Molecular determinants of blood-brain barrier permeation.

PubMed

Geldenhuys, Werner J; Mohammad, Afroz S; Adkins, Chris E; Lockman, Paul R

2015-01-01

The blood-brain barrier (BBB) is a microvascular unit which selectively regulates the permeability of drugs to the brain. With the rise in CNS drug targets and diseases, there is a need to be able to accurately predict a priori which compounds in a company database should be pursued for favorable properties. In this review, we will explore the different computational tools available today, as well as underpin these to the experimental methods used to determine BBB permeability. These include in vitro models and the in vivo models that yield the dataset we use to generate predictive models. Understanding of how these models were experimentally derived determines our accurate and predicted use for determining a balance between activity and BBB distribution.

Molecular determinants of blood–brain barrier permeation

PubMed Central

Geldenhuys, Werner J; Mohammad, Afroz S; Adkins, Chris E; Lockman, Paul R

2015-01-01

The blood–brain barrier (BBB) is a microvascular unit which selectively regulates the permeability of drugs to the brain. With the rise in CNS drug targets and diseases, there is a need to be able to accurately predict a priori which compounds in a company database should be pursued for favorable properties. In this review, we will explore the different computational tools available today, as well as underpin these to the experimental methods used to determine BBB permeability. These include in vitro models and the in vivo models that yield the dataset we use to generate predictive models. Understanding of how these models were experimentally derived determines our accurate and predicted use for determining a balance between activity and BBB distribution. PMID:26305616

Drug release control and system understanding of sucrose esters matrix tablets by artificial neural networks.

PubMed

Chansanroj, Krisanin; Petrović, Jelena; Ibrić, Svetlana; Betz, Gabriele

2011-10-09

Artificial neural networks (ANNs) were applied for system understanding and prediction of drug release properties from direct compacted matrix tablets using sucrose esters (SEs) as matrix-forming agents for controlled release of a highly water soluble drug, metoprolol tartrate. Complexity of the system was presented through the effects of SE concentration and tablet porosity at various hydrophilic-lipophilic balance (HLB) values of SEs ranging from 0 to 16. Both effects contributed to release behaviors especially in the system containing hydrophilic SEs where swelling phenomena occurred. A self-organizing map neural network (SOM) was applied for visualizing interrelation among the variables and multilayer perceptron neural networks (MLPs) were employed to generalize the system and predict the drug release properties based on HLB value and concentration of SEs and tablet properties, i.e., tablet porosity, volume and tensile strength. Accurate prediction was obtained after systematically optimizing network performance based on learning algorithm of MLP. Drug release was mainly attributed to the effects of SEs, tablet volume and tensile strength in multi-dimensional interrelation whereas tablet porosity gave a small impact. Ability of system generalization and accurate prediction of the drug release properties proves the validity of SOM and MLPs for the formulation modeling of direct compacted matrix tablets containing controlled release agents of different material properties. Copyright © 2011 Elsevier B.V. All rights reserved.

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

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

Global vision of druggability issues: applications and perspectives.

PubMed

Abi Hussein, Hiba; Geneix, Colette; Petitjean, Michel; Borrel, Alexandre; Flatters, Delphine; Camproux, Anne-Claude

2017-02-01

During the preliminary stage of a drug discovery project, the lack of druggability information and poor target selection are the main causes of frequent failures. Elaborating on accurate computational druggability prediction methods is a requirement for prioritizing target selection, designing new drugs and avoiding side effects. In this review, we describe a survey of recently reported druggability prediction methods mainly based on networks, statistical pocket druggability predictions and virtual screening. An application for a frequent mutation of p53 tumor suppressor is presented, illustrating the complementarity of druggability prediction approaches, the remaining challenges and potential new drug development perspectives. Copyright © 2016 Elsevier Ltd. All rights reserved.

Personalized Cancer Medicine: An Organoid Approach.

PubMed

Aboulkheyr Es, Hamidreza; Montazeri, Leila; Aref, Amir Reza; Vosough, Massoud; Baharvand, Hossein

2018-04-01

Personalized cancer therapy applies specific treatments to each patient. Using personalized tumor models with similar characteristics to the original tumors may result in more accurate predictions of drug responses in patients. Tumor organoid models have several advantages over pre-existing models, including conserving the molecular and cellular composition of the original tumor. These advantages highlight the tremendous potential of tumor organoids in personalized cancer therapy, particularly preclinical drug screening and predicting patient responses to selected treatment regimens. Here, we highlight the advantages, challenges, and translational potential of tumor organoids in personalized cancer therapy and focus on gene-drug associations, drug response prediction, and treatment selection. Finally, we discuss how microfluidic technology can contribute to immunotherapy drug screening in tumor organoids. Copyright © 2017 Elsevier Ltd. All rights reserved.

Possibility of Predicting Serotonin Transporter Occupancy From the In Vitro Inhibition Constant for Serotonin Transporter, the Clinically Relevant Plasma Concentration of Unbound Drugs, and Their Profiles for Substrates of Transporters.

PubMed

Yahata, Masahiro; Chiba, Koji; Watanabe, Takao; Sugiyama, Yuichi

2017-09-01

Accurate prediction of target occupancy facilitates central nervous system drug development. In this review, we discuss the predictability of serotonin transporter (SERT) occupancy in human brain estimated from in vitro K i values for human SERT and plasma concentrations of unbound drug (C u,plasma ), as well as the impact of drug transporters in the blood-brain barrier. First, the geometric means of in vitro K i values were compared with the means of in vivo K i values (K i,u,plasma ) which were calculated as C u,plasma values at 50% occupancy of SERT obtained from previous clinical positron emission tomography/single photon emission computed tomography imaging studies for 6 selective serotonin transporter reuptake inhibitors and 3 serotonin norepinephrine reuptake inhibitors. The in vitro K i values for 7 drugs were comparable to their in vivo K i,u,plasma values within 3-fold difference. SERT occupancy was overestimated for 5 drugs (P-glycoprotein substrates) and underestimated for 2 drugs (presumably uptake transporter substrates, although no evidence exists as yet). In conclusion, prediction of human SERT occupancy from in vitro K i values and C u,plasma was successful for drugs that are not transporter substrates and will become possible in future even for transporter substrates, once the transporter activities will be accurately estimated from in vitro experiments. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Predicting drug hydrolysis based on moisture uptake in various packaging designs.

PubMed

Naversnik, Klemen; Bohanec, Simona

2008-12-18

An attempt was made to predict the stability of a moisture sensitive drug product based on the knowledge of the dependence of the degradation rate on tablet moisture. The moisture increase inside a HDPE bottle with the drug formulation was simulated with the sorption-desorption moisture transfer model, which, in turn, allowed an accurate prediction of the drug degradation kinetics. The stability prediction, obtained by computer simulation, was made in a considerably shorter time frame and required little resources compared to a conventional stability study. The prediction was finally upgraded to a stochastic Monte Carlo simulation, which allowed quantitative incorporation of uncertainty, stemming from various sources. The resulting distribution of the outcome of interest (amount of degradation product at expiry) is a comprehensive way of communicating the result along with its uncertainty, superior to single-value results or confidence intervals.

Solubility prediction, solvate and cocrystal screening as tools for rational crystal engineering.

PubMed

Loschen, Christoph; Klamt, Andreas

2015-06-01

The fact that novel drug candidates are becoming increasingly insoluble is a major problem of current drug development. Computational tools may address this issue by screening for suitable solvents or by identifying potential novel cocrystal formers that increase bioavailability. In contrast to other more specialized methods, the fluid phase thermodynamics approach COSMO-RS (conductor-like screening model for real solvents) allows for a comprehensive treatment of drug solubility, solvate and cocrystal formation and many other thermodynamics properties in liquids. This article gives an overview of recent COSMO-RS developments that are of interest for drug development and contains several new application examples for solubility prediction and solvate/cocrystal screening. For all property predictions COSMO-RS has been used. The basic concept of COSMO-RS consists of using the screening charge density as computed from first principles calculations in combination with fast statistical thermodynamics to compute the chemical potential of a compound in solution. The fast and accurate assessment of drug solubility and the identification of suitable solvents, solvate or cocrystal formers is nowadays possible and may be used to complement modern drug development. Efficiency is increased by avoiding costly quantum-chemical computations using a database of previously computed molecular fragments. COSMO-RS theory can be applied to a range of physico-chemical properties, which are of interest in rational crystal engineering. Most notably, in combination with experimental reference data, accurate quantitative solubility predictions in any solvent or solvent mixture are possible. Additionally, COSMO-RS can be extended to the prediction of cocrystal formation, which results in considerable predictive accuracy concerning coformer screening. In a recent variant costly quantum chemical calculations are avoided resulting in a significant speed-up and ease-of-use. © 2015 Royal Pharmaceutical Society.

Predicting the Metabolic Sites by Flavin-Containing Monooxygenase on Drug Molecules Using SVM Classification on Computed Quantum Mechanics and Circular Fingerprints Molecular Descriptors

PubMed Central

Fu, Chien-wei; Lin, Thy-Hou

2017-01-01

As an important enzyme in Phase I drug metabolism, the flavin-containing monooxygenase (FMO) also metabolizes some xenobiotics with soft nucleophiles. The site of metabolism (SOM) on a molecule is the site where the metabolic reaction is exerted by an enzyme. Accurate prediction of SOMs on drug molecules will assist the search for drug leads during the optimization process. Here, some quantum mechanics features such as the condensed Fukui function and attributes from circular fingerprints (called Molprint2D) are computed and classified using the support vector machine (SVM) for predicting some potential SOMs on a series of drugs that can be metabolized by FMO enzymes. The condensed Fukui function fA− representing the nucleophilicity of central atom A and the attributes from circular fingerprints accounting the influence of neighbors on the central atom. The total number of FMO substrates and non-substrates collected in the study is 85 and they are equally divided into the training and test sets with each carrying roughly the same number of potential SOMs. However, only N-oxidation and S-oxidation features were considered in the prediction since the available C-oxidation data was scarce. In the training process, the LibSVM package of WEKA package and the option of 10-fold cross validation are employed. The prediction performance on the test set evaluated by accuracy, Matthews correlation coefficient and area under ROC curve computed are 0.829, 0.659, and 0.877 respectively. This work reveals that the SVM model built can accurately predict the potential SOMs for drug molecules that are metabolizable by the FMO enzymes. PMID:28072829

Cell-specific prediction and application of drug-induced gene expression profiles.

PubMed

Hodos, Rachel; Zhang, Ping; Lee, Hao-Chih; Duan, Qiaonan; Wang, Zichen; Clark, Neil R; Ma'ayan, Avi; Wang, Fei; Kidd, Brian; Hu, Jianying; Sontag, David; Dudley, Joel

2018-01-01

Gene expression profiling of in vitro drug perturbations is useful for many biomedical discovery applications including drug repurposing and elucidation of drug mechanisms. However, limited data availability across cell types has hindered our capacity to leverage or explore the cell-specificity of these perturbations. While recent efforts have generated a large number of drug perturbation profiles across a variety of human cell types, many gaps remain in this combinatorial drug-cell space. Hence, we asked whether it is possible to fill these gaps by predicting cell-specific drug perturbation profiles using available expression data from related conditions--i.e. from other drugs and cell types. We developed a computational framework that first arranges existing profiles into a three-dimensional array (or tensor) indexed by drugs, genes, and cell types, and then uses either local (nearest-neighbors) or global (tensor completion) information to predict unmeasured profiles. We evaluate prediction accuracy using a variety of metrics, and find that the two methods have complementary performance, each superior in different regions in the drug-cell space. Predictions achieve correlations of 0.68 with true values, and maintain accurate differentially expressed genes (AUC 0.81). Finally, we demonstrate that the predicted profiles add value for making downstream associations with drug targets and therapeutic classes.

Cell-specific prediction and application of drug-induced gene expression profiles

PubMed Central

Hodos, Rachel; Zhang, Ping; Lee, Hao-Chih; Duan, Qiaonan; Wang, Zichen; Clark, Neil R.; Ma'ayan, Avi; Wang, Fei; Kidd, Brian; Hu, Jianying; Sontag, David

2017-01-01

Gene expression profiling of in vitro drug perturbations is useful for many biomedical discovery applications including drug repurposing and elucidation of drug mechanisms. However, limited data availability across cell types has hindered our capacity to leverage or explore the cell-specificity of these perturbations. While recent efforts have generated a large number of drug perturbation profiles across a variety of human cell types, many gaps remain in this combinatorial drug-cell space. Hence, we asked whether it is possible to fill these gaps by predicting cell-specific drug perturbation profiles using available expression data from related conditions--i.e. from other drugs and cell types. We developed a computational framework that first arranges existing profiles into a three-dimensional array (or tensor) indexed by drugs, genes, and cell types, and then uses either local (nearest-neighbors) or global (tensor completion) information to predict unmeasured profiles. We evaluate prediction accuracy using a variety of metrics, and find that the two methods have complementary performance, each superior in different regions in the drug-cell space. Predictions achieve correlations of 0.68 with true values, and maintain accurate differentially expressed genes (AUC 0.81). Finally, we demonstrate that the predicted profiles add value for making downstream associations with drug targets and therapeutic classes. PMID:29218867

Molecular Dynamics Simulations and Kinetic Measurements to Estimate and Predict Protein-Ligand Residence Times.

PubMed

Mollica, Luca; Theret, Isabelle; Antoine, Mathias; Perron-Sierra, Françoise; Charton, Yves; Fourquez, Jean-Marie; Wierzbicki, Michel; Boutin, Jean A; Ferry, Gilles; Decherchi, Sergio; Bottegoni, Giovanni; Ducrot, Pierre; Cavalli, Andrea

2016-08-11

Ligand-target residence time is emerging as a key drug discovery parameter because it can reliably predict drug efficacy in vivo. Experimental approaches to binding and unbinding kinetics are nowadays available, but we still lack reliable computational tools for predicting kinetics and residence time. Most attempts have been based on brute-force molecular dynamics (MD) simulations, which are CPU-demanding and not yet particularly accurate. We recently reported a new scaled-MD-based protocol, which showed potential for residence time prediction in drug discovery. Here, we further challenged our procedure's predictive ability by applying our methodology to a series of glucokinase activators that could be useful for treating type 2 diabetes mellitus. We combined scaled MD with experimental kinetics measurements and X-ray crystallography, promptly checking the protocol's reliability by directly comparing computational predictions and experimental measures. The good agreement highlights the potential of our scaled-MD-based approach as an innovative method for computationally estimating and predicting drug residence times.

Prediction and validation of diffusion coefficients in a model drug delivery system using microsecond atomistic molecular dynamics simulation and vapour sorption analysis.

PubMed

Forrey, Christopher; Saylor, David M; Silverstein, Joshua S; Douglas, Jack F; Davis, Eric M; Elabd, Yossef A

2014-10-14

Diffusion of small to medium sized molecules in polymeric medical device materials underlies a broad range of public health concerns related to unintended leaching from or uptake into implantable medical devices. However, obtaining accurate diffusion coefficients for such systems at physiological temperature represents a formidable challenge, both experimentally and computationally. While molecular dynamics simulation has been used to accurately predict the diffusion coefficients, D, of a handful of gases in various polymers, this success has not been extended to molecules larger than gases, e.g., condensable vapours, liquids, and drugs. We present atomistic molecular dynamics simulation predictions of diffusion in a model drug eluting system that represent a dramatic improvement in accuracy compared to previous simulation predictions for comparable systems. We find that, for simulations of insufficient duration, sub-diffusive dynamics can lead to dramatic over-prediction of D. We present useful metrics for monitoring the extent of sub-diffusive dynamics and explore how these metrics correlate to error in D. We also identify a relationship between diffusion and fast dynamics in our system, which may serve as a means to more rapidly predict diffusion in slowly diffusing systems. Our work provides important precedent and essential insights for utilizing atomistic molecular dynamics simulations to predict diffusion coefficients of small to medium sized molecules in condensed soft matter systems.

Organ-on-a-Chip Technology for Reproducing Multiorgan Physiology.

PubMed

Lee, Seung Hwan; Sung, Jong Hwan

2018-01-01

In the drug development process, the accurate prediction of drug efficacy and toxicity is important in order to reduce the cost, labor, and effort involved. For this purpose, conventional 2D cell culture models are used in the early phase of drug development. However, the differences between the in vitro and the in vivo systems have caused the failure of drugs in the later phase of the drug-development process. Therefore, there is a need for a novel in vitro model system that can provide accurate information for evaluating the drug efficacy and toxicity through a closer recapitulation of the in vivo system. Recently, the idea of using microtechnology for mimicking the microscale tissue environment has become widespread, leading to the development of "organ-on-a-chip." Furthermore, the system is further developed for realizing a multiorgan model for mimicking interactions between multiple organs. These advancements are still ongoing and are aimed at ultimately developing "body-on-a-chip" or "human-on-a-chip" devices for predicting the response of the whole body. This review summarizes recently developed organ-on-a-chip technologies, and their applications for reproducing multiorgan functions. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Quantitative prediction of drug side effects based on drug-related features.

PubMed

Niu, Yanqing; Zhang, Wen

2017-09-01

Unexpected side effects of drugs are great concern in the drug development, and the identification of side effects is an important task. Recently, machine learning methods are proposed to predict the presence or absence of interested side effects for drugs, but it is difficult to make the accurate prediction for all of them. In this paper, we transform side effect profiles of drugs as their quantitative scores, by summing up their side effects with weights. The quantitative scores may measure the dangers of drugs, and thus help to compare the risk of different drugs. Here, we attempt to predict quantitative scores of drugs, namely the quantitative prediction. Specifically, we explore a variety of drug-related features and evaluate their discriminative powers for the quantitative prediction. Then, we consider several feature combination strategies (direct combination, average scoring ensemble combination) to integrate three informative features: chemical substructures, targets, and treatment indications. Finally, the average scoring ensemble model which produces the better performances is used as the final quantitative prediction model. Since weights for side effects are empirical values, we randomly generate different weights in the simulation experiments. The experimental results show that the quantitative method is robust to different weights, and produces satisfying results. Although other state-of-the-art methods cannot make the quantitative prediction directly, the prediction results can be transformed as the quantitative scores. By indirect comparison, the proposed method produces much better results than benchmark methods in the quantitative prediction. In conclusion, the proposed method is promising for the quantitative prediction of side effects, which may work cooperatively with existing state-of-the-art methods to reveal dangers of drugs.

A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury

PubMed Central

Kohonen, Pekka; Parkkinen, Juuso A.; Willighagen, Egon L.; Ceder, Rebecca; Wennerberg, Krister; Kaski, Samuel; Grafström, Roland C.

2017-01-01

Predicting unanticipated harmful effects of chemicals and drug molecules is a difficult and costly task. Here we utilize a ‘big data compacting and data fusion’—concept to capture diverse adverse outcomes on cellular and organismal levels. The approach generates from transcriptomics data set a ‘predictive toxicogenomics space’ (PTGS) tool composed of 1,331 genes distributed over 14 overlapping cytotoxicity-related gene space components. Involving ∼2.5 × 108 data points and 1,300 compounds to construct and validate the PTGS, the tool serves to: explain dose-dependent cytotoxicity effects, provide a virtual cytotoxicity probability estimate intrinsic to omics data, predict chemically-induced pathological states in liver resulting from repeated dosing of rats, and furthermore, predict human drug-induced liver injury (DILI) from hepatocyte experiments. Analysing 68 DILI-annotated drugs, the PTGS tool outperforms and complements existing tests, leading to a hereto-unseen level of DILI prediction accuracy. PMID:28671182

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.

Evaluation and modeling of the eutectic composition of various drug-polyethylene glycol solid dispersions.

PubMed

Baird, Jared A; Taylor, Lynne S

2011-06-01

The purpose of this study was to gain a better understanding of which factors contribute to the eutectic composition of drug-polyethylene glycol (PEG) blends and to compare experimental values with predictions from the semi-empirical model developed by Lacoulonche et al. Eutectic compositions of various drug-PEG 3350 solid dispersions were predicted, assuming athermal mixing, and compared to experimentally determined eutectic points. The presence or absence of specific interactions between the drug and PEG 3350 were investigated using Fourier transform infrared (FT-IR) spectroscopy. The eutectic composition for haloperidol-PEG and loratadine-PEG solid dispersions was accurately predicted using the model, while predictions for aceclofenac-PEG and chlorpropamide-PEG were very different from those experimentally observed. Deviations in the model prediction from ideal behavior for the systems evaluated were confirmed to be due to the presence of specific interactions between the drug and polymer, as demonstrated by IR spectroscopy. Detailed analysis showed that the eutectic composition prediction from the model is interdependent on the crystal lattice energy of the drug compound (evaluated from the melting temperature and the heat of fusion) as well as the nature of the drug-polymer interactions. In conclusion, for compounds with melting points less than 200°C, the model is ideally suited for predicting the eutectic composition of systems where there is an absence of drug-polymer interactions.

Corneal cell culture models: a tool to study corneal drug absorption.

PubMed

Dey, Surajit

2011-05-01

In recent times, there has been an ever increasing demand for ocular drugs to treat sight threatening diseases such as glaucoma, age-related macular degeneration and diabetic retinopathy. As more drugs are developed, there is a great need to test in vitro permeability of these drugs to predict their efficacy and bioavailability in vivo. Corneal cell culture models are the only tool that can predict drug absorption across ocular layers accurately and rapidly. Cell culture studies are also valuable in reducing the number of animals needed for in vivo studies which can increase the cost of the drug developmental process. Currently, rabbit corneal cell culture models are used to predict human corneal absorption due to the difficulty in human corneal studies. More recently, a three dimensional human corneal equivalent has been developed using three different cell types to mimic the human cornea. In the future, human corneal cell culture systems need to be developed to be used as a standardized model for drug permeation.

Accounting for receptor flexibility and enhanced sampling methods in computer-aided drug design.

PubMed

Sinko, William; Lindert, Steffen; McCammon, J Andrew

2013-01-01

Protein flexibility plays a major role in biomolecular recognition. In many cases, it is not obvious how molecular structure will change upon association with other molecules. In proteins, these changes can be major, with large deviations in overall backbone structure, or they can be more subtle as in a side-chain rotation. Either way the algorithms that predict the favorability of biomolecular association require relatively accurate predictions of the bound structure to give an accurate assessment of the energy involved in association. Here, we review a number of techniques that have been proposed to accommodate receptor flexibility in the simulation of small molecules binding to protein receptors. We investigate modifications to standard rigid receptor docking algorithms and also explore enhanced sampling techniques, and the combination of free energy calculations and enhanced sampling techniques. The understanding and allowance for receptor flexibility are helping to make computer simulations of ligand protein binding more accurate. These developments may help improve the efficiency of drug discovery and development. Efficiency will be essential as we begin to see personalized medicine tailored to individual patients, which means specific drugs are needed for each patient's genetic makeup. © 2012 John Wiley & Sons A/S.

Perfusion kinetics in human brain tumor with DCE-MRI derived model and CFD analysis.

PubMed

Bhandari, A; Bansal, A; Singh, A; Sinha, N

2017-07-05

Cancer is one of the leading causes of death all over the world. Among the strategies that are used for cancer treatment, the effectiveness of chemotherapy is often hindered by factors such as irregular and non-uniform uptake of drugs inside tumor. Thus, accurate prediction of drug transport and deposition inside tumor is crucial for increasing the effectiveness of chemotherapeutic treatment. In this study, a computational model of human brain tumor is developed that incorporates dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) data into a voxelized porous media model. The model takes into account realistic transport and perfusion kinetics parameters together with realistic heterogeneous tumor vasculature and accurate arterial input function (AIF), which makes it patient specific. The computational results for interstitial fluid pressure (IFP), interstitial fluid velocity (IFV) and tracer concentration show good agreement with the experimental results. The computational model can be extended further for predicting the deposition of chemotherapeutic drugs in tumor environment as well as selection of the best chemotherapeutic drug for a specific patient. Copyright © 2017 Elsevier Ltd. All rights reserved.

CRISPR-Cas9-mediated saturated mutagenesis screen predicts clinical drug resistance with improved accuracy.

PubMed

Ma, Leyuan; Boucher, Jeffrey I; Paulsen, Janet; Matuszewski, Sebastian; Eide, Christopher A; Ou, Jianhong; Eickelberg, Garrett; Press, Richard D; Zhu, Lihua Julie; Druker, Brian J; Branford, Susan; Wolfe, Scot A; Jensen, Jeffrey D; Schiffer, Celia A; Green, Michael R; Bolon, Daniel N

2017-10-31

Developing tools to accurately predict the clinical prevalence of drug-resistant mutations is a key step toward generating more effective therapeutics. Here we describe a high-throughput CRISPR-Cas9-based saturated mutagenesis approach to generate comprehensive libraries of point mutations at a defined genomic location and systematically study their effect on cell growth. As proof of concept, we mutagenized a selected region within the leukemic oncogene BCR-ABL1 Using bulk competitions with a deep-sequencing readout, we analyzed hundreds of mutations under multiple drug conditions and found that the effects of mutations on growth in the presence or absence of drug were critical for predicting clinically relevant resistant mutations, many of which were cancer adaptive in the absence of drug pressure. Using this approach, we identified all clinically isolated BCR-ABL1 mutations and achieved a prediction score that correlated highly with their clinical prevalence. The strategy described here can be broadly applied to a variety of oncogenes to predict patient mutations and evaluate resistance susceptibility in the development of new therapeutics. Published under the PNAS license.

Improvement of experimental testing and network training conditions with genome-wide microarrays for more accurate predictions of drug gene targets

PubMed Central

2014-01-01

Background Genome-wide microarrays have been useful for predicting chemical-genetic interactions at the gene level. However, interpreting genome-wide microarray results can be overwhelming due to the vast output of gene expression data combined with off-target transcriptional responses many times induced by a drug treatment. This study demonstrates how experimental and computational methods can interact with each other, to arrive at more accurate predictions of drug-induced perturbations. We present a two-stage strategy that links microarray experimental testing and network training conditions to predict gene perturbations for a drug with a known mechanism of action in a well-studied organism. Results S. cerevisiae cells were treated with the antifungal, fluconazole, and expression profiling was conducted under different biological conditions using Affymetrix genome-wide microarrays. Transcripts were filtered with a formal network-based method, sparse simultaneous equation models and Lasso regression (SSEM-Lasso), under different network training conditions. Gene expression results were evaluated using both gene set and single gene target analyses, and the drug’s transcriptional effects were narrowed first by pathway and then by individual genes. Variables included: (i) Testing conditions – exposure time and concentration and (ii) Network training conditions – training compendium modifications. Two analyses of SSEM-Lasso output – gene set and single gene – were conducted to gain a better understanding of how SSEM-Lasso predicts perturbation targets. Conclusions This study demonstrates that genome-wide microarrays can be optimized using a two-stage strategy for a more in-depth understanding of how a cell manifests biological reactions to a drug treatment at the transcription level. Additionally, a more detailed understanding of how the statistical model, SSEM-Lasso, propagates perturbations through a network of gene regulatory interactions is achieved. PMID:24444313

Mechanistic modelling of drug release from a polymer matrix using magnetic resonance microimaging.

PubMed

Kaunisto, Erik; Tajarobi, Farhad; Abrahmsen-Alami, Susanna; Larsson, Anette; Nilsson, Bernt; Axelsson, Anders

2013-03-12

In this paper a new model describing drug release from a polymer matrix tablet is presented. The utilization of the model is described as a two step process where, initially, polymer parameters are obtained from a previously published pure polymer dissolution model. The results are then combined with drug parameters obtained from literature data in the new model to predict solvent and drug concentration profiles and polymer and drug release profiles. The modelling approach was applied to the case of a HPMC matrix highly loaded with mannitol (model drug). The results showed that the drug release rate can be successfully predicted, using the suggested modelling approach. However, the model was not able to accurately predict the polymer release profile, possibly due to the sparse amount of usable pure polymer dissolution data. In addition to the case study, a sensitivity analysis of model parameters relevant to drug release was performed. The analysis revealed important information that can be useful in the drug formulation process. Copyright © 2013 Elsevier B.V. All rights reserved.

Advancing Drug Discovery through Enhanced Free Energy Calculations.

PubMed

Abel, Robert; Wang, Lingle; Harder, Edward D; Berne, B J; Friesner, Richard A

2017-07-18

A principal goal of drug discovery project is to design molecules that can tightly and selectively bind to the target protein receptor. Accurate prediction of protein-ligand binding free energies is therefore of central importance in computational chemistry and computer aided drug design. Multiple recent improvements in computing power, classical force field accuracy, enhanced sampling methods, and simulation setup have enabled accurate and reliable calculations of protein-ligands binding free energies, and position free energy calculations to play a guiding role in small molecule drug discovery. In this Account, we outline the relevant methodological advances, including the REST2 (Replica Exchange with Solute Temperting) enhanced sampling, the incorporation of REST2 sampling with convential FEP (Free Energy Perturbation) through FEP/REST, the OPLS3 force field, and the advanced simulation setup that constitute our FEP+ approach, followed by the presentation of extensive comparisons with experiment, demonstrating sufficient accuracy in potency prediction (better than 1 kcal/mol) to substantially impact lead optimization campaigns. The limitations of the current FEP+ implementation and best practices in drug discovery applications are also discussed followed by the future methodology development plans to address those limitations. We then report results from a recent drug discovery project, in which several thousand FEP+ calculations were successfully deployed to simultaneously optimize potency, selectivity, and solubility, illustrating the power of the approach to solve challenging drug design problems. The capabilities of free energy calculations to accurately predict potency and selectivity have led to the advance of ongoing drug discovery projects, in challenging situations where alternative approaches would have great difficulties. The ability to effectively carry out projects evaluating tens of thousands, or hundreds of thousands, of proposed drug candidates, is potentially transformative in enabling hard to drug targets to be attacked, and in facilitating the development of superior compounds, in various dimensions, for a wide range of targets. More effective integration of FEP+ calculations into the drug discovery process will ensure that the results are deployed in an optimal fashion for yielding the best possible compounds entering the clinic; this is where the greatest payoff is in the exploitation of computer driven design capabilities. A key conclusion from the work described is the surprisingly robust and accurate results that are attainable within the conventional classical simulation, fixed charge paradigm. No doubt there are individual cases that would benefit from a more sophisticated energy model or dynamical treatment, and properties other than protein-ligand binding energies may be more sensitive to these approximations. We conclude that an inflection point in the ability of MD simulations to impact drug discovery has now been attained, due to the confluence of hardware and software development along with the formulation of "good enough" theoretical methods and models.

Advancing viral RNA structure prediction: measuring the thermodynamics of pyrimidine-rich internal loops

PubMed Central

Phan, Andy; Mailey, Katherine; Saeki, Jessica; Gu, Xiaobo

2017-01-01

Accurate thermodynamic parameters improve RNA structure predictions and thus accelerate understanding of RNA function and the identification of RNA drug binding sites. Many viral RNA structures, such as internal ribosome entry sites, have internal loops and bulges that are potential drug target sites. Current models used to predict internal loops are biased toward small, symmetric purine loops, and thus poorly predict asymmetric, pyrimidine-rich loops with >6 nucleotides (nt) that occur frequently in viral RNA. This article presents new thermodynamic data for 40 pyrimidine loops, many of which can form UU or protonated CC base pairs. Uracil and protonated cytosine base pairs stabilize asymmetric internal loops. Accurate prediction rules are presented that account for all thermodynamic measurements of RNA asymmetric internal loops. New loop initiation terms for loops with >6 nt are presented that do not follow previous assumptions that increasing asymmetry destabilizes loops. Since the last 2004 update, 126 new loops with asymmetry or sizes greater than 2 × 2 have been measured. These new measurements significantly deepen and diversify the thermodynamic database for RNA. These results will help better predict internal loops that are larger, pyrimidine-rich, and occur within viral structures such as internal ribosome entry sites. PMID:28213527

DeSigN: connecting gene expression with therapeutics for drug repurposing and development.

PubMed

Lee, Bernard Kok Bang; Tiong, Kai Hung; Chang, Jit Kang; Liew, Chee Sun; Abdul Rahman, Zainal Ariff; Tan, Aik Choon; Khang, Tsung Fei; Cheong, Sok Ching

2017-01-25

The drug discovery and development pipeline is a long and arduous process that inevitably hampers rapid drug development. Therefore, strategies to improve the efficiency of drug development are urgently needed to enable effective drugs to enter the clinic. Precision medicine has demonstrated that genetic features of cancer cells can be used for predicting drug response, and emerging evidence suggest that gene-drug connections could be predicted more accurately by exploring the cumulative effects of many genes simultaneously. We developed DeSigN, a web-based tool for predicting drug efficacy against cancer cell lines using gene expression patterns. The algorithm correlates phenotype-specific gene signatures derived from differentially expressed genes with pre-defined gene expression profiles associated with drug response data (IC 50 ) from 140 drugs. DeSigN successfully predicted the right drug sensitivity outcome in four published GEO studies. Additionally, it predicted bosutinib, a Src/Abl kinase inhibitor, as a sensitive inhibitor for oral squamous cell carcinoma (OSCC) cell lines. In vitro validation of bosutinib in OSCC cell lines demonstrated that indeed, these cell lines were sensitive to bosutinib with IC 50 of 0.8-1.2 μM. As further confirmation, we demonstrated experimentally that bosutinib has anti-proliferative activity in OSCC cell lines, demonstrating that DeSigN was able to robustly predict drug that could be beneficial for tumour control. DeSigN is a robust method that is useful for the identification of candidate drugs using an input gene signature obtained from gene expression analysis. This user-friendly platform could be used to identify drugs with unanticipated efficacy against cancer cell lines of interest, and therefore could be used for the repurposing of drugs, thus improving the efficiency of drug development.

How good is "evidence" from clinical studies of drug effects and why might such evidence fail in the prediction of the clinical utility of drugs?

PubMed

Naci, Huseyin; Ioannidis, John P A

2015-01-01

Promising evidence from clinical studies of drug effects does not always translate to improvements in patient outcomes. In this review, we discuss why early evidence is often ill suited to the task of predicting the clinical utility of drugs. The current gap between initially described drug effects and their subsequent clinical utility results from deficits in the design, conduct, analysis, reporting, and synthesis of clinical studies-often creating conditions that generate favorable, but ultimately incorrect, conclusions regarding drug effects. There are potential solutions that could improve the relevance of clinical evidence in predicting the real-world effectiveness of drugs. What is needed is a new emphasis on clinical utility, with nonconflicted entities playing a greater role in the generation, synthesis, and interpretation of clinical evidence. Clinical studies should adopt strong design features, reflect clinical practice, and evaluate outcomes and comparisons that are meaningful to patients. Transformative changes to the research agenda may generate more meaningful and accurate evidence on drug effects to guide clinical decision making.

Modeling methodology for the accurate and prompt prediction of symptomatic events in chronic diseases.

PubMed

Pagán, Josué; Risco-Martín, José L; Moya, José M; Ayala, José L

2016-08-01

Prediction of symptomatic crises in chronic diseases allows to take decisions before the symptoms occur, such as the intake of drugs to avoid the symptoms or the activation of medical alarms. The prediction horizon is in this case an important parameter in order to fulfill the pharmacokinetics of medications, or the time response of medical services. This paper presents a study about the prediction limits of a chronic disease with symptomatic crises: the migraine. For that purpose, this work develops a methodology to build predictive migraine models and to improve these predictions beyond the limits of the initial models. The maximum prediction horizon is analyzed, and its dependency on the selected features is studied. A strategy for model selection is proposed to tackle the trade off between conservative but robust predictive models, with respect to less accurate predictions with higher horizons. The obtained results show a prediction horizon close to 40min, which is in the time range of the drug pharmacokinetics. Experiments have been performed in a realistic scenario where input data have been acquired in an ambulatory clinical study by the deployment of a non-intrusive Wireless Body Sensor Network. Our results provide an effective methodology for the selection of the future horizon in the development of prediction algorithms for diseases experiencing symptomatic crises. Copyright © 2016 Elsevier Inc. All rights reserved.

Drug Repositioning by Kernel-Based Integration of Molecular Structure, Molecular Activity, and Phenotype Data

PubMed Central

Wang, Yongcui; Chen, Shilong; Deng, Naiyang; Wang, Yong

2013-01-01

Computational inference of novel therapeutic values for existing drugs, i.e., drug repositioning, offers the great prospect for faster and low-risk drug development. Previous researches have indicated that chemical structures, target proteins, and side-effects could provide rich information in drug similarity assessment and further disease similarity. However, each single data source is important in its own way and data integration holds the great promise to reposition drug more accurately. Here, we propose a new method for drug repositioning, PreDR (Predict Drug Repositioning), to integrate molecular structure, molecular activity, and phenotype data. Specifically, we characterize drug by profiling in chemical structure, target protein, and side-effects space, and define a kernel function to correlate drugs with diseases. Then we train a support vector machine (SVM) to computationally predict novel drug-disease interactions. PreDR is validated on a well-established drug-disease network with 1,933 interactions among 593 drugs and 313 diseases. By cross-validation, we find that chemical structure, drug target, and side-effects information are all predictive for drug-disease relationships. More experimentally observed drug-disease interactions can be revealed by integrating these three data sources. Comparison with existing methods demonstrates that PreDR is competitive both in accuracy and coverage. Follow-up database search and pathway analysis indicate that our new predictions are worthy of further experimental validation. Particularly several novel predictions are supported by clinical trials databases and this shows the significant prospects of PreDR in future drug treatment. In conclusion, our new method, PreDR, can serve as a useful tool in drug discovery to efficiently identify novel drug-disease interactions. In addition, our heterogeneous data integration framework can be applied to other problems. PMID:24244318

Accurate and Reliable Prediction of the Binding Affinities of Macrocycles to Their Protein Targets.

PubMed

Yu, Haoyu S; Deng, Yuqing; Wu, Yujie; Sindhikara, Dan; Rask, Amy R; Kimura, Takayuki; Abel, Robert; Wang, Lingle

2017-12-12

Macrocycles have been emerging as a very important drug class in the past few decades largely due to their expanded chemical diversity benefiting from advances in synthetic methods. Macrocyclization has been recognized as an effective way to restrict the conformational space of acyclic small molecule inhibitors with the hope of improving potency, selectivity, and metabolic stability. Because of their relatively larger size as compared to typical small molecule drugs and the complexity of the structures, efficient sampling of the accessible macrocycle conformational space and accurate prediction of their binding affinities to their target protein receptors poses a great challenge of central importance in computational macrocycle drug design. In this article, we present a novel method for relative binding free energy calculations between macrocycles with different ring sizes and between the macrocycles and their corresponding acyclic counterparts. We have applied the method to seven pharmaceutically interesting data sets taken from recent drug discovery projects including 33 macrocyclic ligands covering a diverse chemical space. The predicted binding free energies are in good agreement with experimental data with an overall root-mean-square error (RMSE) of 0.94 kcal/mol. This is to our knowledge the first time where the free energy of the macrocyclization of linear molecules has been directly calculated with rigorous physics-based free energy calculation methods, and we anticipate the outstanding accuracy demonstrated here across a broad range of target classes may have significant implications for macrocycle drug discovery.

Application of a handheld NIR spectrometer in prediction of drug content in inkjet printed orodispersible formulations containing prednisolone and levothyroxine.

PubMed

Vakili, Hossein; Wickström, Henrika; Desai, Diti; Preis, Maren; Sandler, Niklas

2017-05-30

Quality control tools to assess the quality of printable orodispersible formulations are yet to be defined. Four different orodispersible dosage forms containing two poorly soluble drugs, levothyroxine and prednisolone, were produced on two different edible substrates by piezoelectric inkjet printing. Square shaped units of 4cm 2 were printed in different resolutions to achieve an escalating drug dose by highly accurate and uniform displacement of droplets in picoliter range from the printhead onto the substrates. In addition, the stability of drug inks in a course of 24h as well as the mechanical properties and disintegration behavior of the printed units were examined. A compact handheld near-infrared (NIR) spectral device in the range of 1550-1950nm was used for quantitative estimation of the drug amount in printed formulations. The spectral data was treated with mean centering, Savitzky-Golay filtering and a third derivative approach. Principal component analysis (PCA) and orthogonal partial least squares (OPLS) regression were applied to build predictive models for quality control of the printed dosage forms. The accurate tuning of the dose in each formulation was confirmed by UV spectrophotometry for prednisolone (0.43-1.95mg with R 2 =0.999) and high performance liquid chromatography for levothyroxine (0.15-0.86mg with R 2 =0.997). It was verified that the models were capable of clustering and predicting the drug dose in the formulations with both Q 2 and R 2 Y values between 0.94-0.99. Copyright © 2017 Elsevier B.V. All rights reserved.

Ligand Binding Site Detection by Local Structure Alignment and Its Performance Complementarity

PubMed Central

Lee, Hui Sun; Im, Wonpil

2013-01-01

Accurate determination of potential ligand binding sites (BS) is a key step for protein function characterization and structure-based drug design. Despite promising results of template-based BS prediction methods using global structure alignment (GSA), there is a room to improve the performance by properly incorporating local structure alignment (LSA) because BS are local structures and often similar for proteins with dissimilar global folds. We present a template-based ligand BS prediction method using G-LoSA, our LSA tool. A large benchmark set validation shows that G-LoSA predicts drug-like ligands’ positions in single-chain protein targets more precisely than TM-align, a GSA-based method, while the overall success rate of TM-align is better. G-LoSA is particularly efficient for accurate detection of local structures conserved across proteins with diverse global topologies. Recognizing the performance complementarity of G-LoSA to TM-align and a non-template geometry-based method, fpocket, a robust consensus scoring method, CMCS-BSP (Complementary Methods and Consensus Scoring for ligand Binding Site Prediction), is developed and shows improvement on prediction accuracy. The G-LoSA source code is freely available at http://im.bioinformatics.ku.edu/GLoSA. PMID:23957286

The human placental perfusion model: a systematic review and development of a model to predict in vivo transfer of therapeutic drugs.

PubMed

Hutson, J R; Garcia-Bournissen, F; Davis, A; Koren, G

2011-07-01

Dual perfusion of a single placental lobule is the only experimental model to study human placental transfer of substances in organized placental tissue. To date, there has not been any attempt at a systematic evaluation of this model. The aim of this study was to systematically evaluate the perfusion model in predicting placental drug transfer and to develop a pharmacokinetic model to account for nonplacental pharmacokinetic parameters in the perfusion results. In general, the fetal-to-maternal drug concentration ratios matched well between placental perfusion experiments and in vivo samples taken at the time of delivery of the infant. After modeling for differences in maternal and fetal/neonatal protein binding and blood pH, the perfusion results were able to accurately predict in vivo transfer at steady state (R² = 0.85, P < 0.0001). Placental perfusion experiments can be used to predict placental drug transfer when adjusting for extra parameters and can be useful for assessing drug therapy risks and benefits in pregnancy.

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.

Predicting perturbation patterns from the topology of biological networks.

PubMed

Santolini, Marc; Barabási, Albert-László

2018-06-20

High-throughput technologies, offering an unprecedented wealth of quantitative data underlying the makeup of living systems, are changing biology. Notably, the systematic mapping of the relationships between biochemical entities has fueled the rapid development of network biology, offering a suitable framework to describe disease phenotypes and predict potential drug targets. However, our ability to develop accurate dynamical models remains limited, due in part to the limited knowledge of the kinetic parameters underlying these interactions. Here, we explore the degree to which we can make reasonably accurate predictions in the absence of the kinetic parameters. We find that simple dynamically agnostic models are sufficient to recover the strength and sign of the biochemical perturbation patterns observed in 87 biological models for which the underlying kinetics are known. Surprisingly, a simple distance-based model achieves 65% accuracy. We show that this predictive power is robust to topological and kinetic parameter perturbations, and we identify key network properties that can increase up to 80% the recovery rate of the true perturbation patterns. We validate our approach using experimental data on the chemotactic pathway in bacteria, finding that a network model of perturbation spreading predicts with ∼80% accuracy the directionality of gene expression and phenotype changes in knock-out and overproduction experiments. These findings show that the steady advances in mapping out the topology of biochemical interaction networks opens avenues for accurate perturbation spread modeling, with direct implications for medicine and drug development.

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.

Predicting new drug indications from network analysis

NASA Astrophysics Data System (ADS)

Mohd Ali, Yousoff Effendy; Kwa, Kiam Heong; Ratnavelu, Kurunathan

This work adapts centrality measures commonly used in social network analysis to identify drugs with better positions in drug-side effect network and drug-indication network for the purpose of drug repositioning. Our basic hypothesis is that drugs having similar phenotypic profiles such as side effects may also share similar therapeutic properties based on related mechanism of action and vice versa. The networks were constructed from Side Effect Resource (SIDER) 4.1 which contains 1430 unique drugs with side effects and 1437 unique drugs with indications. Within the giant components of these networks, drugs were ranked based on their centrality scores whereby 18 prominent drugs from the drug-side effect network and 15 prominent drugs from the drug-indication network were identified. Indications and side effects of prominent drugs were deduced from the profiles of their neighbors in the networks and compared to existing clinical studies while an optimum threshold of similarity among drugs was sought for. The threshold can then be utilized for predicting indications and side effects of all drugs. Similarities of drugs were measured by the extent to which they share phenotypic profiles and neighbors. To improve the likelihood of accurate predictions, only profiles such as side effects of common or very common frequencies were considered. In summary, our work is an attempt to offer an alternative approach to drug repositioning using centrality measures commonly used for analyzing social networks.

Kernelized rank learning for personalized drug recommendation.

PubMed

He, Xiao; Folkman, Lukas; Borgwardt, Karsten

2018-03-08

Large-scale screenings of cancer cell lines with detailed molecular profiles against libraries of pharmacological compounds are currently being performed in order to gain a better understanding of the genetic component of drug response and to enhance our ability to recommend therapies given a patient's molecular profile. These comprehensive screens differ from the clinical setting in which (1) medical records only contain the response of a patient to very few drugs, (2) drugs are recommended by doctors based on their expert judgment, and (3) selecting the most promising therapy is often more important than accurately predicting the sensitivity to all potential drugs. Current regression models for drug sensitivity prediction fail to account for these three properties. We present a machine learning approach, named Kernelized Rank Learning (KRL), that ranks drugs based on their predicted effect per cell line (patient), circumventing the difficult problem of precisely predicting the sensitivity to the given drug. Our approach outperforms several state-of-the-art predictors in drug recommendation, particularly if the training dataset is sparse, and generalizes to patient data. Our work phrases personalized drug recommendation as a new type of machine learning problem with translational potential to the clinic. The Python implementation of KRL and scripts for running our experiments are available at https://github.com/BorgwardtLab/Kernelized-Rank-Learning. xiao.he@bsse.ethz.ch, lukas.folkman@bsse.ethz.ch. Supplementary data are available at Bioinformatics online.

Advancing viral RNA structure prediction: measuring the thermodynamics of pyrimidine-rich internal loops.

PubMed

Phan, Andy; Mailey, Katherine; Saeki, Jessica; Gu, Xiaobo; Schroeder, Susan J

2017-05-01

Accurate thermodynamic parameters improve RNA structure predictions and thus accelerate understanding of RNA function and the identification of RNA drug binding sites. Many viral RNA structures, such as internal ribosome entry sites, have internal loops and bulges that are potential drug target sites. Current models used to predict internal loops are biased toward small, symmetric purine loops, and thus poorly predict asymmetric, pyrimidine-rich loops with >6 nucleotides (nt) that occur frequently in viral RNA. This article presents new thermodynamic data for 40 pyrimidine loops, many of which can form UU or protonated CC base pairs. Uracil and protonated cytosine base pairs stabilize asymmetric internal loops. Accurate prediction rules are presented that account for all thermodynamic measurements of RNA asymmetric internal loops. New loop initiation terms for loops with >6 nt are presented that do not follow previous assumptions that increasing asymmetry destabilizes loops. Since the last 2004 update, 126 new loops with asymmetry or sizes greater than 2 × 2 have been measured. These new measurements significantly deepen and diversify the thermodynamic database for RNA. These results will help better predict internal loops that are larger, pyrimidine-rich, and occur within viral structures such as internal ribosome entry sites. © 2017 Phan et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.

Computational modeling of human oral bioavailability: what will be next?

PubMed

Cabrera-Pérez, Miguel Ángel; Pham-The, Hai

2018-06-01

The oral route is the most convenient way of administrating drugs. Therefore, accurate determination of oral bioavailability is paramount during drug discovery and development. Quantitative structure-property relationship (QSPR), rule-of-thumb (RoT) and physiologically based-pharmacokinetic (PBPK) approaches are promising alternatives to the early oral bioavailability prediction. Areas covered: The authors give insight into the factors affecting bioavailability, the fundamental theoretical framework and the practical aspects of computational methods for predicting this property. They also give their perspectives on future computational models for estimating oral bioavailability. Expert opinion: Oral bioavailability is a multi-factorial pharmacokinetic property with its accurate prediction challenging. For RoT and QSPR modeling, the reliability of datasets, the significance of molecular descriptor families and the diversity of chemometric tools used are important factors that define model predictability and interpretability. Likewise, for PBPK modeling the integrity of the pharmacokinetic data, the number of input parameters, the complexity of statistical analysis and the software packages used are relevant factors in bioavailability prediction. Although these approaches have been utilized independently, the tendency to use hybrid QSPR-PBPK approaches together with the exploration of ensemble and deep-learning systems for QSPR modeling of oral bioavailability has opened new avenues for development promising tools for oral bioavailability prediction.

Altered drug metabolism during pregnancy: hormonal regulation of drug-metabolizing enzymes.

PubMed

Jeong, Hyunyoung

2010-06-01

Medication use during pregnancy is prevalent, but pharmacokinetic information of most drugs used during pregnancy is lacking in spite of known effects of pregnancy on drug disposition. Accurate pharmacokinetic information is essential for optimal drug therapy in mother and fetus. Thus, understanding how pregnancy influences drug disposition is important for better prediction of pharmacokinetic changes of drugs in pregnant women. Pregnancy is known to affect hepatic drug metabolism, but the underlying mechanisms remain unknown. Physiological changes accompanying pregnancy are probably responsible for the reported alteration in drug metabolism during pregnancy. These include elevated concentrations of various hormones such as estrogen, progesterone, placental growth hormones and prolactin. This review covers how these hormones influence expression of drug-metabolizing enzymes (DMEs), thus potentially responsible for altered drug metabolism during pregnancy. The reader will gain a greater understanding of the altered drug metabolism in pregnant women and the regulatory effects of pregnancy hormones on expression of DMEs. In-depth studies in hormonal regulatory mechanisms as well as confirmatory studies in pregnant women are warranted for systematic understanding and prediction of the changes in hepatic drug metabolism during pregnancy.

Multiple grid arrangement improves ligand docking with unknown binding sites: Application to the inverse docking problem.

PubMed

Ban, Tomohiro; Ohue, Masahito; Akiyama, Yutaka

2018-04-01

The identification of comprehensive drug-target interactions is important in drug discovery. Although numerous computational methods have been developed over the years, a gold standard technique has not been established. Computational ligand docking and structure-based drug design allow researchers to predict the binding affinity between a compound and a target protein, and thus, they are often used to virtually screen compound libraries. In addition, docking techniques have also been applied to the virtual screening of target proteins (inverse docking) to predict target proteins of a drug candidate. Nevertheless, a more accurate docking method is currently required. In this study, we proposed a method in which a predicted ligand-binding site is covered by multiple grids, termed multiple grid arrangement. Notably, multiple grid arrangement facilitates the conformational search for a grid-based ligand docking software and can be applied to the state-of-the-art commercial docking software Glide (Schrödinger, LLC). We validated the proposed method by re-docking with the Astex diverse benchmark dataset and blind binding site situations, which improved the correct prediction rate of the top scoring docking pose from 27.1% to 34.1%; however, only a slight improvement in target prediction accuracy was observed with inverse docking scenarios. These findings highlight the limitations and challenges of current scoring functions and the need for more accurate docking methods. The proposed multiple grid arrangement method was implemented in Glide by modifying a cross-docking script for Glide, xglide.py. The script of our method is freely available online at http://www.bi.cs.titech.ac.jp/mga_glide/. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations.

PubMed

Cournia, Zoe; Allen, Bryce; Sherman, Woody

2017-12-26

Accurate in silico prediction of protein-ligand binding affinities has been a primary objective of structure-based drug design for decades due to the putative value it would bring to the drug discovery process. However, computational methods have historically failed to deliver value in real-world drug discovery applications due to a variety of scientific, technical, and practical challenges. Recently, a family of approaches commonly referred to as relative binding free energy (RBFE) calculations, which rely on physics-based molecular simulations and statistical mechanics, have shown promise in reliably generating accurate predictions in the context of drug discovery projects. This advance arises from accumulating developments in the underlying scientific methods (decades of research on force fields and sampling algorithms) coupled with vast increases in computational resources (graphics processing units and cloud infrastructures). Mounting evidence from retrospective validation studies, blind challenge predictions, and prospective applications suggests that RBFE simulations can now predict the affinity differences for congeneric ligands with sufficient accuracy and throughput to deliver considerable value in hit-to-lead and lead optimization efforts. Here, we present an overview of current RBFE implementations, highlighting recent advances and remaining challenges, along with examples that emphasize practical considerations for obtaining reliable RBFE results. We focus specifically on relative binding free energies because the calculations are less computationally intensive than absolute binding free energy (ABFE) calculations and map directly onto the hit-to-lead and lead optimization processes, where the prediction of relative binding energies between a reference molecule and new ideas (virtual molecules) can be used to prioritize molecules for synthesis. We describe the critical aspects of running RBFE calculations, from both theoretical and applied perspectives, using a combination of retrospective literature examples and prospective studies from drug discovery projects. This work is intended to provide a contemporary overview of the scientific, technical, and practical issues associated with running relative binding free energy simulations, with a focus on real-world drug discovery applications. We offer guidelines for improving the accuracy of RBFE simulations, especially for challenging cases, and emphasize unresolved issues that could be improved by further research in the field.

Inferences of drug responses in cancer cells from cancer genomic features and compound chemical and therapeutic properties

PubMed Central

Wang, Yongcui; Fang, Jianwen; Chen, Shilong

2016-01-01

Accurately predicting the response of a cancer patient to a therapeutic agent is a core goal of precision medicine. Existing approaches were mainly relied primarily on genomic alterations in cancer cells that have been treated with different drugs. Here we focus on predicting drug response based on integration of the heterogeneously pharmacogenomics data from both cell and drug sides. Through a systematical approach, named as PDRCC (Predict Drug Response in Cancer Cells), the cancer genomic alterations and compound chemical and therapeutic properties were incorporated to determine the chemotherapeutic response in cancer patients. Using the Cancer Cell Line Encyclopedia (CCLE) study as the benchmark dataset, all pharmacogenomics data exhibited their roles in inferring the relationships between cancer cells and drugs. When integrating both genomic resources and compound information, the prediction coverage was significantly increased. The validity of PDRCC was also supported by its effective in uncovering the unknown cell-drug associations with database and literature evidences. It set the stage for clinical testing of novel therapeutic strategies, such as the sensitive association between cancer cell ‘A549_LUNG’ and compound ‘Topotecan’. In conclusion, PDRCC offers the possibility for faster, safer, and cheaper the development of novel anti-cancer therapeutics in the early-stage clinical trails. PMID:27645580

Inferences of drug responses in cancer cells from cancer genomic features and compound chemical and therapeutic properties

NASA Astrophysics Data System (ADS)

Wang, Yongcui; Fang, Jianwen; Chen, Shilong

2016-09-01

Accurately predicting the response of a cancer patient to a therapeutic agent is a core goal of precision medicine. Existing approaches were mainly relied primarily on genomic alterations in cancer cells that have been treated with different drugs. Here we focus on predicting drug response based on integration of the heterogeneously pharmacogenomics data from both cell and drug sides. Through a systematical approach, named as PDRCC (Predict Drug Response in Cancer Cells), the cancer genomic alterations and compound chemical and therapeutic properties were incorporated to determine the chemotherapeutic response in cancer patients. Using the Cancer Cell Line Encyclopedia (CCLE) study as the benchmark dataset, all pharmacogenomics data exhibited their roles in inferring the relationships between cancer cells and drugs. When integrating both genomic resources and compound information, the prediction coverage was significantly increased. The validity of PDRCC was also supported by its effective in uncovering the unknown cell-drug associations with database and literature evidences. It set the stage for clinical testing of novel therapeutic strategies, such as the sensitive association between cancer cell ‘A549_LUNG’ and compound ‘Topotecan’. In conclusion, PDRCC offers the possibility for faster, safer, and cheaper the development of novel anti-cancer therapeutics in the early-stage clinical trails.

In vitro transcriptomic prediction of hepatotoxicity for early drug discovery

PubMed Central

Cheng, Feng; Theodorescu, Dan; Schulman, Ira G.; Lee, Jae K.

2012-01-01

Liver toxicity (hepatotoxicity) is a critical issue in drug discovery and development. Standard preclinical evaluation of drug hepatotoxicity is generally performed using in vivo animal systems. However, only a small number of preselected compounds can be examined in vivo due to high experimental costs. A more efficient yet accurate screening technique which can identify potentially hepatotoxic compounds in the early stages of drug development would thus be valuable. Here, we develop and apply a novel genomic prediction technique for screening hepatotoxic compounds based on in vitro human liver cell tests. Using a training set of in vivo rodent experiments for drug hepatotoxicity evaluation, we discovered common biomarkers of drug-induced liver toxicity among six heterogeneous compounds. This gene set was further triaged to a subset of 32 genes that can be used as a multi-gene expression signature to predict hepatotoxicity. This multi-gene predictor was independently validated and showed consistently high prediction performance on five test sets of in vitro human liver cell and in vivo animal toxicity experiments. The predictor also demonstrated utility in evaluating different degrees of toxicity in response to drug concentrations which may be useful not only for discerning a compound’s general hepatotoxicity but also for determining its toxic concentration. PMID:21884709

Inferences of drug responses in cancer cells from cancer genomic features and compound chemical and therapeutic properties.

PubMed

Wang, Yongcui; Fang, Jianwen; Chen, Shilong

2016-09-20

Accurately predicting the response of a cancer patient to a therapeutic agent is a core goal of precision medicine. Existing approaches were mainly relied primarily on genomic alterations in cancer cells that have been treated with different drugs. Here we focus on predicting drug response based on integration of the heterogeneously pharmacogenomics data from both cell and drug sides. Through a systematical approach, named as PDRCC (Predict Drug Response in Cancer Cells), the cancer genomic alterations and compound chemical and therapeutic properties were incorporated to determine the chemotherapeutic response in cancer patients. Using the Cancer Cell Line Encyclopedia (CCLE) study as the benchmark dataset, all pharmacogenomics data exhibited their roles in inferring the relationships between cancer cells and drugs. When integrating both genomic resources and compound information, the prediction coverage was significantly increased. The validity of PDRCC was also supported by its effective in uncovering the unknown cell-drug associations with database and literature evidences. It set the stage for clinical testing of novel therapeutic strategies, such as the sensitive association between cancer cell 'A549_LUNG' and compound 'Topotecan'. In conclusion, PDRCC offers the possibility for faster, safer, and cheaper the development of novel anti-cancer therapeutics in the early-stage clinical trails.

A systematic investigation of computation models for predicting Adverse Drug Reactions (ADRs).

PubMed

Kuang, Qifan; Wang, MinQi; Li, Rong; Dong, YongCheng; Li, Yizhou; Li, Menglong

2014-01-01

Early and accurate identification of adverse drug reactions (ADRs) is critically important for drug development and clinical safety. Computer-aided prediction of ADRs has attracted increasing attention in recent years, and many computational models have been proposed. However, because of the lack of systematic analysis and comparison of the different computational models, there remain limitations in designing more effective algorithms and selecting more useful features. There is therefore an urgent need to review and analyze previous computation models to obtain general conclusions that can provide useful guidance to construct more effective computational models to predict ADRs. In the current study, the main work is to compare and analyze the performance of existing computational methods to predict ADRs, by implementing and evaluating additional algorithms that have been earlier used for predicting drug targets. Our results indicated that topological and intrinsic features were complementary to an extent and the Jaccard coefficient had an important and general effect on the prediction of drug-ADR associations. By comparing the structure of each algorithm, final formulas of these algorithms were all converted to linear model in form, based on this finding we propose a new algorithm called the general weighted profile method and it yielded the best overall performance among the algorithms investigated in this paper. Several meaningful conclusions and useful findings regarding the prediction of ADRs are provided for selecting optimal features and algorithms.

BRCA-Monet: a breast cancer specific drug treatment mode-of-action network for treatment effective prediction using large scale microarray database.

PubMed

Ma, Chifeng; Chen, Hung-I; Flores, Mario; Huang, Yufei; Chen, Yidong

2013-01-01

Connectivity map (cMap) is a recent developed dataset and algorithm for uncovering and understanding the treatment effect of small molecules on different cancer cell lines. It is widely used but there are still remaining challenges for accurate predictions. Here, we propose BRCA-MoNet, a network of drug mode of action (MoA) specific to breast cancer, which is constructed based on the cMap dataset. A drug signature selection algorithm fitting the characteristic of cMap data, a quality control scheme as well as a novel query algorithm based on BRCA-MoNet are developed for more effective prediction of drug effects. BRCA-MoNet was applied to three independent data sets obtained from the GEO database: Estrodial treated MCF7 cell line, BMS-754807 treated MCF7 cell line, and a breast cancer patient microarray dataset. In the first case, BRCA-MoNet could identify drug MoAs likely to share same and reverse treatment effect. In the second case, the result demonstrated the potential of BRCA-MoNet to reposition drugs and predict treatment effects for drugs not in cMap data. In the third case, a possible procedure of personalized drug selection is showcased. The results clearly demonstrated that the proposed BRCA-MoNet approach can provide increased prediction power to cMap and thus will be useful for identification of new therapeutic candidates.

A hybrid approach to advancing quantitative prediction of tissue distribution of basic drugs in human

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

Poulin, Patrick, E-mail: patrick-poulin@videotron.ca; Ekins, Sean; Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, 20 Penn Street, Baltimore, MD 21201

A general toxicity of basic drugs is related to phospholipidosis in tissues. Therefore, it is essential to predict the tissue distribution of basic drugs to facilitate an initial estimate of that toxicity. The objective of the present study was to further assess the original prediction method that consisted of using the binding to red blood cells measured in vitro for the unbound drug (RBCu) as a surrogate for tissue distribution, by correlating it to unbound tissue:plasma partition coefficients (Kpu) of several tissues, and finally to predict volume of distribution at steady-state (V{sub ss}) in humans under in vivo conditions. Thismore » correlation method demonstrated inaccurate predictions of V{sub ss} for particular basic drugs that did not follow the original correlation principle. Therefore, the novelty of this study is to provide clarity on the actual hypotheses to identify i) the impact of pharmacological mode of action on the generic correlation of RBCu-Kpu, ii) additional mechanisms of tissue distribution for the outlier drugs, iii) molecular features and properties that differentiate compounds as outliers in the original correlation analysis in order to facilitate its applicability domain alongside the properties already used so far, and finally iv) to present a novel and refined correlation method that is superior to what has been previously published for the prediction of human V{sub ss} of basic drugs. Applying a refined correlation method after identifying outliers would facilitate the prediction of more accurate distribution parameters as key inputs used in physiologically based pharmacokinetic (PBPK) and phospholipidosis models.« less

DR2DI: a powerful computational tool for predicting novel drug-disease associations

NASA Astrophysics Data System (ADS)

Lu, Lu; Yu, Hua

2018-05-01

Finding the new related candidate diseases for known drugs provides an effective method for fast-speed and low-risk drug development. However, experimental identification of drug-disease associations is expensive and time-consuming. This motivates the need for developing in silico computational methods that can infer true drug-disease pairs with high confidence. In this study, we presented a novel and powerful computational tool, DR2DI, for accurately uncovering the potential associations between drugs and diseases using high-dimensional and heterogeneous omics data as information sources. Based on a unified and extended similarity kernel framework, DR2DI inferred the unknown relationships between drugs and diseases using Regularized Kernel Classifier. Importantly, DR2DI employed a semi-supervised and global learning algorithm which can be applied to uncover the diseases (drugs) associated with known and novel drugs (diseases). In silico global validation experiments showed that DR2DI significantly outperforms recent two approaches for predicting drug-disease associations. Detailed case studies further demonstrated that the therapeutic indications and side effects of drugs predicted by DR2DI could be validated by existing database records and literature, suggesting that DR2DI can be served as a useful bioinformatic tool for identifying the potential drug-disease associations and guiding drug repositioning. Our software and comparison codes are freely available at https://github.com/huayu1111/DR2DI.

DR2DI: a powerful computational tool for predicting novel drug-disease associations

NASA Astrophysics Data System (ADS)

Lu, Lu; Yu, Hua

2018-04-01

Finding the new related candidate diseases for known drugs provides an effective method for fast-speed and low-risk drug development. However, experimental identification of drug-disease associations is expensive and time-consuming. This motivates the need for developing in silico computational methods that can infer true drug-disease pairs with high confidence. In this study, we presented a novel and powerful computational tool, DR2DI, for accurately uncovering the potential associations between drugs and diseases using high-dimensional and heterogeneous omics data as information sources. Based on a unified and extended similarity kernel framework, DR2DI inferred the unknown relationships between drugs and diseases using Regularized Kernel Classifier. Importantly, DR2DI employed a semi-supervised and global learning algorithm which can be applied to uncover the diseases (drugs) associated with known and novel drugs (diseases). In silico global validation experiments showed that DR2DI significantly outperforms recent two approaches for predicting drug-disease associations. Detailed case studies further demonstrated that the therapeutic indications and side effects of drugs predicted by DR2DI could be validated by existing database records and literature, suggesting that DR2DI can be served as a useful bioinformatic tool for identifying the potential drug-disease associations and guiding drug repositioning. Our software and comparison codes are freely available at https://github.com/huayu1111/DR2DI.

Humanizing the zebrafish liver shifts drug metabolic profiles and improves pharmacokinetics of CYP3A4 substrates.

PubMed

Poon, Kar Lai; Wang, Xingang; Ng, Ashley S; Goh, Wei Huang; McGinnis, Claudia; Fowler, Stephen; Carney, Tom J; Wang, Haishan; Ingham, Phillip W

2017-03-01

Understanding and predicting whether new drug candidates will be safe in the clinic is a critical hurdle in pharmaceutical development, that relies in part on absorption, distribution, metabolism, excretion and toxicology studies in vivo. Zebrafish is a relatively new model system for drug metabolism and toxicity studies, offering whole organism screening coupled with small size and potential for high-throughput screening. Through toxicity and absorption analyses of a number of drugs, we find that zebrafish is generally predictive of drug toxicity, although assay outcomes are influenced by drug lipophilicity which alters drug uptake. In addition, liver microsome assays reveal specific differences in metabolism of compounds between human and zebrafish livers, likely resulting from the divergence of the cytochrome P450 superfamily between species. To reflect human metabolism more accurately, we generated a transgenic "humanized" zebrafish line that expresses the major human phase I detoxifying enzyme, CYP3A4, in the liver. Here, we show that this humanized line shows an elevated metabolism of CYP3A4-specific substrates compared to wild-type zebrafish. The generation of this first described humanized zebrafish liver suggests such approaches can enhance the accuracy of the zebrafish model for toxicity prediction.

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.

2018 update to the HIV-TRePS system: the development of new computational models to predict HIV treatment outcomes, with or without a genotype, with enhanced usability for low-income settings.

PubMed

Revell, Andrew D; Wang, Dechao; Perez-Elias, Maria-Jesus; Wood, Robin; Cogill, Dolphina; Tempelman, Hugo; Hamers, Raph L; Reiss, Peter; van Sighem, Ard I; Rehm, Catherine A; Pozniak, Anton; Montaner, Julio S G; Lane, H Clifford; Larder, Brendan A

2018-06-08

Optimizing antiretroviral drug combination on an individual basis can be challenging, particularly in settings with limited access to drugs and genotypic resistance testing. Here we describe our latest computational models to predict treatment responses, with or without a genotype, and compare their predictive accuracy with that of genotyping. Random forest models were trained to predict the probability of virological response to a new therapy introduced following virological failure using up to 50 000 treatment change episodes (TCEs) without a genotype and 18 000 TCEs including genotypes. Independent data sets were used to evaluate the models. This study tested the effects on model accuracy of relaxing the baseline data timing windows, the use of a new filter to exclude probable non-adherent cases and the addition of maraviroc, tipranavir and elvitegravir to the system. The no-genotype models achieved area under the receiver operator characteristic curve (AUC) values of 0.82 and 0.81 using the standard and relaxed baseline data windows, respectively. The genotype models achieved AUC values of 0.86 with the new non-adherence filter and 0.84 without. Both sets of models were significantly more accurate than genotyping with rules-based interpretation, which achieved AUC values of only 0.55-0.63, and were marginally more accurate than previous models. The models were able to identify alternative regimens that were predicted to be effective for the vast majority of cases in which the new regimen prescribed in the clinic failed. These latest global models predict treatment responses accurately even without a genotype and have the potential to help optimize therapy, particularly in resource-limited settings.

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients.

PubMed

Freitas, Alex A; Limbu, Kriti; Ghafourian, Taravat

2015-01-01

Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Graphical AbstractDecision trees for the prediction of tissue partition coefficient and volume of distribution of drugs.

A Study on Pharmacokinetics of Bosentan with Systems Modeling, Part 1: Translating Systemic Plasma Concentration to Liver Exposure in Healthy Subjects.

PubMed

Li, Rui; Niosi, Mark; Johnson, Nathaniel; Tess, David A; Kimoto, Emi; Lin, Jian; Yang, Xin; Riccardi, Keith A; Ryu, Sangwoo; El-Kattan, Ayman F; Maurer, Tristan S; Tremaine, Larry M; Di, Li

2018-04-01

Understanding liver exposure of hepatic transporter substrates in clinical studies is often critical, as it typically governs pharmacodynamics, drug-drug interactions, and toxicity for certain drugs. However, this is a challenging task since there is currently no easy method to directly measure drug concentration in the human liver. Using bosentan as an example, we demonstrate a new approach to estimate liver exposure based on observed systemic pharmacokinetics from clinical studies using physiologically based pharmacokinetic modeling. The prediction was verified to be both accurate and precise using sensitivity analysis. For bosentan, the predicted pseudo steady-state unbound liver-to-unbound systemic plasma concentration ratio was 34.9 (95% confidence interval: 4.2, 50). Drug-drug interaction (i.e., CYP3A and CYP2B6 induction) and inhibition of hepatic transporters (i.e., bile salt export pump, multidrug resistance-associated proteins, and sodium-taurocholate cotransporting polypeptide) were predicted based on the estimated unbound liver tissue or plasma concentrations. With further validation and refinement, we conclude that this approach may serve to predict human liver exposure and complement other methods involving tissue biopsy and imaging. Copyright © 2018 by The American Society for Pharmacology and Experimental Therapeutics.

pKa prediction of monoprotic small molecules the SMARTS way.

PubMed

Lee, Adam C; Yu, Jing-Yu; Crippen, Gordon M

2008-10-01

Realizing favorable absorption, distribution, metabolism, elimination, and toxicity profiles is a necessity due to the high attrition rate of lead compounds in drug development today. The ability to accurately predict bioavailability can help save time and money during the screening and optimization processes. As several robust programs already exist for predicting logP, we have turned our attention to the fast and robust prediction of pK(a) for small molecules. Using curated data from the Beilstein Database and Lange's Handbook of Chemistry, we have created a decision tree based on a novel set of SMARTS strings that can accurately predict the pK(a) for monoprotic compounds with R(2) of 0.94 and root mean squared error of 0.68. Leave-some-out (10%) cross-validation achieved Q(2) of 0.91 and root mean squared error of 0.80.

[Effect of heat transfer in the packages on the stability of thiamine nitrate under uncontrolled temperature conditions].

PubMed

Nakamura, Toru; Yamaji, Takayuki; Takayama, Kozo

2013-01-01

To accurately predict the stability of thiamine nitrate as a model drug in pharmaceutical products under uncontrolled temperature conditions, the average reaction rate constant was determined, taking into account the heat transfer from the atmosphere to the product. The stability tests of thiamine nitrate in the three packages with different heat transfers were performed under non-isothermal conditions. The stability data observed were compared with the predictions based on a newly developed method, showing that the stability was well predicted by the method involving the heat transfer. By contrast, there were some deviations observed from the predicted data, without considering heat transfer in the packages with low heat transfer. The above-mentioned result clearly shows that heat transfer should be considered to ensure accurate prediction of the stability of commercial pharmaceutical products under non-isothermal atmospheres.

A rule of unity for human intestinal absorption 3: Application to pharmaceuticals.

PubMed

Patel, Raj B; Yalkowsky, Samuel H

2018-02-01

The rule of unity is based on a simple absorption parameter, Π, that can accurately predict whether or not an orally administered drug will be well absorbed or poorly absorbed. The intrinsic aqueous solubility and octanol-water partition coefficient, along with the drug dose are used to calculate Π. We show that a single delineator value for Π exist that can distinguish whether a drug is likely to be well absorbed (FA ≥ 0.5) or poorly absorbed (FA < 0.5) at any specified dose. The model is shown to give 82.5% correct predictions for over 938 pharmaceuticals. The maximum well-absorbed dose (i.e. the maximum dose that will be more than 50% absorbed) calculated using this model can be utilized as a guideline for drug design and synthesis. Copyright © 2017 John Wiley & Sons, Ltd.

Free energy landscape for the binding process of Huperzine A to acetylcholinesterase

PubMed Central

Bai, Fang; Xu, Yechun; Chen, Jing; Liu, Qiufeng; Gu, Junfeng; Wang, Xicheng; Ma, Jianpeng; Li, Honglin; Onuchic, José N.; Jiang, Hualiang

2013-01-01

Drug-target residence time (t = 1/koff, where koff is the dissociation rate constant) has become an important index in discovering better- or best-in-class drugs. However, little effort has been dedicated to developing computational methods that can accurately predict this kinetic parameter or related parameters, koff and activation free energy of dissociation (). In this paper, energy landscape theory that has been developed to understand protein folding and function is extended to develop a generally applicable computational framework that is able to construct a complete ligand-target binding free energy landscape. This enables both the binding affinity and the binding kinetics to be accurately estimated. We applied this method to simulate the binding event of the anti-Alzheimer’s disease drug (−)−Huperzine A to its target acetylcholinesterase (AChE). The computational results are in excellent agreement with our concurrent experimental measurements. All of the predicted values of binding free energy and activation free energies of association and dissociation deviate from the experimental data only by less than 1 kcal/mol. The method also provides atomic resolution information for the (−)−Huperzine A binding pathway, which may be useful in designing more potent AChE inhibitors. We expect this methodology to be widely applicable to drug discovery and development. PMID:23440190

Free energy landscape for the binding process of Huperzine A to acetylcholinesterase.

PubMed

Bai, Fang; Xu, Yechun; Chen, Jing; Liu, Qiufeng; Gu, Junfeng; Wang, Xicheng; Ma, Jianpeng; Li, Honglin; Onuchic, José N; Jiang, Hualiang

2013-03-12

Drug-target residence time (t = 1/k(off), where k(off) is the dissociation rate constant) has become an important index in discovering better- or best-in-class drugs. However, little effort has been dedicated to developing computational methods that can accurately predict this kinetic parameter or related parameters, k(off) and activation free energy of dissociation (ΔG(off)≠). In this paper, energy landscape theory that has been developed to understand protein folding and function is extended to develop a generally applicable computational framework that is able to construct a complete ligand-target binding free energy landscape. This enables both the binding affinity and the binding kinetics to be accurately estimated. We applied this method to simulate the binding event of the anti-Alzheimer's disease drug (-)-Huperzine A to its target acetylcholinesterase (AChE). The computational results are in excellent agreement with our concurrent experimental measurements. All of the predicted values of binding free energy and activation free energies of association and dissociation deviate from the experimental data only by less than 1 kcal/mol. The method also provides atomic resolution information for the (-)-Huperzine A binding pathway, which may be useful in designing more potent AChE inhibitors. We expect this methodology to be widely applicable to drug discovery and development.

The evaluation of the abuse liability of drugs.

PubMed

Johanson, C E

1990-01-01

In order to place appropriate restrictions upon the availability of certain therapeutic agents to limit their abuse, it is important to assess abuse liability, an important aspect of drug safety evaluation. However, the negative consequences of restriction must also be considered. Drugs most likely to be tested are psychoactive compounds with therapeutic indications similar to known drugs of abuse. Methods include assays of pharmacological profile, drug discrimination procedures, self-administration procedures, and measures of drug-induced toxicity including evaluations of tolerance and physical dependence. Furthermore, the evaluation of toxicity using behavioural end-points is an important component of the assessment, and it is generally believed that the most valid procedure in this evaluation is the measurement of drug self-administration. However, even this method rarely predicts the extent of abuse of a specific drug. Although methods are available which appear to measure relative abuse liability, these procedures are not validated for all drug classes. Thus, additional strategies, including abuse liability studies in humans, modelled after those used with animals, must be used in order to make a more informed prediction. Although there is pressure to place restrictions on new drugs at the time of marketing, in light of the difficulty of predicting relative abuse potential, a better strategy might be to market a drug without restrictions, but require postmarketing surveillance in order to obtain more accurate information on which to base a final decision.

Estimation of elimination half-lives of organic chemicals in humans using gradient boosting machine.

PubMed

Lu, Jing; Lu, Dong; Zhang, Xiaochen; Bi, Yi; Cheng, Keguang; Zheng, Mingyue; Luo, Xiaomin

2016-11-01

Elimination half-life is an important pharmacokinetic parameter that determines exposure duration to approach steady state of drugs and regulates drug administration. The experimental evaluation of half-life is time-consuming and costly. Thus, it is attractive to build an accurate prediction model for half-life. In this study, several machine learning methods, including gradient boosting machine (GBM), support vector regressions (RBF-SVR and Linear-SVR), local lazy regression (LLR), SA, SR, and GP, were employed to build high-quality prediction models. Two strategies of building consensus models were explored to improve the accuracy of prediction. Moreover, the applicability domains (ADs) of the models were determined by using the distance-based threshold. Among seven individual models, GBM showed the best performance (R(2)=0.820 and RMSE=0.555 for the test set), and Linear-SVR produced the inferior prediction accuracy (R(2)=0.738 and RMSE=0.672). The use of distance-based ADs effectively determined the scope of QSAR models. However, the consensus models by combing the individual models could not improve the prediction performance. Some essential descriptors relevant to half-life were identified and analyzed. An accurate prediction model for elimination half-life was built by GBM, which was superior to the reference model (R(2)=0.723 and RMSE=0.698). Encouraged by the promising results, we expect that the GBM model for elimination half-life would have potential applications for the early pharmacokinetic evaluations, and provide guidance for designing drug candidates with favorable in vivo exposure profile. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang. Copyright © 2016 Elsevier B.V. All rights reserved.

Current State and Future Perspectives in QSAR Models to Predict Blood- Brain Barrier Penetration in Central Nervous System Drug R&D.

PubMed

Morales, Juan F; Montoto, Sebastian Scioli; Fagiolino, Pietro; Ruiz, Maria E

2017-01-01

The Blood-Brain Barrier (BBB) is a physical and biochemical barrier that restricts the entry of certain drugs to the Central Nervous System (CNS), while allowing the passage of others. The ability to predict the permeability of a given molecule through the BBB is a key aspect in CNS drug discovery and development, since neurotherapeutic agents with molecular targets in the CNS should be able to cross the BBB, whereas peripherally acting agents should not, to minimize the risk of CNS adverse effects. In this review we examine and discuss QSAR approaches and current availability of experimental data for the construction of BBB permeability predictive models, focusing on the modeling of the biorelevant parameter unbound partitioning coefficient (Kp,uu). Emphasis is made on two possible strategies to overcome the current limitations of in silico models: considering the prediction of brain penetration as a multifactorial problem, and increasing experimental datasets through accurate and standardized experimental techniques.

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.

A Systematic Investigation of Computation Models for Predicting Adverse Drug Reactions (ADRs)

PubMed Central

Kuang, Qifan; Wang, MinQi; Li, Rong; Dong, YongCheng; Li, Yizhou; Li, Menglong

2014-01-01

Background Early and accurate identification of adverse drug reactions (ADRs) is critically important for drug development and clinical safety. Computer-aided prediction of ADRs has attracted increasing attention in recent years, and many computational models have been proposed. However, because of the lack of systematic analysis and comparison of the different computational models, there remain limitations in designing more effective algorithms and selecting more useful features. There is therefore an urgent need to review and analyze previous computation models to obtain general conclusions that can provide useful guidance to construct more effective computational models to predict ADRs. Principal Findings In the current study, the main work is to compare and analyze the performance of existing computational methods to predict ADRs, by implementing and evaluating additional algorithms that have been earlier used for predicting drug targets. Our results indicated that topological and intrinsic features were complementary to an extent and the Jaccard coefficient had an important and general effect on the prediction of drug-ADR associations. By comparing the structure of each algorithm, final formulas of these algorithms were all converted to linear model in form, based on this finding we propose a new algorithm called the general weighted profile method and it yielded the best overall performance among the algorithms investigated in this paper. Conclusion Several meaningful conclusions and useful findings regarding the prediction of ADRs are provided for selecting optimal features and algorithms. PMID:25180585

BRCA-Monet: a breast cancer specific drug treatment mode-of-action network for treatment effective prediction using large scale microarray database

PubMed Central

2013-01-01

Background Connectivity map (cMap) is a recent developed dataset and algorithm for uncovering and understanding the treatment effect of small molecules on different cancer cell lines. It is widely used but there are still remaining challenges for accurate predictions. Method Here, we propose BRCA-MoNet, a network of drug mode of action (MoA) specific to breast cancer, which is constructed based on the cMap dataset. A drug signature selection algorithm fitting the characteristic of cMap data, a quality control scheme as well as a novel query algorithm based on BRCA-MoNet are developed for more effective prediction of drug effects. Result BRCA-MoNet was applied to three independent data sets obtained from the GEO database: Estrodial treated MCF7 cell line, BMS-754807 treated MCF7 cell line, and a breast cancer patient microarray dataset. In the first case, BRCA-MoNet could identify drug MoAs likely to share same and reverse treatment effect. In the second case, the result demonstrated the potential of BRCA-MoNet to reposition drugs and predict treatment effects for drugs not in cMap data. In the third case, a possible procedure of personalized drug selection is showcased. Conclusions The results clearly demonstrated that the proposed BRCA-MoNet approach can provide increased prediction power to cMap and thus will be useful for identification of new therapeutic candidates. Website: The web based application is developed and can be access through the following link http://compgenomics.utsa.edu/BRCAMoNet/ PMID:24564956

New strategy for protein interactions and application to structure-based drug design

NASA Astrophysics Data System (ADS)

Zou, Xiaoqin

One of the greatest challenges in computational biophysics is to predict interactions between biological molecules, which play critical roles in biological processes and rational design of therapeutic drugs. Biomolecular interactions involve delicate interplay between multiple interactions, including electrostatic interactions, van der Waals interactions, solvent effect, and conformational entropic effect. Accurate determination of these complex and subtle interactions is challenging. Moreover, a biological molecule such as a protein usually consists of thousands of atoms, and thus occupies a huge conformational space. The large degrees of freedom pose further challenges for accurate prediction of biomolecular interactions. Here, I will present our development of physics-based theory and computational modeling on protein interactions with other molecules. The major strategy is to extract microscopic energetics from the information embedded in the experimentally-determined structures of protein complexes. I will also present applications of the methods to structure-based therapeutic design. Supported by NSF CAREER Award DBI-0953839, NIH R01GM109980, and the American Heart Association (Midwest Affiliate) [13GRNT16990076].

XenoSite: accurately predicting CYP-mediated sites of metabolism with neural networks.

PubMed

Zaretzki, Jed; Matlock, Matthew; Swamidass, S Joshua

2013-12-23

Understanding how xenobiotic molecules are metabolized is important because it influences the safety, efficacy, and dose of medicines and how they can be modified to improve these properties. The cytochrome P450s (CYPs) are proteins responsible for metabolizing 90% of drugs on the market, and many computational methods can predict which atomic sites of a molecule--sites of metabolism (SOMs)--are modified during CYP-mediated metabolism. This study improves on prior methods of predicting CYP-mediated SOMs by using new descriptors and machine learning based on neural networks. The new method, XenoSite, is faster to train and more accurate by as much as 4% or 5% for some isozymes. Furthermore, some "incorrect" predictions made by XenoSite were subsequently validated as correct predictions by revaluation of the source literature. Moreover, XenoSite output is interpretable as a probability, which reflects both the confidence of the model that a particular atom is metabolized and the statistical likelihood that its prediction for that atom is correct.

Predictability of drug release from water-insoluble polymeric matrix tablets.

PubMed

Grund, Julia; Körber, Martin; Bodmeier, Roland

2013-11-01

The purpose of this study was to extend the predictability of an established solution of Fick's second law of diffusion with formulation-relevant parameters and including percolation theory. Kollidon SR (polyvinyl acetate/polyvinylpyrrolidone, 80/20 w/w) matrix tablets with various porosities (10-30% v/v) containing model drugs with different solubilities (Cs=10-170 mg/ml) and in different amounts (A=10-90% w/w) were prepared by direct compression and characterized by drug release and mass loss studies. Drug release was fitted to Fick's second law to obtain the apparent diffusion coefficient. Its changes were correlated with the total porosity of the matrix and the solubility of the drug. The apparent diffusion coefficient was best described by a cumulative normal distribution over the range of total porosities. The mean of the distribution coincided with the polymer percolation threshold, and the minimum and maximum of the distribution were represented by the diffusion coefficient in pore-free polymer and in aqueous medium, respectively. The derived model was verified, and the applicability further extended to a drug solubility range of 10-1000 mg/ml. The developed mathematical model accurately describes and predicts drug release from Kollidon SR matrix tablets. It can efficiently reduce experimental trials during formulation development. Copyright © 2013 Elsevier B.V. All rights reserved.

TMDIM: an improved algorithm for the structure prediction of transmembrane domains of bitopic dimers.

PubMed

Cao, Han; Ng, Marcus C K; Jusoh, Siti Azma; Tai, Hio Kuan; Siu, Shirley W I

2017-09-01

[Formula: see text]-Helical transmembrane proteins are the most important drug targets in rational drug development. However, solving the experimental structures of these proteins remains difficult, therefore computational methods to accurately and efficiently predict the structures are in great demand. We present an improved structure prediction method TMDIM based on Park et al. (Proteins 57:577-585, 2004) for predicting bitopic transmembrane protein dimers. Three major algorithmic improvements are introduction of the packing type classification, the multiple-condition decoy filtering, and the cluster-based candidate selection. In a test of predicting nine known bitopic dimers, approximately 78% of our predictions achieved a successful fit (RMSD <2.0 Å) and 78% of the cases are better predicted than the two other methods compared. Our method provides an alternative for modeling TM bitopic dimers of unknown structures for further computational studies. TMDIM is freely available on the web at https://cbbio.cis.umac.mo/TMDIM . Website is implemented in PHP, MySQL and Apache, with all major browsers supported.

TMDIM: an improved algorithm for the structure prediction of transmembrane domains of bitopic dimers

NASA Astrophysics Data System (ADS)

Cao, Han; Ng, Marcus C. K.; Jusoh, Siti Azma; Tai, Hio Kuan; Siu, Shirley W. I.

2017-09-01

α-Helical transmembrane proteins are the most important drug targets in rational drug development. However, solving the experimental structures of these proteins remains difficult, therefore computational methods to accurately and efficiently predict the structures are in great demand. We present an improved structure prediction method TMDIM based on Park et al. (Proteins 57:577-585, 2004) for predicting bitopic transmembrane protein dimers. Three major algorithmic improvements are introduction of the packing type classification, the multiple-condition decoy filtering, and the cluster-based candidate selection. In a test of predicting nine known bitopic dimers, approximately 78% of our predictions achieved a successful fit (RMSD <2.0 Å) and 78% of the cases are better predicted than the two other methods compared. Our method provides an alternative for modeling TM bitopic dimers of unknown structures for further computational studies. TMDIM is freely available on the web at https://cbbio.cis.umac.mo/TMDIM. Website is implemented in PHP, MySQL and Apache, with all major browsers supported.

Drug-gene modeling in pediatric T-cell acute lymphoblastic leukemia highlights importance of 6-mercaptopurine for outcome.

PubMed

Beesley, Alex H; Firth, Martin J; Anderson, Denise; Samuels, Amy L; Ford, Jette; Kees, Ursula R

2013-05-01

Patients relapsing with T-cell acute lymphoblastic leukemia (T-ALL) face a dismal outcome. The aim of this study was to identify new markers of drug resistance and clinical response in T-ALL. We measured gene expression and drug sensitivity in 15 pediatric T-ALL cell lines to find signatures predictive of resistance to 10 agents used in therapy. These were used to generate a model for outcome prediction in patient cohorts using microarray data from diagnosis specimens. In three independent T-ALL cohorts, the 10-drug model was able to accurately identify patient outcome, indicating that the in vitro-derived drug-gene profiles were clinically relevant. Importantly, predictions of outcome within each cohort were linked to distinct drugs, suggesting that different mechanisms contribute to relapse. Sulfite oxidase (SUOX) expression and the drug-transporter ABCC1 (MRP1) were linked to thiopurine sensitivity, suggesting novel pathways for targeting resistance. This study advances our understanding of drug resistance in T-ALL and provides new markers for patient stratification. The results suggest potential benefit from the earlier use of 6-mercaptopurine in T-ALL therapy or the development of adjuvants that may sensitize blasts to this drug. The methodology developed in this study could be applied to other cancers to achieve patient stratification at the time of diagnosis.

DrugECs: An Ensemble System with Feature Subspaces for Accurate Drug-Target Interaction Prediction

PubMed Central

Jiang, Jinjian; Wang, Nian; Zhang, Jun

2017-01-01

Background Drug-target interaction is key in drug discovery, especially in the design of new lead compound. However, the work to find a new lead compound for a specific target is complicated and hard, and it always leads to many mistakes. Therefore computational techniques are commonly adopted in drug design, which can save time and costs to a significant extent. Results To address the issue, a new prediction system is proposed in this work to identify drug-target interaction. First, drug-target pairs are encoded with a fragment technique and the software “PaDEL-Descriptor.” The fragment technique is for encoding target proteins, which divides each protein sequence into several fragments in order and encodes each fragment with several physiochemical properties of amino acids. The software “PaDEL-Descriptor” creates encoding vectors for drug molecules. Second, the dataset of drug-target pairs is resampled and several overlapped subsets are obtained, which are then input into kNN (k-Nearest Neighbor) classifier to build an ensemble system. Conclusion Experimental results on the drug-target dataset showed that our method performs better and runs faster than the state-of-the-art predictors. PMID:28744468

Altered drug metabolism during pregnancy: Hormonal regulation of drug-metabolizing enzymes

PubMed Central

Jeong, Hyunyoung

2013-01-01

Importance of the field Medication use during pregnancy is prevalent, but pharmacokinetic information of most drugs used during pregnancy is lacking in spite of known effects of pregnancy on drug disposition. Accurate pharmacokinetic information is essential for optimal drug therapy in mother and fetus. Thus, understanding how pregnancy influences drug disposition is important for better prediction of pharmacokinetic changes of drugs in pregnant women. Areas covered in this review Pregnancy is known to affect hepatic drug metabolism, but the underlying mechanisms remain unknown. Physiological changes accompanying pregnancy are likely responsible for the reported alteration in drug metabolism during pregnancy. These include elevated concentrations of various hormones such as estrogen, progesterone, placental growth hormones and prolactin. This review covers how these hormones influence expression of drug-metabolizing enzymes, thus potentially responsible for altered drug metabolism during pregnancy. What the reader will gain The reader will gain a greater understanding of the altered drug metabolism in pregnant women and the regulatory effects of pregnancy hormones on expression of drug-metabolizing enzymes. Take home message In-depth studies in hormonal regulatory mechanisms as well as confirmatory studies in pregnant women are warranted for systematic understanding and prediction of the changes in hepatic drug metabolism during pregnancy. PMID:20367533

Optimization of Paclitaxel Containing pH-Sensitive Liposomes By 3 Factor, 3 Level Box-Behnken Design.

PubMed

Rane, Smita; Prabhakar, Bala

2013-07-01

The aim of this study was to investigate the combined influence of 3 independent variables in the preparation of paclitaxel containing pH-sensitive liposomes. A 3 factor, 3 levels Box-Behnken design was used to derive a second order polynomial equation and construct contour plots to predict responses. The independent variables selected were molar ratio phosphatidylcholine:diolylphosphatidylethanolamine (X1), molar concentration of cholesterylhemisuccinate (X2), and amount of drug (X3). Fifteen batches were prepared by thin film hydration method and evaluated for percent drug entrapment, vesicle size, and pH sensitivity. The transformed values of the independent variables and the percent drug entrapment were subjected to multiple regression to establish full model second order polynomial equation. F was calculated to confirm the omission of insignificant terms from the full model equation to derive a reduced model polynomial equation to predict the dependent variables. Contour plots were constructed to show the effects of X1, X2, and X3 on the percent drug entrapment. A model was validated for accurate prediction of the percent drug entrapment by performing checkpoint analysis. The computer optimization process and contour plots predicted the levels of independent variables X1, X2, and X3 (0.99, -0.06, 0, respectively), for maximized response of percent drug entrapment with constraints on vesicle size and pH sensitivity.

Prediction of human pharmacokinetics using physiologically based modeling: a retrospective analysis of 26 clinically tested drugs.

PubMed

De Buck, Stefan S; Sinha, Vikash K; Fenu, Luca A; Nijsen, Marjoleen J; Mackie, Claire E; Gilissen, Ron A H J

2007-10-01

The aim of this study was to evaluate different physiologically based modeling strategies for the prediction of human pharmacokinetics. Plasma profiles after intravenous and oral dosing were simulated for 26 clinically tested drugs. Two mechanism-based predictions of human tissue-to-plasma partitioning (P(tp)) from physicochemical input (method Vd1) were evaluated for their ability to describe human volume of distribution at steady state (V(ss)). This method was compared with a strategy that combined predicted and experimentally determined in vivo rat P(tp) data (method Vd2). Best V(ss) predictions were obtained using method Vd2, providing that rat P(tp) input was corrected for interspecies differences in plasma protein binding (84% within 2-fold). V(ss) predictions from physicochemical input alone were poor (32% within 2-fold). Total body clearance (CL) was predicted as the sum of scaled rat renal clearance and hepatic clearance projected from in vitro metabolism data. Best CL predictions were obtained by disregarding both blood and microsomal or hepatocyte binding (method CL2, 74% within 2-fold), whereas strong bias was seen using both blood and microsomal or hepatocyte binding (method CL1, 53% within 2-fold). The physiologically based pharmacokinetics (PBPK) model, which combined methods Vd2 and CL2 yielded the most accurate predictions of in vivo terminal half-life (69% within 2-fold). The Gastroplus advanced compartmental absorption and transit model was used to construct an absorption-disposition model and provided accurate predictions of area under the plasma concentration-time profile, oral apparent volume of distribution, and maximum plasma concentration after oral dosing, with 74%, 70%, and 65% within 2-fold, respectively. This evaluation demonstrates that PBPK models can lead to reasonable predictions of human pharmacokinetics.

THE ART OF DATA MINING THE MINEFIELDS OF TOXICITY DATABASES TO LINK CHEMISTRY TO BIOLOGY

EPA Science Inventory

Toxicity databases have a special role in predictive toxicology, providing ready access to historical information throughout the workflow of discovery, development, and product safety processes in drug development as well as in review by regulatory agencies. To provide accurate i...

Do MCI criteria in drug trials accurately identify subjects with predementia Alzheimer's disease?

PubMed Central

Visser, P; Scheltens, P; Verhey, F

2005-01-01

Background: Drugs effective in Alzheimer-type dementia have been tested in subjects with mild cognitive impairment (MCI) because these are supposed to have Alzheimer's disease in the predementia stage. Objectives: To investigate whether MCI criteria used in these drug trials can accurately diagnose subjects with predementia Alzheimer's disease. Methods: MCI criteria of the Gal-Int 11 study, InDDEx study, ADCS memory impairment study, ampakine CX 516 study, piracetam study, and Merck rofecoxib study were applied retrospectively in a cohort of 150 non-demented subjects from a memory clinic. Forty two had progressed to Alzheimer type dementia during a five year follow up period and were considered to have predementia Alzheimer's disease at baseline. Outcome measures were the odds ratio, sensitivity, specificity, and positive and negative predictive value. Results: The odds ratio of the MCI criteria for predementia Alzheimer's disease varied between 0.84 and 11. Sensitivity varied between 0.46 and 0.83 and positive predictive value between 0.43 and 0.76. None of the criteria combined a high sensitivity with a high positive predictive value. Exclusion criteria for depression led to an increase in positive predictive value and specificity at the cost of sensitivity. In subjects older than 65 years the positive predictive value was higher than in younger subjects. Conclusions: The diagnostic accuracy of MCI criteria used in trials for predementia Alzheimer's disease is low to moderate. Their use may lead to inclusion of many patients who do not have predementia Alzheimer's disease or to exclusion of many who do. Subjects with moderately severe depression should not be excluded from trials in order not to reduce the sensitivity. PMID:16170074

Human leukocyte antigen (HLA) pharmacogenomic tests: potential and pitfalls.

PubMed

Daly, Ann K

2014-02-01

Adverse drug reactions involving a range of prescribed drugs and affecting the skin, liver and other organs show strong associations with particular HLA alleles. For some reactions, HLA typing prior to prescription, so that those positive for the risk allele are not given the drug associated with the reaction, shows high positive and negative predictive values. The best example of clinical implementation relates to the hypersensitivity reaction induced by the anti-HIV drug abacavir. When this reaction is phenotyped accurately, 100% of those who develop it are positive for HLA-B*57:01. Drug regulators worldwide now recommend genotyping for HLA-B*57:01 before abacavir is prescribed. Serious skin rashes including Stevens-Johnson syndrome and toxic epidermal necrosis can be induced by carbamazepine and other anticonvulsant drugs. In certain East Asians, these reactions are significantly associated with HLA-B*15:02, and typing for this allele is now recommended prior to carbamazepine prescription in these populations. Other HLA associations have been described for skin rash induced by carbamazepine, allopurinol and nevirapine and for liver injury induced by flucloxacillin, amoxicillin-clavulanate, lapatanib, lumiracoxib and ticlopidine. However, the predictive values for typing HLA alleles associated with these adverse reactions are lower. Clinical implementation therefore seems unlikely. Performing HLA typing is relatively complex compared with genotyping assays for single nucleotide polymorphisms. With emphasis on HLA-B*57:01, the approaches used commonly, including use of sequence-specific oligonucleotide PCR primers and DNA sequencing are considered, together with their successful implementation. Genotyping single nucleotide polymorphisms tagging HLA alleles is a simpler alternative to HLA typing but appears insufficiently accurate for clinical use.

A computational approach for predicting off-target toxicity of antiviral ribonucleoside analogues to mitochondrial RNA polymerase.

PubMed

Freedman, Holly; Winter, Philip; Tuszynski, Jack; Tyrrell, D Lorne; Houghton, Michael

2018-06-22

In the development of antiviral drugs that target viral RNA-dependent RNA polymerases, off-target toxicity caused by the inhibition of the human mitochondrial RNA polymerase (POLRMT) is a major liability. Therefore, it is essential that all new ribonucleoside analogue drugs be accurately screened for POLRMT inhibition. A computational tool that can accurately predict NTP binding to POLRMT could assist in evaluating any potential toxicity and in designing possible salvaging strategies. Using the available crystal structure of POLRMT bound to an RNA transcript, here we created a model of POLRMT with an NTP molecule bound in the active site. Furthermore, we implemented a computational screening procedure that determines the relative binding free energy of an NTP analogue to POLRMT by free energy perturbation (FEP), i.e. a simulation in which the natural NTP molecule is slowly transformed into the analogue and back. In each direction, the transformation was performed over 40 ns of simulation on our IBM Blue Gene Q supercomputer. This procedure was validated across a panel of drugs for which experimental dissociation constants were available, showing that NTP relative binding free energies could be predicted to within 0.97 kcal/mol of the experimental values on average. These results demonstrate for the first time that free-energy simulation can be a useful tool for predicting binding affinities of NTP analogues to a polymerase. We expect that our model, together with similar models of viral polymerases, will be very useful in the screening and future design of NTP inhibitors of viral polymerases that have no mitochondrial toxicity. © 2018 Freedman et al.

Rapid and accurate prediction and scoring of water molecules in protein binding sites.

PubMed

Ross, Gregory A; Morris, Garrett M; Biggin, Philip C

2012-01-01

Water plays a critical role in ligand-protein interactions. However, it is still challenging to predict accurately not only where water molecules prefer to bind, but also which of those water molecules might be displaceable. The latter is often seen as a route to optimizing affinity of potential drug candidates. Using a protocol we call WaterDock, we show that the freely available AutoDock Vina tool can be used to predict accurately the binding sites of water molecules. WaterDock was validated using data from X-ray crystallography, neutron diffraction and molecular dynamics simulations and correctly predicted 97% of the water molecules in the test set. In addition, we combined data-mining, heuristic and machine learning techniques to develop probabilistic water molecule classifiers. When applied to WaterDock predictions in the Astex Diverse Set of protein ligand complexes, we could identify whether a water molecule was conserved or displaced to an accuracy of 75%. A second model predicted whether water molecules were displaced by polar groups or by non-polar groups to an accuracy of 80%. These results should prove useful for anyone wishing to undertake rational design of new compounds where the displacement of water molecules is being considered as a route to improved affinity.

Concordance and predictive value of two adverse drug event data sets.

PubMed

Cami, Aurel; Reis, Ben Y

2014-08-22

Accurate prediction of adverse drug events (ADEs) is an important means of controlling and reducing drug-related morbidity and mortality. Since no single "gold standard" ADE data set exists, a range of different drug safety data sets are currently used for developing ADE prediction models. There is a critical need to assess the degree of concordance between these various ADE data sets and to validate ADE prediction models against multiple reference standards. We systematically evaluated the concordance of two widely used ADE data sets - Lexi-comp from 2010 and SIDER from 2012. The strength of the association between ADE (drug) counts in Lexi-comp and SIDER was assessed using Spearman rank correlation, while the differences between the two data sets were characterized in terms of drug categories, ADE categories and ADE frequencies. We also performed a comparative validation of the Predictive Pharmacosafety Networks (PPN) model using both ADE data sets. The predictive power of PPN using each of the two validation sets was assessed using the area under Receiver Operating Characteristic curve (AUROC). The correlations between the counts of ADEs and drugs in the two data sets were 0.84 (95% CI: 0.82-0.86) and 0.92 (95% CI: 0.91-0.93), respectively. Relative to an earlier snapshot of Lexi-comp from 2005, Lexi-comp 2010 and SIDER 2012 introduced a mean of 1,973 and 4,810 new drug-ADE associations per year, respectively. The difference between these two data sets was most pronounced for Nervous System and Anti-infective drugs, Gastrointestinal and Nervous System ADEs, and postmarketing ADEs. A minor difference of 1.1% was found in the AUROC of PPN when SIDER 2012 was used for validation instead of Lexi-comp 2010. In conclusion, the ADE and drug counts in Lexi-comp and SIDER data sets were highly correlated and the choice of validation set did not greatly affect the overall prediction performance of PPN. Our results also suggest that it is important to be aware of the differences that exist among ADE data sets, especially in modeling applications focused on specific drug and ADE categories.

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

Humanized mouse lines and their application for prediction of human drug metabolism and toxicological risk assessment

PubMed Central

Cheung, Connie; Gonzalez, Frank J

2008-01-01

Cytochrome P450s (P450s) are important enzymes involved in the metabolism of xenobiotics, particularly clinically used drugs, and are also responsible for metabolic activation of chemical carcinogens and toxins. Many xenobiotics can activate nuclear receptors that in turn induce the expression of genes encoding xenobiotic metabolizing enzymes and drug transporters. Marked species differences in the expression and regulation of cytochromes P450 and xenobiotic nuclear receptors exist. Thus obtaining reliable rodent models to accurately reflect human drug and carcinogen metabolism is severely limited. Humanized transgenic mice were developed in an effort to create more reliable in vivo systems to study and predict human responses to xenobiotics. Human P450s or human xenobiotic-activated nuclear receptors were introduced directly or replaced the corresponding mouse gene, thus creating “humanized” transgenic mice. Mice expressing human CYP1A1/CYP1A2, CYP2E1, CYP2D6, CYP3A4, CY3A7, PXR, PPARα were generated and characterized. These humanized mouse models offers a broad utility in the evaluation and prediction of toxicological risk that may aid in the development of safer drugs. PMID:18682571

Availability of human induced pluripotent stem cell-derived cardiomyocytes in assessment of drug potential for QT prolongation

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

Nozaki, Yumiko, E-mail: yumiko-nozaki@ds-pharma.co.jp; Honda, Yayoi, E-mail: yayoi-honda@ds-pharma.co.jp; Tsujimoto, Shinji, E-mail: shinji-tsujimoto@ds-pharma.co.jp

2014-07-01

Field potential duration (FPD) in human-induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs), which can express QT interval in an electrocardiogram, is reported to be a useful tool to predict K{sup +} channel and Ca{sup 2+} channel blocker effects on QT interval. However, there is no report showing that this technique can be used to predict multichannel blocker potential for QT prolongation. The aim of this study is to show that FPD from MEA (Multielectrode array) of hiPS-CMs can detect QT prolongation induced by multichannel blockers. hiPS-CMs were seeded onto MEA and FPD was measured for 2 min every 10 min formore » 30 min after drug exposure for the vehicle and each drug concentration. I{sub Kr} and I{sub Ks} blockers concentration-dependently prolonged corrected FPD (FPDc), whereas Ca{sup 2+} channel blockers concentration-dependently shortened FPDc. Also, the multichannel blockers Amiodarone, Paroxetine, Terfenadine and Citalopram prolonged FPDc in a concentration dependent manner. Finally, the I{sub Kr} blockers, Terfenadine and Citalopram, which are reported to cause Torsade de Pointes (TdP) in clinical practice, produced early afterdepolarization (EAD). hiPS-CMs using MEA system and FPDc can predict the effects of drug candidates on QT interval. This study also shows that this assay can help detect EAD for drugs with TdP potential. - Highlights: • We focused on hiPS-CMs to replace in vitro assays in preclinical screening studies. • hiPS-CMs FPD is useful as an indicator to predict drug potential for QT prolongation. • MEA assay can help detect EAD for drugs with TdP potentials. • MEA assay in hiPS-CMs is useful for accurately predicting drug TdP risk in humans.« less

Prevalidation of a model for predicting acute neutropenia by colony forming unit granulocyte/macrophage (CFU-GM) assay.

PubMed

Pessina, A; Albella, B; Bueren, J; Brantom, P; Casati, S; Gribaldo, L; Croera, C; Gagliardi, G; Foti, P; Parchment, R; Parent-Massin, D; Sibiril, Y; Van Den Heuvel, R

2001-12-01

This report describes an international prevalidation study conducted to optimise the Standard Operating Procedure (SOP) for detecting myelosuppressive agents by CFU-GM assay and to study a model for predicting (by means of this in vitro hematopoietic assay) the acute xenobiotic exposure levels that cause maximum tolerated decreases in absolute neutrophil counts (ANC). In the first phase of the study (Protocol Refinement), two SOPs were assessed, by using two cell culture media (Test A, containing GM-CSF; and Test B, containing G-CSF, GM-CSF, IL-3, IL-6 and SCF), and the two tests were applied to cells from both human (bone marrow and umbilical cord blood) and mouse (bone marrow) CFU-GM. In the second phase (Protocol Transfer), the SOPs were transferred to four laboratories to verify the linearity of the assay response and its interlaboratory reproducibility. After a further phase (Protocol Performance), dedicated to a training set of six anticancer drugs (adriamycin, flavopindol, morpholino-doxorubicin, pyrazoloacridine, taxol and topotecan), a model for predicting neutropenia was verified. Results showed that the assay is linear under SOP conditions, and that the in vitro endpoints used by the clinical prediction model of neutropenia are highly reproducible within and between laboratories. Valid tests represented 95% of all tests attempted. The 90% inhibitory concentration values (IC(90)) from Test A and Test B accurately predicted the human maximum tolerated dose (MTD) for five of six and for four of six myelosuppressive anticancer drugs, respectively, that were selected as prototype xenobiotics. As expected, both tests failed to accurately predict the human MTD of a drug that is a likely protoxicant. It is concluded that Test A offers significant cost advantages compared to Test B, without any loss of performance or predictive accuracy. On the basis of these results, we proposed a formal Phase II validation study using the Test A SOP for 16-18 additional xenobiotics that represent the spectrum of haematotoxic potential.

Assessment of juvenile pigs to serve as human pediatric surrogates for preclinical formulation pharmacokinetic testing

USDA-ARS?s Scientific Manuscript database

Pediatric drug development is hampered by the various biological, clinical, and formulation challenges associated with age-based populations. A primary cause for this lack of development is the inability to accurately predict ontogenic changes that affect pharmacokinetics (PK) in children using trad...

Risk factors for early adolescent drug use in four ethnic and racial groups.

PubMed

Vega, W A; Zimmerman, R S; Warheit, G J; Apospori, E; Gil, A G

1993-02-01

It is widely believed that risk factors identified in previous epidemiologic studies accurately predict adolescent drug use. Comparative studies are needed to determine how risk factors vary in prevalence, distribution, sensitivity, and pattern across the major US ethnic/racial groups. Baseline questionnaire data from a 3-year epidemiologic study of early adolescent development and drug use were used to conduct bivariate and multivariate risk factor analyses. Respondents (n = 6760) were sixth- and seventh-grade Cuban, other Hispanic, Black, and White non-Hispanic boys in the 48 middle schools of the greater Miami (Dade County) area. Findings indicate 5% lifetime illicit drug use, 4% lifetime inhalant use, 37% lifetime alcohol use, and 21% lifetime tobacco use, with important intergroup differences. Monotonic relationships were found between 10 risk factors and alcohol and illicit drug use. Individual risk factors were distributed disproportionately, and sensitivity and patterning of risk factors varied widely by ethnic/racial subsample. While the cumulative prevalence of risk factors bears a monotonic relationship to drug use, ethnic/racial differences in risk factor profiles, especially for Blacks, suggest differential predictive value based on cultural differences.

Early Diagnosis and Prediction of Anticancer Drug-induced Cardiotoxicity: From Cardiac Imaging to "Omics" Technologies.

PubMed

Madonna, Rosalinda

2017-07-01

Heart failure due to antineoplastic therapy remains a major cause of morbidity and mortality in oncological patients. These patients often have no prior manifestation of disease. There is therefore a need for accurate identification of individuals at risk of such events before the appearance of clinical manifestations. The present article aims to provide an overview of cardiac imaging as well as new "-omics" technologies, especially with regard to genomics and proteomics as promising tools for the early detection and prediction of cardiotoxicity and individual responses to antineoplastic drugs. Copyright © 2017 Sociedad Española de Cardiología. Published by Elsevier España, S.L.U. All rights reserved.

Computer-Assisted Drug Formulation Design: Novel Approach in Drug Delivery.

PubMed

Metwally, Abdelkader A; Hathout, Rania M

2015-08-03

We hypothesize that, by using several chemo/bio informatics tools and statistical computational methods, we can study and then predict the behavior of several drugs in model nanoparticulate lipid and polymeric systems. Accordingly, two different matrices comprising tripalmitin, a core component of solid lipid nanoparticles (SLN), and PLGA were first modeled using molecular dynamics simulation, and then the interaction of drugs with these systems was studied by means of computing the free energy of binding using the molecular docking technique. These binding energies were hence correlated with the loadings of these drugs in the nanoparticles obtained experimentally from the available literature. The obtained relations were verified experimentally in our laboratory using curcumin as a model drug. Artificial neural networks were then used to establish the effect of the drugs' molecular descriptors on the binding energies and hence on the drug loading. The results showed that the used soft computing methods can provide an accurate method for in silico prediction of drug loading in tripalmitin-based and PLGA nanoparticulate systems. These results have the prospective of being applied to other nano drug-carrier systems, and this integrated statistical and chemo/bio informatics approach offers a new toolbox to the formulation science by proposing what we present as computer-assisted drug formulation design (CADFD).

PHOENIX: a scoring function for affinity prediction derived using high-resolution crystal structures and calorimetry measurements.

PubMed

Tang, Yat T; Marshall, Garland R

2011-02-28

Binding affinity prediction is one of the most critical components to computer-aided structure-based drug design. Despite advances in first-principle methods for predicting binding affinity, empirical scoring functions that are fast and only relatively accurate are still widely used in structure-based drug design. With the increasing availability of X-ray crystallographic structures in the Protein Data Bank and continuing application of biophysical methods such as isothermal titration calorimetry to measure thermodynamic parameters contributing to binding free energy, sufficient experimental data exists that scoring functions can now be derived by separating enthalpic (ΔH) and entropic (TΔS) contributions to binding free energy (ΔG). PHOENIX, a scoring function to predict binding affinities of protein-ligand complexes, utilizes the increasing availability of experimental data to improve binding affinity predictions by the following: model training and testing using high-resolution crystallographic data to minimize structural noise, independent models of enthalpic and entropic contributions fitted to thermodynamic parameters assumed to be thermodynamically biased to calculate binding free energy, use of shape and volume descriptors to better capture entropic contributions. A set of 42 descriptors and 112 protein-ligand complexes were used to derive functions using partial least-squares for change of enthalpy (ΔH) and change of entropy (TΔS) to calculate change of binding free energy (ΔG), resulting in a predictive r2 (r(pred)2) of 0.55 and a standard error (SE) of 1.34 kcal/mol. External validation using the 2009 version of the PDBbind "refined set" (n = 1612) resulted in a Pearson correlation coefficient (R(p)) of 0.575 and a mean error (ME) of 1.41 pK(d). Enthalpy and entropy predictions were of limited accuracy individually. However, their difference resulted in a relatively accurate binding free energy. While the development of an accurate and applicable scoring function was an objective of this study, the main focus was evaluation of the use of high-resolution X-ray crystal structures with high-quality thermodynamic parameters from isothermal titration calorimetry for scoring function development. With the increasing application of structure-based methods in molecular design, this study suggests that using high-resolution crystal structures, separating enthalpy and entropy contributions to binding free energy, and including descriptors to better capture entropic contributions may prove to be effective strategies toward rapid and accurate calculation of binding affinity.

Organic Ion Transporters and Statin Drug Interactions.

PubMed

Kellick, Kenneth

2017-11-25

Statin drug-drug interactions (DDIs) are both troublesome to patients as well as costly to medical resources. The ability to predict and avoid these events could lead to improved outcomes as well as patient satisfaction. This review will explore efforts to better understand and predict these interactions specifically related to one drug transport system, the organic anion-transporting polypeptides (OATPs) specifically OATP1B1 and OATP1B3. Since the publication of the discovery of OATPs, there have been various pharmacokinetic models that have been proposed to explain the variation in pharmacokinetic and clinical effects related to the OATPs. The effects in transport activity appear to be partially related to the individual polymorphisms studied. Drug-drug interactions can occur when other drugs compete for the metabolic site on the OATPs. Various medications are identified as substrates and/or inhibitors of the OATPs, thereby complicating the ability to fully predict the impact on levels and effects. All of the models reviewed claim successes but show limited clinical utility. There are specific populations that have been identified, predominately various Asian descendants that require lower doses of statins to avoid adverse events. The concept of attributing these actions to the OATPs has been explored, but current models cannot accurately predict statin blood levels or elimination constants. The current research only points to the differences in the human genome and the single-nucleotide polymorphisms that exist between us. Based upon the currently available studies, there is beginning to be a glimmer in the understanding how different populations respond to statin transport and elimination. Additionally and unfortunately, there are other enzymes to be studied to better predict patient differences. Clearly, there has been much work completed, yet many more questions require answering to better understand these transport proteins.

Prediction of Human Pharmacokinetic Profile After Transdermal Drug Application Using Excised Human Skin.

PubMed

Yamamoto, Syunsuke; Karashima, Masatoshi; Arai, Yuta; Tohyama, Kimio; Amano, Nobuyuki

2017-09-01

Although several mathematical models have been reported for the estimation of human plasma concentration profiles of drug substances after dermal application, the successful cases that can predict human pharmacokinetic profiles are limited. Therefore, the aim of this study is to investigate the prediction of human plasma concentrations after dermal application using in vitro permeation parameters obtained from excised human skin. The in vitro skin permeability of 7 marketed drug products was evaluated. The plasma concentration-time profiles of the drug substances in humans after their dermal application were simulated using compartment models and the clinical pharmacokinetic parameters. The transdermal process was simulated using the in vitro skin permeation rate and lag time assuming a zero-order absorption. These simulated plasma concentration profiles were compared with the clinical data. The result revealed that the steady-state plasma concentration of diclofenac and the maximum concentrations of nicotine, bisoprolol, rivastigmine, and lidocaine after topical application were within 2-fold of the clinical data. Furthermore, the simulated concentration profiles of bisoprolol, nicotine, and rivastigmine reproduced the decrease in absorption due to drug depletion from the formulation. In conclusion, this simple compartment model using in vitro human skin permeation parameters as zero-order absorption predicted the human plasma concentrations accurately. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Impact of gastrointestinal disease states on oral drug absorption - implications for formulation design - a PEARRL review.

PubMed

Effinger, Angela; O'Driscoll, Caitriona M; McAllister, Mark; Fotaki, Nikoletta

2018-05-16

Drug product performance in patients with gastrointestinal (GI) diseases can be altered compared to healthy subjects due to pathophysiological changes. In this review, relevant differences in patients with inflammatory bowel diseases, coeliac disease, irritable bowel syndrome and short bowel syndrome are discussed and possible in vitro and in silico tools to predict drug product performance in this patient population are assessed. Drug product performance was altered in patients with GI diseases compared to healthy subjects, as assessed in a limited number of studies for some drugs. Underlying causes can be observed pathophysiological alterations such as the differences in GI transit time, the composition of the GI fluids and GI permeability. Additionally, alterations in the abundance of metabolising enzymes and transporter systems were observed. The effect of the GI diseases on each parameter is not always evident as it may depend on the location and the state of the disease. The impact of the pathophysiological change on drug bioavailability depends on the physicochemical characteristics of the drug, the pharmaceutical formulation and drug metabolism. In vitro and in silico methods to predict drug product performance in patients with GI diseases are currently limited but could be a useful tool to improve drug therapy. Development of suitable in vitro dissolution and in silico models for patients with GI diseases can improve their drug therapy. The likeliness of the models to provide accurate predictions depends on the knowledge of pathophysiological alterations, and thus, further assessment of physiological differences is essential. © 2018 Royal Pharmaceutical Society.

FDA approved drugs complexed to their targets: evaluating pose prediction accuracy of docking protocols.

PubMed

Bohari, Mohammed H; Sastry, G Narahari

2012-09-01

Efficient drug discovery programs can be designed by utilizing existing pools of knowledge from the already approved drugs. This can be achieved in one way by repositioning of drugs approved for some indications to newer indications. Complex of drug to its target gives fundamental insight into molecular recognition and a clear understanding of putative binding site. Five popular docking protocols, Glide, Gold, FlexX, Cdocker and LigandFit have been evaluated on a dataset of 199 FDA approved drug-target complexes for their accuracy in predicting the experimental pose. Performance for all the protocols is assessed at default settings, with root mean square deviation (RMSD) between the experimental ligand pose and the docked pose of less than 2.0 Å as the success criteria in predicting the pose. Glide (38.7 %) is found to be the most accurate in top ranked pose and Cdocker (58.8 %) in top RMSD pose. Ligand flexibility is a major bottleneck in failure of docking protocols to correctly predict the pose. Resolution of the crystal structure shows an inverse relationship with the performance of docking protocol. All the protocols perform optimally when a balanced type of hydrophilic and hydrophobic interaction or dominant hydrophilic interaction exists. Overall in 16 different target classes, hydrophobic interactions dominate in the binding site and maximum success is achieved for all the docking protocols in nuclear hormone receptor class while performance for the rest of the classes varied based on individual protocol.

Computational tools and resources for metabolism-related property predictions. 1. Overview of publicly available (free and commercial) databases and software.

PubMed

Peach, Megan L; Zakharov, Alexey V; Liu, Ruifeng; Pugliese, Angelo; Tawa, Gregory; Wallqvist, Anders; Nicklaus, Marc C

2012-10-01

Metabolism has been identified as a defining factor in drug development success or failure because of its impact on many aspects of drug pharmacology, including bioavailability, half-life and toxicity. In this article, we provide an outline and descriptions of the resources for metabolism-related property predictions that are currently either freely or commercially available to the public. These resources include databases with data on, and software for prediction of, several end points: metabolite formation, sites of metabolic transformation, binding to metabolizing enzymes and metabolic stability. We attempt to place each tool in historical context and describe, wherever possible, the data it was based on. For predictions of interactions with metabolizing enzymes, we show a typical set of results for a small test set of compounds. Our aim is to give a clear overview of the areas and aspects of metabolism prediction in which the currently available resources are useful and accurate, and the areas in which they are inadequate or missing entirely.

Translational Biomarkers of Neurotoxicity: A Health and Environmental Sciences Institute Perspective on The Way Forward

EPA Science Inventory

Neurotoxicity has been linked to a number of common drugs and chemicals, yet efficient and accurate methods to detect it are lacking. There is a need for more sensitive and specific biomarkers of neurotoxicity that can help diagnose and predict neurotoxicity that are relevant acr...

The Juvenile Addiction Risk Rating: Development and Initial Psychometrics

ERIC Educational Resources Information Center

Powell, Michael; Newgent, Rebecca A.

2016-01-01

This article describes the development and psychometrics of the Juvenile Addiction Risk Rating. The Juvenile Addiction Risk Rating is a brief screening of addiction potential based on 10 risk factors predictive of youth alcohol and drug-related problems that assists examiners in more accurate treatment planning when self-report information is…

Empirical model for conveniently predicting total and regional lung deposition of inhaled aerosols

EPA Science Inventory

Accurate estimate of a dose of inhaled aerosols is a key factor for estimating potential health risks to exposure to ambient pollutant particulate matter on the one hand, and the therapeutic efficacy of inhaled drug aerosols on the other hand. Particle deposition in the lung is d...

Improved Predictions of Drug-Drug Interactions Mediated by Time-Dependent Inhibition of CYP3A.

PubMed

Yadav, Jaydeep; Korzekwa, Ken; Nagar, Swati

2018-05-07

Time-dependent inactivation (TDI) of cytochrome P450s (CYPs) is a leading cause of clinical drug-drug interactions (DDIs). Current methods tend to overpredict DDIs. In this study, a numerical approach was used to model complex CYP3A TDI in human-liver microsomes. The inhibitors evaluated included troleandomycin (TAO), erythromycin (ERY), verapamil (VER), and diltiazem (DTZ) along with the primary metabolites N-demethyl erythromycin (NDE), norverapamil (NV), and N-desmethyl diltiazem (NDD). The complexities incorporated into the models included multiple-binding kinetics, quasi-irreversible inactivation, sequential metabolism, inhibitor depletion, and membrane partitioning. The resulting inactivation parameters were incorporated into static in vitro-in vivo correlation (IVIVC) models to predict clinical DDIs. For 77 clinically observed DDIs, with a hepatic-CYP3A-synthesis-rate constant of 0.000 146 min -1 , the average fold difference between the observed and predicted DDIs was 3.17 for the standard replot method and 1.45 for the numerical method. Similar results were obtained using a synthesis-rate constant of 0.000 32 min -1 . These results suggest that numerical methods can successfully model complex in vitro TDI kinetics and that the resulting DDI predictions are more accurate than those obtained with the standard replot approach.

Simple heuristics in over-the-counter drug choices: a new hint for medical education and practice.

PubMed

Riva, Silvia; Monti, Marco; Antonietti, Alessandro

2011-01-01

Over-the-counter (OTC) drugs are widely available and often purchased by consumers without advice from a health care provider. Many people rely on self-management of medications to treat common medical conditions. Although OTC medications are regulated by the National and the International Health and Drug Administration, many people are unaware of proper dosing, side effects, adverse drug reactions, and possible medication interactions. This study examined how subjects make their decisions to select an OTC drug, evaluating the role of cognitive heuristics which are simple and adaptive rules that help the decision-making process of people in everyday contexts. By analyzing 70 subjects' information-search and decision-making behavior when selecting OTC drugs, we examined the heuristics they applied in order to assess whether simple decision-making processes were also accurate and relevant. Subjects were tested with a sequence of two experimental tests based on a computerized Java system devised to analyze participants' choices in a virtual environment. We found that subjects' information-search behavior reflected the use of fast and frugal heuristics. In addition, although the heuristics which correctly predicted subjects' decisions implied significantly fewer cues on average than the subjects did in the information-search task, they were accurate in describing order of information search. A simple combination of a fast and frugal tree and a tallying rule predicted more than 78% of subjects' decisions. The current emphasis in health care is to shift some responsibility onto the consumer through expansion of self medication. To know which cognitive mechanisms are behind the choice of OTC drugs is becoming a relevant purpose of current medical education. These findings have implications both for the validity of simple heuristics describing information searches in the field of OTC drug choices and for current medical education, which has to prepare competent health specialists to orientate and support the choices of their patients.

Simple heuristics in over-the-counter drug choices: a new hint for medical education and practice

PubMed Central

Riva, Silvia; Monti, Marco; Antonietti, Alessandro

2011-01-01

Introduction Over-the-counter (OTC) drugs are widely available and often purchased by consumers without advice from a health care provider. Many people rely on self-management of medications to treat common medical conditions. Although OTC medications are regulated by the National and the International Health and Drug Administration, many people are unaware of proper dosing, side effects, adverse drug reactions, and possible medication interactions. Purpose This study examined how subjects make their decisions to select an OTC drug, evaluating the role of cognitive heuristics which are simple and adaptive rules that help the decision-making process of people in everyday contexts. Subjects and methods By analyzing 70 subjects’ information-search and decision-making behavior when selecting OTC drugs, we examined the heuristics they applied in order to assess whether simple decision-making processes were also accurate and relevant. Subjects were tested with a sequence of two experimental tests based on a computerized Java system devised to analyze participants’ choices in a virtual environment. Results We found that subjects’ information-search behavior reflected the use of fast and frugal heuristics. In addition, although the heuristics which correctly predicted subjects’ decisions implied significantly fewer cues on average than the subjects did in the information-search task, they were accurate in describing order of information search. A simple combination of a fast and frugal tree and a tallying rule predicted more than 78% of subjects’ decisions. Conclusion The current emphasis in health care is to shift some responsibility onto the consumer through expansion of self medication. To know which cognitive mechanisms are behind the choice of OTC drugs is becoming a relevant purpose of current medical education. These findings have implications both for the validity of simple heuristics describing information searches in the field of OTC drug choices and for current medical education, which has to prepare competent health specialists to orientate and support the choices of their patients. PMID:23745077

Controlling Release Kinetics of PLG Microspheres Using a Manufacturing Technique

NASA Astrophysics Data System (ADS)

Berchane, Nader

2005-11-01

Controlled drug delivery offers numerous advantages compared with conventional free dosage forms, in particular: improved efficacy and patient compliance. Emulsification is a widely used technique to entrap drugs in biodegradable microspheres for controlled drug delivery. The size of the formed microspheres has a significant influence on drug release kinetics. Despite the advantages of controlled drug delivery, previous attempts to achieve predetermined release rates have seen limited success. This study develops a tool to tailor desired release kinetics by combining microsphere batches of specified mean diameter and size distribution. A fluid mechanics based correlation that predicts the average size of Poly(Lactide-co-Glycolide) [PLG] microspheres from the manufacturing technique, is constructed and validated by comparison with experimental results. The microspheres produced are accurately represented by the Rosin-Rammler mathematical distribution function. A mathematical model is formulated that incorporates the microsphere distribution function to predict the release kinetics from mono-dispersed and poly-dispersed populations. Through this mathematical model, different release kinetics can be achieved by combining different sized populations in different ratios. The resulting design tool should prove useful for the pharmaceutical industry to achieve designer release kinetics.

Machine Learning of Human Pluripotent Stem Cell-Derived Engineered Cardiac Tissue Contractility for Automated Drug Classification.

PubMed

Lee, Eugene K; Tran, David D; Keung, Wendy; Chan, Patrick; Wong, Gabriel; Chan, Camie W; Costa, Kevin D; Li, Ronald A; Khine, Michelle

2017-11-14

Accurately predicting cardioactive effects of new molecular entities for therapeutics remains a daunting challenge. Immense research effort has been focused toward creating new screening platforms that utilize human pluripotent stem cell (hPSC)-derived cardiomyocytes and three-dimensional engineered cardiac tissue constructs to better recapitulate human heart function and drug responses. As these new platforms become increasingly sophisticated and high throughput, the drug screens result in larger multidimensional datasets. Improved automated analysis methods must therefore be developed in parallel to fully comprehend the cellular response across a multidimensional parameter space. Here, we describe the use of machine learning to comprehensively analyze 17 functional parameters derived from force readouts of hPSC-derived ventricular cardiac tissue strips (hvCTS) electrically paced at a range of frequencies and exposed to a library of compounds. A generated metric is effective for then determining the cardioactivity of a given drug. Furthermore, we demonstrate a classification model that can automatically predict the mechanistic action of an unknown cardioactive drug. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Aminoglycoside Therapy Manager: An Advanced Computer Program for Decision Support for Drug Dosing and Therapeutic Monitoring

PubMed Central

Lenert, Leslie; Lurie, Jon; Coleman, Robert; Klosterman, Heidrun; Blaschke, Terrence

1990-01-01

In this paper, we will describe an advanced drug dosing program, Aminoglycoside Therapy Manager that reasons using Bayesian pharmacokinetic modeling and symbolic modeling of patient status and drug response. Our design is similar to the design of the Digitalis Therapy Advisor program, but extends previous work by incorporating a Bayesian pharmacokinetic model, a “meta-level” analysis of drug concentrations to identify sampling errors and changes in pharmacokinetics, and including the results of the “meta-level” analysis in reasoning for dosing and therapeutic monitoring recommendations. The program is user friendly and runs on low cost general-purpose hardware. Validation studies show that the program is as accurate in predicting future drug concentrations as an expert using commercial Bayesian forecasting software.

Biomimetic three-dimensional tissue models for advanced high-throughput drug screening

PubMed Central

Nam, Ki-Hwan; Smith, Alec S.T.; Lone, Saifullah; Kwon, Sunghoon; Kim, Deok-Ho

2015-01-01

Most current drug screening assays used to identify new drug candidates are 2D cell-based systems, even though such in vitro assays do not adequately recreate the in vivo complexity of 3D tissues. Inadequate representation of the human tissue environment during a preclinical test can result in inaccurate predictions of compound effects on overall tissue functionality. Screening for compound efficacy by focusing on a single pathway or protein target, coupled with difficulties in maintaining long-term 2D monolayers, can serve to exacerbate these issues when utilizing such simplistic model systems for physiological drug screening applications. Numerous studies have shown that cell responses to drugs in 3D culture are improved from those in 2D, with respect to modeling in vivo tissue functionality, which highlights the advantages of using 3D-based models for preclinical drug screens. In this review, we discuss the development of microengineered 3D tissue models which accurately mimic the physiological properties of native tissue samples, and highlight the advantages of using such 3D micro-tissue models over conventional cell-based assays for future drug screening applications. We also discuss biomimetic 3D environments, based-on engineered tissues as potential preclinical models for the development of more predictive drug screening assays for specific disease models. PMID:25385716

MOWGLI: prediction of protein-MannOse interacting residues With ensemble classifiers usinG evoLutionary Information.

PubMed

Pai, Priyadarshini P; Mondal, Sukanta

2016-10-01

Proteins interact with carbohydrates to perform various cellular interactions. Of the many carbohydrate ligands that proteins bind with, mannose constitute an important class, playing important roles in host defense mechanisms. Accurate identification of mannose-interacting residues (MIR) may provide important clues to decipher the underlying mechanisms of protein-mannose interactions during infections. This study proposes an approach using an ensemble of base classifiers for prediction of MIR using their evolutionary information in the form of position-specific scoring matrix. The base classifiers are random forests trained by different subsets of training data set Dset128 using 10-fold cross-validation. The optimized ensemble of base classifiers, MOWGLI, is then used to predict MIR on protein chains of the test data set Dtestset29 which showed a promising performance with 92.0% accurate prediction. An overall improvement of 26.6% in precision was observed upon comparison with the state-of-art. It is hoped that this approach, yielding enhanced predictions, could be eventually used for applications in drug design and vaccine development.

gCUP: rapid GPU-based HIV-1 co-receptor usage prediction for next-generation sequencing.

PubMed

Olejnik, Michael; Steuwer, Michel; Gorlatch, Sergei; Heider, Dominik

2014-11-15

Next-generation sequencing (NGS) has a large potential in HIV diagnostics, and genotypic prediction models have been developed and successfully tested in the recent years. However, albeit being highly accurate, these computational models lack computational efficiency to reach their full potential. In this study, we demonstrate the use of graphics processing units (GPUs) in combination with a computational prediction model for HIV tropism. Our new model named gCUP, parallelized and optimized for GPU, is highly accurate and can classify >175 000 sequences per second on an NVIDIA GeForce GTX 460. The computational efficiency of our new model is the next step to enable NGS technologies to reach clinical significance in HIV diagnostics. Moreover, our approach is not limited to HIV tropism prediction, but can also be easily adapted to other settings, e.g. drug resistance prediction. The source code can be downloaded at http://www.heiderlab.de d.heider@wz-straubing.de. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Toward Improved Force-Field Accuracy through Sensitivity Analysis of Host-Guest Binding Thermodynamics

PubMed Central

Yin, Jian; Fenley, Andrew T.; Henriksen, Niel M.; Gilson, Michael K.

2015-01-01

Improving the capability of atomistic computer models to predict the thermodynamics of noncovalent binding is critical for successful structure-based drug design, and the accuracy of such calculations remains limited by non-optimal force field parameters. Ideally, one would incorporate protein-ligand affinity data into force field parametrization, but this would be inefficient and costly. We now demonstrate that sensitivity analysis can be used to efficiently tune Lennard-Jones parameters of aqueous host-guest systems for increasingly accurate calculations of binding enthalpy. These results highlight the promise of a comprehensive use of calorimetric host-guest binding data, along with existing validation data sets, to improve force field parameters for the simulation of noncovalent binding, with the ultimate goal of making protein-ligand modeling more accurate and hence speeding drug discovery. PMID:26181208

Drug delivery optimization through Bayesian networks.

PubMed Central

Bellazzi, R.

1992-01-01

This paper describes how Bayesian Networks can be used in combination with compartmental models to plan Recombinant Human Erythropoietin (r-HuEPO) delivery in the treatment of anemia of chronic uremic patients. Past measurements of hematocrit or hemoglobin concentration in a patient during the therapy can be exploited to adjust the parameters of a compartmental model of the erythropoiesis. This adaptive process allows more accurate patient-specific predictions, and hence a more rational dosage planning. We describe a drug delivery optimization protocol, based on our approach. Some results obtained on real data are presented. PMID:1482938

Evaluation of a Novel Renewable Hepatic Cell Model for Prediction of Clinical CYP3A4 Induction Using a Correlation-Based Relative Induction Score Approach.

PubMed

Zuo, Rongjun; Li, Feng; Parikh, Sweta; Cao, Li; Cooper, Kirsten L; Hong, Yulong; Liu, Jin; Faris, Ronald A; Li, Daochuan; Wang, Hongbing

2017-02-01

Metabolism enzyme induction-mediated drug-drug interactions need to be carefully characterized in vitro for drug candidates to predict in vivo safety risk and therapeutic efficiency. Currently, both the Food and Drug Administration and European Medicines Agency recommend using primary human hepatocytes as the gold standard in vitro test system for studying the induction potential of candidate drugs on cytochrome P450 (CYP), CYP3A4, CYP1A2, and CYP2B6. However, primary human hepatocytes are known to bear inherent limitations such as limited supply and large lot-to-lot variations, which result in an experimental burden to qualify new lots. To overcome these shortcomings, a renewable source of human hepatocytes (i.e., Corning HepatoCells) was developed from primary human hepatocytes and was evaluated for in vitro CYP3A4 induction using methods well established by the pharmaceutical industry. HepatoCells have shown mature hepatocyte-like morphology and demonstrated primary hepatocyte-like response to prototypical inducers of all three CYP enzymes with excellent consistency. Importantly, HepatoCells retain a phenobarbital-responsive nuclear translocation of human constitutive androstane receptor from the cytoplasm, characteristic to primary hepatocytes. To validate HepatoCells as a useful tool to predict potential clinical relevant CYP3A4 induction, we tested three different lots of HepatoCells with a group of clinical strong, moderate/weak CYP3A4 inducers, and noninducers. A relative induction score calibration curve-based approach was used for prediction. HepatoCells showed accurate prediction comparable to primary human hepatocytes. Together, these results demonstrate that Corning HepatoCells is a reliable in vitro model for drug-drug interaction studies during the early phase of drug testing. Copyright © 2017 by The Author(s).

Bitterness intensity prediction of berberine hydrochloride using an electronic tongue and a GA-BP neural network.

PubMed

Liu, Ruixin; Zhang, Xiaodong; Zhang, Lu; Gao, Xiaojie; Li, Huiling; Shi, Junhan; Li, Xuelin

2014-06-01

The aim of this study was to predict the bitterness intensity of a drug using an electronic tongue (e-tongue). The model drug of berberine hydrochloride was used to establish a bitterness prediction model (BPM), based on the taste evaluation of bitterness intensity by a taste panel, the data provided by the e-tongue and a genetic algorithm-back-propagation neural network (GA-BP) modeling method. The modeling characteristics of the GA-BP were compared with those of multiple linear regression, partial least square regression and BP methods. The determination coefficient of the BPM was 0.99965±0.00004, the root mean square error of cross-validation was 0.1398±0.0488 and the correlation coefficient of the cross-validation between the true and predicted values was 0.9959±0.0027. The model is superior to the other three models based on these indicators. In conclusion, the model established in this study has a high fitting degree and may be used for the bitterness prediction modeling of berberine hydrochloride of different concentrations. The model also provides a reference for the generation of BPMs of other drugs. Additionally, the algorithm of the study is able to conduct a rapid and accurate quantitative analysis of the data provided by the e-tongue.

Bitterness intensity prediction of berberine hydrochloride using an electronic tongue and a GA-BP neural network

PubMed Central

LIU, RUIXIN; ZHANG, XIAODONG; ZHANG, LU; GAO, XIAOJIE; LI, HUILING; SHI, JUNHAN; LI, XUELIN

2014-01-01

The aim of this study was to predict the bitterness intensity of a drug using an electronic tongue (e-tongue). The model drug of berberine hydrochloride was used to establish a bitterness prediction model (BPM), based on the taste evaluation of bitterness intensity by a taste panel, the data provided by the e-tongue and a genetic algorithm-back-propagation neural network (GA-BP) modeling method. The modeling characteristics of the GA-BP were compared with those of multiple linear regression, partial least square regression and BP methods. The determination coefficient of the BPM was 0.99965±0.00004, the root mean square error of cross-validation was 0.1398±0.0488 and the correlation coefficient of the cross-validation between the true and predicted values was 0.9959±0.0027. The model is superior to the other three models based on these indicators. In conclusion, the model established in this study has a high fitting degree and may be used for the bitterness prediction modeling of berberine hydrochloride of different concentrations. The model also provides a reference for the generation of BPMs of other drugs. Additionally, the algorithm of the study is able to conduct a rapid and accurate quantitative analysis of the data provided by the e-tongue. PMID:24926369

Identifying predictive features in drug response using machine learning: opportunities and challenges.

PubMed

Vidyasagar, Mathukumalli

2015-01-01

This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction problems are divided into two categories: sparse classification and sparse regression. In classification, the clinical parameter to be predicted is binary, whereas in regression, the parameter is a real number. Well-known methods for both classes of problems are briefly discussed. These include the SVM (support vector machine) for classification and various algorithms such as ridge regression, LASSO (least absolute shrinkage and selection operator), and EN (elastic net) for regression. In addition, several well-established methods that do not directly fall into machine learning theory are also reviewed, including neural networks, PAM (pattern analysis for microarrays), SAM (significance analysis for microarrays), GSEA (gene set enrichment analysis), and k-means clustering. Several references indicative of the application of these methods to cancer biology are discussed.

An unexpected way forward: towards a more accurate and rigorous protein-protein binding affinity scoring function by eliminating terms from an already simple scoring function.

PubMed

Swanson, Jon; Audie, Joseph

2018-01-01

A fundamental and unsolved problem in biophysical chemistry is the development of a computationally simple, physically intuitive, and generally applicable method for accurately predicting and physically explaining protein-protein binding affinities from protein-protein interaction (PPI) complex coordinates. Here, we propose that the simplification of a previously described six-term PPI scoring function to a four term function results in a simple expression of all physically and statistically meaningful terms that can be used to accurately predict and explain binding affinities for a well-defined subset of PPIs that are characterized by (1) crystallographic coordinates, (2) rigid-body association, (3) normal interface size, and hydrophobicity and hydrophilicity, and (4) high quality experimental binding affinity measurements. We further propose that the four-term scoring function could be regarded as a core expression for future development into a more general PPI scoring function. Our work has clear implications for PPI modeling and structure-based drug design.

Application of optical action potentials in human induced pluripotent stem cells-derived cardiomyocytes to predict drug-induced cardiac arrhythmias.

PubMed

Lu, H R; Hortigon-Vinagre, M P; Zamora, V; Kopljar, I; De Bondt, A; Gallacher, D J; Smith, G

2017-09-01

Human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs) are emerging as new and human-relevant source in vitro model for cardiac safety assessment that allow us to investigate a set of 20 reference drugs for predicting cardiac arrhythmogenic liability using optical action potential (oAP) assay. Here, we describe our examination of the oAP measurement using a voltage sensitive dye (Di-4-ANEPPS) to predict adverse compound effects using hiPS-CMs and 20 cardioactive reference compounds. Fluorescence signals were digitized at 10kHz and the records subsequently analyzed off-line. Cells were exposed to 30min incubation to vehicle or compound (n=5/dose, 4 doses/compound) that were blinded to the investigating laboratory. Action potential parameters were measured, including rise time (T rise ) of the optical action potential duration (oAPD). Significant effects on oAPD were sensitively detected with 11 QT-prolonging drugs, while oAPD shortening was observed with I Ca -antagonists, I Kr -activator or ATP-sensitive K + channel (K ATP )-opener. Additionally, the assay detected varied effects induced by 6 different sodium channel blockers. The detection threshold for these drug effects was at or below the published values of free effective therapeutic plasma levels or effective concentrations by other studies. The results of this blinded study indicate that OAP is a sensitive method to accurately detect drug-induced effects (i.e., duration/QT-prolongation, shortening, beat rate, and incidence of early after depolarizations) in hiPS-CMs; therefore, this technique will potentially be useful in predicting drug-induced arrhythmogenic liabilities in early de-risking within the drug discovery phase. Copyright © 2017 Elsevier Inc. All rights reserved.

Companion Diagnostic 64Cu-Liposome Positron Emission Tomography Enables Characterization of Drug Delivery to Tumors and Predicts Response to Cancer Nanomedicines.

PubMed

Lee, Helen; Gaddy, Daniel; Ventura, Manuela; Bernards, Nicholas; de Souza, Raquel; Kirpotin, Dmitri; Wickham, Thomas; Fitzgerald, Jonathan; Zheng, Jinzi; Hendriks, Bart S

2018-01-01

Deposition of liposomal drugs into solid tumors is a potentially rate-limiting step for drug delivery and has substantial variability that may influence probability of response. Tumor deposition is a shared mechanism for liposomal therapeutics such that a single companion diagnostic agent may have utility in predicting response to multiple nanomedicines. Methods: We describe the development, characterization and preclinical proof-of-concept of the positron emission tomography (PET) agent, MM-DX-929, a drug-free untargeted 100 nm PEGylated liposome stably entrapping a chelated complex of 4-DEAP-ATSC and 64 Cu (copper-64). MM-DX-929 is designed to mimic the biodistribution of similarly sized therapeutic agents and enable quantification of deposition in solid tumors. Results: MM-DX-929 demonstrated sufficient in vitro and in vivo stability with PET images accurately reflecting the disposition of liposome nanoparticles over the time scale of imaging. MM-DX-929 is also representative of the tumor deposition and intratumoral distribution of three different liposomal drugs, including targeted liposomes and those with different degrees of PEGylation. Furthermore, stratification using a single pre-treatment MM-DX-929 PET assessment of tumor deposition demonstrated that tumors with high MM-DX-929 deposition predicted significantly greater anti-tumor activity after multi-cycle treatments with different liposomal drugs. In contrast, MM-DX-929 tumor deposition was not prognostic in untreated tumor-bearing xenografts, nor predictive in animals treated with small molecule chemotherapeutics. Conclusions: These data illustrate the potential of MM-DX-929 PET as a companion diagnostic strategy to prospectively select patients likely to respond to liposomal drugs or nanomedicines of similar molecular size.

Major Source of Error in QSPR Prediction of Intrinsic Thermodynamic Solubility of Drugs: Solid vs Nonsolid State Contributions?

PubMed

Abramov, Yuriy A

2015-06-01

The main purpose of this study is to define the major limiting factor in the accuracy of the quantitative structure-property relationship (QSPR) models of the thermodynamic intrinsic aqueous solubility of the drug-like compounds. For doing this, the thermodynamic intrinsic aqueous solubility property was suggested to be indirectly "measured" from the contributions of solid state, ΔGfus, and nonsolid state, ΔGmix, properties, which are estimated by the corresponding QSPR models. The QSPR models of ΔGfus and ΔGmix properties were built based on a set of drug-like compounds with available accurate measurements of fusion and thermodynamic solubility properties. For consistency ΔGfus and ΔGmix models were developed using similar algorithms and descriptor sets, and validated against the similar test compounds. Analysis of the relative performances of these two QSPR models clearly demonstrates that it is the solid state contribution which is the limiting factor in the accuracy and predictive power of the QSPR models of the thermodynamic intrinsic solubility. The performed analysis outlines a necessity of development of new descriptor sets for an accurate description of the long-range order (periodicity) phenomenon in the crystalline state. The proposed approach to the analysis of limitations and suggestions for improvement of QSPR-type models may be generalized to other applications in the pharmaceutical industry.

Development of estrogen receptor beta binding prediction model using large sets of chemicals.

PubMed

Sakkiah, Sugunadevi; Selvaraj, Chandrabose; Gong, Ping; Zhang, Chaoyang; Tong, Weida; Hong, Huixiao

2017-11-03

We developed an ER β binding prediction model to facilitate identification of chemicals specifically bind ER β or ER α together with our previously developed ER α binding model. Decision Forest was used to train ER β binding prediction model based on a large set of compounds obtained from EADB. Model performance was estimated through 1000 iterations of 5-fold cross validations. Prediction confidence was analyzed using predictions from the cross validations. Informative chemical features for ER β binding were identified through analysis of the frequency data of chemical descriptors used in the models in the 5-fold cross validations. 1000 permutations were conducted to assess the chance correlation. The average accuracy of 5-fold cross validations was 93.14% with a standard deviation of 0.64%. Prediction confidence analysis indicated that the higher the prediction confidence the more accurate the predictions. Permutation testing results revealed that the prediction model is unlikely generated by chance. Eighteen informative descriptors were identified to be important to ER β binding prediction. Application of the prediction model to the data from ToxCast project yielded very high sensitivity of 90-92%. Our results demonstrated ER β binding of chemicals could be accurately predicted using the developed model. Coupling with our previously developed ER α prediction model, this model could be expected to facilitate drug development through identification of chemicals that specifically bind ER β or ER α .

Precision Oncology beyond Targeted Therapy: Combining Omics Data with Machine Learning Matches the Majority of Cancer Cells to Effective Therapeutics.

PubMed

Ding, Michael Q; Chen, Lujia; Cooper, Gregory F; Young, Jonathan D; Lu, Xinghua

2018-02-01

Precision oncology involves identifying drugs that will effectively treat a tumor and then prescribing an optimal clinical treatment regimen. However, most first-line chemotherapy drugs do not have biomarkers to guide their application. For molecularly targeted drugs, using the genomic status of a drug target as a therapeutic indicator has limitations. In this study, machine learning methods (e.g., deep learning) were used to identify informative features from genome-scale omics data and to train classifiers for predicting the effectiveness of drugs in cancer cell lines. The methodology introduced here can accurately predict the efficacy of drugs, regardless of whether they are molecularly targeted or nonspecific chemotherapy drugs. This approach, on a per-drug basis, can identify sensitive cancer cells with an average sensitivity of 0.82 and specificity of 0.82; on a per-cell line basis, it can identify effective drugs with an average sensitivity of 0.80 and specificity of 0.82. This report describes a data-driven precision medicine approach that is not only generalizable but also optimizes therapeutic efficacy. The framework detailed herein, when successfully translated to clinical environments, could significantly broaden the scope of precision oncology beyond targeted therapies, benefiting an expanded proportion of cancer patients. Mol Cancer Res; 16(2); 269-78. ©2017 AACR . ©2017 American Association for Cancer Research.

Computational tools and resources for metabolism-related property predictions. 1. Overview of publicly available (free and commercial) databases and software

PubMed Central

Peach, Megan L; Zakharov, Alexey V; Liu, Ruifeng; Pugliese, Angelo; Tawa, Gregory; Wallqvist, Anders; Nicklaus, Marc C

2014-01-01

Metabolism has been identified as a defining factor in drug development success or failure because of its impact on many aspects of drug pharmacology, including bioavailability, half-life and toxicity. In this article, we provide an outline and descriptions of the resources for metabolism-related property predictions that are currently either freely or commercially available to the public. These resources include databases with data on, and software for prediction of, several end points: metabolite formation, sites of metabolic transformation, binding to metabolizing enzymes and metabolic stability. We attempt to place each tool in historical context and describe, wherever possible, the data it was based on. For predictions of interactions with metabolizing enzymes, we show a typical set of results for a small test set of compounds. Our aim is to give a clear overview of the areas and aspects of metabolism prediction in which the currently available resources are useful and accurate, and the areas in which they are inadequate or missing entirely. PMID:23088273

Application of the Refined Integral Method in the mathematical modeling of drug delivery from one-layer torus-shaped devices.

PubMed

Helbling, Ignacio M; Ibarra, Juan C D; Luna, Julio A

2012-02-28

A mathematical modeling of controlled release of drug from one-layer torus-shaped devices is presented. Analytical solutions based on Refined Integral Method (RIM) are derived. The validity and utility of the model are ascertained by comparison of the simulation results with matrix-type vaginal rings experimental release data reported in the literature. For the comparisons, the pair-wise procedure is used to measure quantitatively the fit of the theoretical predictions to the experimental data. A good agreement between the model prediction and the experimental data is observed. A comparison with a previously reported model is also presented. More accurate results are achieved for small A/C(s) ratios. Copyright © 2011 Elsevier B.V. All rights reserved.

The impact of supersaturation level for oral absorption of BCS class IIb drugs, dipyridamole and ketoconazole, using in vivo predictive dissolution system: Gastrointestinal Simulator (GIS).

PubMed

Tsume, Yasuhiro; Matsui, Kazuki; Searls, Amanda L; Takeuchi, Susumu; Amidon, Gregory E; Sun, Duxin; Amidon, Gordon L

2017-05-01

The development of formulations and the assessment of oral drug absorption for Biopharmaceutical Classification System (BCS) class IIb drugs is often a difficult issue due to the potential for supersaturation and precipitation in the gastrointestinal (GI) tract. The physiological environment in the GI tract largely influences in vivo drug dissolution rates of those drugs. Thus, those physiological factors should be incorporated into the in vitro system to better assess in vivo performance of BCS class IIb drugs. In order to predict oral bioperformance, an in vitro dissolution system with multiple compartments incorporating physiologically relevant factors would be expected to more accurately predict in vivo phenomena than a one-compartment dissolution system like USP Apparatus 2 because, for example, the pH change occurring in the human GI tract can be better replicated in a multi-compartmental platform. The Gastrointestinal Simulator (GIS) consists of three compartments, the gastric, duodenal and jejunal chambers, and is a practical in vitro dissolution apparatus to predict in vivo dissolution for oral dosage forms. This system can demonstrate supersaturation and precipitation and, therefore, has the potential to predict in vivo bioperformance of oral dosage forms where this phenomenon may occur. In this report, in vitro studies were performed with dipyridamole and ketoconazole to evaluate the precipitation rates and the relationship between the supersaturation levels and oral absorption of BCS class II weak base drugs. To evaluate the impact of observed supersaturation levels on oral absorption, a study utilizing the GIS in combination with mouse intestinal infusion was conducted. Supersaturation levels observed in the GIS enhanced dipyridamole and ketoconazole absorption in mouse, and a good correlation between their supersaturation levels and their concentration in plasma was observed. The GIS, therefore, appears to represent in vivo dissolution phenomena and demonstrate supersaturation and precipitation of dipyridamole and ketoconazole. We therefore conclude that the GIS has been shown to be a good biopredictive tool to predict in vivo bioperformance of BCS class IIb drugs that can be used to optimize oral formulations. Copyright © 2017. Published by Elsevier B.V.

Disrupting the old order of imaging.

PubMed

Jha, Saurabh; Lexa, Frank J

2013-06-01

The purpose of this article is to expand on the economic concepts of creative destruction and disruptive innovation to imagine scenarios in which diagnostic imaging modalities and certain imaging paradigms can be rendered obsolete. Potential disrupters of imaging are novel drugs, clinical trials, accurate biomarkers, and government regulations. A taxonomic schema can be used to better predict the decline of certain imaging modalities.

Mixture toxicity of the anti-inflammatory drugs diclofenac, ibuprofen, naproxen, and acetylsalicylic acid.

PubMed

Cleuvers, Michael

2004-11-01

The ecotoxicity of the nonsteroidal anti-inflammatory drugs (NSAIDs) diclofenac, ibuprofen, naproxen, and acetylsalicylic acid (ASA) has been evaluated using acute Daphnia and algal tests. Toxicities were relatively low, with half-maximal effective concentration (EC50) values obtained using Daphnia in the range from 68 to 166 mg L(-1) and from 72 to 626 mg L(-1) in the algal test. Acute effects of these substances seem to be quite improbable. The quantitative structure-activity relationships (QSAR) approach showed that all substances act by nonpolar narcosis; thus, the higher the n-octanol/water partitioning coefficient (log Kow) of the substances, the higher is their toxicity. Mixture toxicity of the compounds could be accurately predicted using the concept of concentration addition. Toxicity of the mixture was considerable, even at concentrations at which the single substances showed no or only very slight effects, with some deviations in the Daphnia test, which could be explained by incompatibility of the very steep dose-response curves and the probit analysis of the data. Because pharmaceuticals in the aquatic environment occur usually as mixtures, an accurate prediction of the mixture toxicity is indispensable for environmental risk assessment.

Optical metabolic imaging measures early drug response in an allograft murine breast cancer model (Conference Presentation)

NASA Astrophysics Data System (ADS)

Sharick, Joe T.; Cook, Rebecca S.; Skala, Melissa C.

2017-02-01

Previous work has shown that cellular-level Optical Metabolic Imaging (OMI) of organoids derived from human breast cancer cell-line xenografts accurately and rapidly predicts in vivo response to therapy. To validate OMI as a predictive measure of treatment response in an immune-competent model, we used the polyomavirus middle-T (PyVmT) transgenic mouse breast cancer model. The PyVmT model includes intra-tumoral heterogeneity and a complex tumor microenvironment that can influence treatment responses. Three-dimensional organoids generated from primary PyVmT tumor tissue were treated with a chemotherapy (paclitaxel) and a PI3K inhibitor (XL147), each alone or in combination. Cellular subpopulations of response were measured using the OMI Index, a composite endpoint of metabolic response comprised of the optical redox ratio (ratio of the fluorescence intensities of metabolic co-enzymes NAD(P)H to FAD) as well as the fluorescence lifetimes of NAD(P)H and FAD. Combination treatment significantly decreased the OMI Index of PyVmT tumor organoids (p<0.0001) and in vivo tumors (p<0.0001) versus controls. Subpopulation analyses revealed a homogeneous response to combined therapy in both cultured organoids and in vivo tumors, while single agent treatment with XL147 alone or paclitaxel alone elicited heterogeneous responses in organoids. Tumor volume decreased with combination treatment through treatment day 30. These results indicate that OMI of organoids generated from PyVmT tumors can accurately reflect drug response in heterogeneous allografts with both innate and adaptive immunity. Thus, this method is promising for use in humans to predict long-term treatment responses accurately and rapidly, and could aid in clinical treatment planning.

On the accuracy of estimation of basic pharmacokinetic parameters by the traditional noncompartmental equations and the prediction of the steady-state volume of distribution in obese patients based upon data derived from normal subjects.

PubMed

Berezhkovskiy, Leonid M

2011-06-01

The steady-state and terminal volumes of distribution, as well as the mean residence time of drug in the body (V(ss), V(β), and MRT) are the common pharmacokinetic parameters calculated using the drug plasma concentration-time profile C(p) (t) following intravenous (i.v. bolus or constant rate infusion) drug administration. These calculations are valid for the linear pharmacokinetic system with central elimination (i.e., elimination rate being proportional to drug concentration in plasma). Formally, the assumption of central elimination is not normally met because the rate of drug elimination is proportional to the unbound drug concentration at elimination site, although equilibration between systemic circulation and the site of clearance for majority of small molecule drugs is fast. Thus, the assumption of central elimination is practically quite adequate. It appears reasonable to estimate the extent of possible errors in determination of these pharmacokinetic parameters due to the absence of central elimination. The comparison of V(ss), V(β), and MRT calculated by exact equations and the commonly used ones was made considering a simplified physiologically based pharmacokinetic model. It was found that if the drug plasma concentration profile is detected accurately, determination of drug distribution volumes and MRT using the traditional noncompartmental calculations of these parameters from C(p) (t) yields the values very close to that obtained from exact equations. Though in practice, the accurate measurement of C(p) (t), especially its terminal phase, may not always be possible. This is particularly applicable for obtaining the distribution volumes of lipophilic compounds in obese subjects, when the possibility of late terminal phase at low drug concentration is quite likely, specifically for compounds with high clearance. An accurate determination of V(ss) is much needed in clinical practice because it is critical for the proper selection of drug treatment regimen. For that reason, we developed a convenient method for calculation of V(ss) in obese (or underweight) subjects. It is based on using the V(ss) values obtained from pharmacokinetic studies in normal subjects and the physicochemical properties of drug molecule. A simple criterion that determines either the increase or decrease of V(ss) (per unit body weight) due to obesity is obtained. The accurate determination of adipose tissue-plasma partition coefficient is crucial for the practical application of suggested method. Copyright © 2011 Wiley-Liss, Inc.

FDA-approved drugs that are spermatotoxic in animals and the utility of animal testing for human risk prediction.

PubMed

Rayburn, Elizabeth R; Gao, Liang; Ding, Jiayi; Ding, Hongxia; Shao, Jun; Li, Haibo

2018-02-01

This study reviews FDA-approved drugs that negatively impact spermatozoa in animals, as well as how these findings reflect on observations in human male gametes. The FDA drug warning labels included in the DailyMed database and the peer-reviewed literature in the PubMed database were searched for information to identify single-ingredient, FDA-approved prescription drugs with spermatotoxic effects. A total of 235 unique, single-ingredient, FDA-approved drugs reported to be spermatotoxic in animals were identified in the drug labels. Forty-nine of these had documented negative effects on humans in either the drug label or literature, while 31 had no effect or a positive impact on human sperm. For the other 155 drugs that were spermatotoxic in animals, no human data was available. The current animal models are not very effective for predicting human spermatotoxicity, and there is limited information available about the impact of many drugs on human spermatozoa. New approaches should be designed that more accurately reflect the findings in men, including more studies on human sperm in vitro and studies using other systems (ex vivo tissue culture, xenograft models, in silico studies, etc.). In addition, the present data is often incomplete or reported in a manner that prevents interpretation of their clinical relevance. Changes should be made to the requirements for pre-clinical testing, drug surveillance, and the warning labels of drugs to ensure that the potential risks to human fertility are clearly indicated.

Emerging Tools for Synthetic Genome Design

PubMed Central

Lee, Bo-Rahm; Cho, Suhyung; Song, Yoseb; Kim, Sun Chang; Cho, Byung-Kwan

2013-01-01

Synthetic biology is an emerging discipline for designing and synthesizing predictable, measurable, controllable, and transformable biological systems. These newly designed biological systems have great potential for the development of cheaper drugs, green fuels, biodegradable plastics, and targeted cancer therapies over the coming years. Fortunately, our ability to quickly and accurately engineer biological systems that behave predictably has been dramatically expanded by significant advances in DNA-sequencing, DNA-synthesis, and DNA-editing technologies. Here, we review emerging technologies and methodologies in the field of building designed biological systems, and we discuss their future perspectives. PMID:23708771

Improvement of the Prediction of Drugs Demand Using Spatial Data Mining Tools.

PubMed

Ramos, M Isabel; Cubillas, Juan José; Feito, Francisco R

2016-01-01

The continued availability of products at any store is the major issue in order to provide good customer service. If the store is a drugstore this matter reaches a greater importance, as out of stock of a drug when there is high demand causes problems and tensions in the healthcare system. There are numerous studies of the impact this issue has on patients. The lack of any drug in a pharmacy in certain seasons is very common, especially when some external factors proliferate favoring the occurrence of certain diseases. This study focuses on a particular drug consumed in the city of Jaen, southern Andalucia, Spain. Our goal is to determine in advance the Salbutamol demand. Advanced data mining techniques have been used with spatial variables. These last have a key role to generate an effective model. In this research we have used the attributes that are associated with Salbutamol demand and it has been generated a very accurate prediction model of 5.78% of mean absolute error. This is a very encouraging data considering that the consumption of this drug in Jaen varies 500% from one period to another.

Improving drug safety: From adverse drug reaction knowledge discovery to clinical implementation.

PubMed

Tan, Yuxiang; Hu, Yong; Liu, Xiaoxiao; Yin, Zhinan; Chen, Xue-Wen; Liu, Mei

2016-11-01

Adverse drug reactions (ADRs) are a major public health concern, causing over 100,000 fatalities in the United States every year with an annual cost of $136 billion. Early detection and accurate prediction of ADRs is thus vital for drug development and patient safety. Multiple scientific disciplines, namely pharmacology, pharmacovigilance, and pharmacoinformatics, have been addressing the ADR problem from different perspectives. With the same goal of improving drug safety, this article summarizes and links the research efforts in the multiple disciplines into a single framework from comprehensive understanding of the interactions between drugs and biological system and the identification of genetic and phenotypic predispositions of patients susceptible to higher ADR risks and finally to the current state of implementation of medication-related decision support systems. We start by describing available computational resources for building drug-target interaction networks with biological annotations, which provides a fundamental knowledge for ADR prediction. Databases are classified by functions to help users in selection. Post-marketing surveillance is then introduced where data-driven approach can not only enhance the prediction accuracy of ADRs but also enables the discovery of genetic and phenotypic risk factors of ADRs. Understanding genetic risk factors for ADR requires well organized patient genetics information and analysis by pharmacogenomic approaches. Finally, current state of clinical decision support systems is presented and described how clinicians can be assisted with the integrated knowledgebase to minimize the risk of ADR. This review ends with a discussion of existing challenges in each of disciplines with potential solutions and future directions. Copyright © 2016 Elsevier Inc. All rights reserved.

Development of gold-immobilized P450 platform for exploring the effect of oligomer formation on P450-mediated metabolism for in vitro to in vivo drug metabolism predictions

NASA Astrophysics Data System (ADS)

Kabulski, Jarod L.

The cytochrome P450 (P450) enzyme family is responsible for the biotransformation of a wide range of endogenous and xenobiotic compounds, as well as being the major metabolic enzyme in first pass drug metabolism. In vivo drug metabolism for P450 enzymes is predicted using in vitro data obtained from a reconstituted expressed P450 system, but these systems have not always been proven to accurately represent in vivo enzyme kinetics, due to interactions caused by oligomer formation. These in vitro systems use soluble P450 enzymes prone to oligomer formation and studies have shown that increased states of protein aggregation directly affect the P450 enzyme kinetics. We have developed an immobilized enzyme system that isolates the enzyme and can be used to elucidate the effect of P450 aggregation on metabolism kinetics. The long term goal of my research is to develop a tool that will help improve the assessment of pharmaceuticals by better predicting in vivo kinetics in an in vitro system. The central hypothesis of this research is that P450-mediated kinetics measured in vitro is dependent on oligomer formation and that the accurate prediction of in vivo P450-mediated kinetics requires elucidation of the effect of oligomer formation. The rationale is that the development of a P450 bound to a Au platform can be used to control the aggregation of enzymes and bonding to Au may also permit replacement of the natural redox partners with an electrode capable of supplying a constant flow of electrons. This dissertation explains the details of the enzyme attachment, monitoring substrate binding, and metabolism using physiological and electrochemical methods, determination of enzyme kinetics, and the development of an immobilized-P450 enzyme bioreactor. This work provides alternative approaches to studying P450-mediated kinetics, a platform for controlling enzyme aggregation, electrochemically-driven P450 metabolism, and for investigating the effect of protein-protein interactions on drug metabolism.

Evolution of Antibody-Drug Conjugate Tumor Disposition Model to Predict Preclinical Tumor Pharmacokinetics of Trastuzumab-Emtansine (T-DM1).

PubMed

Singh, Aman P; Maass, Katie F; Betts, Alison M; Wittrup, K Dane; Kulkarni, Chethana; King, Lindsay E; Khot, Antari; Shah, Dhaval K

2016-07-01

A mathematical model capable of accurately characterizing intracellular disposition of ADCs is essential for a priori predicting unconjugated drug concentrations inside the tumor. Towards this goal, the objectives of this manuscript were to: (1) evolve previously published cellular disposition model of ADC with more intracellular details to characterize the disposition of T-DM1 in different HER2 expressing cell lines, (2) integrate the improved cellular model with the ADC tumor disposition model to a priori predict DM1 concentrations in a preclinical tumor model, and (3) identify prominent pathways and sensitive parameters associated with intracellular activation of ADCs. The cellular disposition model was augmented by incorporating intracellular ADC degradation and passive diffusion of unconjugated drug across tumor cells. Different biomeasures and chemomeasures for T-DM1, quantified in the companion manuscript, were incorporated into the modified model of ADC to characterize in vitro pharmacokinetics of T-DM1 in three HER2+ cell lines. When the cellular model was integrated with the tumor disposition model, the model was able to a priori predict tumor DM1 concentrations in xenograft mice. Pathway analysis suggested different contribution of antigen-mediated and passive diffusion pathways for intracellular unconjugated drug exposure between in vitro and in vivo systems. Global and local sensitivity analyses revealed that non-specific deconjugation and passive diffusion of the drug across tumor cell membrane are key parameters for drug exposure inside a cell. Finally, a systems pharmacokinetic model for intracellular processing of ADCs has been proposed to highlight our current understanding about the determinants of ADC activation inside a cell.

Binding free energy predictions of farnesoid X receptor (FXR) agonists using a linear interaction energy (LIE) approach with reliability estimation: application to the D3R Grand Challenge 2

NASA Astrophysics Data System (ADS)

Rifai, Eko Aditya; van Dijk, Marc; Vermeulen, Nico P. E.; Geerke, Daan P.

2018-01-01

Computational protein binding affinity prediction can play an important role in drug research but performing efficient and accurate binding free energy calculations is still challenging. In the context of phase 2 of the Drug Design Data Resource (D3R) Grand Challenge 2 we used our automated eTOX ALLIES approach to apply the (iterative) linear interaction energy (LIE) method and we evaluated its performance in predicting binding affinities for farnesoid X receptor (FXR) agonists. Efficiency was obtained by our pre-calibrated LIE models and molecular dynamics (MD) simulations at the nanosecond scale, while predictive accuracy was obtained for a small subset of compounds. Using our recently introduced reliability estimation metrics, we could classify predictions with higher confidence by featuring an applicability domain (AD) analysis in combination with protein-ligand interaction profiling. The outcomes of and agreement between our AD and interaction-profile analyses to distinguish and rationalize the performance of our predictions highlighted the relevance of sufficiently exploring protein-ligand interactions during training and it demonstrated the possibility to quantitatively and efficiently evaluate if this is achieved by using simulation data only.

Physiologically based pharmacokinetic model of mechanism-based inhibition of CYP3A by clarithromycin.

PubMed

Quinney, Sara K; Zhang, Xin; Lucksiri, Aroonrut; Gorski, J Christopher; Li, Lang; Hall, Stephen D

2010-02-01

The prediction of clinical drug-drug interactions (DDIs) due to mechanism-based inhibitors of CYP3A is complicated when the inhibitor itself is metabolized by CYP3Aas in the case of clarithromycin. Previous attempts to predict the effects of clarithromycin on CYP3A substrates, e.g., midazolam, failed to account for nonlinear metabolism of clarithromycin. A semiphysiologically based pharmacokinetic model was developed for clarithromycin and midazolam metabolism, incorporating hepatic and intestinal metabolism by CYP3A and non-CYP3A mechanisms. CYP3A inactivation by clarithromycin occurred at both sites. K(I) and k(inact) values for clarithromycin obtained from in vitro sources were unable to accurately predict the clinical effect of clarithromycin on CYP3A activity. An iterative approach determined the optimum values to predict in vivo effects of clarithromycin on midazolam to be 5.3 microM for K(i) and 0.4 and 4 h(-1) for k(inact) in the liver and intestines, respectively. The incorporation of CYP3A-dependent metabolism of clarithromycin enabled prediction of its nonlinear pharmacokinetics. The predicted 2.6-fold change in intravenous midazolam area under the plasma concentration-time curve (AUC) after 500 mg of clarithromycin orally twice daily was consistent with clinical observations. Although the mean predicted 5.3-fold change in the AUC of oral midazolam was lower than mean observed values, it was within the range of observations. Intestinal CYP3A activity was less sensitive to changes in K(I), k(inact), and CYP3A half-life than hepatic CYP3A. This semiphysiologically based pharmacokinetic model incorporating CYP3A inactivation in the intestine and liver accurately predicts the nonlinear pharmacokinetics of clarithromycin and the DDI observed between clarithromycin and midazolam. Furthermore, this model framework can be applied to other mechanism-based inhibitors.

In Vivo Predictive Dissolution: Comparing the Effect of Bicarbonate and Phosphate Buffer on the Dissolution of Weak Acids and Weak Bases.

PubMed

Krieg, Brian J; Taghavi, Seyed Mohammad; Amidon, Gordon L; Amidon, Gregory E

2015-09-01

Bicarbonate is the main buffer in the small intestine and it is well known that buffer properties such as pKa can affect the dissolution rate of ionizable drugs. However, bicarbonate buffer is complicated to work with experimentally. Finding a suitable substitute for bicarbonate buffer may provide a way to perform more physiologically relevant dissolution tests. The dissolution of weak acid and weak base drugs was conducted in bicarbonate and phosphate buffer using rotating disk dissolution methodology. Experimental results were compared with the predicted results using the film model approach of (Mooney K, Mintun M, Himmelstein K, Stella V. 1981. J Pharm Sci 70(1):22-32) based on equilibrium assumptions as well as a model accounting for the slow hydration reaction, CO2 + H2 O → H2 CO3 . Assuming carbonic acid is irreversible in the dehydration direction: CO2 + H2 O ← H2 CO3 , the transport analysis can accurately predict rotating disk dissolution of weak acid and weak base drugs in bicarbonate buffer. The predictions show that matching the dissolution of weak acid and weak base drugs in phosphate and bicarbonate buffer is possible. The phosphate buffer concentration necessary to match physiologically relevant bicarbonate buffer [e.g., 10.5 mM (HCO3 (-) ), pH = 6.5] is typically in the range of 1-25 mM and is very dependent upon drug solubility and pKa . © 2015 Wiley Periodicals, Inc. and the American Pharmacists Association.

The prediction of candidate genes for cervix related cancer through gene ontology and graph theoretical approach.

PubMed

Hindumathi, V; Kranthi, T; Rao, S B; Manimaran, P

2014-06-01

With rapidly changing technology, prediction of candidate genes has become an indispensable task in recent years mainly in the field of biological research. The empirical methods for candidate gene prioritization that succors to explore the potential pathway between genetic determinants and complex diseases are highly cumbersome and labor intensive. In such a scenario predicting potential targets for a disease state through in silico approaches are of researcher's interest. The prodigious availability of protein interaction data coupled with gene annotation renders an ease in the accurate determination of disease specific candidate genes. In our work we have prioritized the cervix related cancer candidate genes by employing Csaba Ortutay and his co-workers approach of identifying the candidate genes through graph theoretical centrality measures and gene ontology. With the advantage of the human protein interaction data, cervical cancer gene sets and the ontological terms, we were able to predict 15 novel candidates for cervical carcinogenesis. The disease relevance of the anticipated candidate genes was corroborated through a literature survey. Also the presence of the drugs for these candidates was detected through Therapeutic Target Database (TTD) and DrugMap Central (DMC) which affirms that they may be endowed as potential drug targets for cervical cancer.

Metabolic enzyme microarray coupled with miniaturized cell-culture array technology for high-throughput toxicity screening.

PubMed

Lee, Moo-Yeal; Dordick, Jonathan S; Clark, Douglas S

2010-01-01

Due to poor drug candidate safety profiles that are often identified late in the drug development process, the clinical progression of new chemical entities to pharmaceuticals remains hindered, thus resulting in the high cost of drug discovery. To accelerate the identification of safer drug candidates and improve the clinical progression of drug candidates to pharmaceuticals, it is important to develop high-throughput tools that can provide early-stage predictive toxicology data. In particular, in vitro cell-based systems that can accurately mimic the human in vivo response and predict the impact of drug candidates on human toxicology are needed to accelerate the assessment of drug candidate toxicity and human metabolism earlier in the drug development process. The in vitro techniques that provide a high degree of human toxicity prediction will be perhaps more important in cosmetic and chemical industries in Europe, as animal toxicity testing is being phased out entirely in the immediate future.We have developed a metabolic enzyme microarray (the Metabolizing Enzyme Toxicology Assay Chip, or MetaChip) and a miniaturized three-dimensional (3D) cell-culture array (the Data Analysis Toxicology Assay Chip, or DataChip) for high-throughput toxicity screening of target compounds and their metabolic enzyme-generated products. The human or rat MetaChip contains an array of encapsulated metabolic enzymes that is designed to emulate the metabolic reactions in the human or rat liver. The human or rat DataChip contains an array of 3D human or rat cells encapsulated in alginate gels for cell-based toxicity screening. By combining the DataChip with the complementary MetaChip, in vitro toxicity results are obtained that correlate well with in vivo rat data.

An evaluation of the use of remotely sensed parameters for prediction of incidence and risk associated with Vibrio parahaemolyticus in Gulf Coast oysters (Crassostrea virginica).

PubMed

Phillips, A M B; Depaola, A; Bowers, J; Ladner, S; Grimes, D J

2007-04-01

The U.S. Food and Drug Administration recently published a Vibrio parahaemolyticus risk assessment for consumption of raw oysters that predicts V. parahaemolyticus densities at harvest based on water temperature. We retrospectively compared archived remotely sensed measurements (sea surface temperature, chlorophyll, and turbidity) with previously published data from an environmental study of V. parahaemolyticus in Alabama oysters to assess the utility of the former data for predicting V. parahaemolyticus densities in oysters. Remotely sensed sea surface temperature correlated well with previous in situ measurements (R(2) = 0.86) of bottom water temperature, supporting the notion that remotely sensed sea surface temperature data are a sufficiently accurate substitute for direct measurement. Turbidity and chlorophyll levels were not determined in the previous study, but in comparison with the V. parahaemolyticus data, remotely sensed values for these parameters may explain some of the variation in V. parahaemolyticus levels. More accurate determination of these effects and the temporal and spatial variability of these parameters may further improve the accuracy of prediction models. To illustrate the utility of remotely sensed data as a basis for risk management, predictions based on the U.S. Food and Drug Administration V. parahaemolyticus risk assessment model were integrated with remotely sensed sea surface temperature data to display graphically variations in V. parahaemolyticus density in oysters associated with spatial variations in water temperature. We believe images such as these could be posted in near real time, and that the availability of such information in a user-friendly format could be the basis for timely and informed risk management decisions.

Genome-wide prediction and analysis of human tissue-selective genes using microarray expression data

PubMed Central

2013-01-01

Background Understanding how genes are expressed specifically in particular tissues is a fundamental question in developmental biology. Many tissue-specific genes are involved in the pathogenesis of complex human diseases. However, experimental identification of tissue-specific genes is time consuming and difficult. The accurate predictions of tissue-specific gene targets could provide useful information for biomarker development and drug target identification. Results In this study, we have developed a machine learning approach for predicting the human tissue-specific genes using microarray expression data. The lists of known tissue-specific genes for different tissues were collected from UniProt database, and the expression data retrieved from the previously compiled dataset according to the lists were used for input vector encoding. Random Forests (RFs) and Support Vector Machines (SVMs) were used to construct accurate classifiers. The RF classifiers were found to outperform SVM models for tissue-specific gene prediction. The results suggest that the candidate genes for brain or liver specific expression can provide valuable information for further experimental studies. Our approach was also applied for identifying tissue-selective gene targets for different types of tissues. Conclusions A machine learning approach has been developed for accurately identifying the candidate genes for tissue specific/selective expression. The approach provides an efficient way to select some interesting genes for developing new biomedical markers and improve our knowledge of tissue-specific expression. PMID:23369200

Towards better modelling of drug-loading in solid lipid nanoparticles: Molecular dynamics, docking experiments and Gaussian Processes machine learning.

PubMed

Hathout, Rania M; Metwally, Abdelkader A

2016-11-01

This study represents one of the series applying computer-oriented processes and tools in digging for information, analysing data and finally extracting correlations and meaningful outcomes. In this context, binding energies could be used to model and predict the mass of loaded drugs in solid lipid nanoparticles after molecular docking of literature-gathered drugs using MOE® software package on molecularly simulated tripalmitin matrices using GROMACS®. Consequently, Gaussian processes as a supervised machine learning artificial intelligence technique were used to correlate the drugs' descriptors (e.g. M.W., xLogP, TPSA and fragment complexity) with their molecular docking binding energies. Lower percentage bias was obtained compared to previous studies which allows the accurate estimation of the loaded mass of any drug in the investigated solid lipid nanoparticles by just projecting its chemical structure to its main features (descriptors). Copyright © 2016 Elsevier B.V. All rights reserved.

Solubility advantage of amorphous pharmaceuticals: II. Application of quantitative thermodynamic relationships for prediction of solubility enhancement in structurally diverse insoluble pharmaceuticals.

PubMed

Murdande, Sharad B; Pikal, Michael J; Shanker, Ravi M; Bogner, Robin H

2010-12-01

To quantitatively assess the solubility advantage of amorphous forms of nine insoluble drugs with a wide range of physico-chemical properties utilizing a previously reported thermodynamic approach. Thermal properties of amorphous and crystalline forms of drugs were measured using modulated differential calorimetry. Equilibrium moisture sorption uptake by amorphous drugs was measured by a gravimetric moisture sorption analyzer, and ionization constants were determined from the pH-solubility profiles. Solubilities of crystalline and amorphous forms of drugs were measured in de-ionized water at 25°C. Polarized microscopy was used to provide qualitative information about the crystallization of amorphous drug in solution during solubility measurement. For three out the nine compounds, the estimated solubility based on thermodynamic considerations was within two-fold of the experimental measurement. For one compound, estimated solubility enhancement was lower than experimental value, likely due to extensive ionization in solution and hence its sensitivity to error in pKa measurement. For the remaining five compounds, estimated solubility was about 4- to 53-fold higher than experimental results. In all cases where the theoretical solubility estimates were significantly higher, it was observed that the amorphous drug crystallized rapidly during the experimental determination of solubility, thus preventing an accurate experimental assessment of solubility advantage. It has been demonstrated that the theoretical approach does provide an accurate estimate of the maximum solubility enhancement by an amorphous drug relative to its crystalline form for structurally diverse insoluble drugs when recrystallization during dissolution is minimal.

Quantitative spatiotemporal analysis of antibody fragment diffusion and endocytic consumption in tumor spheroids.

PubMed

Thurber, Greg M; Wittrup, K Dane

2008-05-01

Antibody-based cancer treatment depends upon distribution of the targeting macromolecule throughout tumor tissue, and spatial heterogeneity could significantly limit efficacy in many cases. Antibody distribution in tumor tissue is a function of drug dosage, antigen concentration, binding affinity, antigen internalization, drug extravasation from blood vessels, diffusion in the tumor extracellular matrix, and systemic clearance rates. We have isolated the effects of a subset of these variables by live-cell microscopic imaging of single-chain antibody fragments against carcinoembryonic antigen in LS174T tumor spheroids. The measured rates of scFv penetration and retention were compared with theoretical predictions based on simple scaling criteria. The theory predicts that antibody dose must be large enough to drive a sufficient diffusive flux of antibody to overcome cellular internalization, and exposure time must be long enough to allow penetration to the spheroid center. The experimental results in spheroids are quantitatively consistent with these predictions. Therefore, simple scaling criteria can be applied to accurately predict antibody and antibody fragment penetration distance in tumor tissue.

Quantitative Spatiotemporal Analysis of Antibody Fragment Diffusion and Endocytic Consumption in Tumor Spheroids

PubMed Central

Thurber, Greg M.; Wittrup, K. Dane

2010-01-01

Antibody-based cancer treatment depends upon distribution of the targeting macromolecule throughout tumor tissue, and spatial heterogeneity could significantly limit efficacy in many cases. Antibody distribution in tumor tissue is a function of drug dosage, antigen concentration, binding affinity, antigen internalization, drug extravasation from blood vessels, diffusion in the tumor extracellular matrix, and systemic clearance rates. We have isolated the effects of a subset of these variables by live-cell microscopic imaging of single-chain antibody fragments against carcinoembryonic antigen in LS174T tumor spheroids. The measured rates of scFv penetration and retention were compared with theoretical predictions based on simple scaling criteria. The theory predicts that antibody dose must be large enough to drive a sufficient diffusive flux of antibody to overcome cellular internalization, and exposure time must be long enough to allow penetration to the spheroid center. The experimental results in spheroids are quantitatively consistent with these predictions. Therefore, simple scaling criteria can be applied to accurately predict antibody and antibody fragment penetration distance in tumor tissue. PMID:18451160

Machine learning models for lipophilicity and their domain of applicability.

PubMed

Schroeter, Timon; Schwaighofer, Anton; Mika, Sebastian; Laak, Antonius Ter; Suelzle, Detlev; Ganzer, Ursula; Heinrich, Nikolaus; Müller, Klaus-Robert

2007-01-01

Unfavorable lipophilicity and water solubility cause many drug failures; therefore these properties have to be taken into account early on in lead discovery. Commercial tools for predicting lipophilicity usually have been trained on small and neutral molecules, and are thus often unable to accurately predict in-house data. Using a modern Bayesian machine learning algorithm--a Gaussian process model--this study constructs a log D7 model based on 14,556 drug discovery compounds of Bayer Schering Pharma. Performance is compared with support vector machines, decision trees, ridge regression, and four commercial tools. In a blind test on 7013 new measurements from the last months (including compounds from new projects) 81% were predicted correctly within 1 log unit, compared to only 44% achieved by commercial software. Additional evaluations using public data are presented. We consider error bars for each method (model based error bars, ensemble based, and distance based approaches), and investigate how well they quantify the domain of applicability of each model.

Industry Perspective on Contemporary Protein-Binding Methodologies: Considerations for Regulatory Drug-Drug Interaction and Related Guidelines on Highly Bound Drugs.

PubMed

Di, Li; Breen, Christopher; Chambers, Rob; Eckley, Sean T; Fricke, Robert; Ghosh, Avijit; Harradine, Paul; Kalvass, J Cory; Ho, Stacy; Lee, Caroline A; Marathe, Punit; Perkins, Everett J; Qian, Mark; Tse, Susanna; Yan, Zhengyin; Zamek-Gliszczynski, Maciej J

2017-12-01

Regulatory agencies have recently issued drug-drug interaction guidelines, which require determination of plasma protein binding (PPB). To err on the conservative side, the agencies recommend that a 0.01 lower limit of fraction unbound (f u ) be used for highly bound compounds (>99%), irrespective of the actual measured values. While this may avoid false negatives, the recommendation would likely result in a high rate of false positive predictions, resulting in unnecessary clinical studies and more stringent inclusion/exclusion criteria, which may add cost and time in delivery of new medicines to patients. In this perspective, we provide a review of current approaches to measure PPB, and important determinants in enabling the accuracy and precision in these measurements. The ability to measure f u is further illustrated by a cross-company data comparison of PPB for warfarin and itraconazole, demonstrating good concordance of the measured f u values. The data indicate that f u values of ≤0.01 may be determined accurately across laboratories when appropriate methods are used. These data, along with numerous other examples presented in the literature, support the use of experimentally measured f u values for drug-drug interaction predictions, rather than using the arbitrary cutoff value of 0.01 as recommended in current regulatory guidelines. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. 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.

Predicting Drug-induced Hepatotoxicity Using QSAR and Toxicogenomics Approaches

PubMed Central

Low, Yen; Uehara, Takeki; Minowa, Yohsuke; Yamada, Hiroshi; Ohno, Yasuo; Urushidani, Tetsuro; Sedykh, Alexander; Muratov, Eugene; Fourches, Denis; Zhu, Hao; Rusyn, Ivan; Tropsha, Alexander

2014-01-01

Quantitative Structure-Activity Relationship (QSAR) modeling and toxicogenomics are used independently as predictive tools in toxicology. In this study, we evaluated the power of several statistical models for predicting drug hepatotoxicity in rats using different descriptors of drug molecules, namely their chemical descriptors and toxicogenomic profiles. The records were taken from the Toxicogenomics Project rat liver microarray database containing information on 127 drugs (http://toxico.nibio.go.jp/datalist.html). The model endpoint was hepatotoxicity in the rat following 28 days of exposure, established by liver histopathology and serum chemistry. First, we developed multiple conventional QSAR classification models using a comprehensive set of chemical descriptors and several classification methods (k nearest neighbor, support vector machines, random forests, and distance weighted discrimination). With chemical descriptors alone, external predictivity (Correct Classification Rate, CCR) from 5-fold external cross-validation was 61%. Next, the same classification methods were employed to build models using only toxicogenomic data (24h after a single exposure) treated as biological descriptors. The optimized models used only 85 selected toxicogenomic descriptors and had CCR as high as 76%. Finally, hybrid models combining both chemical descriptors and transcripts were developed; their CCRs were between 68 and 77%. Although the accuracy of hybrid models did not exceed that of the models based on toxicogenomic data alone, the use of both chemical and biological descriptors enriched the interpretation of the models. In addition to finding 85 transcripts that were predictive and highly relevant to the mechanisms of drug-induced liver injury, chemical structural alerts for hepatotoxicity were also identified. These results suggest that concurrent exploration of the chemical features and acute treatment-induced changes in transcript levels will both enrich the mechanistic understanding of sub-chronic liver injury and afford models capable of accurate prediction of hepatotoxicity from chemical structure and short-term assay results. PMID:21699217

The Role of Extracellular Binding Proteins in the Cellular Uptake of Drugs: Impact on Quantitative In Vitro-to-In Vivo Extrapolations of Toxicity and Efficacy in Physiologically Based Pharmacokinetic-Pharmacodynamic Research.

PubMed

Poulin, Patrick; Burczynski, Frank J; Haddad, Sami

2016-02-01

A critical component in the development of physiologically based pharmacokinetic-pharmacodynamic (PBPK/PD) models for estimating target organ dosimetry in pharmacology and toxicology studies is the understanding of the uptake kinetics and accumulation of drugs and chemicals at the cellular level. Therefore, predicting free drug concentrations in intracellular fluid will contribute to our understanding of concentrations at the site of action in cells in PBPK/PD research. Some investigators believe that uptake of drugs in cells is solely driven by the unbound fraction; conversely, others argue that the protein-bound fraction contributes a significant portion of the total amount delivered to cells. Accordingly, the current literature suggests the existence of a so-called albumin-mediated uptake mechanism(s) for the protein-bound fraction (i.e., extracellular protein-facilitated uptake mechanisms) at least in hepatocytes and cardiac myocytes; however, such mechanism(s) and cells from other organs deserve further exploration. Therefore, the main objective of this present study was to discuss further the implication of potential protein-facilitated uptake mechanism(s) on drug distribution in cells under in vivo conditions. The interplay between the protein-facilitated uptake mechanism(s) and the effects of a pH gradient, metabolism, transport, and permeation limitation potentially occurring in cells was also discussed, as this should violate the basic assumption on similar free drug concentration in cells and plasma. This was made because the published equations used to calculate drug concentrations in cells in a PBPK/PD model did not consider potential protein-facilitated uptake mechanism(s). Consequently, we corrected some published equations for calculating the free drug concentrations in cells compared with plasma in PBPK/PD modeling studies, and we proposed a refined strategy for potentially performing more accurate quantitative in vitro-to-in vivo extrapolations (IVIVEs) of toxicity (efficacy) at the cellular level from data generated in cell assays. Overall, this present study may help to optimize the human dose prediction in preclinical and clinical studies, while prescribing drugs with narrow therapeutic windows that are highly bound to extracellular proteins and/or highly ionized at the physiological pH. This may facilitate building a more accurate safety (efficacy) profile for such drugs. Copyright © 2016 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Impact of Upfront Cellular Enrichment by Laser Capture Microdissection on Protein and Phosphoprotein Drug Target Signaling Activation Measurements in Human Lung Cancer: Implications for Personalized Medicine

PubMed Central

Elisa, Baldelli; B., Haura Eric; Lucio, Crinò; Douglas, Cress W.; Vienna, Ludovini; B., Schabath Matthew; A., Liotta Lance; F., Petricoin Emanuel; Mariaelena, Pierobon

2015-01-01

Purpose The aim of this study was to evaluate whether upfront cellular enrichment via laser capture microdissection is necessary for accurately quantifying predictive biomarkers in non-small cell lung cancer tumors. Experimental design Fifteen snap frozen surgical biopsies were analyzed. Whole tissue lysate and matched highly enriched tumor epithelium via laser capture microdissection (LCM) were obtained for each patient. The expression and activation/phosphorylation levels of 26 proteins were measured by reverse phase protein microarray. Differences in signaling architecture of dissected and undissected matched pairs were visualized using unsupervised clustering analysis, bar graphs, and scatter plots. Results Overall patient matched LCM and undissected material displayed very distinct and differing signaling architectures with 93% of the matched pairs clustering separately. These differences were seen regardless of the amount of starting tumor epithelial content present in the specimen. Conclusions and clinical relevance These results indicate that LCM driven upfront cellular enrichment is necessary to accurately determine the expression/activation levels of predictive protein signaling markers although results should be evaluated in larger clinical settings. Upfront cellular enrichment of the target cell appears to be an important part of the workflow needed for the accurate quantification of predictive protein signaling biomarkers. Larger independent studies are warranted. PMID:25676683

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.

Optimization of matrix tablets controlled drug release using Elman dynamic neural networks and decision trees.

PubMed

Petrović, Jelena; Ibrić, Svetlana; Betz, Gabriele; Đurić, Zorica

2012-05-30

The main objective of the study was to develop artificial intelligence methods for optimization of drug release from matrix tablets regardless of the matrix type. Static and dynamic artificial neural networks of the same topology were developed to model dissolution profiles of different matrix tablets types (hydrophilic/lipid) using formulation composition, compression force used for tableting and tablets porosity and tensile strength as input data. Potential application of decision trees in discovering knowledge from experimental data was also investigated. Polyethylene oxide polymer and glyceryl palmitostearate were used as matrix forming materials for hydrophilic and lipid matrix tablets, respectively whereas selected model drugs were diclofenac sodium and caffeine. Matrix tablets were prepared by direct compression method and tested for in vitro dissolution profiles. Optimization of static and dynamic neural networks used for modeling of drug release was performed using Monte Carlo simulations or genetic algorithms optimizer. Decision trees were constructed following discretization of data. Calculated difference (f(1)) and similarity (f(2)) factors for predicted and experimentally obtained dissolution profiles of test matrix tablets formulations indicate that Elman dynamic neural networks as well as decision trees are capable of accurate predictions of both hydrophilic and lipid matrix tablets dissolution profiles. Elman neural networks were compared to most frequently used static network, Multi-layered perceptron, and superiority of Elman networks have been demonstrated. Developed methods allow simple, yet very precise way of drug release predictions for both hydrophilic and lipid matrix tablets having controlled drug release. Copyright © 2012 Elsevier B.V. All rights reserved.

Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies.

PubMed

Geeleher, Paul; Zhang, Zhenyu; Wang, Fan; Gruener, Robert F; Nath, Aritro; Morrison, Gladys; Bhutra, Steven; Grossman, Robert L; Huang, R Stephanie

2017-10-01

Obtaining accurate drug response data in large cohorts of cancer patients is very challenging; thus, most cancer pharmacogenomics discovery is conducted in preclinical studies, typically using cell lines and mouse models. However, these platforms suffer from serious limitations, including small sample sizes. Here, we have developed a novel computational method that allows us to impute drug response in very large clinical cancer genomics data sets, such as The Cancer Genome Atlas (TCGA). The approach works by creating statistical models relating gene expression to drug response in large panels of cancer cell lines and applying these models to tumor gene expression data in the clinical data sets (e.g., TCGA). This yields an imputed drug response for every drug in each patient. These imputed drug response data are then associated with somatic genetic variants measured in the clinical cohort, such as copy number changes or mutations in protein coding genes. These analyses recapitulated drug associations for known clinically actionable somatic genetic alterations and identified new predictive biomarkers for existing drugs. © 2017 Geeleher et al.; Published by Cold Spring Harbor Laboratory Press.

Construction of drug-polymer thermodynamic phase diagrams using Flory-Huggins interaction theory: identifying the relevance of temperature and drug weight fraction to phase separation within solid dispersions.

PubMed

Tian, Yiwei; Booth, Jonathan; Meehan, Elizabeth; Jones, David S; Li, Shu; Andrews, Gavin P

2013-01-07

Amorphous drug-polymer solid dispersions have the potential to enhance the dissolution performance and thus bioavailability of BCS class II drug compounds. The principle drawback of this approach is the limited physical stability of amorphous drug within the dispersion. Accurate determination of the solubility and miscibility of drug in the polymer matrix is the key to the successful design and development of such systems. In this paper, we propose a novel method, based on Flory-Huggins theory, to predict and compare the solubility and miscibility of drug in polymeric systems. The systems chosen for this study are (1) hydroxypropyl methylcellulose acetate succinate HF grade (HPMCAS-HF)-felodipine (FD) and (2) Soluplus (a graft copolymer of polyvinyl caprolactam-polyvinyl acetate-polyethylene glycol)-FD. Samples containing different drug compositions were mixed, ball milled, and then analyzed by differential scanning calorimetry (DSC). The value of the drug-polymer interaction parameter χ was calculated from the crystalline drug melting depression data and extrapolated to lower temperatures. The interaction parameter χ was also calculated at 25 °C for both systems using the van Krevelen solubility parameter method. The rank order of interaction parameters of the two systems obtained at this temperature was comparable. Diagrams of drug-polymer temperature-composition and free energy of mixing (ΔG(mix)) were constructed for both systems. The maximum crystalline drug solubility and amorphous drug miscibility may be predicted based on the phase diagrams. Hyper-DSC was used to assess the validity of constructed phase diagrams by annealing solid dispersions at specific drug loadings. Three different samples for each polymer were selected to represent different regions within the phase diagram.

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.

The Linear Interaction Energy Method for the Prediction of Protein Stability Changes Upon Mutation

PubMed Central

Wickstrom, Lauren; Gallicchio, Emilio; Levy, Ronald M.

2011-01-01

The coupling of protein energetics and sequence changes is a critical aspect of computational protein design, as well as for the understanding of protein evolution, human disease, and drug resistance. In order to study the molecular basis for this coupling, computational tools must be sufficiently accurate and computationally inexpensive enough to handle large amounts of sequence data. We have developed a computational approach based on the linear interaction energy (LIE) approximation to predict the changes in the free energy of the native state induced by a single mutation. This approach was applied to a set of 822 mutations in 10 proteins which resulted in an average unsigned error of 0.82 kcal/mol and a correlation coefficient of 0.72 between the calculated and experimental ΔΔG values. The method is able to accurately identify destabilizing hot spot mutations however it has difficulty in distinguishing between stabilizing and destabilizing mutations due to the distribution of stability changes for the set of mutations used to parameterize the model. In addition, the model also performs quite well in initial tests on a small set of double mutations. Based on these promising results, we can begin to examine the relationship between protein stability and fitness, correlated mutations, and drug resistance. PMID:22038697

A novel mixed phospholipid functionalized monolithic column for early screening of drug induced phospholipidosis risk.

PubMed

Zhao, XiangLong; Chen, WeiJia; Liu, ZhengHua; Guo, JiaLiang; Zhou, ZhengYin; Crommen, Jacques; Moaddel, Ruin; Jiang, ZhengJin

2014-11-07

Drug-induced phospholipidosis (PLD) is characterized by the excessive accumulation of phospholipids, resulting in multilamellar vesicle structure within lysosomes. In the present study, a novel mixed phospholipid functionalized monolithic column was developed for the first time through a facile one-step co-polymerization approach. The phospholipid composition of the monolith can be adjusted quantitatively and accurately to mimic the mixed phospholipid environment of different biomembranes on a solid matrix. The mixed phospholipid functionalized monolith as a promising immobilized artificial membrane technique was used to study drug-phospholipid interaction. Scanning electron microscopy, elemental analysis, FT-IR spectra, ζ-potential analysis and micro-HPLC were carried out to characterize the physicochemical properties and separation performance of the monolith. Mechanism studies revealed that both hydrophobic and electrostatic interactions play an important role in the retention of analytes. The ratio of their contributions to retention can be easily manipulated by adjusting the composition of the mixed phospholipids, in order to better mimic the interaction between drugs and cell membrane. The obtained mixed phospholipid functionalized monolithic columns were applied to the screening of drug-induced PLD potency. Data from 79 drugs on the market demonstrated that the chromatographic hydrophobicity index referring to the mixed phospholipid functionalized monolith at pH 7.4 (CHI IAM7.4) for the selected drugs were highly correlated with the drug-induced PLD potency data obtained from other in vivo or in vitro assays. Moreover, the effect of the acidic phospholipid phosphatidylserine proportion on prediction accuracy was also investigated. The monolith containing 20% phosphatidylserine and 80% phosphatidylcholine exhibited the best prediction ability for the drug-induced PLD potency of the tested compounds. This research has led to the successful development of a novel and facile approach to prepare a mixed phospholipids functionalized monolith, which offers a reliable, cost-effective and high-throughput screening tool for early prediction of the PLD potency of drug candidates. Copyright © 2014 Elsevier B.V. All rights reserved.

Large-scale extraction of accurate drug-disease treatment pairs from biomedical literature for drug repurposing

PubMed Central

2013-01-01

Background A large-scale, highly accurate, machine-understandable drug-disease treatment relationship knowledge base is important for computational approaches to drug repurposing. The large body of published biomedical research articles and clinical case reports available on MEDLINE is a rich source of FDA-approved drug-disease indication as well as drug-repurposing knowledge that is crucial for applying FDA-approved drugs for new diseases. However, much of this information is buried in free text and not captured in any existing databases. The goal of this study is to extract a large number of accurate drug-disease treatment pairs from published literature. Results In this study, we developed a simple but highly accurate pattern-learning approach to extract treatment-specific drug-disease pairs from 20 million biomedical abstracts available on MEDLINE. We extracted a total of 34,305 unique drug-disease treatment pairs, the majority of which are not included in existing structured databases. Our algorithm achieved a precision of 0.904 and a recall of 0.131 in extracting all pairs, and a precision of 0.904 and a recall of 0.842 in extracting frequent pairs. In addition, we have shown that the extracted pairs strongly correlate with both drug target genes and therapeutic classes, therefore may have high potential in drug discovery. Conclusions We demonstrated that our simple pattern-learning relationship extraction algorithm is able to accurately extract many drug-disease pairs from the free text of biomedical literature that are not captured in structured databases. The large-scale, accurate, machine-understandable drug-disease treatment knowledge base that is resultant of our study, in combination with pairs from structured databases, will have high potential in computational drug repurposing tasks. PMID:23742147

Intra-graft expression of genes involved in iron homeostasis predicts the development of operational tolerance in human liver transplantation

PubMed Central

Bohne, Felix; Martínez-Llordella, Marc; Lozano, Juan-José; Miquel, Rosa; Benítez, Carlos; Londoño, María-Carlota; Manzia, Tommaso-María; Angelico, Roberta; Swinkels, Dorine W.; Tjalsma, Harold; López, Marta; Abraldes, Juan G.; Bonaccorsi-Riani, Eliano; Jaeckel, Elmar; Taubert, Richard; Pirenne, Jacques; Rimola, Antoni; Tisone, Giuseppe; Sánchez-Fueyo, Alberto

2011-01-01

Following organ transplantation, lifelong immunosuppressive therapy is required to prevent the host immune system from destroying the allograft. This can cause severe side effects and increased recipient morbidity and mortality. Complete cessation of immunosuppressive drugs has been successfully accomplished in selected transplant recipients, providing proof of principle that operational allograft tolerance is attainable in clinical transplantation. The intra-graft molecular pathways associated with successful drug withdrawal, however, are not well defined. In this study, we analyzed sequential blood and liver tissue samples collected from liver transplant recipients enrolled in a prospective multicenter immunosuppressive drug withdrawal clinical trial. Before initiation of drug withdrawal, operationally tolerant and non-tolerant recipients differed in the intra-graft expression of genes involved in the regulation of iron homeostasis. Furthermore, as compared with non-tolerant recipients, operationally tolerant patients exhibited higher serum levels of hepcidin and ferritin and increased hepatocyte iron deposition. Finally, liver tissue gene expression measurements accurately predicted the outcome of immunosuppressive withdrawal in an independent set of patients. These results point to a critical role for iron metabolism in the regulation of intra-graft alloimmune responses in humans and provide a set of biomarkers to conduct drug-weaning trials in liver transplantation. PMID:22156196

Determination of a Degradation Constant for CYP3A4 by Direct Suppression of mRNA in a Novel Human Hepatocyte Model, HepatoPac.

PubMed

Ramsden, Diane; Zhou, Jin; Tweedie, Donald J

2015-09-01

Accurate determination of rates of de novo synthesis and degradation of cytochrome P450s (P450s) has been challenging. There is a high degree of variability in the multiple published values of turnover for specific P450s that is likely exacerbated by differences in methodologies. For CYP3A4, reported half-life values range from 10 to 140 hours. An accurate value for kdeg has been identified as a major limitation for prediction of drug interactions involving mechanism-based inhibition and/or induction. Estimation of P450 half-life from in vitro test systems, such as human hepatocytes, is complicated by differential decreased enzyme function over culture time, attenuation of the impact of enzyme loss through inclusion of glucocorticoids in media, and viability limitations over long-term culture times. HepatoPac overcomes some of these challenges by providing extended stability of enzymes (2.5 weeks in our hands). As such it is a unique tool for studying rates of enzyme degradation achieved through modulation of enzyme levels. CYP3A4 mRNA levels were rapidly depleted by >90% using either small interfering RNA or addition of interleukin-6, which allowed an estimation of the degradation rate constant for CYP3A protein over an incubation time of 96 hours. The degradation rate constant of 0.0240 ± 0.005 hour(-1) was reproducible in hepatocytes from five different human donors. These donors also reflected the overall population with respect to CYP3A5 genotype. This methodology can be applied to additional enzymes and may provide a more accurate in vitro derived kdeg value for predicting clinical drug-drug interaction outcomes. Copyright © 2015 by The American Society for Pharmacology and Experimental Therapeutics.

Investigating the Release of a Hydrophobic Peptide from Matrices of Biodegradable Polymers: An Integrated Method Approach

PubMed Central

Gubskaya, Anna V.; Khan, I. John; Valenzuela, Loreto M.; Lisnyak, Yuriy V.; Kohn, Joachim

2013-01-01

The objectives of this work were: (1) to select suitable compositions of tyrosine-derived polycarbonates for controlled delivery of voclosporin, a potent drug candidate to treat ocular diseases, (2) to establish a structure-function relationship between key molecular characteristics of biodegradable polymer matrices and drug release kinetics, and (3) to identify factors contributing in the rate of drug release. For the first time, the experimental study of polymeric drug release was accompanied by a hierarchical sequence of three computational methods. First, suitable polymer compositions used in subsequent neural network modeling were determined by means of response surface methodology (RSM). Second, accurate artificial neural network (ANN) models were built to predict drug release profiles for fifteen polymers located outside the initial design space. Finally, thermodynamic properties and hydrogen-bonding patterns of model drug-polymer complexes were studied using molecular dynamics (MD) technique to elucidate a role of specific interactions in drug release mechanism. This research presents further development of methodological approaches to meet challenges in the design of polymeric drug delivery systems. PMID:24039300

Receptor-based 3D-QSAR in Drug Design: Methods and Applications in Kinase Studies.

PubMed

Fang, Cheng; Xiao, Zhiyan

2016-01-01

Receptor-based 3D-QSAR strategy represents a superior integration of structure-based drug design (SBDD) and three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis. It combines the accurate prediction of ligand poses by the SBDD approach with the good predictability and interpretability of statistical models derived from the 3D-QSAR approach. Extensive efforts have been devoted to the development of receptor-based 3D-QSAR methods and two alternative approaches have been exploited. One associates with computing the binding interactions between a receptor and a ligand to generate structure-based descriptors for QSAR analyses. The other concerns the application of various docking protocols to generate optimal ligand poses so as to provide reliable molecular alignments for the conventional 3D-QSAR operations. This review highlights new concepts and methodologies recently developed in the field of receptorbased 3D-QSAR, and in particular, covers its application in kinase studies.

Conformational Transitions upon Ligand Binding: Holo-Structure Prediction from Apo Conformations

PubMed Central

Seeliger, Daniel; de Groot, Bert L.

2010-01-01

Biological function of proteins is frequently associated with the formation of complexes with small-molecule ligands. Experimental structure determination of such complexes at atomic resolution, however, can be time-consuming and costly. Computational methods for structure prediction of protein/ligand complexes, particularly docking, are as yet restricted by their limited consideration of receptor flexibility, rendering them not applicable for predicting protein/ligand complexes if large conformational changes of the receptor upon ligand binding are involved. Accurate receptor models in the ligand-bound state (holo structures), however, are a prerequisite for successful structure-based drug design. Hence, if only an unbound (apo) structure is available distinct from the ligand-bound conformation, structure-based drug design is severely limited. We present a method to predict the structure of protein/ligand complexes based solely on the apo structure, the ligand and the radius of gyration of the holo structure. The method is applied to ten cases in which proteins undergo structural rearrangements of up to 7.1 Å backbone RMSD upon ligand binding. In all cases, receptor models within 1.6 Å backbone RMSD to the target were predicted and close-to-native ligand binding poses were obtained for 8 of 10 cases in the top-ranked complex models. A protocol is presented that is expected to enable structure modeling of protein/ligand complexes and structure-based drug design for cases where crystal structures of ligand-bound conformations are not available. PMID:20066034

Quantitative Estimation of Plasma Free Drug Fraction in Patients With Varying Degrees of Hepatic Impairment: A Methodological Evaluation.

PubMed

Li, Guo-Fu; Yu, Guo; Li, Yanfei; Zheng, Yi; Zheng, Qing-Shan; Derendorf, Hartmut

2018-07-01

Quantitative prediction of unbound drug fraction (f u ) is essential for scaling pharmacokinetics through physiologically based approaches. However, few attempts have been made to evaluate the projection of f u values under pathological conditions. The primary objective of this study was to predict f u values (n = 105) of 56 compounds with or without the information of predominant binding protein in patients with varying degrees of hepatic insufficiency by accounting for quantitative changes in molar concentrations of either the major binding protein or albumin plus alpha 1-acid glycoprotein associated with differing levels of hepatic dysfunction. For the purpose of scaling, data pertaining to albumin and α1-acid glycoprotein levels in response to differing degrees of hepatic impairment were systematically collected from 919 adult donors. The results of the present study demonstrate for the first time the feasibility of physiologically based scaling f u in hepatic dysfunction after verifying with experimentally measured data of a wide variety of compounds from individuals with varying degrees of hepatic insufficiency. Furthermore, the high level of predictive accuracy indicates that the inter-relation between the severity of hepatic impairment and these plasma protein levels are physiologically accurate. The present study enhances the confidence in predicting f u in hepatic insufficiency, particularly for albumin-bound drugs. Copyright © 2018 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Using physics-based pose predictions and free energy perturbation calculations to predict binding poses and relative binding affinities for FXR ligands in the D3R Grand Challenge 2

NASA Astrophysics Data System (ADS)

Athanasiou, Christina; Vasilakaki, Sofia; Dellis, Dimitris; Cournia, Zoe

2018-01-01

Computer-aided drug design has become an integral part of drug discovery and development in the pharmaceutical and biotechnology industry, and is nowadays extensively used in the lead identification and lead optimization phases. The drug design data resource (D3R) organizes challenges against blinded experimental data to prospectively test computational methodologies as an opportunity for improved methods and algorithms to emerge. We participated in Grand Challenge 2 to predict the crystallographic poses of 36 Farnesoid X Receptor (FXR)-bound ligands and the relative binding affinities for two designated subsets of 18 and 15 FXR-bound ligands. Here, we present our methodology for pose and affinity predictions and its evaluation after the release of the experimental data. For predicting the crystallographic poses, we used docking and physics-based pose prediction methods guided by the binding poses of native ligands. For FXR ligands with known chemotypes in the PDB, we accurately predicted their binding modes, while for those with unknown chemotypes the predictions were more challenging. Our group ranked #1st (based on the median RMSD) out of 46 groups, which submitted complete entries for the binding pose prediction challenge. For the relative binding affinity prediction challenge, we performed free energy perturbation (FEP) calculations coupled with molecular dynamics (MD) simulations. FEP/MD calculations displayed a high success rate in identifying compounds with better or worse binding affinity than the reference (parent) compound. Our studies suggest that when ligands with chemical precedent are available in the literature, binding pose predictions using docking and physics-based methods are reliable; however, predictions are challenging for ligands with completely unknown chemotypes. We also show that FEP/MD calculations hold predictive value and can nowadays be used in a high throughput mode in a lead optimization project provided that crystal structures of sufficiently high quality are available.

Noninvasive imaging of absolute PpIX concentration distribution in nonmelanoma skin tumors at pre-PDT

NASA Astrophysics Data System (ADS)

Sunar, Ulas; Rohrbach, Daniel; Morgan, Janet; Zeitouni, Natalie

2013-03-01

Photodynamic Therapy (PDT) has proven to be an effective treatment option for nonmelanoma skin cancers. The ability to quantify the concentration of drug in the treated area is crucial for effective treatment planning as well as predicting outcomes. We utilized spatial frequency domain imaging for quantifying the accurate concentration of protoporphyrin IX (PpIX) in phantoms and in vivo. We correct fluorescence against the effects of native tissue absorption and scattering parameters. First we quantified the absorption and scattering of the tissue non-invasively. Then, we corrected raw fluorescence signal by compensating for optical properties to get the absolute drug concentration. After phantom experiments, we used basal cell carcinoma (BCC) model in Gli mice to determine optical properties and drug concentration in vivo at pre-PDT.

Pharmacogenomic prediction of anthracycline-induced cardiotoxicity in children.

PubMed

Visscher, Henk; Ross, Colin J D; Rassekh, S Rod; Barhdadi, Amina; Dubé, Marie-Pierre; Al-Saloos, Hesham; Sandor, George S; Caron, Huib N; van Dalen, Elvira C; Kremer, Leontien C; van der Pal, Helena J; Brown, Andrew M K; Rogers, Paul C; Phillips, Michael S; Rieder, Michael J; Carleton, Bruce C; Hayden, Michael R

2012-05-01

Anthracycline-induced cardiotoxicity (ACT) is a serious adverse drug reaction limiting anthracycline use and causing substantial morbidity and mortality. Our aim was to identify genetic variants associated with ACT in patients treated for childhood cancer. We carried out a study of 2,977 single-nucleotide polymorphisms (SNPs) in 220 key drug biotransformation genes in a discovery cohort of 156 anthracycline-treated children from British Columbia, with replication in a second cohort of 188 children from across Canada and further replication of the top SNP in a third cohort of 96 patients from Amsterdam, the Netherlands. We identified a highly significant association of a synonymous coding variant rs7853758 (L461L) within the SLC28A3 gene with ACT (odds ratio, 0.35; P = 1.8 × 10(-5) for all cohorts combined). Additional associations (P < .01) with risk and protective variants in other genes including SLC28A1 and several adenosine triphosphate-binding cassette transporters (ABCB1, ABCB4, and ABCC1) were present. We further explored combining multiple variants into a single-prediction model together with clinical risk factors and classification of patients into three risk groups. In the high-risk group, 75% of patients were accurately predicted to develop ACT, with 36% developing this within the first year alone, whereas in the low-risk group, 96% of patients were accurately predicted not to develop ACT. We have identified multiple genetic variants in SLC28A3 and other genes associated with ACT. Combined with clinical risk factors, genetic risk profiling might be used to identify high-risk patients who can then be provided with safer treatment options.

The VACS index accurately predicts mortality and treatment response among multi-drug resistant HIV infected patients participating in the options in management with antiretrovirals (OPTIMA) study.

PubMed

Brown, Sheldon T; Tate, Janet P; Kyriakides, Tassos C; Kirkwood, Katherine A; Holodniy, Mark; Goulet, Joseph L; Angus, Brian J; Cameron, D William; Justice, Amy C

2014-01-01

The VACS Index is highly predictive of all-cause mortality among HIV infected individuals within the first few years of combination antiretroviral therapy (cART). However, its accuracy among highly treatment experienced individuals and its responsiveness to treatment interventions have yet to be evaluated. We compared the accuracy and responsiveness of the VACS Index with a Restricted Index of age and traditional HIV biomarkers among patients enrolled in the OPTIMA study. Using data from 324/339 (96%) patients in OPTIMA, we evaluated associations between indices and mortality using Kaplan-Meier estimates, proportional hazards models, Harrel's C-statistic and net reclassification improvement (NRI). We also determined the association between study interventions and risk scores over time, and change in score and mortality. Both the Restricted Index (c = 0.70) and VACS Index (c = 0.74) predicted mortality from baseline, but discrimination was improved with the VACS Index (NRI = 23%). Change in score from baseline to 48 weeks was more strongly associated with survival for the VACS Index than the Restricted Index with respective hazard ratios of 0.26 (95% CI 0.14-0.49) and 0.39(95% CI 0.22-0.70) among the 25% most improved scores, and 2.08 (95% CI 1.27-3.38) and 1.51 (95%CI 0.90-2.53) for the 25% least improved scores. The VACS Index predicts all-cause mortality more accurately among multi-drug resistant, treatment experienced individuals and is more responsive to changes in risk associated with treatment intervention than an index restricted to age and HIV biomarkers. The VACS Index holds promise as an intermediate outcome for intervention research.

Simultaneous multiplexed quantification of nicotine and its metabolites using surface enhanced Raman scattering.

PubMed

Alharbi, Omar; Xu, Yun; Goodacre, Royston

2014-10-07

The detection and quantification of xenobiotics and their metabolites in man is important for drug dosing, therapy and for substance abuse monitoring where longer-lived metabolic products from illicit materials can be assayed after the drug of abuse has been cleared from the system. Raman spectroscopy offers unique specificity for molecular characterization and this usually weak signal can be significantly enhanced using surface enhanced Raman scattering (SERS). We report here the novel development of SERS with chemometrics for the simultaneous analysis of the drug nicotine and its major xenometabolites cotinine and trans-3'-hydroxycotinine. Initial experiments optimized the SERS conditions and we found that when these three determinands were analysed individually that the maximum SERS signals were found at three different pH. These were pH 3 for nicotine and pH 10 and 11 for cotinine and trans-3'-hydroxycotinine, respectively. Tertiary mixtures containing nicotine, cotinine and trans-3'-hydroxycotinine were generated in the concentration range 10(-7)-10(-5) M and SERS spectra were collected at all three pH values. Chemometric analysis using kernel-partial least squares (K-PLS) and artificial neural networks (ANNs) were conducted and these models were validated using bootstrap resampling. All three analytes were accurately quantified with typical root mean squared error of prediction on the test set data being 5-9%; nicotine was most accurately predicted followed by cotinine and then trans-3'-hydroxycotinine. We believe that SERS is a powerful approach for the simultaneous analysis of multiple determinands without recourse to lengthy chromatography, as demonstrated here for the xenobiotic nicotine and its two major xenometabolites.

Consumer confusion between prescription drug precautions and side effects.

PubMed

Amoozegar, Jacqueline B; Rupert, Douglas J; Sullivan, Helen W; O'Donoghue, Amie C

2017-06-01

Multiple studies have identified consumers' difficulty correctly interpreting risk information provided about prescription drugs, whether in printed format or online. This study's purpose was to explore whether consumers can distinguish between prescription drug precautions and side effects presented on brand-name drug websites. Participants (n=873) viewed fictitious drug websites that presented both precautions and side effects for one of four drugs, and they completed a survey assessing recall and comprehension. We coded open-ended recall data to identify whether drug precautions were mentioned and, if so, how they were interpreted. Approximately 15% of participants mentioned at least one drug precaution. The majority (59.7%) misinterpreted precautions as potential side effects. Participants who misinterpreted precautions rated the drugs as significantly more likely to cause side effects than participants who accurately interpreted the precautions. Age, education, literacy, and other factors did not appear to predict precaution interpretation. At least some consumers are likely to interpret precautions on drug websites as potential side effects, which might affect consumer preferences, treatment decisions, and medication safety. Healthcare providers should be aware of this potential confusion, assess patients' understanding of precautions and potential side effects, and address any misunderstandings. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Enhancing Peripheral Nerve Regeneration with a Novel Drug-Delivering Nerve Conduit

DTIC Science & Technology

2015-10-01

data with the release of fluorescently labeled dextran that indicate the new device is sealed and able to release therapeutics in a controlled manner...accurately predict the release of fluorescently labeled dextran , with a similar diffusion coefficient to NGF, over a period of approximately 40 days...enables the release of NGF from the reservoir into the inner chamber. 7 Figure 2. Fluorescently labeled dextran (blue) was released from our PLGA nerve

Comparative Proteomic Analysis of Human Liver Tissue and Isolated Hepatocytes with a Focus on Proteins Determining Drug Exposure.

PubMed

Vildhede, Anna; Wiśniewski, Jacek R; Norén, Agneta; Karlgren, Maria; Artursson, Per

2015-08-07

Freshly isolated human hepatocytes are considered the gold standard for in vitro studies of liver functions, including drug transport, metabolism, and toxicity. For accurate predictions of the in vivo outcome, the isolated hepatocytes should reflect the phenotype of their in vivo counterpart, i.e., hepatocytes in human liver tissue. Here, we quantified and compared the membrane proteomes of freshly isolated hepatocytes and human liver tissue using a label-free shotgun proteomics approach. A total of 5144 unique proteins were identified, spanning over 6 orders of magnitude in abundance. There was a good global correlation in protein abundance. However, the expression of many plasma membrane proteins was lower in the isolated hepatocytes than in the liver tissue. This included transport proteins that determine hepatocyte exposure to many drugs and endogenous compounds. Pathway analysis of the differentially expressed proteins confirmed that hepatocytes are exposed to oxidative stress during isolation and suggested that plasma membrane proteins were degraded via the protein ubiquitination pathway. Finally, using pitavastatin as an example, we show how protein quantifications can improve in vitro predictions of in vivo liver clearance. We tentatively conclude that our data set will be a useful resource for improved hepatocyte predictions of the in vivo outcome.

Automated patch clamp on mESC-derived cardiomyocytes for cardiotoxicity prediction.

PubMed

Stoelzle, Sonja; Haythornthwaite, Alison; Kettenhofen, Ralf; Kolossov, Eugen; Bohlen, Heribert; George, Michael; Brüggemann, Andrea; Fertig, Niels

2011-09-01

Cardiovascular side effects are critical in drug development and have frequently led to late-stage project terminations or even drug withdrawal from the market. Physiologically relevant and predictive assays for cardiotoxicity are hence strongly demanded by the pharmaceutical industry. To identify a potential impact of test compounds on ventricular repolarization, typically a variety of ion channels in diverse heterologously expressing cells have to be investigated. Similar to primary cells, in vitro-generated stem cell-derived cardiomyocytes simultaneously express cardiac ion channels. Thus, they more accurately represent the native situation compared with cell lines overexpressing only a single type of ion channel. The aim of this study was to determine if stem cell-derived cardiomyocytes are suited for use in an automated patch clamp system. The authors show recordings of cardiac ion currents as well as action potential recordings in readily available stem cell-derived cardiomyocytes. Besides monitoring inhibitory effects of reference compounds on typical cardiac ion currents, the authors revealed for the first time drug-induced modulation of cardiac action potentials in an automated patch clamp system. The combination of an in vitro cardiac cell model with higher throughput patch clamp screening technology allows for a cost-effective cardiotoxicity prediction in a physiologically relevant cell system.

3D tumor microtissues as an in vitro testing platform for microenvironmentally-triggered drug delivery systems.

PubMed

Brancato, Virginia; Gioiella, Filomena; Profeta, Martina; Imparato, Giorgia; Guarnieri, Daniela; Urciuolo, Francesco; Melone, Pietro; Netti, Paolo A

2017-07-15

Therapeutic approaches based on nanomedicine have garnered great attention in cancer research. In vitro biological models that better mimic in vivo conditions are crucial tools to more accurately predict their therapeutic efficacy in vivo. In this work, a new 3D breast cancer microtissue has been developed to recapitulate the complexity of the tumor microenvironment and to test its efficacy as screening platform for drug delivery systems. The proposed 3D cancer model presents human breast adenocarcinoma cells and cancer-associated fibroblasts embedded in their own ECM, thus showing several features of an in vivo tumor, such as overexpression of metallo-proteinases (MMPs). After demonstrating at molecular and protein level the MMP2 overexpression in such tumor microtissues, we used them to test a recently validated formulation of endogenous MMP2-responsive nanoparticles (NP). The presence of the MMP2-sensitive linker allows doxorubicin release from NP only upon specific enzymatic cleavage of the peptide. The same NP without the MMP-sensitive linker and healthy breast microtissues were also produced to demonstrate NP specificity and selectivity. Cell viability after NP treatment confirmed that controlled drug delivery is achieved only in 3D tumor microtissues suggesting that the validation of therapeutic strategies in such 3D tumor model could predict human response. A major issue of modern cancer research is the development of accurate and predictive experimental models of human tumors consistent with tumor microenvironment and applicable as screening platforms for novel therapeutic strategies. In this work, we developed and validated a new 3D microtissue model of human breast tumor as a testing platform of anti-cancer drug delivery systems. To this aim, biodegradable nanoparticles responsive to physiological changes specifically occurring in tumor microenvironment were used. Our findings clearly demonstrate that the breast tumor microtissue well recapitulates in vivo physiological features of tumor tissue and elicits a specific response to microenvironmentally-responsive nanoparticles compared to healthy tissue. We believe this study is of particular interest for cancer research and paves the way to exploit tumor microtissues for several testing purposes. Copyright © 2017 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Investigation into metastatic processes and the therapeutic effects of gemcitabine on human pancreatic cancer using an orthotopic SUIT-2 pancreatic cancer mouse model

PubMed Central

Higuchi, Tamami; Yokobori, Takehiko; Naito, Tomoharu; Kakinuma, Chihaya; Hagiwara, Shinji; Nishiyama, Masahiko; Asao, Takayuki

2018-01-01

Prognosis of pancreatic cancer is poor, thus the development of novel therapeutic drugs is necessary. During preclinical studies, appropriate models are essential for evaluating drug efficacy. The present study sought to determine the ideal pancreatic cancer mouse model for reliable preclinical testing. Such a model could accurately reflect human pancreatic cancer phenotypes and predict future clinical trial results. Systemic pathology analysis was performed in an orthotopic transplantation model to prepare model mice for use in preclinical studies, mimicking the progress of human pancreatic cancer. The location and the timing of inoculated cancer cell metastases, pathogenesis and cause of fatality were analyzed. Furthermore, the efficacy of gemcitabine, a key pancreatic cancer drug, was evaluated in this model where liver metastasis and peritoneal dissemination occur. Results indicated that the SUIT-2 orthotopic pancreatic cancer model was similar to the phenotypic sequential progression of human pancreatic cancer, with extra-pancreatic invasion, intra-peritoneal dissemination and other hematogenous organ metastases. Notably, survival was prolonged by administering gemcitabine to mice with metastasized pancreatic cancer. Furthermore, the detailed effects of gemcitabine on the primary tumor and metastatic tumor lesions were pathologically evaluated in mice. The present study indicated the model accurately depicted pancreatic cancer development and metastasis. Furthermore, the detailed effects of pancreatic cancer drugs on the primary tumor and on metastatic tumor lesions. We present this model as a potential new standard for new drug development in pancreatic cancer. PMID:29435042

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.

2D MI-DRAGON: a new predictor for protein-ligands interactions and theoretic-experimental studies of US FDA drug-target network, oxoisoaporphine inhibitors for MAO-A and human parasite proteins.

PubMed

Prado-Prado, Francisco; García-Mera, Xerardo; Escobar, Manuel; Sobarzo-Sánchez, Eduardo; Yañez, Matilde; Riera-Fernandez, Pablo; González-Díaz, Humberto

2011-12-01

There are many pairs of possible Drug-Proteins Interactions that may take place or not (DPIs/nDPIs) between drugs with high affinity/non-affinity for different proteins. This fact makes expensive in terms of time and resources, for instance, the determination of all possible ligands-protein interactions for a single drug. In this sense, we can use Quantitative Structure-Activity Relationships (QSAR) models to carry out rational DPIs prediction. Unfortunately, almost all QSAR models predict activity against only one target. To solve this problem we can develop multi-target QSAR (mt-QSAR) models. In this work, we introduce the technique 2D MI-DRAGON a new predictor for DPIs based on two different well-known software. We use the software MARCH-INSIDE (MI) to calculate 3D structural parameters for targets and the software DRAGON was used to calculated 2D molecular descriptors all drugs showing known DPIs present in the Drug Bank (US FDA benchmark dataset). Both classes of parameters were used as input of different Artificial Neural Network (ANN) algorithms to seek an accurate non-linear mt-QSAR predictor. The best ANN model found is a Multi-Layer Perceptron (MLP) with profile MLP 21:21-31-1:1. This MLP classifies correctly 303 out of 339 DPIs (Sensitivity = 89.38%) and 480 out of 510 nDPIs (Specificity = 94.12%), corresponding to training Accuracy = 92.23%. The validation of the model was carried out by means of external predicting series with Sensitivity = 92.18% (625/678 DPIs; Specificity = 90.12% (730/780 nDPIs) and Accuracy = 91.06%. 2D MI-DRAGON offers a good opportunity for fast-track calculation of all possible DPIs of one drug enabling us to re-construct large drug-target or DPIs Complex Networks (CNs). For instance, we reconstructed the CN of the US FDA benchmark dataset with 855 nodes 519 drugs+336 targets). We predicted CN with similar topology (observed and predicted values of average distance are equal to 6.7 vs. 6.6). These CNs can be used to explore large DPIs databases in order to discover both new drugs and/or targets. Finally, we illustrated in one theoretic-experimental study the practical use of 2D MI-DRAGON. We reported the prediction, synthesis, and pharmacological assay of 10 different oxoisoaporphines with MAO-A inhibitory activity. The more active compound OXO5 presented IC(50) = 0.00083 μM, notably better than the control drug Clorgyline. Copyright © 2011 Elsevier Masson SAS. All rights reserved.

Accurate Prediction of Drug-Induced Liver Injury Using Stem Cell-Derived Populations

PubMed Central

Szkolnicka, Dagmara; Farnworth, Sarah L.; Lucendo-Villarin, Baltasar; Storck, Christopher; Zhou, Wenli; Iredale, John P.; Flint, Oliver

2014-01-01

Despite major progress in the knowledge and management of human liver injury, there are millions of people suffering from chronic liver disease. Currently, the only cure for end-stage liver disease is orthotopic liver transplantation; however, this approach is severely limited by organ donation. Alternative approaches to restoring liver function have therefore been pursued, including the use of somatic and stem cell populations. Although such approaches are essential in developing scalable treatments, there is also an imperative to develop predictive human systems that more effectively study and/or prevent the onset of liver disease and decompensated organ function. We used a renewable human stem cell resource, from defined genetic backgrounds, and drove them through developmental intermediates to yield highly active, drug-inducible, and predictive human hepatocyte populations. Most importantly, stem cell-derived hepatocytes displayed equivalence to primary adult hepatocytes, following incubation with known hepatotoxins. In summary, we have developed a serum-free, scalable, and shippable cell-based model that faithfully predicts the potential for human liver injury. Such a resource has direct application in human modeling and, in the future, could play an important role in developing renewable cell-based therapies. PMID:24375539

Cytochrome p450 turnover: regulation of synthesis and degradation, methods for determining rates, and implications for the prediction of drug interactions.

PubMed

Yang, Jiansong; Liao, Mingxiang; Shou, Magang; Jamei, Masoud; Yeo, Karen Rowland; Tucker, Geoffrey T; Rostami-Hodjegan, Amin

2008-06-01

In vivo enzyme levels are governed by the rates of de novo enzyme synthesis and degradation. A current lack of consensus on values of the in vivo turnover half-lives of human cytochrome P450 (CYP) enzymes places a significant limitation on the accurate prediction of changes in drug concentration-time profiles associated with interactions involving enzyme induction and mechanism (time)-based inhibition (MBI). In the case of MBI, the full extent of inhibition is also sensitive to values of enzyme turnover half-life. We review current understanding of CYP regulation, discuss the pros and cons of various in vitro and in vivo approaches used to estimate the turnover of specific CYPs and, by simulation, consider the impact of variability in estimates of CYP turnover on the prediction of enzyme induction and MBI in vivo. In the absence of consensus on values for the in vivo turnover half-lives of key CYPs, a sensitivity analysis of predictions of the pharmacokinetic effects of enzyme induction and MBI to these values should be an integral part of the modelling exercise, and the selective use of values should be avoided.

Improved protocol and data analysis for accelerated shelf-life estimation of solid dosage forms.

PubMed

Waterman, Kenneth C; Carella, Anthony J; Gumkowski, Michael J; Lukulay, Patrick; MacDonald, Bruce C; Roy, Michael C; Shamblin, Sheri L

2007-04-01

To propose and test a new accelerated aging protocol for solid-state, small molecule pharmaceuticals which provides faster predictions for drug substance and drug product shelf-life. The concept of an isoconversion paradigm, where times in different temperature and humidity-controlled stability chambers are set to provide a critical degradant level, is introduced for solid-state pharmaceuticals. Reliable estimates for temperature and relative humidity effects are handled using a humidity-corrected Arrhenius equation, where temperature and relative humidity are assumed to be orthogonal. Imprecision is incorporated into a Monte-Carlo simulation to propagate the variations inherent in the experiment. In early development phases, greater imprecision in predictions is tolerated to allow faster screening with reduced sampling. Early development data are then used to design appropriate test conditions for more reliable later stability estimations. Examples are reported showing that predicted shelf-life values for lower temperatures and different relative humidities are consistent with the measured shelf-life values at those conditions. The new protocols and analyses provide accurate and precise shelf-life estimations in a reduced time from current state of the art.

A New Method of Constructing a Drug-Polymer Temperature-Composition Phase Diagram Using Hot-Melt Extrusion.

PubMed

Tian, Yiwei; Jones, David S; Donnelly, Conor; Brannigan, Timothy; Li, Shu; Andrews, Gavin P

2018-04-02

Current experimental methodologies used to determine the thermodynamic solubility of an API within a polymer typically involves establishing the dissolution/melting end point of the crystalline API within a physical mixture or through the use of the glass transition temperature measurement of a demixed amorphous solid dispersion. The measurable "equilibrium" points for solubility are normally well above the glass transition temperature of the system, meaning extrapolation is required to predict the drug solubility at pharmaceutically relevant temperatures. In this manuscript, we argue that the presence of highly viscous polymers in these systems results in experimental data that exhibits an under or overestimated value relative to the true thermodynamic solubility. In previous work, we demonstrated the effects of experimental conditions and their impact on measured and predicted thermodynamic solubility points. In light of current understanding, we have developed a new method to limit error associated with viscosity effects for application in small-scale hot-melt extrusion (HME). In this study, HME was used to generate an intermediate (multiphase) system containing crystalline drug, amorphous drug/polymer-rich regions as well as drug that was molecularly dispersed in polymer. An extended annealing method was used together with high-speed differential scanning calorimetry to accurately determine the upper and lower boundaries of the thermodynamic solubility of a model drug-polymer system (felodipine and Soluplus). Compared to our previously published data, the current results confirmed our hypothesis that the prediction of the liquid-solid curve using dynamic determination of dissolution/melting end point of the crystalline API physical mixture presents an underestimation relative to the thermodynamic solubility point. With this proposed method, we were able to experimentally measure the upper and lower boundaries of the liquid-solid curve for the model system. The relationship between inverse temperature and drug-polymer solubility parameter (χ) remained linear at lower drug loadings. Significantly higher solubility and miscibility between the felodipine-Soluplus system were derived from the new χ values.

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.

Accurate van der Waals coefficients from density functional theory

PubMed Central

Tao, Jianmin; Perdew, John P.; Ruzsinszky, Adrienn

2012-01-01

The van der Waals interaction is a weak, long-range correlation, arising from quantum electronic charge fluctuations. This interaction affects many properties of materials. A simple and yet accurate estimate of this effect will facilitate computer simulation of complex molecular materials and drug design. Here we develop a fast approach for accurate evaluation of dynamic multipole polarizabilities and van der Waals (vdW) coefficients of all orders from the electron density and static multipole polarizabilities of each atom or other spherical object, without empirical fitting. Our dynamic polarizabilities (dipole, quadrupole, octupole, etc.) are exact in the zero- and high-frequency limits, and exact at all frequencies for a metallic sphere of uniform density. Our theory predicts dynamic multipole polarizabilities in excellent agreement with more expensive many-body methods, and yields therefrom vdW coefficients C6, C8, C10 for atom pairs with a mean absolute relative error of only 3%. PMID:22205765

Design of Drug Delivery Methods for the Brain and Central Nervous System

NASA Astrophysics Data System (ADS)

Lueshen, Eric

Due to the impermeability of the blood-brain barrier (BBB) to macromolecules delivered systemically, drug delivery to the brain and central nervous system (CNS) is quite difficult and has become an area of intense research. Techniques such as convection-enhanced intraparenchymal delivery and intrathecal magnetic drug targeting offer a means of circumventing the blood-brain barrier for targeted delivery of therapeutics. This dissertation focuses on three aspects of drug delivery: pharmacokinetics, convection-enhanced delivery, and intrathecal magnetic drug targeting. Classical pharmacokinetics mainly uses black-box curve fitting techniques without biochemical or biological basis. This dissertation advances the state-of-the-art of pharmacokinetics and pharmacodynamics by incorporating first principles and biochemical/biotransport mechanisms in the prediction of drug fate in vivo. A whole body physiologically-based pharmacokinetics (PBPK) modeling framework is engineered which creates multiscale mathematical models for entire organisms composed of organs, tissues, and a detailed vasculature network to predict drug bioaccumulation and to rigorously determine kinetic parameters. These models can be specialized to account for species, weight, gender, age, and pathology. Systematic individual therapy design using the proposed mechanistic PBPK modeling framework is also a possibility. Biochemical, anatomical, and physiological scaling laws are also developed to accurately project drug kinetics in humans from small animal experiments. Our promising results demonstrate that the whole-body mechanistic PBPK modeling approach not only elucidates drug mechanisms from a biochemical standpoint, but offers better scaling precision. Better models can substantially accelerate the introduction of drug leads to clinical trials and eventually to the market by offering more understanding of the drug mechanisms, aiding in therapy design, and serving as an accurate dosing tool. Convection-enhanced drug delivery (CED) is a technique used to bypass the BBB via direct intracranial injection using a catheter driven by a positive pressure gradient from an infusion pump. Although CED boasts the advantage of achieving larger drug distribution volumes compared to diffusion driven methods, difficulty in predicting drug spread and preventing backflow along the catheter shaft commonly occur. In this dissertation, a method for predicting drug distributions in the brain using diffusion tensor imaging (DTI) data is employed to show how small variations in catheter placement can lead to drastically different volumes of drug distribution in vivo. The impact that microfluid flow has on deformable brain phantom gel is studied in order to elucidate the causes of backflow, and the results are used to develop backflow-free catheters with safe volumetric flow rates up to 10 ?l/min. Through implementation of our backflow-free catheter designs, physicians will be able to target specific regions of the brain with improved accuracy, increased drug concentration, and larger drug distribution geometries. Intrathecal (IT) drug delivery involves direct drug infusion into the spinal canal and has become standard practice for treating many CNS diseases. Although IT drug delivery boasts the advantage of reduced systemic toxicity compared to oral and intravenous techniques, current IT delivery protocols lack a means of sufficient drug targeting at specific locations of interest within the CNS. In this dissertation, the method of intrathecal magnetic drug targeting (IT-MDT) is developed to overcome the limited targeting capabilities of standard IT drug delivery protocols. The basic idea behind IT-MDT is to guide intrathecally-injected, drug-functionalized magnetic nanoparticles (MNPs) using an external magnetic field to diseased regions within the spinal canal. Cerebrospinal fluid (CSF) transport phenomena are studied, and in vitro human spine surrogates are built. Experiments are run on the in vitro human spine model to determine the feasibility of IT-MDT and to develop novel treatment therapies. Computer simulations are performed to optimize magnetic field placement and/or implant design for generating high gradient magnetic fields, as well as to study how these fields aid in therapeutic nanoparticle localization. Large collection efficiencies of MNPs were achieved during in vitro IT-MDT and implant-assisted IT-MDT experiments with concentration levels nearly nine times that of the control when no magnetic field was present. Testing different magnetizable implants showed that implant design is a key factor in achieving the largest MNP collection efficiency within the targeting region. Knowledge gained from the in vitro IT-MDT experiments and simulations will be used in the future to develop IT-MDT methods in animals and humans.

Structure-Based Predictions of Activity Cliffs

PubMed Central

Husby, Jarmila; Bottegoni, Giovanni; Kufareva, Irina; Abagyan, Ruben; Cavalli, Andrea

2015-01-01

In drug discovery, it is generally accepted that neighboring molecules in a given descriptors' space display similar activities. However, even in regions that provide strong predictability, structurally similar molecules can occasionally display large differences in potency. In QSAR jargon, these discontinuities in the activity landscape are known as ‘activity cliffs’. In this study, we assessed the reliability of ligand docking and virtual ligand screening schemes in predicting activity cliffs. We performed our calculations on a diverse, independently collected database of cliff-forming co-crystals. Starting from ideal situations, which allowed us to establish our baseline, we progressively moved toward simulating more realistic scenarios. Ensemble- and template-docking achieved a significant level of accuracy, suggesting that, despite the well-known limitations of empirical scoring schemes, activity cliffs can be accurately predicted by advanced structure-based methods. PMID:25918827

Height and Weight Estimation From Anthropometric Measurements Using Machine Learning Regressions

PubMed Central

Fernandes, Bruno J. T.; Roque, Alexandre

2018-01-01

Height and weight are measurements explored to tracking nutritional diseases, energy expenditure, clinical conditions, drug dosages, and infusion rates. Many patients are not ambulant or may be unable to communicate, and a sequence of these factors may not allow accurate estimation or measurements; in those cases, it can be estimated approximately by anthropometric means. Different groups have proposed different linear or non-linear equations which coefficients are obtained by using single or multiple linear regressions. In this paper, we present a complete study of the application of different learning models to estimate height and weight from anthropometric measurements: support vector regression, Gaussian process, and artificial neural networks. The predicted values are significantly more accurate than that obtained with conventional linear regressions. In all the cases, the predictions are non-sensitive to ethnicity, and to gender, if more than two anthropometric parameters are analyzed. The learning model analysis creates new opportunities for anthropometric applications in industry, textile technology, security, and health care. PMID:29651366

Toward Fast and Accurate Binding Affinity Prediction with pmemdGTI: An Efficient Implementation of GPU-Accelerated Thermodynamic Integration.

PubMed

Lee, Tai-Sung; Hu, Yuan; Sherborne, Brad; Guo, Zhuyan; York, Darrin M

2017-07-11

We report the implementation of the thermodynamic integration method on the pmemd module of the AMBER 16 package on GPUs (pmemdGTI). The pmemdGTI code typically delivers over 2 orders of magnitude of speed-up relative to a single CPU core for the calculation of ligand-protein binding affinities with no statistically significant numerical differences and thus provides a powerful new tool for drug discovery applications.

MTB-DR-RIF 9G test: Detection and discrimination of tuberculosis and multi-drug resistant tuberculosis strains.

PubMed

Song, Keum-Soo; Nimse, Satish Balasaheb; Cho, Nam Hoon; Sung, Nackmoon; Kim, Hee-Jin; Yang, Jeongseong; Kim, Taisun

2015-12-01

This report describes the evaluation of the novel MTB-DR-RIF 9G test for the accurate detection and discrimination of Mycobacterium tuberculosis (MTB) and rifampicin-resistant M. tuberculosis (MTB-DR-RIF) in the clinical samples. The procedure included the amplification of a nucleotide fragment of the rpoB gene of the MTB and MTB-DR-RIF strains and their hybridization with the immobilized probes. The MTB-DR-RIF 9G test was evaluated for its ability to detect and discriminate MTB and MTB-DR-RIF strains in 113 known clinical samples. The accuracy of the MTB-DR-RIF 9G test was determined by comparing its results with sequencing analysis and drug susceptibility testing. The sensitivity and specificity of the MTB-DR-RIF 9G test at 95% confidence interval were found to be 95.4% (89.5-98.5) and 100% (69.2-100), respectively. The positive predictive value and negative predictive value of the MTB-DR-RIF 9G test at 95% confidence interval were found to be 100% (85.0-95.9) and 66.7% (38.4-88.18), respectively. Sequencing analysis of all samples indicated that the mutations present in the regions identified with the MTB-DR-RIF 9G assay can be detected accurately. Copyright © 2015 Elsevier Ltd. All rights reserved.

Intra- and interspecies gene expression models for predicting drug response in canine osteosarcoma.

PubMed

Fowles, Jared S; Brown, Kristen C; Hess, Ann M; Duval, Dawn L; Gustafson, Daniel L

2016-02-19

Genomics-based predictors of drug response have the potential to improve outcomes associated with cancer therapy. Osteosarcoma (OS), the most common primary bone cancer in dogs, is commonly treated with adjuvant doxorubicin or carboplatin following amputation of the affected limb. We evaluated the use of gene-expression based models built in an intra- or interspecies manner to predict chemosensitivity and treatment outcome in canine OS. Models were built and evaluated using microarray gene expression and drug sensitivity data from human and canine cancer cell lines, and canine OS tumor datasets. The "COXEN" method was utilized to filter gene signatures between human and dog datasets based on strong co-expression patterns. Models were built using linear discriminant analysis via the misclassification penalized posterior algorithm. The best doxorubicin model involved genes identified in human lines that were co-expressed and trained on canine OS tumor data, which accurately predicted clinical outcome in 73 % of dogs (p = 0.0262, binomial). The best carboplatin model utilized canine lines for gene identification and model training, with canine OS tumor data for co-expression. Dogs whose treatment matched our predictions had significantly better clinical outcomes than those that didn't (p = 0.0006, Log Rank), and this predictor significantly associated with longer disease free intervals in a Cox multivariate analysis (hazard ratio = 0.3102, p = 0.0124). Our data show that intra- and interspecies gene expression models can successfully predict response in canine OS, which may improve outcome in dogs and serve as pre-clinical validation for similar methods in human cancer research.

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

Drug-carrying microbubbles as a theranostic tool in convection-enhanced delivery for brain tumor therapy.

PubMed

Chen, Pin-Yuan; Yeh, Chih-Kuang; Hsu, Po-Hung; Lin, Chung-Yin; Huang, Chiung-Yin; Wei, Kuo-Chen; Liu, Hao-Li

2017-06-27

Convection-enhanced delivery (CED) is a promising technique for infusing a therapeutic agent through a catheter with a pressure gradient to create bulk flow for improving drug spread into the brain. So far, gadopentetate dimeglumine (Gd-DTPA) is the most commonly applied surrogate agent for predicting drug distribution through magnetic resonance imaging (MRI). However, Gd-DTPA provides only a short observation duration, and concurrent infusion provides an indirect measure of the exact drug distribution. In this study, we propose using microbubbles as a contrast agent for MRI monitoring, and evaluate their use as a drug-carrying vehicle to directly monitor the infused drug. Results show that microbubbles can provide excellent detectability through MRI relaxometry and accurately represent drug distribution during CED infusion. Compared with the short half-life of Gd-DTPA (1-2 hours), microbubbles allow an extended observation period of up to 12 hours. Moreover, microbubbles provide a sufficiently high drug payload, and glioma mice that underwent a CED infusion of microbubbles carrying doxorubicin presented considerable tumor growth suppression and a significantly improved survival rate. This study recommends microbubbles as a new theranostic tool for CED procedures.

Low-Turnover Drug Molecules: A Current Challenge for Drug Metabolism Scientists.

PubMed

Hutzler, J Matthew; Ring, Barbara J; Anderson, Shelby R

2015-12-01

In vitro assays using liver subcellular fractions or suspended hepatocytes for characterizing the metabolism of drug candidates play an integral role in the optimization strategy employed by medicinal chemists. However, conventional in vitro assays have limitations in their ability to predict clearance and generate metabolites for low-turnover (slowly metabolized) drug molecules. Due to a rapid loss in the activity of the drug-metabolizing enzymes, in vitro incubations are typically performed for a maximum of 1 hour with liver microsomes to 4 hours with suspended hepatocytes. Such incubations are insufficient to generate a robust metabolic response for compounds that are slowly metabolized. Thus, the challenge of accurately estimating low human clearance with confidence has emerged to be among the top challenges that drug metabolism scientists are confronted with today. In response, investigators have evaluated novel methodologies to extend incubation times and more sufficiently measure metabolism of low-turnover drugs. These methods include plated human hepatocytes in monoculture, and a novel in vitro methodology using a relay of sequential incubations with suspended cryopreserved hepatocytes. In addition, more complex in vitro cellular models, such as HepatoPac (Hepregen, Medford, MA), a micropatterned hepatocyte-fibroblast coculture system, and the HµREL (Beverley Hills, CA) hepatic coculture system, have been developed and characterized that demonstrate prolonged enzyme activity. In this review, the advantages and disadvantages of each of these in vitro methodologies as it relates to the prediction of clearance and metabolite identification will be described in an effort to provide drug metabolism scientists with the most up-to-date experimental options for dealing with the complex issue of low-turnover drug candidates. Copyright © 2015 by The American Society for Pharmacology and Experimental Therapeutics.

Properties of Protein Drug Target Classes

PubMed Central

Bull, Simon C.; Doig, Andrew J.

2015-01-01

Accurate identification of drug targets is a crucial part of any drug development program. We mined the human proteome to discover properties of proteins that may be important in determining their suitability for pharmaceutical modulation. Data was gathered concerning each protein’s sequence, post-translational modifications, secondary structure, germline variants, expression profile and drug target status. The data was then analysed to determine features for which the target and non-target proteins had significantly different values. This analysis was repeated for subsets of the proteome consisting of all G-protein coupled receptors, ion channels, kinases and proteases, as well as proteins that are implicated in cancer. Machine learning was used to quantify the proteins in each dataset in terms of their potential to serve as a drug target. This was accomplished by first inducing a random forest that could distinguish between its targets and non-targets, and then using the random forest to quantify the drug target likeness of the non-targets. The properties that can best differentiate targets from non-targets were primarily those that are directly related to a protein’s sequence (e.g. secondary structure). Germline variants, expression levels and interactions between proteins had minimal discriminative power. Overall, the best indicators of drug target likeness were found to be the proteins’ hydrophobicities, in vivo half-lives, propensity for being membrane bound and the fraction of non-polar amino acids in their sequences. In terms of predicting potential targets, datasets of proteases, ion channels and cancer proteins were able to induce random forests that were highly capable of distinguishing between targets and non-targets. The non-target proteins predicted to be targets by these random forests comprise the set of the most suitable potential future drug targets, and should therefore be prioritised when building a drug development programme. PMID:25822509

Clearance Prediction Methodology Needs Fundamental Improvement: Trends Common to Rat and Human Hepatocytes/Microsomes and Implications for Experimental Methodology.

PubMed

Wood, F L; Houston, J B; Hallifax, D

2017-11-01

Although prediction of clearance using hepatocytes and liver microsomes has long played a decisive role in drug discovery, it is widely acknowledged that reliably accurate prediction is not yet achievable despite the predominance of hepatically cleared drugs. Physiologically mechanistic methodology tends to underpredict clearance by several fold, and empirical correction of this bias is confounded by imprecision across drugs. Understanding the causes of prediction uncertainty has been slow, possibly reflecting poor resolution of variables associated with donor source and experimental methods, particularly for the human situation. It has been reported that among published human hepatocyte predictions there was a tendency for underprediction to increase with increasing in vivo intrinsic clearance, suggesting an inherent limitation using this particular system. This implied an artifactual rate limitation in vitro, although preparative effects on cell stability and performance were not yet resolved from assay design limitations. Here, to resolve these issues further, we present an up-to-date and comprehensive examination of predictions from published rat as well as human studies (where n = 128 and 101 hepatocytes and n = 71 and 83 microsomes, respectively) to assess system performance more independently. We report a clear trend of increasing underprediction with increasing in vivo intrinsic clearance, which is similar both between species and between in vitro systems. Hence, prior concerns arising specifically from human in vitro systems may be unfounded and the focus of investigation in the future should be to minimize the potential in vitro assay limitations common to whole cells and subcellular fractions. Copyright © 2017 by The American Society for Pharmacology and Experimental Therapeutics.

Utility of human hepatocyte spheroids without feeder cells for evaluation of hepatotoxicity.

PubMed

Ogihara, Takuo; Arakawa, Hiroshi; Jomura, Tomoko; Idota, Yoko; Koyama, Satoshi; Yano, Kentaro; Kojima, Hajime

2017-01-01

We investigated the utility of three-dimensionally cultured hepatocytes (spheroids) without feeder cells (Sph(f-)) for the prediction of drug-induced liver injury (DILI) in humans. Sph(f-) and spheroids cultured on feeder cells (Sph(f+)) were exposed to the hepatotoxic drugs flutamide, diclofenac, isoniazid and chlorpromazine at various concentrations for 14 days, and albumin secretion and cumulative leakages of toxicity marker enzymes, aspartate aminotransferase (AST), alanine aminotransferase (ALT), lactate dehydrogenase (LDH) and γ-glutamyl transpeptidase (γ-GTP), were measured. The cumulative AST, LDH or γ-GTP leakages from Sph(f-) were similar to or greater than those from Sph(f+) for all drugs tested, although ALT leakages showed no consistent difference between Sph(f+) and Sph(f-). In the case of Sph(f-), significant correlations among all the toxicity markers except for γ-GTP were observed. As regards the drug concentrations causing 1.2-fold elevation of enzyme leakage (F 1.2 ), no consistent difference between Sph(f+) and Sph(f-) was found, although several F 1.2 values were undetermined, especially in Sph(f+). The IC 50 of albumin secretion and F 1.2 of AST leakage from Sph(f-) were equal to or lower than those of Sph(f+) for all the tested drugs. These results indicate that feeder cells might contribute to resistance to hepatotoxicity, suggesting DILI could be evaluated more accurately by using Sph(f-). We suggest that long-term exposure of Sph(f-) to drugs might be a versatile method to predict and reproduce clinical chronic toxicity, especially in response to repeated drug administration.

The usefulness and scientific accuracy of private sector Arabic language patient drug information leaflets.

PubMed

Sukkari, Sana R; Al Humaidan, Abdullah S; Sasich, Larry D

2012-07-01

Inadequate access to useful scientifically accurate patient information is a major cause of the inappropriate use of drugs resulting in serious personal injury and related costs to the health care system. The definition of useful scientifically accurate patient information for prescription drugs was accepted by the US Secretary of the Department of Health and Human Services in 1996 as that derived from or consistent with the US FDA approved professional product label for a drug. Previous quality content studies found that English language patient drug information leaflets distributed by US pharmacies failed to meet minimum criteria defining useful and scientifically accurate information. Evaluation forms containing the explicit elements that define useful scientifically accurate information for three drugs with known serious adverse drug reactions were created based on the current US FDA approved professional product labels. The Arabic language patient drug information leaflets for celecoxib, paroxetine, and lamotrigine were obtained locally and evaluated using a methodology similar to that used in previous quality content patient drug information studies in the US. The Arabic leaflets failed to meet the definition of useful scientifically accurate information. The celecoxib leaflet contained 30% of the required information and the paroxetine and lamotrigine leaflets contained 24% and 20%, respectively. There are several limitations to this study. The Arabic leaflets from only one commercial North American vendor were evaluated and the evaluation included a limited number of drugs. A larger study is necessary to be able to generalize these results. The study results are consistent with those of previous quality content studies of commercially available English patient drug information leaflets. The results have important implications for patients as access to a reliable source of drug information may prevent harm or limit the suffering from serious adverse drug reactions.

Predicting bioactive conformations and binding modes of macrocycles

NASA Astrophysics Data System (ADS)

Anighoro, Andrew; de la Vega de León, Antonio; Bajorath, Jürgen

2016-10-01

Macrocyclic compounds experience increasing interest in drug discovery. It is often thought that these large and chemically complex molecules provide promising candidates to address difficult targets and interfere with protein-protein interactions. From a computational viewpoint, these molecules are difficult to treat. For example, flexible docking of macrocyclic compounds is hindered by the limited ability of current docking approaches to optimize conformations of extended ring systems for pose prediction. Herein, we report predictions of bioactive conformations of macrocycles using conformational search and binding modes using docking. Conformational ensembles generated using specialized search technique of about 70 % of the tested macrocycles contained accurate bioactive conformations. However, these conformations were difficult to identify on the basis of conformational energies. Moreover, docking calculations with limited ligand flexibility starting from individual low energy conformations rarely yielded highly accurate binding modes. In about 40 % of the test cases, binding modes were approximated with reasonable accuracy. However, when conformational ensembles were subjected to rigid body docking, an increase in meaningful binding mode predictions to more than 50 % of the test cases was observed. Electrostatic effects did not contribute to these predictions in a positive or negative manner. Rather, achieving shape complementarity at macrocycle-target interfaces was a decisive factor. In summary, a combined computational protocol using pre-computed conformational ensembles of macrocycles as a starting point for docking shows promise in modeling binding modes of macrocyclic compounds.

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.

Towards the comprehensive, rapid, and accurate prediction of the favorable tautomeric states of drug-like molecules in aqueous solution

NASA Astrophysics Data System (ADS)

Greenwood, Jeremy R.; Calkins, David; Sullivan, Arron P.; Shelley, John C.

2010-06-01

Generating the appropriate protonation states of drug-like molecules in solution is important for success in both ligand- and structure-based virtual screening. Screening collections of millions of compounds requires a method for determining tautomers and their energies that is sufficiently rapid, accurate, and comprehensive. To maximise enrichment, the lowest energy tautomers must be determined from heterogeneous input, without over-enumerating unfavourable states. While computationally expensive, the density functional theory (DFT) method M06-2X/aug-cc-pVTZ(-f) [PB-SCRF] provides accurate energies for enumerated model tautomeric systems. The empirical Hammett-Taft methodology can very rapidly extrapolate substituent effects from model systems to drug-like molecules via the relationship between pKT and pKa. Combining the two complementary approaches transforms the tautomer problem from a scientific challenge to one of engineering scale-up, and avoids issues that arise due to the very limited number of measured pKT values, especially for the complicated heterocycles often favoured by medicinal chemists for their novelty and versatility. Several hundreds of pre-calculated tautomer energies and substituent pKa effects are tabulated in databases for use in structural adjustment by the program Epik, which treats tautomers as a subset of the larger problem of the protonation states in aqueous ensembles and their energy penalties. Accuracy and coverage is continually improved and expanded by parameterizing new systems of interest using DFT and experimental data. Recommendations are made for how to best incorporate tautomers in molecular design and virtual screening workflows.

Using beta binomials to estimate classification uncertainty for ensemble models.

PubMed

Clark, Robert D; Liang, Wenkel; Lee, Adam C; Lawless, Michael S; Fraczkiewicz, Robert; Waldman, Marvin

2014-01-01

Quantitative structure-activity (QSAR) models have enormous potential for reducing drug discovery and development costs as well as the need for animal testing. Great strides have been made in estimating their overall reliability, but to fully realize that potential, researchers and regulators need to know how confident they can be in individual predictions. Submodels in an ensemble model which have been trained on different subsets of a shared training pool represent multiple samples of the model space, and the degree of agreement among them contains information on the reliability of ensemble predictions. For artificial neural network ensembles (ANNEs) using two different methods for determining ensemble classification - one using vote tallies and the other averaging individual network outputs - we have found that the distribution of predictions across positive vote tallies can be reasonably well-modeled as a beta binomial distribution, as can the distribution of errors. Together, these two distributions can be used to estimate the probability that a given predictive classification will be in error. Large data sets comprised of logP, Ames mutagenicity, and CYP2D6 inhibition data are used to illustrate and validate the method. The distributions of predictions and errors for the training pool accurately predicted the distribution of predictions and errors for large external validation sets, even when the number of positive and negative examples in the training pool were not balanced. Moreover, the likelihood of a given compound being prospectively misclassified as a function of the degree of consensus between networks in the ensemble could in most cases be estimated accurately from the fitted beta binomial distributions for the training pool. Confidence in an individual predictive classification by an ensemble model can be accurately assessed by examining the distributions of predictions and errors as a function of the degree of agreement among the constituent submodels. Further, ensemble uncertainty estimation can often be improved by adjusting the voting or classification threshold based on the parameters of the error distribution. Finally, the profiles for models whose predictive uncertainty estimates are not reliable provide clues to that effect without the need for comparison to an external test set.

Calculating Water Thermodynamics in the Binding Site of Proteins - Applications of WaterMap to Drug Discovery.

PubMed

Cappel, Daniel; Sherman, Woody; Beuming, Thijs

2017-01-01

The ability to accurately characterize the solvation properties (water locations and thermodynamics) of biomolecules is of great importance to drug discovery. While crystallography, NMR, and other experimental techniques can assist in determining the structure of water networks in proteins and protein-ligand complexes, most water molecules are not fully resolved and accurately placed. Furthermore, understanding the energetic effects of solvation and desolvation on binding requires an analysis of the thermodynamic properties of solvent involved in the interaction between ligands and proteins. WaterMap is a molecular dynamics-based computational method that uses statistical mechanics to describe the thermodynamic properties (entropy, enthalpy, and free energy) of water molecules at the surface of proteins. This method can be used to assess the solvent contributions to ligand binding affinity and to guide lead optimization. In this review, we provide a comprehensive summary of published uses of WaterMap, including applications to lead optimization, virtual screening, selectivity analysis, ligand pose prediction, and druggability assessment. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

THE ART OF DATA MINING THE MINEFIELDS OF TOXICITY ...

EPA Pesticide Factsheets

Toxicity databases have a special role in predictive toxicology, providing ready access to historical information throughout the workflow of discovery, development, and product safety processes in drug development as well as in review by regulatory agencies. To provide accurate information within a hypothesesbuilding environment, the content of the databases needs to be rigorously modeled using standards and controlled vocabulary. The utilitarian purposes of databases widely vary, ranging from a source for (Q)SAR datasets for modelers to a basis for

A Practical Framework Toward Prediction of Breaking Force and Disintegration of Tablet Formulations Using Machine Learning Tools.

PubMed

Akseli, Ilgaz; Xie, Jingjin; Schultz, Leon; Ladyzhynsky, Nadia; Bramante, Tommasina; He, Xiaorong; Deanne, Rich; Horspool, Keith R; Schwabe, Robert

2017-01-01

Enabling the paradigm of quality by design requires the ability to quantitatively correlate material properties and process variables to measureable product performance attributes. Conventional, quality-by-test methods for determining tablet breaking force and disintegration time usually involve destructive tests, which consume significant amount of time and labor and provide limited information. Recent advances in material characterization, statistical analysis, and machine learning have provided multiple tools that have the potential to develop nondestructive, fast, and accurate approaches in drug product development. In this work, a methodology to predict the breaking force and disintegration time of tablet formulations using nondestructive ultrasonics and machine learning tools was developed. The input variables to the model include intrinsic properties of formulation and extrinsic process variables influencing the tablet during manufacturing. The model has been applied to predict breaking force and disintegration time using small quantities of active pharmaceutical ingredient and prototype formulation designs. The novel approach presented is a step forward toward rational design of a robust drug product based on insight into the performance of common materials during formulation and process development. It may also help expedite drug product development timeline and reduce active pharmaceutical ingredient usage while improving efficiency of the overall process. Copyright © 2016 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Ex vivo cultures of glioblastoma in three-dimensional hydrogel maintain the original tumor growth behavior and are suitable for preclinical drug and radiation sensitivity screening

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

Jiguet Jiglaire, Carine, E-mail: carine.jiguet-jiglaire@univ-amu.fr; CRO2, UMR 911, Faculté de Médecine de la Timone, 27 boulevard Jean Moulin, 13284 Marseille Cedex; INSERM, U911, 13005 Marseille

Identification of new drugs and predicting drug response are major challenges in oncology, especially for brain tumors, because total surgical resection is difficult and radiation therapy or chemotherapy is often ineffective. With the aim of developing a culture system close to in vivo conditions for testing new drugs, we characterized an ex vivo three-dimensional culture system based on a hyaluronic acid-rich hydrogel and compared it with classical two-dimensional culture conditions. U87-MG glioblastoma cells and seven primary cell cultures of human glioblastomas were subjected to radiation therapy and chemotherapy drugs. It appears that 3D hydrogel preserves the original cancer growth behaviormore » and enables assessment of the sensitivity of malignant gliomas to radiation and drugs with regard to inter-tumoral heterogeneity of therapeutic response. It could be used for preclinical assessment of new therapies. - Highlights: • We have compared primary glioblastoma cell culture in a 2D versus 3D-matrix system. • In 3D morphology, organization and markers better recapitulate the original tumor. • 3D-matrix culture might represent a relevant system for more accurate drug screening.« less

Protein asparagine deamidation prediction based on structures with machine learning methods.

PubMed

Jia, Lei; Sun, Yaxiong

2017-01-01

Chemical stability is a major concern in the development of protein therapeutics due to its impact on both efficacy and safety. Protein "hotspots" are amino acid residues that are subject to various chemical modifications, including deamidation, isomerization, glycosylation, oxidation etc. A more accurate prediction method for potential hotspot residues would allow their elimination or reduction as early as possible in the drug discovery process. In this work, we focus on prediction models for asparagine (Asn) deamidation. Sequence-based prediction method simply identifies the NG motif (amino acid asparagine followed by a glycine) to be liable to deamidation. It still dominates deamidation evaluation process in most pharmaceutical setup due to its convenience. However, the simple sequence-based method is less accurate and often causes over-engineering a protein. We introduce structure-based prediction models by mining available experimental and structural data of deamidated proteins. Our training set contains 194 Asn residues from 25 proteins that all have available high-resolution crystal structures. Experimentally measured deamidation half-life of Asn in penta-peptides as well as 3D structure-based properties, such as solvent exposure, crystallographic B-factors, local secondary structure and dihedral angles etc., were used to train prediction models with several machine learning algorithms. The prediction tools were cross-validated as well as tested with an external test data set. The random forest model had high enrichment in ranking deamidated residues higher than non-deamidated residues while effectively eliminated false positive predictions. It is possible that such quantitative protein structure-function relationship tools can also be applied to other protein hotspot predictions. In addition, we extensively discussed metrics being used to evaluate the performance of predicting unbalanced data sets such as the deamidation case.

Designing Predictive Models for Beta-Lactam Allergy Using the Drug Allergy and Hypersensitivity Database.

PubMed

Chiriac, Anca Mirela; Wang, Youna; Schrijvers, Rik; Bousquet, Philippe Jean; Mura, Thibault; Molinari, Nicolas; Demoly, Pascal

Beta-lactam antibiotics represent the main cause of allergic reactions to drugs, inducing both immediate and nonimmediate allergies. The diagnosis is well established, usually based on skin tests and drug provocation tests, but cumbersome. To design predictive models for the diagnosis of beta-lactam allergy, based on the clinical history of patients with suspicions of allergic reactions to beta-lactams. The study included a retrospective phase, in which records of patients explored for a suspicion of beta-lactam allergy (in the Allergy Unit of the University Hospital of Montpellier between September 1996 and September 2012) were used to construct predictive models based on a logistic regression and decision tree method; a prospective phase, in which we performed an external validation of the chosen models in patients with suspicion of beta-lactam allergy recruited from 3 allergy centers (Montpellier, Nîmes, Narbonne) between March and November 2013. Data related to clinical history and allergy evaluation results were retrieved and analyzed. The retrospective and prospective phases included 1991 and 200 patients, respectively, with a different prevalence of confirmed beta-lactam allergy (23.6% vs 31%, P = .02). For the logistic regression method, performances of the models were similar in both samples: sensitivity was 51% (vs 60%), specificity 75% (vs 80%), positive predictive value 40% (vs 57%), and negative predictive value 83% (vs 82%). The decision tree method reached a sensitivity of 29.5% (vs 43.5%), specificity of 96.4% (vs 94.9%), positive predictive value of 71.6% (vs 79.4%), and negative predictive value of 81.6% (vs 81.3%). Two different independent methods using clinical history predictors were unable to accurately predict beta-lactam allergy and replace a conventional allergy evaluation for suspected beta-lactam allergy. Copyright © 2017 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

Synthetic Tumor Networks for Screening Drug Delivery Systems

PubMed Central

Prabhakarpandian, Balabhaskar; Shen, Ming-Che; Nichols, Joseph B.; Garson, Charles J.; Mills, Ivy R.; Matar, Majed M.; Fewell, Jason G.; Pant, Kapil

2015-01-01

Tumor drug delivery is a complex phenomenon affected by several elements in addition to drug or delivery vehicle’s physico-chemical properties. A key factor is tumor microvasculature with complex effects including convective transport, high interstitial pressure and enhanced vascular permeability due to the presence of “leaky vessels”. Current in vitro models of the tumor microenvironment for evaluating drug delivery are oversimplified and, as a result, show poor correlation with in vivo performance. In this study, we report on the development of a novel microfluidic platform that models the tumor microenvironment more accurately, with physiologically and morphologically realistic microvasculature including endothelial cell lined leaky capillary vessels along with 3D solid tumors. Endothelial cells and 3D spheroids of cervical tumor cells were co-cultured in the networks. Drug vehicle screening was demonstrated using GFP gene delivery by different formulations of nanopolymers. The synthetic tumor network was successful in predicting in vivo delivery efficiencies of the drug vehicles. The developed assay will have critical applications both in basic research, where it can be used to develop next generation delivery vehicles, and in drug discovery where it can be used to study drug transport and delivery efficacy in realistic tumor microenvironment, thereby enabling drug compound and/or delivery vehicle screening. PMID:25599856

The Use of Gene Ontology Term and KEGG Pathway Enrichment for Analysis of Drug Half-Life

PubMed Central

Chen, Lei; Lu, Jing; Kong, XiangYin; Huang, Tao; Li, HaiPeng

2016-01-01

A drug’s biological half-life is defined as the time required for the human body to metabolize or eliminate 50% of the initial drug dosage. Correctly measuring the half-life of a given drug is helpful for the safe and accurate usage of the drug. In this study, we investigated which gene ontology (GO) terms and biological pathways were highly related to the determination of drug half-life. The investigated drugs, with known half-lives, were analyzed based on their enrichment scores for associated GO terms and KEGG pathways. These scores indicate which GO terms or KEGG pathways the drug targets. The feature selection method, minimum redundancy maximum relevance, was used to analyze these GO terms and KEGG pathways and to identify important GO terms and pathways, such as sodium-independent organic anion transmembrane transporter activity (GO:0015347), monoamine transmembrane transporter activity (GO:0008504), negative regulation of synaptic transmission (GO:0050805), neuroactive ligand-receptor interaction (hsa04080), serotonergic synapse (hsa04726), and linoleic acid metabolism (hsa00591), among others. This analysis confirmed our results and may show evidence for a new method in studying drug half-lives and building effective computational methods for the prediction of drug half-lives. PMID:27780226

Cross-species chemogenomic profiling reveals evolutionarily conserved drug mode of action

PubMed Central

Kapitzky, Laura; Beltrao, Pedro; Berens, Theresa J; Gassner, Nadine; Zhou, Chunshui; Wüster, Arthur; Wu, Julie; Babu, M Madan; Elledge, Stephen J; Toczyski, David; Lokey, R Scott; Krogan, Nevan J

2010-01-01

We present a cross-species chemogenomic screening platform using libraries of haploid deletion mutants from two yeast species, Saccharomyces cerevisiae and Schizosaccharomyces pombe. We screened a set of compounds of known and unknown mode of action (MoA) and derived quantitative drug scores (or D-scores), identifying mutants that are either sensitive or resistant to particular compounds. We found that compound–functional module relationships are more conserved than individual compound–gene interactions between these two species. Furthermore, we observed that combining data from both species allows for more accurate prediction of MoA. Finally, using this platform, we identified a novel small molecule that acts as a DNA damaging agent and demonstrate that its MoA is conserved in human cells. PMID:21179023

DockBench as docking selector tool: the lesson learned from D3R Grand Challenge 2015

NASA Astrophysics Data System (ADS)

Salmaso, Veronica; Sturlese, Mattia; Cuzzolin, Alberto; Moro, Stefano

2016-09-01

Structure-based drug design (SBDD) has matured within the last two decades as a valuable tool for the optimization of low molecular weight lead compounds to highly potent drugs. The key step in SBDD requires knowledge of the three-dimensional structure of the target-ligand complex, which is usually determined by X-ray crystallography. In the absence of structural information for the complex, SBDD relies on the generation of plausible molecular docking models. However, molecular docking protocols suffer from inaccuracies in the description of the interaction energies between the ligand and the target molecule, and often fail in the prediction of the correct binding mode. In this context, the appropriate selection of the most accurate docking protocol is absolutely relevant for the final molecular docking result, even if addressing this point is absolutely not a trivial task. D3R Grand Challenge 2015 has represented a precious opportunity to test the performance of DockBench, an integrate informatics platform to automatically compare RMDS-based molecular docking performances of different docking/scoring methods. The overall performance resulted in the blind prediction are encouraging in particular for the pose prediction task, in which several complex were predicted with a sufficient accuracy for medicinal chemistry purposes.

DockBench as docking selector tool: the lesson learned from D3R Grand Challenge 2015.

PubMed

Salmaso, Veronica; Sturlese, Mattia; Cuzzolin, Alberto; Moro, Stefano

2016-09-01

Structure-based drug design (SBDD) has matured within the last two decades as a valuable tool for the optimization of low molecular weight lead compounds to highly potent drugs. The key step in SBDD requires knowledge of the three-dimensional structure of the target-ligand complex, which is usually determined by X-ray crystallography. In the absence of structural information for the complex, SBDD relies on the generation of plausible molecular docking models. However, molecular docking protocols suffer from inaccuracies in the description of the interaction energies between the ligand and the target molecule, and often fail in the prediction of the correct binding mode. In this context, the appropriate selection of the most accurate docking protocol is absolutely relevant for the final molecular docking result, even if addressing this point is absolutely not a trivial task. D3R Grand Challenge 2015 has represented a precious opportunity to test the performance of DockBench, an integrate informatics platform to automatically compare RMDS-based molecular docking performances of different docking/scoring methods. The overall performance resulted in the blind prediction are encouraging in particular for the pose prediction task, in which several complex were predicted with a sufficient accuracy for medicinal chemistry purposes.

Drug-Eluting Beads Loaded With Doxorubicin (DEBDOX) Chemoembolisation Before Liver Transplantation for Hepatocellular Carcinoma: An Imaging/Histologic Correlation Study

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

Pauwels, Xavier, E-mail: xpauwels@hotmail.com; Azahaf, Mustapha, E-mail: mustapha.azahaf@chru-lille.fr; Lassailly, Guillaume, E-mail: guillaume.lassailly@chru-lille.fr

Purpose Most transplant centers use chemoembolisation as locoregional bridge therapy for hepatocellular carcinoma (HCC) before liver transplantation (LT). Chemoembolisation using beads loaded with doxorubicin (DEBDOX) is a promising technique that enables delivery of a large quantity of drugs against HCC. We sought to assess the imaging–histologic correlation after DEBDOX chemoembolisation.Materials and Methods All consecutive patients who had undergone DEBDOX chemoembolisation before receiving liver graft for HCC were included. Tumour response was evaluated according to Response Evaluation Criteria in Solid Tumours (RECIST) and modified RECIST (mRECIST) criteria. The result of final imaging made before LT was correlated with histological data to predict tumourmore » necrosis.ResultsTwenty-eight patients underwent 43 DEBDOX procedures for 45 HCC. Therapy had a significant effect as shown by a decrease in the mean size of the largest nodule (p = 0.02) and the sum of viable part of tumour sizes according to mRECIST criteria (p < 0.001). An objective response using mRECIST criteria was significantly correlated with mean tumour necrosis ≥90 % (p = 0.03). A complete response using mRECIST criteria enabled accurate prediction of complete tumour necrosis (p = 0.01). Correlations using RECIST criteria were not significant.ConclusionOur data confirm the potential benefit of DEBDOX chemoembolisation as bridge therapy before LT, and they provide a rational basis for new studies focusing on recurrence-free survival after LT. Radiologic evaluation according to mRECIST criteria enables accurate prediction of tumour necrosis, whereas RECIST criteria do not.« less

Presenting efficacy information in direct-to-consumer prescription drug advertisements.

PubMed

O'Donoghue, Amie C; Sullivan, Helen W; Aikin, Kathryn J; Chowdhury, Dhuly; Moultrie, Rebecca R; Rupert, Douglas J

2014-05-01

We evaluated whether presenting prescription drug efficacy information in direct-to-consumer (DTC) advertising helps individuals accurately report a drug's benefits and, if so, which numerical format is most helpful. We conducted a randomized, controlled study of individuals diagnosed with high cholesterol (n=2807) who viewed fictitious prescription drug print or television ads containing either no drug efficacy information or efficacy information in one of five numerical formats. We measured drug efficacy recall, drug perceptions and attitudes, behavioral intentions, and drug risk recall. Individuals who viewed absolute frequency and/or percentage information more accurately reported drug efficacy than participants who viewed no efficacy information. Participants who viewed relative frequency information generally reported drug efficacy less accurately than participants who viewed other numerical formats. Adding efficacy information to DTC ads-both in print and on television-may potentially increase an individual's knowledge of a drug's efficacy, which may improve patient-provider communication and promote more informed decisions. Providing quantitative efficacy information in a combination of formats (e.g., absolute frequency and percent) may help patients remember information and make decisions about prescription drugs. Published by Elsevier Ireland Ltd.

Integration of element specific persistent homology and machine learning for protein-ligand binding affinity prediction.

PubMed

Cang, Zixuan; Wei, Guo-Wei

2018-02-01

Protein-ligand binding is a fundamental biological process that is paramount to many other biological processes, such as signal transduction, metabolic pathways, enzyme construction, cell secretion, and gene expression. Accurate prediction of protein-ligand binding affinities is vital to rational drug design and the understanding of protein-ligand binding and binding induced function. Existing binding affinity prediction methods are inundated with geometric detail and involve excessively high dimensions, which undermines their predictive power for massive binding data. Topology provides the ultimate level of abstraction and thus incurs too much reduction in geometric information. Persistent homology embeds geometric information into topological invariants and bridges the gap between complex geometry and abstract topology. However, it oversimplifies biological information. This work introduces element specific persistent homology (ESPH) or multicomponent persistent homology to retain crucial biological information during topological simplification. The combination of ESPH and machine learning gives rise to a powerful paradigm for macromolecular analysis. Tests on 2 large data sets indicate that the proposed topology-based machine-learning paradigm outperforms other existing methods in protein-ligand binding affinity predictions. ESPH reveals protein-ligand binding mechanism that can not be attained from other conventional techniques. The present approach reveals that protein-ligand hydrophobic interactions are extended to 40Å  away from the binding site, which has a significant ramification to drug and protein design. Copyright © 2017 John Wiley & Sons, Ltd.

On the nature of cavities on protein surfaces: application to the identification of drug-binding sites.

PubMed

Nayal, Murad; Honig, Barry

2006-06-01

In this article we introduce a new method for the identification and the accurate characterization of protein surface cavities. The method is encoded in the program SCREEN (Surface Cavity REcognition and EvaluatioN). As a first test of the utility of our approach we used SCREEN to locate and analyze the surface cavities of a nonredundant set of 99 proteins cocrystallized with drugs. We find that this set of proteins has on average about 14 distinct cavities per protein. In all cases, a drug is bound at one (and sometimes more than one) of these cavities. Using cavity size alone as a criterion for predicting drug-binding sites yields a high balanced error rate of 15.7%, with only 71.7% coverage. Here we characterize each surface cavity by computing a comprehensive set of 408 physicochemical, structural, and geometric attributes. By applying modern machine learning techniques (Random Forests) we were able to develop a classifier that can identify drug-binding cavities with a balanced error rate of 7.2% and coverage of 88.9%. Only 18 of the 408 cavity attributes had a statistically significant role in the prediction. Of these 18 important attributes, almost all involved size and shape rather than physicochemical properties of the surface cavity. The implications of these results are discussed. A SCREEN Web server is available at http://interface.bioc.columbia.edu/screen. 2006 Wiley-Liss, Inc.

Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network

PubMed Central

2016-01-01

Most small-molecule drug candidates fail before entering the market, frequently because of unexpected toxicity. Often, toxicity is detected only late in drug development, because many types of toxicities, especially idiosyncratic adverse drug reactions (IADRs), are particularly hard to predict and detect. Moreover, drug-induced liver injury (DILI) is the most frequent reason drugs are withdrawn from the market and causes 50% of acute liver failure cases in the United States. A common mechanism often underlies many types of drug toxicities, including both DILI and IADRs. Drugs are bioactivated by drug-metabolizing enzymes into reactive metabolites, which then conjugate to sites in proteins or DNA to form adducts. DNA adducts are often mutagenic and may alter the reading and copying of genes and their regulatory elements, causing gene dysregulation and even triggering cancer. Similarly, protein adducts can disrupt their normal biological functions and induce harmful immune responses. Unfortunately, reactive metabolites are not reliably detected by experiments, and it is also expensive to test drug candidates for potential to form DNA or protein adducts during the early stages of drug development. In contrast, computational methods have the potential to quickly screen for covalent binding potential, thereby flagging problematic molecules and reducing the total number of necessary experiments. Here, we train a deep convolution neural network—the XenoSite reactivity model—using literature data to accurately predict both sites and probability of reactivity for molecules with glutathione, cyanide, protein, and DNA. On the site level, cross-validated predictions had area under the curve (AUC) performances of 89.8% for DNA and 94.4% for protein. Furthermore, the model separated molecules electrophilically reactive with DNA and protein from nonreactive molecules with cross-validated AUC performances of 78.7% and 79.8%, respectively. On both the site- and molecule-level, the model’s performances significantly outperformed reactivity indices derived from quantum simulations that are reported in the literature. Moreover, we developed and applied a selectivity score to assess preferential reactions with the macromolecules as opposed to the common screening traps. For the entire data set of 2803 molecules, this approach yielded totals of 257 (9.2%) and 227 (8.1%) molecules predicted to be reactive only with DNA and protein, respectively, and hence those that would be missed by standard reactivity screening experiments. Site of reactivity data is an underutilized resource that can be used to not only predict if molecules are reactive, but also show where they might be modified to reduce toxicity while retaining efficacy. The XenoSite reactivity model is available at http://swami.wustl.edu/xenosite/p/reactivity. PMID:27610414

In Vitro and In Silico Risk Assessment in Acquired Long QT Syndrome: The Devil Is in the Details.

PubMed

Lee, William; Windley, Monique J; Vandenberg, Jamie I; Hill, Adam P

2017-01-01

Acquired long QT syndrome, mostly as a result of drug block of the Kv11. 1 potassium channel in the heart, is characterized by delayed cardiac myocyte repolarization, prolongation of the T interval on the ECG, syncope and sudden cardiac death due to the polymorphic ventricular arrhythmia Torsade de Pointes (TdP). In recent years, efforts are underway through the Comprehensive in vitro proarrhythmic assay (CiPA) initiative, to develop better tests for this drug induced arrhythmia based in part on in silico simulations of pharmacological disruption of repolarization. However, drug binding to Kv11.1 is more complex than a simple binary molecular reaction, meaning simple steady state measures of potency are poor surrogates for risk. As a result, there is a plethora of mechanistic detail describing the drug/Kv11.1 interaction-such as drug binding kinetics, state preference, temperature dependence and trapping-that needs to be considered when developing in silico models for risk prediction. In addition to this, other factors, such as multichannel pharmacological profile and the nature of the ventricular cell models used in simulations also need to be considered in the search for the optimum in silico approach. Here we consider how much of mechanistic detail needs to be included for in silico models to accurately predict risk and further, how much of this detail can be retrieved from protocols that are practical to implement in high throughout screens as part of next generation of preclinical in silico drug screening approaches?

Part I---Evaluating Effects of Oligomer Formation on Cytochrome P450 2C9 Electron Transfer and Drug Metabolism, Part II---Utilizing Molecular Modeling Techniques to Study the Src-Interacting Proteins Actin Filament Associated Protein of 110 kDa (AFAP-110) and Cortactin

NASA Astrophysics Data System (ADS)

Jett, John Edward, Jr.

The dissertation has been divided into two parts to accurately reflect the two distinct areas of interest pursued during my matriculation in the School of Pharmacy at West Virginia University. In Part I, I discuss research probing the nature of electron transfer in the Cytochrome P450 family of proteins, a group of proteins well-known for their role in drug metabolism. In Part II, I focus on in silico and in vitro work developed in concert to probe protein structure and protein-protein interactions involved in actin filament reorganization and cellular motility. Part I. Cytochrome P450s (P450s) are an important class of enzymes known to metabolize a variety of endogenous and xenobiotic compounds. P450s are most commonly found in liver and intestinal endothelial cells and are responsible for the metabolism of approximately 75% of pharmaceutical drugs on the market. CYP2C9---one of the six major P450 isoforms---is responsible for ˜20% of drug metabolism. Elucidation of the factors that affect in vitro drug metabolism is crucial to the accurate prediction of in vivo drug metabolism kinetics. Currently, the two major techniques for studying in vitro drug metabolism are solution-based. However, it is known that the results of solution-based studies can vary from in vivo drug metabolism. One reason suggested to account for this variation is the state of P450 oligomer formation in solution compared to the in vivo environment, where P450s are membrane-bound. To understand the details of how oligomer formation affects in vitro drug metabolism, it is imperative that techniques be developed which will allow for the unequivocal control of oligomer formation without altering other experimental parameters. Our long term goal of this research is to develop methods to more accurately predict in vivo drug metabolism from in vitro data. This section of the dissertation will discuss the development of a platform consisting of a doped silicon surface containing a large array of gold nanopillars, the immobilization of CYP2C9 enzymes to those nanopillars, and the utilization of the array to perform conductive probe atomic force microscopy experiments examining the electron transfer process of CYP2C9 in the absence and presence of substrate molecules. Part II. The Src protein has been known to play a role in cancer cell progression for over 30 years. The function of a non-receptor tyrosine kinase such as Src is to relay extracellular signals through intracellular tyrosine phosphorylation. As a tyrosine kinase, Src and the cellular signaling pathways it is involved in play many functional roles in the cell, both in cellular proliferation and in cytoskeletal dynamics, cell adhesion, motility and invasion. Two of the many proteins comprising Src cellular signaling pathways are actin filament associated protein of 110 kDa (AFAP-110) and cortactin. AFAP-110 is a known activator of Src; one mechanism to abrogate the AFAP-110-induced activation of Src is to inhibit their colocalization within the cell. This colocalization is expected to occur when the pleckstrin homology (PH1 and PH2) domains of AFAP-110 are allowed to interact with membrane-bound phospholipids. Cortactin, on the other hand, is a cytosolic protein capable of being phosphorylated on various tyrosine residues, activating it and allowing it to interact with actin. The Src homology 2 (SH2) domain of Src has been shown to be capable of interacting with cortactin, an association which will be probed here. This section of the dissertation will discuss the use of molecular modeling techniques to develop structural models of the AFAP-110 PH1 and PH2 domains and use them to make predictions about how the protein interacts with phospholipids in the plasma membrane and how they might be stabilized to interact with other proteins. Structural models were designed using homology modeling methods, docking programs were used to predict key residues of AFAP-110 involved in binding to phospholipids and mutational analyses was used to test those predictions. This section will also discuss the use of molecular modeling techniques to explore protein-protein interactions between cortactin and Src. These include docking experiments and binding interaction analyses between Src and key areas of cortactin known to be involved in protein-protein interactions with Src. The data point to a cysteine-cysteine interaction between the two proteins, a result which is confirmed through in vitro experiments in collaboration with the lab of Dr. Scott Weed.

Large scale free energy calculations for blind predictions of protein-ligand binding: the D3R Grand Challenge 2015.

PubMed

Deng, Nanjie; Flynn, William F; Xia, Junchao; Vijayan, R S K; Zhang, Baofeng; He, Peng; Mentes, Ahmet; Gallicchio, Emilio; Levy, Ronald M

2016-09-01

We describe binding free energy calculations in the D3R Grand Challenge 2015 for blind prediction of the binding affinities of 180 ligands to Hsp90. The present D3R challenge was built around experimental datasets involving Heat shock protein (Hsp) 90, an ATP-dependent molecular chaperone which is an important anticancer drug target. The Hsp90 ATP binding site is known to be a challenging target for accurate calculations of ligand binding affinities because of the ligand-dependent conformational changes in the binding site, the presence of ordered waters and the broad chemical diversity of ligands that can bind at this site. Our primary focus here is to distinguish binders from nonbinders. Large scale absolute binding free energy calculations that cover over 3000 protein-ligand complexes were performed using the BEDAM method starting from docked structures generated by Glide docking. Although the ligand dataset in this study resembles an intermediate to late stage lead optimization project while the BEDAM method is mainly developed for early stage virtual screening of hit molecules, the BEDAM binding free energy scoring has resulted in a moderate enrichment of ligand screening against this challenging drug target. Results show that, using a statistical mechanics based free energy method like BEDAM starting from docked poses offers better enrichment than classical docking scoring functions and rescoring methods like Prime MM-GBSA for the Hsp90 data set in this blind challenge. Importantly, among the three methods tested here, only the mean value of the BEDAM binding free energy scores is able to separate the large group of binders from the small group of nonbinders with a gap of 2.4 kcal/mol. None of the three methods that we have tested provided accurate ranking of the affinities of the 147 active compounds. We discuss the possible sources of errors in the binding free energy calculations. The study suggests that BEDAM can be used strategically to discriminate binders from nonbinders in virtual screening and to more accurately predict the ligand binding modes prior to the more computationally expensive FEP calculations of binding affinity.

Large scale free energy calculations for blind predictions of protein-ligand binding: the D3R Grand Challenge 2015

NASA Astrophysics Data System (ADS)

Deng, Nanjie; Flynn, William F.; Xia, Junchao; Vijayan, R. S. K.; Zhang, Baofeng; He, Peng; Mentes, Ahmet; Gallicchio, Emilio; Levy, Ronald M.

2016-09-01

We describe binding free energy calculations in the D3R Grand Challenge 2015 for blind prediction of the binding affinities of 180 ligands to Hsp90. The present D3R challenge was built around experimental datasets involving Heat shock protein (Hsp) 90, an ATP-dependent molecular chaperone which is an important anticancer drug target. The Hsp90 ATP binding site is known to be a challenging target for accurate calculations of ligand binding affinities because of the ligand-dependent conformational changes in the binding site, the presence of ordered waters and the broad chemical diversity of ligands that can bind at this site. Our primary focus here is to distinguish binders from nonbinders. Large scale absolute binding free energy calculations that cover over 3000 protein-ligand complexes were performed using the BEDAM method starting from docked structures generated by Glide docking. Although the ligand dataset in this study resembles an intermediate to late stage lead optimization project while the BEDAM method is mainly developed for early stage virtual screening of hit molecules, the BEDAM binding free energy scoring has resulted in a moderate enrichment of ligand screening against this challenging drug target. Results show that, using a statistical mechanics based free energy method like BEDAM starting from docked poses offers better enrichment than classical docking scoring functions and rescoring methods like Prime MM-GBSA for the Hsp90 data set in this blind challenge. Importantly, among the three methods tested here, only the mean value of the BEDAM binding free energy scores is able to separate the large group of binders from the small group of nonbinders with a gap of 2.4 kcal/mol. None of the three methods that we have tested provided accurate ranking of the affinities of the 147 active compounds. We discuss the possible sources of errors in the binding free energy calculations. The study suggests that BEDAM can be used strategically to discriminate binders from nonbinders in virtual screening and to more accurately predict the ligand binding modes prior to the more computationally expensive FEP calculations of binding affinity.

Random forest models to predict aqueous solubility.

PubMed

Palmer, David S; O'Boyle, Noel M; Glen, Robert C; Mitchell, John B O

2007-01-01

Random Forest regression (RF), Partial-Least-Squares (PLS) regression, Support Vector Machines (SVM), and Artificial Neural Networks (ANN) were used to develop QSPR models for the prediction of aqueous solubility, based on experimental data for 988 organic molecules. The Random Forest regression model predicted aqueous solubility more accurately than those created by PLS, SVM, and ANN and offered methods for automatic descriptor selection, an assessment of descriptor importance, and an in-parallel measure of predictive ability, all of which serve to recommend its use. The prediction of log molar solubility for an external test set of 330 molecules that are solid at 25 degrees C gave an r2 = 0.89 and RMSE = 0.69 log S units. For a standard data set selected from the literature, the model performed well with respect to other documented methods. Finally, the diversity of the training and test sets are compared to the chemical space occupied by molecules in the MDL drug data report, on the basis of molecular descriptors selected by the regression analysis.

Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

NASA Astrophysics Data System (ADS)

Costanzi, Stefano; Tikhonova, Irina G.; Harden, T. Kendall; Jacobson, Kenneth A.

2009-11-01

Accurate in silico models for the quantitative prediction of the activity of G protein-coupled receptor (GPCR) ligands would greatly facilitate the process of drug discovery and development. Several methodologies have been developed based on the properties of the ligands, the direct study of the receptor-ligand interactions, or a combination of both approaches. Ligand-based three-dimensional quantitative structure-activity relationships (3D-QSAR) techniques, not requiring knowledge of the receptor structure, have been historically the first to be applied to the prediction of the activity of GPCR ligands. They are generally endowed with robustness and good ranking ability; however they are highly dependent on training sets. Structure-based techniques generally do not provide the level of accuracy necessary to yield meaningful rankings when applied to GPCR homology models. However, they are essentially independent from training sets and have a sufficient level of accuracy to allow an effective discrimination between binders and nonbinders, thus qualifying as viable lead discovery tools. The combination of ligand and structure-based methodologies in the form of receptor-based 3D-QSAR and ligand and structure-based consensus models results in robust and accurate quantitative predictions. The contribution of the structure-based component to these combined approaches is expected to become more substantial and effective in the future, as more sophisticated scoring functions are developed and more detailed structural information on GPCRs is gathered.

A liquid chromatography-tandem mass spectrometry-based targeted proteomics assay for monitoring P-glycoprotein levels in human breast tissue.

PubMed

Yang, Ting; Chen, Fei; Xu, Feifei; Wang, Fengliang; Xu, Qingqing; Chen, Yun

2014-09-25

P-glycoprotein (P-gp) can efflux drugs from cancer cells, and its overexpression is commonly associated with multi-drug resistance (MDR). Thus, the accurate quantification of P-gp would help predict the response to chemotherapy and for prognosis of breast cancer patients. An advanced liquid chromatography-tandem mass spectrometry (LC/MS/MS)-based targeted proteomics assay was developed and validated for monitoring P-gp levels in breast tissue. Tryptic peptide 368IIDNKPSIDSYSK380 was selected as a surrogate analyte for quantification, and immuno-depleted tissue extract was used as a surrogate matrix. Matched pairs of breast tissue samples from 60 patients who were suspected to have drug resistance were subject to analysis. The levels of P-gp were quantified. Using data from normal tissue, we suggested a P-gp reference interval. The experimental values of tumor tissue samples were compared with those obtained from Western blotting and immunohistochemistry (IHC). The result indicated that the targeted proteomics approach was comparable to IHC but provided a lower limit of quantification (LOQ) and could afford more reliable results at low concentrations than the other two methods. LC/MS/MS-based targeted proteomics may allow the quantification of P-gp in breast tissue in a more accurate manner. Copyright © 2014 Elsevier B.V. All rights reserved.

Interventional MRI-guided catheter placement and real time drug delivery to the central nervous system.

PubMed

Han, Seunggu J; Bankiewicz, Krystof; Butowski, Nicholas A; Larson, Paul S; Aghi, Manish K

2016-06-01

Local delivery of therapeutic agents into the brain has many advantages; however, the inability to predict, visualize and confirm the infusion into the intended target has been a major hurdle in its clinical development. Here, we describe the current workflow and application of the interventional MRI (iMRI) system for catheter placement and real time visualization of infusion. We have applied real time convection-enhanced delivery (CED) of therapeutic agents with iMRI across a number of different clinical trials settings in neuro-oncology and movement disorders. Ongoing developments and accumulating experience with the technique and technology of drug formulations, CED platforms, and iMRI systems will continue to make local therapeutic delivery into the brain more accurate, efficient, effective and safer.

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.

Use of phylogenetical analysis to predict susceptibility of pathogenic Candida spp. to antifungal drugs.

PubMed

Maheux, Andrée F; Sellam, Adnane; Piché, Yves; Boissinot, Maurice; Pelletier, René; Boudreau, Dominique K; Picard, François J; Trépanier, Hélène; Boily, Marie-Josée; Ouellette, Marc; Roy, Paul H; Bergeron, Michel G

2016-12-01

Successful treatment of a Candida infection relies on 1) an accurate identification of the pathogenic fungus and 2) on its susceptibility to antifungal drugs. In the present study we investigated the level of correlation between phylogenetical evolution and susceptibility of pathogenic Candida spp. to antifungal drugs. For this, we compared a phylogenetic tree, assembled with the concatenated sequences (2475-bp) of the ATP2, TEF1, and TUF1 genes from 20 representative Candida species, with published minimal inhibitory concentrations (MIC) of the four principal antifungal drug classes commonly used in the treatment of candidiasis: polyenes, triazoles, nucleoside analogues, and echinocandins. The phylogenetic tree revealed three distinct phylogenetic clusters among Candida species. Species within a given phylogenetic cluster have generally similar susceptibility profiles to antifungal drugs and species within Clusters II and III were less sensitive to antifungal drugs than Cluster I species. These results showed that phylogenetical relationship between clusters and susceptibility to several antifungal drugs could be used to guide therapy when only species identification is available prior to information pertaining to its resistance profile. An extended study comprising a large panel of clinical samples should be conducted to confirm the efficiency of this approach in the treatment of candidiasis. Copyright © 2016. Published by Elsevier B.V.

Evolving regulatory paradigm for proarrhythmic risk assessment for new drugs.

PubMed

Vicente, Jose; Stockbridge, Norman; Strauss, David G

Fourteen drugs were removed from the market worldwide because their potential to cause torsade de pointes (torsade), a potentially fatal ventricular arrhythmia. The observation that most drugs that cause torsade block the potassium channel encoded by the human ether-à-go-go related gene (hERG) and prolong the heart rate corrected QT interval (QTc) on the ECG, led to a focus on screening new drugs for their potential to block the hERG potassium channel and prolong QTc. This has been a successful strategy keeping torsadogenic drugs off the market, but has resulted in drugs being dropped from development, sometimes inappropriately. This is because not all drugs that block the hERG potassium channel and prolong QTc cause torsade, sometimes because they block other channels. The regulatory paradigm is evolving to improve proarrhythmic risk prediction. ECG studies can now use exposure-response modeling for assessing the effect of a drug on the QTc in small sample size first-in-human studies. Furthermore, the Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative is developing and validating a new in vitro paradigm for cardiac safety evaluation of new drugs that provides a more accurate and comprehensive mechanistic-based assessment of proarrhythmic potential. Under CiPA, the prediction of proarrhythmic potential will come from in vitro ion channel assessments coupled with an in silico model of the human ventricular myocyte. The preclinical assessment will be checked with an assessment of human phase 1 ECG data to determine if there are unexpected ion channel effects in humans compared to preclinical ion channel data. While there is ongoing validation work, the heart rate corrected J-T peak interval is likely to be assessed under CiPA to detect inward current block in presence of hERG potassium channel block. Copyright © 2016 Elsevier Inc. All rights reserved.

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

Systems-level modeling of mycobacterial metabolism for the identification of new (multi-)drug targets.

PubMed

Rienksma, Rienk A; Suarez-Diez, Maria; Spina, Lucie; Schaap, Peter J; Martins dos Santos, Vitor A P

2014-12-01

Systems-level metabolic network reconstructions and the derived constraint-based (CB) mathematical models are efficient tools to explore bacterial metabolism. Approximately one-fourth of the Mycobacterium tuberculosis (Mtb) genome contains genes that encode proteins directly involved in its metabolism. These represent potential drug targets that can be systematically probed with CB models through the prediction of genes essential (or the combination thereof) for the pathogen to grow. However, gene essentiality depends on the growth conditions and, so far, no in vitro model precisely mimics the host at the different stages of mycobacterial infection, limiting model predictions. These limitations can be circumvented by combining expression data from in vivo samples with a validated CB model, creating an accurate description of pathogen metabolism in the host. To this end, we present here a thoroughly curated and extended genome-scale CB metabolic model of Mtb quantitatively validated using 13C measurements. We describe some of the efforts made in integrating CB models and high-throughput data to generate condition specific models, and we will discuss challenges ahead. This knowledge and the framework herein presented will enable to identify potential new drug targets, and will foster the development of optimal therapeutic strategies. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

A pharmaco-metabolomics approach in a clinical trial of ALS: Identification of predictive markers of progression.

PubMed

Blasco, Hélène; Patin, Franck; Descat, Amandine; Garçon, Guillaume; Corcia, Philippe; Gelé, Patrick; Lenglet, Timothée; Bede, Peter; Meininger, Vincent; Devos, David; Gossens, Jean François; Pradat, Pierre-François

2018-01-01

There is an urgent and unmet need for accurate biomarkers in Amyotrophic Lateral Sclerosis. A pharmaco-metabolomics study was conducted using plasma samples from the TRO19622 (olesoxime) trial to assess the link between early metabolomic profiles and clinical outcomes. Patients included in this trial were randomized into either Group O receiving olesoxime (n = 38) or Group P receiving placebo (n = 36). The metabolomic profile was assessed at time-point one (V1) and 12 months (V12) after the initiation of the treatment. High performance liquid chromatography coupled with tandem mass spectrometry was used to quantify 188 metabolites (Biocrates® commercial kit). Multivariate analysis based on machine learning approaches (i.e. Biosigner algorithm) was performed. Metabolomic profiles at V1 and V12 and changes in metabolomic profiles between V1 and V12 accurately discriminated between Groups O and P (p<5×10-6), and identified glycine, kynurenine and citrulline/arginine as the best predictors of group membership. Changes in metabolomic profiles were closely linked to clinical progression, and correlated with glutamine levels in Group P and amino acids, lipids and spermidine levels in Group O. Multivariate models accurately predicted disease progression and highlighted the discriminant role of sphingomyelins (SM C22:3, SM C24:1, SM OH C22:2, SM C16:1). To predict SVC from SM C24:1 in group O and SVC from SM OH C22:2 and SM C16:1 in group P+O, we noted a median sensitivity between 67% and 100%, a specificity between 66.7 and 71.4%, a positive predictive value between 66 and 75% and a negative predictive value between 70% and 100% in the test sets. This proof-of-concept study demonstrates that the metabolomics has a role in evaluating the biological effect of an investigational drug and may be a candidate biomarker as a secondary outcome measure in clinical trials.

Structural and functional screening in human induced-pluripotent stem cell-derived cardiomyocytes accurately identifies cardiotoxicity of multiple drug types

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

Doherty, Kimberly R., E-mail: kimberly.doherty@quintiles.com; Talbert, Dominique R.; Trusk, Patricia B.

Safety pharmacology studies that evaluate new drug entities for potential cardiac liability remain a critical component of drug development. Current studies have shown that in vitro tests utilizing human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CM) may be beneficial for preclinical risk evaluation. We recently demonstrated that an in vitro multi-parameter test panel assessing overall cardiac health and function could accurately reflect the associated clinical cardiotoxicity of 4 FDA-approved targeted oncology agents using hiPS-CM. The present studies expand upon this initial observation to assess whether this in vitro screen could detect cardiotoxicity across multiple drug classes with known clinical cardiac risks.more » Thus, 24 drugs were examined for their effect on both structural (viability, reactive oxygen species generation, lipid formation, troponin secretion) and functional (beating activity) endpoints in hiPS-CM. Using this screen, the cardiac-safe drugs showed no effects on any of the tests in our panel. However, 16 of 18 compounds with known clinical cardiac risk showed drug-induced changes in hiPS-CM by at least one method. Moreover, when taking into account the Cmax values, these 16 compounds could be further classified depending on whether the effects were structural, functional, or both. Overall, the most sensitive test assessed cardiac beating using the xCELLigence platform (88.9%) while the structural endpoints provided additional insight into the mechanism of cardiotoxicity for several drugs. These studies show that a multi-parameter approach examining both cardiac cell health and function in hiPS-CM provides a comprehensive and robust assessment that can aid in the determination of potential cardiac liability. - Highlights: • 24 drugs were tested for cardiac liability using an in vitro multi-parameter screen. • Changes in beating activity were the most sensitive in predicting cardiac risk. • Structural effects add in-depth insight towards mechanism of cardiac toxicity. • Testing functional and structural endpoints enhances early cardiac risk assessment.« less

Model-based prediction of myelosuppression and recovery based on frequent neutrophil monitoring.

PubMed

Netterberg, Ida; Nielsen, Elisabet I; Friberg, Lena E; Karlsson, Mats O

2017-08-01

To investigate whether a more frequent monitoring of the absolute neutrophil counts (ANC) during myelosuppressive chemotherapy, together with model-based predictions, can improve therapy management, compared to the limited clinical monitoring typically applied today. Daily ANC in chemotherapy-treated cancer patients were simulated from a previously published population model describing docetaxel-induced myelosuppression. The simulated values were used to generate predictions of the individual ANC time-courses, given the myelosuppression model. The accuracy of the predicted ANC was evaluated under a range of conditions with reduced amount of ANC measurements. The predictions were most accurate when more data were available for generating the predictions and when making short forecasts. The inaccuracy of ANC predictions was highest around nadir, although a high sensitivity (≥90%) was demonstrated to forecast Grade 4 neutropenia before it occurred. The time for a patient to recover to baseline could be well forecasted 6 days (±1 day) before the typical value occurred on day 17. Daily monitoring of the ANC, together with model-based predictions, could improve anticancer drug treatment by identifying patients at risk for severe neutropenia and predicting when the next cycle could be initiated.

Alignment-Based Prediction of Sites of Metabolism.

PubMed

de Bruyn Kops, Christina; Friedrich, Nils-Ole; Kirchmair, Johannes

2017-06-26

Prediction of metabolically labile atom positions in a molecule (sites of metabolism) is a key component of the simulation of xenobiotic metabolism as a whole, providing crucial information for the development of safe and effective drugs. In 2008, an exploratory study was published in which sites of metabolism were derived based on molecular shape- and chemical feature-based alignment to a molecule whose site of metabolism (SoM) had been determined by experiments. We present a detailed analysis of the breadth of applicability of alignment-based SoM prediction, including transfer of the approach from a structure- to ligand-based method and extension of the applicability of the models from cytochrome P450 2C9 to all cytochrome P450 isozymes involved in drug metabolism. We evaluate the effect of molecular similarity of the query and reference molecules on the ability of this approach to accurately predict SoMs. In addition, we combine the alignment-based method with a leading chemical reactivity model to take reactivity into account. The combined model yielded superior performance in comparison to the alignment-based approach and the reactivity models with an average area under the receiver operating characteristic curve of 0.85 in cross-validation experiments. In particular, early enrichment was improved, as evidenced by higher BEDROC scores (mean BEDROC = 0.59 for α = 20.0, mean BEDROC = 0.73 for α = 80.5).

Pharmacological MRI (phMRI) of the Human Central Nervous System.

PubMed

Lanfermann, H; Schindler, C; Jordan, J; Krug, N; Raab, P

2015-10-01

Pharmacological magnetic resonance imaging (phMRI) of the central nervous system (CNS) addresses the increasing demands in the biopharma industry for new methods that can accurately predict, as early as possible, whether novel CNS agents will be effective and safe. Imaging of physiological and molecular-level function can provide a more direct measure of a drug mechanism of action, enabling more predictive measures of drug activity. The availability of phMRI of the nervous system within the professional infrastructure of the Clinical Research Center (CRC) Hannover as proof of concept center ensures that advances in basic science progress swiftly into benefits for patients. Advanced standardized MRI techniques including quantitative MRI, kurtosis determination, functional MRI, and spectroscopic imaging of the entire brain are necessary for phMRI. As a result, MR scanners will evolve into high-precision measuring instruments for assessment of desirable and undesirable effects of drugs as the basic precondition for individually tailored therapy. The CRC's Imaging Unit with high-end large-scale equipment will allow the following unique opportunities: for example, identification of MR-based biomarkers to assess the effect of drugs (surrogate parameters), establishment of normal levels and reference ranges for MRI-based biomarkers, evaluation of the most relevant MRI sequences for drug monitoring in outpatient care. Another very important prerequisite for phMRI is the MHH Core Facility as the scientific and operational study unit of the CRC partner Hannover Medical School. This unit is responsible for the study coordination, conduction, complete study logistics, administration, and application of the quality assurance system based on required industry standards.

Simulation with quantum mechanics/molecular mechanics for drug discovery.

PubMed

Barbault, Florent; Maurel, François

2015-10-01

Biological macromolecules, such as proteins or nucleic acids, are (still) molecules and thus they follow the same chemical rules that any simple molecule follows, even if their size generally renders accurate studies unhelpful. However, in the context of drug discovery, a detailed analysis of ligand association is required for understanding or predicting their interactions and hybrid quantum mechanics/molecular mechanics (QM/MM) computations are relevant tools to help elucidate this process. In this review, the authors explore the use of QM/MM for drug discovery. After a brief description of the molecular mechanics (MM) technique, the authors describe the subtractive and additive techniques for QM/MM computations. The authors then present several application cases in topics involved in drug discovery. QM/MM have been widely employed during the last decades to study chemical processes such as enzyme-inhibitor interactions. However, despite the enthusiasm around this area, plain MM simulations may be more meaningful than QM/MM. To obtain reliable results, the authors suggest fixing several keystone parameters according to the underlying chemistry of each studied system.

Simulation with quantum mechanics/molecular mechanics for drug discovery.

PubMed

Barbault, Florent; Maurel, François

2015-08-08

Biological macromolecules, such as proteins or nucleic acids, are (still) molecules and thus they follow the same chemical rules that any simple molecule follows, even if their size generally renders accurate studies unhelpful. However, in the context of drug discovery, a detailed analysis of ligand association is required for understanding or predicting their interactions and hybrid quantum mechanics/molecular mechanics (QM/MM) computations are relevant tools to help elucidate this process. Areas covered: In this review, the authors explore the use of QM/MM for drug discovery. After a brief description of the molecular mechanics (MM) technique, the authors describe the subtractive and additive techniques for QM/MM computations. The authors then present several application cases in topics involved in drug discovery. Expert opinion: QM/MM have been widely employed during the last decades to study chemical processes such as enzyme-inhibitor interactions. However, despite the enthusiasm around this area, plain MM simulations may be more meaningful than QM/MM. To obtain reliable results, the authors suggest fixing several keystone parameters according to the underlying chemistry of each studied system.

Evaluation of a New Handheld Instrument for the Detection of Counterfeit Artesunate by Visual Fluorescence Comparison

PubMed Central

Ranieri, Nicola; Tabernero, Patricia; Green, Michael D.; Verbois, Leigh; Herrington, James; Sampson, Eric; Satzger, R. Duane; Phonlavong, Chindaphone; Thao, Khamxay; Newton, Paul N.; Witkowski, Mark R.

2014-01-01

There is an urgent need for accurate and inexpensive handheld instruments for the evaluation of medicine quality in the field. A blinded evaluation of the diagnostic accuracy of the Counterfeit Detection Device 3 (CD-3), developed by the US Food and Drug Administration Forensic Chemistry Center, was conducted in the Lao People's Democratic Republic. Two hundred three samples of the oral antimalarial artesunate were compared with authentic products using the CD-3 by a trainer and two trainees. The specificity (95% confidence interval [95% CI]), sensitivity (95% CI), positive predictive value (95% CI), and negative predictive value (95% CI) of the CD-3 for detecting counterfeit (falsified) artesunate were 100% (93.8–100%), 98.4% (93.8–99.7%), 100% (96.2–100%), and 97.4% (90.2–99.6%), respectively. Interobserver agreement for 203 samples of artesunate was 100%. The CD-3 holds promise as a relatively inexpensive and easy to use instrument for field evaluation of medicines, potentially empowering drug inspectors, customs agents, and pharmacists. PMID:25266348

The current status of biomarkers for predicting toxicity

PubMed Central

Campion, Sarah; Aubrecht, Jiri; Boekelheide, Kim; Brewster, David W; Vaidya, Vishal S; Anderson, Linnea; Burt, Deborah; Dere, Edward; Hwang, Kathleen; Pacheco, Sara; Saikumar, Janani; Schomaker, Shelli; Sigman, Mark; Goodsaid, Federico

2013-01-01

Introduction There are significant rates of attrition in drug development. A number of compounds fail to progress past preclinical development due to limited tools that accurately monitor toxicity in preclinical studies and in the clinic. Research has focused on improving tools for the detection of organ-specific toxicity through the identification and characterization of biomarkers of toxicity. Areas covered This article reviews what we know about emerging biomarkers in toxicology, with a focus on the 2012 Northeast Society of Toxicology meeting titled ‘Translational Biomarkers in Toxicology.’ The areas covered in this meeting are summarized and include biomarkers of testicular injury and dysfunction, emerging biomarkers of kidney injury and translation of emerging biomarkers from preclinical species to human populations. The authors also provide a discussion about the biomarker qualification process and possible improvements to this process. Expert opinion There is currently a gap between the scientific work in the development and qualification of novel biomarkers for nonclinical drug safety assessment and how these biomarkers are actually used in drug safety assessment. A clear and efficient path to regulatory acceptance is needed so that breakthroughs in the biomarker toolkit for nonclinical drug safety assessment can be utilized to aid in the drug development process. PMID:23961847

Development and validation of a risk-prediction nomogram for in-hospital mortality in adults poisoned with drugs and nonpharmaceutical agents

PubMed Central

Lionte, Catalina; Sorodoc, Victorita; Jaba, Elisabeta; Botezat, Alina

2017-01-01

Abstract Acute poisoning with drugs and nonpharmaceutical agents represents an important challenge in the emergency department (ED). The objective is to create and validate a risk-prediction nomogram for use in the ED to predict the risk of in-hospital mortality in adults from acute poisoning with drugs and nonpharmaceutical agents. This was a prospective cohort study involving adults with acute poisoning from drugs and nonpharmaceutical agents admitted to a tertiary referral center for toxicology between January and December 2015 (derivation cohort) and between January and June 2016 (validation cohort). We used a program to generate nomograms based on binary logistic regression predictive models. We included variables that had significant associations with death. Using regression coefficients, we calculated scores for each variable, and estimated the event probability. Model validation was performed using bootstrap to quantify our modeling strategy and using receiver operator characteristic (ROC) analysis. The nomogram was tested on a separate validation cohort using ROC analysis and goodness-of-fit tests. Data from 315 patients aged 18 to 91 years were analyzed (n = 180 in the derivation cohort; n = 135 in the validation cohort). In the final model, the following variables were significantly associated with mortality: age, laboratory test results (lactate, potassium, MB isoenzyme of creatine kinase), electrocardiogram parameters (QTc interval), and echocardiography findings (E wave velocity deceleration time). Sex was also included to use the same model for men and women. The resulting nomogram showed excellent survival/mortality discrimination (area under the curve [AUC] 0.976, 95% confidence interval [CI] 0.954–0.998, P < 0.0001 for the derivation cohort; AUC 0.957, 95% CI 0.892–1, P < 0.0001 for the validation cohort). This nomogram provides more precise, rapid, and simple risk-analysis information for individual patients acutely exposed to drugs and nonpharmaceutical agents, and accurately estimates the probability of in-hospital death, exclusively using the results of objective tests available in the ED. PMID:28328838

Robust and Accurate Modeling Approaches for Migraine Per-Patient Prediction from Ambulatory Data

PubMed Central

Pagán, Josué; Irene De Orbe, M.; Gago, Ana; Sobrado, Mónica; Risco-Martín, José L.; Vivancos Mora, J.; Moya, José M.; Ayala, José L.

2015-01-01

Migraine is one of the most wide-spread neurological disorders, and its medical treatment represents a high percentage of the costs of health systems. In some patients, characteristic symptoms that precede the headache appear. However, they are nonspecific, and their prediction horizon is unknown and pretty variable; hence, these symptoms are almost useless for prediction, and they are not useful to advance the intake of drugs to be effective and neutralize the pain. To solve this problem, this paper sets up a realistic monitoring scenario where hemodynamic variables from real patients are monitored in ambulatory conditions with a wireless body sensor network (WBSN). The acquired data are used to evaluate the predictive capabilities and robustness against noise and failures in sensors of several modeling approaches. The obtained results encourage the development of per-patient models based on state-space models (N4SID) that are capable of providing average forecast windows of 47 min and a low rate of false positives. PMID:26134103

[Application analysis of adverse drug reaction terminology WHOART and MedDRA].

PubMed

Liu, Jing; Xie, Yan-ming; Gai, Guo-zhong; Liao, Xing

2015-12-01

Drug safety has always been a global focus. Discovery and accurate information acquisition of adverse drug reaction have been the most crucial concern. Terminology of adverse drug reaction makes adverse reaction medical report meaningful, standardized and accurate. This paper discussed the domestic use of the terminology WHOART and MedDRA in terms of content, structure, and application situation. It also analysed the differences between the two terminologies and discusses the future trend of application in our country

Evaluation of limited sampling models for prediction of oral midazolam AUC for CYP3A phenotyping and drug interaction studies.

PubMed

Mueller, Silke C; Drewelow, Bernd

2013-05-01

The area under the concentration-time curve (AUC) after oral midazolam administration is commonly used for cytochrome P450 (CYP) 3A phenotyping studies. The aim of this investigation was to evaluate a limited sampling strategy for the prediction of AUC with oral midazolam. A total of 288 concentration-time profiles from 123 healthy volunteers who participated in four previously performed drug interaction studies with intense sampling after a single oral dose of 7.5 mg midazolam were available for evaluation. Of these, 45 profiles served for model building, which was performed by stepwise multiple linear regression, and the remaining 243 datasets served for validation. Mean prediction error (MPE), mean absolute error (MAE) and root mean squared error (RMSE) were calculated to determine bias and precision The one- to four-sampling point models with the best coefficient of correlation were the one-sampling point model (8 h; r (2) = 0.84), the two-sampling point model (0.5 and 8 h; r (2) = 0.93), the three-sampling point model (0.5, 2, and 8 h; r (2) = 0.96), and the four-sampling point model (0.5,1, 2, and 8 h; r (2) = 0.97). However, the one- and two-sampling point models were unable to predict the midazolam AUC due to unacceptable bias and precision. Only the four-sampling point model predicted the very low and very high midazolam AUC of the validation dataset with acceptable precision and bias. The four-sampling point model was also able to predict the geometric mean ratio of the treatment phase over the baseline (with 90 % confidence interval) results of three drug interaction studies in the categories of strong, moderate, and mild induction, as well as no interaction. A four-sampling point limited sampling strategy to predict the oral midazolam AUC for CYP3A phenotyping is proposed. The one-, two- and three-sampling point models were not able to predict midazolam AUC accurately.

Dose imprecision and resistance: free-choice medicated feeds in industrial food animal production in the United States.

PubMed

Love, David C; Davis, Meghan F; Bassett, Anna; Gunther, Andrew; Nachman, Keeve E

2011-03-01

Industrial food animal production employs many of the same antibiotics or classes of antibiotics that are used in human medicine. These drugs can be administered to food animals in the form of free-choice medicated feeds (FCMF), where animals choose how much feed to consume. Routine administration of these drugs to livestock selects for microorganisms that are resistant to medications critical to the treatment of clinical infections in humans. In this commentary, we discuss the history of medicated feeds, the nature of FCMF use with regard to dose delivery, and U.S. policies that address antimicrobial drug use in food animals. FCMF makes delivering a predictable, accurate, and intended dose difficult. Overdosing can lead to animal toxicity; underdosing or inconsistent dosing can result in a failure to resolve animal diseases and in the development of antimicrobial-resistant microorganisms. The delivery of antibiotics to food animals for reasons other than the treatment of clinically diagnosed disease, especially via free-choice feeding methods, should be reconsidered.

A novel approach to the investigation of passive molecular permeation through lipid bilayers from atomistic simulations.

PubMed

Ghaemi, Zhaleh; Minozzi, Manuela; Carloni, Paolo; Laio, Alessandro

2012-07-26

Predicting the permeability coefficient (P) of drugs permeating through the cell membrane is of paramount importance in drug discovery. We here propose an approach for calculating P based on bias-exchange metadynamics. The approach allows constructing from atomistic simulations a model of permeation taking explicitly into account not only the "trivial" reaction coordinate, the position of the drug along the direction normal to the lipid membrane plane, but also other degrees of freedom, for example, the torsional angles of the permeating molecule, or variables describing its solvation/desolvation. This allows deriving an accurate picture of the permeation process, and constructing a detailed molecular model of the transition state, making a rational control of permeation properties possible. We benchmarked this approach on the permeation of ethanol molecules through a POPC membrane, showing that the value of P calculated with our model agrees with the one calculated by a long unbiased molecular dynamics of the same system.

Arrhythmic hazard map for a 3D whole-ventricles model under multiple ion channel block.

PubMed

Okada, Jun-Ichi; Yoshinaga, Takashi; Kurokawa, Junko; Washio, Takumi; Furukawa, Tetsushi; Sawada, Kohei; Sugiura, Seiryo; Hisada, Toshiaki

2018-05-10

To date, proposed in silico models for preclinical cardiac safety testing are limited in their predictability and usability. We previously reported a multi-scale heart simulation that accurately predicts arrhythmogenic risk for benchmark drugs. We extend this approach and report the first comprehensive hazard map of drug-induced arrhythmia based on the exhaustive in silico electrocardiogram (ECG) database of drug effects, developed using a petaflop computer. A total of 9075 electrocardiograms constitute the five-dimensional hazard map, with coordinates representing the extent of the block of each of the five ionic currents (rapid delayed rectifier potassium current (IKr), fast (INa) and late (INa,L) components of the sodium current, L-type calcium current (ICa,L) and slow delayed rectifier current (IKs)), involved in arrhythmogenesis. Results of the evaluation of arrhythmogenic risk based on this hazard map agreed well with the risk assessments reported in three references. ECG database also suggested that the interval between the J-point and the T-wave peak is a superior index of arrhythmogenicity compared to other ECG biomarkers including the QT interval. Because concentration-dependent effects on electrocardiograms of any drug can be traced on this map based on in vitro current assay data, its arrhythmogenic risk can be evaluated without performing costly and potentially risky human electrophysiological assays. Hence, the map serves as a novel tool for use in pharmaceutical research and development. This article is protected by copyright. All rights reserved.

Can we predict the blood pressure response to renal denervation?

PubMed Central

Fink, Gregory D.; Phelps, Jeremiah T.

2016-01-01

Renal denervation (RDN) is a new therapy used to treat drug-resistant hypertension in the clinical setting. Published human trials show substantial inter-individual variability in the blood pressure (BP) response to RDN, even when technical aspects of the treatment are standardized as much as possible between patients. Widespread acceptance of RDN for treating hypertension will require accurate identification of patients likely to respond to RDN with a fall in BP that is clinically significant in magnitude, well-maintained over time and does not cause adverse consequences. In this paper we review and evaluate clinical studies that address possible predictors of the BP response to RDN. We conclude that only one generally reliable predictor has been identified to date, namely pre-RDN BP level, although there is some evidence for a few other factors. Experimental interventions in laboratory animals provide the opportunity to explore potential predictors that are difficult to investigate in human patients. Therefore we also describe results (from our lab and others) with RDN in spontaneously hypertensive rats. Since virtually all patients receiving RDN are taking three or more antihypertensive drugs, a particular focus of our work was on how ongoing antihypertensive drug treatment might alter the BP response to RDN. We conclude that patient age (or duration of hypertension) and concomitant treatment with certain drugs can affect the blood pressure response to RDN and that this information could help predict a favorable clinical response. PMID:27530600

Solubilities of crystalline drugs in polymers: an improved analytical method and comparison of solubilities of indomethacin and nifedipine in PVP, PVP/VA, and PVAc.

PubMed

Sun, Ye; Tao, Jing; Zhang, Geoff G Z; Yu, Lian

2010-09-01

A previous method for measuring solubilities of crystalline drugs in polymers has been improved to enable longer equilibration and used to survey the solubilities of indomethacin (IMC) and nifedipine (NIF) in two homo-polymers [polyvinyl pyrrolidone (PVP) and polyvinyl acetate (PVAc)] and their co-polymer (PVP/VA). These data are important for understanding the stability of amorphous drug-polymer dispersions, a strategy actively explored for delivering poorly soluble drugs. Measuring solubilities in polymers is difficult because their high viscosities impede the attainment of solubility equilibrium. In this method, a drug-polymer mixture prepared by cryo-milling is annealed at different temperatures and analyzed by differential scanning calorimetry to determine whether undissolved crystals remain and thus the upper and lower bounds of the equilibrium solution temperature. The new annealing method yielded results consistent with those obtained with the previous scanning method at relatively high temperatures, but revised slightly the previous results at lower temperatures. It also lowered the temperature of measurement closer to the glass transition temperature. For D-mannitol and IMC dissolving in PVP, the polymer's molecular weight has little effect on the weight-based solubility. For IMC and NIF, the dissolving powers of the polymers follow the order PVP > PVP/VA > PVAc. In each polymer studied, NIF is less soluble than IMC. The activities of IMC and NIF dissolved in various polymers are reasonably well fitted to the Flory-Huggins model, yielding the relevant drug-polymer interaction parameters. The new annealing method yields more accurate data than the previous scanning method when solubility equilibrium is slow to achieve. In practice, these two methods can be combined for efficiency. The measured solubilities are not readily anticipated, which underscores the importance of accurate experimental data for developing predictive models.

New potent and selective cytochrome P450 2B6 (CYP2B6) inhibitors based on three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis

PubMed Central

Korhonen, L E; Turpeinen, M; Rahnasto, M; Wittekindt, C; Poso, A; Pelkonen, O; Raunio, H; Juvonen, R O

2007-01-01

Background and purpose: The cytochrome P450 2B6 (CYP2B6) enzyme metabolises a number of clinically important drugs. Drug-drug interactions resulting from inhibition or induction of CYP2B6 activity may cause serious adverse effects. The aims of this study were to construct a three-dimensional structure-activity relationship (3D-QSAR) model of the CYP2B6 protein and to identify novel potent and selective inhibitors of CYP2B6 for in vitro research purposes. Experimental approach: The inhibition potencies (IC50 values) of structurally diverse chemicals were determined with recombinant human CYP2B6 enzyme. Two successive models were constructed using Comparative Molecular Field Analysis (CoMFA). Key results: Three compounds proved to be very potent and selective competitive inhibitors of CYP2B6 in vitro (IC50<1 μM): 4-(4-chlorobenzyl)pyridine (CBP), 4-(4-nitrobenzyl)pyridine (NBP), and 4-benzylpyridine (BP). A complete inhibition of CYP2B6 activity was achieved with 0.1 μM CBP, whereas other CYP-related activities were not affected. Forty-one compounds were selected for further testing and construction of the final CoMFA model. The created CoMFA model was of high quality and predicted accurately the inhibition potency of a test set (n=7) of structurally diverse compounds. Conclusions and implications: Two CoMFA models were created which revealed the key molecular characteristics of inhibitors of the CYP2B6 enzyme. The final model accurately predicted the inhibitory potencies of several structurally unrelated compounds. CBP, BP and NBP were identified as novel potent and selective inhibitors of CYP2B6 and CBP especially is a suitable inhibitor for in vitro screening studies. PMID:17325652

In silico toxicology for the pharmaceutical sciences

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

Valerio, Luis G., E-mail: Luis.Valerio@fda.hhs.go

2009-12-15

The applied use of in silico technologies (a.k.a. computational toxicology, in silico toxicology, computer-assisted tox, e-tox, i-drug discovery, predictive ADME, etc.) for predicting preclinical toxicological endpoints, clinical adverse effects, and metabolism of pharmaceutical substances has become of high interest to the scientific community and the public. The increased accessibility of these technologies for scientists and recent regulations permitting their use for chemical risk assessment supports this notion. The scientific community is interested in the appropriate use of such technologies as a tool to enhance product development and safety of pharmaceuticals and other xenobiotics, while ensuring the reliability and accuracy ofmore » in silico approaches for the toxicological and pharmacological sciences. For pharmaceutical substances, this means active and impurity chemicals in the drug product may be screened using specialized software and databases designed to cover these substances through a chemical structure-based screening process and algorithm specific to a given software program. A major goal for use of these software programs is to enable industry scientists not only to enhance the discovery process but also to ensure the judicious use of in silico tools to support risk assessments of drug-induced toxicities and in safety evaluations. However, a great amount of applied research is still needed, and there are many limitations with these approaches which are described in this review. Currently, there is a wide range of endpoints available from predictive quantitative structure-activity relationship models driven by many different computational software programs and data sources, and this is only expected to grow. For example, there are models based on non-proprietary and/or proprietary information specific to assessing potential rodent carcinogenicity, in silico screens for ICH genetic toxicity assays, reproductive and developmental toxicity, theoretical prediction of human drug metabolism, mechanisms of action for pharmaceuticals, and newer models for predicting human adverse effects. How accurate are these approaches is both a statistical issue and challenge in toxicology. In this review, fundamental concepts and the current capabilities and limitations of this technology will be critically addressed.« less

Evaluation of factors important in modeling plasma concentrations of tetracycline hydrochloride administered in water in swine.

PubMed

Mason, Sharon E; Almond, Glen W; Riviere, Jim E; Baynes, Ronald E

2012-10-01

To model the plasma tetracycline concentrations in swine (Sus scrofa domestica) treated with medication administered in water and determine the factors that contribute to the most accurate predictions of measured plasma drug concentrations. Plasma tetracycline concentrations measured in blood samples from 3 populations of swine. Data from previous studies provided plasma tetracycline concentrations that were measured in blood samples collected from 1 swine population at 0, 4, 8, 12, 24, 32, 48, 56, 72, 80, 96, and 104 hours and from 2 swine populations at 0, 12, 24, 48, and 72 hours hours during administration of tetracycline hydrochloride dissolved in water. A 1-compartment pharmacostatistical model was used to analyze 5 potential covariate schemes and determine factors most important in predicting the plasma concentrations of tetracycline in swine. 2 models most accurately predicted the tetracycline plasma concentrations in the 3 populations of swine. Factors of importance were body weight or age of pig, ambient temperature, concentration of tetracycline in water, and water use per unit of time. The factors found to be of importance, combined with knowledge of the individual pharmacokinetic and chemical properties of medications currently approved for administration in water, may be useful in more prudent administration of approved medications administered to swine. Factors found to be important in pharmacostatistical models may allow prediction of plasma concentrations of tetracycline or other commonly used medications administered in water. The ability to predict in vivo concentrations of medication in a population of food animals can be combined with bacterial minimum inhibitory concentrations to decrease the risk of developing antimicrobial resistance.

"Rate My Therapist": Automated Detection of Empathy in Drug and Alcohol Counseling via Speech and Language Processing.

PubMed

Xiao, Bo; Imel, Zac E; Georgiou, Panayiotis G; Atkins, David C; Narayanan, Shrikanth S

2015-01-01

The technology for evaluating patient-provider interactions in psychotherapy-observational coding-has not changed in 70 years. It is labor-intensive, error prone, and expensive, limiting its use in evaluating psychotherapy in the real world. Engineering solutions from speech and language processing provide new methods for the automatic evaluation of provider ratings from session recordings. The primary data are 200 Motivational Interviewing (MI) sessions from a study on MI training methods with observer ratings of counselor empathy. Automatic Speech Recognition (ASR) was used to transcribe sessions, and the resulting words were used in a text-based predictive model of empathy. Two supporting datasets trained the speech processing tasks including ASR (1200 transcripts from heterogeneous psychotherapy sessions and 153 transcripts and session recordings from 5 MI clinical trials). The accuracy of computationally-derived empathy ratings were evaluated against human ratings for each provider. Computationally-derived empathy scores and classifications (high vs. low) were highly accurate against human-based codes and classifications, with a correlation of 0.65 and F-score (a weighted average of sensitivity and specificity) of 0.86, respectively. Empathy prediction using human transcription as input (as opposed to ASR) resulted in a slight increase in prediction accuracies, suggesting that the fully automatic system with ASR is relatively robust. Using speech and language processing methods, it is possible to generate accurate predictions of provider performance in psychotherapy from audio recordings alone. This technology can support large-scale evaluation of psychotherapy for dissemination and process studies.

Unprecedently Large-Scale Kinase Inhibitor Set Enabling the Accurate Prediction of Compound–Kinase Activities: A Way toward Selective Promiscuity by Design?

PubMed Central

2016-01-01

Drug discovery programs frequently target members of the human kinome and try to identify small molecule protein kinase inhibitors, primarily for cancer treatment, additional indications being increasingly investigated. One of the challenges is controlling the inhibitors degree of selectivity, assessed by in vitro profiling against panels of protein kinases. We manually extracted, compiled, and standardized such profiles published in the literature: we collected 356 908 data points corresponding to 482 protein kinases, 2106 inhibitors, and 661 patents. We then analyzed this data set in terms of kinome coverage, results reproducibility, popularity, and degree of selectivity of both kinases and inhibitors. We used the data set to create robust proteochemometric models capable of predicting kinase activity (the ligand–target space was modeled with an externally validated RMSE of 0.41 ± 0.02 log units and R02 0.74 ± 0.03), in order to account for missing or unreliable measurements. The influence on the prediction quality of parameters such as number of measurements, Murcko scaffold frequency or inhibitor type was assessed. Interpretation of the models enabled to highlight inhibitors and kinases properties correlated with higher affinities, and an analysis in the context of kinases crystal structures was performed. Overall, the models quality allows the accurate prediction of kinase-inhibitor activities and their structural interpretation, thus paving the way for the rational design of compounds with a targeted selectivity profile. PMID:27482722

From sequence to enzyme mechanism using multi-label machine learning.

PubMed

De Ferrari, Luna; Mitchell, John B O

2014-05-19

In this work we predict enzyme function at the level of chemical mechanism, providing a finer granularity of annotation than traditional Enzyme Commission (EC) classes. Hence we can predict not only whether a putative enzyme in a newly sequenced organism has the potential to perform a certain reaction, but how the reaction is performed, using which cofactors and with susceptibility to which drugs or inhibitors, details with important consequences for drug and enzyme design. Work that predicts enzyme catalytic activity based on 3D protein structure features limits the prediction of mechanism to proteins already having either a solved structure or a close relative suitable for homology modelling. In this study, we evaluate whether sequence identity, InterPro or Catalytic Site Atlas sequence signatures provide enough information for bulk prediction of enzyme mechanism. By splitting MACiE (Mechanism, Annotation and Classification in Enzymes database) mechanism labels to a finer granularity, which includes the role of the protein chain in the overall enzyme complex, the method can predict at 96% accuracy (and 96% micro-averaged precision, 99.9% macro-averaged recall) the MACiE mechanism definitions of 248 proteins available in the MACiE, EzCatDb (Database of Enzyme Catalytic Mechanisms) and SFLD (Structure Function Linkage Database) databases using an off-the-shelf K-Nearest Neighbours multi-label algorithm. We find that InterPro signatures are critical for accurate prediction of enzyme mechanism. We also find that incorporating Catalytic Site Atlas attributes does not seem to provide additional accuracy. The software code (ml2db), data and results are available online at http://sourceforge.net/projects/ml2db/ and as supplementary files.

OSPREY Predicts Resistance Mutations Using Positive and Negative Computational Protein Design.

PubMed

Ojewole, Adegoke; Lowegard, Anna; Gainza, Pablo; Reeve, Stephanie M; Georgiev, Ivelin; Anderson, Amy C; Donald, Bruce R

2017-01-01

Drug resistance in protein targets is an increasingly common phenomenon that reduces the efficacy of both existing and new antibiotics. However, knowledge of future resistance mutations during pre-clinical phases of drug development would enable the design of novel antibiotics that are robust against not only known resistant mutants, but also against those that have not yet been clinically observed. Computational structure-based protein design (CSPD) is a transformative field that enables the prediction of protein sequences with desired biochemical properties such as binding affinity and specificity to a target. The use of CSPD to predict previously unseen resistance mutations represents one of the frontiers of computational protein design. In a recent study (Reeve et al. Proc Natl Acad Sci U S A 112(3):749-754, 2015), we used our OSPREY (Open Source Protein REdesign for You) suite of CSPD algorithms to prospectively predict resistance mutations that arise in the active site of the dihydrofolate reductase enzyme from methicillin-resistant Staphylococcus aureus (SaDHFR) in response to selective pressure from an experimental competitive inhibitor. We demonstrated that our top predicted candidates are indeed viable resistant mutants. Since that study, we have significantly enhanced the capabilities of OSPREY with not only improved modeling of backbone flexibility, but also efficient multi-state design, fast sparse approximations, partitioned continuous rotamers for more accurate energy bounds, and a computationally efficient representation of molecular-mechanics and quantum-mechanical energy functions. Here, using SaDHFR as an example, we present a protocol for resistance prediction using the latest version of OSPREY. Specifically, we show how to use a combination of positive and negative design to predict active site escape mutations that maintain the enzyme's catalytic function but selectively ablate binding of an inhibitor.

Immobilized Cytochrome P450 2C9 (CYP2C9): Applications for Metabolite Generation, Monitoring Protein-Protein Interactions, and Improving In-vivo Predictions Using Enhanced In-vitro Models

NASA Astrophysics Data System (ADS)

Wollenberg, Lance A.

Cytochrome P450 (P450) enzymes are a family of oxoferroreductase enzymes containing a heme moiety and are well known to be involved in the metabolism of a wide variety of endogenous and xenobiotic materials. It is estimated that roughly 75% of all pharmaceutical compounds are metabolized by these enzymes. Traditional reconstituted in-vitro incubation studies using recombinant P450 enzymes are often used to predict in-vivo kinetic parameters of a drug early in development. However, in many cases, these reconstituted incubations are prone to aggregation which has been shown to affect the catalytic activity of an enzyme. Moreover, the presence of other isoforms of P450 enzymes present in a metabolic incubation, as is the case with microsomal systems, may affect the catalytic activity of an enzyme through isoform-specific protein-protein interactions. Both of these effects may result in inaccurate prediction of in-vivo drug metabolism using in-vitro experiments. Here we described the development of immobilized P450 constructs designed to elucidate the effects of aggregation and protein-protein interactions between P450 isoforms on catalytic activities. The long term objective of this project is to develop a system to control the oligomeric state of Cytochrome P450 enzymes to accurately elucidate discrepancies between in vitro reconstituted systems and actual in vivo drug metabolism for the precise prediction of metabolic activity. This approach will serve as a system to better draw correlations between in-vivo and in-vitro drug metabolism data. The central hypothesis is that Cytochrome P450 enzymes catalytic activity can be altered by protein-protein interactions occurring between Cytochrome P450 enzymes involved in drug metabolism, and is dependent on varying states of protein aggregation. This dissertation explains the details of the construction and characterization of a nanostructure device designed to control the state of aggregation of a P450 enzyme. Moreover, applications of immobilized P450 enzyme constructs will also be used for monitoring protein-protein interaction and metabolite production with the use of immobilized-P450 bioreactor constructs. This work provides insight into the effect on catalytic activity caused by both P450 aggregation as well as isoform-specific protein-protein interactions and provides insight in the production of biosynthetically produced drug metabolites

Modeling Free Energies of Solvation in Olive Oil

PubMed Central

Chamberlin, Adam C.; Levitt, David G.; Cramer, Christopher J.; Truhlar, Donald G.

2009-01-01

Olive oil partition coefficients are useful for modeling the bioavailability of drug-like compounds. We have recently developed an accurate solvation model called SM8 for aqueous and organic solvents (Marenich, A. V.; Olson, R. M.; Kelly, C. P.; Cramer, C. J.; Truhlar, D. G. J. Chem. Theory Comput. 2007, 3, 2011) and a temperature-dependent solvation model called SM8T for aqueous solution (Chamberlin, A. C.; Cramer, C. J.; Truhlar, D. G. J. Phys. Chem. B 2008, 112, 3024). Here we describe an extension of SM8T to predict air–olive oil and water–olive oil partitioning for drug-like solutes as functions of temperature. We also describe the database of experimental partition coefficients used to parameterize the model; this database includes 371 entries for 304 compounds spanning the 291–310 K temperature range. PMID:19434923

Evaluation of protein-ligand affinity prediction using steered molecular dynamics simulations.

PubMed

Okimoto, Noriaki; Suenaga, Atsushi; Taiji, Makoto

2017-11-01

In computational drug design, ranking a series of compound analogs in a manner that is consistent with experimental affinities remains a challenge. In this study, we evaluated the prediction of protein-ligand binding affinities using steered molecular dynamics simulations. First, we investigated the appropriate conditions for accurate predictions in these simulations. A conic harmonic restraint was applied to the system for efficient sampling of work values on the ligand unbinding pathway. We found that pulling velocity significantly influenced affinity predictions, but that the number of collectable trajectories was less influential. We identified the appropriate pulling velocity and collectable trajectories for binding affinity predictions as 1.25 Å/ns and 100, respectively, and these parameters were used to evaluate three target proteins (FK506 binding protein, trypsin, and cyclin-dependent kinase 2). For these proteins using our parameters, the accuracy of affinity prediction was higher and more stable when Jarzynski's equality was employed compared with the second-order cumulant expansion equation of Jarzynski's equality. Our results showed that steered molecular dynamics simulations are effective for predicting the rank order of ligands; thus, they are a potential tool for compound selection in hit-to-lead and lead optimization processes.

Accurate prediction of subcellular location of apoptosis proteins combining Chou's PseAAC and PsePSSM based on wavelet denoising.

PubMed

Yu, Bin; Li, Shan; Qiu, Wen-Ying; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Wang, Ming-Hui; Zhang, Yan

2017-12-08

Apoptosis proteins subcellular localization information are very important for understanding the mechanism of programmed cell death and the development of drugs. The prediction of subcellular localization of an apoptosis protein is still a challenging task because the prediction of apoptosis proteins subcellular localization can help to understand their function and the role of metabolic processes. In this paper, we propose a novel method for protein subcellular localization prediction. Firstly, the features of the protein sequence are extracted by combining Chou's pseudo amino acid composition (PseAAC) and pseudo-position specific scoring matrix (PsePSSM), then the feature information of the extracted is denoised by two-dimensional (2-D) wavelet denoising. Finally, the optimal feature vectors are input to the SVM classifier to predict subcellular location of apoptosis proteins. Quite promising predictions are obtained using the jackknife test on three widely used datasets and compared with other state-of-the-art methods. The results indicate that the method proposed in this paper can remarkably improve the prediction accuracy of apoptosis protein subcellular localization, which will be a supplementary tool for future proteomics research.

Accurate prediction of subcellular location of apoptosis proteins combining Chou’s PseAAC and PsePSSM based on wavelet denoising

PubMed Central

Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Wang, Ming-Hui; Zhang, Yan

2017-01-01

Apoptosis proteins subcellular localization information are very important for understanding the mechanism of programmed cell death and the development of drugs. The prediction of subcellular localization of an apoptosis protein is still a challenging task because the prediction of apoptosis proteins subcellular localization can help to understand their function and the role of metabolic processes. In this paper, we propose a novel method for protein subcellular localization prediction. Firstly, the features of the protein sequence are extracted by combining Chou's pseudo amino acid composition (PseAAC) and pseudo-position specific scoring matrix (PsePSSM), then the feature information of the extracted is denoised by two-dimensional (2-D) wavelet denoising. Finally, the optimal feature vectors are input to the SVM classifier to predict subcellular location of apoptosis proteins. Quite promising predictions are obtained using the jackknife test on three widely used datasets and compared with other state-of-the-art methods. The results indicate that the method proposed in this paper can remarkably improve the prediction accuracy of apoptosis protein subcellular localization, which will be a supplementary tool for future proteomics research. PMID:29296195

An integrated approach to improved toxicity prediction for the safety assessment during preclinical drug development using Hep G2 cells

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

Noor, Fozia; Niklas, Jens; Mueller-Vieira, Ursula

2009-06-01

Efficient and accurate safety assessment of compounds is extremely important in the preclinical development of drugs especially when hepatotoxicty is in question. Multiparameter and time resolved assays are expected to greatly improve the prediction of toxicity by assessing complex mechanisms of toxicity. An integrated approach is presented in which Hep G2 cells and primary rat hepatocytes are compared in frequently used cytotoxicity assays for parent compound toxicity. The interassay variability was determined. The cytotoxicity assays were also compared with a reliable alternative time resolved respirometric assay. The set of training compounds consisted of well known hepatotoxins; amiodarone, carbamazepine, clozapine, diclofenac,more » tacrine, troglitazone and verapamil. The sensitivity of both cell systems in each tested assay was determined. Results show that careful selection of assay parameters and inclusion of a kinetic time resolved assay improves prediction for non-metabolism mediated toxicity using Hep G2 cells as indicated by a sensitivity ratio of 1. The drugs with EC{sub 50} values 100 {mu}M or lower were considered toxic. The difference in the sensitivity of the two cell systems to carbamazepine which causes toxicity via reactive metabolites emphasizes the importance of human cell based in-vitro assays. Using the described system, primary rat hepatocytes do not offer advantage over the Hep G2 cells in parent compound toxicity evaluation. Moreover, respiration method is non invasive, highly sensitive and allows following the time course of toxicity. Respiration assay could serve as early indicator of changes that subsequently lead to toxicity.« less

CSAHi study-2: Validation of multi-electrode array systems (MEA60/2100) for prediction of drug-induced proarrhythmia using human iPS cell-derived cardiomyocytes: Assessment of reference compounds and comparison with non-clinical studies and clinical information.

PubMed

Nozaki, Yumiko; Honda, Yayoi; Watanabe, Hitoshi; Saiki, Shota; Koyabu, Kiyotaka; Itoh, Tetsuji; Nagasawa, Chiho; Nakamori, Chiaki; Nakayama, Chiaki; Iwasaki, Hiroshi; Suzuki, Shinobu; Tanaka, Kohji; Takahashi, Etsushi; Miyamoto, Kaori; Morimura, Kaoru; Yamanishi, Atsuhiro; Endo, Hiroko; Shinozaki, Junko; Nogawa, Hisashi; Shinozawa, Tadahiro; Saito, Fumiyo; Kunimatsu, Takeshi

2017-08-01

With the aim of reconsidering ICH S7B and E14 guidelines, a new in vitro assay system has been subjected to worldwide validation to establish a better prediction platform for potential drug-induced QT prolongation and the consequent TdP in clinical practice. In Japan, CSAHi HEART team has been working on hiPS-CMs in the MEA (hiPS-CMs/MEA) under a standardized protocol and found no inter-facility or lot-to-lot variability for proarrhythmic risk assessment of 7 reference compounds. In this study, we evaluated the responses of hiPS-CMs/MEA to another 31 reference compounds associated with cardiac toxicities, and gene expression to further clarify the electrophysiological characteristics over the course of culture period. The hiPS-CMs/MEA assay accurately predicted reference compounds potential for arrhythmogenesis, and yielded results that showed better correlation with target concentrations of QTc prolongation or TdP in clinical setting than other current in vitro and in vivo assays. Gene expression analyses revealed consistent profiles in all samples within and among the testing facilities. This report would provide CiPA with informative guidance on the use of the hiPS-CMs/MEA assay, and promote the establishment of a new paradigm, beyond conventional in vitro and in vivo assays for cardiac safety assessment of new drugs. Copyright © 2017 Elsevier Inc. All rights reserved.

Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field.

PubMed

Wang, Lingle; Wu, Yujie; Deng, Yuqing; Kim, Byungchan; Pierce, Levi; Krilov, Goran; Lupyan, Dmitry; Robinson, Shaughnessy; Dahlgren, Markus K; Greenwood, Jeremy; Romero, Donna L; Masse, Craig; Knight, Jennifer L; Steinbrecher, Thomas; Beuming, Thijs; Damm, Wolfgang; Harder, Ed; Sherman, Woody; Brewer, Mark; Wester, Ron; Murcko, Mark; Frye, Leah; Farid, Ramy; Lin, Teng; Mobley, David L; Jorgensen, William L; Berne, Bruce J; Friesner, Richard A; Abel, Robert

2015-02-25

Designing tight-binding ligands is a primary objective of small-molecule drug discovery. Over the past few decades, free-energy calculations have benefited from improved force fields and sampling algorithms, as well as the advent of low-cost parallel computing. However, it has proven to be challenging to reliably achieve the level of accuracy that would be needed to guide lead optimization (∼5× in binding affinity) for a wide range of ligands and protein targets. Not surprisingly, widespread commercial application of free-energy simulations has been limited due to the lack of large-scale validation coupled with the technical challenges traditionally associated with running these types of calculations. Here, we report an approach that achieves an unprecedented level of accuracy across a broad range of target classes and ligands, with retrospective results encompassing 200 ligands and a wide variety of chemical perturbations, many of which involve significant changes in ligand chemical structures. In addition, we have applied the method in prospective drug discovery projects and found a significant improvement in the quality of the compounds synthesized that have been predicted to be potent. Compounds predicted to be potent by this approach have a substantial reduction in false positives relative to compounds synthesized on the basis of other computational or medicinal chemistry approaches. Furthermore, the results are consistent with those obtained from our retrospective studies, demonstrating the robustness and broad range of applicability of this approach, which can be used to drive decisions in lead optimization.

Physical stability and recrystallization kinetics of amorphous ibipinabant drug product by fourier transform raman spectroscopy.

PubMed

Sinclair, Wayne; Leane, Michael; Clarke, Graham; Dennis, Andrew; Tobyn, Mike; Timmins, Peter

2011-11-01

The solid-state physical stability and recrystallization kinetics during storage stability are described for an amorphous solid dispersed drug substance, ibipinabant, at a low concentration (1.0%, w/w) in a solid oral dosage form (tablet). The recrystallization behavior of the amorphous ibipinabant-polyvinylpyrrolidone solid dispersion in the tablet product was characterized by Fourier transform (FT) Raman spectroscopy. A partial least-square analysis used for multivariate calibration based on Raman spectra was developed and validated to detect less than 5% (w/w) of the crystalline form (equivalent to less than 0.05% of the total mass of the tablet). The method provided reliable and highly accurate predictive crystallinity assessments after exposure to a variety of stability storage conditions. It was determined that exposure to moisture had a significant impact on the crystallinity of amorphous ibipinabant. The information provided by the method has potential utility for predictive physical stability assessments. Dissolution testing demonstrated that the predicted crystallinity had a direct correlation with this physical property of the drug product. Recrystallization kinetics was measured using FT Raman spectroscopy for the solid dispersion from the tablet product stored at controlled temperature and relative humidity. The measurements were evaluated by application of the Johnson-Mehl-Avrami (JMA) kinetic model to determine recrystallization rate constants and Avrami exponent (n = 2). The analysis showed that the JMA equation could describe the process very well, and indicated that the recrystallization kinetics observed was a two-step process with an induction period (nucleation) followed by rod-like crystal growth. Copyright © 2011 Wiley-Liss, Inc.

A unified frame of predicting side effects of drugs by using linear neighborhood similarity.

PubMed

Zhang, Wen; Yue, Xiang; Liu, Feng; Chen, Yanlin; Tu, Shikui; Zhang, Xining

2017-12-14

Drug side effects are one of main concerns in the drug discovery, which gains wide attentions. Investigating drug side effects is of great importance, and the computational prediction can help to guide wet experiments. As far as we known, a great number of computational methods have been proposed for the side effect predictions. The assumption that similar drugs may induce same side effects is usually employed for modeling, and how to calculate the drug-drug similarity is critical in the side effect predictions. In this paper, we present a novel measure of drug-drug similarity named "linear neighborhood similarity", which is calculated in a drug feature space by exploring linear neighborhood relationship. Then, we transfer the similarity from the feature space into the side effect space, and predict drug side effects by propagating known side effect information through a similarity-based graph. Under a unified frame based on the linear neighborhood similarity, we propose method "LNSM" and its extension "LNSM-SMI" to predict side effects of new drugs, and propose the method "LNSM-MSE" to predict unobserved side effect of approved drugs. We evaluate the performances of LNSM and LNSM-SMI in predicting side effects of new drugs, and evaluate the performances of LNSM-MSE in predicting missing side effects of approved drugs. The results demonstrate that the linear neighborhood similarity can improve the performances of side effect prediction, and the linear neighborhood similarity-based methods can outperform existing side effect prediction methods. More importantly, the proposed methods can predict side effects of new drugs as well as unobserved side effects of approved drugs under a unified frame.

Electrophysiological evidence of early attentional bias to drug-related pictures in chronic cannabis users.

PubMed

Asmaro, Deyar; Carolan, Patrick L; Liotti, Mario

2014-01-01

Behavioral and electrophysiological correlates of attentional bias to cannabis-related cues were investigated in a marijuana dependent group and a non-user group employing a drug Stroop task in which cannabis-related, negative and neutral images were presented. Behaviorally, cannabis users were less accurate during drug-containing blocks than non-users. Electrophysiologically, in chronic marijuana-users, an early positive ERP enhancement over left frontal scalp (EAP, 200-350ms) was present in response to drug-containing blocks relative to negative blocks. This effect was absent in the non-user group. Furthermore, drug-containing blocks gave rise to enhanced voltage of a posterior P300 (300-400ms), and a posterior sustained slow wave (LPP, 400-700ms) relative to negative blocks. However, such effects were similar between cannabis users and non-users. Brain source imaging in cannabis users revealed a generator for the EAP effect to drug stimuli in left ventromedial prefrontal cortex/medial orbitofrontal cortex, a region active in fMRI studies of drug cue-reactivity and a target of the core dopaminergic mesolimbic pathway involved in the processing of substances of abuse. This study identifies the timing and brain localization of an ERP correlate of early attentional capture to drug-related pictures in chronic marijuana users. The EAP to drug cues may identify a new electrophysiological marker with clinical implications for predicting abstinence versus relapse or to evaluate treatment interventions. © 2013.

Using PEGylated magnetic nanoparticles to describe the EPR effect in tumor for predicting therapeutic efficacy of micelle drugs.

PubMed

Chen, Ling; Zang, Fengchao; Wu, Haoan; Li, Jianzhong; Xie, Jun; Ma, Ming; Gu, Ning; Zhang, Yu

2018-01-25

Micelle drugs based on a polymeric platform offer great advantages over liposomal drugs for tumor treatment. Although nearly all of the nanomedicines approved in the clinical use can passively target to the tumor tissues on the basis of an enhanced permeability and retention (EPR) effect, the nanodrugs have shown heterogenous responses in the patients. This phenomenon may be traced back to the EPR effect of tumor, which is extremely variable in the individuals from extensive studies. Nevertheless, there is a lack of experimental data describing the EPR effect and predicting its impact on therapeutic efficacy of nanoagents. Herein, we developed 32 nm magnetic iron oxide nanoparticles (MION) as a T 2 -weighted contrast agent to describe the EPR effect of each tumor by in vivo magnetic resonance imaging (MRI). The MION were synthesized by a thermal decomposition method and modified with DSPE-PEG2000 for biological applications. The PEGylated MION (Fe 3 O 4 @PEG) exhibited high r 2 of 571 mM -1 s -1 and saturation magnetization (M s ) of 94 emu g -1 Fe as well as long stability and favorable biocompatibility through the in vitro studies. The enhancement intensities of the tumor tissue from the MR images were quantitatively measured as TNR (Tumor/Normal tissue signal Ratio) values, which were correlated with the delay of tumor growth after intravenous administration of the PLA-PEG/PTX micelle drug. The results demonstrated that the group with the smallest TNR values (TNR < 0.5) displayed the best tumor inhibitory effect. In addition, there was a superior correlation between TNR value and relative tumor delay in individual mice. These analysis results indicated that the TNR value of the tumor region enhanced by Fe 3 O 4 @PEG (d = 32 nm) could be used to predict the therapeutic efficacy of the micelle drugs (d ≤ 32 nm) in a certain period of time. Fe 3 O 4 @PEG has a potential to serve as an ideal MRI contrast agent to visualize the EPR effect in patients for accurate medication guidance of micelle drugs in the future treatment of tumors.

Computational intelligence techniques for biological data mining: An overview

NASA Astrophysics Data System (ADS)

Faye, Ibrahima; Iqbal, Muhammad Javed; Said, Abas Md; Samir, Brahim Belhaouari

2014-10-01

Computational techniques have been successfully utilized for a highly accurate analysis and modeling of multifaceted and raw biological data gathered from various genome sequencing projects. These techniques are proving much more effective to overcome the limitations of the traditional in-vitro experiments on the constantly increasing sequence data. However, most critical problems that caught the attention of the researchers may include, but not limited to these: accurate structure and function prediction of unknown proteins, protein subcellular localization prediction, finding protein-protein interactions, protein fold recognition, analysis of microarray gene expression data, etc. To solve these problems, various classification and clustering techniques using machine learning have been extensively used in the published literature. These techniques include neural network algorithms, genetic algorithms, fuzzy ARTMAP, K-Means, K-NN, SVM, Rough set classifiers, decision tree and HMM based algorithms. Major difficulties in applying the above algorithms include the limitations found in the previous feature encoding and selection methods while extracting the best features, increasing classification accuracy and decreasing the running time overheads of the learning algorithms. The application of this research would be potentially useful in the drug design and in the diagnosis of some diseases. This paper presents a concise overview of the well-known protein classification techniques.

An improved kinetics approach to describe the physical stability of amorphous solid dispersions.

PubMed

Yang, Jiao; Grey, Kristin; Doney, John

2010-01-15

The recrystallization of amorphous solid dispersions may lead to a loss in the dissolution rate, and consequently reduce bioavailability. The purpose of this work is to understand factors governing the recrystallization of amorphous drug-polymer solid dispersions, and develop a kinetics model capable of accurately predicting their physical stability. Recrystallization kinetics was measured using differential scanning calorimetry for initially amorphous efavirenz-polyvinylpyrrolidone solid dispersions stored at controlled temperature and relative humidity. The experimental measurements were fitted by a new kinetic model to estimate the recrystallization rate constant and microscopic geometry of crystal growth. The new kinetics model was used to illustrate the governing factors of amorphous solid dispersions stability. Temperature was found to affect efavirenz recrystallization in an Arrhenius manner, while recrystallization rate constant was shown to increase linearly with relative humidity. Polymer content tremendously inhibited the recrystallization process by increasing the crystallization activation energy and decreasing the equilibrium crystallinity. The new kinetic model was validated by the good agreement between model fits and experiment measurements. A small increase in polyvinylpyrrolidone resulted in substantial stability enhancements of efavirenz amorphous solid dispersion. The new established kinetics model provided more accurate predictions than the Avrami equation.

Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go

PubMed Central

Moitessier, N; Englebienne, P; Lee, D; Lawandi, J; Corbeil, C R

2008-01-01

Accelerating the drug discovery process requires predictive computational protocols capable of reducing or simplifying the synthetic and/or combinatorial challenge. Docking-based virtual screening methods have been developed and successfully applied to a number of pharmaceutical targets. In this review, we first present the current status of docking and scoring methods, with exhaustive lists of these. We next discuss reported comparative studies, outlining criteria for their interpretation. In the final section, we describe some of the remaining developments that would potentially lead to a universally applicable docking/scoring method. PMID:18037925

Biomarker Surrogates Do Not Accurately Predict Sputum Eosinophils and Neutrophils in Asthma

PubMed Central

Hastie, Annette T.; Moore, Wendy C.; Li, Huashi; Rector, Brian M.; Ortega, Victor E.; Pascual, Rodolfo M.; Peters, Stephen P.; Meyers, Deborah A.; Bleecker, Eugene R.

2013-01-01

Background Sputum eosinophils (Eos) are a strong predictor of airway inflammation, exacerbations, and aid asthma management, whereas sputum neutrophils (Neu) indicate a different severe asthma phenotype, potentially less responsive to TH2-targeted therapy. Variables such as blood Eos, total IgE, fractional exhaled nitric oxide (FeNO) or FEV1% predicted, may predict airway Eos, while age, FEV1%predicted, or blood Neu may predict sputum Neu. Availability and ease of measurement are useful characteristics, but accuracy in predicting airway Eos and Neu, individually or combined, is not established. Objectives To determine whether blood Eos, FeNO, and IgE accurately predict sputum eosinophils, and age, FEV1% predicted, and blood Neu accurately predict sputum neutrophils (Neu). Methods Subjects in the Wake Forest Severe Asthma Research Program (N=328) were characterized by blood and sputum cells, healthcare utilization, lung function, FeNO, and IgE. Multiple analytical techniques were utilized. Results Despite significant association with sputum Eos, blood Eos, FeNO and total IgE did not accurately predict sputum Eos, and combinations of these variables failed to improve prediction. Age, FEV1%predicted and blood Neu were similarly unsatisfactory for prediction of sputum Neu. Factor analysis and stepwise selection found FeNO, IgE and FEV1% predicted, but not blood Eos, correctly predicted 69% of sputum Eos

An investigation into the crystallization tendency/kinetics of amorphous active pharmaceutical ingredients: A case study with dipyridamole and cinnarizine.

PubMed

Baghel, Shrawan; Cathcart, Helen; Redington, Wynette; O'Reilly, Niall J

2016-07-01

Amorphous drug formulations have great potential to enhance solubility and thus bioavailability of BCS class II drugs. However, the higher free energy and molecular mobility of the amorphous form drive them towards the crystalline state which makes them unstable. Accurate determination of the crystallization tendency/kinetics is the key to the successful design and development of such systems. In this study, dipyridamole (DPM) and cinnarizine (CNZ) have been selected as model compounds. Thermodynamic fragility (mT) was measured from the heat capacity change at the glass transition temperature (Tg) whereas dynamic fragility (mD) was evaluated using methods based on extrapolation of configurational entropy to zero [Formula: see text] , and heating rate dependence of Tg [Formula: see text] . The mean relaxation time of amorphous drugs was calculated from the Vogel-Tammann-Fulcher (VTF) equation. Furthermore, the correlation between fragility and glass forming ability (GFA) of the model drugs has been established and the relevance of these parameters to crystallization of amorphous drugs is also assessed. Moreover, the crystallization kinetics of model drugs under isothermal conditions has been studied using Johnson-Mehl-Avrami (JMA) approach to determine the Avrami constant 'n' which provides an insight into the mechanism of crystallization. To further probe into the crystallization mechanism, the non-isothermal crystallization kinetics of model systems were also analysed by statistically fitting the crystallization data to 15 different kinetic models and the relevance of model-free kinetic approach has been established. The crystallization mechanism for DPM and CNZ at each extent of transformation has been predicted. The calculated fragility, glass forming ability (GFA) and crystallization kinetics are found to be in good correlation with the stability prediction of amorphous solid dispersions. Thus, this research work involves a multidisciplinary approach to establish fragility, GFA and crystallization kinetics as stability predictors for amorphous drug formulations. Copyright © 2016 Elsevier B.V. All rights reserved.

Elucidating Hyperconjugation from Electronegativity to Predict Drug Conformational Energy in a High Throughput Manner.

PubMed

Liu, Zhaomin; Pottel, Joshua; Shahamat, Moeed; Tomberg, Anna; Labute, Paul; Moitessier, Nicolas

2016-04-25

Computational chemists use structure-based drug design and molecular dynamics of drug/protein complexes which require an accurate description of the conformational space of drugs. Organic chemists use qualitative chemical principles such as the effect of electronegativity on hyperconjugation, the impact of steric clashes on stereochemical outcome of reactions, and the consequence of resonance on the shape of molecules to rationalize experimental observations. While computational chemists speak about electron densities and molecular orbitals, organic chemists speak about partial charges and localized molecular orbitals. Attempts to reconcile these two parallel approaches such as programs for natural bond orbitals and intrinsic atomic orbitals computing Lewis structures-like orbitals and reaction mechanism have appeared. In the past, we have shown that encoding and quantifying chemistry knowledge and qualitative principles can lead to predictive methods. In the same vein, we thought to understand the conformational behaviors of molecules and to encode this knowledge back into a molecular mechanics tool computing conformational potential energy and to develop an alternative to atom types and training of force fields on large sets of molecules. Herein, we describe a conceptually new approach to model torsion energies based on fundamental chemistry principles. To demonstrate our approach, torsional energy parameters were derived on-the-fly from atomic properties. When the torsional energy terms implemented in GAFF, Parm@Frosst, and MMFF94 were substituted by our method, the accuracy of these force fields to reproduce MP2-derived torsional energy profiles and their transferability to a variety of functional groups and drug fragments were overall improved. In addition, our method did not rely on atom types and consequently did not suffer from poor automated atom type assignments.

NLLSS: Predicting Synergistic Drug Combinations Based on Semi-supervised Learning

PubMed Central

Chen, Ming; Wang, Quanxin; Zhang, Lixin; Yan, Guiying

2016-01-01

Fungal infection has become one of the leading causes of hospital-acquired infections with high mortality rates. Furthermore, drug resistance is common for fungus-causing diseases. Synergistic drug combinations could provide an effective strategy to overcome drug resistance. Meanwhile, synergistic drug combinations can increase treatment efficacy and decrease drug dosage to avoid toxicity. Therefore, computational prediction of synergistic drug combinations for fungus-causing diseases becomes attractive. In this study, we proposed similar nature of drug combinations: principal drugs which obtain synergistic effect with similar adjuvant drugs are often similar and vice versa. Furthermore, we developed a novel algorithm termed Network-based Laplacian regularized Least Square Synergistic drug combination prediction (NLLSS) to predict potential synergistic drug combinations by integrating different kinds of information such as known synergistic drug combinations, drug-target interactions, and drug chemical structures. We applied NLLSS to predict antifungal synergistic drug combinations and showed that it achieved excellent performance both in terms of cross validation and independent prediction. Finally, we performed biological experiments for fungal pathogen Candida albicans to confirm 7 out of 13 predicted antifungal synergistic drug combinations. NLLSS provides an efficient strategy to identify potential synergistic antifungal combinations. PMID:27415801

Lost in translation: preclinical studies on 3,4-methylenedioxymethamphetamine provide information on mechanisms of action, but do not allow accurate prediction of adverse events in humans

PubMed Central

Green, AR; King, MV; Shortall, SE; Fone, KCF

2012-01-01

3,4-Methylenedioxymethamphetamine (MDMA) induces both acute adverse effects and long-term neurotoxic loss of brain 5-HT neurones in laboratory animals. However, when choosing doses, most preclinical studies have paid little attention to the pharmacokinetics of the drug in humans or animals. The recreational use of MDMA and current clinical investigations of the drug for therapeutic purposes demand better translational pharmacology to allow accurate risk assessment of its ability to induce adverse events. Recent pharmacokinetic studies on MDMA in animals and humans are reviewed and indicate that the risks following MDMA ingestion should be re-evaluated. Acute behavioural and body temperature changes result from rapid MDMA-induced monoamine release, whereas long-term neurotoxicity is primarily caused by metabolites of the drug. Therefore acute physiological changes in humans are fairly accurately mimicked in animals by appropriate dosing, although allometric dosing calculations have little value. Long-term changes require MDMA to be metabolized in a similar manner in experimental animals and humans. However, the rate of metabolism of MDMA and its major metabolites is slower in humans than rats or monkeys, potentially allowing endogenous neuroprotective mechanisms to function in a species specific manner. Furthermore acute hyperthermia in humans probably limits the chance of recreational users ingesting sufficient MDMA to produce neurotoxicity, unlike in the rat. MDMA also inhibits the major enzyme responsible for its metabolism in humans thereby also assisting in preventing neurotoxicity. These observations question whether MDMA alone produces long-term 5-HT neurotoxicity in human brain, although when taken in combination with other recreational drugs it may induce neurotoxicity. LINKED ARTICLES This article is commented on by Parrott, pp. 1518–1520 of this issue. To view this commentary visit http://dx.doi.org/10.1111/j.1476-5381.2012.01941.x and to view the the rebuttal by the authors (Green et al., pp. 1521–1522 of this issue) visit http://dx.doi.org/10.1111/j.1476-5381.2012.01940.x PMID:22188379

Laser based thermo-conductometry as an approach to determine ribbon solid fraction off-line and in-line.

PubMed

Wiedey, Raphael; Šibanc, Rok; Kleinebudde, Peter

2018-06-06

Ribbon solid fraction is one of the most important quality attributes during roll compaction/dry granulation. Accurate and precise determination is challenging and no in-line measurement tool has been generally accepted, yet. In this study, a new analytical tool with potential off-line as well as in-line applicability is described. It is based on the thermo-conductivity of the compacted material, which is known to depend on the solid fraction. A laser diode was used to punctually heat the ribbon and the heat propagation monitored by infrared thermography. After performing a Gaussian fit of the transverse ribbon profile, the scale parameter σ showed correlation to ribbon solid fraction in off-line as well as in-line studies. Accurate predictions of the solid fraction were possible for a relevant range of process settings. Drug stability was not affected, as could be demonstrated for the model drug nifedipine. The application of this technique was limited when using certain fillers and working at higher roll speeds. This study showed the potentials of this new technique and is a starting point for additional work that has to be done to overcome these challenges. Copyright © 2018 Elsevier B.V. All rights reserved.

21 CFR 203.39 - Donation of drug samples to charitable institutions.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 4 2014-04-01 2014-04-01 false Donation of drug samples to charitable... SERVICES (CONTINUED) DRUGS: GENERAL PRESCRIPTION DRUG MARKETING Samples § 203.39 Donation of drug samples... donation record accurately describes the drug sample delivered and that no drug sample is adulterated or...

21 CFR 203.39 - Donation of drug samples to charitable institutions.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 4 2012-04-01 2012-04-01 false Donation of drug samples to charitable... SERVICES (CONTINUED) DRUGS: GENERAL PRESCRIPTION DRUG MARKETING Samples § 203.39 Donation of drug samples... donation record accurately describes the drug sample delivered and that no drug sample is adulterated or...

21 CFR 203.39 - Donation of drug samples to charitable institutions.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 4 2013-04-01 2013-04-01 false Donation of drug samples to charitable... SERVICES (CONTINUED) DRUGS: GENERAL PRESCRIPTION DRUG MARKETING Samples § 203.39 Donation of drug samples... donation record accurately describes the drug sample delivered and that no drug sample is adulterated or...

Surrogate endpoints in clinical trials of chronic kidney disease progression: moving from single to multiple risk marker response scores.

PubMed

Schievink, Bauke; Mol, Peter G M; Lambers Heerspink, Hiddo J

2015-11-01

There is increased interest in developing surrogate endpoints for clinical trials of chronic kidney disease progression, as the established clinically meaningful endpoint end-stage renal disease requires large and lengthy trials to assess drug efficacy. We describe recent developments in the search for novel surrogate endpoints. Declines in estimated glomerular filtration rate (eGFR) of 30% or 40% and albuminuria have been proposed as surrogates for end-stage renal disease. However, changes in eGFR or albuminuria may not be valid under all circumstances as drugs always have effects on multiple renal risk markers. Changes in each of these other 'off-target' risk markers can alter renal risk (either beneficially or adversely), and can thereby confound the relationship between surrogates that are based on single risk markers and renal outcome. Risk algorithms that integrate the short-term drug effects on multiple risk markers to predict drug effects on hard renal outcomes may therefore be more accurate. The validity of these risk algorithms is currently investigated. Given that drugs affect multiple renal risk markers, risk scores that integrate these effects are a promising alternative to using eGFR decline or albuminuria. Proper validation is required before these risk scores can be implemented.

Multilayer Spheroids To Quantify Drug Uptake and Diffusion in 3D

PubMed Central

2015-01-01

There is a need for new quantitative in vitro models of drug uptake and diffusion to help assess drug toxicity/efficacy as well as new more predictive models for drug discovery. We report a three-dimensional (3D) multilayer spheroid model and a new algorithm to quantitatively study uptake and inward diffusion of fluorescent calcein via gap junction intercellular communication (GJIC). When incubated with calcein-AM, a substrate of the efflux transporter P-glycoprotein (Pgp), spheroids from a variety of cell types accumulated calcein over time. Accumulation decreased in spheroids overexpressing Pgp (HEK-MDR) and was increased in the presence of Pgp inhibitors (verapamil, loperamide, cyclosporin A). Inward diffusion of calcein was negligible in spheroids that lacked GJIC (OVCAR-3, SK-OV-3) and was reduced in the presence of an inhibitor of GJIC (carbenoxolone). In addition to inhibiting Pgp, verapamil and loperamide, but not cyclosporin A, inhibited inward diffusion of calcein, suggesting that they also inhibit GJIC. The dose response curves of verapamil’s inhibition of Pgp and GJIC were similar (IC50: 8 μM). The method is amenable to many different cell types and may serve as a quantitative 3D model that more accurately replicates in vivo barriers to drug uptake and diffusion. PMID:24641346

Disease identification based on ambulatory drugs dispensation and in-hospital ICD-10 diagnoses: a comparison.

PubMed

Halfon, Patricia; Eggli, Yves; Decollogny, Anne; Seker, Erol

2013-10-31

Pharmacy-based case mix measures are an alternative source of information to the relatively scarce outpatient diagnoses data. But most published tools use national drug nomenclatures and offer no head-to-head comparisons between drugs-related and diagnoses-based categories. The objective of the study was to test the accuracy of drugs-based morbidity groups derived from the World Health Organization Anatomical Therapeutic Chemical Classification of drugs by checking them against diagnoses-based groups. We compared drugs-based categories with their diagnoses-based analogues using anonymous data on 108,915 individuals insured with one of four companies. They were followed throughout 2005 and 2006 and hospitalized at least once during this period. The agreement between the two approaches was measured by weighted kappa coefficients. The reproducibility of the drugs-based morbidity measure over the 2 years was assessed for all enrollees. Eighty percent used a drug associated with at least one of the 60 morbidity categories derived from drugs dispensation. After accounting for inpatient under-coding, fifteen conditions agreed sufficiently with their diagnoses-based counterparts to be considered alternative strategies to diagnoses. In addition, they exhibited good reproducibility and allowed prevalence estimates in accordance with national estimates. For 22 conditions, drugs-based information identified accurately a subset of the population defined by diagnoses. Most categories provide insurers with health status information that could be exploited for healthcare expenditure prediction or ambulatory cost control, especially when ambulatory diagnoses are not available. However, due to insufficient concordance with their diagnoses-based analogues, their use for morbidity indicators is limited.

Drug Metabolism by the Host and Gut Microbiota: A Partnership or Rivalry?

PubMed

Swanson, Hollie I

2015-10-01

The importance of the gut microbiome in determining not only overall health, but also in the metabolism of drugs and xenobiotics, is rapidly emerging. It is becoming increasingly clear that the gut microbiota can act in concert with the host cells to maintain intestinal homeostasis, cometabolize drugs and xenobiotics, and alter the expression levels of drug-metabolizing enzymes and transporters and the expression and activity levels of nuclear receptors. In this myriad of activities, the impact of the microbiota may be beneficial or detrimental to the host. Given that the interplay between the gut microbiota and host cells is likely subject to high interindividual variability, this work has tremendous implications for our ability to predict accurately a particular drug's pharmacokinetics and a given patient population's response to drugs. In this issue of Drug Metabolism and Disposition, a series of articles is presented that illustrate the progress and challenges that lie ahead as we unravel the intricacies associated with drug and xenobiotic metabolism by the gut microbiota. These articles highlight the underlying mechanisms that are involved and the use of in vivo and in vitro approaches that are currently available for elucidating the role of the gut microbiota in drug and xenobiotic metabolism. These articles also shed light on exciting new avenues of research that may be pursued as we consider the role of the gut microbiota as an endocrine organ, a component of the brain-gut axis, and whether the gut microbiota is an appropriate and amenable target for new drugs. Copyright © 2015 by The American Society for Pharmacology and Experimental Therapeutics.

Using radiance predicted by the P3 approximation in a spherical geometry to predict tissue optical properties

NASA Astrophysics Data System (ADS)

Dickey, Dwayne J.; Moore, Ronald B.; Tulip, John

2001-01-01

For photodynamic therapy of solid tumors, such as prostatic carcinoma, to be achieved, an accurate model to predict tissue parameters and light dose must be found. Presently, most analytical light dosimetry models are fluence based and are not clinically viable for tissue characterization. Other methods of predicting optical properties, such as Monet Carlo, are accurate but far too time consuming for clinical application. However, radiance predicted by the P3-Approximation, an anaylitical solution to the transport equation, may be a viable and accurate alternative. The P3-Approximation accurately predicts optical parameters in intralipid/methylene blue based phantoms in a spherical geometry. The optical parameters furnished by the radiance, when introduced into fluence predicted by both P3- Approximation and Grosjean Theory, correlate well with experimental data. The P3-Approximation also predicts the optical properties of prostate tissue, agreeing with documented optical parameters. The P3-Approximation could be the clinical tool necessary to facilitate PDT of solid tumors because of the limited number of invasive measurements required and the speed in which accurate calculations can be performed.

Comparison of the uptake of methacrylate-based nanoparticles in static and dynamic in vitro systems as well as in vivo.

PubMed

Rinkenauer, Alexandra C; Press, Adrian T; Raasch, Martin; Pietsch, Christian; Schweizer, Simon; Schwörer, Simon; Rudolph, Karl L; Mosig, Alexander; Bauer, Michael; Traeger, Anja; Schubert, Ulrich S

2015-10-28

Polymer-based nanoparticles are promising drug delivery systems allowing the development of new drug and treatment strategies with reduced side effects. However, it remains a challenge to screen for new and effective nanoparticle-based systems in vitro. Important factors influencing the behavior of nanoparticles in vivo cannot be simulated in screening assays in vitro, which still represent the main tools in academic research and pharmaceutical industry. These systems have serious drawbacks in the development of nanoparticle-based drug delivery systems, since they do not consider the highly complex processes influencing nanoparticle clearance, distribution, and uptake in vivo. In particular, the transfer of in vitro nanoparticle performance to in vivo models often fails, demonstrating the urgent need for novel in vitro tools that can imitate aspects of the in vivo situation more accurate. Dynamic cell culture, where cells are cultured and incubated in the presence of shear stress has the potential to bridge this gap by mimicking key-features of organs and vessels. Our approach implements and compares a chip-based dynamic cell culture model to the common static cell culture and mouse model to assess its capability to predict the in vivo success more accurately, by using a well-defined poly((methyl methacrylate)-co-(methacrylic acid)) and poly((methyl methacrylate)-co-(2-dimethylamino ethylmethacrylate)) based nanoparticle library. After characterization in static and dynamic in vitro cell culture we were able to show that physiological conditions such as cell-cell communication of co-cultured endothelial cells and macrophages as well as mechanotransductive signaling through shear stress significantly alter cellular nanoparticle uptake. In addition, it could be demonstrated by using dynamic cell cultures that the in vivo situation is simulated more accurately and thereby can be applied as a novel system to investigate the performance of nanoparticle systems in vivo more reliable. Copyright © 2015. Published by Elsevier B.V.

Population pharmacokinetic model of THC integrates oral, intravenous, and pulmonary dosing and characterizes short- and long-term pharmacokinetics.

PubMed

Heuberger, Jules A A C; Guan, Zheng; Oyetayo, Olubukayo-Opeyemi; Klumpers, Linda; Morrison, Paul D; Beumer, Tim L; van Gerven, Joop M A; Cohen, Adam F; Freijer, Jan

2015-02-01

Δ(9)-Tetrahydrocannobinol (THC), the main psychoactive compound of Cannabis, is known to have a long terminal half-life. However, this characteristic is often ignored in pharmacokinetic (PK) studies of THC, which may affect the accuracy of predictions in different pharmacologic areas. For therapeutic use for example, it is important to accurately describe the terminal phase of THC to describe accumulation of the drug. In early clinical research, the THC challenge test can be optimized through more accurate predictions of the dosing sequence and the wash-out between occasions in a crossover setting, which is mainly determined by the terminal half-life of the compound. The purpose of this study is to better quantify the long-term pharmacokinetics of THC. A population-based PK model for THC was developed describing the profile up to 48 h after an oral, intravenous, and pulmonary dose of THC in humans. In contrast to earlier models, the current model integrates all three major administration routes and covers the long terminal phase of THC. Results show that THC has a fast initial and intermediate half-life, while the apparent terminal half-life is long (21.5 h), with a clearance of 38.8 L/h. Because the current model characterizes the long-term pharmacokinetics, it can be used to assess the accumulation of THC in a multiple-dose setting and to forecast concentration profiles of the drug under many different dosing regimens or administration routes. Additionally, this model could provide helpful insights into the THC challenge test used for the development of (novel) compounds targeting the cannabinoid system for different therapeutic applications and could improve decision making in future clinical trials.

A Critical Review of Validation, Blind Testing, and Real- World Use of Alchemical Protein-Ligand Binding Free Energy Calculations.

PubMed

Abel, Robert; Wang, Lingle; Mobley, David L; Friesner, Richard A

2017-01-01

Protein-ligand binding is among the most fundamental phenomena underlying all molecular biology, and a greater ability to more accurately and robustly predict the binding free energy of a small molecule ligand for its cognate protein is expected to have vast consequences for improving the efficiency of pharmaceutical drug discovery. We briefly reviewed a number of scientific and technical advances that have enabled alchemical free energy calculations to recently emerge as a preferred approach, and critically considered proper validation and effective use of these techniques. In particular, we characterized a selection bias effect which may be important in prospective free energy calculations, and introduced a strategy to improve the accuracy of the free energy predictions. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

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.

Quantum-Mechanics Methodologies in Drug Discovery: Applications of Docking and Scoring in Lead Optimization.

PubMed

Crespo, Alejandro; Rodriguez-Granillo, Agustina; Lim, Victoria T

2017-01-01

The development and application of quantum mechanics (QM) methodologies in computer- aided drug design have flourished in the last 10 years. Despite the natural advantage of QM methods to predict binding affinities with a higher level of theory than those methods based on molecular mechanics (MM), there are only a few examples where diverse sets of protein-ligand targets have been evaluated simultaneously. In this work, we review recent advances in QM docking and scoring for those cases in which a systematic analysis has been performed. In addition, we introduce and validate a simplified QM/MM expression to compute protein-ligand binding energies. Overall, QMbased scoring functions are generally better to predict ligand affinities than those based on classical mechanics. However, the agreement between experimental activities and calculated binding energies is highly dependent on the specific chemical series considered. The advantage of more accurate QM methods is evident in cases where charge transfer and polarization effects are important, for example when metals are involved in the binding process or when dispersion forces play a significant role as in the case of hydrophobic or stacking interactions. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

PredSTP: a highly accurate SVM based model to predict sequential cystine stabilized peptides.

PubMed

Islam, S M Ashiqul; Sajed, Tanvir; Kearney, Christopher Michel; Baker, Erich J

2015-07-05

Numerous organisms have evolved a wide range of toxic peptides for self-defense and predation. Their effective interstitial and macro-environmental use requires energetic and structural stability. One successful group of these peptides includes a tri-disulfide domain arrangement that offers toxicity and high stability. Sequential tri-disulfide connectivity variants create highly compact disulfide folds capable of withstanding a variety of environmental stresses. Their combination of toxicity and stability make these peptides remarkably valuable for their potential as bio-insecticides, antimicrobial peptides and peptide drug candidates. However, the wide sequence variation, sources and modalities of group members impose serious limitations on our ability to rapidly identify potential members. As a result, there is a need for automated high-throughput member classification approaches that leverage their demonstrated tertiary and functional homology. We developed an SVM-based model to predict sequential tri-disulfide peptide (STP) toxins from peptide sequences. One optimized model, called PredSTP, predicted STPs from training set with sensitivity, specificity, precision, accuracy and a Matthews correlation coefficient of 94.86%, 94.11%, 84.31%, 94.30% and 0.86, respectively, using 200 fold cross validation. The same model outperforms existing prediction approaches in three independent out of sample testsets derived from PDB. PredSTP can accurately identify a wide range of cystine stabilized peptide toxins directly from sequences in a species-agnostic fashion. The ability to rapidly filter sequences for potential bioactive peptides can greatly compress the time between peptide identification and testing structural and functional properties for possible antimicrobial and insecticidal candidates. A web interface is freely available to predict STP toxins from http://crick.ecs.baylor.edu/.

Motor system contribution to action prediction: Temporal accuracy depends on motor experience.

PubMed

Stapel, Janny C; Hunnius, Sabine; Meyer, Marlene; Bekkering, Harold

2016-03-01

Predicting others' actions is essential for well-coordinated social interactions. In two experiments including an infant population, this study addresses to what extent motor experience of an observer determines prediction accuracy for others' actions. Results show that infants who were proficient crawlers but inexperienced walkers predicted crawling more accurately than walking, whereas age groups mastering both skills (i.e. toddlers and adults) were equally accurate in predicting walking and crawling. Regardless of experience, human movements were predicted more accurately by all age groups than non-human movement control stimuli. This suggests that for predictions to be accurate, the observed act needs to be established in the motor repertoire of the observer. Through the acquisition of new motor skills, we also become better at predicting others' actions. The findings thus stress the relevance of motor experience for social-cognitive development. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

Evolutionary Diagnosis of non-synonymous variants involved in differential drug response

PubMed Central

2015-01-01

Background Many pharmaceutical drugs are known to be ineffective or have negative side effects in a substantial proportion of patients. Genomic advances are revealing that some non-synonymous single nucleotide variants (nsSNVs) may cause differences in drug efficacy and side effects. Therefore, it is desirable to evaluate nsSNVs of interest in their ability to modulate the drug response. Results We found that the available data on the link between drug response and nsSNV is rather modest. There were only 31 distinct drug response-altering (DR-altering) and 43 distinct drug response-neutral (DR-neutral) nsSNVs in the whole Pharmacogenomics Knowledge Base (PharmGKB). However, even with this modest dataset, it was clear that existing bioinformatics tools have difficulties in correctly predicting the known DR-altering and DR-neutral nsSNVs. They exhibited an overall accuracy of less than 50%, which was not better than random diagnosis. We found that the underlying problem is the markedly different evolutionary properties between positions harboring nsSNVs linked to drug responses and those observed for inherited diseases. To solve this problem, we developed a new diagnosis method, Drug-EvoD, which was trained on the evolutionary properties of nsSNVs associated with drug responses in a sparse learning framework. Drug-EvoD achieves a TPR of 84% and a TNR of 53%, with a balanced accuracy of 69%, which improves upon other methods significantly. Conclusions The new tool will enable researchers to computationally identify nsSNVs that may affect drug responses. However, much larger training and testing datasets are needed to develop more reliable and accurate tools. PMID:25952014

"Rate My Therapist": Automated Detection of Empathy in Drug and Alcohol Counseling via Speech and Language Processing

PubMed Central

Xiao, Bo; Imel, Zac E.; Georgiou, Panayiotis G.; Atkins, David C.; Narayanan, Shrikanth S.

2015-01-01

The technology for evaluating patient-provider interactions in psychotherapy–observational coding–has not changed in 70 years. It is labor-intensive, error prone, and expensive, limiting its use in evaluating psychotherapy in the real world. Engineering solutions from speech and language processing provide new methods for the automatic evaluation of provider ratings from session recordings. The primary data are 200 Motivational Interviewing (MI) sessions from a study on MI training methods with observer ratings of counselor empathy. Automatic Speech Recognition (ASR) was used to transcribe sessions, and the resulting words were used in a text-based predictive model of empathy. Two supporting datasets trained the speech processing tasks including ASR (1200 transcripts from heterogeneous psychotherapy sessions and 153 transcripts and session recordings from 5 MI clinical trials). The accuracy of computationally-derived empathy ratings were evaluated against human ratings for each provider. Computationally-derived empathy scores and classifications (high vs. low) were highly accurate against human-based codes and classifications, with a correlation of 0.65 and F-score (a weighted average of sensitivity and specificity) of 0.86, respectively. Empathy prediction using human transcription as input (as opposed to ASR) resulted in a slight increase in prediction accuracies, suggesting that the fully automatic system with ASR is relatively robust. Using speech and language processing methods, it is possible to generate accurate predictions of provider performance in psychotherapy from audio recordings alone. This technology can support large-scale evaluation of psychotherapy for dissemination and process studies. PMID:26630392

CaFE: a tool for binding affinity prediction using end-point free energy methods.

PubMed

Liu, Hui; Hou, Tingjun

2016-07-15

Accurate prediction of binding free energy is of particular importance to computational biology and structure-based drug design. Among those methods for binding affinity predictions, the end-point approaches, such as MM/PBSA and LIE, have been widely used because they can achieve a good balance between prediction accuracy and computational cost. Here we present an easy-to-use pipeline tool named Calculation of Free Energy (CaFE) to conduct MM/PBSA and LIE calculations. Powered by the VMD and NAMD programs, CaFE is able to handle numerous static coordinate and molecular dynamics trajectory file formats generated by different molecular simulation packages and supports various force field parameters. CaFE source code and documentation are freely available under the GNU General Public License via GitHub at https://github.com/huiliucode/cafe_plugin It is a VMD plugin written in Tcl and the usage is platform-independent. tingjunhou@zju.edu.cn. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Clinical responses to ERK inhibition in BRAFV600E-mutant colorectal cancer predicted using a computational model.

PubMed

Kirouac, Daniel C; Schaefer, Gabriele; Chan, Jocelyn; Merchant, Mark; Orr, Christine; Huang, Shih-Min A; Moffat, John; Liu, Lichuan; Gadkar, Kapil; Ramanujan, Saroja

2017-01-01

Approximately 10% of colorectal cancers harbor BRAF V600E mutations, which constitutively activate the MAPK signaling pathway. We sought to determine whether ERK inhibitor (GDC-0994)-containing regimens may be of clinical benefit to these patients based on data from in vitro (cell line) and in vivo (cell- and patient-derived xenograft) studies of cetuximab (EGFR), vemurafenib (BRAF), cobimetinib (MEK), and GDC-0994 (ERK) combinations. Preclinical data was used to develop a mechanism-based computational model linking cell surface receptor (EGFR) activation, the MAPK signaling pathway, and tumor growth. Clinical predictions of anti-tumor activity were enabled by the use of tumor response data from three Phase 1 clinical trials testing combinations of EGFR, BRAF, and MEK inhibitors. Simulated responses to GDC-0994 monotherapy (overall response rate = 17%) accurately predicted results from a Phase 1 clinical trial regarding the number of responding patients (2/18) and the distribution of tumor size changes ("waterfall plot"). Prospective simulations were then used to evaluate potential drug combinations and predictive biomarkers for increasing responsiveness to MEK/ERK inhibitors in these patients.

Prediction and Dissection of Protein-RNA Interactions by Molecular Descriptors.

PubMed

Liu, Zhi-Ping; Chen, Luonan

2016-01-01

Protein-RNA interactions play crucial roles in numerous biological processes. However, detecting the interactions and binding sites between protein and RNA by traditional experiments is still time consuming and labor costing. Thus, it is of importance to develop bioinformatics methods for predicting protein-RNA interactions and binding sites. Accurate prediction of protein-RNA interactions and recognitions will highly benefit to decipher the interaction mechanisms between protein and RNA, as well as to improve the RNA-related protein engineering and drug design. In this work, we summarize the current bioinformatics strategies of predicting protein-RNA interactions and dissecting protein-RNA interaction mechanisms from local structure binding motifs. In particular, we focus on the feature-based machine learning methods, in which the molecular descriptors of protein and RNA are extracted and integrated as feature vectors of representing the interaction events and recognition residues. In addition, the available methods are classified and compared comprehensively. The molecular descriptors are expected to elucidate the binding mechanisms of protein-RNA interaction and reveal the functional implications from structural complementary perspective.

A paradigm shift in pharmacokinetic-pharmacodynamic (PKPD) modeling: rule of thumb for estimating free drug level in tissue compared with plasma to guide drug design.

PubMed

Poulin, Patrick

2015-07-01

A basic assumption in pharmacokinetics-pharmacodynamics research is that the free drug concentration is similar in plasma and tissue, and, hence, in vitro plasma data can be used to estimate the in vivo condition in tissue. However, in a companion manuscript, it has been demonstrated that this assumption is violated for the ionized drugs. Nonetheless, these observations focus on in vitro static environments and do not challenge data with an in vivo dynamic system. Therefore, an extension from an in vitro to an in vivo system becomes the necessary next step. The objective of this study was to perform theoretical simulations of the free drug concentration in tissue and plasma by using a physiologically based pharmacokinetics (PBPK) model reproducing the in vivo conditions in human. Therefore, the effects of drug ionization, lipophilicity, and clearance have been taken into account in a dynamic system. This modeling exercise was performed as a proof of concept to demonstrate that free drug concentration in tissue and plasma may also differ in a dynamic system for passively permeable drugs that are ionized at the physiological pH. The PBPK model simulations indicated that free drug concentrations in tissue cells and plasma significantly differ for the ionized drugs because of the pH gradient effect between cells and interstitial space. Hence, a rule of thumb for potentially performing more accurate PBPK/PD modeling is suggested, which states that the free drug concentration in tissue and plasma will differ for the ionizable drugs in contrast to the neutral drugs. In addition to the pH gradient effect for the ionizable drugs, lipophilicity and clearance effects will increase or decrease the free drug concentration in tissue and plasma for each class of drugs; thus, higher will be the drug lipophilicity and clearance, lower would be the free drug concentration in plasma, and, hence, in tissue, in a dynamic in vivo system. Therefore, only considering the value of free fraction in plasma derived from a static in vitro environment might be biased to guide drug design (the old paradigm), and, hence, it is recommended to use a PBPK model to reproduce more accurately the in vivo condition in tissue (the new paradigm). This newly developed approach can be used to predict free drug concentration in diverse tissue compartments for small molecules in toxicology and pharmacology studies, which can be leveraged to optimize the pharmacokinetics drivers of tissue distribution based upon physicochemical and physiological input parameters in an attempt to optimize free drug level in tissue. Overall, this present study provides guidance on the application of plasma and tissue concentration information in PBPK/PD research in preclinical and clinical studies, which is in accordance with the recent literature. © 2015 Wiley Periodicals, Inc. and the American Pharmacists Association.

Clinical implications of omics and systems medicine: focus on predictive and individualized treatment.

PubMed

Benson, M

2016-03-01

Many patients with common diseases do not respond to treatment. This is a key challenge to modern health care, which causes both suffering and enormous costs. One important reason for the lack of treatment response is that common diseases are associated with altered interactions between thousands of genes, in combinations that differ between subgroups of patients who do or do not respond to a given treatment. Such subgroups, or even distinct disease entities, have been described recently in asthma, diabetes, autoimmune diseases and cancer. High-throughput techniques (omics) allow identification and characterization of such subgroups or entities. This may have important clinical implications, such as identification of diagnostic markers for individualized medicine, as well as new therapeutic targets for patients who do not respond to existing drugs. For example, whole-genome sequencing may be applied to more accurately guide treatment of neurodevelopmental diseases, or to identify drugs specifically targeting mutated genes in cancer. A study published in 2015 showed that 28% of hepatocellular carcinomas contained mutated genes that potentially could be targeted by drugs already approved by the US Food and Drug Administration. A translational study, which is described in detail, showed how combined omics, computational, functional and clinical studies could identify and validate a novel diagnostic and therapeutic candidate gene in allergy. Another important clinical implication is the identification of potential diagnostic markers and therapeutic targets for predictive and preventative medicine. By combining computational and experimental methods, early disease regulators may be identified and potentially used to predict and treat disease before it becomes symptomatic. Systems medicine is an emerging discipline, which may contribute to such developments through combining omics with computational, functional and clinical studies. The aims of this review are to provide a brief introduction to systems medicine and discuss how it may contribute to the clinical implementation of individualized treatment, using clinically relevant examples. © 2015 The Association for the Publication of the Journal of Internal Medicine.

Drug Metabolism by the Host and Gut Microbiota: A Partnership or Rivalry?

PubMed Central

2015-01-01

The importance of the gut microbiome in determining not only overall health, but also in the metabolism of drugs and xenobiotics, is rapidly emerging. It is becoming increasingly clear that the gut microbiota can act in concert with the host cells to maintain intestinal homeostasis, cometabolize drugs and xenobiotics, and alter the expression levels of drug-metabolizing enzymes and transporters and the expression and activity levels of nuclear receptors. In this myriad of activities, the impact of the microbiota may be beneficial or detrimental to the host. Given that the interplay between the gut microbiota and host cells is likely subject to high interindividual variability, this work has tremendous implications for our ability to predict accurately a particular drug’s pharmacokinetics and a given patient population’s response to drugs. In this issue of Drug Metabolism and Disposition, a series of articles is presented that illustrate the progress and challenges that lie ahead as we unravel the intricacies associated with drug and xenobiotic metabolism by the gut microbiota. These articles highlight the underlying mechanisms that are involved and the use of in vivo and in vitro approaches that are currently available for elucidating the role of the gut microbiota in drug and xenobiotic metabolism. These articles also shed light on exciting new avenues of research that may be pursued as we consider the role of the gut microbiota as an endocrine organ, a component of the brain-gut axis, and whether the gut microbiota is an appropriate and amenable target for new drugs. PMID:26261284

Dose Imprecision and Resistance: Free-Choice Medicated Feeds in Industrial Food Animal Production in the United States

PubMed Central

Love, David C.; Davis, Meghan F.; Bassett, Anna; Gunther, Andrew; Nachman, Keeve E.

2011-01-01

Background Industrial food animal production employs many of the same antibiotics or classes of antibiotics that are used in human medicine. These drugs can be administered to food animals in the form of free-choice medicated feeds (FCMF), where animals choose how much feed to consume. Routine administration of these drugs to livestock selects for microorganisms that are resistant to medications critical to the treatment of clinical infections in humans. Objectives In this commentary, we discuss the history of medicated feeds, the nature of FCMF use with regard to dose delivery, and U.S. policies that address antimicrobial drug use in food animals. Discussion FCMF makes delivering a predictable, accurate, and intended dose difficult. Overdosing can lead to animal toxicity; underdosing or inconsistent dosing can result in a failure to resolve animal diseases and in the development of antimicrobial-resistant microorganisms. Conclusions The delivery of antibiotics to food animals for reasons other than the treatment of clinically diagnosed disease, especially via free-choice feeding methods, should be reconsidered. PMID:21030337

Global Analysis Reveals Families of Chemical Motifs Enriched for hERG Inhibitors

PubMed Central

Du, Fang; Babcock, Joseph J.; Yu, Haibo; Zou, Beiyan; Li, Min

2015-01-01

Promiscuous inhibition of the human ether-à-go-go-related gene (hERG) potassium channel by drugs poses a major risk for life threatening arrhythmia and costly drug withdrawals. Current knowledge of this phenomenon is derived from a limited number of known drugs and tool compounds. However, in a diverse, naïve chemical library, it remains unclear which and to what degree chemical motifs or scaffolds might be enriched for hERG inhibition. Here we report electrophysiology measurements of hERG inhibition and computational analyses of >300,000 diverse small molecules. We identify chemical ‘communities’ with high hERG liability, containing both canonical scaffolds and structurally distinctive molecules. These data enable the development of more effective classifiers to computationally assess hERG risk. The resultant predictive models now accurately classify naïve compound libraries for tendency of hERG inhibition. Together these results provide a more complete reference map of characteristic chemical motifs for hERG liability and advance a systematic approach to rank chemical collections for cardiotoxicity risk. PMID:25700001

Taxi-Out Time Prediction for Departures at Charlotte Airport Using Machine Learning Techniques

NASA Technical Reports Server (NTRS)

Lee, Hanbong; Malik, Waqar; Jung, Yoon C.

2016-01-01

Predicting the taxi-out times of departures accurately is important for improving airport efficiency and takeoff time predictability. In this paper, we attempt to apply machine learning techniques to actual traffic data at Charlotte Douglas International Airport for taxi-out time prediction. To find the key factors affecting aircraft taxi times, surface surveillance data is first analyzed. From this data analysis, several variables, including terminal concourse, spot, runway, departure fix and weight class, are selected for taxi time prediction. Then, various machine learning methods such as linear regression, support vector machines, k-nearest neighbors, random forest, and neural networks model are applied to actual flight data. Different traffic flow and weather conditions at Charlotte airport are also taken into account for more accurate prediction. The taxi-out time prediction results show that linear regression and random forest techniques can provide the most accurate prediction in terms of root-mean-square errors. We also discuss the operational complexity and uncertainties that make it difficult to predict the taxi times accurately.

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

Exploring the knowledge behind predictions in everyday cognition: an iterated learning study.

PubMed

Stephens, Rachel G; Dunn, John C; Rao, Li-Lin; Li, Shu

2015-10-01

Making accurate predictions about events is an important but difficult task. Recent work suggests that people are adept at this task, making predictions that reflect surprisingly accurate knowledge of the distributions of real quantities. Across three experiments, we used an iterated learning procedure to explore the basis of this knowledge: to what extent is domain experience critical to accurate predictions and how accurate are people when faced with unfamiliar domains? In Experiment 1, two groups of participants, one resident in Australia, the other in China, predicted the values of quantities familiar to both (movie run-times), unfamiliar to both (the lengths of Pharaoh reigns), and familiar to one but unfamiliar to the other (cake baking durations and the lengths of Beijing bus routes). While predictions from both groups were reasonably accurate overall, predictions were inaccurate in the selectively unfamiliar domains and, surprisingly, predictions by the China-resident group were also inaccurate for a highly familiar domain: local bus route lengths. Focusing on bus routes, two follow-up experiments with Australia-resident groups clarified the knowledge and strategies that people draw upon, plus important determinants of accurate predictions. For unfamiliar domains, people appear to rely on extrapolating from (not simply directly applying) related knowledge. However, we show that people's predictions are subject to two sources of error: in the estimation of quantities in a familiar domain and extension to plausible values in an unfamiliar domain. We propose that the key to successful predictions is not simply domain experience itself, but explicit experience of relevant quantities.

Modeling covalent-modifier drugs.

PubMed

Awoonor-Williams, Ernest; Walsh, Andrew G; Rowley, Christopher N

2017-11-01

In this review, we present a summary of how computer modeling has been used in the development of covalent-modifier drugs. Covalent-modifier drugs bind by forming a chemical bond with their target. This covalent binding can improve the selectivity of the drug for a target with complementary reactivity and result in increased binding affinities due to the strength of the covalent bond formed. In some cases, this results in irreversible inhibition of the target, but some targeted covalent inhibitor (TCI) drugs bind covalently but reversibly. Computer modeling is widely used in drug discovery, but different computational methods must be used to model covalent modifiers because of the chemical bonds formed. Structural and bioinformatic analysis has identified sites of modification that could yield selectivity for a chosen target. Docking methods, which are used to rank binding poses of large sets of inhibitors, have been augmented to support the formation of protein-ligand bonds and are now capable of predicting the binding pose of covalent modifiers accurately. The pK a 's of amino acids can be calculated in order to assess their reactivity towards electrophiles. QM/MM methods have been used to model the reaction mechanisms of covalent modification. The continued development of these tools will allow computation to aid in the development of new covalent-modifier drugs. This article is part of a Special Issue entitled: Biophysics in Canada, edited by Lewis Kay, John Baenziger, Albert Berghuis and Peter Tieleman. Copyright © 2017 Elsevier B.V. All rights reserved.

Compound Structure-Independent Activity Prediction in High-Dimensional Target Space.

PubMed

Balfer, Jenny; Hu, Ye; Bajorath, Jürgen

2014-08-01

Profiling of compound libraries against arrays of targets has become an important approach in pharmaceutical research. The prediction of multi-target compound activities also represents an attractive task for machine learning with potential for drug discovery applications. Herein, we have explored activity prediction in high-dimensional target space. Different types of models were derived to predict multi-target activities. The models included naïve Bayesian (NB) and support vector machine (SVM) classifiers based upon compound structure information and NB models derived on the basis of activity profiles, without considering compound structure. Because the latter approach can be applied to incomplete training data and principally depends on the feature independence assumption, SVM modeling was not applicable in this case. Furthermore, iterative hybrid NB models making use of both activity profiles and compound structure information were built. In high-dimensional target space, NB models utilizing activity profile data were found to yield more accurate activity predictions than structure-based NB and SVM models or hybrid models. An in-depth analysis of activity profile-based models revealed the presence of correlation effects across different targets and rationalized prediction accuracy. Taken together, the results indicate that activity profile information can be effectively used to predict the activity of test compounds against novel targets. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Predicting Drug Concentration‐Time Profiles in Multiple CNS Compartments Using a Comprehensive Physiologically‐Based Pharmacokinetic Model

PubMed Central

Yamamoto, Yumi; Välitalo, Pyry A.; Huntjens, Dymphy R.; Proost, Johannes H.; Vermeulen, An; Krauwinkel, Walter; Beukers, Margot W.; van den Berg, Dirk‐Jan; Hartman, Robin; Wong, Yin Cheong; Danhof, Meindert; van Hasselt, John G. C.

2017-01-01

Drug development targeting the central nervous system (CNS) is challenging due to poor predictability of drug concentrations in various CNS compartments. We developed a generic physiologically based pharmacokinetic (PBPK) model for prediction of drug concentrations in physiologically relevant CNS compartments. System‐specific and drug‐specific model parameters were derived from literature and in silico predictions. The model was validated using detailed concentration‐time profiles from 10 drugs in rat plasma, brain extracellular fluid, 2 cerebrospinal fluid sites, and total brain tissue. These drugs, all small molecules, were selected to cover a wide range of physicochemical properties. The concentration‐time profiles for these drugs were adequately predicted across the CNS compartments (symmetric mean absolute percentage error for the model prediction was <91%). In conclusion, the developed PBPK model can be used to predict temporal concentration profiles of drugs in multiple relevant CNS compartments, which we consider valuable information for efficient CNS drug development. PMID:28891201

In silico screening of the impact of hERG channel kinetic abnormalities on channel block and susceptibility to acquired long QT syndrome

PubMed Central

Romero, Lucia; Trenor, Beatriz; Yang, Pei-Chi; Saiz, Javier; Clancy, Colleen E.

2014-01-01

Accurate diagnosis of predisposition to long QT syndrome is crucial for reducing the risk of cardiac arrhythmias. In recent years, drug-induced provocative tests have proved useful to unmask some latent mutations linked to cardiac arrhythmias. In this study we expanded this concept by developing a prototype for a computational provocative screening test to reveal genetic predisposition to acquired Long-QT Syndrome (aLTQS). We developed a computational approach to reveal the pharmacological properties of IKr blocking drugs that are most likely to cause aLQTS in the setting of subtle alterations in IKr channel gating that would be expected to result from benign genetic variants. We used the model to predict the most potentially lethal combinations of kinetic anomalies and drug properties. In doing so, we also implicitly predicted ideal inverse therapeutic properties of K channel openers that would be expected to remedy a specific defect. We systematically performed “in silico mutagenesis” by altering discrete kinetic transition rates of the Fink et al. Markov model of human IKr channels, corresponding to activation, inactivation, deactivation and recovery from inactivation of IKr channels. We then screened and identified the properties of IKr blockers that caused acquired Long QT and therefore unmasked mutant phenotypes for mild, moderate and severe variants. Mutant IKr channels were incorporated into the O’Hara et al. human ventricular action potential (AP) model and subjected to simulated application of a wide variety of IKr-drug interactions in order to identify the characteristics that selectively exacerbate the AP duration (APD) differences between wild-type and IKr mutated cells. Our results show that drugs with disparate affinities to conformation states of the IKr channel are key to amplify variants underlying susceptibility to acquired Long QT Syndrome, an effect that is especially pronounced at slow frequencies. Finally, we developed a mathematical formulation of the M54T MiRP1 latent mutation and simulated a provocative test. In this setting, application of dofetilide dramatically amplified the predicted QT interval duration in the M54T hMiRP1 mutation compared to wild-type. PMID:24631769

Towards Organs on Demand: Breakthroughs and Challenges in Models of Organogenesis.

PubMed

Francipane, Maria Giovanna; Lagasse, Eric

2016-09-01

In recent years, functional three-dimensional (3D) tissue generation in vitro has been significantly advanced by tissue-engineering methods, achieving better reproduction of complex native organs compared to conventional culture systems. This review will discuss traditional 3D cell culture techniques as well as newly developed technology platforms. These recent techniques provide new possibilities in the creation of human body parts and provide more accurate predictions of tissue response to drug and chemical challenges. Given the rapid advancement in the human induced pluripotent stem cell (iPSC) field, these platforms also hold great promise in the development of patient-specific, transplantable tissues and organs on demand.

Estimation of thermodynamic acidity constants of some penicillinase-resistant penicillins.

PubMed

Demiralay, Ebru Çubuk; Üstün, Zehra; Daldal, Y Doğan

2014-03-01

In this work, thermodynamic acidity constants (pssKa) of methicillin, oxacillin, nafcillin, cloxacilin, dicloxacillin were determined with reverse phase liquid chromatographic method (RPLC) by taking into account the effect of the activity coefficients in hydro-organic water-acetonitrile binary mixtures. From these values, thermodynamic aqueous acidity constants of these drugs were calculated by different approaches. The linear relationships established between retention factors of the species and the polarity parameter of the mobile phase (ET(N)) was proved to predict accurately retention in LC as a function of the acetonitrile content (38%, 40% and 42%, v/v). Copyright © 2013 Elsevier B.V. All rights reserved.

PhenoPredict: A disease phenome-wide drug repositioning approach towards schizophrenia drug discovery.

PubMed

Xu, Rong; Wang, QuanQiu

2015-08-01

Schizophrenia (SCZ) is a common complex disorder with poorly understood mechanisms and no effective drug treatments. Despite the high prevalence and vast unmet medical need represented by the disease, many drug companies have moved away from the development of drugs for SCZ. Therefore, alternative strategies are needed for the discovery of truly innovative drug treatments for SCZ. Here, we present a disease phenome-driven computational drug repositioning approach for SCZ. We developed a novel drug repositioning system, PhenoPredict, by inferring drug treatments for SCZ from diseases that are phenotypically related to SCZ. The key to PhenoPredict is the availability of a comprehensive drug treatment knowledge base that we recently constructed. PhenoPredict retrieved all 18 FDA-approved SCZ drugs and ranked them highly (recall=1.0, and average ranking of 8.49%). When compared to PREDICT, one of the most comprehensive drug repositioning systems currently available, in novel predictions, PhenoPredict represented clear improvements over PREDICT in Precision-Recall (PR) curves, with a significant 98.8% improvement in the area under curve (AUC) of the PR curves. In addition, we discovered many drug candidates with mechanisms of action fundamentally different from traditional antipsychotics, some of which had published literature evidence indicating their treatment benefits in SCZ patients. In summary, although the fundamental pathophysiological mechanisms of SCZ remain unknown, integrated systems approaches to studying phenotypic connections among diseases may facilitate the discovery of innovative SCZ drugs. Copyright © 2015 Elsevier Inc. All rights reserved.

Cancer Precision Medicine: Why More Is More and DNA Is Not Enough.

PubMed

Schütte, Moritz; Ogilvie, Lesley A; Rieke, Damian T; Lange, Bodo M H; Yaspo, Marie-Laure; Lehrach, Hans

2017-01-01

Every tumour is different. They arise in patients with different genomes, from cells with different epigenetic modifications, and by random processes affecting the genome and/or epigenome of a somatic cell, allowing it to escape the usual controls on its growth. Tumours and patients therefore often respond very differently to the drugs they receive. Cancer precision medicine aims to characterise the tumour (and often also the patient) to be able to predict, with high accuracy, its response to different treatments, with options ranging from the selective characterisation of a few genomic variants considered particularly important to predict the response of the tumour to specific drugs, to deep genome analysis of both tumour and patient, combined with deep transcriptome analysis of the tumour. Here, we compare the expected results of carrying out such analyses at different levels, from different size panels to a comprehensive analysis incorporating both patient and tumour at the DNA and RNA levels. In doing so, we illustrate the additional power gained by this unusually deep analysis strategy, a potential basis for a future precision medicine first strategy in cancer drug therapy. However, this is only a step along the way of increasingly detailed molecular characterisation, which in our view will, in the future, introduce additional molecular characterisation techniques, including systematic analysis of proteins and protein modification states and different types of metabolites in the tumour, systematic analysis of circulating tumour cells and nucleic acids, the use of spatially resolved analysis techniques to address the problem of tumour heterogeneity as well as the deep analyses of the immune system of the patient to, e.g., predict the response of the patient to different types of immunotherapy. Such analyses will generate data sets of even greater complexity, requiring mechanistic modelling approaches to capture enough of the complex situation in the real patient to be able to accurately predict his/her responses to all available therapies. © 2017 S. Karger AG, Basel.

First study of the evolution of the SeDeM expert system parameters based on percolation theory: Monitoring of their critical behavior.

PubMed

Galdón, Eduardo; Casas, Marta; Gayango, Manuel; Caraballo, Isidoro

2016-12-01

The deep understanding of products and processes has become a requirement for pharmaceutical industries to follow the Quality by Design principles promoted by the regulatory authorities. With this aim, SeDeM expert system was developed as a useful preformulation tool to predict the likelihood to process drugs and excipients through direct compression. SeDeM system is a step forward in the rational development of a formulation, allowing the normalisation of the rheological parameters and the identification of the weaknesses and strengths of a powder or a powder blend. However, this method is based on the assumption of a linear behavior of disordered systems. As percolation theory has demonstrated, powder blends behave as non-linear systems that can suffer abrupt changes in their properties near to geometrical phase transitions of the components. The aim of this paper was to analyze for the first time the evolution of the SeDeM parameters in drug/excipient powder blends from the point of view of the percolation theory and to compare the changes predicted by SeDeM with the predictions of Percolation theory. For this purpose, powder blends of lactose and theophylline with varying concentrations of the model drug have been prepared and the SeDeM analysis has been applied to each blend in order to monitor the evolution of their properties. On the other hand, percolation thresholds have been estimated for these powder blends where critical points have been found for important rheological parameters as the powder flow. Finally, the predictions of percolation theory and SeDeM have been compared concluding that percolation theory can complement the SeDeM method for a more accurate estimation of the Design Space. Copyright © 2016 Elsevier B.V. All rights reserved.

Developing global regression models for metabolite concentration prediction regardless of cell line.

PubMed

André, Silvère; Lagresle, Sylvain; Da Sliva, Anthony; Heimendinger, Pierre; Hannas, Zahia; Calvosa, Éric; Duponchel, Ludovic

2017-11-01

Following the Process Analytical Technology (PAT) of the Food and Drug Administration (FDA), drug manufacturers are encouraged to develop innovative techniques in order to monitor and understand their processes in a better way. Within this framework, it has been demonstrated that Raman spectroscopy coupled with chemometric tools allow to predict critical parameters of mammalian cell cultures in-line and in real time. However, the development of robust and predictive regression models clearly requires many batches in order to take into account inter-batch variability and enhance models accuracy. Nevertheless, this heavy procedure has to be repeated for every new line of cell culture involving many resources. This is why we propose in this paper to develop global regression models taking into account different cell lines. Such models are finally transferred to any culture of the cells involved. This article first demonstrates the feasibility of developing regression models, not only for mammalian cell lines (CHO and HeLa cell cultures), but also for insect cell lines (Sf9 cell cultures). Then global regression models are generated, based on CHO cells, HeLa cells, and Sf9 cells. Finally, these models are evaluated considering a fourth cell line(HEK cells). In addition to suitable predictions of glucose and lactate concentration of HEK cell cultures, we expose that by adding a single HEK-cell culture to the calibration set, the predictive ability of the regression models are substantially increased. In this way, we demonstrate that using global models, it is not necessary to consider many cultures of a new cell line in order to obtain accurate models. Biotechnol. Bioeng. 2017;114: 2550-2559. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Prediction of polypharmacological profiles of drugs by the integration of chemical, side effect, and therapeutic space.

PubMed

Cheng, Feixiong; Li, Weihua; Wu, Zengrui; Wang, Xichuan; Zhang, Chen; Li, Jie; Liu, Guixia; Tang, Yun

2013-04-22

Prediction of polypharmacological profiles of drugs enables us to investigate drug side effects and further find their new indications, i.e. drug repositioning, which could reduce the costs while increase the productivity of drug discovery. Here we describe a new computational framework to predict polypharmacological profiles of drugs by the integration of chemical, side effect, and therapeutic space. On the basis of our previous developed drug side effects database, named MetaADEDB, a drug side effect similarity inference (DSESI) method was developed for drug-target interaction (DTI) prediction on a known DTI network connecting 621 approved drugs and 893 target proteins. The area under the receiver operating characteristic curve was 0.882 ± 0.011 averaged from 100 simulated tests of 10-fold cross-validation for the DSESI method, which is comparative with drug structural similarity inference and drug therapeutic similarity inference methods. Seven new predicted candidate target proteins for seven approved drugs were confirmed by published experiments, with the successful hit rate more than 15.9%. Moreover, network visualization of drug-target interactions and off-target side effect associations provide new mechanism-of-action of three approved antipsychotic drugs in a case study. The results indicated that the proposed methods could be helpful for prediction of polypharmacological profiles of drugs.

Protein location prediction using atomic composition and global features of the amino acid sequence

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

Cherian, Betsy Sheena, E-mail: betsy.skb@gmail.com; Nair, Achuthsankar S.

2010-01-22

Subcellular location of protein is constructive information in determining its function, screening for drug candidates, vaccine design, annotation of gene products and in selecting relevant proteins for further studies. Computational prediction of subcellular localization deals with predicting the location of a protein from its amino acid sequence. For a computational localization prediction method to be more accurate, it should exploit all possible relevant biological features that contribute to the subcellular localization. In this work, we extracted the biological features from the full length protein sequence to incorporate more biological information. A new biological feature, distribution of atomic composition is effectivelymore » used with, multiple physiochemical properties, amino acid composition, three part amino acid composition, and sequence similarity for predicting the subcellular location of the protein. Support Vector Machines are designed for four modules and prediction is made by a weighted voting system. Our system makes prediction with an accuracy of 100, 82.47, 88.81 for self-consistency test, jackknife test and independent data test respectively. Our results provide evidence that the prediction based on the biological features derived from the full length amino acid sequence gives better accuracy than those derived from N-terminal alone. Considering the features as a distribution within the entire sequence will bring out underlying property distribution to a greater detail to enhance the prediction accuracy.« less

Prediction of Protein-Protein Interaction Sites with Machine-Learning-Based Data-Cleaning and Post-Filtering Procedures.

PubMed

Liu, Guang-Hui; Shen, Hong-Bin; Yu, Dong-Jun

2016-04-01

Accurately predicting protein-protein interaction sites (PPIs) is currently a hot topic because it has been demonstrated to be very useful for understanding disease mechanisms and designing drugs. Machine-learning-based computational approaches have been broadly utilized and demonstrated to be useful for PPI prediction. However, directly applying traditional machine learning algorithms, which often assume that samples in different classes are balanced, often leads to poor performance because of the severe class imbalance that exists in the PPI prediction problem. In this study, we propose a novel method for improving PPI prediction performance by relieving the severity of class imbalance using a data-cleaning procedure and reducing predicted false positives with a post-filtering procedure: First, a machine-learning-based data-cleaning procedure is applied to remove those marginal targets, which may potentially have a negative effect on training a model with a clear classification boundary, from the majority samples to relieve the severity of class imbalance in the original training dataset; then, a prediction model is trained on the cleaned dataset; finally, an effective post-filtering procedure is further used to reduce potential false positive predictions. Stringent cross-validation and independent validation tests on benchmark datasets demonstrated the efficacy of the proposed method, which exhibits highly competitive performance compared with existing state-of-the-art sequence-based PPIs predictors and should supplement existing PPI prediction methods.

Effects of packaging and heat transfer kinetics on drug-product stability during storage under uncontrolled temperature conditions.

PubMed

Nakamura, Toru; Yamaji, Takayuki; Takayama, Kozo

2013-05-01

To predict the stability of pharmaceutical preparations under uncontrolled temperature conditions accurately, a method to compute the average reaction rate constant taking into account the heat transfer from the atmosphere to the product was developed. The average reaction rate constants computed with taken into consideration heat transfer (κ(re) ) were then compared with those computed without taking heat transfer into consideration (κ(in) ). The apparent thermal diffusivity (κ(a) ) exerted some influence on the average reaction rate constant ratio (R, R = κ(re) /κ(in) ). In the regions where the κ(a) was large (above 1 h(-1) ) or very small, the value of R was close to 1. On the contrary, in the middle region (0.001-1 h(-1) ), the value of R was less than 1.The κ(a) of the central part of a large-size container and that of the central part of a paper case of 10 bottles of liquid medicine (100 mL) fell within this middle region. On the basis of the above-mentioned considerations, heat transfer may need to be taken into consideration to enable a more accurate prediction of the stability of actual pharmaceutical preparations under nonisothermal atmospheres. Copyright © 2013 Wiley Periodicals, Inc.

Raman spectroscopy detects deterioration in biomechanical properties of bone in a glucocorticoid-treated mouse model of rheumatoid arthritis

NASA Astrophysics Data System (ADS)

Maher, Jason R.; Takahata, Masahiko; Awad, Hani A.; Berger, Andrew J.

2011-08-01

Although glucocorticoids are frequently prescribed for the symptomatic management of inflammatory disorders such as rheumatoid arthritis, extended glucocorticoid exposure is the leading cause of physician-induced osteoporosis and leaves patients at a high risk of fracture. To study the biochemical effects of glucocorticoid exposure and how they might affect biomechanical properties of the bone, Raman spectra were acquired from ex vivo tibiae of glucocorticoid- and placebo-treated wild-type mice and a transgenic mouse model of rheumatoid arthritis. Statistically significant spectral differences were observed due to both treatment regimen and mouse genotype. These differences are attributed to changes in the overall bone mineral composition, as well as the degree of phosphate mineralization in tibial cortical bone. In addition, partial least squares regression was used to generate a Raman-based prediction of each tibia's biomechanical strength as quantified by a torsion test. The Raman-based predictions were as accurate as those produced by microcomputed tomography derived parameters, and more accurate than the clinically-used parameter of bone mineral density. These results suggest that Raman spectroscopy could be a valuable tool for monitoring bone biochemistry in studies of bone diseases such as osteoporosis, including tests of drugs being developed to combat these diseases.

Advantages and disadvantages of technologies for HER2 testing in breast cancer specimens.

PubMed

Furrer, Daniela; Sanschagrin, François; Jacob, Simon; Diorio, Caroline

2015-11-01

Human epidermal growth factor receptor 2 (HER2) plays a central role as a prognostic and predictive marker in breast cancer specimens. Reliable HER2 evaluation is central to determine the eligibility of patients with breast cancer to targeted anti-HER2 therapies such as trastuzumab and lapatinib. Presently, several methods exist for the determination of HER2 status at different levels (protein, RNA, and DNA level). In this review, we discuss the main advantages and disadvantages of the techniques developed so far for the evaluation of HER2 status in breast cancer specimens. Each technique has its own advantages and disadvantages. It is therefore not surprising that no consensus has been reached so far on which technique is the best for the determination of HER2 status. Currently, emphasis must be put on standardization of procedures, internal and external quality control assessment, and competency evaluation of already existing methods to ensure accurate, reliable, and clinically meaningful test results. Development of new robust and accurate diagnostic assays should also be encouraged. In addition, large clinical trials are warranted to identify the technique that most reliably predicts a positive response to anti-HER2 drugs. Copyright© by the American Society for Clinical Pathology.

The mouse beam walking assay offers improved sensitivity over the mouse rotarod in determining motor coordination deficits induced by benzodiazepines.

PubMed

Stanley, Joanna L; Lincoln, Rachael J; Brown, Terry A; McDonald, Louise M; Dawson, Gerard R; Reynolds, David S

2005-05-01

The mouse rotarod test of motor coordination/sedation is commonly used to predict clinical sedation caused by novel drugs. However, past experience suggests that it lacks the desired degree of sensitivity to be predictive of effects in humans. For example, the benzodiazepine, bretazenil, showed little impairment of mouse rotarod performance, but marked sedation in humans. The aim of the present study was to assess whether the mouse beam walking assay demonstrates: (i) an increased sensitivity over the rotarod and (ii) an increased ability to predict clinically sedative doses of benzodiazepines. The study compared the effects of the full benzodiazepine agonists, diazepam and lorazepam, and the partial agonist, bretazenil, on the mouse rotarod and beam walking assays. Diazepam and lorazepam significantly impaired rotarod performance, although relatively high GABA-A receptor occupancy was required (72% and 93%, respectively), whereas beam walking performance was significantly affected at approximately 30% receptor occupancy. Bretazenil produced significant deficits at 90% and 53% receptor occupancy on the rotarod and beam walking assays, respectively. The results suggest that the mouse beam walking assay is a more sensitive tool for determining benzodiazepine-induced motor coordination deficits than the rotarod. Furthermore, the GABA-A receptor occupancy values at which significant deficits were determined in the beam walking assay are comparable with those observed in clinical positron emission tomography studies using sedative doses of benzodiazepines. These data suggest that the beam walking assay may be able to more accurately predict the clinically sedative doses of novel benzodiazepine-like drugs.

Data-driven prediction of adverse drug reactions induced by drug drug interactions

DTIC Science & Technology

2017-06-08

currently on the market and for which drug-protein interaction information is available . These predictions are publicly accessible at http://avoid...associated with these ADRs via DDIs. We made the predictions publicly available via internet access. Keywords: Drug-drug interactions, Adverse drug reactions...ˆDeceased Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research

Drug-Target Interaction Prediction through Label Propagation with Linear Neighborhood Information.

PubMed

Zhang, Wen; Chen, Yanlin; Li, Dingfang

2017-11-25

Interactions between drugs and target proteins provide important information for the drug discovery. Currently, experiments identified only a small number of drug-target interactions. Therefore, the development of computational methods for drug-target interaction prediction is an urgent task of theoretical interest and practical significance. In this paper, we propose a label propagation method with linear neighborhood information (LPLNI) for predicting unobserved drug-target interactions. Firstly, we calculate drug-drug linear neighborhood similarity in the feature spaces, by considering how to reconstruct data points from neighbors. Then, we take similarities as the manifold of drugs, and assume the manifold unchanged in the interaction space. At last, we predict unobserved interactions between known drugs and targets by using drug-drug linear neighborhood similarity and known drug-target interactions. The experiments show that LPLNI can utilize only known drug-target interactions to make high-accuracy predictions on four benchmark datasets. Furthermore, we consider incorporating chemical structures into LPLNI models. Experimental results demonstrate that the model with integrated information (LPLNI-II) can produce improved performances, better than other state-of-the-art methods. The known drug-target interactions are an important information source for computational predictions. The usefulness of the proposed method is demonstrated by cross validation and the case study.

Organ/body-on-a-chip based on microfluidic technology for drug discovery.

PubMed

Kimura, Hiroshi; Sakai, Yasuyuki; Fujii, Teruo

2018-02-01

Although animal experiments are indispensable for preclinical screening in the drug discovery process, various issues such as ethical considerations and species differences remain. To solve these issues, cell-based assays using human-derived cells have been actively pursued. However, it remains difficult to accurately predict drug efficacy, toxicity, and organs interactions, because cultivated cells often do not retain their original organ functions and morphologies in conventional in vitro cell culture systems. In the μTAS research field, which is a part of biochemical engineering, the technologies of organ-on-a-chip, based on microfluidic devices built using microfabrication, have been widely studied recently as a novel in vitro organ model. Since it is possible to physically and chemically mimic the in vitro environment by using microfluidic device technology, maintenance of cellular function and morphology, and replication of organ interactions can be realized using organ-on-a-chip devices. So far, functions of various organs and tissues, such as the lung, liver, kidney, and gut have been reproduced as in vitro models. Furthermore, a body-on-a-chip, integrating multi organ functions on a microfluidic device, has also been proposed for prediction of organ interactions. We herein provide a background of microfluidic systems, organ-on-a-chip, Body-on-a-chip technologies, and their challenges in the future. Copyright © 2017 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.

Chimeric mice with humanized liver: Application in drug metabolism and pharmacokinetics studies for drug discovery.

PubMed

Naritomi, Yoichi; Sanoh, Seigo; Ohta, Shigeru

2018-02-01

Predicting human drug metabolism and pharmacokinetics (PK) is key to drug discovery. In particular, it is important to predict human PK, metabolite profiles and drug-drug interactions (DDIs). Various methods have been used for such predictions, including in vitro metabolic studies using human biological samples, such as hepatic microsomes and hepatocytes, and in vivo studies using experimental animals. However, prediction studies using these methods are often inconclusive due to discrepancies between in vitro and in vivo results, and interspecies differences in drug metabolism. Further, the prediction methods have changed from qualitative to quantitative to solve these issues. Chimeric mice with humanized liver have been developed, in which mouse liver cells are mostly replaced with human hepatocytes. Since human drug metabolizing enzymes are expressed in the liver of these mice, they are regarded as suitable models for mimicking the drug metabolism and PK observed in humans; therefore, these mice are useful for predicting human drug metabolism and PK. In this review, we discuss the current state, issues, and future directions of predicting human drug metabolism and PK using chimeric mice with humanized liver in drug discovery. Copyright © 2017 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.

Drug-induced life-threatening arrhythmias and sudden cardiac death: A clinical perspective of long QT, short QT and Brugada syndromes.

PubMed

Ramalho, Diogo; Freitas, João

2018-05-01

Sudden cardiac death is a major public health challenge, which can be caused by genetic or acquired structural or electrophysiological abnormalities. These abnormalities include hereditary channelopathies: long QT, short QT and Brugada syndromes. These syndromes are a notable concern, particularly in young people, due to their high propensity for severe ventricular arrhythmias and sudden cardiac death. Current evidence suggests the involvement of an increasing number of drugs in acquired forms of long QT and Brugada syndromes. However, drug-induced short QT syndrome is still a rarely reported condition. Therefore, there has been speculation on its clinical significance, since few fatal arrhythmias and sudden cardiac death cases have been described so far. Drug-induced proarrhythmia is a growing challenge for physicians, regulatory agencies and the pharmaceutical industry. Physicians should weigh the risks of potentially fatal outcomes against the therapeutic benefits, when making decisions about drug prescriptions. Growing concerns about its safety and the need for more accurate predictive models for drug-induced fatal outcomes justify further research in these fields. The aim of this article is to comprehensively and critically review the recently published evidence with regard to drug-induced life-threatening arrhythmias and sudden cardiac death. This article will take into account the provision of data to physicians that are useful in the identification of the culprit drugs, and thus, contribute to the prompt recognition and management of these serious clinical conditions. Copyright © 2018 Sociedade Portuguesa de Cardiologia. Publicado por Elsevier España, S.L.U. All rights reserved.

Logic models to predict continuous outputs based on binary inputs with an application to personalized cancer therapy

PubMed Central

Knijnenburg, Theo A.; Klau, Gunnar W.; Iorio, Francesco; Garnett, Mathew J.; McDermott, Ultan; Shmulevich, Ilya; Wessels, Lodewyk F. A.

2016-01-01

Mining large datasets using machine learning approaches often leads to models that are hard to interpret and not amenable to the generation of hypotheses that can be experimentally tested. We present ‘Logic Optimization for Binary Input to Continuous Output’ (LOBICO), a computational approach that infers small and easily interpretable logic models of binary input features that explain a continuous output variable. Applying LOBICO to a large cancer cell line panel, we find that logic combinations of multiple mutations are more predictive of drug response than single gene predictors. Importantly, we show that the use of the continuous information leads to robust and more accurate logic models. LOBICO implements the ability to uncover logic models around predefined operating points in terms of sensitivity and specificity. As such, it represents an important step towards practical application of interpretable logic models. PMID:27876821

Logic models to predict continuous outputs based on binary inputs with an application to personalized cancer therapy.

PubMed

Knijnenburg, Theo A; Klau, Gunnar W; Iorio, Francesco; Garnett, Mathew J; McDermott, Ultan; Shmulevich, Ilya; Wessels, Lodewyk F A

2016-11-23

Mining large datasets using machine learning approaches often leads to models that are hard to interpret and not amenable to the generation of hypotheses that can be experimentally tested. We present 'Logic Optimization for Binary Input to Continuous Output' (LOBICO), a computational approach that infers small and easily interpretable logic models of binary input features that explain a continuous output variable. Applying LOBICO to a large cancer cell line panel, we find that logic combinations of multiple mutations are more predictive of drug response than single gene predictors. Importantly, we show that the use of the continuous information leads to robust and more accurate logic models. LOBICO implements the ability to uncover logic models around predefined operating points in terms of sensitivity and specificity. As such, it represents an important step towards practical application of interpretable logic models.

Investigation of an alternative generic model for predicting pharmacokinetic changes during physiological stress.

PubMed

Peng, Henry T; Edginton, Andrea N; Cheung, Bob

2013-10-01

Physiologically based pharmacokinetic models were developed using MATLAB Simulink® and PK-Sim®. We compared the capability and usefulness of these two models by simulating pharmacokinetic changes of midazolam under exercise and heat stress to verify the usefulness of MATLAB Simulink® as a generic PBPK modeling software. Although both models show good agreement with experimental data obtained under resting condition, their predictions of pharmacokinetics changes are less accurate in the stressful conditions. However, MATLAB Simulink® may be more flexible to include physiologically based processes such as oral absorption and simulate various stress parameters such as stress intensity, duration and timing of drug administration to improve model performance. Further work will be conducted to modify algorithms in our generic model developed using MATLAB Simulink® and to investigate pharmacokinetics under other physiological stress such as trauma. © The Author(s) 2013.

Prediction of Surface and pH-Specific Binding of Peptides to Metal and Oxide Nanoparticles

NASA Astrophysics Data System (ADS)

Heinz, Hendrik; Lin, Tzu-Jen; Emami, Fateme Sadat; Ramezani-Dakhel, Hadi; Naik, Rajesh; Knecht, Marc; Perry, Carole C.; Huang, Yu

2015-03-01

The mechanism of specific peptide adsorption onto metallic and oxidic nanostructures has been elucidated in atomic resolution using novel force fields and surface models in comparison to measurements. As an example, variations in peptide adsorption on Pd and Pt nanoparticles depending on shape, size, and location of peptides on specific bounding facets are explained. Accurate computational predictions of reaction rates in C-C coupling reactions using particle models derived from HE-XRD and PDF data illustrate the utility of computational methods for the rational design of new catalysts. On oxidic nanoparticles such as silica and apatites, it is revealed how changes in pH lead to similarity scores of attracted peptides lower than 20%, supported by appropriate model surfaces and data from adsorption isotherms. The results demonstrate how new computational methods can support the design of nanoparticle carriers for drug release and the understanding of calcification mechanisms in the human body.

Drug Distribution. Part 1. Models to Predict Membrane Partitioning.

PubMed

Nagar, Swati; Korzekwa, Ken

2017-03-01

Tissue partitioning is an important component of drug distribution and half-life. Protein binding and lipid partitioning together determine drug distribution. Two structure-based models to predict partitioning into microsomal membranes are presented. An orientation-based model was developed using a membrane template and atom-based relative free energy functions to select drug conformations and orientations for neutral and basic drugs. The resulting model predicts the correct membrane positions for nine compounds tested, and predicts the membrane partitioning for n = 67 drugs with an average fold-error of 2.4. Next, a more facile descriptor-based model was developed for acids, neutrals and bases. This model considers the partitioning of neutral and ionized species at equilibrium, and can predict membrane partitioning with an average fold-error of 2.0 (n = 92 drugs). Together these models suggest that drug orientation is important for membrane partitioning and that membrane partitioning can be well predicted from physicochemical properties.

Remaining dischargeable time prediction for lithium-ion batteries using unscented Kalman filter

NASA Astrophysics Data System (ADS)

Dong, Guangzhong; Wei, Jingwen; Chen, Zonghai; Sun, Han; Yu, Xiaowei

2017-10-01

To overcome the range anxiety, one of the important strategies is to accurately predict the range or dischargeable time of the battery system. To accurately predict the remaining dischargeable time (RDT) of a battery, a RDT prediction framework based on accurate battery modeling and state estimation is presented in this paper. Firstly, a simplified linearized equivalent-circuit-model is developed to simulate the dynamic characteristics of a battery. Then, an online recursive least-square-algorithm method and unscented-Kalman-filter are employed to estimate the system matrices and SOC at every prediction point. Besides, a discrete wavelet transform technique is employed to capture the statistical information of past dynamics of input currents, which are utilized to predict the future battery currents. Finally, the RDT can be predicted based on the battery model, SOC estimation results and predicted future battery currents. The performance of the proposed methodology has been verified by a lithium-ion battery cell. Experimental results indicate that the proposed method can provide an accurate SOC and parameter estimation and the predicted RDT can solve the range anxiety issues.

Predicting drug-target interactions using restricted Boltzmann machines.

PubMed

Wang, Yuhao; Zeng, Jianyang

2013-07-01

In silico prediction of drug-target interactions plays an important role toward identifying and developing new uses of existing or abandoned drugs. Network-based approaches have recently become a popular tool for discovering new drug-target interactions (DTIs). Unfortunately, most of these network-based approaches can only predict binary interactions between drugs and targets, and information about different types of interactions has not been well exploited for DTI prediction in previous studies. On the other hand, incorporating additional information about drug-target relationships or drug modes of action can improve prediction of DTIs. Furthermore, the predicted types of DTIs can broaden our understanding about the molecular basis of drug action. We propose a first machine learning approach to integrate multiple types of DTIs and predict unknown drug-target relationships or drug modes of action. We cast the new DTI prediction problem into a two-layer graphical model, called restricted Boltzmann machine, and apply a practical learning algorithm to train our model and make predictions. Tests on two public databases show that our restricted Boltzmann machine model can effectively capture the latent features of a DTI network and achieve excellent performance on predicting different types of DTIs, with the area under precision-recall curve up to 89.6. In addition, we demonstrate that integrating multiple types of DTIs can significantly outperform other predictions either by simply mixing multiple types of interactions without distinction or using only a single interaction type. Further tests show that our approach can infer a high fraction of novel DTIs that has been validated by known experiments in the literature or other databases. These results indicate that our approach can have highly practical relevance to DTI prediction and drug repositioning, and hence advance the drug discovery process. Software and datasets are available on request. Supplementary data are available at Bioinformatics online.

Understanding and predicting binding between human leukocyte antigens (HLAs) and peptides by network analysis.

PubMed

Luo, Heng; Ye, Hao; Ng, Hui; Shi, Leming; Tong, Weida; Mattes, William; Mendrick, Donna; Hong, Huixiao

2015-01-01

As the major histocompatibility complex (MHC), human leukocyte antigens (HLAs) are one of the most polymorphic genes in humans. Patients carrying certain HLA alleles may develop adverse drug reactions (ADRs) after taking specific drugs. Peptides play an important role in HLA related ADRs as they are the necessary co-binders of HLAs with drugs. Many experimental data have been generated for understanding HLA-peptide binding. However, efficiently utilizing the data for understanding and accurately predicting HLA-peptide binding is challenging. Therefore, we developed a network analysis based method to understand and predict HLA-peptide binding. Qualitative Class I HLA-peptide binding data were harvested and prepared from four major databases. An HLA-peptide binding network was constructed from this dataset and modules were identified by the fast greedy modularity optimization algorithm. To examine the significance of signals in the yielded models, the modularity was compared with the modularity values generated from 1,000 random networks. The peptides and HLAs in the modules were characterized by similarity analysis. The neighbor-edges based and unbiased leverage algorithm (Nebula) was developed for predicting HLA-peptide binding. Leave-one-out (LOO) validations and two-fold cross-validations were conducted to evaluate the performance of Nebula using the constructed HLA-peptide binding network. Nine modules were identified from analyzing the HLA-peptide binding network with a highest modularity compared to all the random networks. Peptide length and functional side chains of amino acids at certain positions of the peptides were different among the modules. HLA sequences were module dependent to some extent. Nebula archived an overall prediction accuracy of 0.816 in the LOO validations and average accuracy of 0.795 in the two-fold cross-validations and outperformed the method reported in the literature. Network analysis is a useful approach for analyzing large and sparse datasets such as the HLA-peptide binding dataset. The modules identified from the network analysis clustered peptides and HLAs with similar sequences and properties of amino acids. Nebula performed well in the predictions of HLA-peptide binding. We demonstrated that network analysis coupled with Nebula is an efficient approach to understand and predict HLA-peptide binding interactions and thus, could further our understanding of ADRs.

Understanding and predicting binding between human leukocyte antigens (HLAs) and peptides by network analysis

PubMed Central

2015-01-01

Background As the major histocompatibility complex (MHC), human leukocyte antigens (HLAs) are one of the most polymorphic genes in humans. Patients carrying certain HLA alleles may develop adverse drug reactions (ADRs) after taking specific drugs. Peptides play an important role in HLA related ADRs as they are the necessary co-binders of HLAs with drugs. Many experimental data have been generated for understanding HLA-peptide binding. However, efficiently utilizing the data for understanding and accurately predicting HLA-peptide binding is challenging. Therefore, we developed a network analysis based method to understand and predict HLA-peptide binding. Methods Qualitative Class I HLA-peptide binding data were harvested and prepared from four major databases. An HLA-peptide binding network was constructed from this dataset and modules were identified by the fast greedy modularity optimization algorithm. To examine the significance of signals in the yielded models, the modularity was compared with the modularity values generated from 1,000 random networks. The peptides and HLAs in the modules were characterized by similarity analysis. The neighbor-edges based and unbiased leverage algorithm (Nebula) was developed for predicting HLA-peptide binding. Leave-one-out (LOO) validations and two-fold cross-validations were conducted to evaluate the performance of Nebula using the constructed HLA-peptide binding network. Results Nine modules were identified from analyzing the HLA-peptide binding network with a highest modularity compared to all the random networks. Peptide length and functional side chains of amino acids at certain positions of the peptides were different among the modules. HLA sequences were module dependent to some extent. Nebula archived an overall prediction accuracy of 0.816 in the LOO validations and average accuracy of 0.795 in the two-fold cross-validations and outperformed the method reported in the literature. Conclusions Network analysis is a useful approach for analyzing large and sparse datasets such as the HLA-peptide binding dataset. The modules identified from the network analysis clustered peptides and HLAs with similar sequences and properties of amino acids. Nebula performed well in the predictions of HLA-peptide binding. We demonstrated that network analysis coupled with Nebula is an efficient approach to understand and predict HLA-peptide binding interactions and thus, could further our understanding of ADRs. PMID:26424483

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.

Toward preclinical predictive drug testing for metabolism and hepatotoxicity by using in vitro models derived from human embryonic stem cells and human cell lines - a report on the Vitrocellomics EU-project.

PubMed

Mandenius, Carl-Fredrik; Andersson, Tommy B; Alves, Paula M; Batzl-Hartmann, Christine; Björquist, Petter; Carrondo, Manuel J T; Chesne, Christophe; Coecke, Sandra; Edsbagge, Josefina; Fredriksson, J Magnus; Gerlach, Jörg C; Heinzle, Elmar; Ingelman-Sundberg, Magnus; Johansson, Inger; Küppers-Munther, Barbara; Müller-Vieira, Ursula; Noor, Fozia; Zeilinger, Katrin

2011-05-01

Drug-induced liver injury is a common reason for drug attrition in late clinical phases, and even for post-launch withdrawals. As a consequence, there is a broad consensus in the pharmaceutical industry, and within regulatory authorities, that a significant improvement of the current in vitro test methodologies for accurate assessment and prediction of such adverse effects is needed. For this purpose, appropriate in vivo-like hepatic in vitro models are necessary, in addition to novel sources of human hepatocytes. In this report, we describe recent and ongoing research toward the use of human embryonic stem cell (hESC)-derived hepatic cells, in conjunction with new and improved test methods, for evaluating drug metabolism and hepatotoxicity. Recent progress on the directed differentiation of human embryonic stem cells to the functional hepatic phenotype is reported, as well as the development and adaptation of bioreactors and toxicity assay technologies for the testing of hepatic cells. The aim of achieving a testing platform for metabolism and hepatotoxicity assessment, based on hESC-derived hepatic cells, has advanced markedly in the last 2-3 years. However, great challenges still remain, before such new test systems could be routinely used by the industry. In particular, we give an overview of results from the Vitrocellomics project (EU Framework 6) and discuss these in relation to the current state-of-the-art and the remaining difficulties, with suggestions on how to proceed before such in vitro systems can be implemented in industrial discovery and development settings and in regulatory acceptance. 2011 FRAME.

Carprofen pharmacokinetics in plasma and in control and inflamed canine tissue fluid using in vivo ultrafiltration.

PubMed

Messenger, K M; Wofford, J A; Papich, M G

2016-02-01

Measurement of unbound drug concentrations at their sites of action is necessary for accurate PK/PD modeling. The objective of this study was to determine the unbound concentration of carprofen in canine interstitial fluid (ISF) using in vivo ultrafiltration and to compare pharmacokinetic parameters of free carprofen concentrations between inflamed and control tissue sites. We hypothesized that active concentrations of carprofen would exhibit different dispositions in ISF between inflamed vs. normal tissues. Bilateral ultrafiltration probes were placed subcutaneously in six healthy Beagle dogs 12 h prior to induction of inflammation. Two milliliters of either 2% carrageenan or saline control was injected subcutaneously at each probe site, 12 h prior to intravenous carprofen (4 mg/kg) administration. Plasma and ISF samples were collected at regular intervals for 72 h, and carprofen concentrations were determined using HPLC. Prostaglandin E2 (PGE2 ) concentrations were quantified in ISF using ELISA. Unbound carprofen concentrations were higher in ISF compared with predicted unbound plasma drug concentrations. Concentrations were not significantly higher in inflamed ISF compared with control ISF. Compartmental modeling was used to generate pharmacokinetic parameter estimates, which were not significantly different between sites. Terminal half-life (T½) was longer in the ISF compared with plasma. PGE2 in ISF decreased following administration of carprofen. In vivo ultrafiltration is a reliable method to determine unbound carprofen in ISF, and that disposition of unbound drug into tissue is much higher than predicted from unbound drug concentration in plasma. However, concentrations and pharmacokinetic parameter estimates are not significantly different in inflamed vs. un-inflamed tissues. © 2015 John Wiley & Sons Ltd.

On the validity of the dispersion model of hepatic drug elimination when intravascular transit time densities are long-tailed.

PubMed

Weiss, M; Stedtler, C; Roberts, M S

1997-09-01

The dispersion model with mixed boundary conditions uses a single parameter, the dispersion number, to describe the hepatic elimination of xenobiotics and endogenous substances. An implicit a priori assumption of the model is that the transit time density of intravascular indicators is approximately by an inverse Gaussian distribution. This approximation is limited in that the model poorly describes the tail part of the hepatic outflow curves of vascular indicators. A sum of two inverse Gaussian functions is proposed as an alternative, more flexible empirical model for transit time densities of vascular references. This model suggests that a more accurate description of the tail portion of vascular reference curves yields an elimination rate constant (or intrinsic clearance) which is 40% less than predicted by the dispersion model with mixed boundary conditions. The results emphasize the need to accurately describe outflow curves in using them as a basis for determining pharmacokinetic parameters using hepatic elimination models.

Interspecies scaling and prediction of human clearance: comparison of small- and macro-molecule drugs

PubMed Central

Huh, Yeamin; Smith, David E.; Feng, Meihau Rose

2014-01-01

Human clearance prediction for small- and macro-molecule drugs was evaluated and compared using various scaling methods and statistical analysis.Human clearance is generally well predicted using single or multiple species simple allometry for macro- and small-molecule drugs excreted renally.The prediction error is higher for hepatically eliminated small-molecules using single or multiple species simple allometry scaling, and it appears that the prediction error is mainly associated with drugs with low hepatic extraction ratio (Eh). The error in human clearance prediction for hepatically eliminated small-molecules was reduced using scaling methods with a correction of maximum life span (MLP) or brain weight (BRW).Human clearance of both small- and macro-molecule drugs is well predicted using the monkey liver blood flow method. Predictions using liver blood flow from other species did not work as well, especially for the small-molecule drugs. PMID:21892879

Guidelines for School-Based Alcohol and Drug Abuse Prevention Programs.

ERIC Educational Resources Information Center

California State Dept. of Education, Sacramento.

This paper contains the revised drug education guidelines for the state of California, which emphasize prevention of alcohol and drug abuse. The materials define school-based alcohol and drug abuse prevention programming as a comprehensive process that not only provides students with accurate information about alcohol and drugs, but also enhances…

75 FR 60308 - New Animal Drugs for Use in Animal Feeds; Melengestrol

Federal Register 2010, 2011, 2012, 2013, 2014

2010-09-30

.... FDA-2010-N-0002] New Animal Drugs for Use in Animal Feeds; Melengestrol AGENCY: Food and Drug... amending the animal drug regulations to more accurately reflect the recent approval of two supplemental new animal drug applications (NADAs) filed by Pharmacia & Upjohn Co., a Division of Pfizer, Inc. The...

Prediction of the cause, effects, and prevention of drug-nutrient interactions using attributes and attribute values.

PubMed

Roe, D A

1985-01-01

Drug-nutrient interactions and their adverse outcomes have previously been identified by observation, investigation, and literature reports. Knowing the attributes of the drugs, availability of knowledge base management systems for microcomputer use can facilitate prediction of the mechanism and the effects of drug-nutrient interactions. Examples used to illustrate this approach are prediction of lactose intolerance in drug-induced malabsorption, and prediction of the mechanism responsible for drug-induced flush reactions. In the future we see that there may be many opportunities to use this system further in the investigation of complex drug-nutrient interactions.

Semi-supervised protein subcellular localization.

PubMed

Xu, Qian; Hu, Derek Hao; Xue, Hong; Yu, Weichuan; Yang, Qiang

2009-01-30

Protein subcellular localization is concerned with predicting the location of a protein within a cell using computational method. The location information can indicate key functionalities of proteins. Accurate predictions of subcellular localizations of protein can aid the prediction of protein function and genome annotation, as well as the identification of drug targets. Computational methods based on machine learning, such as support vector machine approaches, have already been widely used in the prediction of protein subcellular localization. However, a major drawback of these machine learning-based approaches is that a large amount of data should be labeled in order to let the prediction system learn a classifier of good generalization ability. However, in real world cases, it is laborious, expensive and time-consuming to experimentally determine the subcellular localization of a protein and prepare instances of labeled data. In this paper, we present an approach based on a new learning framework, semi-supervised learning, which can use much fewer labeled instances to construct a high quality prediction model. We construct an initial classifier using a small set of labeled examples first, and then use unlabeled instances to refine the classifier for future predictions. Experimental results show that our methods can effectively reduce the workload for labeling data using the unlabeled data. Our method is shown to enhance the state-of-the-art prediction results of SVM classifiers by more than 10%.

Uniting Cheminformatics and Chemical Theory To Predict the Intrinsic Aqueous Solubility of Crystalline Druglike Molecules

PubMed Central

2014-01-01

We present four models of solution free-energy prediction for druglike molecules utilizing cheminformatics descriptors and theoretically calculated thermodynamic values. We make predictions of solution free energy using physics-based theory alone and using machine learning/quantitative structure–property relationship (QSPR) models. We also develop machine learning models where the theoretical energies and cheminformatics descriptors are used as combined input. These models are used to predict solvation free energy. While direct theoretical calculation does not give accurate results in this approach, machine learning is able to give predictions with a root mean squared error (RMSE) of ∼1.1 log S units in a 10-fold cross-validation for our Drug-Like-Solubility-100 (DLS-100) dataset of 100 druglike molecules. We find that a model built using energy terms from our theoretical methodology as descriptors is marginally less predictive than one built on Chemistry Development Kit (CDK) descriptors. Combining both sets of descriptors allows a further but very modest improvement in the predictions. However, in some cases, this is a statistically significant enhancement. These results suggest that there is little complementarity between the chemical information provided by these two sets of descriptors, despite their different sources and methods of calculation. Our machine learning models are also able to predict the well-known Solubility Challenge dataset with an RMSE value of 0.9–1.0 log S units. PMID:24564264

G‐LoSA: An efficient computational tool for local structure‐centric biological studies and drug design

PubMed Central

2016-01-01

Abstract Molecular recognition by protein mostly occurs in a local region on the protein surface. Thus, an efficient computational method for accurate characterization of protein local structural conservation is necessary to better understand biology and drug design. We present a novel local structure alignment tool, G‐LoSA. G‐LoSA aligns protein local structures in a sequence order independent way and provides a GA‐score, a chemical feature‐based and size‐independent structure similarity score. Our benchmark validation shows the robust performance of G‐LoSA to the local structures of diverse sizes and characteristics, demonstrating its universal applicability to local structure‐centric comparative biology studies. In particular, G‐LoSA is highly effective in detecting conserved local regions on the entire surface of a given protein. In addition, the applications of G‐LoSA to identifying template ligands and predicting ligand and protein binding sites illustrate its strong potential for computer‐aided drug design. We hope that G‐LoSA can be a useful computational method for exploring interesting biological problems through large‐scale comparison of protein local structures and facilitating drug discovery research and development. G‐LoSA is freely available to academic users at http://im.compbio.ku.edu/GLoSA/. PMID:26813336

G-LoSA: An efficient computational tool for local structure-centric biological studies and drug design.

PubMed

Lee, Hui Sun; Im, Wonpil

2016-04-01

Molecular recognition by protein mostly occurs in a local region on the protein surface. Thus, an efficient computational method for accurate characterization of protein local structural conservation is necessary to better understand biology and drug design. We present a novel local structure alignment tool, G-LoSA. G-LoSA aligns protein local structures in a sequence order independent way and provides a GA-score, a chemical feature-based and size-independent structure similarity score. Our benchmark validation shows the robust performance of G-LoSA to the local structures of diverse sizes and characteristics, demonstrating its universal applicability to local structure-centric comparative biology studies. In particular, G-LoSA is highly effective in detecting conserved local regions on the entire surface of a given protein. In addition, the applications of G-LoSA to identifying template ligands and predicting ligand and protein binding sites illustrate its strong potential for computer-aided drug design. We hope that G-LoSA can be a useful computational method for exploring interesting biological problems through large-scale comparison of protein local structures and facilitating drug discovery research and development. G-LoSA is freely available to academic users at http://im.compbio.ku.edu/GLoSA/. © 2016 The Protein Society.

Predicting the extent of metabolism using in vitro permeability rate measurements and in silico permeability rate predictions

PubMed Central

Hosey, Chelsea M; Benet, Leslie Z

2015-01-01

The Biopharmaceutics Drug Disposition Classification System (BDDCS) can be utilized to predict drug disposition, including interactions with other drugs and transporter or metabolizing enzyme effects based on the extent of metabolism and solubility of a drug. However, defining the extent of metabolism relies upon clinical data. Drugs exhibiting high passive intestinal permeability rates are extensively metabolized. Therefore, we aimed to determine if in vitro measures of permeability rate or in silico permeability rate predictions could predict the extent of metabolism, to determine a reference compound representing the permeability rate above which compounds would be expected to be extensively metabolized, and to predict the major route of elimination of compounds in a two-tier approach utilizing permeability rate and a previously published model predicting the major route of elimination of parent drug. Twenty-two in vitro permeability rate measurement data sets in Caco-2 and MDCK cell lines and PAMPA were collected from the literature, while in silico permeability rate predictions were calculated using ADMET Predictor™ or VolSurf+. The potential for permeability rate to differentiate between extensively and poorly metabolized compounds was analyzed with receiver operating characteristic curves. Compounds that yielded the highest sensitivity-specificity average were selected as permeability rate reference standards. The major route of elimination of poorly permeable drugs was predicted by our previously published model and the accuracies and predictive values were calculated. The areas under the receiver operating curves were >0.90 for in vitro measures of permeability rate and >0.80 for the VolSurf+ model of permeability rate, indicating they were able to predict the extent of metabolism of compounds. Labetalol and zidovudine predicted greater than 80% of extensively metabolized drugs correctly and greater than 80% of poorly metabolized drugs correctly in Caco-2 and MDCK, respectively, while theophylline predicted greater than 80% of extensively and poorly metabolized drugs correctly in PAMPA. A two-tier approach predicting elimination route predicts 72±9%, 49±10%, and 66±7% of extensively metabolized, biliarily eliminated, and renally eliminated parent drugs correctly when the permeability rate is predicted in silico and 74±7%, 85±2%, and 73±8% of extensively metabolized, biliarily eliminated, and renally eliminated parent drugs correctly, respectively when the permeability rate is determined in vitro. PMID:25816851

Secretome profile analysis of multidrug-resistant, monodrug-resistant and drug-susceptible Mycobacterium tuberculosis.

PubMed

Putim, Chanyanuch; Phaonakrop, Narumon; Jaresitthikunchai, Janthima; Gamngoen, Ratikorn; Tragoolpua, Khajornsak; Intorasoot, Sorasak; Anukool, Usanee; Tharincharoen, Chayada Sitthidet; Phunpae, Ponrut; Tayapiwatana, Chatchai; Kasinrerk, Watchara; Roytrakul, Sittiruk; Butr-Indr, Bordin

2018-03-01

The emergence of drug-resistant tuberculosis has generated great concern in the control of tuberculosis and HIV/TB patients have established severe complications that are difficult to treat. Although, the gold standard of drug-susceptibility testing is highly accurate and efficient, it is time-consuming. Diagnostic biomarkers are, therefore, necessary in discriminating between infection from drug-resistant and drug-susceptible strains. One strategy that aids to effectively control tuberculosis is understanding the function of secreting proteins that mycobacteria use to manipulate the host cellular defenses. In this study, culture filtrate proteins from Mycobacterium tuberculosis H37Rv, isoniazid-resistant, rifampicin-resistant and multidrug-resistant strains were gathered and profiled by shotgun-proteomics technique. Mass spectrometric analysis of the secreted proteome identified several proteins, of which 837, 892, 838 and 850 were found in M. tuberculosis H37Rv, isoniazid-resistant, rifampicin-resistant and multidrug-resistant strains, respectively. These proteins have been implicated in various cellular processes, including biological adhesion, biological regulation, developmental process, immune system process localization, cellular process, cellular component organization or biogenesis, metabolic process, and response to stimulus. Analysis based on STITCH database predicted the interaction of DNA topoisomerase I, 3-oxoacyl-(acyl-carrier protein) reductase, ESAT-6-like protein, putative prophage phiRv2 integrase, and 3-phosphoshikimate 1-carboxyvinyltransferase with isoniazid, rifampicin, pyrazinamide, ethambutol and streptomycin, suggesting putative roles in controlling the anti-tuberculosis ability. However, several proteins with no interaction with all first-line anti-tuberculosis drugs might be used as markers for mycobacterial identification.

Computational Analysis of Molecular Interaction Networks Underlying Change of HIV-1 Resistance to Selected Reverse Transcriptase Inhibitors

PubMed Central

Kierczak, Marcin; Dramiński, Michał; Koronacki, Jacek; Komorowski, Jan

2010-01-01

Motivation Despite more than two decades of research, HIV resistance to drugs remains a serious obstacle in developing efficient AIDS treatments. Several computational methods have been developed to predict resistance level from the sequence of viral proteins such as reverse transcriptase (RT) or protease. These methods, while powerful and accurate, give very little insight into the molecular interactions that underly acquisition of drug resistance/hypersusceptibility. Here, we attempt at filling this gap by using our Monte Carlo feature selection and interdependency discovery method (MCFS-ID) to elucidate molecular interaction networks that characterize viral strains with altered drug resistance levels. Results We analyzed a number of HIV-1 RT sequences annotated with drug resistance level using the MCFS-ID method. This let us expound interdependency networks that characterize change of drug resistance to six selected RT inhibitors: Abacavir, Lamivudine, Stavudine, Zidovudine, Tenofovir and Nevirapine. The networks consider interdependencies at the level of physicochemical properties of mutating amino acids, eg,: polarity. We mapped each network on the 3D structure of RT in attempt to understand the molecular meaning of interacting pairs. The discovered interactions describe several known drug resistance mechanisms and, importantly, some previously unidentified ones. Our approach can be easily applied to a whole range of problems from the domain of protein engineering. Availability A portable Java implementation of our MCFS-ID method is freely available for academic users and can be obtained at: http://www.ipipan.eu/staff/m.draminski/software.htm. PMID:21234299

Computational Analysis of Molecular Interaction Networks Underlying Change of HIV-1 Resistance to Selected Reverse Transcriptase Inhibitors.

PubMed

Kierczak, Marcin; Dramiński, Michał; Koronacki, Jacek; Komorowski, Jan

2010-12-12

Despite more than two decades of research, HIV resistance to drugs remains a serious obstacle in developing efficient AIDS treatments. Several computational methods have been developed to predict resistance level from the sequence of viral proteins such as reverse transcriptase (RT) or protease. These methods, while powerful and accurate, give very little insight into the molecular interactions that underly acquisition of drug resistance/hypersusceptibility. Here, we attempt at filling this gap by using our Monte Carlo feature selection and interdependency discovery method (MCFS-ID) to elucidate molecular interaction networks that characterize viral strains with altered drug resistance levels. We analyzed a number of HIV-1 RT sequences annotated with drug resistance level using the MCFS-ID method. This let us expound interdependency networks that characterize change of drug resistance to six selected RT inhibitors: Abacavir, Lamivudine, Stavudine, Zidovudine, Tenofovir and Nevirapine. The networks consider interdependencies at the level of physicochemical properties of mutating amino acids, eg,: polarity. We mapped each network on the 3D structure of RT in attempt to understand the molecular meaning of interacting pairs. The discovered interactions describe several known drug resistance mechanisms and, importantly, some previously unidentified ones. Our approach can be easily applied to a whole range of problems from the domain of protein engineering. A portable Java implementation of our MCFS-ID method is freely available for academic users and can be obtained at: http://www.ipipan.eu/staff/m.draminski/software.htm.

Immunopharmacotherapy: vaccination strategies as a treatment for drug abuse and dependence.

PubMed

Moreno, Amira Y; Janda, Kim D

2009-04-01

Despite intensive efforts for its eradication, addiction to both legal and illicit drugs continues to be a major worldwide medical and social problem. Drug addiction is defined as a disease state in which the body relies on a substance for normal functioning and develops physical dependence leading to compulsive and repetitive use despite negative consequences to the user's health, mental state or social life. Psychoactive substances such as cocaine, nicotine, alcohol, and amphetamines are able to cross the blood-brain barrier once ingested and temporarily alter the chemical balance of the brain. Current medications used for the treatment of dependence are typically agonists or antagonists of the drugs of abuse. The complex interrelations of the neuronal circuits have made it difficult to accurately predict the actions of potential agonist/antagonist drugs and have led to undesirable side effects within the central nervous system. Nearly forty years ago, a handful of groups began to explore the possibility of utilizing an individual's own immune machinery to counteract the effects of drug exposure in an approach later termed by our laboratory, immunopharmacotherapy.Immunopharmacotherapy aims to use highly specific antibodies to sequester the drug of interest while the latter is still in the bloodstream. Thus, creation of the antibody-drug complex will blunt crossing of the blood brain barrier (BBB) not only counteracting the reinforcing effects of the drug but also preventing any detrimental side effects on the CNS. In the present mini-review we aim to present a focused summary, including relevant challenges and future directions, of the current state of cocaine and nicotine vaccines as these two programs have been the most successful to date.

Two-Drug Antimicrobial Chemotherapy: A Mathematical Model and Experiments with Mycobacterium marinum

PubMed Central

Ankomah, Peter; Levin, Bruce R.

2012-01-01

Multi-drug therapy is the standard-of-care treatment for tuberculosis. Despite this, virtually all studies of the pharmacodynamics (PD) of mycobacterial drugs employed for the design of treatment protocols are restricted to single agents. In this report, mathematical models and in vitro experiments with Mycobacterium marinum and five antimycobacterial drugs are used to quantitatively evaluate the pharmaco-, population and evolutionary dynamics of two-drug antimicrobial chemotherapy regimes. Time kill experiments with single and pairs of antibiotics are used to estimate the parameters and evaluate the fit of Hill-function-based PD models. While Hill functions provide excellent fits for the PD of each single antibiotic studied, rifampin, amikacin, clarithromycin, streptomycin and moxifloxacin, two-drug Hill functions with a unique interaction parameter cannot account for the PD of any of the 10 pairs of these drugs. If we assume two antibiotic-concentration dependent functions for the interaction parameter, one for sub-MIC and one for supra-MIC drug concentrations, the modified biphasic Hill function provides a reasonably good fit for the PD of all 10 pairs of antibiotics studied. Monte Carlo simulations of antibiotic treatment based on the experimentally-determined PD functions are used to evaluate the potential microbiological efficacy (rate of clearance) and evolutionary consequences (likelihood of generating multi-drug resistance) of these different drug combinations as well as their sensitivity to different forms of non-adherence to therapy. These two-drug treatment simulations predict varying outcomes for the different pairs of antibiotics with respect to the aforementioned measures of efficacy. In summary, Hill functions with biphasic drug-drug interaction terms provide accurate analogs for the PD of pairs of antibiotics and M. marinum. The models, experimental protocols and computer simulations used in this study can be applied to evaluate the potential microbiological and evolutionary efficacy of two-drug therapy for any bactericidal antibiotics and bacteria that can be cultured in vitro. PMID:22253599

Prediction and assimilation of surf-zone processes using a Bayesian network: Part I: Forward models

USGS Publications Warehouse

Plant, Nathaniel G.; Holland, K. Todd

2011-01-01

Prediction of coastal processes, including waves, currents, and sediment transport, can be obtained from a variety of detailed geophysical-process models with many simulations showing significant skill. This capability supports a wide range of research and applied efforts that can benefit from accurate numerical predictions. However, the predictions are only as accurate as the data used to drive the models and, given the large temporal and spatial variability of the surf zone, inaccuracies in data are unavoidable such that useful predictions require corresponding estimates of uncertainty. We demonstrate how a Bayesian-network model can be used to provide accurate predictions of wave-height evolution in the surf zone given very sparse and/or inaccurate boundary-condition data. The approach is based on a formal treatment of a data-assimilation problem that takes advantage of significant reduction of the dimensionality of the model system. We demonstrate that predictions of a detailed geophysical model of the wave evolution are reproduced accurately using a Bayesian approach. In this surf-zone application, forward prediction skill was 83%, and uncertainties in the model inputs were accurately transferred to uncertainty in output variables. We also demonstrate that if modeling uncertainties were not conveyed to the Bayesian network (i.e., perfect data or model were assumed), then overly optimistic prediction uncertainties were computed. More consistent predictions and uncertainties were obtained by including model-parameter errors as a source of input uncertainty. Improved predictions (skill of 90%) were achieved because the Bayesian network simultaneously estimated optimal parameters while predicting wave heights.

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.

Obtaining Accurate Probabilities Using Classifier Calibration

ERIC Educational Resources Information Center

Pakdaman Naeini, Mahdi

2016-01-01

Learning probabilistic classification and prediction models that generate accurate probabilities is essential in many prediction and decision-making tasks in machine learning and data mining. One way to achieve this goal is to post-process the output of classification models to obtain more accurate probabilities. These post-processing methods are…

Metabolomics: building on a century of biochemistry to guide human health

PubMed Central

German, J. Bruce; Hammock, Bruce D.; Watkins, Steven M.

2006-01-01

Medical diagnosis and treatment efficacy will improve significantly when a more personalized system for health assessment is implemented. This system will require diagnostics that provide sufficiently detailed information about the metabolic status of individuals such that assay results will be able to guide food, drug and lifestyle choices to maintain or improve distinct aspects of health without compromising others. Achieving this goal will use the new science of metabolomics – comprehensive metabolic profiling of individuals linked to the biological understanding of human integrative metabolism. Candidate technologies to accomplish this goal are largely available, yet they have not been brought into practice for this purpose. Metabolomic technologies must be sufficiently rapid, accurate and affordable to be routinely accessible to both healthy and acutely ill individuals. The use of metabolomic data to predict the health trajectories of individuals will require bioinformatic tools and quantitative reference databases. These databases containing metabolite profiles from the population must be built, stored and indexed according to metabolic and health status. Building and annotating these databases with the knowledge to predict how a specific metabolic pattern from an individual can be adjusted with diet, drugs and lifestyle to improve health represents a logical application of the biochemistry knowledge that the life sciences have produced over the past 100 years. PMID:16680201

A case study on the in silico absorption simulations of levothyroxine sodium immediate-release tablets.

PubMed

Kocic, Ivana; Homsek, Irena; Dacevic, Mirjana; Grbic, Sandra; Parojcic, Jelena; Vucicevic, Katarina; Prostran, Milica; Miljkovic, Branislava

2012-04-01

The aim of this case study was to develop a drug-specific absorption model for levothyroxine (LT4) using mechanistic gastrointestinal simulation technology (GIST) implemented in the GastroPlus™ software package. The required input parameters were determined experimentally, in silico predicted and/or taken from the literature. The simulated plasma profile was similar and in a good agreement with the data observed in the in vivo bioequivalence study, indicating that the GIST model gave an accurate prediction of LT4 oral absorption. Additionally, plasma concentration-time profiles were simulated based on a set of experimental and virtual in vitro dissolution data in order to estimate the influence of different in vitro drug dissolution kinetics on the simulated plasma profiles and to identify biorelevant dissolution specification for LT4 immediate-release (IR) tablets. A set of experimental and virtual in vitro data was also used for correlation purposes. In vitro-in vivo correlation model based on the convolution approach was applied in order to assess the relationship between the in vitro and in vivo data. The obtained results suggest that dissolution specification of more than 85% LT4 dissolved in 60 min might be considered as biorelevant dissolution specification criteria for LT4 IR tablets. Copyright © 2012 John Wiley & Sons, Ltd.

Recapitulating physiological and pathological shear stress and oxygen to model vasculature in health and disease

NASA Astrophysics Data System (ADS)

Abaci, Hasan Erbil; Shen, Yu-I.; Tan, Scott; Gerecht, Sharon

2014-05-01

Studying human vascular disease in conventional cell cultures and in animal models does not effectively mimic the complex vascular microenvironment and may not accurately predict vascular responses in humans. We utilized a microfluidic device to recapitulate both shear stress and O2 levels in health and disease, establishing a microfluidic vascular model (μVM). Maintaining human endothelial cells (ECs) in healthy-mimicking conditions resulted in conversion to a physiological phenotype namely cell elongation, reduced proliferation, lowered angiogenic gene expression and formation of actin cortical rim and continuous barrier. We next examined the responses of the healthy μVM to a vasotoxic cancer drug, 5-Fluorouracil (5-FU), in comparison with an in vivo mouse model. We found that 5-FU does not induce apoptosis rather vascular hyperpermeability, which can be alleviated by Resveratrol treatment. This effect was confirmed by in vivo findings identifying a vasoprotecting strategy by the adjunct therapy of 5-FU with Resveratrol. The μVM of ischemic disease demonstrated the transition of ECs from a quiescent to an activated state, with higher proliferation rate, upregulation of angiogenic genes, and impaired barrier integrity. The μVM offers opportunities to study and predict human ECs with physiologically relevant phenotypes in healthy, pathological and drug-treated environments.

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.

Drug-target interaction prediction via class imbalance-aware ensemble learning.

PubMed

Ezzat, Ali; Wu, Min; Li, Xiao-Li; Kwoh, Chee-Keong

2016-12-22

Multiple computational methods for predicting drug-target interactions have been developed to facilitate the drug discovery process. These methods use available data on known drug-target interactions to train classifiers with the purpose of predicting new undiscovered interactions. However, a key challenge regarding this data that has not yet been addressed by these methods, namely class imbalance, is potentially degrading the prediction performance. Class imbalance can be divided into two sub-problems. Firstly, the number of known interacting drug-target pairs is much smaller than that of non-interacting drug-target pairs. This imbalance ratio between interacting and non-interacting drug-target pairs is referred to as the between-class imbalance. Between-class imbalance degrades prediction performance due to the bias in prediction results towards the majority class (i.e. the non-interacting pairs), leading to more prediction errors in the minority class (i.e. the interacting pairs). Secondly, there are multiple types of drug-target interactions in the data with some types having relatively fewer members (or are less represented) than others. This variation in representation of the different interaction types leads to another kind of imbalance referred to as the within-class imbalance. In within-class imbalance, prediction results are biased towards the better represented interaction types, leading to more prediction errors in the less represented interaction types. We propose an ensemble learning method that incorporates techniques to address the issues of between-class imbalance and within-class imbalance. Experiments show that the proposed method improves results over 4 state-of-the-art methods. In addition, we simulated cases for new drugs and targets to see how our method would perform in predicting their interactions. New drugs and targets are those for which no prior interactions are known. Our method displayed satisfactory prediction performance and was able to predict many of the interactions successfully. Our proposed method has improved the prediction performance over the existing work, thus proving the importance of addressing problems pertaining to class imbalance in the data.

Individualized drug dosing using RBF-Galerkin method: Case of anemia management in chronic kidney disease.

PubMed

Mirinejad, Hossein; Gaweda, Adam E; Brier, Michael E; Zurada, Jacek M; Inanc, Tamer

2017-09-01

Anemia is a common comorbidity in patients with chronic kidney disease (CKD) and is frequently associated with decreased physical component of quality of life, as well as adverse cardiovascular events. Current treatment methods for renal anemia are mostly population-based approaches treating individual patients with a one-size-fits-all model. However, FDA recommendations stipulate individualized anemia treatment with precise control of the hemoglobin concentration and minimal drug utilization. In accordance with these recommendations, this work presents an individualized drug dosing approach to anemia management by leveraging the theory of optimal control. A Multiple Receding Horizon Control (MRHC) approach based on the RBF-Galerkin optimization method is proposed for individualized anemia management in CKD patients. Recently developed by the authors, the RBF-Galerkin method uses the radial basis function approximation along with the Galerkin error projection to solve constrained optimal control problems numerically. The proposed approach is applied to generate optimal dosing recommendations for individual patients. Performance of the proposed approach (MRHC) is compared in silico to that of a population-based anemia management protocol and an individualized multiple model predictive control method for two case scenarios: hemoglobin measurement with and without observational errors. In silico comparison indicates that hemoglobin concentration with MRHC method has less variation among the methods, especially in presence of measurement errors. In addition, the average achieved hemoglobin level from the MRHC is significantly closer to the target hemoglobin than that of the other two methods, according to the analysis of variance (ANOVA) statistical test. Furthermore, drug dosages recommended by the MRHC are more stable and accurate and reach the steady-state value notably faster than those generated by the other two methods. The proposed method is highly efficient for the control of hemoglobin level, yet provides accurate dosage adjustments in the treatment of CKD anemia. Copyright © 2017 Elsevier B.V. All rights reserved.

Rational optimization of drug-target residence time: Insights from inhibitor binding to the S. aureus FabI enzyme-product complex

PubMed Central

Chang, Andrew; Schiebel, Johannes; Yu, Weixuan; Bommineni, Gopal R.; Pan, Pan; Baxter, Michael V.; Khanna, Avinash; Sotriffer, Christoph A.; Kisker, Caroline; Tonge, Peter J.

2013-01-01

Drug-target kinetics has recently emerged as an especially important facet of the drug discovery process. In particular, prolonged drug-target residence times may confer enhanced efficacy and selectivity in the open in vivo system. However, the lack of accurate kinetic and structural data for series of congeneric compounds hinders the rational design of inhibitors with decreased off-rates. Therefore, we chose the Staphylococcus aureus enoyl-ACP reductase (saFabI) - an important target for the development of new anti-staphylococcal drugs - as a model system to rationalize and optimize the drug-target residence time on a structural basis. Using our new, efficient and widely applicable mechanistically informed kinetic approach, we obtained a full characterization of saFabI inhibition by a series of 20 diphenyl ethers complemented by a collection of 9 saFabI-inhibitor crystal structures. We identified a strong correlation between the affinities of the investigated saFabI diphenyl ether inhibitors and their corresponding residence times, which can be rationalized on a structural basis. Due to its favorable interactions with the enzyme, the residence time of our most potent compound exceeds 10 hours. In addition, we found that affinity and residence time in this system can be significantly enhanced by modifications predictable by a careful consideration of catalysis. Our study provides a blueprint for investigating and prolonging drug-target kinetics and may aid in the rational design of long-residence-time inhibitors targeting the essential saFabI enzyme. PMID:23697754

Comparative physiology of mice and rats: radiometric measurement of vascular parameters in rodent tissues.

PubMed

Boswell, C Andrew; Mundo, Eduardo E; Ulufatu, Sheila; Bumbaca, Daniela; Cahaya, Hendry S; Majidy, Nicholas; Van Hoy, Marjie; Schweiger, Michelle G; Fielder, Paul J; Prabhu, Saileta; Khawli, Leslie A

2014-05-05

A solid understanding of physiology is beneficial in optimizing drug delivery and in the development of clinically predictive models of drug disposition kinetics. Although an abundance of data exists in the literature, it is often confounded by the use of various experimental methods and a lack of consensus in values from different sources. To help address this deficiency, we sought to directly compare three important vascular parameters at the tissue level using the same experimental approach in both mice and rats. Interstitial volume, vascular volume, and blood flow were radiometrically measured in selected harvested tissues of both species by extracellular marker infusion, red blood cell labeling, and rubidium chloride bolus distribution, respectively. The latter two parameters were further compared by whole-body autoradiographic imaging. An overall good interspecies agreement was observed for interstitial volume and blood flow on a weight-normalized basis in most tissues. In contrast, the measured vascular volumes of most rat tissues were higher than for mouse. Mice and rats, the two most commonly utilized rodent species in translational drug development, should not be considered as interchangeable in terms of vascular volume per gram of tissue. This will be particularly critical in biodistribution studies of drugs, as the amount of drug in the residual blood of tissues is often not negligible, especially for biologic drugs (e.g., antibodies) having long circulation half-lives. Physiologically based models of drug pharmacokinetics and/or pharmacodynamics also rely on accurate knowledge of biological parameters in tissues. For tissue parameters with poor interspecies agreement, the significance and possible drivers are discussed.

Dose-response relationship in the treatment of gastrointestinal disorders.

PubMed

Weihrauch, T R; Demol, P

1989-08-01

Numerous clinical studies have been performed to establish efficacy and safety of drugs in gastroenterological disorders. Only in a few if any of these studies, however, the rationale for the optimal dose and the dose regimens, respectively, have been addressed. Adequate and well-controlled dose finding studies play a key role in the clinical assessment of new drugs and in the evaluation of new indications. Hereby the range from the minimal effective dose to the maximal effective and well tolerated dose can be assessed and thus the optimal dose-range and dosage regimen be determined. Meaningful pharmacodynamic studies can be performed in the gastrointestinal tract also in healthy volunteers provided that a method with a high predictability for the desired therapeutic effect is available such as measurement of gastric acid secretion and its inhibition by a drug. Dose finding studies in gastroenterology can be carried out under two main aspects: First, to assess the pharmacodynamic and therapeutic effect of a compound on the gastrointestinal tract (e.g. anti-ulcer drug). Second, to evaluate the side effects of a drug on the gastrointestinal tract (e.g. gastric mucosal damage by non-steroidal anti-inflammatory drugs). For the evaluation of new drugs in gastrointestinal therapy a number of methods are available which yield accurate and reproducible data. While careful clinical-pharmacological dose-response studies using these methods have been carried out already more than a decade ago, it is surprising that therapeutic dose finding studies have become available only during the past few years. For scientific as well as for ethical reasons more trials which determine the optimal therapeutic dose are warranted.

Prediction of Metabolism of Drugs using Artificial Intelligence: How far have we reached?

PubMed

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

2016-01-01

Information about drug metabolism is an essential component of drug development. Modeling the drug metabolism requires identification of the involved enzymes, rate and extent of metabolism, the sites of metabolism etc. There has been continuous attempts in the prediction of metabolism of drugs using artificial intelligence in effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are number of predictive models available for metabolism using Support vector machines, Artificial neural networks, Bayesian classifiers etc. There is an urgent need to review their progress so far and address the existing challenges in prediction of metabolism. In this attempt, we are presenting the currently available literature models and some of the critical issues regarding prediction of drug metabolism.

Near infrared and Raman spectroscopy as Process Analytical Technology tools for the manufacturing of silicone-based drug reservoirs.

PubMed

Mantanus, J; Rozet, E; Van Butsele, K; De Bleye, C; Ceccato, A; Evrard, B; Hubert, Ph; Ziémons, E

2011-08-05

Using near infrared (NIR) and Raman spectroscopy as PAT tools, 3 critical quality attributes of a silicone-based drug reservoir were studied. First, the Active Pharmaceutical Ingredient (API) homogeneity in the reservoir was evaluated using Raman spectroscopy (mapping): the API distribution within the industrial drug reservoirs was found to be homogeneous while API aggregates were detected in laboratory scale samples manufactured with a non optimal mixing process. Second, the crosslinking process of the reservoirs was monitored at different temperatures with NIR spectroscopy. Conformity tests and Principal Component Analysis (PCA) were performed on the collected data to find out the relation between the temperature and the time necessary to reach the crosslinking endpoints. An agreement was found between the conformity test results and the PCA results. Compared to the conformity test method, PCA had the advantage to discriminate the heating effect from the crosslinking effect occurring together during the monitored process. Therefore the 2 approaches were found to be complementary. Third, based on the HPLC reference method, a NIR model able to quantify the API in the drug reservoir was developed and thoroughly validated. Partial Least Squares (PLS) regression on the calibration set was performed to build prediction models of which the ability to quantify accurately was tested with the external validation set. The 1.2% Root Mean Squared Error of Prediction (RMSEP) of the NIR model indicated the global accuracy of the model. The accuracy profile based on tolerance intervals was used to generate a complete validation report. The 95% tolerance interval calculated on the validation results indicated that each future result will have a relative error below ±5% with a probability of at least 95%. In conclusion, 3 critical quality attributes of silicone-based drug reservoirs were quickly and efficiently evaluated by NIR and Raman spectroscopy. Copyright © 2011 Elsevier B.V. All rights reserved.

Predicting drug side-effect profiles: a chemical fragment-based approach

PubMed Central

2011-01-01

Background Drug side-effects, or adverse drug reactions, have become a major public health concern. It is one of the main causes of failure in the process of drug development, and of drug withdrawal once they have reached the market. Therefore, in silico prediction of potential side-effects early in the drug discovery process, before reaching the clinical stages, is of great interest to improve this long and expensive process and to provide new efficient and safe therapies for patients. Results In the present work, we propose a new method to predict potential side-effects of drug candidate molecules based on their chemical structures, applicable on large molecular databanks. A unique feature of the proposed method is its ability to extract correlated sets of chemical substructures (or chemical fragments) and side-effects. This is made possible using sparse canonical correlation analysis (SCCA). In the results, we show the usefulness of the proposed method by predicting 1385 side-effects in the SIDER database from the chemical structures of 888 approved drugs. These predictions are performed with simultaneous extraction of correlated ensembles formed by a set of chemical substructures shared by drugs that are likely to have a set of side-effects. We also conduct a comprehensive side-effect prediction for many uncharacterized drug molecules stored in DrugBank, and were able to confirm interesting predictions using independent source of information. Conclusions The proposed method is expected to be useful in various stages of the drug development process. PMID:21586169

The study of forensic toxicology should not be neglected in Japanese universities.

PubMed

Ishihara, Kenji; Yajima, Daisuke; Abe, Hiroko; Nagasawa, Sayaka; Nara, Akina; Iwase, Hirotaro

2015-04-01

Forensic toxicology is aimed at identifying the relationship between drugs or poison and the cause of death or crime. In the authors' toxicology laboratory at Chiba University, the authors analyze almost every body for drugs and poisons. A simple inspection kit was used in an attempt to ascertain drug abuse. A mass spectrometer is used to perform highly accurate screening. When a poison is detected, quantitative analyses are required. A recent topic of interest is new psychoactive substances (NPS). Although NPS-related deaths may be decreasing, use of NPS as a cause of death is difficult to ascertain. Forensic institutes have recently begun to perform drug and poison tests on corpses. However, this approach presents several problems, as are discussed here. The hope is that highly accurate analyses of drugs and poisons will be performed throughout the country.

A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care.

PubMed

Alanazi, Hamdan O; Abdullah, Abdul Hanan; Qureshi, Kashif Naseer

2017-04-01

Recently, Artificial Intelligence (AI) has been used widely in medicine and health care sector. In machine learning, the classification or prediction is a major field of AI. Today, the study of existing predictive models based on machine learning methods is extremely active. Doctors need accurate predictions for the outcomes of their patients' diseases. In addition, for accurate predictions, timing is another significant factor that influences treatment decisions. In this paper, existing predictive models in medicine and health care have critically reviewed. Furthermore, the most famous machine learning methods have explained, and the confusion between a statistical approach and machine learning has clarified. A review of related literature reveals that the predictions of existing predictive models differ even when the same dataset is used. Therefore, existing predictive models are essential, and current methods must be improved.

Accurate Prediction of Motor Failures by Application of Multi CBM Tools: A Case Study

NASA Astrophysics Data System (ADS)

Dutta, Rana; Singh, Veerendra Pratap; Dwivedi, Jai Prakash

2018-02-01

Motor failures are very difficult to predict accurately with a single condition-monitoring tool as both electrical and the mechanical systems are closely related. Electrical problem, like phase unbalance, stator winding insulation failures can, at times, lead to vibration problem and at the same time mechanical failures like bearing failure, leads to rotor eccentricity. In this case study of a 550 kW blower motor it has been shown that a rotor bar crack was detected by current signature analysis and vibration monitoring confirmed the same. In later months in a similar motor vibration monitoring predicted bearing failure and current signature analysis confirmed the same. In both the cases, after dismantling the motor, the predictions were found to be accurate. In this paper we will be discussing the accurate predictions of motor failures through use of multi condition monitoring tools with two case studies.

Comprehensive prediction of drug-protein interactions and side effects for the human proteome

PubMed Central

Zhou, Hongyi; Gao, Mu; Skolnick, Jeffrey

2015-01-01

Identifying unexpected drug-protein interactions is crucial for drug repurposing. We develop a comprehensive proteome scale approach that predicts human protein targets and side effects of drugs. For drug-protein interaction prediction, FINDSITEcomb, whose average precision is ~30% and recall ~27%, is employed. For side effect prediction, a new method is developed with a precision of ~57% and a recall of ~24%. Our predictions show that drugs are quite promiscuous, with the average (median) number of human targets per drug of 329 (38), while a given protein interacts with 57 drugs. The result implies that drug side effects are inevitable and existing drugs may be useful for repurposing, with only ~1,000 human proteins likely causing serious side effects. A killing index derived from serious side effects has a strong correlation with FDA approved drugs being withdrawn. Therefore, it provides a pre-filter for new drug development. The methodology is free to the academic community on the DR. PRODIS (DRugome, PROteome, and DISeasome) webserver at http://cssb.biology.gatech.edu/dr.prodis/. DR. PRODIS provides protein targets of drugs, drugs for a given protein target, associated diseases and side effects of drugs, as well as an interface for the virtual target screening of new compounds. PMID:26057345

Aggregation Trade Offs in Family Based Recommendations

NASA Astrophysics Data System (ADS)

Berkovsky, Shlomo; Freyne, Jill; Coombe, Mac

Personalized information access tools are frequently based on collaborative filtering recommendation algorithms. Collaborative filtering recommender systems typically suffer from a data sparsity problem, where systems do not have sufficient user data to generate accurate and reliable predictions. Prior research suggested using group-based user data in the collaborative filtering recommendation process to generate group-based predictions and partially resolve the sparsity problem. Although group recommendations are less accurate than personalized recommendations, they are more accurate than general non-personalized recommendations, which are the natural fall back when personalized recommendations cannot be generated. In this work we present initial results of a study that exploits the browsing logs of real families of users gathered in an eHealth portal. The browsing logs allowed us to experimentally compare the accuracy of two group-based recommendation strategies: aggregated group models and aggregated predictions. Our results showed that aggregating individual models into group models resulted in more accurate predictions than aggregating individual predictions into group predictions.

Identifying Drug-Target Interactions with Decision Templates.

PubMed

Yan, Xiao-Ying; Zhang, Shao-Wu

2018-01-01

During the development process of new drugs, identification of the drug-target interactions wins primary concerns. However, the chemical or biological experiments bear the limitation in coverage as well as the huge cost of both time and money. Based on drug similarity and target similarity, chemogenomic methods can be able to predict potential drug-target interactions (DTIs) on a large scale and have no luxurious need about target structures or ligand entries. In order to reflect the cases that the drugs having variant structures interact with common targets and the targets having dissimilar sequences interact with same drugs. In addition, though several other similarity metrics have been developed to predict DTIs, the combination of multiple similarity metrics (especially heterogeneous similarities) is too naïve to sufficiently explore the multiple similarities. In this paper, based on Gene Ontology and pathway annotation, we introduce two novel target similarity metrics to address above issues. More importantly, we propose a more effective strategy via decision template to integrate multiple classifiers designed with multiple similarity metrics. In the scenarios that predict existing targets for new drugs and predict approved drugs for new protein targets, the results on the DTI benchmark datasets show that our target similarity metrics are able to enhance the predictive accuracies in two scenarios. And the elaborate fusion strategy of multiple classifiers has better predictive power than the naïve combination of multiple similarity metrics. Compared with other two state-of-the-art approaches on the four popular benchmark datasets of binary drug-target interactions, our method achieves the best results in terms of AUC and AUPR for predicting available targets for new drugs (S2), and predicting approved drugs for new protein targets (S3).These results demonstrate that our method can effectively predict the drug-target interactions. The software package can freely available at https://github.com/NwpuSY/DT_all.git for academic users. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

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.

Computational prediction of multidisciplinary team decision-making for adjuvant breast cancer drug therapies: a machine learning approach.

PubMed

Lin, Frank P Y; Pokorny, Adrian; Teng, Christina; Dear, Rachel; Epstein, Richard J

2016-12-01

Multidisciplinary team (MDT) meetings are used to optimise expert decision-making about treatment options, but such expertise is not digitally transferable between centres. To help standardise medical decision-making, we developed a machine learning model designed to predict MDT decisions about adjuvant breast cancer treatments. We analysed MDT decisions regarding adjuvant systemic therapy for 1065 breast cancer cases over eight years. Machine learning classifiers with and without bootstrap aggregation were correlated with MDT decisions (recommended, not recommended, or discussable) regarding adjuvant cytotoxic, endocrine and biologic/targeted therapies, then tested for predictability using stratified ten-fold cross-validations. The predictions so derived were duly compared with those based on published (ESMO and NCCN) cancer guidelines. Machine learning more accurately predicted adjuvant chemotherapy MDT decisions than did simple application of guidelines. No differences were found between MDT- vs. ESMO/NCCN- based decisions to prescribe either adjuvant endocrine (97%, p = 0.44/0.74) or biologic/targeted therapies (98%, p = 0.82/0.59). In contrast, significant discrepancies were evident between MDT- and guideline-based decisions to prescribe chemotherapy (87%, p < 0.01, representing 43% and 53% variations from ESMO/NCCN guidelines, respectively). Using ten-fold cross-validation, the best classifiers achieved areas under the receiver operating characteristic curve (AUC) of 0.940 for chemotherapy (95% C.I., 0.922-0.958), 0.899 for the endocrine therapy (95% C.I., 0.880-0.918), and 0.977 for trastuzumab therapy (95% C.I., 0.955-0.999) respectively. Overall, bootstrap aggregated classifiers performed better among all evaluated machine learning models. A machine learning approach based on clinicopathologic characteristics can predict MDT decisions about adjuvant breast cancer drug therapies. The discrepancy between MDT- and guideline-based decisions regarding adjuvant chemotherapy implies that certain non-clincopathologic criteria, such as patient preference and resource availability, are factored into clinical decision-making by local experts but not captured by guidelines.

A Physiologically Based Pharmacokinetic Model to Predict the Pharmacokinetics of Highly Protein-Bound Drugs and Impact of Errors in Plasma Protein Binding

PubMed Central

Ye, Min; Nagar, Swati; Korzekwa, Ken

2015-01-01

Predicting the pharmacokinetics of highly protein-bound drugs is difficult. Also, since historical plasma protein binding data was often collected using unbuffered plasma, the resulting inaccurate binding data could contribute to incorrect predictions. This study uses a generic physiologically based pharmacokinetic (PBPK) model to predict human plasma concentration-time profiles for 22 highly protein-bound drugs. Tissue distribution was estimated from in vitro drug lipophilicity data, plasma protein binding, and blood: plasma ratio. Clearance was predicted with a well-stirred liver model. Underestimated hepatic clearance for acidic and neutral compounds was corrected by an empirical scaling factor. Predicted values (pharmacokinetic parameters, plasma concentration-time profile) were compared with observed data to evaluate model accuracy. Of the 22 drugs, less than a 2-fold error was obtained for terminal elimination half-life (t1/2, 100% of drugs), peak plasma concentration (Cmax, 100%), area under the plasma concentration-time curve (AUC0–t, 95.4%), clearance (CLh, 95.4%), mean retention time (MRT, 95.4%), and steady state volume (Vss, 90.9%). The impact of fup errors on CLh and Vss prediction was evaluated. Errors in fup resulted in proportional errors in clearance prediction for low-clearance compounds, and in Vss prediction for high-volume neutral drugs. For high-volume basic drugs, errors in fup did not propagate to errors in Vss prediction. This is due to the cancellation of errors in the calculations for tissue partitioning of basic drugs. Overall, plasma profiles were well simulated with the present PBPK model. PMID:26531057

A hybrid method for prediction and repositioning of drug Anatomical Therapeutic Chemical classes.

PubMed

Chen, Lei; Lu, Jing; Zhang, Ning; Huang, Tao; Cai, Yu-Dong

2014-04-01

In the Anatomical Therapeutic Chemical (ATC) classification system, therapeutic drugs are divided into 14 main classes according to the organ or system on which they act and their chemical, pharmacological and therapeutic properties. This system, recommended by the World Health Organization (WHO), provides a global standard for classifying medical substances and serves as a tool for international drug utilization research to improve quality of drug use. In view of this, it is necessary to develop effective computational prediction methods to identify the ATC-class of a given drug, which thereby could facilitate further analysis of this system. In this study, we initiated an attempt to develop a prediction method and to gain insights from it by utilizing ontology information of drug compounds. Since only about one-fourth of drugs in the ATC classification system have ontology information, a hybrid prediction method combining the ontology information, chemical interaction information and chemical structure information of drug compounds was proposed for the prediction of drug ATC-classes. As a result, by using the Jackknife test, the 1st prediction accuracies for identifying the 14 main ATC-classes in the training dataset, the internal validation dataset and the external validation dataset were 75.90%, 75.70% and 66.36%, respectively. Analysis of some samples with false-positive predictions in the internal and external validation datasets indicated that some of them may even have a relationship with the false-positive predicted ATC-class, suggesting novel uses of these drugs. It was conceivable that the proposed method could be used as an efficient tool to identify ATC-classes of novel drugs or to discover novel uses of known drugs.

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

Canini, Laetitia; Guedj, Jeremie; Chatterjee, Anushree

In this study, modelling HCV RNA decline kinetics under therapy has proven useful for characterizing treatment effectiveness. Here we model HCV viral kinetics (VK) in 72 patients given a combination of danoprevir, a protease inhibitor, and mericitabine, a nucleoside polymerase inhibitor, for 14 days in the INFORM-1 trial. A biphasic VK model with time-varying danoprevir and mericitabine effectiveness and Bliss independence for characterizing the interaction between both drugs provided the best fit to the VK data. As a result, the average final antiviral effectiveness of the drug combination varied between 0.998 for 100 mg three times daily of danoprevir andmore » 500 mg twice daily of mericitabine and 0.9998 for 600 mg twice daily of danoprevir and 1,000 mg twice daily of mericitabine. Using the individual parameters estimated from the VK data collected over 2 weeks, we were not able to reproduce the low sustained virological response rates obtained in a more recent study where patients were treated with a combination of mericitabine and ritonavir-boosted danoprevir for 24 weeks. In conclusion, this suggests that drug-resistant viruses emerge after 2 weeks of treatment and that longer studies are necessary to provide accurate predictions of longer treatment outcomes.« less

In Vitro Tissue-Engineered Skeletal Muscle Models for Studying Muscle Physiology and Disease.

PubMed

Khodabukus, Alastair; Prabhu, Neel; Wang, Jason; Bursac, Nenad

2018-04-25

Healthy skeletal muscle possesses the extraordinary ability to regenerate in response to small-scale injuries; however, this self-repair capacity becomes overwhelmed with aging, genetic myopathies, and large muscle loss. The failure of small animal models to accurately replicate human muscle disease, injury and to predict clinically-relevant drug responses has driven the development of high fidelity in vitro skeletal muscle models. Herein, the progress made and challenges ahead in engineering biomimetic human skeletal muscle tissues that can recapitulate muscle development, genetic diseases, regeneration, and drug response is discussed. Bioengineering approaches used to improve engineered muscle structure and function as well as the functionality of satellite cells to allow modeling muscle regeneration in vitro are also highlighted. Next, a historical overview on the generation of skeletal muscle cells and tissues from human pluripotent stem cells, and a discussion on the potential of these approaches to model and treat genetic diseases such as Duchenne muscular dystrophy, is provided. Finally, the need to integrate multiorgan microphysiological systems to generate improved drug discovery technologies with the potential to complement or supersede current preclinical animal models of muscle disease is described. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Acute and chronic toxicity of six anticancer drugs on rotifers and crustaceans.

PubMed

Parrella, Alfredo; Lavorgna, Margherita; Criscuolo, Emma; Russo, Chiara; Fiumano, Vittorio; Isidori, Marina

2014-11-01

The growing use of cytostatic drugs is gaining relevance as an environmental concern. Environmental and distribution studies are increasing due to the development of accurate analytical methods, whereas ecotoxicological studies are still lacking. The aim of the present study was to investigate the acute and chronic toxicity of six cytostatics (5-fluorouracil, capecitabine, cisplatin, doxorubicin, etoposide, and imatinib) belonging to five classes of Anatomical Therapeutic Classification (ATC) on primary consumers of the aquatic chain (Daphnia magna, Ceriodaphnia dubia, Brachionus calyciflorus, and Thamnocephalus platyurus). Acute ecotoxicological effects occurred at concentrations in the order of mgL(-)(1), higher than those predicted in the environment, and the most acutely toxic drugs among those tested were cisplatin and doxorubicin for most aquatic organisms. For chronic toxicity, cisplatin and 5-fluorouracil showed the highest toxic potential in all test organisms, inducing 50% reproduction inhibition in crustaceans at concentrations on the order of μgL(-)(1). Rotifers were less susceptible to these pharmaceuticals. On the basis of chronic results, the low effective concentrations suggest a potential environmental risk of cytostatics. Thus, this study could be an important starting point for establishing the real environmental impact of these substances. Copyright © 2014 Elsevier Ltd. All rights reserved.

Advancing Predictive Hepatotoxicity at the Intersection of Experimental, in Silico, and Artificial Intelligence Technologies.

PubMed

Fraser, Keith; Bruckner, Dylan M; Dordick, Jonathan S

2018-06-18

Adverse drug reactions, particularly those that result in drug-induced liver injury (DILI), are a major cause of drug failure in clinical trials and drug withdrawals. Hepatotoxicity-mediated drug attrition occurs despite substantial investments of time and money in developing cellular assays, animal models, and computational models to predict its occurrence in humans. Underperformance in predicting hepatotoxicity associated with drugs and drug candidates has been attributed to existing gaps in our understanding of the mechanisms involved in driving hepatic injury after these compounds perfuse and are metabolized by the liver. Herein we assess in vitro, in vivo (animal), and in silico strategies used to develop predictive DILI models. We address the effectiveness of several two- and three-dimensional in vitro cellular methods that are frequently employed in hepatotoxicity screens and how they can be used to predict DILI in humans. We also explore how humanized animal models can recapitulate human drug metabolic profiles and associated liver injury. Finally, we highlight the maturation of computational methods for predicting hepatotoxicity, the untapped potential of artificial intelligence for improving in silico DILI screens, and how knowledge acquired from these predictions can shape the refinement of experimental methods.

Drug-disease association and drug-repositioning predictions in complex diseases using causal inference-probabilistic matrix factorization.

PubMed

Yang, Jihong; Li, Zheng; Fan, Xiaohui; Cheng, Yiyu

2014-09-22

The high incidence of complex diseases has become a worldwide threat to human health. Multiple targets and pathways are perturbed during the pathological process of complex diseases. Systematic investigation of complex relationship between drugs and diseases is necessary for new association discovery and drug repurposing. For this purpose, three causal networks were constructed herein for cardiovascular diseases, diabetes mellitus, and neoplasms, respectively. A causal inference-probabilistic matrix factorization (CI-PMF) approach was proposed to predict and classify drug-disease associations, and further used for drug-repositioning predictions. First, multilevel systematic relations between drugs and diseases were integrated from heterogeneous databases to construct causal networks connecting drug-target-pathway-gene-disease. Then, the association scores between drugs and diseases were assessed by evaluating a drug's effects on multiple targets and pathways. Furthermore, PMF models were learned based on known interactions, and associations were then classified into three types by trained models. Finally, therapeutic associations were predicted based upon the ranking of association scores and predicted association types. In terms of drug-disease association prediction, modified causal inference included in CI-PMF outperformed existing causal inference with a higher AUC (area under receiver operating characteristic curve) score and greater precision. Moreover, CI-PMF performed better than single modified causal inference in predicting therapeutic drug-disease associations. In the top 30% of predicted associations, 58.6% (136/232), 50.8% (31/61), and 39.8% (140/352) hit known therapeutic associations, while precisions obtained by the latter were only 10.2% (231/2264), 8.8% (36/411), and 9.7% (189/1948). Clinical verifications were further conducted for the top 100 newly predicted therapeutic associations. As a result, 21, 12, and 32 associations have been studied and many treatment effects of drugs on diseases were investigated for cardiovascular diseases, diabetes mellitus, and neoplasms, respectively. Related chains in causal networks were extracted for these 65 clinical-verified associations, and we further illustrated the therapeutic role of etodolac in breast cancer by inferred chains. Overall, CI-PMF is a useful approach for associating drugs with complex diseases and provides potential values for drug repositioning.

The Development of Teaching Efficacy for Drug-Dosage Calculation Instruction: A Nursing Faculty Perspective

ERIC Educational Resources Information Center

Vitale, Gail A.

2011-01-01

The purpose of this study was to examine how nursing efficacy for drug-dosage calculation instruction is determined. Medication administration is a critical function of nurses in healthcare settings. An essential component of safe medication administration is accurate drug-dosage calculation, but instruction in drug-dosage calculation methods…

BDDCS Class Prediction for New Molecular Entities

PubMed Central

Broccatelli, Fabio; Cruciani, Gabriele; Benet, Leslie Z.; Oprea, Tudor I.

2012-01-01

The Biopharmaceutics Drug Disposition Classification System (BDDCS) was successfully employed for predicting drug-drug interactions (DDIs) with respect to drug metabolizing enzymes (DMEs), drug transporters and their interplay. The major assumption of BDDCS is that the extent of metabolism (EoM) predicts high versus low intestinal permeability rate, and vice versa, at least when uptake transporters or paracellular transport are not involved. We recently published a collection of over 900 marketed drugs classified for BDDCS. We suggest that a reliable model for predicting BDDCS class, integrated with in vitro assays, could anticipate disposition and potential DDIs of new molecular entities (NMEs). Here we describe a computational procedure for predicting BDDCS class from molecular structures. The model was trained on a set of 300 oral drugs, and validated on an external set of 379 oral drugs, using 17 descriptors calculated or derived from the VolSurf+ software. For each molecule, a probability of BDDCS class membership was given, based on predicted EoM, FDA solubility (FDAS) and their confidence scores. The accuracy in predicting FDAS was 78% in training and 77% in validation, while for EoM prediction the accuracy was 82% in training and 79% in external validation. The actual BDDCS class corresponded to the highest ranked calculated class for 55% of the validation molecules, and it was within the top two ranked more than 92% of the times. The unbalanced stratification of the dataset didn’t affect the prediction, which showed highest accuracy in predicting classes 2 and 3 with respect to the most populated class 1. For class 4 drugs a general lack of predictability was observed. A linear discriminant analysis (LDA) confirmed the degree of accuracy for the prediction of the different BDDCS classes is tied to the structure of the dataset. This model could routinely be used in early drug discovery to prioritize in vitro tests for NMEs (e.g., affinity to transporters, intestinal metabolism, intestinal absorption and plasma protein binding). We further applied the BDDCS prediction model on a large set of medicinal chemistry compounds (over 30,000 chemicals). Based on this application, we suggest that solubility, and not permeability, is the major difference between NMEs and drugs. We anticipate that the forecast of BDDCS categories in early drug discovery may lead to a significant R&D cost reduction. PMID:22224483

Prediction of Antimicrobial Peptides Based on Sequence Alignment and Feature Selection Methods

PubMed Central

Wang, Ping; Hu, Lele; Liu, Guiyou; Jiang, Nan; Chen, Xiaoyun; Xu, Jianyong; Zheng, Wen; Li, Li; Tan, Ming; Chen, Zugen; Song, Hui; Cai, Yu-Dong; Chou, Kuo-Chen

2011-01-01

Antimicrobial peptides (AMPs) represent a class of natural peptides that form a part of the innate immune system, and this kind of ‘nature's antibiotics’ is quite promising for solving the problem of increasing antibiotic resistance. In view of this, it is highly desired to develop an effective computational method for accurately predicting novel AMPs because it can provide us with more candidates and useful insights for drug design. In this study, a new method for predicting AMPs was implemented by integrating the sequence alignment method and the feature selection method. It was observed that, the overall jackknife success rate by the new predictor on a newly constructed benchmark dataset was over 80.23%, and the Mathews correlation coefficient is 0.73, indicating a good prediction. Moreover, it is indicated by an in-depth feature analysis that the results are quite consistent with the previously known knowledge that some amino acids are preferential in AMPs and that these amino acids do play an important role for the antimicrobial activity. For the convenience of most experimental scientists who want to use the prediction method without the interest to follow the mathematical details, a user-friendly web-server is provided at http://amp.biosino.org/. PMID:21533231

Numerical Investigation of the Residual Stress Distribution of Flat-Faced and Convexly Curved Tablets Using the Finite Element Method.

PubMed

Otoguro, Saori; Hayashi, Yoshihiro; Miura, Takahiro; Uehara, Naoto; Utsumi, Shunichi; Onuki, Yoshinori; Obata, Yasuko; Takayama, Kozo

2015-01-01

The stress distribution of tablets after compression was simulated using a finite element method, where the powder was defined by the Drucker-Prager cap model. The effect of tablet shape, identified by the surface curvature, on the residual stress distribution was investigated. In flat-faced tablets, weak positive shear stress remained from the top and bottom die walls toward the center of the tablet. In the case of the convexly curved tablet, strong positive shear stress remained on the upper side and in the intermediate part between the die wall and the center of the tablet. In the case of x-axial stress, negative values were observed for all tablets, suggesting that the x-axial force always acts from the die wall toward the center of the tablet. In the flat tablet, negative x-axial stress remained from the upper edge to the center bottom. The x-axial stress distribution differed between the flat and convexly curved tablets. Weak stress remained in the y-axial direction of the flat tablet, whereas an upward force remained at the center of the convexly curved tablet. By employing multiple linear regression analysis, the mechanical properties of the tablets were predicted accurately as functions of their residual stress distribution. However, the multiple linear regression prediction of the dissolution parameters of acetaminophen, used here as a model drug, was limited, suggesting that the dissolution of active ingredients is not a simple process; further investigation is needed to enable accurate predictions of dissolution parameters.

Virtual screening using molecular simulations.

PubMed

Yang, Tianyi; Wu, Johnny C; Yan, Chunli; Wang, Yuanfeng; Luo, Ray; Gonzales, Michael B; Dalby, Kevin N; Ren, Pengyu

2011-06-01

Effective virtual screening relies on our ability to make accurate prediction of protein-ligand binding, which remains a great challenge. In this work, utilizing the molecular-mechanics Poisson-Boltzmann (or Generalized Born) surface area approach, we have evaluated the binding affinity of a set of 156 ligands to seven families of proteins, trypsin β, thrombin α, cyclin-dependent kinase (CDK), cAMP-dependent kinase (PKA), urokinase-type plasminogen activator, β-glucosidase A, and coagulation factor Xa. The effect of protein dielectric constant in the implicit-solvent model on the binding free energy calculation is shown to be important. The statistical correlations between the binding energy calculated from the implicit-solvent approach and experimental free energy are in the range of 0.56-0.79 across all the families. This performance is better than that of typical docking programs especially given that the latter is directly trained using known binding data whereas the molecular mechanics is based on general physical parameters. Estimation of entropic contribution remains the barrier to accurate free energy calculation. We show that the traditional rigid rotor harmonic oscillator approximation is unable to improve the binding free energy prediction. Inclusion of conformational restriction seems to be promising but requires further investigation. On the other hand, our preliminary study suggests that implicit-solvent based alchemical perturbation, which offers explicit sampling of configuration entropy, can be a viable approach to significantly improve the prediction of binding free energy. Overall, the molecular mechanics approach has the potential for medium to high-throughput computational drug discovery. Copyright © 2011 Wiley-Liss, Inc.

Prediction of new onset of end stage renal disease in Chinese patients with type 2 diabetes mellitus - a population-based retrospective cohort study.

PubMed

Wan, Eric Yuk Fai; Fong, Daniel Yee Tak; Fung, Colman Siu Cheung; Yu, Esther Yee Tak; Chin, Weng Yee; Chan, Anca Ka Chun; Lam, Cindy Lo Kuen

2017-08-01

Since diabetes mellitus (DM) is the leading cause of end stage renal disease (ESRD), this study aimed to develop a 5-year ESRD risk prediction model among Chinese patients with Type 2 DM (T2DM) in primary care. A retrospective cohort study was conducted on 149,333 Chinese adult T2DM primary care patients without ESRD in 2010. Using the derivation cohort over a median of 5 years follow-up, the gender-specific models including the interaction effect between predictors and age were derived using Cox regression with a forward stepwise approach. Harrell's C-statistic and calibration plot were applied to the validation cohort to assess discrimination and calibration of the models. Prediction models showed better discrimination with Harrell's C-statistics of 0.866 (males) and 0.862 (females) and calibration power from the plots than other established models. The predictors included age, usages of anti-hypertensive drugs, anti-glucose drugs, and Hemogloblin A1c, blood pressure, urine albumin/creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR). Specific predictors for male were smoking and presence of sight threatening diabetic retinopathy while additional predictors for female included longer duration of diabetes and quadratic effect of body mass index. Interaction factors with age showed a greater weighting of insulin and urine ACR in younger males, and eGFR in younger females. Our newly developed gender-specific models provide a more accurate 5-year ESRD risk predictions for Chinese diabetic primary care patients than other existing models. The models included several modifiable risk factors that clinicians can use to counsel patients, and to target at in the delivery of care to patients.

An Ensemble Method to Distinguish Bacteriophage Virion from Non-Virion Proteins Based on Protein Sequence Characteristics.

PubMed

Zhang, Lina; Zhang, Chengjin; Gao, Rui; Yang, Runtao

2015-09-09

Bacteriophage virion proteins and non-virion proteins have distinct functions in biological processes, such as specificity determination for host bacteria, bacteriophage replication and transcription. Accurate identification of bacteriophage virion proteins from bacteriophage protein sequences is significant to understand the complex virulence mechanism in host bacteria and the influence of bacteriophages on the development of antibacterial drugs. In this study, an ensemble method for bacteriophage virion protein prediction from bacteriophage protein sequences is put forward with hybrid feature spaces incorporating CTD (composition, transition and distribution), bi-profile Bayes, PseAAC (pseudo-amino acid composition) and PSSM (position-specific scoring matrix). When performing on the training dataset 10-fold cross-validation, the presented method achieves a satisfactory prediction result with a sensitivity of 0.870, a specificity of 0.830, an accuracy of 0.850 and Matthew's correlation coefficient (MCC) of 0.701, respectively. To evaluate the prediction performance objectively, an independent testing dataset is used to evaluate the proposed method. Encouragingly, our proposed method performs better than previous studies with a sensitivity of 0.853, a specificity of 0.815, an accuracy of 0.831 and MCC of 0.662 on the independent testing dataset. These results suggest that the proposed method can be a potential candidate for bacteriophage virion protein prediction, which may provide a useful tool to find novel antibacterial drugs and to understand the relationship between bacteriophage and host bacteria. For the convenience of the vast majority of experimental Int. J. Mol. Sci. 2015, 16,21735 scientists, a user-friendly and publicly-accessible web-server for the proposed ensemble method is established.

Predicting perceived safety to drive the morning after drinking: The importance of hangover symptoms.

PubMed

Cameron, Elaine; French, David P

2016-07-01

People driving the day after drinking are at risk of impaired performance and accidents due to continued intoxication or the effects of alcohol hangover. Drivers are poor at estimating their own blood alcohol concentration, and some drive despite believing they are over the legal limit. It is therefore important to identify other factors influencing perceived ability to drive 'the morning after'. This study tested how accurately participants estimated their legal driving status, and the contribution of beliefs and hangover symptoms to the prediction of perceived driving safety. This cross-sectional study involved 193 students completing a questionnaire and alcohol breath test the morning after heavy alcohol consumption. Indicators of subjective intoxication, severity of hangover symptoms, estimated legal status and perceived safety to drive were measured. A hierarchical linear regression analysis was conducted. No participants thought they were under the English legal limit when they were not, and 47% thought they were over the limit when they were in fact legally permissible to drive. However, 20% of those believing they were over the limit nevertheless rated themselves as safe to drive. Hangover symptoms added 17% variance to the prediction of perceived safety to drive, over and above objective and subjective measures of intoxication. Perceived severity of hangover symptoms influence beliefs about driving ability: When judging safety to drive, people experiencing less severe symptoms believe they are less impaired. If this finding is robust, health promotion campaigns should aim to correct this misapprehension. [Cameron E, French D. Predicting perceived safety to drive the morning after drinking: The importance of hangover symptoms. Drug Alcohol Rev 2016;35:442-446]. © 2015 Australasian Professional Society on Alcohol and other Drugs.

Prediction of Individual Serum Infliximab Concentrations in Inflammatory Bowel Disease by a Bayesian Dashboard System.

PubMed

Eser, Alexander; Primas, Christian; Reinisch, Sieglinde; Vogelsang, Harald; Novacek, Gottfried; Mould, Diane R; Reinisch, Walter

2018-01-30

Despite a robust exposure-response relationship of infliximab in inflammatory bowel disease (IBD), attempts to adjust dosing to individually predicted serum concentrations of infliximab (SICs) are lacking. Compared with labor-intensive conventional software for pharmacokinetic (PK) modeling (eg, NONMEM) dashboards are easy-to-use programs incorporating complex Bayesian statistics to determine individual pharmacokinetics. We evaluated various infliximab detection assays and the number of samples needed to precisely forecast individual SICs using a Bayesian dashboard. We assessed long-term infliximab retention in patients being dosed concordantly versus discordantly with Bayesian dashboard recommendations. Three hundred eighty-two serum samples from 117 adult IBD patients on infliximab maintenance therapy were analyzed by 3 commercially available assays. Data from each assay was modeled using NONMEM and a Bayesian dashboard. PK parameter precision and residual variability were assessed. Forecast concentrations from both systems were compared with observed concentrations. Infliximab retention was assessed by prediction for dose intensification via Bayesian dashboard versus real-life practice. Forecast precision of SICs varied between detection assays. At least 3 SICs from a reliable assay are needed for an accurate forecast. The Bayesian dashboard performed similarly to NONMEM to predict SICs. Patients dosed concordantly with Bayesian dashboard recommendations had a significantly longer median drug survival than those dosed discordantly (51.5 versus 4.6 months, P < .0001). The Bayesian dashboard helps to assess the diagnostic performance of infliximab detection assays. Three, not single, SICs provide sufficient information for individualized dose adjustment when incorporated into the Bayesian dashboard. Treatment adjusted to forecasted SICs is associated with longer drug retention of infliximab. © 2018, The American College of Clinical Pharmacology.

Base pair probability estimates improve the prediction accuracy of RNA non-canonical base pairs

PubMed Central

2017-01-01

Prediction of RNA tertiary structure from sequence is an important problem, but generating accurate structure models for even short sequences remains difficult. Predictions of RNA tertiary structure tend to be least accurate in loop regions, where non-canonical pairs are important for determining the details of structure. Non-canonical pairs can be predicted using a knowledge-based model of structure that scores nucleotide cyclic motifs, or NCMs. In this work, a partition function algorithm is introduced that allows the estimation of base pairing probabilities for both canonical and non-canonical interactions. Pairs that are predicted to be probable are more likely to be found in the true structure than pairs of lower probability. Pair probability estimates can be further improved by predicting the structure conserved across multiple homologous sequences using the TurboFold algorithm. These pairing probabilities, used in concert with prior knowledge of the canonical secondary structure, allow accurate inference of non-canonical pairs, an important step towards accurate prediction of the full tertiary structure. Software to predict non-canonical base pairs and pairing probabilities is now provided as part of the RNAstructure software package. PMID:29107980

Heart rate during basketball game play and volleyball drills accurately predicts oxygen uptake and energy expenditure.

PubMed

Scribbans, T D; Berg, K; Narazaki, K; Janssen, I; Gurd, B J

2015-09-01

There is currently little information regarding the ability of metabolic prediction equations to accurately predict oxygen uptake and exercise intensity from heart rate (HR) during intermittent sport. The purpose of the present study was to develop and, cross-validate equations appropriate for accurately predicting oxygen cost (VO2) and energy expenditure from HR during intermittent sport participation. Eleven healthy adult males (19.9±1.1yrs) were recruited to establish the relationship between %VO2peak and %HRmax during low-intensity steady state endurance (END), moderate-intensity interval (MOD) and high intensity-interval exercise (HI), as performed on a cycle ergometer. Three equations (END, MOD, and HI) for predicting %VO2peak based on %HRmax were developed. HR and VO2 were directly measured during basketball games (6 male, 20.8±1.0 yrs; 6 female, 20.0±1.3yrs) and volleyball drills (12 female; 20.8±1.0yrs). Comparisons were made between measured and predicted VO2 and energy expenditure using the 3 equations developed and 2 previously published equations. The END and MOD equations accurately predicted VO2 and energy expenditure, while the HI equation underestimated, and the previously published equations systematically overestimated VO2 and energy expenditure. Intermittent sport VO2 and energy expenditure can be accurately predicted from heart rate data using either the END (%VO2peak=%HRmax x 1.008-17.17) or MOD (%VO2peak=%HRmax x 1.2-32) equations. These 2 simple equations provide an accessible and cost-effective method for accurate estimation of exercise intensity and energy expenditure during intermittent sport.

Absolute Measurements of Macrophage Migration Inhibitory Factor and Interleukin-1-β mRNA Levels Accurately Predict Treatment Response in Depressed Patients.

PubMed

Cattaneo, Annamaria; Ferrari, Clarissa; Uher, Rudolf; Bocchio-Chiavetto, Luisella; Riva, Marco Andrea; Pariante, Carmine M

2016-10-01

Increased levels of inflammation have been associated with a poorer response to antidepressants in several clinical samples, but these findings have had been limited by low reproducibility of biomarker assays across laboratories, difficulty in predicting response probability on an individual basis, and unclear molecular mechanisms. Here we measured absolute mRNA values (a reliable quantitation of number of molecules) of Macrophage Migration Inhibitory Factor and interleukin-1β in a previously published sample from a randomized controlled trial comparing escitalopram vs nortriptyline (GENDEP) as well as in an independent, naturalistic replication sample. We then used linear discriminant analysis to calculate mRNA values cutoffs that best discriminated between responders and nonresponders after 12 weeks of antidepressants. As Macrophage Migration Inhibitory Factor and interleukin-1β might be involved in different pathways, we constructed a protein-protein interaction network by the Search Tool for the Retrieval of Interacting Genes/Proteins. We identified cutoff values for the absolute mRNA measures that accurately predicted response probability on an individual basis, with positive predictive values and specificity for nonresponders of 100% in both samples (negative predictive value=82% to 85%, sensitivity=52% to 61%). Using network analysis, we identified different clusters of targets for these 2 cytokines, with Macrophage Migration Inhibitory Factor interacting predominantly with pathways involved in neurogenesis, neuroplasticity, and cell proliferation, and interleukin-1β interacting predominantly with pathways involved in the inflammasome complex, oxidative stress, and neurodegeneration. We believe that these data provide a clinically suitable approach to the personalization of antidepressant therapy: patients who have absolute mRNA values above the suggested cutoffs could be directed toward earlier access to more assertive antidepressant strategies, including the addition of other antidepressants or antiinflammatory drugs. © The Author 2016. Published by Oxford University Press on behalf of CINP.

Effective heating of magnetic nanoparticle aggregates for in vivo nano-theranostic hyperthermia.

PubMed

Wang, Chencai; Hsu, Chao-Hsiung; Li, Zhao; Hwang, Lian-Pin; Lin, Ying-Chih; Chou, Pi-Tai; Lin, Yung-Ya

2017-01-01

Magnetic resonance (MR) nano-theranostic hyperthermia uses magnetic nanoparticles to target and accumulate at the lesions and generate heat to kill lesion cells directly through hyperthermia or indirectly through thermal activation and control releasing of drugs. Preclinical and translational applications of MR nano-theranostic hyperthermia are currently limited by a few major theoretical difficulties and experimental challenges in in vivo conditions. For example, conventional models for estimating the heat generated and the optimal magnetic nanoparticle sizes for hyperthermia do not accurately reproduce reported in vivo experimental results. In this work, a revised cluster-based model was proposed to predict the specific loss power (SLP) by explicitly considering magnetic nanoparticle aggregation in in vivo conditions. By comparing with the reported experimental results of magnetite Fe 3 O 4 and cobalt ferrite CoFe 2 O 4 magnetic nanoparticles, it is shown that the revised cluster-based model provides a more accurate prediction of the experimental values than the conventional models that assume magnetic nanoparticles act as single units. It also provides a clear physical picture: the aggregation of magnetic nanoparticles increases the cluster magnetic anisotropy while reducing both the cluster domain magnetization and the average magnetic moment, which, in turn, shift the predicted SLP toward a smaller magnetic nanoparticle diameter with lower peak values. As a result, the heating efficiency and the SLP values are decreased. The improvement in the prediction accuracy in in vivo conditions is particularly pronounced when the magnetic nanoparticle diameter is in the range of ~10-20 nm. This happens to be an important size range for MR cancer nano-theranostics, as it exhibits the highest efficacy against both primary and metastatic tumors in vivo. Our studies show that a relatively 20%-25% smaller magnetic nanoparticle diameter should be chosen to reach the maximal heating efficiency in comparison with the optimal size predicted by previous models.

Effective heating of magnetic nanoparticle aggregates for in vivo nano-theranostic hyperthermia

PubMed Central

Wang, Chencai; Hsu, Chao-Hsiung; Li, Zhao; Hwang, Lian-Pin; Lin, Ying-Chih; Chou, Pi-Tai; Lin, Yung-Ya

2017-01-01

Magnetic resonance (MR) nano-theranostic hyperthermia uses magnetic nanoparticles to target and accumulate at the lesions and generate heat to kill lesion cells directly through hyperthermia or indirectly through thermal activation and control releasing of drugs. Preclinical and translational applications of MR nano-theranostic hyperthermia are currently limited by a few major theoretical difficulties and experimental challenges in in vivo conditions. For example, conventional models for estimating the heat generated and the optimal magnetic nanoparticle sizes for hyperthermia do not accurately reproduce reported in vivo experimental results. In this work, a revised cluster-based model was proposed to predict the specific loss power (SLP) by explicitly considering magnetic nanoparticle aggregation in in vivo conditions. By comparing with the reported experimental results of magnetite Fe3O4 and cobalt ferrite CoFe2O4 magnetic nanoparticles, it is shown that the revised cluster-based model provides a more accurate prediction of the experimental values than the conventional models that assume magnetic nanoparticles act as single units. It also provides a clear physical picture: the aggregation of magnetic nanoparticles increases the cluster magnetic anisotropy while reducing both the cluster domain magnetization and the average magnetic moment, which, in turn, shift the predicted SLP toward a smaller magnetic nanoparticle diameter with lower peak values. As a result, the heating efficiency and the SLP values are decreased. The improvement in the prediction accuracy in in vivo conditions is particularly pronounced when the magnetic nanoparticle diameter is in the range of ~10–20 nm. This happens to be an important size range for MR cancer nano-theranostics, as it exhibits the highest efficacy against both primary and metastatic tumors in vivo. Our studies show that a relatively 20%–25% smaller magnetic nanoparticle diameter should be chosen to reach the maximal heating efficiency in comparison with the optimal size predicted by previous models. PMID:28894366

Relationship between the Prediction Accuracy of Tsunami Inundation and Relative Distribution of Tsunami Source and Observation Arrays: A Case Study in Tokyo Bay

NASA Astrophysics Data System (ADS)

Takagawa, T.

2017-12-01

A rapid and precise tsunami forecast based on offshore monitoring is getting attention to reduce human losses due to devastating tsunami inundation. We developed a forecast method based on the combination of hierarchical Bayesian inversion with pre-computed database and rapid post-computing of tsunami inundation. The method was applied to Tokyo bay to evaluate the efficiency of observation arrays against three tsunamigenic earthquakes. One is a scenario earthquake at Nankai trough and the other two are historic ones of Genroku in 1703 and Enpo in 1677. In general, rich observation array near the tsunami source has an advantage in both accuracy and rapidness of tsunami forecast. To examine the effect of observation time length we used four types of data with the lengths of 5, 10, 20 and 45 minutes after the earthquake occurrences. Prediction accuracy of tsunami inundation was evaluated by the simulated tsunami inundation areas around Tokyo bay due to target earthquakes. The shortest time length of accurate prediction varied with target earthquakes. Here, accurate prediction means the simulated values fall within the 95% credible intervals of prediction. In Enpo earthquake case, 5-minutes observation is enough for accurate prediction for Tokyo bay, but 10-minutes and 45-minutes are needed in the case of Nankai trough and Genroku, respectively. The difference of the shortest time length for accurate prediction shows the strong relationship with the relative distance from the tsunami source and observation arrays. In the Enpo case, offshore tsunami observation points are densely distributed even in the source region. So, accurate prediction can be rapidly achieved within 5 minutes. This precise prediction is useful for early warnings. Even in the worst case of Genroku, where less observation points are available near the source, accurate prediction can be obtained within 45 minutes. This information can be useful to figure out the outline of the hazard in an early stage of reaction.

A physiologically based pharmacokinetic model to predict the pharmacokinetics of highly protein-bound drugs and the impact of errors in plasma protein binding.

PubMed

Ye, Min; Nagar, Swati; Korzekwa, Ken

2016-04-01

Predicting the pharmacokinetics of highly protein-bound drugs is difficult. Also, since historical plasma protein binding data were often collected using unbuffered plasma, the resulting inaccurate binding data could contribute to incorrect predictions. This study uses a generic physiologically based pharmacokinetic (PBPK) model to predict human plasma concentration-time profiles for 22 highly protein-bound drugs. Tissue distribution was estimated from in vitro drug lipophilicity data, plasma protein binding and the blood: plasma ratio. Clearance was predicted with a well-stirred liver model. Underestimated hepatic clearance for acidic and neutral compounds was corrected by an empirical scaling factor. Predicted values (pharmacokinetic parameters, plasma concentration-time profile) were compared with observed data to evaluate the model accuracy. Of the 22 drugs, less than a 2-fold error was obtained for the terminal elimination half-life (t1/2 , 100% of drugs), peak plasma concentration (Cmax , 100%), area under the plasma concentration-time curve (AUC0-t , 95.4%), clearance (CLh , 95.4%), mean residence time (MRT, 95.4%) and steady state volume (Vss , 90.9%). The impact of fup errors on CLh and Vss prediction was evaluated. Errors in fup resulted in proportional errors in clearance prediction for low-clearance compounds, and in Vss prediction for high-volume neutral drugs. For high-volume basic drugs, errors in fup did not propagate to errors in Vss prediction. This is due to the cancellation of errors in the calculations for tissue partitioning of basic drugs. Overall, plasma profiles were well simulated with the present PBPK model. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Drug-target interaction prediction: A Bayesian ranking approach.

PubMed

Peska, Ladislav; Buza, Krisztian; Koller, Júlia

2017-12-01

In silico prediction of drug-target interactions (DTI) could provide valuable information and speed-up the process of drug repositioning - finding novel usage for existing drugs. In our work, we focus on machine learning algorithms supporting drug-centric repositioning approach, which aims to find novel usage for existing or abandoned drugs. We aim at proposing a per-drug ranking-based method, which reflects the needs of drug-centric repositioning research better than conventional drug-target prediction approaches. We propose Bayesian Ranking Prediction of Drug-Target Interactions (BRDTI). The method is based on Bayesian Personalized Ranking matrix factorization (BPR) which has been shown to be an excellent approach for various preference learning tasks, however, it has not been used for DTI prediction previously. In order to successfully deal with DTI challenges, we extended BPR by proposing: (i) the incorporation of target bias, (ii) a technique to handle new drugs and (iii) content alignment to take structural similarities of drugs and targets into account. Evaluation on five benchmark datasets shows that BRDTI outperforms several state-of-the-art approaches in terms of per-drug nDCG and AUC. BRDTI results w.r.t. nDCG are 0.929, 0.953, 0.948, 0.897 and 0.690 for G-Protein Coupled Receptors (GPCR), Ion Channels (IC), Nuclear Receptors (NR), Enzymes (E) and Kinase (K) datasets respectively. Additionally, BRDTI significantly outperformed other methods (BLM-NII, WNN-GIP, NetLapRLS and CMF) w.r.t. nDCG in 17 out of 20 cases. Furthermore, BRDTI was also shown to be able to predict novel drug-target interactions not contained in the original datasets. The average recall at top-10 predicted targets for each drug was 0.762, 0.560, 1.000 and 0.404 for GPCR, IC, NR, and E datasets respectively. Based on the evaluation, we can conclude that BRDTI is an appropriate choice for researchers looking for an in silico DTI prediction technique to be used in drug-centric repositioning scenarios. BRDTI Software and supplementary materials are available online at www.ksi.mff.cuni.cz/∼peska/BRDTI. Copyright © 2017 Elsevier B.V. All rights reserved.

Forecasting malaria in a highly endemic country using environmental and clinical predictors.

PubMed

Zinszer, Kate; Kigozi, Ruth; Charland, Katia; Dorsey, Grant; Brewer, Timothy F; Brownstein, John S; Kamya, Moses R; Buckeridge, David L

2015-06-18

Malaria thrives in poor tropical and subtropical countries where local resources are limited. Accurate disease forecasts can provide public and clinical health services with the information needed to implement targeted approaches for malaria control that make effective use of limited resources. The objective of this study was to determine the relevance of environmental and clinical predictors of malaria across different settings in Uganda. Forecasting models were based on health facility data collected by the Uganda Malaria Surveillance Project and satellite-derived rainfall, temperature, and vegetation estimates from 2006 to 2013. Facility-specific forecasting models of confirmed malaria were developed using multivariate autoregressive integrated moving average models and produced weekly forecast horizons over a 52-week forecasting period. The model with the most accurate forecasts varied by site and by forecast horizon. Clinical predictors were retained in the models with the highest predictive power for all facility sites. The average error over the 52 forecasting horizons ranged from 26 to 128% whereas the cumulative burden forecast error ranged from 2 to 22%. Clinical data, such as drug treatment, could be used to improve the accuracy of malaria predictions in endemic settings when coupled with environmental predictors. Further exploration of malaria forecasting is necessary to improve its accuracy and value in practice, including examining other environmental and intervention predictors, including insecticide-treated nets.

HIT'nDRIVE: patient-specific multidriver gene prioritization for precision oncology

PubMed Central

Hodzic, Ermin; Sauerwald, Thomas; Dao, Phuong; Wang, Kendric; Yeung, Jake; Anderson, Shawn; Vandin, Fabio; Haffari, Gholamreza; Collins, Colin C.; Sahinalp, S. Cenk

2017-01-01

Prioritizing molecular alterations that act as drivers of cancer remains a crucial bottleneck in therapeutic development. Here we introduce HIT'nDRIVE, a computational method that integrates genomic and transcriptomic data to identify a set of patient-specific, sequence-altered genes, with sufficient collective influence over dysregulated transcripts. HIT'nDRIVE aims to solve the “random walk facility location” (RWFL) problem in a gene (or protein) interaction network, which differs from the standard facility location problem by its use of an alternative distance measure: “multihitting time,” the expected length of the shortest random walk from any one of the set of sequence-altered genes to an expression-altered target gene. When applied to 2200 tumors from four major cancer types, HIT'nDRIVE revealed many potentially clinically actionable driver genes. We also demonstrated that it is possible to perform accurate phenotype prediction for tumor samples by only using HIT'nDRIVE-seeded driver gene modules from gene interaction networks. In addition, we identified a number of breast cancer subtype-specific driver modules that are associated with patients’ survival outcome. Furthermore, HIT'nDRIVE, when applied to a large panel of pan-cancer cell lines, accurately predicted drug efficacy using the driver genes and their seeded gene modules. Overall, HIT'nDRIVE may help clinicians contextualize massive multiomics data in therapeutic decision making, enabling widespread implementation of precision oncology. PMID:28768687

DenguePredict: An Integrated Drug Repositioning Approach towards Drug Discovery for Dengue.

PubMed

Wang, QuanQiu; Xu, Rong

2015-01-01

Dengue is a viral disease of expanding global incidence without cures. Here we present a drug repositioning system (DenguePredict) leveraging upon a unique drug treatment database and vast amounts of disease- and drug-related data. We first constructed a large-scale genetic disease network with enriched dengue genetics data curated from biomedical literature. We applied a network-based ranking algorithm to find dengue-related diseases from the disease network. We then developed a novel algorithm to prioritize FDA-approved drugs from dengue-related diseases to treat dengue. When tested in a de-novo validation setting, DenguePredict found the only two drugs tested in clinical trials for treating dengue and ranked them highly: chloroquine ranked at top 0.96% and ivermectin at top 22.75%. We showed that drugs targeting immune systems and arachidonic acid metabolism-related apoptotic pathways might represent innovative drugs to treat dengue. In summary, DenguePredict, by combining comprehensive disease- and drug-related data and novel algorithms, may greatly facilitate drug discovery for dengue.

A Bayesian prediction model between a biomarker and the clinical endpoint for dichotomous variables.

PubMed

Jiang, Zhiwei; Song, Yang; Shou, Qiong; Xia, Jielai; Wang, William

2014-12-20

Early biomarkers are helpful for predicting clinical endpoints and for evaluating efficacy in clinical trials even if the biomarker cannot replace clinical outcome as a surrogate. The building and evaluation of an association model between biomarkers and clinical outcomes are two equally important concerns regarding the prediction of clinical outcome. This paper is to address both issues in a Bayesian framework. A Bayesian meta-analytic approach is proposed to build a prediction model between the biomarker and clinical endpoint for dichotomous variables. Compared with other Bayesian methods, the proposed model only requires trial-level summary data of historical trials in model building. By using extensive simulations, we evaluate the link function and the application condition of the proposed Bayesian model under scenario (i) equal positive predictive value (PPV) and negative predictive value (NPV) and (ii) higher NPV and lower PPV. In the simulations, the patient-level data is generated to evaluate the meta-analytic model. PPV and NPV are employed to describe the patient-level relationship between the biomarker and the clinical outcome. The minimum number of historical trials to be included in building the model is also considered. It is seen from the simulations that the logit link function performs better than the odds and cloglog functions under both scenarios. PPV/NPV ≥0.5 for equal PPV and NPV, and PPV + NPV ≥1 for higher NPV and lower PPV are proposed in order to predict clinical outcome accurately and precisely when the proposed model is considered. Twenty historical trials are required to be included in model building when PPV and NPV are equal. For unequal PPV and NPV, the minimum number of historical trials for model building is proposed to be five. A hypothetical example shows an application of the proposed model in global drug development. The proposed Bayesian model is able to predict well the clinical endpoint from the observed biomarker data for dichotomous variables as long as the conditions are satisfied. It could be applied in drug development. But the practical problems in applications have to be studied in further research.

Multi-parameter in vitro toxicity testing of crizotinib, sunitinib, erlotinib, and nilotinib in human cardiomyocytes

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

Doherty, Kimberly R., E-mail: kimberly.doherty@quintiles.com; Wappel, Robert L.; Talbert, Dominique R.

2013-10-01

Tyrosine kinase inhibitors (TKi) have greatly improved the treatment and prognosis of multiple cancer types. However, unexpected cardiotoxicity has arisen in a subset of patients treated with these agents that was not wholly predicted by pre-clinical testing, which centers around animal toxicity studies and inhibition of the human Ether-à-go-go-Related Gene (hERG) channel. Therefore, we sought to determine whether a multi-parameter test panel assessing the effect of drug treatment on cellular, molecular, and electrophysiological endpoints could accurately predict cardiotoxicity. We examined how 4 FDA-approved TKi agents impacted cell viability, apoptosis, reactive oxygen species (ROS) generation, metabolic status, impedance, and ion channelmore » function in human cardiomyocytes. The 3 drugs clinically associated with severe cardiac adverse events (crizotinib, sunitinib, nilotinib) all proved to be cardiotoxic in our in vitro tests while the relatively cardiac-safe drug erlotinib showed only minor changes in cardiac cell health. Crizotinib, an ALK/MET inhibitor, led to increased ROS production, caspase activation, cholesterol accumulation, disruption in cardiac cell beat rate, and blockage of ion channels. The multi-targeted TKi sunitinib showed decreased cardiomyocyte viability, AMPK inhibition, increased lipid accumulation, disrupted beat pattern, and hERG block. Nilotinib, a second generation Bcr-Abl inhibitor, led to increased ROS generation, caspase activation, hERG block, and an arrhythmic beat pattern. Thus, each drug showed a unique toxicity profile that may reflect the multiple mechanisms leading to cardiotoxicity. This study demonstrates that a multi-parameter approach can provide a robust characterization of drug-induced cardiomyocyte damage that can be leveraged to improve drug safety during early phase development. - Highlights: • TKi with known adverse effects show unique cardiotoxicity profiles in this panel. • Crizotinib increases ROS, apoptosis, and cholesterol as well as alters beat rate. • Sunitinib inhibits AMPK, increases lipids and alters the cardiac beat pattern. • Nilotinib causes ROS and caspase activation, decreased lipids and arrhythmia. • Erlotinib did not impact ROS, caspase, or lipid levels or affect the beat pattern.« less

Xpert MTB/RIF for rapid detection of rifampicin-resistant Mycobacterium tuberculosis from pulmonary tuberculosis patients in Southwest Ethiopia.

PubMed

Tadesse, Mulualem; Aragaw, Dossegnaw; Dimah, Belayneh; Efa, Feyisa; Abebe, Gemeda

2016-12-01

Accurate and rapid detection of drug-resistant strains of tuberculosis (TB) is critical for early initiation of treatment and for limiting the transmission of drug-resistant TB. Here, we investigated the accuracy of Xpert MTB/RIF for detection of rifampicin (RIF) resistance, and whether this detection predicts the presence of multidrug resistant (MDR) TB in Southwest Ethiopia. Smear- or culture-positive sputa obtained from TB patients with increased suspicion of drug resistance were included in this study. GenoType MTBDRplus line-probe assays (LPAs) and Xpert MTB/RIF tests were performed on smear-positive sputum specimens and on cultured isolates for smear-negative specimens. We performed routine drug-susceptibility testing using LPA as the reference standard for confirmation of RIF and isoniazid (INH) resistance. First-line drug-susceptibility results were available for 67 Mycobacterium tuberculosis complex-positive sputum specimens using the LPA test, with our preliminary results indicating that 30% (20/67) were MDR-TB, 3% (2/67) were RIF monoresistant, 6% (4/67) were INH monoresistant, and 61% (41/67) were susceptible to both RIF and INH. Relative to routine RIF-susceptibility testing (LPA), Xpert MTB/RIF detected all RIF resistance correctly, with 100% sensitivity and 97.8% specificity and a positive-predictive value of 95.7%. Of the 23 RIF-resistant strains according to Xpert MTB/RIF, 87% (20/23) were resistant to both RIF and INH (MDR), 8.7% (2/23) were RIF monoresistant, and 4.3% (1/23) were sensitive to RIF according to the LPA test. A high proportion of RIF resistance was documented among patients previously categorized as failure cases (50%, 10/20), followed by relapse cases (31.6%, 6/19) and defaulters (28.6%, 2/7). Xpert MTB/RIF was highly effective at identifying RIF-resistant strains in smear- or culture-positive samples. RIF resistance based on Xpert MTB/RIF results could be used to estimate MDR and allow rapid initiation of MDR-TB treatment in regions with high levels of drug-resistant TB. Copyright © 2016.

High prevalence of risk factors in elderly patients using drugs associated with acquired torsades de pointes chronically in Colombia.

PubMed

Moreno-Gutiérrez, Paula Andrea; Gaviria-Mendoza, Andrés; Cañón, Mauricio Montoya; Machado-Alba, Jorge Enrique

2016-08-01

Medication is one of the main causes of long QT syndrome (LQTS) and torsades de pointes (TdP), and the older adult population is at particularly high risk. The aim of the present study was to describe the prescription patterns of drugs with a risk of TdP in the Colombian older adult population. Patients older than 65 years who received medication with a risk of TdP during three consecutive months were selected. The medication was obtained and classified according to the QT Drug List from Crediblemeds.org. The data were analysed using SPSS-22. A total of 55 932 patients were chronically receiving QT-prolonging drugs; 61.9% (n = 34 ,632) were women and the mean age of the sample was 75.6 years. Drugs with a conditional risk were consumed by 95.2% of patients, 5.3% received drugs with a known risk and 2.9% received drugs with a possible risk. Two or more QT-prolonging drugs were consumed by 10.3% of the patients (n = 5786). Most of the sample (96.8%, n = 54 170) had at least one additional risk factor for LQTS, with a mean of 3.1 ± 0.9 risk factors. Patients receiving QT-prolonging drugs for psychiatric and neurological disease were at a higher risk of major polypharmacy [odds ratio (OR) 3.0; 95% confidence interval (CI) 2.80, 3.22) and of receiving high doses of QT-prolonging drugs (OR 3.8; 95% CI 3.52, 4.05). The widespread use of medication that causes TdP and the high prevalence of additional risks in the older adult population raise the need for accurate prediction of risk and constant patient monitoring. Patients taking psychiatric drugs are at a higher risk of TdP. © 2016 The British Pharmacological Society.

High prevalence of risk factors in elderly patients using drugs associated with acquired torsades de pointes chronically in Colombia

PubMed Central

Moreno‐Gutiérrez, Paula Andrea; Gaviria‐Mendoza, Andrés; Cañón, Mauricio Montoya

2016-01-01

Aims Medication is one of the main causes of long QT syndrome (LQTS) and torsades de pointes (TdP), and the older adult population is at particularly high risk. The aim of the present study was to describe the prescription patterns of drugs with a risk of TdP in the Colombian older adult population. Methods Patients older than 65 years who received medication with a risk of TdP during three consecutive months were selected. The medication was obtained and classified according to the QT Drug List from Crediblemeds.org. The data were analysed using SPSS‐22. Results A total of 55 932 patients were chronically receiving QT‐prolonging drugs; 61.9% (n = 34 ,632) were women and the mean age of the sample was 75.6 years. Drugs with a conditional risk were consumed by 95.2% of patients, 5.3% received drugs with a known risk and 2.9% received drugs with a possible risk. Two or more QT‐prolonging drugs were consumed by 10.3% of the patients (n = 5786). Most of the sample (96.8%, n = 54 170) had at least one additional risk factor for LQTS, with a mean of 3.1 ± 0.9 risk factors. Patients receiving QT‐prolonging drugs for psychiatric and neurological disease were at a higher risk of major polypharmacy [odds ratio (OR) 3.0; 95% confidence interval (CI) 2.80, 3.22) and of receiving high doses of QT‐prolonging drugs (OR 3.8; 95% CI 3.52, 4.05). Conclusions The widespread use of medication that causes TdP and the high prevalence of additional risks in the older adult population raise the need for accurate prediction of risk and constant patient monitoring. Patients taking psychiatric drugs are at a higher risk of TdP. PMID:27060989

Metrics for quantifying antimicrobial use in beef feedlots.

PubMed

Benedict, Katharine M; Gow, Sheryl P; Reid-Smith, Richard J; Booker, Calvin W; Morley, Paul S

2012-08-01

Accurate antimicrobial drug use data are needed to enlighten discussions regarding the impact of antimicrobial drug use in agriculture. The primary objective of this study was to investigate the perceived accuracy and clarity of different methods for reporting antimicrobial drug use information collected regarding beef feedlots. Producers, veterinarians, industry representatives, public health officials, and other knowledgeable beef industry leaders were invited to complete a web-based survey. A total of 156 participants in 33 US states, 4 Canadian provinces, and 8 other countries completed the survey. No single metric was considered universally optimal for all use circumstances or for all audiences. To effectively communicate antimicrobial drug use data, evaluation of the target audience is critical to presenting the information. Metrics that are most accurate need to be carefully and repeatedly explained to the audience.

Prediction of Central Nervous System Side Effects Through Drug Permeability to Blood-Brain Barrier and Recommendation Algorithm.

PubMed

Fan, Jun; Yang, Jing; Jiang, Zhenran

2018-04-01

Drug side effects are one of the public health concerns. Using powerful machine-learning methods to predict potential side effects before the drugs reach the clinical stages is of great importance to reduce time consumption and protect the security of patients. Recently, researchers have proved that the central nervous system (CNS) side effects of a drug are closely related to its permeability to the blood-brain barrier (BBB). Inspired by this, we proposed an extended neighborhood-based recommendation method to predict CNS side effects using drug permeability to the BBB and other known features of drug. To the best of our knowledge, this is the first attempt to predict CNS side effects considering drug permeability to the BBB. Computational experiments demonstrated that drug permeability to the BBB is an important factor in CNS side effects prediction. Moreover, we built an ensemble recommendation model and obtained higher AUC score (area under the receiver operating characteristic curve) and AUPR score (area under the precision-recall curve) on the data set of CNS side effects by integrating various features of drug.

Rapid identification and validation of novel targeted approaches for Glioblastoma: A combined ex vivo-in vivo pharmaco-omic model.

PubMed

Daher, Ahmad; de Groot, John

2018-01-01

Tumor heterogeneity is a major factor in glioblastoma's poor response to therapy and seemingly inevitable recurrence. Only two glioblastoma drugs have received Food and Drug Administration approval since 1998, highlighting the urgent need for new therapies. Profiling "omics" analyses have helped characterize glioblastoma molecularly and have thus identified multiple molecular targets for precision medicine. These molecular targets have influenced clinical trial design; many "actionable" mutation-focused trials are underway, but because they have not yet led to therapeutic breakthroughs, new strategies for treating glioblastoma, especially those with a pharmacological functional component, remain in high demand. In that regard, high-throughput screening that allows for expedited preclinical drug testing and the use of GBM models that represent tumor heterogeneity more accurately than traditional cancer cell lines is necessary to maximize the successful translation of agents into the clinic. High-throughput screening has been successfully used in the testing, discovery, and validation of potential therapeutics in various cancer models, but it has not been extensively utilized in glioblastoma models. In this report, we describe the basic aspects of high-throughput screening and propose a modified high-throughput screening model in which ex vivo and in vivo drug testing is complemented by post-screening pharmacological, pan-omic analysis to expedite anti-glioma drugs' preclinical testing and develop predictive biomarker datasets that can aid in personalizing glioblastoma therapy and inform clinical trial design. Copyright © 2017 Elsevier Inc. All rights reserved.

Drugs on the College Campus. A Guide for College Administrators.

ERIC Educational Resources Information Center

Nowlis, Helen H.

This guide to drugs on the college campus provides accurate information to help administrators and other college officials understand and cope with the use of drugs by college students. The problem is defined, and facts about drugs, and the implications and issues occasioned by their use, are presented. Information is also offered in the following…

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.

Predicting early cognitive decline in newly-diagnosed Parkinson's patients: A practical model.

PubMed

Hogue, Olivia; Fernandez, Hubert H; Floden, Darlene P

2018-06-19

To create a multivariable model to predict early cognitive decline among de novo patients with Parkinson's disease, using brief, inexpensive assessments that are easily incorporated into clinical flow. Data for 351 drug-naïve patients diagnosed with idiopathic Parkinson's disease were obtained from the Parkinson's Progression Markers Initiative. Baseline demographic, disease history, motor, and non-motor features were considered as candidate predictors. Best subsets selection was used to determine the multivariable baseline symptom profile that most accurately predicted individual cognitive decline within three years. Eleven per cent of the sample experienced cognitive decline. The final logistic regression model predicting decline included five baseline variables: verbal memory retention, right-sided bradykinesia, years of education, subjective report of cognitive impairment, and REM behavior disorder. Model discrimination was good (optimism-adjusted concordance index = .749). The associated nomogram provides a tool to determine individual patient risk of meaningful cognitive change in the early stages of the disease. Through the consideration of easily-implemented or routinely-gathered assessments, we have identified a multidimensional baseline profile and created a convenient, inexpensive tool to predict cognitive decline in the earliest stages of Parkinson's disease. The use of this tool would generate prediction at the individual level, allowing clinicians to tailor medical management for each patient and identify at-risk patients for clinical trials aimed at disease modifying therapies. Copyright © 2018. Published by Elsevier Ltd.

Prediction of small molecule binding property of protein domains with Bayesian classifiers based on Markov chains.

PubMed

Bulashevska, Alla; Stein, Martin; Jackson, David; Eils, Roland

2009-12-01

Accurate computational methods that can help to predict biological function of a protein from its sequence are of great interest to research biologists and pharmaceutical companies. One approach to assume the function of proteins is to predict the interactions between proteins and other molecules. In this work, we propose a machine learning method that uses a primary sequence of a domain to predict its propensity for interaction with small molecules. By curating the Pfam database with respect to the small molecule binding ability of its component domains, we have constructed a dataset of small molecule binding and non-binding domains. This dataset was then used as training set to learn a Bayesian classifier, which should distinguish members of each class. The domain sequences of both classes are modelled with Markov chains. In a Jack-knife test, our classification procedure achieved the predictive accuracies of 77.2% and 66.7% for binding and non-binding classes respectively. We demonstrate the applicability of our classifier by using it to identify previously unknown small molecule binding domains. Our predictions are available as supplementary material and can provide very useful information to drug discovery specialists. Given the ubiquitous and essential role small molecules play in biological processes, our method is important for identifying pharmaceutically relevant components of complete proteomes. The software is available from the author upon request.

Formulation and evaluation of chitosan/polyethylene oxide nanofibers loaded with metronidazole for local infections.

PubMed

Zupančič, Špela; Potrč, Tanja; Baumgartner, Saša; Kocbek, Petra; Kristl, Julijana

2016-12-01

Nanofibers combined with an antimicrobial represent a powerful strategy for treatment of various infections. Local infections usually have a low fluid volume available for drug release, whereas pharmacopoeian dissolution tests include a much larger receptor volume. Therefore, the development of novel drug-release methods that more closely resemble the in-vivo conditions is necessary. We first developed novel biocompatible and biodegradable chitosan/polyethylene oxide nanofibers using environmentally friendly electrospinning of aqueous polymer solutions, with the inclusion of the antimicrobial metronidazole. Here, the focus is on the characterization of these nanofibers, which have high potential for bioadhesion and retention at the site of application. These can be used where prolonged retention of the delivery system at an infected target site is needed. Drug release was studied using three in-vitro methods: a dissolution apparatus (Apparatus 1 of the European Pharmacopoeia), vials, and a Franz diffusion cell. In contrast to other studies, here the Franz diffusion cell method was modified to introduce a small volume of medium with the nanofibers in the donor compartment, where the nanofibers swelled, eroded, and released the metronidazole, which then diffused into the receptor compartment. This set-up with nanofibers in a limited amount of medium released the drug more slowly compared to the other two in-vitro methods that included larger volumes of medium. These findings show that drug release from nanofibers strongly depends on the release method used. Therefore, in-vitro test methods should closely resemble the in-vivo conditions for more accurate prediction of drug release at a therapeutic site. Copyright © 2016 Elsevier B.V. All rights reserved.

The SPOTS System: An Ocular Scoring System Optimized for Use in Modern Preclinical Drug Development and Toxicology.

PubMed

Eaton, Joshua Seth; Miller, Paul E; Bentley, Ellison; Thomasy, Sara M; Murphy, Christopher J

2017-12-01

To present a semiquantitative ocular scoring system comprising elements and criteria that address many of the limitations associated with systems commonly used in preclinical studies, providing enhanced cross-species applicability and predictive value in modern ocular drug and device development. Revisions to the ocular scoring systems of McDonald-Shadduck and Hackett-McDonald were conducted by board-certified veterinary ophthalmologists at Ocular Services On Demand (OSOD) over the execution of hundreds of in vivo preclinical ocular drug and device development studies and general toxicological investigations. This semiquantitative preclinical ocular toxicology scoring (SPOTS) system was driven by limitations of previously published systems identified by our group's recent review of slit lamp-based scoring systems in clinical ophthalmology, toxicology, and vision science. The SPOTS system provides scoring criteria for the anterior segment, posterior segment, and characterization of intravitreal test articles. Key elements include: standardized slit lamp settings; expansion of criteria to enhance applicability to nonrabbit species; refinement and disambiguation of scoring criteria for corneal opacity, fluorescein staining severity, and aqueous flare; introduction of novel criteria for scoring of aqueous and anterior vitreous cell; and introduction of criteria for findings observed with drugs/devices targeting the posterior segment. A modified Standardization of Uveitis Nomenclature (SUN) system is also introduced to facilitate accurate use of SUN's criteria in laboratory species. The SPOTS systems provide criteria that stand to enhance the applicability of semiquantitative scoring criteria to the full range of laboratory species, in the context of modern approaches to ocular therapeutics and drug delivery and drug and device development.

Pharmacological mechanism-based drug safety assessment and prediction.

PubMed

Abernethy, D R; Woodcock, J; Lesko, L J

2011-06-01

Advances in cheminformatics, bioinformatics, and pharmacology in the context of biological systems are now at a point that these tools can be applied to mechanism-based drug safety assessment and prediction. The development of such predictive tools at the US Food and Drug Administration (FDA) will complement ongoing efforts in drug safety that are focused on spontaneous adverse event reporting and active surveillance to monitor drug safety. This effort will require the active collaboration of scientists in the pharmaceutical industry, academe, and the National Institutes of Health, as well as those at the FDA, to reach its full potential. Here, we describe the approaches and goals for the mechanism-based drug safety assessment and prediction program.

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.

A dual-process account of auditory change detection.

PubMed

McAnally, Ken I; Martin, Russell L; Eramudugolla, Ranmalee; Stuart, Geoffrey W; Irvine, Dexter R F; Mattingley, Jason B

2010-08-01

Listeners can be "deaf" to a substantial change in a scene comprising multiple auditory objects unless their attention has been directed to the changed object. It is unclear whether auditory change detection relies on identification of the objects in pre- and post-change scenes. We compared the rates at which listeners correctly identify changed objects with those predicted by change-detection models based on signal detection theory (SDT) and high-threshold theory (HTT). Detected changes were not identified as accurately as predicted by models based on either theory, suggesting that some changes are detected by a process that does not support change identification. Undetected changes were identified as accurately as predicted by the HTT model but much less accurately than predicted by the SDT models. The process underlying change detection was investigated further by determining receiver-operating characteristics (ROCs). ROCs did not conform to those predicted by either a SDT or a HTT model but were well modeled by a dual-process that incorporated HTT and SDT components. The dual-process model also accurately predicted the rates at which detected and undetected changes were correctly identified.

Asymmetric bagging and feature selection for activities prediction of drug molecules.

PubMed

Li, Guo-Zheng; Meng, Hao-Hua; Lu, Wen-Cong; Yang, Jack Y; Yang, Mary Qu

2008-05-28

Activities of drug molecules can be predicted by QSAR (quantitative structure activity relationship) models, which overcomes the disadvantages of high cost and long cycle by employing the traditional experimental method. With the fact that the number of drug molecules with positive activity is rather fewer than that of negatives, it is important to predict molecular activities considering such an unbalanced situation. Here, asymmetric bagging and feature selection are introduced into the problem and asymmetric bagging of support vector machines (asBagging) is proposed on predicting drug activities to treat the unbalanced problem. At the same time, the features extracted from the structures of drug molecules affect prediction accuracy of QSAR models. Therefore, a novel algorithm named PRIFEAB is proposed, which applies an embedded feature selection method to remove redundant and irrelevant features for asBagging. Numerical experimental results on a data set of molecular activities show that asBagging improve the AUC and sensitivity values of molecular activities and PRIFEAB with feature selection further helps to improve the prediction ability. Asymmetric bagging can help to improve prediction accuracy of activities of drug molecules, which can be furthermore improved by performing feature selection to select relevant features from the drug molecules data sets.

A probabilistic and adaptive approach to modeling performance of pavement infrastructure

DOT National Transportation Integrated Search

2007-08-01

Accurate prediction of pavement performance is critical to pavement management agencies. Reliable and accurate predictions of pavement infrastructure performance can save significant amounts of money for pavement infrastructure management agencies th...

A prediction model of drug-induced ototoxicity developed by an optimal support vector machine (SVM) method.

PubMed

Zhou, Shu; Li, Guo-Bo; Huang, Lu-Yi; Xie, Huan-Zhang; Zhao, Ying-Lan; Chen, Yu-Zong; Li, Lin-Li; Yang, Sheng-Yong

2014-08-01

Drug-induced ototoxicity, as a toxic side effect, is an important issue needed to be considered in drug discovery. Nevertheless, current experimental methods used to evaluate drug-induced ototoxicity are often time-consuming and expensive, indicating that they are not suitable for a large-scale evaluation of drug-induced ototoxicity in the early stage of drug discovery. We thus, in this investigation, established an effective computational prediction model of drug-induced ototoxicity using an optimal support vector machine (SVM) method, GA-CG-SVM. Three GA-CG-SVM models were developed based on three training sets containing agents bearing different risk levels of drug-induced ototoxicity. For comparison, models based on naïve Bayesian (NB) and recursive partitioning (RP) methods were also used on the same training sets. Among all the prediction models, the GA-CG-SVM model II showed the best performance, which offered prediction accuracies of 85.33% and 83.05% for two independent test sets, respectively. Overall, the good performance of the GA-CG-SVM model II indicates that it could be used for the prediction of drug-induced ototoxicity in the early stage of drug discovery. Copyright © 2014 Elsevier Ltd. All rights reserved.

Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference

PubMed Central

Jiang, Jing; Lu, Weiqiang; Li, Weihua; Liu, Guixia; Zhou, Weixing; Huang, Jin; Tang, Yun

2012-01-01

Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 µM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning. PMID:22589709

Systematic drug repositioning for a wide range of diseases with integrative analyses of phenotypic and molecular data.

PubMed

Iwata, Hiroaki; Sawada, Ryusuke; Mizutani, Sayaka; Yamanishi, Yoshihiro

2015-02-23

Drug repositioning, or the application of known drugs to new indications, is a challenging issue in pharmaceutical science. In this study, we developed a new computational method to predict unknown drug indications for systematic drug repositioning in a framework of supervised network inference. We defined a descriptor for each drug-disease pair based on the phenotypic features of drugs (e.g., medicinal effects and side effects) and various molecular features of diseases (e.g., disease-causing genes, diagnostic markers, disease-related pathways, and environmental factors) and constructed a statistical model to predict new drug-disease associations for a wide range of diseases in the International Classification of Diseases. Our results show that the proposed method outperforms previous methods in terms of accuracy and applicability, and its performance does not depend on drug chemical structure similarity. Finally, we performed a comprehensive prediction of a drug-disease association network consisting of 2349 drugs and 858 diseases and described biologically meaningful examples of newly predicted drug indications for several types of cancers and nonhereditary diseases.

Drug-therapy networks and the prediction of novel drug targets

PubMed Central

Spiro, Zoltan; Kovacs, Istvan A; Csermely, Peter

2008-01-01

A recent study in BMC Pharmacology presents a network of drugs and the therapies in which they are used. Network approaches open new ways of predicting novel drug targets and overcoming the cellular robustness that can prevent drugs from working. PMID:18710588

Use of refractometry and colorimetry as field methods to rapidly assess antimalarial drug quality.

PubMed

Green, Michael D; Nettey, Henry; Villalva Rojas, Ofelia; Pamanivong, Chansapha; Khounsaknalath, Lamphet; Grande Ortiz, Miguel; Newton, Paul N; Fernández, Facundo M; Vongsack, Latsamy; Manolin, Ot

2007-01-04

The proliferation of counterfeit and poor-quality drugs is a major public health problem; especially in developing countries lacking adequate resources to effectively monitor their prevalence. Simple and affordable field methods provide a practical means of rapidly monitoring drug quality in circumstances where more advanced techniques are not available. Therefore, we have evaluated refractometry, colorimetry and a technique combining both processes as simple and accurate field assays to rapidly test the quality of the commonly available antimalarial drugs; artesunate, chloroquine, quinine, and sulfadoxine. Method bias, sensitivity, specificity and accuracy relative to high-performance liquid chromatographic (HPLC) analysis of drugs collected in the Lao PDR were assessed for each technique. The HPLC method for each drug was evaluated in terms of assay variability and accuracy. The accuracy of the combined method ranged from 0.96 to 1.00 for artesunate tablets, chloroquine injectables, quinine capsules, and sulfadoxine tablets while the accuracy was 0.78 for enterically coated chloroquine tablets. These techniques provide a generally accurate, yet simple and affordable means to assess drug quality in resource-poor settings.

A Rapid Survival Assay to Measure Drug-Induced Cytotoxicity and Cell Cycle Effects

PubMed Central

Valiathan, Chandni; McFaline, Jose L.

2012-01-01

We describe a rapid method to accurately measure the cytotoxicity of mammalian cells upon exposure to various drugs. Using this assay, we obtain survival data in a fraction of the time required to perform the traditional clonogenic survival assay, considered the gold standard. The dynamic range of the assay allows sensitivity measurements on a multi-log scale allowing better resolution of comparative sensitivities. Moreover, the results obtained contain additional information on cell cycle effects of the drug treatment. Cell survival is obtained from a quantitative comparison of proliferation between drug-treated and untreated cells. During the assay, cells are treated with a drug and, following a recovery period, allowed to proliferate in the presence of BrdU. Cells that synthesize DNA in the presence of bromodeoxyuridine (BrdU) exhibit quenched Hoechst fluorescence easily detected by flow cytometry; quenching is used to determine relative proliferation in treated versus untreated cells. Finally, the multi-well setup of this assay allows the simultaneous screening of multiple cell lines, multiple doses, or multiple drugs to accurately measure cell survival and cell cycle changes after drug treatment. PMID:22133811

Drug Testing.

ERIC Educational Resources Information Center

Legal Memorandum, 1987

1987-01-01

A number of legal issues are involved in conducting a drug testing program to determine whether students--and occasionally teachers--are using illegal drugs. Two legal issues have been raised concerning the accuracy of the urinalysis test: whether it is chemically accurate and whether appropriate procedures have been followed to make certain that…

Drug-Target Interactions: Prediction Methods and Applications.

PubMed

Anusuya, Shanmugam; Kesherwani, Manish; Priya, K Vishnu; Vimala, Antonydhason; Shanmugam, Gnanendra; Velmurugan, Devadasan; Gromiha, M Michael

2018-01-01

Identifying the interactions between drugs and target proteins is a key step in drug discovery. This not only aids to understand the disease mechanism, but also helps to identify unexpected therapeutic activity or adverse side effects of drugs. Hence, drug-target interaction prediction becomes an essential tool in the field of drug repurposing. The availability of heterogeneous biological data on known drug-target interactions enabled many researchers to develop various computational methods to decipher unknown drug-target interactions. This review provides an overview on these computational methods for predicting drug-target interactions along with available webservers and databases for drug-target interactions. Further, the applicability of drug-target interactions in various diseases for identifying lead compounds has been outlined. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

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.

Preclinical models used for immunogenicity prediction of therapeutic proteins.

PubMed

Brinks, Vera; Weinbuch, Daniel; Baker, Matthew; Dean, Yann; Stas, Philippe; Kostense, Stefan; Rup, Bonita; Jiskoot, Wim

2013-07-01

All therapeutic proteins are potentially immunogenic. Antibodies formed against these drugs can decrease efficacy, leading to drastically increased therapeutic costs and in rare cases to serious and sometimes life threatening side-effects. Many efforts are therefore undertaken to develop therapeutic proteins with minimal immunogenicity. For this, immunogenicity prediction of candidate drugs during early drug development is essential. Several in silico, in vitro and in vivo models are used to predict immunogenicity of drug leads, to modify potentially immunogenic properties and to continue development of drug candidates with expected low immunogenicity. Despite the extensive use of these predictive models, their actual predictive value varies. Important reasons for this uncertainty are the limited/insufficient knowledge on the immune mechanisms underlying immunogenicity of therapeutic proteins, the fact that different predictive models explore different components of the immune system and the lack of an integrated clinical validation. In this review, we discuss the predictive models in use, summarize aspects of immunogenicity that these models predict and explore the merits and the limitations of each of the models.

A Window into the Intoxicated Mind? Speech as an Index of Psychoactive Drug Effects

PubMed Central

Bedi, Gillinder; Cecchi, Guillermo A; Slezak, Diego F; Carrillo, Facundo; Sigman, Mariano; de Wit, Harriet

2014-01-01

Abused drugs can profoundly alter mental states in ways that may motivate drug use. These effects are usually assessed with self-report, an approach that is vulnerable to biases. Analyzing speech during intoxication may present a more direct, objective measure, offering a unique ‘window' into the mind. Here, we employed computational analyses of speech semantic and topological structure after ±3,4-methylenedioxymethamphetamine (MDMA; ‘ecstasy') and methamphetamine in 13 ecstasy users. In 4 sessions, participants completed a 10-min speech task after MDMA (0.75 and 1.5 mg/kg), methamphetamine (20 mg), or placebo. Latent Semantic Analyses identified the semantic proximity between speech content and concepts relevant to drug effects. Graph-based analyses identified topological speech characteristics. Group-level drug effects on semantic distances and topology were assessed. Machine-learning analyses (with leave-one-out cross-validation) assessed whether speech characteristics could predict drug condition in the individual subject. Speech after MDMA (1.5 mg/kg) had greater semantic proximity than placebo to the concepts friend, support, intimacy, and rapport. Speech on MDMA (0.75 mg/kg) had greater proximity to empathy than placebo. Conversely, speech on methamphetamine was further from compassion than placebo. Classifiers discriminated between MDMA (1.5 mg/kg) and placebo with 88% accuracy, and MDMA (1.5 mg/kg) and methamphetamine with 84% accuracy. For the two MDMA doses, the classifier performed at chance. These data suggest that automated semantic speech analyses can capture subtle alterations in mental state, accurately discriminating between drugs. The findings also illustrate the potential for automated speech-based approaches to characterize clinically relevant alterations to mental state, including those occurring in psychiatric illness. PMID:24694926

A window into the intoxicated mind? Speech as an index of psychoactive drug effects.

PubMed

Bedi, Gillinder; Cecchi, Guillermo A; Slezak, Diego F; Carrillo, Facundo; Sigman, Mariano; de Wit, Harriet

2014-09-01

Abused drugs can profoundly alter mental states in ways that may motivate drug use. These effects are usually assessed with self-report, an approach that is vulnerable to biases. Analyzing speech during intoxication may present a more direct, objective measure, offering a unique 'window' into the mind. Here, we employed computational analyses of speech semantic and topological structure after ±3,4-methylenedioxymethamphetamine (MDMA; 'ecstasy') and methamphetamine in 13 ecstasy users. In 4 sessions, participants completed a 10-min speech task after MDMA (0.75 and 1.5 mg/kg), methamphetamine (20 mg), or placebo. Latent Semantic Analyses identified the semantic proximity between speech content and concepts relevant to drug effects. Graph-based analyses identified topological speech characteristics. Group-level drug effects on semantic distances and topology were assessed. Machine-learning analyses (with leave-one-out cross-validation) assessed whether speech characteristics could predict drug condition in the individual subject. Speech after MDMA (1.5 mg/kg) had greater semantic proximity than placebo to the concepts friend, support, intimacy, and rapport. Speech on MDMA (0.75 mg/kg) had greater proximity to empathy than placebo. Conversely, speech on methamphetamine was further from compassion than placebo. Classifiers discriminated between MDMA (1.5 mg/kg) and placebo with 88% accuracy, and MDMA (1.5 mg/kg) and methamphetamine with 84% accuracy. For the two MDMA doses, the classifier performed at chance. These data suggest that automated semantic speech analyses can capture subtle alterations in mental state, accurately discriminating between drugs. The findings also illustrate the potential for automated speech-based approaches to characterize clinically relevant alterations to mental state, including those occurring in psychiatric illness.

Constructing a Foundational Platform Driven by Japan's K Supercomputer for Next-Generation Drug Design.

PubMed

Brown, J B; Nakatsui, Masahiko; Okuno, Yasushi

2014-12-01

The cost of pharmaceutical R&D has risen enormously, both worldwide and in Japan. However, Japan faces a particularly difficult situation in that its population is aging rapidly, and the cost of pharmaceutical R&D affects not only the industry but the entire medical system as well. To attempt to reduce costs, the newly launched K supercomputer is available for big data drug discovery and structural simulation-based drug discovery. We have implemented both primary (direct) and secondary (infrastructure, data processing) methods for the two types of drug discovery, custom tailored to maximally use the 88 128 compute nodes/CPUs of K, and evaluated the implementations. We present two types of results. In the first, we executed the virtual screening of nearly 19 billion compound-protein interactions, and calculated the accuracy of predictions against publicly available experimental data. In the second investigation, we implemented a very computationally intensive binding free energy algorithm, and found that comparison of our binding free energies was considerably accurate when validated against another type of publicly available experimental data. The common feature of both result types is the scale at which computations were executed. The frameworks presented in this article provide prospectives and applications that, while tuned to the computing resources available in Japan, are equally applicable to any equivalent large-scale infrastructure provided elsewhere. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Combining clinical variables to optimize prediction of antidepressant treatment outcomes.

PubMed

Iniesta, Raquel; Malki, Karim; Maier, Wolfgang; Rietschel, Marcella; Mors, Ole; Hauser, Joanna; Henigsberg, Neven; Dernovsek, Mojca Zvezdana; Souery, Daniel; Stahl, Daniel; Dobson, Richard; Aitchison, Katherine J; Farmer, Anne; Lewis, Cathryn M; McGuffin, Peter; Uher, Rudolf

2016-07-01

The outcome of treatment with antidepressants varies markedly across people with the same diagnosis. A clinically significant prediction of outcomes could spare the frustration of trial and error approach and improve the outcomes of major depressive disorder through individualized treatment selection. It is likely that a combination of multiple predictors is needed to achieve such prediction. We used elastic net regularized regression to optimize prediction of symptom improvement and remission during treatment with escitalopram or nortriptyline and to identify contributing predictors from a range of demographic and clinical variables in 793 adults with major depressive disorder. A combination of demographic and clinical variables, with strong contributions from symptoms of depressed mood, reduced interest, decreased activity, indecisiveness, pessimism and anxiety significantly predicted treatment outcomes, explaining 5-10% of variance in symptom improvement with escitalopram. Similar combinations of variables predicted remission with area under the curve 0.72, explaining approximately 15% of variance (pseudo R(2)) in who achieves remission, with strong contributions from body mass index, appetite, interest-activity symptom dimension and anxious-somatizing depression subtype. Escitalopram-specific outcome prediction was more accurate than generic outcome prediction, and reached effect sizes that were near or above a previously established benchmark for clinical significance. Outcome prediction on the nortriptyline arm did not significantly differ from chance. These results suggest that easily obtained demographic and clinical variables can predict therapeutic response to escitalopram with clinically meaningful accuracy, suggesting a potential for individualized prescription of this antidepressant drug. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Engineering and validation of a novel lipid thin film for biomembrane modeling in lipophilicity determination of drugs and xenobiotics

PubMed Central

Idowu, Sunday Olakunle; Adeyemo, Morenikeji Ambali; Ogbonna, Udochi Ihechiluru

2009-01-01

Background Determination of lipophilicity as a tool for predicting pharmacokinetic molecular behavior is limited by the predictive power of available experimental models of the biomembrane. There is current interest, therefore, in models that accurately simulate the biomembrane structure and function. A novel bio-device; a lipid thin film, was engineered as an alternative approach to the previous use of hydrocarbon thin films in biomembrane modeling. Results Retention behavior of four structurally diverse model compounds; 4-amino-3,5-dinitrobenzoic acid (ADBA), naproxen (NPX), nabumetone (NBT) and halofantrine (HF), representing 4 broad classes of varying molecular polarities and aqueous solubility behavior, was investigated on the lipid film, liquid paraffin, and octadecylsilane layers. Computational, thermodynamic and image analysis confirms the peculiar amphiphilic configuration of the lipid film. Effect of solute-type, layer-type and variables interactions on retention behavior was delineated by 2-way analysis of variance (ANOVA) and quantitative structure property relationships (QSPR). Validation of the lipid film was implemented by statistical correlation of a unique chromatographic metric with Log P (octanol/water) and several calculated molecular descriptors of bulk and solubility properties. Conclusion The lipid film signifies a biomimetic artificial biological interface capable of both hydrophobic and specific electrostatic interactions. It captures the hydrophilic-lipophilic balance (HLB) in the determination of lipophilicity of molecules unlike the pure hydrocarbon film of the prior art. The potentials and performance of the bio-device gives the promise of its utility as a predictive analytic tool for early-stage drug discovery science. PMID:19735551

Predictability of the 2012 Great Arctic Cyclone on medium-range timescales

NASA Astrophysics Data System (ADS)

Yamagami, Akio; Matsueda, Mio; Tanaka, Hiroshi L.

2018-03-01

Arctic Cyclones (ACs) can have a significant impact on the Arctic region. Therefore, the accurate prediction of ACs is important in anticipating their associated environmental and societal costs. This study investigates the predictability of the 2012 Great Arctic Cyclone (AC12) that exhibited a minimum central pressure of 964 hPa on 6 August 2012, using five medium-range ensemble forecasts. We show that the development and position of AC12 were better predicted in forecasts initialized on and after 4 August 2012. In addition, the position of AC12 was more predictable than its development. A comparison of ensemble members, classified by the error in predictability of the development and position of AC12, revealed that an accurate prediction of upper-level fields, particularly temperature, was important for the prediction of this event. The predicted position of AC12 was influenced mainly by the prediction of the polar vortex, whereas the predicted development of AC12 was dependent primarily on the prediction of the merging of upper-level warm cores. Consequently, an accurate prediction of the polar vortex position and the development of the warm core through merging resulted in better prediction of AC12.

Drug Screening Using a Library of Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes Reveals Disease Specific Patterns of Cardiotoxicity

PubMed Central

Liang, Ping; Lan, Feng; Lee, Andrew S.; Gong, Tingyu; Sanchez-Freire, Veronica; Wang, Yongming; Diecke, Sebastian; Sallam, Karim; Knowles, Joshua W.; Wang, Paul J.; Nguyen, Patricia K.; Bers, Donald M.; Robbins, Robert C.; Wu, Joseph C.

2013-01-01

Background Cardiotoxicity is a leading cause for drug attrition during pharmaceutical development and has resulted in numerous preventable patient deaths. Incidents of adverse cardiac drug reactions are more common in patients with pre-existing heart disease than the general population. Here we generated a library of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) from patients with various hereditary cardiac disorders to model differences in cardiac drug toxicity susceptibility for patients of different genetic backgrounds. Methods and Results Action potential duration (APD) and drug-induced arrhythmia were measured at the single cell level in hiPSC-CMs derived from healthy subjects and patients with hereditary long QT syndrome (LQT), familial hypertrophic cardiomyopathy (HCM), and familial dilated cardiomyopathy (DCM). Disease phenotypes were verified in LQT, HCM, and DCM iPSC-CMs by immunostaining and single cell patch clamp. Human embryonic stem cell-derived cardiomyocytes (hESC-CMs) and the human ether-a-go-go-related gene (hERG) expressing human embryonic kidney (HEK293) cells were used as controls. Single cell PCR confirmed expression of all cardiac ion channels in patient-specific hiPSC-CMs as well as hESC-CMs, but not in HEK293 cells. Disease-specific hiPSC-CMs demonstrated increased susceptibility to known cardiotoxic drugs as measured by APD and quantification of drug-induced arrhythmias such as early after depolarizations (EADs) and delayed after depolarizations (DADs). Conclusions We have recapitulated drug-induced cardiotoxicity profiles for healthy subjects, LQT, HCM, and DCM patients at the single cell level for the first time. Our data indicate that healthy and diseased individuals exhibit different susceptibilities to cardiotoxic drugs and that use of disease-specific hiPSC-CMs may predict adverse drug responses more accurately than standard hERG test or healthy control hiPSC-CM/hESC-CM screening assays. PMID:23519760

Visual presentations of efficacy data in direct-to-consumer prescription drug print and television advertisements: A randomized study.

PubMed

Sullivan, Helen W; O'Donoghue, Amie C; Aikin, Kathryn J; Chowdhury, Dhuly; Moultrie, Rebecca R; Rupert, Douglas J

2016-05-01

To determine whether visual aids help people recall quantitative efficacy information in direct-to-consumer (DTC) prescription drug advertisements, and if so, which types of visual aids are most helpful. Individuals diagnosed with high cholesterol (n=2504) were randomized to view a fictional DTC print or television advertisement with no visual aid or one of four visual aids (pie chart, bar chart, table, or pictograph) depicting drug efficacy. We measured drug efficacy and risk recall, drug perceptions and attitudes, and behavioral intentions. For print advertisements, a bar chart or table, compared with no visual aid, elicited more accurate drug efficacy recall. The bar chart was better at this than the pictograph and the table was better than the pie chart. For television advertisements, any visual aid, compared with no visual aid, elicited more accurate drug efficacy recall. The bar chart was better at this than the pictograph or the table. Visual aids depicting quantitative efficacy information in DTC print and television advertisements increased drug efficacy recall, which may help people make informed decisions about prescription drugs. Adding visual aids to DTC advertising may increase the public's knowledge of how well prescription drugs work. Published by Elsevier Ireland Ltd.

28 CFR 16.98 - Exemption of the Drug Enforcement Administration (DEA)-limited access.

Code of Federal Regulations, 2012 CFR

2012-07-01

... (Justice/DEA-013) (7) System to Retrieve Information from Drug Evidence (STRIDE/Ballistics) (Justice/DEA... Retrieve Information from Drug Evidence (STRIDE/Ballistics) (Justice/DEA-014) only to the extent that..., implemented internal quality assurance procedures to ensure that ESS data is as thorough, accurate, and...

28 CFR 16.98 - Exemption of the Drug Enforcement Administration (DEA)-limited access.

Code of Federal Regulations, 2011 CFR

2011-07-01

... (Justice/DEA-013) (7) System to Retrieve Information from Drug Evidence (STRIDE/Ballistics) (Justice/DEA... Retrieve Information from Drug Evidence (STRIDE/Ballistics) (Justice/DEA-014) only to the extent that..., implemented internal quality assurance procedures to ensure that ESS data is as thorough, accurate, and...

Automatic detection of adverse events to predict drug label changes using text and data mining techniques.

PubMed

Gurulingappa, Harsha; Toldo, Luca; Rajput, Abdul Mateen; Kors, Jan A; Taweel, Adel; Tayrouz, Yorki

2013-11-01

The aim of this study was to assess the impact of automatically detected adverse event signals from text and open-source data on the prediction of drug label changes. Open-source adverse effect data were collected from FAERS, Yellow Cards and SIDER databases. A shallow linguistic relation extraction system (JSRE) was applied for extraction of adverse effects from MEDLINE case reports. Statistical approach was applied on the extracted datasets for signal detection and subsequent prediction of label changes issued for 29 drugs by the UK Regulatory Authority in 2009. 76% of drug label changes were automatically predicted. Out of these, 6% of drug label changes were detected only by text mining. JSRE enabled precise identification of four adverse drug events from MEDLINE that were undetectable otherwise. Changes in drug labels can be predicted automatically using data and text mining techniques. Text mining technology is mature and well-placed to support the pharmacovigilance tasks. Copyright © 2013 John Wiley & Sons, Ltd.

A hadoop-based method to predict potential effective drug combination.

PubMed

Sun, Yifan; Xiong, Yi; Xu, Qian; Wei, Dongqing

2014-01-01

Combination drugs that impact multiple targets simultaneously are promising candidates for combating complex diseases due to their improved efficacy and reduced side effects. However, exhaustive screening of all possible drug combinations is extremely time-consuming and impractical. Here, we present a novel Hadoop-based approach to predict drug combinations by taking advantage of the MapReduce programming model, which leads to an improvement of scalability of the prediction algorithm. By integrating the gene expression data of multiple drugs, we constructed data preprocessing and the support vector machines and naïve Bayesian classifiers on Hadoop for prediction of drug combinations. The experimental results suggest that our Hadoop-based model achieves much higher efficiency in the big data processing steps with satisfactory performance. We believed that our proposed approach can help accelerate the prediction of potential effective drugs with the increasing of the combination number at an exponential rate in future. The source code and datasets are available upon request.

A Hadoop-Based Method to Predict Potential Effective Drug Combination

PubMed Central

Xiong, Yi; Xu, Qian; Wei, Dongqing

2014-01-01

Combination drugs that impact multiple targets simultaneously are promising candidates for combating complex diseases due to their improved efficacy and reduced side effects. However, exhaustive screening of all possible drug combinations is extremely time-consuming and impractical. Here, we present a novel Hadoop-based approach to predict drug combinations by taking advantage of the MapReduce programming model, which leads to an improvement of scalability of the prediction algorithm. By integrating the gene expression data of multiple drugs, we constructed data preprocessing and the support vector machines and naïve Bayesian classifiers on Hadoop for prediction of drug combinations. The experimental results suggest that our Hadoop-based model achieves much higher efficiency in the big data processing steps with satisfactory performance. We believed that our proposed approach can help accelerate the prediction of potential effective drugs with the increasing of the combination number at an exponential rate in future. The source code and datasets are available upon request. PMID:25147789

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.

Pharmacological validation of a novel nonhuman primate measure of thermal responsivity with utility for predicting analgesic effects.

PubMed

Vardigan, Joshua D; Houghton, Andrea K; Lange, Henry S; Adarayan, Emily D; Pall, Parul S; Ballard, Jeanine E; Henze, Darrell A; Uslaner, Jason M

2018-01-01

The development of novel analgesics to treat acute or chronic pain has been a challenge due to a lack of translatable measurements. Preclinical end points with improved translatability are necessary to more accurately inform clinical testing paradigms, which may help guide selection of viable drug candidates. In this study, a nonhuman primate biomarker which is sensitive to standard analgesics at clinically relevant plasma concentrations, can differentiate analgesia from sedation and utilizes a protocol very similar to that which can be employed in human clinical studies is described. Specifically, acute heat stimuli were delivered to the volar forearm using a contact heat thermode in the same manner as the clinical setting. Clinically efficacious exposures of morphine, fentanyl, and tramadol produced robust analgesic effects, whereas doses of diazepam that produce sedation had no effect. We propose that this assay has predictive utility that can help improve the probability of success for developing novel analgesics.

[Optimization of calcium alginate floating microspheres loading aspirin by artificial neural networks and response surface methodology].

PubMed

Zhang, An-yang; Fan, Tian-yuan

2010-04-18

To investigate the preparation and optimization of calcium alginate floating microspheres loading aspirin. A model was used to predict the in vitro release of aspirin and optimize the formulation by artificial neural networks (ANNs) and response surface methodology (RSM). The amounts of the material in the formulation were used as inputs, while the release and floating rate of the microspheres were used as outputs. The performances of ANNs and RSM were compared. ANNs were more accurate in prediction. There was no significant difference between ANNs and RSM in optimization. Approximately 90% of the optimized microspheres could float on the artificial gastric juice over 4 hours. 42.12% of aspirin was released in 60 min, 60.97% in 120 min and 78.56% in 240 min. The release of the drug from the microspheres complied with Higuchi equation. The aspirin floating microspheres with satisfying in vitro release were prepared successfully by the methods of ANNs and RSM.

Pharmacological validation of a novel nonhuman primate measure of thermal responsivity with utility for predicting analgesic effects

PubMed Central

Vardigan, Joshua D; Houghton, Andrea K; Lange, Henry S; Adarayan, Emily D; Pall, Parul S; Ballard, Jeanine E; Henze, Darrell A; Uslaner, Jason M

2018-01-01

Introduction The development of novel analgesics to treat acute or chronic pain has been a challenge due to a lack of translatable measurements. Preclinical end points with improved translatability are necessary to more accurately inform clinical testing paradigms, which may help guide selection of viable drug candidates. Methods In this study, a nonhuman primate biomarker which is sensitive to standard analgesics at clinically relevant plasma concentrations, can differentiate analgesia from sedation and utilizes a protocol very similar to that which can be employed in human clinical studies is described. Specifically, acute heat stimuli were delivered to the volar forearm using a contact heat thermode in the same manner as the clinical setting. Results Clinically efficacious exposures of morphine, fentanyl, and tramadol produced robust analgesic effects, whereas doses of diazepam that produce sedation had no effect. Conclusion We propose that this assay has predictive utility that can help improve the probability of success for developing novel analgesics. PMID:29692626

Measuring drug absorption improves interpretation of behavioral responses in a larval zebrafish locomotor assay for predicting seizure liability.

PubMed

Cassar, Steven; Breidenbach, Laura; Olson, Amanda; Huang, Xin; Britton, Heather; Woody, Clarissa; Sancheti, Pankajkumar; Stolarik, DeAnne; Wicke, Karsten; Hempel, Katja; LeRoy, Bruce

2017-11-01

Unanticipated effects on the central nervous system are a concern during new drug development. A larval zebrafish locomotor assay can reveal seizure liability of experimental molecules before testing in mammals. Relative absorption of compounds by larvae is lacking in prior reports of such assays; having those data may be valuable for interpreting seizure liability assay performance. Twenty-eight reference drugs were tested at multiple dose levels in fish water and analyzed by a blinded investigator. Responses of larval zebrafish were quantified during a 30min dosing period. Predictive metrics were calculated by comparing fish activity to mammalian seizure liability for each drug. Drug level analysis was performed to calculate concentrations in dose solutions and larvae. Fifteen drug candidates with neuronal targets, some having preclinical convulsion findings in mammals, were tested similarly. The assay has good predictive value of established mammalian responses for reference drugs. Analysis of drug absorption by larval fish revealed a positive correlation between hyperactive behavior and pro-convulsive drug absorption. False negative results were associated with significantly lower compound absorption compared to true negative, or true positive results. The predictive value for preclinical toxicology findings was inferior to that suggested by reference drugs. Disproportionately low exposures in larvae giving false negative results demonstrate that drug exposure analysis can help interpret results. Due to the rigorous testing commonly performed in preclinical toxicology, predicting convulsions in those studies may be more difficult than predicting effects from marketed drugs. Copyright © 2017 Elsevier Inc. All rights reserved.

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.

Evaluation of Turbulence-Model Performance as Applied to Jet-Noise Prediction

NASA Technical Reports Server (NTRS)

Woodruff, S. L.; Seiner, J. M.; Hussaini, M. Y.; Erlebacher, G.

1998-01-01

The accurate prediction of jet noise is possible only if the jet flow field can be predicted accurately. Predictions for the mean velocity and turbulence quantities in the jet flowfield are typically the product of a Reynolds-averaged Navier-Stokes solver coupled with a turbulence model. To evaluate the effectiveness of solvers and turbulence models in predicting those quantities most important to jet noise prediction, two CFD codes and several turbulence models were applied to a jet configuration over a range of jet temperatures for which experimental data is available.

Messages discriminated from the media about illicit drugs.

PubMed

Patterson, S J

1994-01-01

The electronic media have been an instrumental tool in the most recent efforts to address the issue of illicit drug abuse in the United States. Messages about illicit drugs appear in three places in the media: advertising content, news content, and entertainment content. Many studies have documented the amount and types of messages that appear on the electronic media, but few have asked the audience how they interpret these messages. The purpose of this study is to investigate how much and what type of information college students receive from the media about drugs. Interviews were conducted with 228 students using the message discrimination protocol. The messages were then content analyzed into theme areas. Results indicate the majority of messages discriminated from advertising content were fear appeals; that the majority of messages discriminated from news content documented the enforcement efforts in the war on drugs; and that messages about drugs in entertainment content were more likely to provide clear accurate information about drugs than the other two content sources. The results are discussed in terms of the audience receiving fear and fight messages from the electronic media rather than clear, accurate information necessary to make informed decisions about drugs.

Validity of the BodyGem calorimeter and prediction equations for the assessment of resting energy expenditure in overweight and obese Saudi males.

PubMed

Almajwal, Ali M; Williams, Peter G; Batterham, Marijka J

2011-07-01

To assess the accuracy of resting energy expenditure (REE) measurement in a sample of overweight and obese Saudi males, using the BodyGem device (BG) with whole room calorimetry (WRC) as a reference, and to evaluate the accuracy of predictive equations. Thirty-eight subjects (mean +/- SD, age 26.8+/- 3.7 years, body mass index 31.0+/- 4.8) were recruited during the period from 5 February 2007 to 28 March 2008. Resting energy expenditure was measured using a WRC and BG device, and also calculated using 7 prediction equations. Mean differences, bias, percent of bias (%bias), accurate estimation, underestimation and overestimation were calculated. Repeated measures with the BG were not significantly different (accurate prediction: 81.6%; %bias 1.1+/- 6.3, p>0.24) with limits of agreement ranging from +242 to -200 kcal. Resting energy expenditure measured by BG was significantly less than WRC values (accurate prediction: 47.4%; %bias: 11.0+/- 14.6, p = 0.0001) with unacceptably wide limits of agreement. Harris-Benedict, Schofield and World Health Organization equations were the most accurate, estimating REE within 10% of measured REE, but none seem appropriate to predict the REE of individuals. There was a poor agreement between the REE measured by WRC compared to BG or predictive equations. The BG assessed REE accurately in 47.4% of the subjects on an individual level.

Using DFT methodology for more reliable predictive models: Design of inhibitors of Golgi α-Mannosidase II.

PubMed

Bobovská, Adela; Tvaroška, Igor; Kóňa, Juraj

2016-05-01

Human Golgi α-mannosidase II (GMII), a zinc ion co-factor dependent glycoside hydrolase (E.C.3.2.1.114), is a pharmaceutical target for the design of inhibitors with anti-cancer activity. The discovery of an effective inhibitor is complicated by the fact that all known potent inhibitors of GMII are involved in unwanted co-inhibition with lysosomal α-mannosidase (LMan, E.C.3.2.1.24), a relative to GMII. Routine empirical QSAR models for both GMII and LMan did not work with a required accuracy. Therefore, we have developed a fast computational protocol to build predictive models combining interaction energy descriptors from an empirical docking scoring function (Glide-Schrödinger), Linear Interaction Energy (LIE) method, and quantum mechanical density functional theory (QM-DFT) calculations. The QSAR models were built and validated with a library of structurally diverse GMII and LMan inhibitors and non-active compounds. A critical role of QM-DFT descriptors for the more accurate prediction abilities of the models is demonstrated. The predictive ability of the models was significantly improved when going from the empirical docking scoring function to mixed empirical-QM-DFT QSAR models (Q(2)=0.78-0.86 when cross-validation procedures were carried out; and R(2)=0.81-0.83 for a testing set). The average error for the predicted ΔGbind decreased to 0.8-1.1kcalmol(-1). Also, 76-80% of non-active compounds were successfully filtered out from GMII and LMan inhibitors. The QSAR models with the fragmented QM-DFT descriptors may find a useful application in structure-based drug design where pure empirical and force field methods reached their limits and where quantum mechanics effects are critical for ligand-receptor interactions. The optimized models will apply in lead optimization processes for GMII drug developments. Copyright © 2016 Elsevier Inc. All rights reserved.

PREDICTION OF RELAPSE FROM HYPERTHYROIDISM FOLLOWING ANTITHYROID MEDICATION WITHDRAWAL USING TECHNETIUM THYROID UPTAKE SCANNING.

PubMed

Nakhjavani, Manouchehr; Abdollahi, Soraya; Farzanefar, Saeed; Abousaidi, Mohammadtagi; Esteghamati, Alireza; Naseri, Maryam; Eftekhari, Mohamad; Abbasi, Mehrshad

2017-04-02

Technetium thyroid uptake (TTU) is not inhibited by antithyroid drugs (ATD) and reflects the degree of thyroid stimulation. We intended to predict the relapse rate from hyperthyroidism based on TTU measurement. Out of 44 initially enrolled subjects, 38 patients aged 41.6 ± 14.6 with Graves disease (duration: 84 ± 78 months) completed the study. TTU was performed with 40-second imaging of the neck and mediastinum 20 minutes after injection of 1 mCi technetium-99m pertechnetate. TTU was measured as the percentage of the count of activity accumulated in the thyroidal region minus the mediastinal background uptake to the count of 1 mCi technetium-99m under the same acquisition conditions. Then methimazole was stopped and patients were followed. The optimal TTU cutoff value for Graves relapse prediction was calculated using Youden's J statistic. Hyperthyroidism relapsed in 11 (28.9%) patients 122 ± 96 (range: 15-290) days post-ATD withdrawal. The subjects in remission were followed for 209 ± 81 days (range: 88-390). TTU was significantly higher in patients with forthcoming relapse (12.0 ± 8.0 vs. 3.9 ± 2.0, P = .007). The difference was significant after adjustment for age, sex, history of previous relapse, disease duration, and thyroid-stimulating hormone (TSH) levels before withdrawal. The area under the receiver operative characteristic (ROC) curve was 0.87. The optimal TTU cutoff value for classification of subjects with relapse and remission was 8.7 with sensitivity, specificity, and positive and negative predictive value of 73%, 100%, 100%, and 90%, respectively (odds ratio [OR] = 10.0; 95% confidence interval [CI]: 3.4-29.3). TTU evaluation in hyperthyroid patients receiving antithyroid medication is an accurate and practical method for predicting relapse after ATD withdrawal. ATD = antithyroid drugs RIU = radio-iodine uptake TSH = thyroid-stimulating hormone TSI = thyroid-stimulating immunoglobulin TTU = technetium thyroid uptake.

Quantification of Wilms' tumor 1 mRNA by digital polymerase chain reaction.

PubMed

Koizumi, Yuki; Furuya, Daisuke; Endo, Teruo; Asanuma, Kouichi; Yanagihara, Nozomi; Takahashi, Satoshi

2018-02-01

Wilms' tumor 1 (WT1) is overexpressed in various hematopoietic tumors and widely used as a marker of minimal residual disease. WT1 mRNA has been analyzed using quantitative real-time polymerase chain reaction (real-time PCR). In the present study, we analyzed 40 peripheral blood and bone marrow samples obtained from cases of acute myeloid leukemia, acute lymphoblastic leukemia, and myelodysplastic syndrome at Sapporo Medical University Hospital from April 2012 to January 2015. We performed quantification of WT1 was performed using QuantStudio 3D Digital PCR System (Thermo Fisher Scientific‎) and compared the results between digital PCR and real-time PCR technology. The correlation between digital PCR and real-time PCR was very strong (R = 0.99), and the detection limits of the two methods were equivalent. Digital PCR was able to accurately detect lower WT levels compared with real-time PCR. Digital PCR technology can thus be utilized to predict WT1/ABL1 expression level accurately and should thus be useful for diagnosis or the evaluation of drug efficiency in patients with leukemia.

ASTRAL, DRAGON and SEDAN scores predict stroke outcome more accurately than physicians.

PubMed

Ntaios, G; Gioulekas, F; Papavasileiou, V; Strbian, D; Michel, P

2016-11-01

ASTRAL, SEDAN and DRAGON scores are three well-validated scores for stroke outcome prediction. Whether these scores predict stroke outcome more accurately compared with physicians interested in stroke was investigated. Physicians interested in stroke were invited to an online anonymous survey to provide outcome estimates in randomly allocated structured scenarios of recent real-life stroke patients. Their estimates were compared to scores' predictions in the same scenarios. An estimate was considered accurate if it was within 95% confidence intervals of actual outcome. In all, 244 participants from 32 different countries responded assessing 720 real scenarios and 2636 outcomes. The majority of physicians' estimates were inaccurate (1422/2636, 53.9%). 400 (56.8%) of physicians' estimates about the percentage probability of 3-month modified Rankin score (mRS) > 2 were accurate compared with 609 (86.5%) of ASTRAL score estimates (P < 0.0001). 394 (61.2%) of physicians' estimates about the percentage probability of post-thrombolysis symptomatic intracranial haemorrhage were accurate compared with 583 (90.5%) of SEDAN score estimates (P < 0.0001). 160 (24.8%) of physicians' estimates about post-thrombolysis 3-month percentage probability of mRS 0-2 were accurate compared with 240 (37.3%) DRAGON score estimates (P < 0.0001). 260 (40.4%) of physicians' estimates about the percentage probability of post-thrombolysis mRS 5-6 were accurate compared with 518 (80.4%) DRAGON score estimates (P < 0.0001). ASTRAL, DRAGON and SEDAN scores predict outcome of acute ischaemic stroke patients with higher accuracy compared to physicians interested in stroke. © 2016 EAN.

Kidney Disease and the Nexus of Chronic Kidney Disease and Acute Kidney Injury: The Role of Novel Biomarkers as Early and Accurate Diagnostics.

PubMed

Yerramilli, Murthy; Farace, Giosi; Quinn, John; Yerramilli, Maha

2016-11-01

Chronic kidney disease (CKD) and acute kidney injury (AKI) are interconnected and the presence of one is a risk for the other. CKD is an important predictor of AKI after exposure to nephrotoxic drugs or major surgery, whereas persistent or repetitive injury could result in the progression of CKD. This brings new perspectives to the diagnosis and monitoring of kidney diseases highlighting the need for a panel of kidney-specific biomarkers that reflect functional as well as structural damage and recovery, predict potential risk and provide prognosis. This article discusses the kidney-specific biomarkers, symmetric dimethylarginine (SDMA), clusterin, cystatin B, and inosine. Copyright © 2016 Elsevier Inc. All rights reserved.

Structural DNA Nanotechnology: State of the Art and Future Perspective

PubMed Central

2015-01-01

Over the past three decades DNA has emerged as an exceptional molecular building block for nanoconstruction due to its predictable conformation and programmable intra- and intermolecular Watson–Crick base-pairing interactions. A variety of convenient design rules and reliable assembly methods have been developed to engineer DNA nanostructures of increasing complexity. The ability to create designer DNA architectures with accurate spatial control has allowed researchers to explore novel applications in many directions, such as directed material assembly, structural biology, biocatalysis, DNA computing, nanorobotics, disease diagnosis, and drug delivery. This Perspective discusses the state of the art in the field of structural DNA nanotechnology and presents some of the challenges and opportunities that exist in DNA-based molecular design and programming. PMID:25029570

A multi-organ chip co-culture of neurospheres and liver equivalents for long-term substance testing.

PubMed

Materne, Eva-Maria; Ramme, Anja Patricia; Terrasso, Ana Paula; Serra, Margarida; Alves, Paula Marques; Brito, Catarina; Sakharov, Dmitry A; Tonevitsky, Alexander G; Lauster, Roland; Marx, Uwe

2015-07-10

Current in vitro and animal tests for drug development are failing to emulate the systemic organ complexity of the human body and, therefore, often do not accurately predict drug toxicity, leading to high attrition rates in clinical studies (Paul et al., 2010). The phylogenetic distance between humans and laboratory animals is enormous, this affects the transferability of animal data on the efficacy of neuroprotective drugs. Therefore, many neuroprotective treatments that have shown promise in animals have not been successful when transferred to humans (Dragunow, 2008; Gibbons and Dragunow, 2010). We present a multi-organ chip capable of maintaining 3D tissues derived from various cell sources in a combined media circuit which bridges the gap in systemic and human tests. A steady state co-culture of human artificial liver microtissues and human neurospheres exposed to fluid flow over two weeks in the multi-organ chip has successfully proven its long-term performance. Daily lactate dehydrogenase activity measurements of the medium and immunofluorescence end-point staining proved the viability of the tissues and the maintenance of differentiated cellular phenotypes. Moreover, the lactate production and glucose consumption values of the tissues cultured indicated that a stable steady-state was achieved after 6 days of co-cultivation. The neurospheres remained differentiated neurons over the two-week cultivation in the multi-organ chip, proven by qPCR and immunofluorescence of the neuronal markers βIII-tubulin and microtubule-associated protein-2. Additionally, a two-week toxicity assay with a repeated substance exposure to the neurotoxic 2,5-hexanedione in two different concentrations induced high apoptosis within the neurospheres and liver microtissues, as shown by a strong increase of lactate dehydrogenase activity in the medium. The principal finding of the exposure of the co-culture to 2,5-hexanedione was that not only toxicity profiles of two different doses could be discriminated, but also that the co-cultures were more sensitive to the substance compared to respective single-tissue cultures in the multi-organ-chip. Thus, we provide here a new in vitro tool which might be utilized to predict the safety and efficacy of substances in clinical studies more accurately in the future. Copyright © 2015 Elsevier B.V. All rights reserved.

Metrics for quantifying antimicrobial use in beef feedlots

PubMed Central

Benedict, Katharine M.; Gow, Sheryl P.; Reid-Smith, Richard J.; Booker, Calvin W.; Morley, Paul S.

2012-01-01

Accurate antimicrobial drug use data are needed to enlighten discussions regarding the impact of antimicrobial drug use in agriculture. The primary objective of this study was to investigate the perceived accuracy and clarity of different methods for reporting antimicrobial drug use information collected regarding beef feedlots. Producers, veterinarians, industry representatives, public health officials, and other knowledgeable beef industry leaders were invited to complete a web-based survey. A total of 156 participants in 33 US states, 4 Canadian provinces, and 8 other countries completed the survey. No single metric was considered universally optimal for all use circumstances or for all audiences. To effectively communicate antimicrobial drug use data, evaluation of the target audience is critical to presenting the information. Metrics that are most accurate need to be carefully and repeatedly explained to the audience. PMID:23372190

Advanced systems biology methods in drug discovery and translational biomedicine.

PubMed

Zou, Jun; Zheng, Ming-Wu; Li, Gen; Su, Zhi-Guang

2013-01-01

Systems biology is in an exponential development stage in recent years and has been widely utilized in biomedicine to better understand the molecular basis of human disease and the mechanism of drug action. Here, we discuss the fundamental concept of systems biology and its two computational methods that have been commonly used, that is, network analysis and dynamical modeling. The applications of systems biology in elucidating human disease are highlighted, consisting of human disease networks, treatment response prediction, investigation of disease mechanisms, and disease-associated gene prediction. In addition, important advances in drug discovery, to which systems biology makes significant contributions, are discussed, including drug-target networks, prediction of drug-target interactions, investigation of drug adverse effects, drug repositioning, and drug combination prediction. The systems biology methods and applications covered in this review provide a framework for addressing disease mechanism and approaching drug discovery, which will facilitate the translation of research findings into clinical benefits such as novel biomarkers and promising therapies.

Predicting vapor-liquid phase equilibria with augmented ab initio interatomic potentials

NASA Astrophysics Data System (ADS)

Vlasiuk, Maryna; Sadus, Richard J.

2017-06-01

The ability of ab initio interatomic potentials to accurately predict vapor-liquid phase equilibria is investigated. Monte Carlo simulations are reported for the vapor-liquid equilibria of argon and krypton using recently developed accurate ab initio interatomic potentials. Seventeen interatomic potentials are studied, formulated from different combinations of two-body plus three-body terms. The simulation results are compared to either experimental or reference data for conditions ranging from the triple point to the critical point. It is demonstrated that the use of ab initio potentials enables systematic improvements to the accuracy of predictions via the addition of theoretically based terms. The contribution of three-body interactions is accounted for using the Axilrod-Teller-Muto plus other multipole contributions and the effective Marcelli-Wang-Sadus potentials. The results indicate that the predictive ability of recent interatomic potentials, obtained from quantum chemical calculations, is comparable to that of accurate empirical models. It is demonstrated that the Marcelli-Wang-Sadus potential can be used in combination with accurate two-body ab initio models for the computationally inexpensive and accurate estimation of vapor-liquid phase equilibria.

Predicting vapor-liquid phase equilibria with augmented ab initio interatomic potentials.

PubMed

Vlasiuk, Maryna; Sadus, Richard J

2017-06-28

The ability of ab initio interatomic potentials to accurately predict vapor-liquid phase equilibria is investigated. Monte Carlo simulations are reported for the vapor-liquid equilibria of argon and krypton using recently developed accurate ab initio interatomic potentials. Seventeen interatomic potentials are studied, formulated from different combinations of two-body plus three-body terms. The simulation results are compared to either experimental or reference data for conditions ranging from the triple point to the critical point. It is demonstrated that the use of ab initio potentials enables systematic improvements to the accuracy of predictions via the addition of theoretically based terms. The contribution of three-body interactions is accounted for using the Axilrod-Teller-Muto plus other multipole contributions and the effective Marcelli-Wang-Sadus potentials. The results indicate that the predictive ability of recent interatomic potentials, obtained from quantum chemical calculations, is comparable to that of accurate empirical models. It is demonstrated that the Marcelli-Wang-Sadus potential can be used in combination with accurate two-body ab initio models for the computationally inexpensive and accurate estimation of vapor-liquid phase equilibria.

Accurate Evaluation Method of Molecular Binding Affinity from Fluctuation Frequency

NASA Astrophysics Data System (ADS)

Hoshino, Tyuji; Iwamoto, Koji; Ode, Hirotaka; Ohdomari, Iwao

2008-05-01

Exact estimation of the molecular binding affinity is significantly important for drug discovery. The energy calculation is a direct method to compute the strength of the interaction between two molecules. This energetic approach is, however, not accurate enough to evaluate a slight difference in binding affinity when distinguishing a prospective substance from dozens of candidates for medicine. Hence more accurate estimation of drug efficacy in a computer is currently demanded. Previously we proposed a concept of estimating molecular binding affinity, focusing on the fluctuation at an interface between two molecules. The aim of this paper is to demonstrate the compatibility between the proposed computational technique and experimental measurements, through several examples for computer simulations of an association of human immunodeficiency virus type-1 (HIV-1) protease and its inhibitor (an example for a drug-enzyme binding), a complexation of an antigen and its antibody (an example for a protein-protein binding), and a combination of estrogen receptor and its ligand chemicals (an example for a ligand-receptor binding). The proposed affinity estimation has proven to be a promising technique in the advanced stage of the discovery and the design of drugs.

Type- and Subtype-Specific Influenza Forecast.

PubMed

Kandula, Sasikiran; Yang, Wan; Shaman, Jeffrey

2017-03-01

Prediction of the growth and decline of infectious disease incidence has advanced considerably in recent years. As these forecasts improve, their public health utility should increase, particularly as interventions are developed that make explicit use of forecast information. It is the task of the research community to increase the content and improve the accuracy of these infectious disease predictions. Presently, operational real-time forecasts of total influenza incidence are produced at the municipal and state level in the United States. These forecasts are generated using ensemble simulations depicting local influenza transmission dynamics, which have been optimized prior to forecast with observations of influenza incidence and data assimilation methods. Here, we explore whether forecasts targeted to predict influenza by type and subtype during 2003-2015 in the United States were more or less accurate than forecasts targeted to predict total influenza incidence. We found that forecasts separated by type/subtype generally produced more accurate predictions and, when summed, produced more accurate predictions of total influenza incidence. These findings indicate that monitoring influenza by type and subtype not only provides more detailed observational content but supports more accurate forecasting. More accurate forecasting can help officials better respond to and plan for current and future influenza activity. © The Author 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Rapid detection of multidrug-resistant Mycobacterium tuberculosis using the malachite green decolourisation assay

PubMed Central

Coban, Ahmet Yilmaz; Uzun, Meltem

2013-01-01

Early detection of drug resistance in Mycobacterium tuberculosis isolates allows for earlier and more effective treatment of patients. The aim of this study was to investigate the performance of the malachite green decolourisation assay (MGDA) in detecting isoniazid (INH) and rifampicin (RIF) resistance in M. tuberculosis clinical isolates. Fifty M. tuberculosis isolates, including 19 multidrug-resistant, eight INH-resistant and 23 INH and RIF-susceptible samples, were tested. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and agreement of the assay for INH were 92.5%, 91.3%, 92.5%, 91.3% and 92%, respectively. Similarly, the sensitivity, specificity, PPV, NPV and agreement of the assay for RIF were 94.7%, 100%, 100%, 96.8% and 98%, respectively. There was a major discrepancy in the tests of two isolates, as they were sensitive to INH by the MGDA test, but resistant by the reference method. There was a minor discrepancy in the tests of two additional isolates, as they were sensitive to INH by the reference method, but resistant by the MGDA test. The drug susceptibility test results were obtained within eight-nine days. In conclusion, the MGDA test is a reliable and accurate method for the rapid detection of INH and RIF resistance compared with the reference method and the MGDA test additionally requires less time to obtain results. PMID:24402143

IFPTarget: A Customized Virtual Target Identification Method Based on Protein-Ligand Interaction Fingerprinting Analyses.

PubMed

Li, Guo-Bo; Yu, Zhu-Jun; Liu, Sha; Huang, Lu-Yi; Yang, Ling-Ling; Lohans, Christopher T; Yang, Sheng-Yong

2017-07-24

Small-molecule target identification is an important and challenging task for chemical biology and drug discovery. Structure-based virtual target identification has been widely used, which infers and prioritizes potential protein targets for the molecule of interest (MOI) principally via a scoring function. However, current "universal" scoring functions may not always accurately identify targets to which the MOI binds from the retrieved target database, in part due to a lack of consideration of the important binding features for an individual target. Here, we present IFPTarget, a customized virtual target identification method, which uses an interaction fingerprinting (IFP) method for target-specific interaction analyses and a comprehensive index (Cvalue) for target ranking. Evaluation results indicate that the IFP method enables substantially improved binding pose prediction, and Cvalue has an excellent performance in target ranking for the test set. When applied to screen against our established target library that contains 11,863 protein structures covering 2842 unique targets, IFPTarget could retrieve known targets within the top-ranked list and identified new potential targets for chemically diverse drugs. IFPTarget prediction led to the identification of the metallo-β-lactamase VIM-2 as a target for quercetin as validated by enzymatic inhibition assays. This study provides a new in silico target identification tool and will aid future efforts to develop new target-customized methods for target identification.

Large-Scale Chemical Similarity Networks for Target Profiling of Compounds Identified in Cell-Based Chemical Screens

PubMed Central

Lo, Yu-Chen; Senese, Silvia; Li, Chien-Ming; Hu, Qiyang; Huang, Yong; Damoiseaux, Robert; Torres, Jorge Z.

2015-01-01

Target identification is one of the most critical steps following cell-based phenotypic chemical screens aimed at identifying compounds with potential uses in cell biology and for developing novel disease therapies. Current in silico target identification methods, including chemical similarity database searches, are limited to single or sequential ligand analysis that have limited capabilities for accurate deconvolution of a large number of compounds with diverse chemical structures. Here, we present CSNAP (Chemical Similarity Network Analysis Pulldown), a new computational target identification method that utilizes chemical similarity networks for large-scale chemotype (consensus chemical pattern) recognition and drug target profiling. Our benchmark study showed that CSNAP can achieve an overall higher accuracy (>80%) of target prediction with respect to representative chemotypes in large (>200) compound sets, in comparison to the SEA approach (60–70%). Additionally, CSNAP is capable of integrating with biological knowledge-based databases (Uniprot, GO) and high-throughput biology platforms (proteomic, genetic, etc) for system-wise drug target validation. To demonstrate the utility of the CSNAP approach, we combined CSNAP's target prediction with experimental ligand evaluation to identify the major mitotic targets of hit compounds from a cell-based chemical screen and we highlight novel compounds targeting microtubules, an important cancer therapeutic target. The CSNAP method is freely available and can be accessed from the CSNAP web server (http://services.mbi.ucla.edu/CSNAP/). PMID:25826798

Physics-based scoring of protein-ligand interactions: explicit polarizability, quantum mechanics and free energies.

PubMed

Bryce, Richard A

2011-04-01

The ability to accurately predict the interaction of a ligand with its receptor is a key limitation in computer-aided drug design approaches such as virtual screening and de novo design. In this article, we examine current strategies for a physics-based approach to scoring of protein-ligand affinity, as well as outlining recent developments in force fields and quantum chemical techniques. We also consider advances in the development and application of simulation-based free energy methods to study protein-ligand interactions. Fuelled by recent advances in computational algorithms and hardware, there is the opportunity for increased integration of physics-based scoring approaches at earlier stages in computationally guided drug discovery. Specifically, we envisage increased use of implicit solvent models and simulation-based scoring methods as tools for computing the affinities of large virtual ligand libraries. Approaches based on end point simulations and reference potentials allow the application of more advanced potential energy functions to prediction of protein-ligand binding affinities. Comprehensive evaluation of polarizable force fields and quantum mechanical (QM)/molecular mechanical and QM methods in scoring of protein-ligand interactions is required, particularly in their ability to address challenging targets such as metalloproteins and other proteins that make highly polar interactions. Finally, we anticipate increasingly quantitative free energy perturbation and thermodynamic integration methods that are practical for optimization of hits obtained from screened ligand libraries.

Multiscale rescaled range analysis of EEG recordings in sevoflurane anesthesia.

PubMed

Liang, Zhenhu; Li, Duan; Ouyang, Gaoxiang; Wang, Yinghua; Voss, Logan J; Sleigh, Jamie W; Li, Xiaoli

2012-04-01

The Hurst exponent (HE) is a nonlinear method measuring the smoothness of a fractal time series. In this study we applied the HE index, extracted from electroencephalographic (EEG) recordings, as a measure of anesthetic drug effects on brain activity. In 19 adult patients undergoing sevoflurane general anesthesia, we calculated the HE of the raw EEG; comparing the maximal overlap discrete wavelet transform (MODWT) with the traditional rescaled range (R/S) analysis techniques, and with a commercial index of depth of anesthesia - the response entropy (RE). We analyzed each wavelet-decomposed sub-band as well as the combined low frequency bands (HEOLFBs). The methods were compared in regard to pharmacokinetic/pharmacodynamic (PK/PD) modeling, and prediction probability. All the low frequency band HE indices decreased when anesthesia deepened. However the HEOLFB was the best index because: it was less sensitive to artifacts, most closely tracked the exact point of loss of consciousness, showed a better prediction probability in separating the awake and unconscious states, and tracked sevoflurane concentration better - as estimated by the PK/PD models. The HE is a useful measure for estimating the depth of anesthesia. It was noted that HEOLFB showed the best performance for tracking drug effect. The HEOLFB could be used as an index for accurately estimating the effect of anesthesia on brain activity. Copyright © 2011 International Federation of Clinical Neurophysiology. All rights reserved.

Network-based de-noising improves prediction from microarray data.

PubMed

Kato, Tsuyoshi; Murata, Yukio; Miura, Koh; Asai, Kiyoshi; Horton, Paul B; Koji, Tsuda; Fujibuchi, Wataru

2006-03-20

Prediction of human cell response to anti-cancer drugs (compounds) from microarray data is a challenging problem, due to the noise properties of microarrays as well as the high variance of living cell responses to drugs. Hence there is a strong need for more practical and robust methods than standard methods for real-value prediction. We devised an extended version of the off-subspace noise-reduction (de-noising) method to incorporate heterogeneous network data such as sequence similarity or protein-protein interactions into a single framework. Using that method, we first de-noise the gene expression data for training and test data and also the drug-response data for training data. Then we predict the unknown responses of each drug from the de-noised input data. For ascertaining whether de-noising improves prediction or not, we carry out 12-fold cross-validation for assessment of the prediction performance. We use the Pearson's correlation coefficient between the true and predicted response values as the prediction performance. De-noising improves the prediction performance for 65% of drugs. Furthermore, we found that this noise reduction method is robust and effective even when a large amount of artificial noise is added to the input data. We found that our extended off-subspace noise-reduction method combining heterogeneous biological data is successful and quite useful to improve prediction of human cell cancer drug responses from microarray data.

21 CFR 868.1890 - Predictive pulmonary-function value calculator.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Predictive pulmonary-function value calculator. 868.1890 Section 868.1890 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN... pulmonary-function value calculator. (a) Identification. A predictive pulmonary-function value calculator is...