixue; Li, Xuan; Hao, Pei

2012-01-01

Animal models are indispensable tools in studying the cause of human diseases and searching for the treatments. The scientific value of an animal model depends on the accurate mimicry of human diseases. The primary goal of the current study was to develop a cross-species method by using the animal models' expression data to evaluate the similarity to human diseases' and assess drug molecules' efficiency in drug research. Therefore, we hoped to reveal that it is feasible and useful to compare gene expression profiles across species in the studies of pathology, toxicology, drug repositioning, and drug action mechanism. We developed a cross-species analysis method to analyze animal models' similarity to human diseases and effectiveness in drug research by utilizing the existing animal gene expression data in the public database, and mined some meaningful information to help drug research, such as potential drug candidates, possible drug repositioning, side effects and analysis in pharmacology. New animal models could be evaluated by our method before they are used in drug discovery. We applied the method to several cases of known animal model expression profiles and obtained some useful information to help drug research. We found that trichostatin A and some other HDACs could have very similar response across cell lines and species at gene expression level. Mouse hypoxia model could accurately mimic the human hypoxia, while mouse diabetes drug model might have some limitation. The transgenic mouse of Alzheimer was a useful model and we deeply analyzed the biological mechanisms of some drugs in this case. In addition, all the cases could provide some ideas for drug discovery and drug repositioning. We developed a new cross-species gene expression module comparison method to use animal models' expression data to analyse the effectiveness of animal models in drug research. Moreover, through data integration, our method could be applied for drug research, such as potential drug candidates, possible drug repositioning, side effects and information about pharmacology.

Fragment-based quantitative structure-activity relationship (FB-QSAR) for fragment-based drug design.

PubMed

Du, Qi-Shi; Huang, Ri-Bo; Wei, Yu-Tuo; Pang, Zong-Wen; Du, Li-Qin; Chou, Kuo-Chen

2009-01-30

In cooperation with the fragment-based design a new drug design method, the so-called "fragment-based quantitative structure-activity relationship" (FB-QSAR) is proposed. The essence of the new method is that the molecular framework in a family of drug candidates are divided into several fragments according to their substitutes being investigated. The bioactivities of molecules are correlated with the physicochemical properties of the molecular fragments through two sets of coefficients in the linear free energy equations. One coefficient set is for the physicochemical properties and the other for the weight factors of the molecular fragments. Meanwhile, an iterative double least square (IDLS) technique is developed to solve the two sets of coefficients in a training data set alternately and iteratively. The IDLS technique is a feedback procedure with machine learning ability. The standard Two-dimensional quantitative structure-activity relationship (2D-QSAR) is a special case, in the FB-QSAR, when the whole molecule is treated as one entity. The FB-QSAR approach can remarkably enhance the predictive power and provide more structural insights into rational drug design. As an example, the FB-QSAR is applied to build a predictive model of neuraminidase inhibitors for drug development against H5N1 influenza virus. (c) 2008 Wiley Periodicals, Inc.

Personalizing oncology treatments by predicting drug efficacy, side-effects, and improved therapy: mathematics, statistics, and their integration.

PubMed

Agur, Zvia; Elishmereni, Moran; Kheifetz, Yuri

2014-01-01

Despite its great promise, personalized oncology still faces many hurdles, and it is increasingly clear that targeted drugs and molecular biomarkers alone yield only modest clinical benefit. One reason is the complex relationships between biomarkers and the patient's response to drugs, obscuring the true weight of the biomarkers in the overall patient's response. This complexity can be disentangled by computational models that integrate the effects of personal biomarkers into a simulator of drug-patient dynamic interactions, for predicting the clinical outcomes. Several computational tools have been developed for personalized oncology, notably evidence-based tools for simulating pharmacokinetics, Bayesian-estimated tools for predicting survival, etc. We describe representative statistical and mathematical tools, and discuss their merits, shortcomings and preliminary clinical validation attesting to their potential. Yet, the individualization power of mathematical models alone, or statistical models alone, is limited. More accurate and versatile personalization tools can be constructed by a new application of the statistical/mathematical nonlinear mixed effects modeling (NLMEM) approach, which until recently has been used only in drug development. Using these advanced tools, clinical data from patient populations can be integrated with mechanistic models of disease and physiology, for generating personal mathematical models. Upon a more substantial validation in the clinic, this approach will hopefully be applied in personalized clinical trials, P-trials, hence aiding the establishment of personalized medicine within the main stream of clinical oncology. © 2014 Wiley Periodicals, Inc.

Engineered cell and tissue models of pulmonary fibrosis.

PubMed

Sundarakrishnan, Aswin; Chen, Ying; Black, Lauren D; Aldridge, Bree B; Kaplan, David L

2018-04-01

Pulmonary fibrosis includes several lung disorders characterized by scar formation and Idiopathic Pulmonary Fibrosis (IPF) is a particularly severe form of pulmonary fibrosis of unknown etiology with a mean life expectancy of 3years' post-diagnosis. Treatments for IPF are limited to two FDA approved drugs, pirfenidone and nintedanib. Most lead candidate drugs that are identified in pre-clinical animal studies fail in human clinical trials. Thus, there is a need for advanced humanized in vitro models of the lung to improve candidate treatments prior to moving to human clinical trials. The development of 3D tissue models has created systems capable of emulating human lung structure, function, and cell and matrix interactions. The specific models accomplish these features and preliminary studies conducted using some of these systems have shown potential for in vitro anti-fibrotic drug testing. Further characterization and improvements will enable these tissue models to extend their utility for in vitro drug testing, to help identify signaling pathways and mechanisms for new drug targets, and potentially reduce animal models as standard pre-clinical models of study. In the current review, we contrast different in vitro models based on increasing dimensionality (2D, 2.5D and 3D), with added focus on contemporary 3D pulmonary models of fibrosis. Copyright © 2017. Published by Elsevier B.V.

Microphysiological modeling of the reproductive tract: a fertile endeavor.

PubMed

Eddie, Sharon L; Kim, J Julie; Woodruff, Teresa K; Burdette, Joanna E

2014-09-01

Preclinical toxicity testing in animal models is a cornerstone of the drug development process, yet it is often unable to predict adverse effects and tolerability issues in human subjects. Species-specific responses to investigational drugs have led researchers to utilize human tissues and cells to better estimate human toxicity. Unfortunately, human cell-derived models are imperfect because toxicity is assessed in isolation, removed from the normal physiologic microenvironment. Microphysiological modeling often referred to as 'organ-on-a-chip' or 'human-on-a-chip' places human tissue into a microfluidic system that mimics the complexity of human in vivo physiology, thereby allowing for toxicity testing on several cell types, tissues, and organs within a more biologically relevant environment. Here we describe important concepts when developing a repro-on-a-chip model. The development of female and male reproductive microfluidic systems is critical to sex-based in vitro toxicity and drug testing. This review addresses the biological and physiological aspects of the male and female reproductive systems in vivo and what should be considered when designing a microphysiological human-on-a-chip model. Additionally, interactions between the reproductive tract and other systems are explored, focusing on the impact of factors and hormones produced by the reproductive tract and disease pathophysiology. © 2014 by the Society for Experimental Biology and Medicine.

Payer and Pharmaceutical Manufacturer Considerations for Outcomes-Based Agreements in the United States.

PubMed

Brown, Joshua D; Sheer, Rich; Pasquale, Margaret; Sudharshan, Lavanya; Axelsen, Kirsten; Subedi, Prasun; Wiederkehr, Daniel; Brownfield, Fred; Kamal-Bahl, Sachin

2018-01-01

Considerable interest exists among health care payers and pharmaceutical manufacturers in designing outcomes-based agreements (OBAs) for medications for which evidence on real-world effectiveness is limited at product launch. To build hypothetical OBA models in which both payer and manufacturer can benefit. Models were developed for a hypothetical hypercholesterolemia OBA, in which the OBA was assumed to increase market access for a newly marketed medication. Fixed inputs were drug and outcome event costs from the literature over a 1-year OBA period. Model estimates were developed using a range of inputs for medication effectiveness, medical cost offsets, and the treated population size. Positive or negative feedback to the manufacturer was incorporated on the basis of expectations of drug performance through changes in the reimbursement level. Model simulations demonstrated that parameters had the greatest impact on payer cost and manufacturer reimbursement. Models suggested that changes in the size of the population treated and drug effectiveness had the largest influence on reimbursement and costs. Despite sharing risk for potential product underperformance, manufacturer reimbursement increased relative to having no OBA, if the OBA improved market access for the new product. Although reduction in medical costs did not fully offset the cost of the medication, the payer could still save on net costs per patient relative to having no OBA by tying reimbursement to drug effectiveness. Pharmaceutical manufacturers and health care payers have demonstrated interest in OBAs, and under a certain set of assumptions both may benefit. Copyright © 2018 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Controlled drug-release system based on pH-sensitive chloride-triggerable liposomes.

PubMed

Wehunt, Mark P; Winschel, Christine A; Khan, Ali K; Guo, Tai L; Abdrakhmanova, Galya R; Sidorov, Vladimir

2013-03-01

New pH-sensitive lipids were synthesized and utilized in formulations of liposomes suitable for controlled drug release. These liposomes contain various amounts of NaCl in the internal aqueous compartments. The release of the drug model is triggered by an application of HCl cotransporter and exogenous physiologically relevant NaCl solution. HCl cotransporter allows an uptake of HCl by liposomes to the extent of their being proportional to the transmembrane Cl(-) gradient. Therefore, each set of liposomes undergoes internal acidification, which, ultimately, leads to the hydrolysis of the pH-sensitive lipids and content release at the desired time. The developed system releases the drug model in a stepwise fashion, with the release stages separated by periods of low activity. These liposomes were found to be insensitive to physiological concentrations of human serum albumin and to be nontoxic to cells at concentrations exceeding pharmacological relevance. These results render this new drug-release model potentially suitable for in vivo applications.

Addressing Therapeutic Options for Ebola Virus Infection in Current and Future Outbreaks.

PubMed

Haque, Azizul; Hober, Didier; Blondiaux, Joel

2015-10-01

Ebola virus can cause severe hemorrhagic disease with high fatality rates. Currently, no specific therapeutic agent or vaccine has been approved for treatment and prevention of Ebola virus infection of humans. Although the number of Ebola cases has fallen in the last few weeks, multiple outbreaks of Ebola virus infection and the likelihood of future exposure highlight the need for development and rapid evaluation of pre- and postexposure treatments. Here, we briefly review the existing and future options for anti-Ebola therapy, based on the data coming from rare clinical reports, studies on animals, and results from in vitro models. We also project the mechanistic hypotheses of several potential drugs against Ebola virus, including small-molecule-based drugs, which are under development and being tested in animal models or in vitro using various cell types. Our paper discusses strategies toward identifying and testing anti-Ebola virus properties of known and medically approved drugs, especially those that can limit the pathological inflammatory response in Ebola patients and thereby provide protection from mortality. We underline the importance of developing combinational therapy for better treatment outcomes for Ebola patients. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Addressing Therapeutic Options for Ebola Virus Infection in Current and Future Outbreaks

PubMed Central

Hober, Didier; Blondiaux, Joel

2015-01-01

Ebola virus can cause severe hemorrhagic disease with high fatality rates. Currently, no specific therapeutic agent or vaccine has been approved for treatment and prevention of Ebola virus infection of humans. Although the number of Ebola cases has fallen in the last few weeks, multiple outbreaks of Ebola virus infection and the likelihood of future exposure highlight the need for development and rapid evaluation of pre- and postexposure treatments. Here, we briefly review the existing and future options for anti-Ebola therapy, based on the data coming from rare clinical reports, studies on animals, and results from in vitro models. We also project the mechanistic hypotheses of several potential drugs against Ebola virus, including small-molecule-based drugs, which are under development and being tested in animal models or in vitro using various cell types. Our paper discusses strategies toward identifying and testing anti-Ebola virus properties of known and medically approved drugs, especially those that can limit the pathological inflammatory response in Ebola patients and thereby provide protection from mortality. We underline the importance of developing combinational therapy for better treatment outcomes for Ebola patients. PMID:26248374

Human topoisomerase I poisoning: docking protoberberines into a structure-based binding site model

NASA Astrophysics Data System (ADS)

Kettmann, Viktor; Košt'álová, Daniela; Höltje, Hans-Dieter

2004-12-01

Using the X-ray crystal structure of the human topoisomerase I (top1) - DNA cleavable complex and the Sybyl software package, we have developed a general model for the ternary cleavable complex formed with four protoberberine alkaloids differing in the substitution on the terminal phenyl rings and covering a broad range of the top1-poisoning activities. This model has the drug intercalated with its planar chromophore between the -1 and +1 base pairs flanking the cleavage site, with the nonplanar portion pointing into the minor groove. The ternary complexes were geometry-optimized and relative interaction energies, computed by using the Tripos force field, were found to rank in correct order the biological potency of the compounds; in addition, the model is also consistent with the top1-poisoning inactivity of berberine, a major prototype of the protoberberine alkaloids. The model might serve as a rational basis for elaboration of the most active compound as a lead structure, in order to develop more potent top1 poisons as next generation anti-cancer drugs.

Non-linear mixed effects modeling - from methodology and software development to driving implementation in drug development science.

PubMed

Pillai, Goonaseelan Colin; Mentré, France; Steimer, Jean-Louis

2005-04-01

Few scientific contributions have made significant impact unless there was a champion who had the vision to see the potential for its use in seemingly disparate areas-and who then drove active implementation. In this paper, we present a historical summary of the development of non-linear mixed effects (NLME) modeling up to the more recent extensions of this statistical methodology. The paper places strong emphasis on the pivotal role played by Lewis B. Sheiner (1940-2004), who used this statistical methodology to elucidate solutions to real problems identified in clinical practice and in medical research and on how he drove implementation of the proposed solutions. A succinct overview of the evolution of the NLME modeling methodology is presented as well as ideas on how its expansion helped to provide guidance for a more scientific view of (model-based) drug development that reduces empiricism in favor of critical quantitative thinking and decision making.

DRUG-NEM: Optimizing drug combinations using single-cell perturbation response to account for intratumoral heterogeneity

PubMed Central

Anchang, Benedict; Davis, Kara L.; Fienberg, Harris G.; Bendall, Sean C.; Karacosta, Loukia G.; Tibshirani, Robert; Nolan, Garry P.; Plevritis, Sylvia K.

2018-01-01

An individual malignant tumor is composed of a heterogeneous collection of single cells with distinct molecular and phenotypic features, a phenomenon termed intratumoral heterogeneity. Intratumoral heterogeneity poses challenges for cancer treatment, motivating the need for combination therapies. Single-cell technologies are now available to guide effective drug combinations by accounting for intratumoral heterogeneity through the analysis of the signaling perturbations of an individual tumor sample screened by a drug panel. In particular, Mass Cytometry Time-of-Flight (CyTOF) is a high-throughput single-cell technology that enables the simultaneous measurements of multiple (>40) intracellular and surface markers at the level of single cells for hundreds of thousands of cells in a sample. We developed a computational framework, entitled Drug Nested Effects Models (DRUG-NEM), to analyze CyTOF single-drug perturbation data for the purpose of individualizing drug combinations. DRUG-NEM optimizes drug combinations by choosing the minimum number of drugs that produce the maximal desired intracellular effects based on nested effects modeling. We demonstrate the performance of DRUG-NEM using single-cell drug perturbation data from tumor cell lines and primary leukemia samples. PMID:29654148

Quality control of the paracetamol drug by chemometrics and imaging spectroscopy in the near infrared region

NASA Astrophysics Data System (ADS)

Baptistao, Mariana; Rocha, Werickson Fortunato de Carvalho; Poppi, Ronei Jesus

2011-09-01

In this work, it was used imaging spectroscopy and chemometric tools for the development and analysis of paracetamol and excipients in pharmaceutical formulations. It was also built concentration maps to study the distribution of the drug in the tablets surface. Multivariate models based on PLS regression were developed for paracetamol and excipients concentrations prediction. For the construction of the models it was used 31 samples in the tablet form containing the active principle in a concentration range of 30.0-90.0% (w/w) and errors below to 5% were obtained for validation samples. Finally, the study of the distribution in the drug was performed through the distribution maps of concentration of active principle and excipients. The analysis of maps showed the complementarity between the active principle and excipients in the tablets. The region with a high concentration of a constituent must have, necessarily, absence or low concentration of the other one. Thus, an alternative method for the paracetamol drug quality monitoring is presented.

Innovative Adolescent Chemical Dependency Treatment and Its Outcome: A Model Based on Outward Bound Programming.

ERIC Educational Resources Information Center

McPeake, John D.; And Others

1991-01-01

Describes adolescent chemical dependency treatment model developed at Beech Hill Hospital (New Hampshire) which integrated Twelve Step-oriented alcohol and drug rehabilitation program with experiential education school, Hurricane Island Outward Bound School. Describes Beech Hill Hurricane Island Outward Bound School Adolescent Chemical Dependency…

Lysosomes as mediators of drug resistance in cancer.

PubMed

Zhitomirsky, Benny; Assaraf, Yehuda G

2016-01-01

Drug resistance remains a leading cause of chemotherapeutic treatment failure and cancer-related mortality. While some mechanisms of anticancer drug resistance have been well characterized, multiple mechanisms remain elusive. In this respect, passive ion trapping-based lysosomal sequestration of multiple hydrophobic weak-base chemotherapeutic agents was found to reduce the accessibility of these drugs to their target sites, resulting in a markedly reduced cytotoxic effect and drug resistance. Recently we have demonstrated that lysosomal sequestration of hydrophobic weak base drugs triggers TFEB-mediated lysosomal biogenesis resulting in an enlarged lysosomal compartment, capable of enhanced drug sequestration. This study further showed that cancer cells with an increased number of drug-accumulating lysosomes are more resistant to lysosome-sequestered drugs, suggesting a model of drug-induced lysosome-mediated chemoresistance. In addition to passive drug sequestration of hydrophobic weak base chemotherapeutics, other mechanisms of lysosome-mediated drug resistance have also been reported; these include active lysosomal drug sequestration mediated by ATP-driven transporters from the ABC superfamily, and a role for lysosomal copper transporters in cancer resistance to platinum-based chemotherapeutics. Furthermore, lysosomal exocytosis was suggested as a mechanism to facilitate the clearance of chemotherapeutics which highly accumulated in lysosomes, thus providing an additional line of resistance, supplementing the organelle entrapment of chemotherapeutics away from their target sites. Along with these mechanisms of lysosome-mediated drug resistance, several approaches were recently developed for the overcoming of drug resistance or exploiting lysosomal drug sequestration, including lysosomal photodestruction and drug-induced lysosomal membrane permeabilization. In this review we explore the current literature addressing the role of lysosomes in mediating cancer drug resistance as well as novel modalities to overcome this chemoresistance. Copyright © 2015 Elsevier Ltd. All rights reserved.

The Nuremberg Code subverts human health and safety by requiring animal modeling

PubMed Central

2012-01-01

Background The requirement that animals be used in research and testing in order to protect humans was formalized in the Nuremberg Code and subsequent national and international laws, codes, and declarations. Discussion We review the history of these requirements and contrast what was known via science about animal models then with what is known now. We further analyze the predictive value of animal models when used as test subjects for human response to drugs and disease. We explore the use of animals for models in toxicity testing as an example of the problem with using animal models. Summary We conclude that the requirements for animal testing found in the Nuremberg Code were based on scientifically outdated principles, compromised by people with a vested interest in animal experimentation, serve no useful function, increase the cost of drug development, and prevent otherwise safe and efficacious drugs and therapies from being implemented. PMID:22769234

The Nuremberg Code subverts human health and safety by requiring animal modeling.

PubMed

Greek, Ray; Pippus, Annalea; Hansen, Lawrence A

2012-07-08

The requirement that animals be used in research and testing in order to protect humans was formalized in the Nuremberg Code and subsequent national and international laws, codes, and declarations. We review the history of these requirements and contrast what was known via science about animal models then with what is known now. We further analyze the predictive value of animal models when used as test subjects for human response to drugs and disease. We explore the use of animals for models in toxicity testing as an example of the problem with using animal models. We conclude that the requirements for animal testing found in the Nuremberg Code were based on scientifically outdated principles, compromised by people with a vested interest in animal experimentation, serve no useful function, increase the cost of drug development, and prevent otherwise safe and efficacious drugs and therapies from being implemented.

Effective screening strategy using ensembled pharmacophore models combined with cascade docking: application to p53-MDM2 interaction inhibitors.

PubMed

Xue, Xin; Wei, Jin-Lian; Xu, Li-Li; Xi, Mei-Yang; Xu, Xiao-Li; Liu, Fang; Guo, Xiao-Ke; Wang, Lei; Zhang, Xiao-Jin; Zhang, Ming-Ye; Lu, Meng-Chen; Sun, Hao-Peng; You, Qi-Dong

2013-10-28

Protein-protein interactions (PPIs) play a crucial role in cellular function and form the backbone of almost all biochemical processes. In recent years, protein-protein interaction inhibitors (PPIIs) have represented a treasure trove of potential new drug targets. Unfortunately, there are few successful drugs of PPIIs on the market. Structure-based pharmacophore (SBP) combined with docking has been demonstrated as a useful Virtual Screening (VS) strategy in drug development projects. However, the combination of target complexity and poor binding affinity prediction has thwarted the application of this strategy in the discovery of PPIIs. Here we report an effective VS strategy on p53-MDM2 PPI. First, we built a SBP model based on p53-MDM2 complex cocrystal structures. The model was then simplified by using a Receptor-Ligand complex-based pharmacophore model considering the critical binding features between MDM2 and its small molecular inhibitors. Cascade docking was subsequently applied to improve the hit rate. Based on this strategy, we performed VS on NCI and SPECS databases and successfully discovered 6 novel compounds from 15 hits with the best, compound 1 (NSC 5359), K(i) = 180 ± 50 nM. These compounds can serve as lead compounds for further optimization.

Similarity-based modeling in large-scale prediction of drug-drug interactions.

PubMed

Vilar, Santiago; Uriarte, Eugenio; Santana, Lourdes; Lorberbaum, Tal; Hripcsak, George; Friedman, Carol; Tatonetti, Nicholas P

2014-09-01

Drug-drug interactions (DDIs) are a major cause of adverse drug effects and a public health concern, as they increase hospital care expenses and reduce patients' quality of life. DDI detection is, therefore, an important objective in patient safety, one whose pursuit affects drug development and pharmacovigilance. In this article, we describe a protocol applicable on a large scale to predict novel DDIs based on similarity of drug interaction candidates to drugs involved in established DDIs. The method integrates a reference standard database of known DDIs with drug similarity information extracted from different sources, such as 2D and 3D molecular structure, interaction profile, target and side-effect similarities. The method is interpretable in that it generates drug interaction candidates that are traceable to pharmacological or clinical effects. We describe a protocol with applications in patient safety and preclinical toxicity screening. The time frame to implement this protocol is 5-7 h, with additional time potentially necessary, depending on the complexity of the reference standard DDI database and the similarity measures implemented.

INTEGRATING GENETIC AND STRUCTURAL DATA ON HUMAN PROTEIN KINOME IN NETWORK-BASED MODELING OF KINASE SENSITIVITIES AND RESISTANCE TO TARGETED AND PERSONALIZED ANTICANCER DRUGS.

PubMed

Verkhivker, Gennady M

2016-01-01

The human protein kinome presents one of the largest protein families that orchestrate functional processes in complex cellular networks, and when perturbed, can cause various cancers. The abundance and diversity of genetic, structural, and biochemical data underlies the complexity of mechanisms by which targeted and personalized drugs can combat mutational profiles in protein kinases. Coupled with the evolution of system biology approaches, genomic and proteomic technologies are rapidly identifying and charactering novel resistance mechanisms with the goal to inform rationale design of personalized kinase drugs. Integration of experimental and computational approaches can help to bring these data into a unified conceptual framework and develop robust models for predicting the clinical drug resistance. In the current study, we employ a battery of synergistic computational approaches that integrate genetic, evolutionary, biochemical, and structural data to characterize the effect of cancer mutations in protein kinases. We provide a detailed structural classification and analysis of genetic signatures associated with oncogenic mutations. By integrating genetic and structural data, we employ network modeling to dissect mechanisms of kinase drug sensitivities to oncogenic EGFR mutations. Using biophysical simulations and analysis of protein structure networks, we show that conformational-specific drug binding of Lapatinib may elicit resistant mutations in the EGFR kinase that are linked with the ligand-mediated changes in the residue interaction networks and global network properties of key residues that are responsible for structural stability of specific functional states. A strong network dependency on high centrality residues in the conformation-specific Lapatinib-EGFR complex may explain vulnerability of drug binding to a broad spectrum of mutations and the emergence of drug resistance. Our study offers a systems-based perspective on drug design by unravelling complex relationships between robustness of targeted kinase genes and binding specificity of targeted kinase drugs. We discuss how these approaches can exploit advances in chemical biology and network science to develop novel strategies for rationally tailored and robust personalized drug therapies.

A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases

PubMed Central

Chernomoretz, Ariel; Agüero, Fernán

2016-01-01

Drug development for neglected diseases has been historically hampered due to lack of market incentives. The advent of public domain resources containing chemical information from high throughput screenings is changing the landscape of drug discovery for these diseases. In this work we took advantage of data from extensively studied organisms like human, mouse, E. coli and yeast, among others, to develop a novel integrative network model to prioritize and identify candidate drug targets in neglected pathogen proteomes, and bioactive drug-like molecules. We modeled genomic (proteins) and chemical (bioactive compounds) data as a multilayer weighted network graph that takes advantage of bioactivity data across 221 species, chemical similarities between 1.7 105 compounds and several functional relations among 1.67 105 proteins. These relations comprised orthology, sharing of protein domains, and shared participation in defined biochemical pathways. We showcase the application of this network graph to the problem of prioritization of new candidate targets, based on the information available in the graph for known compound-target associations. We validated this strategy by performing a cross validation procedure for known mouse and Trypanosoma cruzi targets and showed that our approach outperforms classic alignment-based approaches. Moreover, our model provides additional flexibility as two different network definitions could be considered, finding in both cases qualitatively different but sensible candidate targets. We also showcase the application of the network to suggest targets for orphan compounds that are active against Plasmodium falciparum in high-throughput screens. In this case our approach provided a reduced prioritization list of target proteins for the query molecules and showed the ability to propose new testable hypotheses for each compound. Moreover, we found that some predictions highlighted by our network model were supported by independent experimental validations as found post-facto in the literature. PMID:26735851

A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases.

PubMed

Berenstein, Ariel José; Magariños, María Paula; Chernomoretz, Ariel; Agüero, Fernán

2016-01-01

Drug development for neglected diseases has been historically hampered due to lack of market incentives. The advent of public domain resources containing chemical information from high throughput screenings is changing the landscape of drug discovery for these diseases. In this work we took advantage of data from extensively studied organisms like human, mouse, E. coli and yeast, among others, to develop a novel integrative network model to prioritize and identify candidate drug targets in neglected pathogen proteomes, and bioactive drug-like molecules. We modeled genomic (proteins) and chemical (bioactive compounds) data as a multilayer weighted network graph that takes advantage of bioactivity data across 221 species, chemical similarities between 1.7 105 compounds and several functional relations among 1.67 105 proteins. These relations comprised orthology, sharing of protein domains, and shared participation in defined biochemical pathways. We showcase the application of this network graph to the problem of prioritization of new candidate targets, based on the information available in the graph for known compound-target associations. We validated this strategy by performing a cross validation procedure for known mouse and Trypanosoma cruzi targets and showed that our approach outperforms classic alignment-based approaches. Moreover, our model provides additional flexibility as two different network definitions could be considered, finding in both cases qualitatively different but sensible candidate targets. We also showcase the application of the network to suggest targets for orphan compounds that are active against Plasmodium falciparum in high-throughput screens. In this case our approach provided a reduced prioritization list of target proteins for the query molecules and showed the ability to propose new testable hypotheses for each compound. Moreover, we found that some predictions highlighted by our network model were supported by independent experimental validations as found post-facto in the literature.

A conceptual framework for achieving performance enhancing drug compliance in sport.

PubMed

Donovan, Robert J; Egger, Garry; Kapernick, Vicki; Mendoza, John

2002-01-01

There has been, and continues to be, widespread international concern about athletes' use of banned performance enhancing drugs (PEDs). This concern culminated in the formation of the World Anti-Doping Agency (WADA) in November 1999. To date, the main focus on controlling the use of PEDs has been on testing athletes and the development of tests to detect usage. Although athletes' beliefs and values are known to influence whether or not an athlete will use drugs, little is known about athletes' beliefs and attitudes, and the limited empirical literature shows little use of behavioural science frameworks to guide research methodology, results interpretation, and intervention implications. Mindful of this in preparing its anti-doping strategy for the 2000 Olympics, the Australian Sports Drug Agency (ASDA) in 1997 commissioned a study to assess the extent to which models of attitude-behaviour change in the public health/injury prevention literature had useful implications for compliance campaigns in the sport drug area. A preliminary compliance model was developed from three behavioural science frameworks: social cognition models; threat (or fear) appeals; and instrumental and normative approaches. A subsequent review of the performance enhancing drug literature confirmed that the overall framework was consistent with known empirical data, and therefore had at least face validity if not construct validity. The overall model showed six major inputs to an athlete's attitudes and intentions with respect to performance enhancing drug usage: personality factors, threat appraisal, benefit appraisal, reference group influences, personal morality and legitimacy. The model demonstrated that a comprehensive, fully integrated programme is necessary for maximal effect, and provides anti-doping agencies with a structured framework for strategic planning and implementing interventions. Programmes can be developed in each of the six major areas, with allocation of resources to each area based on needs-assessment research with athletes and other relevant groups.

Phenotypic screening in cancer drug discovery - past, present and future.

PubMed

Moffat, John G; Rudolph, Joachim; Bailey, David

2014-08-01

There has been a resurgence of interest in the use of phenotypic screens in drug discovery as an alternative to target-focused approaches. Given that oncology is currently the most active therapeutic area, and also one in which target-focused approaches have been particularly prominent in the past two decades, we investigated the contribution of phenotypic assays to oncology drug discovery by analysing the origins of all new small-molecule cancer drugs approved by the US Food and Drug Administration (FDA) over the past 15 years and those currently in clinical development. Although the majority of these drugs originated from target-based discovery, we identified a significant number whose discovery depended on phenotypic screening approaches. We postulate that the contribution of phenotypic screening to cancer drug discovery has been hampered by a reliance on 'classical' nonspecific drug effects such as cytotoxicity and mitotic arrest, exacerbated by a paucity of mechanistically defined cellular models for therapeutically translatable cancer phenotypes. However, technical and biological advances that enable such mechanistically informed phenotypic models have the potential to empower phenotypic drug discovery in oncology.

gene2drug: a computational tool for pathway-based rational drug repositioning.

PubMed

Napolitano, Francesco; Carrella, Diego; Mandriani, Barbara; Pisonero-Vaquero, Sandra; Sirci, Francesco; Medina, Diego L; Brunetti-Pierri, Nicola; di Bernardo, Diego

2018-05-01

Drug repositioning has been proposed as an effective shortcut to drug discovery. The availability of large collections of transcriptional responses to drugs enables computational approaches to drug repositioning directly based on measured molecular effects. We introduce a novel computational methodology for rational drug repositioning, which exploits the transcriptional responses following treatment with small molecule. Specifically, given a therapeutic target gene, a prioritization of potential effective drugs is obtained by assessing their impact on the transcription of genes in the pathway(s) including the target. We performed in silico validation and comparison with a state-of-art technique based on similar principles. We next performed experimental validation in two different real-case drug repositioning scenarios: (i) upregulation of the glutamate-pyruvate transaminase (GPT), which has been shown to induce reduction of oxalate levels in a mouse model of primary hyperoxaluria, and (ii) activation of the transcription factor TFEB, a master regulator of lysosomal biogenesis and autophagy, whose modulation may be beneficial in neurodegenerative disorders. A web tool for Gene2drug is freely available at http://gene2drug.tigem.it. An R package is under development and can be obtained from https://github.com/franapoli/gep2pep. dibernardo@tigem.it. Supplementary data are available at Bioinformatics online.

Fighting cancer with nanomedicine---drug-polyester nanoconjugates for targeted cancer therapy

NASA Astrophysics Data System (ADS)

Yin, Qian

The aim of my Ph. D. research is to develop drug-polyester nanoconjugates (NCs) as a novel translational polymeric drug delivery system that can successfully evade non-specific uptake by reticuloendothelial system (RES) and facilitate targeted cancer diagnosis and therapy. By uniquely integrating well-established chemical reaction-controlled ring opening polymerization (ROP) with nanoprecipitation technique, I successfully developed a polymeric NC system based on poly(lactic acid) and poly(O-carboxyanhydrides) (OCA) that allows for the quantitative loading and controlled release of a variety of anticancer drugs. The developed NC system could be easily modified with parmidronate, one of bisphosphonates commonly used as the treatment for disease characterized by osteolysis, to selectively deliver doxorubicin (Doxo) to the bone tissues and substantially to improve their therapeutic efficiency in inhibiting the growth of osteosarcoma in both murine and canine models. More importantly, the developed NCs could avidly bind to human serum albumin, a ubiquitous protein in the blood, to bypass the endothelium barrier and penetrate into tumor tissues more deeply and efficiently. When compared with PEGylated NCs, these albumin-bound NCs showed significantly reduced accumulation in RES and enhanced tumor accumulation, which consequently contributed to higher their tumor inhibition capabilities. In addition, the developed NC system allows easy incorporation of X-ray computed tomography (CT) contrast agents to largely facilitate personalized therapy by improving diagnosis accuracy and monitoring therapeutic efficacy. Through the synthetic and formulation strategy I developed, a large quantity (grams or larger-scale) of drug-polyester NCs can be easily obtained, which can be used as a model drug delivery system for fundamental studies as well as a real drug delivery system for disease treatment in clinical settings.

Accounting- versus economic-based rates of return: implications for profitability measures in the pharmaceutical industry.

PubMed

Skrepnek, Grant H

2004-01-01

Accounting-based profits have indicated that pharmaceutical firms have achieved greater returns relative to other sectors. However, partially due to the theoretically inappropriate reporting of research and development (R&D) expenditures according to generally accepted accounting principles, evidence suggests that a substantial and upward bias is present in accounting-based rates of return for corporations with high levels of intangible assets. Given the intensity of R&D in pharmaceutical firms, accounting-based profit metrics in the drug sector may be affected to a greater extent than other industries. The aim of this work was to address measurement issues associated with corporate performance and factors that contribute to the bias within accounting-based rates of return. Seminal and broadly cited works on the subject of accounting- versus economic-based rates of return were reviewed from the economic and finance literature, with an emphasis placed on issues and scientific evidence directly related to the drug development process and pharmaceutical industry. With international convergence and harmonization of accounting standards being imminent, stricter adherence to theoretically sound economic principles is advocated, particularly those based on discounted cash-flow methods. Researchers, financial analysts, and policy makers must be cognizant of the biases and limitations present within numerous corporate performance measures. Furthermore, the development of more robust and valid economic models of the pharmaceutical industry is required to capture the unique dimensions of risk and return of the drug development process. Empiric work has illustrated that estimates of economic-based rates of return range from approximately 2 to approximately 11 percentage points below various accounting-based rates of return for drug companies. Because differences in the nature of risk and uncertainty borne by drug manufacturers versus other sectors make comparative assessments of performance challenging and often inappropriate, continued work in this area is warranted.

Hindered disulfide bonds to regulate release rate of model drug from mesoporous silica.

PubMed

Nadrah, Peter; Maver, Uroš; Jemec, Anita; Tišler, Tatjana; Bele, Marjan; Dražić, Goran; Benčina, Mojca; Pintar, Albin; Planinšek, Odon; Gaberšček, Miran

2013-05-01

With the advancement of drug delivery systems based on mesoporous silica nanoparticles (MSNs), a simple and efficient method regulating the drug release kinetics is needed. We developed redox-responsive release systems with three levels of hindrance around the disulfide bond. A model drug (rhodamine B dye) was loaded into MSNs' mesoporous voids. The pore opening was capped with β-cyclodextrin in order to prevent leakage of drug. Indeed, in absence of a reducing agent the systems exhibited little leakage, while the addition of dithiothreitol cleaved the disulfide bonds and enabled the release of cargo. The release rate and the amount of released dye were tuned by the level of hindrance around disulfide bonds, with the increased hindrance causing a decrease in the release rate as well as in the amount of released drug. Thus, we demonstrated the ability of the present mesoporous systems to intrinsically control the release rate and the amount of the released cargo by only minor structural variations. Furthermore, an in vivo experiment on zebrafish confirmed that the present model delivery system is nonteratogenic.

Modeling the effects of space structure and combination therapies on phenotypic heterogeneity and drug resistance in solid tumors.

PubMed

Lorz, Alexander; Lorenzi, Tommaso; Clairambault, Jean; Escargueil, Alexandre; Perthame, Benoît

2015-01-01

Histopathological evidence supports the idea that the emergence of phenotypic heterogeneity and resistance to cytotoxic drugs can be considered as a process of selection in tumor cell populations. In this framework, can we explain intra-tumor heterogeneity in terms of selection driven by the local cell environment? Can we overcome the emergence of resistance and favor the eradication of cancer cells by using combination therapies? Bearing these questions in mind, we develop a model describing cell dynamics inside a tumor spheroid under the effects of cytotoxic and cytostatic drugs. Cancer cells are assumed to be structured as a population by two real variables standing for space position and the expression level of a phenotype of resistance to cytotoxic drugs. The model takes explicitly into account the dynamics of resources and anticancer drugs as well as their interactions with the cell population under treatment. We analyze the effects of space structure and combination therapies on phenotypic heterogeneity and chemotherapeutic resistance. Furthermore, we study the efficacy of combined therapy protocols based on constant infusion and bang-bang delivery of cytotoxic and cytostatic drugs.

Introduction/overview: gender-based differences in pharmacologic and toxicologic responses.

PubMed

Christian, M S

2001-01-01

Gender may be the most important factor in mammalian development and response to exogenous agents. From believing sex-related differences required sheltering women to protect their reproductive capacity (Victorians thought exercise, education, train travel, and certain music neuro- and reprotoxic to females) to legislating a status of essential equality of the sexes may have increased women's health issues. Men and women often respond differently to drugs. Inclusion of women in phase I/II clinical trials is insufficient to identify gender-based differences in response; rather, animal models should be the basis for predicting gender-based differences in pharmacologic and toxicologic effects. Unfortunately, current animal models do not consistently demonstrate such differences. Use of commonly used species (e.g., rats and dogs) does not necessarily result in relevant evaluation of an agent in a species at appropriate development (age), physiological state, anatomy, metabolism, or kinetics for estimation of human risks. The need to test agents in relevant animal models and advances in metabolic, pharmacokinetic, and pharmacodynamic capabilities challenge us to improve methods by using the most relevant models for estimating human risk. We need to be concerned about gender-related differences and the dynamics of gender-based growth and development over the entire life cycle. We must also consider potential interactions of dietary supplements and other exogenous agents that can act as drugs or modulate the potential effects of drugs differently in men, women, and developing children of both sexes. To this end, the health benefits of genistein and the effects of this dietary agent in a multigeneration study in rats will be described. It is envisioned that this symposium will assist in re-recognition of the importance of gender-related differences in use and response to pharmaceuticals and result in optimization of nonclinical testing procedures to identify benefits and risks for human use of these agents.

Organizational adoption of preemployment drug testing.

PubMed

Spell, C S; Blum, T C

2001-04-01

This study explored the adoption of preemployment drug testing by 360 organizations. Survival models were developed that included internal organizational and labor market factors hypothesized to affect the likelihood of adoption of drug testing. Also considered was another set of variables that included social and political variables based on institutional theory. An event history analysis using Cox regressions indicated that both internal organizational and environmental variables predicted adoption of drug testing. Results indicate that the higher the proportion of drug testers in the worksite's industry, the more likely it would be to adopt drug testing. Also, the extent to which an organization uses an internal labor market, voluntary turnover rate, and the extent to which management perceives drugs to be a problem were related to likelihood of adoption of drug testing.

A mechanistic framework for in vitro-in vivo extrapolation of liver membrane transporters: prediction of drug-drug interaction between rosuvastatin and cyclosporine.

PubMed

Jamei, M; Bajot, F; Neuhoff, S; Barter, Z; Yang, J; Rostami-Hodjegan, A; Rowland-Yeo, K

2014-01-01

The interplay between liver metabolising enzymes and transporters is a complex process involving system-related parameters such as liver blood perfusion as well as drug attributes including protein and lipid binding, ionisation, relative magnitude of passive and active permeation. Metabolism- and/or transporter-mediated drug-drug interactions (mDDIs and tDDIs) add to the complexity of this interplay. Thus, gaining meaningful insight into the impact of each element on the disposition of a drug and accurately predicting drug-drug interactions becomes very challenging. To address this, an in vitro-in vivo extrapolation (IVIVE)-linked mechanistic physiologically based pharmacokinetic (PBPK) framework for modelling liver transporters and their interplay with liver metabolising enzymes has been developed and implemented within the Simcyp Simulator(®). In this article an IVIVE technique for liver transporters is described and a full-body PBPK model is developed. Passive and active (saturable) transport at both liver sinusoidal and canalicular membranes are accounted for and the impact of binding and ionisation processes is considered. The model also accommodates tDDIs involving inhibition of multiple transporters. Integrating prior in vitro information on the metabolism and transporter kinetics of rosuvastatin (organic-anion transporting polypeptides OATP1B1, OAT1B3 and OATP2B1, sodium-dependent taurocholate co-transporting polypeptide [NTCP] and breast cancer resistance protein [BCRP]) with one clinical dataset, the PBPK model was used to simulate the drug disposition of rosuvastatin for 11 reported studies that had not been used for development of the rosuvastatin model. The simulated area under the plasma concentration-time curve (AUC), maximum concentration (C max) and the time to reach C max (t max) values of rosuvastatin over the dose range of 10-80 mg, were within 2-fold of the observed data. Subsequently, the validated model was used to investigate the impact of coadministration of cyclosporine (ciclosporin), an inhibitor of OATPs, BCRP and NTCP, on the exposure of rosuvastatin in healthy volunteers. The results show the utility of the model to integrate a wide range of in vitro and in vivo data and simulate the outcome of clinical studies, with implications for their design.

Use of multiattribute utility theory for formulary management in a health system.

PubMed

Chung, Seonyoung; Kim, Sooyon; Kim, Jeongmee; Sohn, Kieho

2010-01-15

The application, utility, and flexibility of the multiattribute utility theory (MAUT) when used as a formulary decision methodology in a Korean medical center were evaluated. A drug analysis model using MAUT consisting of 10 steps was designed for two drug classes of dihydropyridine calcium channel blockers (CCBs) and angiotensin II receptor blockers (ARBs). These two drug classes contain the most diverse agents among cardiovascular drugs on Samsung Medical Center's drug formulary. The attributes identified for inclusion in the drug analysis model were effectiveness, safety, patient convenience, and cost, with relative weights of 50%, 30%, 10%, and 10%, respectively. The factors were incorporated into the model to quantify the contribution of each attribute. For each factor, a utility scale of 0-100 was established, and the total utility score for each alternative was calculated. An attempt was made to make the model adaptable to changing health care and regulatory circumstances. The analysis revealed amlodipine besylate to be an alternative agent, with the highest total utility score among the dihydropyridine CCBs, while barnidipine hydrochloride had the lowest score. For ARBs, losartan potassium had the greatest total utility score, while olmesartan medoxomil had the lowest. A drug analysis model based on the MAUT was successfully developed and used in making formulary decisions for dihydropyridine CCBs and ARBs for a Korean health system. The model incorporates sufficient utility and flexibility of a drug's attributes and can be used as an alternative decision-making tool for formulary management in health systems.

Can Drosophila melanogaster represent a model system for the detection of reproductive adverse drug reactions?

PubMed

Avanesian, Agnesa; Semnani, Sahar; Jafari, Mahtab

2009-08-01

Once a molecule is identified as a potential drug, the detection of adverse drug reactions is one of the key components of its development and the FDA approval process. We propose using Drosophila melanogaster to screen for reproductive adverse drug reactions in the early stages of drug development. Compared with other non-mammalian models, D. melanogaster has many similarities to the mammalian reproductive system, including putative sex hormones and conserved proteins involved in genitourinary development. Furthermore, the D. melanogaster model would present significant advantages in time efficiency and cost-effectiveness compared with mammalian models. We present data on methotrexate (MTX) reproductive adverse events in multiple animal models, including fruit flies, as proof-of-concept for the use of the D. melanogaster model.

Realizing the promise of personalized medicine.

PubMed

Aspinall, Mara G; Hamermesh, Richard G

2007-10-01

Scientific advances have begun to give doctors the power to customize therapy for individuals. However, adoption of this approach has progressed slowly and unevenly because the trial-and-error treatment model still governs how the health care system develops, regulates, pays for, and delivers therapies. Aspinall, the president of Genzyme Genetics, and Hamermesh, chair of a Harvard Business School initiative to improve leadership in health care organizations, discuss the barriers to personalized medicine and suggest ways to overcome them. The blockbuster model for developing drugs, the authors point out, is still what most major pharmaceutical companies follow, even though its days are numbered. What the industry must embrace in its place is a business model based on a larger portfolio of targeted--and therefore more effective and profitable--treatments, not a limited palette of one-size-fits-all drugs. The current regulatory environment overemphasizes large-scale clinical trials of broad-based therapies. Instead, the focus should be on enrolling subpopulations, based on diagnostic testing, in trials of targeted drug treatments and on monitoring and assessing effectiveness after drugs are approved. A dysfunctional payment system complicates matters by rewarding providers for performance of procedures rather than for accurate diagnosis and effective prevention. Aspinall and Hamermesh call for coordinating regulation and reimbursement so that incentives are provided for the right outcomes. Finally, the authors urge changing physicians' habits through education about genomics, diagnostic testing, and targeted therapies. They say that medical schools and physician organizations must become committed advocates of personalized medicine so that patients and the medical industry can get all the benefits it offers.

Optimizing Chemotherapy Dose and Schedule by Norton-Simon Mathematical Modeling

PubMed Central

Traina, Tiffany A.; Dugan, Ute; Higgins, Brian; Kolinsky, Kenneth; Theodoulou, Maria; Hudis, Clifford A.; Norton, Larry

2011-01-01

Background To hasten and improve anticancer drug development, we created a novel approach to generating and analyzing preclinical dose-scheduling data so as to optimize benefit-to-toxicity ratios. Methods We applied mathematical methods based upon Norton-Simon growth kinetic modeling to tumor-volume data from breast cancer xenografts treated with capecitabine (Xeloda®, Roche) at the conventional schedule of 14 days of treatment followed by a 7-day rest (14 - 7). Results The model predicted that 7 days of treatment followed by a 7-day rest (7 - 7) would be superior. Subsequent preclinical studies demonstrated that this biweekly capecitabine schedule allowed for safe delivery of higher daily doses, improved tumor response, and prolonged animal survival. Conclusions We demonstrated that the application of Norton-Simon modeling to the design and analysis of preclinical data predicts an improved capecitabine dosing schedule in xenograft models. This method warrants further investigation and application in clinical drug development. PMID:20519801

New experimental models of the blood-brain barrier for CNS drug discovery

PubMed Central

Kaisar, Mohammad A.; Sajja, Ravi K.; Prasad, Shikha; Abhyankar, Vinay V.; Liles, Taylor; Cucullo, Luca

2017-01-01

Introduction The blood-brain barrier (BBB) is a dynamic biological interface which actively controls the passage of substances between the blood and the central nervous system (CNS). From a biological and functional standpoint, the BBB plays a crucial role in maintaining brain homeostasis inasmuch that deterioration of BBB functions are prodromal to many CNS disorders. Conversely, the BBB hinders the delivery of drugs targeting the brain to treat a variety of neurological diseases. Area covered This article reviews recent technological improvements and innovation in the field of BBB modeling including static and dynamic cell-based platforms, microfluidic systems and the use of stem cells and 3D printing technologies. Additionally, the authors laid out a roadmap for the integration of microfluidics and stem cell biology as a holistic approach for the development of novel in vitro BBB platforms. Expert opinion Development of effective CNS drugs has been hindered by the lack of reliable strategies to mimic the BBB and cerebrovascular impairments in vitro. Technological advancements in BBB modeling have fostered the development of highly integrative and quasi- physiological in vitro platforms to support the process of drug discovery. These advanced in vitro tools are likely to further current understanding of the cerebrovascular modulatory mechanisms. PMID:27782770

Models of Latent Tuberculosis: Their Salient Features, Limitations, and Development

PubMed Central

Patel, Kamlesh; Jhamb, Sarbjit Singh; Singh, Prati Pal

2011-01-01

Latent tuberculosis is a subclinical condition caused by Mycobacterium tuberculosis, which affects about one-third of the population across the world. To abridge the chemotherapy of tuberculosis, it is necessary to have active drugs against latent form of M. tuberculosis. Therefore, it is imperative to devise in vitro and models of latent tuberculosis to explore potential drugs. In vitro models such as hypoxia, nutrient starvation, and multiple stresses are based on adverse conditions encountered by bacilli in granuloma. Bacilli experience oxygen depletion condition in hypoxia model, whereas the nutrient starvation model is based on deprivation of total nutrients from a culture medium. In the multiple stress model dormancy is induced by more than one type of stress. In silico mathematical models have also been developed to predict the interactions of bacilli with the host immune system and to propose structures for potential anti tuberculosis compounds. Besides these in vitro and in silico models, there are a number of in vivo animal models like mouse, guinea pig, rabbit, etc. Although they simulate human latent tuberculosis up to a certain extent but do not truly replicate human infection. All these models have their inherent merits and demerits. However, there is no perfect model for latent tuberculosis. Therefore, it is imperative to upgrade and refine existing models or develop a new model. However, battery of models will always be a better alternative to any single model as they will complement each other by overcoming their limitations. PMID:22219558

Pluripotent stem cells: the last 10 years.

PubMed

Kimbrel, Erin A; Lanza, Robert

2016-12-01

Pluripotent stem cells (PSCs) can differentiate into virtually any cell type in the body, making them attractive for both regenerative medicine and drug discovery. Over the past 10 years, technological advances and innovative platforms have yielded first-in-man PSC-based clinical trials and opened up new approaches for disease modeling and drug development. Induced PSCs have become the foremost alternative to embryonic stem cells and accelerated the development of disease-in-a-dish models. Over the years and with each new discovery, PSCs have proven to be extremely versatile. This review article highlights key advancements in PSC research, from 2006 to 2016, and how they will guide the direction of the field over the next decade.

Computational design and multiscale modeling of a nanoactuator using DNA actuation.

PubMed

Hamdi, Mustapha

2009-12-02

Developments in the field of nanobiodevices coupling nanostructures and biological components are of great interest in medical nanorobotics. As the fundamentals of bio/non-bio interaction processes are still poorly understood in the design of these devices, design tools and multiscale dynamics modeling approaches are necessary at the fabrication pre-project stage. This paper proposes a new concept of optimized carbon nanotube based servomotor design for drug delivery and biomolecular transport applications. The design of an encapsulated DNA-multi-walled carbon nanotube actuator is prototyped using multiscale modeling. The system is parametrized by using a quantum level approach and characterized by using a molecular dynamics simulation. Based on the analysis of the simulation results, a servo nanoactuator using ionic current feedback is simulated and analyzed for application as a drug delivery carrier.

Enhanced Delivery of Chemotherapy to Tumors Using a Multi-Component Nanochain with Radiofrequency-Tunable Drug Release

PubMed Central

Peiris, Pubudu M.; Bauer, Lisa; Toy, Randall; Tran, Emily; Pansky, Jenna; Doolittle, Elizabeth; Schmidt, Erik; Hayden, Elliott; Mayer, Aaron; Keri, Ruth A.; Griswold, Mark A.; Karathanasis, Efstathios

2012-01-01

While nanoparticles maximize the amount of chemotherapeutic drug in tumors relative to normal tissues, nanoparticle-based drugs are not accessible to the majority of cancer cells because nanoparticles display patchy, near-perivascular accumulation in tumors. To overcome the limitations of current drugs in their molecular or nanoparticle form, we developed a nanoparticle based on multi-component nanochains to deliver drug to the majority of cancer cells throughout a tumor while reducing off-target delivery. The nanoparticle is composed of three magnetic nanospheres and one doxorubicin-loaded liposome assembled in a 100-nm-long chain. These nanoparticles display prolonged blood circulation and significant intratumoral deposition in tumor models in rodents. Furthermore, the magnetic particles of the chains serve as a mechanical transducer to transfer radiofrequency energy to the drug-loaded liposome. The defects on the liposomal walls trigger the release of free drug capable of spreading throughout the entire tumor, which results in a wide-spread anticancer effect. PMID:22486623

Synthesis and characterization of psyllium-NVP based drug delivery system through radiation crosslinking polymerization

NASA Astrophysics Data System (ADS)

Singh, Baljit; Kumar, S.

2008-08-01

In order to develop the hydrogels meant for the drug delivery, we have prepared psyllium- N-vinylpyrrolidone (NVP) based hydrogels by radiation induced crosslinking. Polymers were characterized with SEMs, FTIR and swelling studies. Swelling of the hydrogels was studied as a function of monomer concentration, total radiation dose, temperature, pH and [NaCl] of the swelling medium. The swelling kinetics of the hydrogels and release dynamics of anticancer model drug (5-fluorouracil) from the hydrogels have been carried out for the evaluation of swelling and drug release mechanism. It has been observed that diffusion exponent ' n' have 0.8, 0.9, 0.8 and gel characteristics constant ' k' have 9.22 × 10 -3, 2.06 × 10 -3, 11.72 × 10 -3 values for the release of drug from the drug loaded hydrogels in distilled water, pH 2.2 buffer and pH 7.4 buffer, respectively. The present study shows that the release of drug from the hydrogels occurred through Non-Fickian diffusion mechanism.

Translating dosages from animal models to human clinical trials--revisiting body surface area scaling.

PubMed

Blanchard, Otis L; Smoliga, James M

2015-05-01

Body surface area (BSA) scaling has been used for prescribing individualized dosages of various drugs and has also been recommended by the U.S. Food and Drug Administration as one method for using data from animal model species to establish safe starting dosages for first-in-human clinical trials. Although BSA conversion equations have been used in certain clinical applications for decades, recent recommendations to use BSA to derive interspecies equivalents for therapeutic dosages of drug and natural products are inappropriate. A thorough review of the literature reveals that BSA conversions are based on antiquated science and have little justification in current translational medicine compared to more advanced allometric and physiologically based pharmacokinetic modeling. Misunderstood and misinterpreted use of BSA conversions may have disastrous consequences, including underdosing leading to abandonment of potentially efficacious investigational drugs, and unexpected deadly adverse events. We aim to demonstrate that recent recommendations for BSA are not appropriate for animal-to-human dosage conversions and use pharmacokinetic data from resveratrol studies to demonstrate how confusion between the "human equivalent dose" and "pharmacologically active dose" can lead to inappropriate dose recommendations. To optimize drug development, future recommendations for interspecies scaling must be scientifically justified using physiologic, pharmacokinetic, and toxicology data rather than simple BSA conversion. © FASEB.

Physiologically Based Pharmacokinetic Modeling Suggests Limited Drug–Drug Interaction Between Clopidogrel and Dasabuvir

PubMed Central

Fu, W; Badri, P; Bow, DAJ; Fischer, V

2017-01-01

Dasabuvir, a nonnucleoside NS5B polymerase inhibitor, is a sensitive substrate of cytochrome P450 (CYP) 2C8 with a potential for drug–drug interaction (DDI) with clopidogrel. A physiologically based pharmacokinetic (PBPK) model was developed for dasabuvir to evaluate the DDI potential with clopidogrel, the acyl‐β‐D glucuronide metabolite of which has been reported as a strong mechanism‐based inhibitor of CYP2C8 based on an interaction with repaglinide. In addition, the PBPK model for clopidogrel and its metabolite were updated with additional in vitro data. Sensitivity analyses using these PBPK models suggested that CYP2C8 inhibition by clopidogrel acyl‐β‐D glucuronide may not be as potent as previously suggested. The dasabuvir and updated clopidogrel PBPK models predict a moderate increase of 1.5–1.9‐fold for Cmax and 1.9–2.8‐fold for AUC of dasabuvir when coadministered with clopidogrel. While the PBPK results suggest there is a potential for DDI between dasabuvir and clopidogrel, the magnitude is not expected to be clinically relevant. PMID:28411400

Animal models of Parkinson's disease: a source of novel treatments and clues to the cause of the disease

PubMed Central

Duty, Susan; Jenner, Peter

2011-01-01

Animal models of Parkinson's disease (PD) have proved highly effective in the discovery of novel treatments for motor symptoms of PD and in the search for clues to the underlying cause of the illness. Models based on specific pathogenic mechanisms may subsequently lead to the development of neuroprotective agents for PD that stop or slow disease progression. The array of available rodent models is large and ranges from acute pharmacological models, such as the reserpine- or haloperidol-treated rats that display one or more parkinsonian signs, to models exhibiting destruction of the dopaminergic nigro-striatal pathway, such as the classical 6-hydroxydopamine (6-OHDA) rat and 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) mouse models. All of these have provided test beds in which new molecules for treating the motor symptoms of PD can be assessed. In addition, the emergence of abnormal involuntary movements (AIMs) with repeated treatment of 6-OHDA-lesioned rats with L-DOPA has allowed for examination of the mechanisms responsible for treatment-related dyskinesia in PD, and the detection of molecules able to prevent or reverse their appearance. Other toxin-based models of nigro-striatal tract degeneration include the systemic administration of the pesticides rotenone and paraquat, but whilst providing clues to disease pathogenesis, these are not so commonly used for drug development. The MPTP-treated primate model of PD, which closely mimics the clinical features of PD and in which all currently used anti-parkinsonian medications have been shown to be effective, is undoubtedly the most clinically-relevant of all available models. The MPTP-treated primate develops clear dyskinesia when repeatedly exposed to L-DOPA, and these parkinsonian animals have shown responses to novel dopaminergic agents that are highly predictive of their effect in man. Whether non-dopaminergic drugs show the same degree of predictability of response is a matter of debate. As our understanding of the pathogenesis of PD has improved, so new rodent models produced by agents mimicking these mechanisms, including proteasome inhibitors such as PSI, lactacystin and epoximycin or inflammogens like lipopolysaccharide (LPS) have been developed. A further generation of models aimed at mimicking the genetic causes of PD has also sprung up. Whilst these newer models have provided further clues to the disease pathology, they have so far been less commonly used for drug development. There is little doubt that the availability of experimental animal models of PD has dramatically altered dopaminergic drug treatment of the illness and the prevention and reversal of drug-related side effects that emerge with disease progression and chronic medication. However, so far, we have made little progress in moving into other pharmacological areas for the treatment of PD, and we have not developed models that reflect the progressive nature of the illness and its complexity in terms of the extent of pathology and biochemical change. Only when this occurs are we likely to make progress in developing agents to stop or slow the disease progression. The overarching question that draws all of these models together in the quest for better drug treatments for PD is how well do they recapitulate the human condition and how predictive are they of successful translation of drugs into the clinic? This article aims to clarify the current position and highlight the strengths and weaknesses of available models. LINKED ARTICLES This article is part of a themed issue on Translational Neuropharmacology. To view the other articles in this issue visit http://dx.doi.org/10.1111/bph.2011.164.issue-4 PMID:21486284

Modelling Ebola virus dynamics: Implications for therapy.

PubMed

Martyushev, Alexey; Nakaoka, Shinji; Sato, Kei; Noda, Takeshi; Iwami, Shingo

2016-11-01

Ebola virus (EBOV) causes a severe, often fatal Ebola virus disease (EVD), for which no approved antivirals exist. Recently, some promising anti-EBOV drugs, which are experimentally potent in animal models, have been developed. However, because the quantitative dynamics of EBOV replication in humans is uncertain, it remains unclear how much antiviral suppression of viral replication affects EVD outcome in patients. Here, we developed a novel mathematical model to quantitatively analyse human viral load data obtained during the 2000/01 Uganda EBOV outbreak and evaluated the effects of different antivirals. We found that nucleoside analogue- and siRNA-based therapies are effective if a therapy with a >50% inhibition rate is initiated within a few days post-symptom-onset. In contrast, antibody-based therapy requires not only a higher inhibition rate but also an earlier administration, especially for otherwise fatal cases. Our results demonstrate that an appropriate choice of EBOV-specific drugs is required for effective EVD treatment. Copyright © 2016 Elsevier B.V. All rights reserved.

Indexing molecules for their hERG liability.

PubMed

Rayan, Anwar; Falah, Mizied; Raiyn, Jamal; Da'adoosh, Beny; Kadan, Sleman; Zaid, Hilal; Goldblum, Amiram

2013-07-01

The human Ether-a-go-go-Related-Gene (hERG) potassium (K(+)) channel is liable to drug-inducing blockage that prolongs the QT interval of the cardiac action potential, triggers arrhythmia and possibly causes sudden cardiac death. Early prediction of drug liability to hERG K(+) channel is therefore highly important and preferably obligatory at earlier stages of any drug discovery process. In vitro assessment of drug binding affinity to hERG K(+) channel involves substantial expenses, time, and labor; and therefore computational models for predicting liabilities of drug candidates for hERG toxicity is of much importance. In the present study, we apply the Iterative Stochastic Elimination (ISE) algorithm to construct a large number of rule-based models (filters) and exploit their combination for developing the concept of hERG Toxicity Index (ETI). ETI estimates the molecular risk to be a blocker of hERG potassium channel. The area under the curve (AUC) of the attained model is 0.94. The averaged ETI of hERG binders, drugs from CMC, clinical-MDDR, endogenous molecules, ACD and ZINC, were found to be 9.17, 2.53, 3.3, -1.98, -2.49 and -3.86 respectively. Applying the proposed hERG Toxicity Index Model on external test set composed of more than 1300 hERG blockers picked from chEMBL shows excellent performance (Matthews Correlation Coefficient of 0.89). The proposed strategy could be implemented for the evaluation of chemicals in the hit/lead optimization stages of the drug discovery process, improve the selection of drug candidates as well as the development of safe pharmaceutical products. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

Improving Parolees' Participation in Drug Treatment and Other Services through Strengths Case Management.

PubMed

Prendergast, Michael; Cartier, Jerome J

2008-01-01

In an effort to increase participation in community aftercare treatment for substance-abusing parolees, an intervention based on a transitional case management (TCM) model that focuses mainly on offenders' strengths has been developed and is under testing. This model consists of completion, by the inmate, of a self-assessment of strengths that informs the development of the continuing care plan, a case conference call shortly before release, and strengths case management for three months post-release to promote retention in substance abuse treatment and support the participant's access to designated services in the community. The post-release component consists of a minimum of one weekly client/case manager meeting (in person or by telephone) for 12 weeks. The intervention is intended to improve the transition process from prison to community at both the individual and systems level. Specifically, the intervention is designed to improve outcomes in parolee admission to, and retention in, community-based substance-abuse treatment, parolee access to other needed services, and recidivism rates during the first year of parole. On the systems level, the intervention is intended to improve the communication and collaboration between criminal justice agencies, community-based treatment organizations, and other social and governmental service providers. The TCM model is being tested in a multisite study through the Criminal Justice Drug Abuse Treatment Studies (CJ-DATS) research cooperative funded by the National Institute of Drug Abuse.

Development of New ADC Technology with Topoisomerase I Inhibitor.

PubMed

Agatsuma, Toshinori

2017-01-01

Antibody-drug conjugates (ADCs) selectively deliver large amounts of antitumor drugs to tumor tissue and show significant antitumor effects with a wide therapeutic window. We developed a new linker-drug technology platform with an exatecan derivative, which is a highly potent topoisomerase I inhibitor. The major advantages of the technology are: 1) high and homogeneous drug-to-antibody ratio (DAR) availability; 2) potent antitumor activity in conjunction with bystander killing; 3) few safety concerns because of the stable linker limiting release of free drug; and 4) a wide application to therapeutic antibodies. Using this linker-drug technology, we generated an anti-HER2 ADC, namely DS-8201a. DS-8201a, in which almost all eight cysteine residues of the antibody are bound to drug, was effective against trastuzumab DM1 (T-DM1)-insensitive patient-derived xenograft (PDX) models with high HER2 expression and also demonstrated antitumor efficacy against several breast cancer PDX models with low HER2 expression. DS-8201a was well tolerated in rats and monkeys following repeated administration. These results suggest that DS-8201a may be efficacious in a broader population of HER2-positive cancer patients and also confirm the importance of this new class of novel topoisomerase I inhibitor-based ADC technology.

Big data to smart data in Alzheimer's disease: The brain health modeling initiative to foster actionable knowledge.

PubMed

Geerts, Hugo; Dacks, Penny A; Devanarayan, Viswanath; Haas, Magali; Khachaturian, Zaven S; Gordon, Mark Forrest; Maudsley, Stuart; Romero, Klaus; Stephenson, Diane

2016-09-01

Massive investment and technological advances in the collection of extensive and longitudinal information on thousands of Alzheimer patients results in large amounts of data. These "big-data" databases can potentially advance CNS research and drug development. However, although necessary, they are not sufficient, and we posit that they must be matched with analytical methods that go beyond retrospective data-driven associations with various clinical phenotypes. Although these empirically derived associations can generate novel and useful hypotheses, they need to be organically integrated in a quantitative understanding of the pathology that can be actionable for drug discovery and development. We argue that mechanism-based modeling and simulation approaches, where existing domain knowledge is formally integrated using complexity science and quantitative systems pharmacology can be combined with data-driven analytics to generate predictive actionable knowledge for drug discovery programs, target validation, and optimization of clinical development. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Leveraging model-informed approaches for drug discovery and development in the cardiovascular space.

PubMed

Dockendorf, Marissa F; Vargo, Ryan C; Gheyas, Ferdous; Chain, Anne S Y; Chatterjee, Manash S; Wenning, Larissa A

2018-06-01

Cardiovascular disease remains a significant global health burden, and development of cardiovascular drugs in the current regulatory environment often demands large and expensive cardiovascular outcome trials. Thus, the use of quantitative pharmacometric approaches which can help enable early Go/No Go decision making, ensure appropriate dose selection, and increase the likelihood of successful clinical trials, have become increasingly important to help reduce the risk of failed cardiovascular outcomes studies. In addition, cardiovascular safety is an important consideration for many drug development programs, whether or not the drug is designed to treat cardiovascular disease; modeling and simulation approaches also have utility in assessing risk in this area. Herein, examples of modeling and simulation applied at various stages of drug development, spanning from the discovery stage through late-stage clinical development, for cardiovascular programs are presented. Examples of how modeling approaches have been utilized in early development programs across various therapeutic areas to help inform strategies to mitigate the risk of cardiovascular-related adverse events, such as QTc prolongation and changes in blood pressure, are also presented. These examples demonstrate how more informed drug development decisions can be enabled by modeling and simulation approaches in the cardiovascular area.

In vitro models for the prediction of in vivo performance of oral dosage forms.

PubMed

Kostewicz, Edmund S; Abrahamsson, Bertil; Brewster, Marcus; Brouwers, Joachim; Butler, James; Carlert, Sara; Dickinson, Paul A; Dressman, Jennifer; Holm, René; Klein, Sandra; Mann, James; McAllister, Mark; Minekus, Mans; Muenster, Uwe; Müllertz, Anette; Verwei, Miriam; Vertzoni, Maria; Weitschies, Werner; Augustijns, Patrick

2014-06-16

Accurate prediction of the in vivo biopharmaceutical performance of oral drug formulations is critical to efficient drug development. Traditionally, in vitro evaluation of oral drug formulations has focused on disintegration and dissolution testing for quality control (QC) purposes. The connection with in vivo biopharmaceutical performance has often been ignored. More recently, the switch to assessing drug products in a more biorelevant and mechanistic manner has advanced the understanding of drug formulation behavior. Notwithstanding this evolution, predicting the in vivo biopharmaceutical performance of formulations that rely on complex intraluminal processes (e.g. solubilization, supersaturation, precipitation…) remains extremely challenging. Concomitantly, the increasing demand for complex formulations to overcome low drug solubility or to control drug release rates urges the development of new in vitro tools. Development and optimizing innovative, predictive Oral Biopharmaceutical Tools is the main target of the OrBiTo project within the Innovative Medicines Initiative (IMI) framework. A combination of physico-chemical measurements, in vitro tests, in vivo methods, and physiology-based pharmacokinetic modeling is expected to create a unique knowledge platform, enabling the bottlenecks in drug development to be removed and the whole process of drug development to become more efficient. As part of the basis for the OrBiTo project, this review summarizes the current status of predictive in vitro assessment tools for formulation behavior. Both pharmacopoeia-listed apparatus and more advanced tools are discussed. Special attention is paid to major issues limiting the predictive power of traditional tools, including the simulation of dynamic changes in gastrointestinal conditions, the adequate reproduction of gastrointestinal motility, the simulation of supersaturation and precipitation, and the implementation of the solubility-permeability interplay. It is anticipated that the innovative in vitro biopharmaceutical tools arising from the OrBiTo project will lead to improved predictions for in vivo behavior of drug formulations in the GI tract. Copyright © 2013 Elsevier B.V. All rights reserved.

Combinatorially-generated library of 6-fluoroquinolone analogs as potential novel antitubercular agents: a chemometric and molecular modeling assessment.

PubMed

Minovski, Nikola; Perdih, Andrej; Solmajer, Tom

2012-05-01

The virtual combinatorial chemistry approach as a methodology for generating chemical libraries of structurally-similar analogs in a virtual environment was employed for building a general mixed virtual combinatorial library with a total of 53.871 6-FQ structural analogs, introducing the real synthetic pathways of three well known 6-FQ inhibitors. The druggability properties of the generated combinatorial 6-FQs were assessed using an in-house developed drug-likeness filter integrating the Lipinski/Veber rule-sets. The compounds recognized as drug-like were used as an external set for prediction of the biological activity values using a neural-networks (NN) model based on an experimentally-determined set of active 6-FQs. Furthermore, a subset of compounds was extracted from the pool of drug-like 6-FQs, with predicted biological activity, and subsequently used in virtual screening (VS) campaign combining pharmacophore modeling and molecular docking studies. This complex scheme, a powerful combination of chemometric and molecular modeling approaches provided novel QSAR guidelines that could aid in the further lead development of 6-FQs agents.

NIPTE: a multi-university partnership supporting academic drug development.

PubMed

Gurvich, Vadim J; Byrn, Stephen R

2013-10-01

The strategic goal of academic translational research is to accelerate translational science through the improvement and development of resources for moving discoveries across translational barriers through 'first in humans' studies. To achieve this goal, access to drug discovery resources and preclinical IND-enabling infrastructure is crucial. One potential approach of research institutions for coordinating preclinical development, based on a model from the National Institute for Pharmaceutical Technology and Education (NIPTE), can provide academic translational and medical centers with access to a wide variety of enabling infrastructure for developing small molecule clinical candidates in an efficient, cost-effective manner. Copyright © 2013 Elsevier Ltd. All rights reserved.

Application of physiologically based absorption modeling to formulation development of a low solubility, low permeability weak base: mechanistic investigation of food effect.

PubMed

Zhang, Hefei; Xia, Binfeng; Sheng, Jennifer; Heimbach, Tycho; Lin, Tsu-Han; He, Handan; Wang, Yanfeng; Novick, Steven; Comfort, Ann

2014-04-01

Physiologically based pharmacokinetic (PBPK) modeling has been broadly used to facilitate drug development, hereby we developed a PBPK model to systematically investigate the underlying mechanisms of the observed positive food effect of compound X (cpd X) and to strategically explore the feasible approaches to mitigate the food effect. Cpd X is a weak base with pH-dependent solubility; the compound displays significant and dose-dependent food effect in humans, leading to a nonadherence of drug administration. A GastroPlus Opt logD Model was selected for pharmacokinetic simulation under both fasted and fed conditions, where the biopharmaceutic parameters (e.g., solubility and permeability) for cpd X were determined in vitro, and human pharmacokinetic disposition properties were predicted from preclinical data and then optimized with clinical pharmacokinetic data. A parameter sensitivity analysis was performed to evaluate the effect of particle size on the cpd X absorption. A PBPK model was successfully developed for cpd X; its pharmacokinetic parameters (e.g., C max, AUCinf, and t max) predicted at different oral doses were within ±25% of the observed mean values. The in vivo solubility (in duodenum) and mean precipitation time under fed conditions were estimated to be 7.4- and 3.4-fold higher than those under fasted conditions, respectively. The PBPK modeling analysis provided a reasonable explanation for the underlying mechanism for the observed positive food effect of the cpd X in humans. Oral absorption of the cpd X can be increased by reducing the particle size (<100 nm) of an active pharmaceutical ingredient under fasted conditions and therefore, reduce the cpd X food effect correspondingly.

Pluripotent stem cell derived hepatocyte like cells and their potential in toxicity screening.

PubMed

Greenhough, Sebastian; Medine, Claire N; Hay, David C

2010-12-30

Despite considerable progress in modelling human liver toxicity, the requirement still exists for efficient, predictive and cost effective in vitro models to reduce attrition during drug development. Thousands of compounds fail in this process, with hepatotoxicity being one of the significant causes of failure. The cost of clinical studies is substantial, therefore it is essential that toxicological screening is performed early on in the drug development process. Human hepatocytes represent the gold standard model for evaluating drug toxicity, but are a limited resource. Current alternative models are based on immortalised cell lines and animal tissue, but these are limited by poor function, exhibit species variability and show instability in culture. Pluripotent stem cells are an attractive alternative as they are capable of self-renewal and differentiation to all three germ layers, and thereby represent a potentially inexhaustible source of somatic cells. The differentiation of human embryonic stem cells and induced pluripotent stem cells to functional hepatocyte like cells has recently been reported. Further development of this technology could lead to the scalable production of hepatocyte like cells for liver toxicity screening and clinical therapies. Additionally, induced pluripotent stem cell derived hepatocyte like cells may permit in vitro modelling of gene polymorphisms and genetic diseases. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Protein-Based Nanomedicine Platforms for Drug Delivery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ma Ham, Aihui; Tang, Zhiwen; Wu, Hong

2009-08-03

Drug delivery systems have been developed for many years, however some limitations still hurdle the pace of going to clinical phase, for example, poor biodistribution, drug molecule cytotoxicity, tissue damage, quick clearance from the circulation system, solubility and stability of drug molecules. To overcome the limitations of drug delivery, biomaterials have to be developed and applied to drug delivery to protect the drug molecules and to enhance the drug’s efficacy. Protein-based nanomedicine platforms for drug delivery are platforms comprised of naturally self-assembled protein subunits of the same protein or a combination of proteins making up a complete system. They aremore » ideal for drug delivery platforms due to their biocompatibility and biodegradability coupled with low toxicity. A variety of proteins have been used and characterized for drug delivery systems including the ferritin/apoferritin protein cage, plant derived viral capsids, the small Heat shock protein (sHsp) cage, albumin, soy and whey protein, collagen, and gelatin. There are many different types and shapes that have been prepared to deliver drug molecules using protein-based platforms including the various protein cages, microspheres, nanoparticles, hydrogels, films, minirods and minipellets. There are over 30 therapeutic compounds that have been investigated with protein-based drug delivery platforms for the potential treatment of various cancers, infectious diseases, chronic diseases, autoimmune diseases. In protein-based drug delivery platforms, protein cage is the most newly developed biomaterials for drug delivery and therapeutic applications. Their uniform sizes, multifunctions, and biodegradability push them to the frontier for drug delivery. In this review, the recent strategic development of drug delivery has been discussed with a special emphasis upon the polymer based, especially protein-based nanomedicine platforms for drug delivery. The advantages and disadvantages are also discussed for each type of protein based drug delivery system.« less

Economic demand predicts addiction-like behavior and therapeutic efficacy of oxytocin in the rat.

PubMed

Bentzley, Brandon S; Jhou, Thomas C; Aston-Jones, Gary

2014-08-12

Development of new treatments for drug addiction will depend on high-throughput screening in animal models. However, an addiction biomarker fit for rapid testing, and useful in both humans and animals, is not currently available. Economic models are promising candidates. They offer a structured quantitative approach to modeling behavior that is mathematically identical across species, and accruing evidence indicates economic-based descriptors of human behavior may be particularly useful biomarkers of addiction severity. However, economic demand has not yet been established as a biomarker of addiction-like behavior in animals, an essential final step in linking animal and human studies of addiction through economic models. We recently developed a mathematical approach for rapidly modeling economic demand in rats trained to self-administer cocaine. We show here that economic demand, as both a spontaneous trait and induced state, predicts addiction-like behavior, including relapse propensity, drug seeking in abstinence, and compulsive (punished) drug taking. These findings confirm economic demand as a biomarker of addiction-like behavior in rats. They also support the view that excessive motivation plays an important role in addiction while extending the idea that drug dependence represents a shift from initially recreational to compulsive drug use. Finally, we found that economic demand for cocaine predicted the efficacy of a promising pharmacotherapy (oxytocin) in attenuating cocaine-seeking behaviors across individuals, demonstrating that economic measures may be used to rapidly identify the clinical utility of prospective addiction treatments.

Economic demand predicts addiction-like behavior and therapeutic efficacy of oxytocin in the rat

PubMed Central

Bentzley, Brandon S.; Jhou, Thomas C.; Aston-Jones, Gary

2014-01-01

Development of new treatments for drug addiction will depend on high-throughput screening in animal models. However, an addiction biomarker fit for rapid testing, and useful in both humans and animals, is not currently available. Economic models are promising candidates. They offer a structured quantitative approach to modeling behavior that is mathematically identical across species, and accruing evidence indicates economic-based descriptors of human behavior may be particularly useful biomarkers of addiction severity. However, economic demand has not yet been established as a biomarker of addiction-like behavior in animals, an essential final step in linking animal and human studies of addiction through economic models. We recently developed a mathematical approach for rapidly modeling economic demand in rats trained to self-administer cocaine. We show here that economic demand, as both a spontaneous trait and induced state, predicts addiction-like behavior, including relapse propensity, drug seeking in abstinence, and compulsive (punished) drug taking. These findings confirm economic demand as a biomarker of addiction-like behavior in rats. They also support the view that excessive motivation plays an important role in addiction while extending the idea that drug dependence represents a shift from initially recreational to compulsive drug use. Finally, we found that economic demand for cocaine predicted the efficacy of a promising pharmacotherapy (oxytocin) in attenuating cocaine-seeking behaviors across individuals, demonstrating that economic measures may be used to rapidly identify the clinical utility of prospective addiction treatments. PMID:25071176

Mathematical modeling and computational prediction of cancer drug resistance.

PubMed

Sun, Xiaoqiang; Hu, Bin

2017-06-23

Diverse forms of resistance to anticancer drugs can lead to the failure of chemotherapy. Drug resistance is one of the most intractable issues for successfully treating cancer in current clinical practice. Effective clinical approaches that could counter drug resistance by restoring the sensitivity of tumors to the targeted agents are urgently needed. As numerous experimental results on resistance mechanisms have been obtained and a mass of high-throughput data has been accumulated, mathematical modeling and computational predictions using systematic and quantitative approaches have become increasingly important, as they can potentially provide deeper insights into resistance mechanisms, generate novel hypotheses or suggest promising treatment strategies for future testing. In this review, we first briefly summarize the current progress of experimentally revealed resistance mechanisms of targeted therapy, including genetic mechanisms, epigenetic mechanisms, posttranslational mechanisms, cellular mechanisms, microenvironmental mechanisms and pharmacokinetic mechanisms. Subsequently, we list several currently available databases and Web-based tools related to drug sensitivity and resistance. Then, we focus primarily on introducing some state-of-the-art computational methods used in drug resistance studies, including mechanism-based mathematical modeling approaches (e.g. molecular dynamics simulation, kinetic model of molecular networks, ordinary differential equation model of cellular dynamics, stochastic model, partial differential equation model, agent-based model, pharmacokinetic-pharmacodynamic model, etc.) and data-driven prediction methods (e.g. omics data-based conventional screening approach for node biomarkers, static network approach for edge biomarkers and module biomarkers, dynamic network approach for dynamic network biomarkers and dynamic module network biomarkers, etc.). Finally, we discuss several further questions and future directions for the use of computational methods for studying drug resistance, including inferring drug-induced signaling networks, multiscale modeling, drug combinations and precision medicine. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Development and characterization of a novel drug nanocarrier for oral delivery, based on self-assembled β-casein micelles.

PubMed

Bachar, Michal; Mandelbaum, Amitai; Portnaya, Irina; Perlstein, Hadas; Even-Chen, Simcha; Barenholz, Yechezkel; Danino, Dganit

2012-06-10

β-casein is an amphiphilic protein that self-organizes into well-defined core-shell micelles. We developed these micelles as efficient nanocarriers for oral drug delivery. Our model drug is celecoxib, an anti-inflammatory hydrophobic drug utilized for treatment of rheumatoid arthritis and osteoarthritis, now also evaluated as a potent anticancer drug. This system is unique as it enables encapsulation loads >100-fold higher than other β-casein/drug formulations, and does not require additives as do other formulations that have high loadings. This is combined with the ability to lyophilize the formulation without a cryoprotectant, long-term physical and chemical stability of the resulting powder, and fully reversible reconstitution of the structures by rehydration. The dry dosage form, in which >95% of the drug is encapsulated, meets the daily dose. Cryo-TEM and DLS prove that drug encapsulation results in micelle swelling, and X-ray diffraction shows that the encapsulated drug is amorphous. Altogether, our novel dosage form is highly advantageous for oral administration. Copyright © 2012 Elsevier B.V. All rights reserved.

How Monte Carlo heuristics aid to identify the physical processes of drug release kinetics.

PubMed

Lecca, Paola

2018-01-01

We implement a Monte Carlo heuristic algorithm to model drug release from a solid dosage form. We show that with Monte Carlo simulations it is possible to identify and explain the causes of the unsatisfactory predictive power of current drug release models. It is well known that the power-law, the exponential models, as well as those derived from or inspired by them accurately reproduce only the first 60% of the release curve of a drug from a dosage form. In this study, by using Monte Carlo simulation approaches, we show that these models fit quite accurately almost the entire release profile when the release kinetics is not governed by the coexistence of different physico-chemical mechanisms. We show that the accuracy of the traditional models are comparable with those of Monte Carlo heuristics when these heuristics approximate and oversimply the phenomenology of drug release. This observation suggests to develop and use novel Monte Carlo simulation heuristics able to describe the complexity of the release kinetics, and consequently to generate data more similar to those observed in real experiments. Implementing Monte Carlo simulation heuristics of the drug release phenomenology may be much straightforward and efficient than hypothesizing and implementing from scratch complex mathematical models of the physical processes involved in drug release. Identifying and understanding through simulation heuristics what processes of this phenomenology reproduce the observed data and then formalize them in mathematics may allow avoiding time-consuming, trial-error based regression procedures. Three bullet points, highlighting the customization of the procedure. •An efficient heuristics based on Monte Carlo methods for simulating drug release from solid dosage form encodes is presented. It specifies the model of the physical process in a simple but accurate way in the formula of the Monte Carlo Micro Step (MCS) time interval.•Given the experimentally observed curve of drug release, we point out how Monte Carlo heuristics can be integrated in an evolutionary algorithmic approach to infer the mode of MCS best fitting the observed data, and thus the observed release kinetics.•The software implementing the method is written in R language, the free most used language in the bioinformaticians community.

Disruptive innovations: new anti-infectives in the age of resistance

PubMed Central

Tegos, George P.; Hamblin, Michael R.

2013-01-01

This special issue of Current Opinion in Pharmacology is concerned with new developments in antimicrobial drugs and covers innovative strategies for dealing with microbial infection in the age of multi-antibiotic resistance. Despite widespread fears that many infectious diseases may become untreatable, disruptive innovations are in the process of being discovered and developed that may go some way to leading the fight-back against the rising threat. Natural products, quorum sensing inhibitors, biofilm disruptors, gallium-based drugs, cyclodextrin inhibitors of pore-forming toxins, anti-fungals that deal with biofilms, and light based antimicrobial strategies are specifically addressed. New non-vertebrate animal models of infection may facilitate high-throughput screening (HTS) of novel anti-infectives. PMID:24012294

The Therapeutic Utility of Employment in Treating Drug Addiction: Science to Application.

PubMed

Silverman, Kenneth; Holtyn, August F; Morrison, Reed

2016-06-01

Research on a model Therapeutic Workplace has allowed for evaluation of the use of employment in the treatment of drug addiction. Under the Therapeutic Workplace intervention, adults with histories of drug addiction are hired and paid to work. To promote drug abstinence or adherence to addiction medications, participants are required to provide drug-free urine samples or take prescribed addiction medications, respectively, to gain access to the workplace and/or to maintain their maximum rate of pay. Research has shown that the Therapeutic Workplace intervention is effective in promoting and maintaining abstinence from heroin, cocaine and alcohol and in promoting adherence to naltrexone. Three models could be used to implement and maintain employment-based reinforcement in the treatment of drug addiction: A Social Business model, a Cooperative Employer model, and a Wage Supplement model. Under all models, participants initiate abstinence in a training and abstinence initiation phase (Phase 1). Under the Social Business model, Phase 1 graduates are hired as employees in a social business and required to maintain abstinence to maintain employment and/or maximum pay. Under the Cooperative Employer model, cooperating community employers hire graduates of Phase 1 and require them to maintain abstinence to maintain employment and/or maximum pay. Under the Wage Supplement Model, graduates of Phase 1 are offered abstinence-contingent wage supplements if they maintain competitive employment in a community job. Given the severity and persistence of the problem of drug addiction and the lack of treatments that can produce lasting effects, continued development of the Therapeutic Workplace is warranted.

Impact of computational structure-based methods on drug discovery.

PubMed

Reynolds, Charles H

2014-01-01

Structure-based drug design has become an indispensible tool in drug discovery. The emergence of structure-based design is due to gains in structural biology that have provided exponential growth in the number of protein crystal structures, new computational algorithms and approaches for modeling protein-ligand interactions, and the tremendous growth of raw computer power in the last 30 years. Computer modeling and simulation have made major contributions to the discovery of many groundbreaking drugs in recent years. Examples are presented that highlight the evolution of computational structure-based design methodology, and the impact of that methodology on drug discovery.

USING PHOTOVOICE WITH YOUTH TO DEVELOP A DRUG PREVENTION PROGRAM IN A RURAL HAWAIIAN COMMUNITY

PubMed Central

Helm, Susana; Lee, Wayde; Hanakahi, Vanda; Gleason, Krissy; McCarthy, Kayne

2015-01-01

Introduction Substance use represents a significant and persistent health disparity among Native Hawaiian youth and communities. A community-university participatory action research project was conducted to develop a Native Hawaiian model of drug prevention. Methods Ten youth participated in eight Photovoice focus groups. Focus group transcripts and the youths’ SHOWED (see, happening, our, why, empower, do) worksheets were analyzed. Results Emergent analyses are described regarding focus group theme identification and the meaning of each theme. Youth-selected exemplary photographs and researcher-selected exemplary quotations are provided. Implications Native Hawaiian drug prevention will be place-based in culturally significant community locations, experiential, and guided by multigenerational teaching and learning. PMID:25768388

Self-assembling toxin-based nanoparticles as self-delivered antitumoral drugs.

PubMed

Sánchez-García, Laura; Serna, Naroa; Álamo, Patricia; Sala, Rita; Céspedes, María Virtudes; Roldan, Mònica; Sánchez-Chardi, Alejandro; Unzueta, Ugutz; Casanova, Isolda; Mangues, Ramón; Vázquez, Esther; Villaverde, Antonio

2018-03-28

Loading capacity and drug leakage from vehicles during circulation in blood is a major concern when developing nanoparticle-based cell-targeted cytotoxics. To circumvent this potential issue it would be convenient the engineering of drugs as self-delivered nanoscale entities, devoid of any heterologous carriers. In this context, we have here engineered potent protein toxins, namely segments of the diphtheria toxin and the Pseudomonas aeruginosa exotoxin as self-assembling, self-delivered therapeutic materials targeted to CXCR4 + cancer stem cells. The systemic administration of both nanostructured drugs in a colorectal cancer xenograft mouse model promotes efficient and specific local destruction of target tumor tissues and a significant reduction of the tumor volume. This observation strongly supports the concept of intrinsically functional protein nanoparticles, which having a dual role as drug and carrier, are designed to be administered without the assistance of heterologous vehicles. Copyright © 2018 Elsevier B.V. All rights reserved.

ADME-Space: a new tool for medicinal chemists to explore ADME properties.

PubMed

Bocci, Giovanni; Carosati, Emanuele; Vayer, Philippe; Arrault, Alban; Lozano, Sylvain; Cruciani, Gabriele

2017-07-25

We introduce a new chemical space for drugs and drug-like molecules, exclusively based on their in silico ADME behaviour. This ADME-Space is based on self-organizing map (SOM) applied to 26,000 molecules. Twenty accurate QSPR models, describing important ADME properties, were developed and, successively, used as new molecular descriptors not related to molecular structure. Applications include permeability, active transport, metabolism and bioavailability studies, but the method can be even used to discuss drug-drug interactions (DDIs) or it can be extended to additional ADME properties. Thus, the ADME-Space opens a new framework for the multi-parametric data analysis in drug discovery where all ADME behaviours of molecules are condensed in one map: it allows medicinal chemists to simultaneously monitor several ADME properties, to rapidly select optimal ADME profiles, retrieve warning on potential ADME problems and DDIs or select proper in vitro experiments.

Nanogel Carrier Design for Targeted Drug Delivery

PubMed Central

Eckmann, D. M.; Composto, R. J.; Tsourkas, A.; Muzykantov, V. R.

2014-01-01

Polymer-based nanogel formulations offer features attractive for drug delivery, including ease of synthesis, controllable swelling and viscoelasticity as well as drug loading and release characteristics, passive and active targeting, and the ability to formulate nanogel carriers that can respond to biological stimuli. These unique features and low toxicity make the nanogels a favorable option for vascular drug targeting. In this review, we address key chemical and biological aspects of nanogel drug carrier design. In particular, we highlight published studies of nanogel design, descriptions of nanogel functional characteristics and their behavior in biological models. These studies form a compendium of information that supports the scientific and clinical rationale for development of this carrier for targeted therapeutic interventions. PMID:25485112

Designing of peptides with desired half-life in intestine-like environment.

PubMed

Sharma, Arun; Singla, Deepak; Rashid, Mamoon; Raghava, Gajendra Pal Singh

2014-08-20

In past, a number of peptides have been reported to possess highly diverse properties ranging from cell penetrating, tumor homing, anticancer, anti-hypertensive, antiviral to antimicrobials. Owing to their excellent specificity, low-toxicity, rich chemical diversity and availability from natural sources, FDA has successfully approved a number of peptide-based drugs and several are in various stages of drug development. Though peptides are proven good drug candidates, their usage is still hindered mainly because of their high susceptibility towards proteases degradation. We have developed an in silico method to predict the half-life of peptides in intestine-like environment and to design better peptides having optimized physicochemical properties and half-life. In this study, we have used 10mer (HL10) and 16mer (HL16) peptides dataset to develop prediction models for peptide half-life in intestine-like environment. First, SVM based models were developed on HL10 dataset which achieved maximum correlation R/R2 of 0.57/0.32, 0.68/0.46, and 0.69/0.47 using amino acid, dipeptide and tripeptide composition, respectively. Secondly, models developed on HL16 dataset showed maximum R/R2 of 0.91/0.82, 0.90/0.39, and 0.90/0.31 using amino acid, dipeptide and tripeptide composition, respectively. Furthermore, models that were developed on selected features, achieved a correlation (R) of 0.70 and 0.98 on HL10 and HL16 dataset, respectively. Preliminary analysis suggests the role of charged residue and amino acid size in peptide half-life/stability. Based on above models, we have developed a web server named HLP (Half Life Prediction), for predicting and designing peptides with desired half-life. The web server provides three facilities; i) half-life prediction, ii) physicochemical properties calculation and iii) designing mutant peptides. In summary, this study describes a web server 'HLP' that has been developed for assisting scientific community for predicting intestinal half-life of peptides and to design mutant peptides with better half-life and physicochemical properties. HLP models were trained using a dataset of peptides whose half-lives have been determined experimentally in crude intestinal proteases preparation. Thus, HLP server will help in designing peptides possessing the potential to be administered via oral route (http://www.imtech.res.in/raghava/hlp/).

Development of concepts on the interaction of drugs with opioid receptors

NASA Astrophysics Data System (ADS)

Kuzmina, N. E.; Kuzmin, V. S.

2011-02-01

The development of concepts on the molecular mechanisms of the action of medicinal drugs on the opioid receptors is briefly surveyed. The modern point of view on the mechanism of activation of opioid receptors is given based on the data from chimeric and site-directed mutagenesis of the cloned opioid receptors and the computer-aided simulations of the reception zone and ligand-receptor complexes. Three-dimensional models of the opioid pharmacophore derived by both conventional methods and a comparative analysis of molecular fields are described in detail.

Evidence based herbal drug standardization approach in coping with challenges of holistic management of diabetes: a dreadful lifestyle disorder of 21st century

PubMed Central

2013-01-01

Plants by virtue of its composition of containing multiple constituents developed during its growth under various environmental stresses providing a plethora of chemical families with medicinal utility. Researchers are exploring this wealth and trying to decode its utility for enhancing health standards of human beings. Diabetes is dreadful lifestyle disorder of 21st century caused due to lack of insulin production or insulin physiological unresponsiveness. The chronic impact of untreated diabetes significantly affects vital organs. The allopathic medicines have five classes of drugs, or otherwise insulin in Type I diabetes, targeting insulin secretion, decreasing effect of glucagon, sensitization of receptors for enhanced glucose uptake etc. In addition, diet management, increased food fiber intake, Resistant Starch intake and routine exercise aid in managing such dangerous metabolic disorder. One of the key factors that limit commercial utility of herbal drugs is standardization. Standardization poses numerous challenges related to marker identification, active principle(s), lack of defined regulations, non-availability of universally acceptable technical standards for testing and implementation of quality control/safety standard (toxicological testing). The present study proposed an integrated herbal drug development & standardization model which is an amalgamation of Classical Approach of Ayurvedic Therapeutics, Reverse Pharmacological Approach based on Observational Therapeutics, Technical Standards for complete product cycle, Chemi-informatics, Herbal Qualitative Structure Activity Relationship and Pharmacophore modeling and, Post-Launch Market Analysis. Further studies are warranted to ensure that an effective herbal drug standardization methodology will be developed, backed by a regulatory standard guide the future research endeavors in more focused manner. PMID:23822656

Evidence based herbal drug standardization approach in coping with challenges of holistic management of diabetes: a dreadful lifestyle disorder of 21st century.

PubMed

Chawla, Raman; Thakur, Pallavi; Chowdhry, Ayush; Jaiswal, Sarita; Sharma, Anamika; Goel, Rajeev; Sharma, Jyoti; Priyadarshi, Smruti Sagar; Kumar, Vinod; Sharma, Rakesh Kumar; Arora, Rajesh

2013-07-04

Plants by virtue of its composition of containing multiple constituents developed during its growth under various environmental stresses providing a plethora of chemical families with medicinal utility. Researchers are exploring this wealth and trying to decode its utility for enhancing health standards of human beings. Diabetes is dreadful lifestyle disorder of 21st century caused due to lack of insulin production or insulin physiological unresponsiveness. The chronic impact of untreated diabetes significantly affects vital organs. The allopathic medicines have five classes of drugs, or otherwise insulin in Type I diabetes, targeting insulin secretion, decreasing effect of glucagon, sensitization of receptors for enhanced glucose uptake etc. In addition, diet management, increased food fiber intake, Resistant Starch intake and routine exercise aid in managing such dangerous metabolic disorder. One of the key factors that limit commercial utility of herbal drugs is standardization. Standardization poses numerous challenges related to marker identification, active principle(s), lack of defined regulations, non-availability of universally acceptable technical standards for testing and implementation of quality control/safety standard (toxicological testing). The present study proposed an integrated herbal drug development & standardization model which is an amalgamation of Classical Approach of Ayurvedic Therapeutics, Reverse Pharmacological Approach based on Observational Therapeutics, Technical Standards for complete product cycle, Chemi-informatics, Herbal Qualitative Structure Activity Relationship and Pharmacophore modeling and, Post-Launch Market Analysis. Further studies are warranted to ensure that an effective herbal drug standardization methodology will be developed, backed by a regulatory standard guide the future research endeavors in more focused manner.

Simulation and Prediction of the Drug-Drug Interaction Potential of Naloxegol by Physiologically Based Pharmacokinetic Modeling.

PubMed

Zhou, D; Bui, K; Sostek, M; Al-Huniti, N

2016-05-01

Naloxegol, a peripherally acting μ-opioid receptor antagonist for the treatment of opioid-induced constipation, is a substrate for cytochrome P450 (CYP) 3A4/3A5 and the P-glycoprotein (P-gp) transporter. By integrating in silico, preclinical, and clinical pharmacokinetic (PK) findings, minimal and full physiologically based pharmacokinetic (PBPK) models were developed to predict the drug-drug interaction (DDI) potential for naloxegol. The models reasonably predicted the observed changes in naloxegol exposure with ketoconazole (increase of 13.1-fold predicted vs. 12.9-fold observed), diltiazem (increase of 2.8-fold predicted vs. 3.4-fold observed), rifampin (reduction of 76% predicted vs. 89% observed), and quinidine (increase of 1.2-fold predicted vs. 1.4-fold observed). The moderate CYP3A4 inducer efavirenz was predicted to reduce naloxegol exposure by ∼50%, whereas weak CYP3A inhibitors were predicted to minimally affect exposure. In summary, the PBPK models reasonably estimated interactions with various CYP3A modulators and can be used to guide dosing in clinical practice when naloxegol is coadministered with such agents. © 2016 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

In Vivo Methods for the Assessment of Topical Drug Bioavailability

PubMed Central

Herkenne, Christophe; Alberti, Ingo; Naik, Aarti; Kalia, Yogeshvar N.; Mathy, François-Xavier; Préat, Véronique

2007-01-01

This paper reviews some current methods for the in vivo assessment of local cutaneous bioavailability in humans after topical drug application. After an introduction discussing the importance of local drug bioavailability assessment and the limitations of model-based predictions, the focus turns to the relevance of experimental studies. The available techniques are then reviewed in detail, with particular emphasis on the tape stripping and microdialysis methodologies. Other less developed techniques, including the skin biopsy, suction blister, follicle removal and confocal Raman spectroscopy techniques are also described. PMID:17985216

Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data.

PubMed

Aliper, Alexander; Plis, Sergey; Artemov, Artem; Ulloa, Alvaro; Mamoshina, Polina; Zhavoronkov, Alex

2016-07-05

Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics, and autonomous driving. In this paper we demonstrate how deep neural networks (DNN) trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based on their transcriptional profiles. We used the perturbation samples of 678 drugs across A549, MCF-7, and PC-3 cell lines from the LINCS Project and linked those to 12 therapeutic use categories derived from MeSH. To train the DNN, we utilized both gene level transcriptomic data and transcriptomic data processed using a pathway activation scoring algorithm, for a pooled data set of samples perturbed with different concentrations of the drug for 6 and 24 hours. In both pathway and gene level classification, DNN achieved high classification accuracy and convincingly outperformed the support vector machine (SVM) model on every multiclass classification problem, however, models based on pathway level data performed significantly better. For the first time we demonstrate a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions. We also propose using deep neural net confusion matrices for drug repositioning. This work is a proof of principle for applying deep learning to drug discovery and development.

Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data

PubMed Central

Aliper, Alexander; Plis, Sergey; Artemov, Artem; Ulloa, Alvaro; Mamoshina, Polina; Zhavoronkov, Alex

2016-01-01

Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics and autonomous driving. In this paper we demonstrate how deep neural networks (DNN) trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based on their transcriptional profiles. We used the perturbation samples of 678 drugs across A549, MCF‐7 and PC‐3 cell lines from the LINCS project and linked those to 12 therapeutic use categories derived from MeSH. To train the DNN, we utilized both gene level transcriptomic data and transcriptomic data processed using a pathway activation scoring algorithm, for a pooled dataset of samples perturbed with different concentrations of the drug for 6 and 24 hours. In both gene and pathway level classification, DNN convincingly outperformed support vector machine (SVM) model on every multiclass classification problem, however, models based on a pathway level classification perform better. For the first time we demonstrate a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions. We also propose using deep neural net confusion matrices for drug repositioning. This work is a proof of principle for applying deep learning to drug discovery and development. PMID:27200455

Highly predictive and interpretable models for PAMPA permeability.

PubMed

Sun, Hongmao; Nguyen, Kimloan; Kerns, Edward; Yan, Zhengyin; Yu, Kyeong Ri; Shah, Pranav; Jadhav, Ajit; Xu, Xin

2017-02-01

Cell membrane permeability is an important determinant for oral absorption and bioavailability of a drug molecule. An in silico model predicting drug permeability is described, which is built based on a large permeability dataset of 7488 compound entries or 5435 structurally unique molecules measured by the same lab using parallel artificial membrane permeability assay (PAMPA). On the basis of customized molecular descriptors, the support vector regression (SVR) model trained with 4071 compounds with quantitative data is able to predict the remaining 1364 compounds with the qualitative data with an area under the curve of receiver operating characteristic (AUC-ROC) of 0.90. The support vector classification (SVC) model trained with half of the whole dataset comprised of both the quantitative and the qualitative data produced accurate predictions to the remaining data with the AUC-ROC of 0.88. The results suggest that the developed SVR model is highly predictive and provides medicinal chemists a useful in silico tool to facilitate design and synthesis of novel compounds with optimal drug-like properties, and thus accelerate the lead optimization in drug discovery. Copyright © 2016 Elsevier Ltd. All rights reserved.

Scaling Pharmacodynamics from In Vitro and Preclinical Animal Studies to Humans

PubMed Central

Mager, Donald E.; Woo, Sukyung; Jusko, William J.

2013-01-01

Summary An important feature of mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) models is the identification of drug- and system-specific factors that determine the intensity and time-course of pharmacological effects. This provides an opportunity to integrate information obtained from in vitro bioassays and preclinical pharmacological studies in animals to anticipate the clinical and adverse responses to drugs in humans. The fact that contemporary PK/PD modeling continues to evolve and seeks to emulate systems level properties should provide enhanced capabilities to scale-up pharmacodynamic data. Critical steps in drug discovery and development, such as lead compound and first in human dose selection, may become more efficient with the implementation and further refinement of translational PK/PD modeling. In this review, we highlight fundamental principles in pharmacodynamics and the basic expectations for in vitro bioassays and traditional allometric scaling in PK/PD modeling. Discussion of PK/PD modeling efforts for recombinant human erythropoietin is also included as a case study showing the potential for advanced systems analysis to facilitate extrapolations and improve understanding of inter-species differences in drug responses. PMID:19252333

Stem cells: a model for screening, discovery and development of drugs.

PubMed

Kitambi, Satish Srinivas; Chandrasekar, Gayathri

2011-01-01

The identification of normal and cancerous stem cells and the recent advances made in isolation and culture of stem cells have rapidly gained attention in the field of drug discovery and regenerative medicine. The prospect of performing screens aimed at proliferation, directed differentiation, and toxicity and efficacy studies using stem cells offers a reliable platform for the drug discovery process. Advances made in the generation of induced pluripotent stem cells from normal or diseased tissue serves as a platform to perform drug screens aimed at developing cell-based therapies against conditions like Parkinson's disease and diabetes. This review discusses the application of stem cells and cancer stem cells in drug screening and their role in complementing, reducing, and replacing animal testing. In addition to this, target identification and major advances in the field of personalized medicine using induced pluripotent cells are also discussed.

Pharmaceutical Product Lead Optimization for Better In vivo Bioequivalence Performance: A case study of Diclofenac Sodium Extended Release Matrix Tablets.

PubMed

Shahiwala, Aliasgar; Zarar, Aisha

2018-01-01

In order to prove the validity of a new formulation, a considerable amount of effort is required to study bioequivalence, which not only increases the burden of carrying out a number of bioequivalence studies but also eventually increases the cost of the optimization process. The aim of the present study was to develop sustained release matrix tablets containing diclofenac sodium using natural polymers and to demonstrate step by step process of product development till the prediction of in vivo marketed product equivalence of the developed product. Different batches of tablets were prepared by direct compression. In vitro drug release studies were performed as per USP. The drug release data were assessed using model-dependent, modelindependent and convolution approaches. Drug release profiles showed that extended release action were in the following order: Gum Tragacanth > Sodium Alginate > Gum Acacia. Amongst the different batches prepared, only F1 and F8 passed the USP criteria of drug release. Developed formulas were found to fit Higuchi kinetics model with Fickian (case I) diffusion-mediated release mechanism. Model- independent kinetics confirmed that total of four batches were passed depending on the similarity factors based on the comparison with the marketed Diclofenac. The results of in vivo predictive convolution model indicated that predicted AUC, Cmax and Tmax values for batch F8 were similar to that of marketed product. This study provides simple yet effective outline of pharmaceutical product development process that will minimize the formulation development trials and maximize the product success in bioequivalence studies. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Administrative Process and Criteria Ranking for Drug Entering Health Insurance List in Iran-TOPSIS-Based Consensus Model.

PubMed

Viyanchi, Amir; Rajabzadeh Ghatari, Ali; Rasekh, Hamid Reza; SafiKhani, HamidReza

2016-01-01

The purposes of our study were to identify a drug entry process, collect, and prioritize criteria for selecting drugs for the list of basic health insurance commitments to prepare an "evidence based reimbursement eligibility plan" in Iran. The 128 noticeable criteria were found when studying the health insurance systems of developed countries. Four parts (involving criteria) formed the first questionnaire: evaluation of evidences quality, clinical evaluation, economic evaluation, and managerial appraisal. The 85 experts (purposed sampling) were asked to mark the importance of each criterion from 1 to 100 as 1 representing the least and 100 the most important criterion and 45 out of them replied completely. Then, in the next questionnaire, we evaluated the 48 remainder criteria by the same45 participants under four sub-criteria (Cost calculation simplicity, Interpretability, Precision, and Updating capability of a criterion). After collecting the replies, the remainder criteria were ranked by TOPSIS method. Softwares "SPSS" 17 and Excel 2007 were used. The ranks of the five most important criteria which were found for drug approval based on TOPSIS are as follows: 1-domestic production (0.556), 2-duration of using (0.399), 3-independence of the assessment group (0.363) 4-impact budgeting (0.362) 5-decisions of other countries about the same drug (0.358). The numbers in parenthesis are relative closeness alternatives in relation to the ideal solution. This model gave a scientific model for judging fairly on the acceptance of novelty medicines.

Structural Biology Fact Sheet

MedlinePlus

... of a medicine developed using structure-based drug design? Researchers used structure-based drug design to develop some anti-HIV drugs. HIV protease ... in their natural state and allow them to design highly specific drugs. What does the future hold ...

A simplified PBPK modeling approach for prediction of pharmacokinetics of four primarily renally excreted and CYP3A metabolized compounds during pregnancy.

PubMed

Xia, Binfeng; Heimbach, Tycho; Gollen, Rakesh; Nanavati, Charvi; He, Handan

2013-10-01

During pregnancy, a drug's pharmacokinetics may be altered and hence anticipation of potential systemic exposure changes is highly desirable. Physiologically based pharmacokinetics (PBPK) models have recently been used to influence clinical trial design or to facilitate regulatory interactions. Ideally, whole-body PBPK models can be used to predict a drug's systemic exposure in pregnant women based on major physiological changes which can impact drug clearance (i.e., in the kidney and liver) and distribution (i.e., adipose and fetoplacental unit). We described a simple and readily implementable multitissue/organ whole-body PBPK model with key pregnancy-related physiological parameters to characterize the PK of reference drugs (metformin, digoxin, midazolam, and emtricitabine) in pregnant women compared with the PK in nonpregnant or postpartum (PP) women. Physiological data related to changes in maternal body weight, tissue volume, cardiac output, renal function, blood flows, and cytochrome P450 activity were collected from the literature and incorporated into the structural PBPK model that describes HV or PP women PK data. Subsequently, the changes in exposure (area under the curve (AUC) and maximum concentration (C max)) in pregnant women were simulated. Model-simulated PK profiles were overall in agreement with observed data. The prediction fold error for C max and AUC ratio (pregnant vs. nonpregnant) was less than 1.3-fold, indicating that the pregnant PBPK model is useful. The utilization of this simplified model in drug development may aid in designing clinical studies to identify potential exposure changes in pregnant women a priori for compounds which are mainly eliminated renally or metabolized by CYP3A4.

[Categories and characteristics of BPH drug evaluation models: a comparative study].

PubMed

Huang, Dong-Yan; Wu, Jian-Hui; Sun, Zu-Yue

2014-02-01

Benign prostatic hyperplasia (BPH) is a worldwide common disease in men over 50 years old, and the exact cause of BPH remains largely unknown. In order to elucidate its pathogenesis and screen effective drugs for the treatment of BPH, many BPH models have been developed at home and abroad. This article presents a comprehensive analysis of the categories and characteristics of BPH drug evaluation models, highlighting the application value of each model, to provide a theoretical basis for the development of BPH drugs.

Human hepatocytes derived from pluripotent stem cells: a promising cell model for drug hepatotoxicity screening.

PubMed

Gómez-Lechón, María José; Tolosa, Laia

2016-09-01

Drug-induced liver injury (DILI) is a frequent cause of failure in both clinical and post-approval stages of drug development, and poses a key challenge to the pharmaceutical industry. Current animal models offer poor prediction of human DILI. Although several human cell-based models have been proposed for the detection of human DILI, human primary hepatocytes remain the gold standard for preclinical toxicological screening. However, their use is hindered by their limited availability, variability and phenotypic instability. In contrast, pluripotent stem cells, which include embryonic and induced pluripotent stem cells (iPSCs), proliferate extensively in vitro and can be differentiated into hepatocytes by the addition of soluble factors. This provides a stable source of hepatocytes for multiple applications, including early preclinical hepatotoxicity screening. In addition, iPSCs also have the potential to establish genotype-specific cells from different individuals, which would increase the predictivity of toxicity assays allowing more successful clinical trials. Therefore, the generation of human hepatocyte-like cells derived from pluripotent stem cells seems to be promising for overcoming limitations of hepatocyte preparations, and it is expected to have a substantial repercussion in preclinical hepatotoxicity risk assessment in early drug development stages.

Preclinical evaluation of anti-HIV microbicide products: New models and biomarkers.

PubMed

Doncel, Gustavo F; Clark, Meredith R

2010-12-01

A safe and effective microbicide product designed to prevent sexual transmission of HIV-1 rests on a solid foundation provided by the proper selection and preclinical characterization of both its active pharmaceutical ingredient (API) and formulation. The evaluation of API and formulation physicochemical properties, drug release, specific antiviral activity, cell and tissue toxicity, organ toxicity, pharmacokinetics, and pharmacodynamics and efficacy provides information to understand the product, make go/no go decisions in the critical path of product development and complete a regulatory dossier to file an investigational new drug (IND) with the US Food and Drug Administration. Incorporation of new models, assays and biomarkers has expanded our ability to understand the mechanisms of action underlying microbicide toxicity and efficacy, enabling a more rational selection of drug and formulation candidates. This review presents an overview of the models and endpoints used to comprehensively evaluate an anti-HIV microbicide in preclinical development. This article forms part of a special supplement on presentations covering HIV transmission and microbicides, based on the symposium "Trends in Microbicide Formulations", held on 25 and 26 January 2010, Arlington, VA. Copyright © 2010 Elsevier B.V. All rights reserved.

iGPCR-Drug: A Web Server for Predicting Interaction between GPCRs and Drugs in Cellular Networking

PubMed Central

Xiao, Xuan; Min, Jian-Liang; Wang, Pu; Chou, Kuo-Chen

2013-01-01

Involved in many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, G-protein-coupled receptors (GPCRs) are among the most frequent targets of therapeutic drugs. It is time-consuming and expensive to determine whether a drug and a GPCR are to interact with each other in a cellular network purely by means of experimental techniques. Although some computational methods were developed in this regard based on the knowledge of the 3D (dimensional) structure of protein, unfortunately their usage is quite limited because the 3D structures for most GPCRs are still unknown. To overcome the situation, a sequence-based classifier, called “iGPCR-drug”, was developed to predict the interactions between GPCRs and drugs in cellular networking. In the predictor, the drug compound is formulated by a 2D (dimensional) fingerprint via a 256D vector, GPCR by the PseAAC (pseudo amino acid composition) generated with the grey model theory, and the prediction engine is operated by the fuzzy K-nearest neighbour algorithm. Moreover, a user-friendly web-server for iGPCR-drug was established at http://www.jci-bioinfo.cn/iGPCR-Drug/. For the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated math equations presented in this paper just for its integrity. The overall success rate achieved by iGPCR-drug via the jackknife test was 85.5%, which is remarkably higher than the rate by the existing peer method developed in 2010 although no web server was ever established for it. It is anticipated that iGPCR-Drug may become a useful high throughput tool for both basic research and drug development, and that the approach presented here can also be extended to study other drug – target interaction networks. PMID:24015221

Bactericidal activity of OPC-67683 against drug-tolerant Mycobacterium tuberculosis.

PubMed

Saliu, Oluwabunmi Y; Crismale, Catina; Schwander, Stephan K; Wallis, Robert S

2007-11-01

There is an urgent need for drugs that hasten sterilization in tuberculosis; however, we presently lack indicators of this activity to guide early drug development. We previously described a novel in vitro assay to study mycobacterial phenotypic drug tolerance, in which sterilizing activity could be assessed. OPC-67,683 is a novel imidazooxazole that accelerates sterilization in the mouse tuberculosis model. The present study was conducted to determine the activity of OPC-67,683 in the in vitro tolerance model using drug-tolerant clinical Mycobacterium tuberculosis strains. Tolerance was assessed in Bactec radiometric culture as: (i) delayed decline in growth index during 14 days of drug exposure; (ii) shorter time to positivity of subcultures following drug exposure. Four isolates were selected from among 16 surveyed, based on delayed killing by isoniazid and OPC-67,683. Unlike isoniazid and rifampicin, whose rates of killing were concentration-independent, OPC-67,683 showed concentration-dependent effects that, at the highest dose levels tested (1.0 microg/mL), were superior to isoniazid and equal to rifampicin. The sterilizing activity of OPC-67683 against drug-tolerant M. tuberculosis in the Bactec model is consistent with its activity in mice. Further studies are warranted to examine the effects of OPC-67683 on mycobacterial persistence in tuberculous patients and to determine the biological basis of tolerance in the model.