Animal modelling of traumatic brain injury in preclinical drug development: where do we go from here?

PubMed Central

Marklund, Niklas; Hillered, Lars

2011-01-01

Traumatic brain injury (TBI) is the leading cause of death and disability in young adults. Survivors of TBI frequently suffer from long-term personality changes and deficits in cognitive and motor performance, urgently calling for novel pharmacological treatment options. To date, all clinical trials evaluating neuroprotective compounds have failed in demonstrating clinical efficacy in cohorts of severely injured TBI patients. The purpose of the present review is to describe the utility of animal models of TBI for preclinical evaluation of pharmacological compounds. No single animal model can adequately mimic all aspects of human TBI owing to the heterogeneity of clinical TBI. To successfully develop compounds for clinical TBI, a thorough evaluation in several TBI models and injury severities is crucial. Additionally, brain pharmacokinetics and the time window must be carefully evaluated. Although the search for a single-compound, ‘silver bullet’ therapy is ongoing, a combination of drugs targeting various aspects of neuroprotection, neuroinflammation and regeneration may be needed. In summary, finding drugs and prove clinical efficacy in TBI is a major challenge ahead for the research community and the drug industry. For a successful translation of basic science knowledge to the clinic to occur we believe that a further refinement of animal models and functional outcome methods is important. In the clinical setting, improved patient classification, more homogenous patient cohorts in clinical trials, standardized treatment strategies, improved central nervous system drug delivery systems and monitoring of target drug levels and drug effects is warranted. LINKED ARTICLES This article is part of a themed issue on Translational Neuropharmacology. To view the other articles in this issue visit http://dx.doi.org/10.1111/bph.2011.164.issue-4 PMID:21175576

Discovery of Anthelmintic Drug Targets and Drugs Using Chokepoints in Nematode Metabolic Pathways

PubMed Central

Taylor, Christina M.; Wang, Qi; Rosa, Bruce A.; Huang, Stanley Ching-Cheng; Powell, Kerrie; Schedl, Tim; Pearce, Edward J.; Abubucker, Sahar; Mitreva, Makedonka

2013-01-01

Parasitic roundworm infections plague more than 2 billion people (1/3 of humanity) and cause drastic losses in crops and livestock. New anthelmintic drugs are urgently needed as new drug resistance and environmental concerns arise. A “chokepoint reaction” is defined as a reaction that either consumes a unique substrate or produces a unique product. A chokepoint analysis provides a systematic method of identifying novel potential drug targets. Chokepoint enzymes were identified in the genomes of 10 nematode species, and the intersection and union of all chokepoint enzymes were found. By studying and experimentally testing available compounds known to target proteins orthologous to nematode chokepoint proteins in public databases, this study uncovers features of chokepoints that make them successful drug targets. Chemogenomic screening was performed on drug-like compounds from public drug databases to find existing compounds that target homologs of nematode chokepoints. The compounds were prioritized based on chemical properties frequently found in successful drugs and were experimentally tested using Caenorhabditis elegans. Several drugs that are already known anthelmintic drugs and novel candidate targets were identified. Seven of the compounds were tested in Caenorhabditis elegans and three yielded a detrimental phenotype. One of these three drug-like compounds, Perhexiline, also yielded a deleterious effect in Haemonchus contortus and Onchocerca lienalis, two nematodes with divergent forms of parasitism. Perhexiline, known to affect the fatty acid oxidation pathway in mammals, caused a reduction in oxygen consumption rates in C. elegans and genome-wide gene expression profiles provided an additional confirmation of its mode of action. Computational modeling of Perhexiline and its target provided structural insights regarding its binding mode and specificity. Our lists of prioritized drug targets and drug-like compounds have potential to expedite the discovery of new anthelmintic drugs with broad-spectrum efficacy. PMID:23935495

Prediction of Human Intestinal Absorption of Compounds Using Artificial Intelligence Techniques.

PubMed

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

2017-01-01

Information about Pharmacokinetics of compounds is an essential component of drug design and development. Modeling the pharmacokinetic properties require identification of the factors effecting absorption, distribution, metabolism and excretion of compounds. There have been continuous attempts in the prediction of intestinal absorption of compounds using various Artificial intelligence methods in the effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are large numbers of individual predictive models available for absorption using machine learning approaches. Six Artificial intelligence methods namely, Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis were used for prediction of absorption of compounds. Prediction accuracy of Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis for prediction of intestinal absorption of compounds was found to be 91.54%, 88.33%, 84.30%, 86.51%, 79.07% and 80.08% respectively. Comparative analysis of all the six prediction models suggested that Support vector machine with Radial basis function based kernel is comparatively better for binary classification of compounds using human intestinal absorption and may be useful at preliminary stages of drug design and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Evaluation of the Percutaneous Absorption of Ketamine HCl, Gabapentin, Clonidine HCl, and Baclofen, in Compounded Transdermal Pain Formulations, Using the Franz Finite Dose Model.

PubMed

Bassani, August S; Banov, Daniel

2016-02-01

This study evaluates the ability of four commonly used analgesics (ketamine HCl, gabapentin, clonidine HCl, and baclofen), when incorporated into two transdermal compounding bases, Lipoderm and Lipoderm ActiveMax, to penetrate human cadaver trunk skin in vitro, using the Franz finite dose model. In vitro experimental study. Methods. Ketamine HCl 5% w/w, gabapentin 10% w/w, clonidine HCl 0.2% w/w, and baclofen 2% w/w were compounded into two transdermal bases, Lipoderm and Lipoderm ActiveMax. Each compounded drug formulation was tested on skin from three different donors and three replicate skin sections per donor. The Franz finite dose model was used in this study to evaluate the percutaneous absorption and distribution of drugs within each formulation. Rapid penetration to peak flux was detected for gabapentin and baclofen at approximately 1 hour after application. Clonidine HCl also had a rapid penetration to peak flux occurring approximately 1 hour after application and had a secondary peak at approximately 40 hours. Ketamine HCl exhibited higher overall absorption rates than the other drugs, and peaked at 6–10 hours. Similar patterns of drug distribution within the skin were also observed using both transdermal bases. This study suggests that the combination of these 4 analgesic drugs can be successfully delivered transdermally, using either Lipoderm or Lipoderm ActiveMax. Compounded transdermal drug preparations may then provide physicians with an alternative to traditional oral pain management regimens that can be personalized to the specific patient with the potential for enhanced pain control.

Synthesis and pharmacological evaluation of polyfunctional benzimidazole-NSAID chimeric molecules combining anti-inflammatory, immunomodulatory and antioxidant activities.

PubMed

Bansal, Yogita; Silakari, Om

2014-11-01

Polyfunctional compounds comprise a novel class of therapeutic agents for treatment of multifactorial diseases. The present study reports a series of benzimidazole-non-steroidal anti-inflammatory drugs (NSAIDs) conjugates (1-10) as novel polyfunctional compounds synthesized in the presence of orthophosphoric acid. The compounds were evaluated for anti-inflammatory (carageenan-induced paw edema model), immunomodulatory (direct haemagglutination test and carbon clearance index models), antioxidant (in vitro and in vivo) and for ulcerogenic effects. Each of the compound has retained the anti-inflammatory activity of the corresponding parent NSAID while exhibiting significantly reduced gastric ulcers. Additionally, the compounds are found to possess potent immunostimulatory and antioxidant activities. The compound 8 was maximally potent (antibody titre value 358.4 ± 140.21, carbon clearance index 0.053 ± 0.002 and antioxidant EC50 value 0.03 ± 0.006). These compounds, exhibiting such multiple pharmacological activities, can be taken as lead for the development of potent drugs for the treatment of chronic multifactorial diseases involving inflammation, immune system modulation and oxidative stress such as cancers. The Lipinski's parameters suggested the compounds to be bear drug like properties.

Fish in a Dish: Drug Discovery for Hearing Habilitation.

PubMed

Esterberg, Robert; Coffin, Allison B; Ou, Henry; Simon, Julian A; Raible, David W; Rubel, Edwin W

2013-01-01

The majority of hearing loss is caused by the permanent loss of inner ear hair cells. The identification of drugs that modulate the susceptibility to hair cell loss or spur their regeneration is often hampered by the difficulties of assaying for such complex phenomena in mammalian models. The zebrafish has emerged as a powerful animal model for chemical screening in many contexts. Several characteristics of the zebrafish, such as its small size and external location of sensory hair cells, uniquely position it as an ideal model organism for the study of hair cell toxicity, protection, and regeneration. We have used this model to screen for drugs that affect each of these aspects of hair cell biology and have identified compounds that affect each of these processes. The identification of such drugs and drug-like compounds holds promise in the future ability to stem hearing loss in the human population.

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

Target-Independent Prediction of Drug Synergies Using Only Drug Lipophilicity

PubMed Central

2015-01-01

Physicochemical properties of compounds have been instrumental in selecting lead compounds with increased drug-likeness. However, the relationship between physicochemical properties of constituent drugs and the tendency to exhibit drug interaction has not been systematically studied. We assembled physicochemical descriptors for a set of antifungal compounds (“drugs”) previously examined for interaction. Analyzing the relationship between molecular weight, lipophilicity, H-bond donor, and H-bond acceptor values for drugs and their propensity to show pairwise antifungal drug synergy, we found that combinations of two lipophilic drugs had a greater tendency to show drug synergy. We developed a more refined decision tree model that successfully predicted drug synergy in stringent cross-validation tests based on only lipophilicity of drugs. Our predictions achieved a precision of 63% and allowed successful prediction for 58% of synergistic drug pairs, suggesting that this phenomenon can extend our understanding for a substantial fraction of synergistic drug interactions. We also generated and analyzed a large-scale synergistic human toxicity network, in which we observed that combinations of lipophilic compounds show a tendency for increased toxicity. Thus, lipophilicity, a simple and easily determined molecular descriptor, is a powerful predictor of drug synergy. It is well established that lipophilic compounds (i) are promiscuous, having many targets in the cell, and (ii) often penetrate into the cell via the cellular membrane by passive diffusion. We discuss the positive relationship between drug lipophilicity and drug synergy in the context of potential drug synergy mechanisms. PMID:25026390

An amphiphilic graft copolymer-based nanoparticle platform for reduction-responsive anticancer and antimalarial drug delivery

NASA Astrophysics Data System (ADS)

Najer, Adrian; Wu, Dalin; Nussbaumer, Martin G.; Schwertz, Geoffrey; Schwab, Anatol; Witschel, Matthias C.; Schäfer, Anja; Diederich, François; Rottmann, Matthias; Palivan, Cornelia G.; Beck, Hans-Peter; Meier, Wolfgang

2016-08-01

Medical applications of anticancer and antimalarial drugs often suffer from low aqueous solubility, high systemic toxicity, and metabolic instability. Smart nanocarrier-based drug delivery systems provide means of solving these problems at once. Herein, we present such a smart nanoparticle platform based on self-assembled, reduction-responsive amphiphilic graft copolymers, which were successfully synthesized through thiol-disulfide exchange reaction between thiolated hydrophilic block and pyridyl disulfide functionalized hydrophobic block. These amphiphilic graft copolymers self-assembled into nanoparticles with mean diameters of about 30-50 nm and readily incorporated hydrophobic guest molecules. Fluorescence correlation spectroscopy (FCS) was used to study nanoparticle stability and triggered release of a model compound in detail. Long-term colloidal stability and model compound retention within the nanoparticles was found when analyzed in cell media at body temperature. In contrast, rapid, complete reduction-triggered disassembly and model compound release was achieved within a physiological reducing environment. The synthesized copolymers revealed no intrinsic cellular toxicity up to 1 mg mL-1. Drug-loaded reduction-sensitive nanoparticles delivered a hydrophobic model anticancer drug (doxorubicin, DOX) to cancer cells (HeLa cells) and an experimental, metabolically unstable antimalarial drug (the serine hydroxymethyltransferase (SHMT) inhibitor (+/-)-1) to Plasmodium falciparum-infected red blood cells (iRBCs), with higher efficacy compared to similar, non-sensitive drug-loaded nanoparticles. These responsive copolymer-based nanoparticles represent a promising candidate as smart nanocarrier platform for various drugs to be applied to different diseases, due to the biocompatibility and biodegradability of the hydrophobic block, and the protein-repellent hydrophilic block.Medical applications of anticancer and antimalarial drugs often suffer from low aqueous solubility, high systemic toxicity, and metabolic instability. Smart nanocarrier-based drug delivery systems provide means of solving these problems at once. Herein, we present such a smart nanoparticle platform based on self-assembled, reduction-responsive amphiphilic graft copolymers, which were successfully synthesized through thiol-disulfide exchange reaction between thiolated hydrophilic block and pyridyl disulfide functionalized hydrophobic block. These amphiphilic graft copolymers self-assembled into nanoparticles with mean diameters of about 30-50 nm and readily incorporated hydrophobic guest molecules. Fluorescence correlation spectroscopy (FCS) was used to study nanoparticle stability and triggered release of a model compound in detail. Long-term colloidal stability and model compound retention within the nanoparticles was found when analyzed in cell media at body temperature. In contrast, rapid, complete reduction-triggered disassembly and model compound release was achieved within a physiological reducing environment. The synthesized copolymers revealed no intrinsic cellular toxicity up to 1 mg mL-1. Drug-loaded reduction-sensitive nanoparticles delivered a hydrophobic model anticancer drug (doxorubicin, DOX) to cancer cells (HeLa cells) and an experimental, metabolically unstable antimalarial drug (the serine hydroxymethyltransferase (SHMT) inhibitor (+/-)-1) to Plasmodium falciparum-infected red blood cells (iRBCs), with higher efficacy compared to similar, non-sensitive drug-loaded nanoparticles. These responsive copolymer-based nanoparticles represent a promising candidate as smart nanocarrier platform for various drugs to be applied to different diseases, due to the biocompatibility and biodegradability of the hydrophobic block, and the protein-repellent hydrophilic block. Electronic supplementary information (ESI) available: Detailed experimental procedures, additional schemes and supplementary data including NMR, FTIR, TEM, DLS, UV-Vis, FCS, and fluorescence microscopy images. See DOI: 10.1039/c6nr04290b

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

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

PubMed

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

2018-05-21

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

Cheminformatics analysis of assertions mined from literature that describe drug-induced liver injury in different species.

PubMed

Fourches, Denis; Barnes, Julie C; Day, Nicola C; Bradley, Paul; Reed, Jane Z; Tropsha, Alexander

2010-01-01

Drug-induced liver injury is one of the main causes of drug attrition. The ability to predict the liver effects of drug candidates from their chemical structures is critical to help guide experimental drug discovery projects toward safer medicines. In this study, we have compiled a data set of 951 compounds reported to produce a wide range of effects in the liver in different species, comprising humans, rodents, and nonrodents. The liver effects for this data set were obtained as assertional metadata, generated from MEDLINE abstracts using a unique combination of lexical and linguistic methods and ontological rules. We have analyzed this data set using conventional cheminformatics approaches and addressed several questions pertaining to cross-species concordance of liver effects, chemical determinants of liver effects in humans, and the prediction of whether a given compound is likely to cause a liver effect in humans. We found that the concordance of liver effects was relatively low (ca. 39-44%) between different species, raising the possibility that species specificity could depend on specific features of chemical structure. Compounds were clustered by their chemical similarity, and similar compounds were examined for the expected similarity of their species-dependent liver effect profiles. In most cases, similar profiles were observed for members of the same cluster, but some compounds appeared as outliers. The outliers were the subject of focused assertion regeneration from MEDLINE as well as other data sources. In some cases, additional biological assertions were identified, which were in line with expectations based on compounds' chemical similarities. The assertions were further converted to binary annotations of underlying chemicals (i.e., liver effect vs no liver effect), and binary quantitative structure-activity relationship (QSAR) models were generated to predict whether a compound would be expected to produce liver effects in humans. Despite the apparent heterogeneity of data, models have shown good predictive power assessed by external 5-fold cross-validation procedures. The external predictive power of binary QSAR models was further confirmed by their application to compounds that were retrieved or studied after the model was developed. To the best of our knowledge, this is the first study for chemical toxicity prediction that applied QSAR modeling and other cheminformatics techniques to observational data generated by the means of automated text mining with limited manual curation, opening up new opportunities for generating and modeling chemical toxicology data.

Prediction of adverse drug reactions using decision tree modeling.

PubMed

Hammann, F; Gutmann, H; Vogt, N; Helma, C; Drewe, J

2010-07-01

Drug safety is of great importance to public health. The detrimental effects of drugs not only limit their application but also cause suffering in individual patients and evoke distrust of pharmacotherapy. For the purpose of identifying drugs that could be suspected of causing adverse reactions, we present a structure-activity relationship analysis of adverse drug reactions (ADRs) in the central nervous system (CNS), liver, and kidney, and also of allergic reactions, for a broad variety of drugs (n = 507) from the Swiss drug registry. Using decision tree induction, a machine learning method, we determined the chemical, physical, and structural properties of compounds that predispose them to causing ADRs. The models had high predictive accuracies (78.9-90.2%) for allergic, renal, CNS, and hepatic ADRs. We show the feasibility of predicting complex end-organ effects using simple models that involve no expensive computations and that can be used (i) in the selection of the compound during the drug discovery stage, (ii) to understand how drugs interact with the target organ systems, and (iii) for generating alerts in postmarketing drug surveillance and pharmacovigilance.

Identification of Drugs Inducing Phospholipidosis by Novel in vitro Data

PubMed Central

Muehlbacher, Markus; Tripal, Philipp; Roas, Florian; Kornhuber, Johannes

2012-01-01

Drug-induced phospholipidosis (PLD) is a lysosomal storage disorder characterized by the accumulation of phospholipids within the lysosome. This adverse drug effect can occur in various tissues and is suspected to impact cellular viability. Therefore, it is important to test chemical compounds for their potential to induce PLD during the drug design process. PLD has been reported to be a side effect of many commonly used drugs, especially those with cationic amphiphilic properties. To predict drug-induced PLD in silico, we established a high-throughput cell-culture-based method to quantitatively determine the induction of PLD by chemical compounds. Using this assay, we tested 297 drug-like compounds at two different concentrations (2.5 μm and 5.0 μm). We were able to identify 28 previously unknown PLD-inducing agents. Furthermore, our experimental results enabled the development of a binary classification model to predict PLD-inducing agents based on their molecular properties. This random forest prediction system yields a bootstrapped validated accuracy of 86 %. PLD-inducing agents overlap with those that target similar biological processes; a high degree of concordance with PLD-inducing agents was identified for cationic amphiphilic compounds, small molecules that inhibit acid sphingomyelinase, compounds that cross the blood–brain barrier, and compounds that violate Lipinski’s rule of five. Furthermore, we were able to show that PLD-inducing compounds applied in combination additively induce PLD. PMID:22945602

pH-sensitive Eudragit nanoparticles for mucosal drug delivery.

PubMed

Yoo, Jin-Wook; Giri, Namita; Lee, Chi H

2011-01-17

Drug delivery via vaginal epithelium has suffered from lack of stability due to acidic and enzymatic environments. The biocompatible pH-sensitive nanoparticles composed of Eudragit S-100 (ES) were developed to protect loaded compounds from being degraded under the rigorous vaginal conditions and achieve their therapeutically effective concentrations in the mucosal epithelium. ES nanoparticles containing a model compound (sodium fluorescein (FNa) or nile red (NR)) were prepared by the modified quasi-emulsion solvent diffusion method. Loading efficiencies were found to be 26% and 71% for a hydrophilic and a hydrophobic compound, respectively. Both hydrophilic and hydrophobic model drugs remained stable in nanoparticles at acidic pH, whereas they are quickly released from nanoparticles upon exposure at physiological pH. The confocal study revealed that ES nanoparticles were taken up by vaginal cells, followed by pH-responsive drug release, with no cytotoxic activities. The pH-sensitive nanoparticles would be a promising carrier for the vaginal-specific delivery of various therapeutic drugs including microbicides and peptides/proteins. Published by Elsevier B.V.

Model for High-Throughput Screening of Multitarget Drugs in Chemical Neurosciences: Synthesis, Assay, and Theoretic Study of Rasagiline Carbamates

PubMed Central

2013-01-01

The disappointing results obtained in recent clinical trials renew the interest in experimental/computational techniques for the discovery of neuroprotective drugs. In this context, multitarget or multiplexing QSAR models (mt-QSAR/mx-QSAR) may help to predict neurotoxicity/neuroprotective effects of drugs in multiple assays, on drug targets, and in model organisms. In this work, we study a data set downloaded from CHEMBL; each data point (>8000) contains the values of one out of 37 possible measures of activity, 493 assays, 169 molecular or cellular targets, and 11 different organisms (including human) for a given compound. In this work, we introduce the first mx-QSAR model for neurotoxicity/neuroprotective effects of drugs based on the MARCH-INSIDE (MI) method. First, we used MI to calculate the stochastic spectral moments (structural descriptors) of all compounds. Next, we found a model that classified correctly 2955 out of 3548 total cases in the training and validation series with Accuracy, Sensitivity, and Specificity values > 80%. The model also showed excellent results in Computational-Chemistry simulations of High-Throughput Screening (CCHTS) experiments, with accuracy = 90.6% for 4671 positive cases. Next, we reported the synthesis, characterization, and experimental assays of new rasagiline derivatives. We carried out three different experimental tests: assay (1) in the absence of neurotoxic agents, assay (2) in the presence of glutamate, and assay (3) in the presence of H2O2. Compounds 11 with 27.4%, 8 with 11.6%, and 9 with 15.4% showed the highest neuroprotective effects in assays (1), (2), and (3), respectively. After that, we used the mx-QSAR model to carry out a CCHTS of the new compounds in >400 unique pharmacological tests not carried out experimentally. Consequently, this model may become a promising auxiliary tool for the discovery of new drugs for the treatment of neurodegenerative diseases. PMID:23855599

Identification of intestinal loss of a drug through physiologically based pharmacokinetic simulation of plasma concentration-time profiles.

PubMed

Peters, Sheila Annie

2008-01-01

Despite recent advances in understanding of the role of the gut as a metabolizing organ, recognition of gut wall metabolism and/or other factors contributing to intestinal loss of a compound has been a challenging task due to the lack of well characterized methods to distinguish it from first-pass hepatic extraction. The implications of identifying intestinal loss of a compound in drug discovery and development can be enormous. Physiologically based pharmacokinetic (PBPK) simulations of pharmacokinetic profiles provide a simple, reliable and cost-effective way to understand the mechanisms underlying pharmacokinetic processes. The purpose of this article is to demonstrate the application of PBPK simulations in bringing to light intestinal loss of orally administered drugs, using two example compounds: verapamil and an in-house compound that is no longer in development (referred to as compound A in this article). A generic PBPK model, built in-house using MATLAB software and incorporating absorption, metabolism, distribution, biliary and renal elimination models, was employed for simulation of concentration-time profiles. Modulation of intrinsic hepatic clearance and tissue distribution parameters in the generic PBPK model was done to achieve a good fit to the observed intravenous pharmacokinetic profiles of the compounds studied. These optimized clearance and distribution parameters are expected to be invariant across different routes of administration, as long as the kinetics are linear, and were therefore employed to simulate the oral profiles of the compounds. For compounds with reasonably good solubility and permeability, an area under the concentration-time curve for the simulated oral profile that far exceeded the observed would indicate some kind of loss in the intestine. PBPK simulations applied to compound A showed substantial loss of the compound in the gastrointestinal tract in humans but not in rats. This accounted for the lower bioavailability of the compound in humans than in rats. PBPK simulations of verapamil identified gut wall metabolism, well established in the literature, and showed large interspecies differences with respect to both gut wall metabolism and drug-induced delays in gastric emptying. Mechanistic insights provided by PBPK simulations can be very valuable in answering vital questions in drug discovery and development. However, such applications of PBPK models are limited by the lack of accurate inputs for clearance and distribution. This article demonstrates a successful application of PBPK simulations to identify and quantify intestinal loss of two model compounds in rats and humans. The limitation of inaccurate inputs for the clearance and distribution parameters was overcome by optimizing these parameters through fitting intravenous profiles. The study also demonstrated that the large interspecies differences associated with gut wall metabolism and gastric emptying, evident for the compounds studied, make animal model extrapolations to humans unreliable. It is therefore important to do PBPK simulations of human pharmacokinetic profiles to understand the relevance of intestinal loss of a compound in humans.

Chemical signatures and new drug targets for gametocytocidal drug development

NASA Astrophysics Data System (ADS)

Sun, Wei; Tanaka, Takeshi Q.; Magle, Crystal T.; Huang, Wenwei; Southall, Noel; Huang, Ruili; Dehdashti, Seameen J.; McKew, John C.; Williamson, Kim C.; Zheng, Wei

2014-01-01

Control of parasite transmission is critical for the eradication of malaria. However, most antimalarial drugs are not active against P. falciparum gametocytes, responsible for the spread of malaria. Consequently, patients can remain infectious for weeks after the clearance of asexual parasites and clinical symptoms. Here we report the identification of 27 potent gametocytocidal compounds (IC50 < 1 μM) from screening 5,215 known drugs and compounds. All these compounds were active against three strains of gametocytes with different drug sensitivities and geographical origins, 3D7, HB3 and Dd2. Cheminformatic analysis revealed chemical signatures for P. falciparum sexual and asexual stages indicative of druggability and suggesting potential targets. Torin 2, a top lead compound (IC50 = 8 nM against gametocytes in vitro), completely blocked oocyst formation in a mouse model of transmission. These results provide critical new leads and potential targets to expand the repertoire of malaria transmission-blocking reagents.

Tackling the conformational sampling of larger flexible compounds and macrocycles in pharmacology and drug discovery.

PubMed

Chen, I-Jen; Foloppe, Nicolas

2013-12-15

Computational conformational sampling underpins much of molecular modeling and design in pharmaceutical work. The sampling of smaller drug-like compounds has been an active area of research. However, few studies have tested in details the sampling of larger more flexible compounds, which are also relevant to drug discovery, including therapeutic peptides, macrocycles, and inhibitors of protein-protein interactions. Here, we investigate extensively mainstream conformational sampling methods on three carefully curated compound sets, namely the 'Drug-like', larger 'Flexible', and 'Macrocycle' compounds. These test molecules are chemically diverse with reliable X-ray protein-bound bioactive structures. The compared sampling methods include Stochastic Search and the recent LowModeMD from MOE, all the low-mode based approaches from MacroModel, and MD/LLMOD recently developed for macrocycles. In addition to default settings, key parameters of the sampling protocols were explored. The performance of the computational protocols was assessed via (i) the reproduction of the X-ray bioactive structures, (ii) the size, coverage and diversity of the output conformational ensembles, (iii) the compactness/extendedness of the conformers, and (iv) the ability to locate the global energy minimum. The influence of the stochastic nature of the searches on the results was also examined. Much better results were obtained by adopting search parameters enhanced over the default settings, while maintaining computational tractability. In MOE, the recent LowModeMD emerged as the method of choice. Mixed torsional/low-mode from MacroModel performed as well as LowModeMD, and MD/LLMOD performed well for macrocycles. The low-mode based approaches yielded very encouraging results with the flexible and macrocycle sets. Thus, one can productively tackle the computational conformational search of larger flexible compounds for drug discovery, including macrocycles. Copyright © 2013 Elsevier Ltd. All rights reserved.

A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases

PubMed Central

Chernomoretz, Ariel; Agüero, Fernán

2016-01-01

Drug development for neglected diseases has been historically hampered due to lack of market incentives. The advent of public domain resources containing chemical information from high throughput screenings is changing the landscape of drug discovery for these diseases. In this work we took advantage of data from extensively studied organisms like human, mouse, E. coli and yeast, among others, to develop a novel integrative network model to prioritize and identify candidate drug targets in neglected pathogen proteomes, and bioactive drug-like molecules. We modeled genomic (proteins) and chemical (bioactive compounds) data as a multilayer weighted network graph that takes advantage of bioactivity data across 221 species, chemical similarities between 1.7 105 compounds and several functional relations among 1.67 105 proteins. These relations comprised orthology, sharing of protein domains, and shared participation in defined biochemical pathways. We showcase the application of this network graph to the problem of prioritization of new candidate targets, based on the information available in the graph for known compound-target associations. We validated this strategy by performing a cross validation procedure for known mouse and Trypanosoma cruzi targets and showed that our approach outperforms classic alignment-based approaches. Moreover, our model provides additional flexibility as two different network definitions could be considered, finding in both cases qualitatively different but sensible candidate targets. We also showcase the application of the network to suggest targets for orphan compounds that are active against Plasmodium falciparum in high-throughput screens. In this case our approach provided a reduced prioritization list of target proteins for the query molecules and showed the ability to propose new testable hypotheses for each compound. Moreover, we found that some predictions highlighted by our network model were supported by independent experimental validations as found post-facto in the literature. PMID:26735851

A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases.

PubMed

Berenstein, Ariel José; Magariños, María Paula; Chernomoretz, Ariel; Agüero, Fernán

2016-01-01

Drug development for neglected diseases has been historically hampered due to lack of market incentives. The advent of public domain resources containing chemical information from high throughput screenings is changing the landscape of drug discovery for these diseases. In this work we took advantage of data from extensively studied organisms like human, mouse, E. coli and yeast, among others, to develop a novel integrative network model to prioritize and identify candidate drug targets in neglected pathogen proteomes, and bioactive drug-like molecules. We modeled genomic (proteins) and chemical (bioactive compounds) data as a multilayer weighted network graph that takes advantage of bioactivity data across 221 species, chemical similarities between 1.7 105 compounds and several functional relations among 1.67 105 proteins. These relations comprised orthology, sharing of protein domains, and shared participation in defined biochemical pathways. We showcase the application of this network graph to the problem of prioritization of new candidate targets, based on the information available in the graph for known compound-target associations. We validated this strategy by performing a cross validation procedure for known mouse and Trypanosoma cruzi targets and showed that our approach outperforms classic alignment-based approaches. Moreover, our model provides additional flexibility as two different network definitions could be considered, finding in both cases qualitatively different but sensible candidate targets. We also showcase the application of the network to suggest targets for orphan compounds that are active against Plasmodium falciparum in high-throughput screens. In this case our approach provided a reduced prioritization list of target proteins for the query molecules and showed the ability to propose new testable hypotheses for each compound. Moreover, we found that some predictions highlighted by our network model were supported by independent experimental validations as found post-facto in the literature.

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

PubMed

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

2016-03-07

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

WhichP450: a multi-class categorical model to predict the major metabolising CYP450 isoform for a compound

NASA Astrophysics Data System (ADS)

Hunt, Peter A.; Segall, Matthew D.; Tyzack, Jonathan D.

2018-02-01

In the development of novel pharmaceuticals, the knowledge of how many, and which, Cytochrome P450 isoforms are involved in the phase I metabolism of a compound is important. Potential problems can arise if a compound is metabolised predominantly by a single isoform in terms of drug-drug interactions or genetic polymorphisms that would lead to variations in exposure in the general population. Combined with models of regioselectivities of metabolism by each isoform, such a model would also aid in the prediction of the metabolites likely to be formed by P450-mediated metabolism. We describe the generation of a multi-class random forest model to predict which, out of a list of the seven leading Cytochrome P450 isoforms, would be the major metabolising isoforms for a novel compound. The model has a 76% success rate with a top-1 criterion and an 88% success rate for a top-2 criterion and shows significant enrichment over randomised models.

Virtual screening and rational drug design method using structure generation system based on 3D-QSAR and docking.

PubMed

Chen, H F; Dong, X C; Zen, B S; Gao, K; Yuan, S G; Panaye, A; Doucet, J P; Fan, B T

2003-08-01

An efficient virtual and rational drug design method is presented. It combines virtual bioactive compound generation with 3D-QSAR model and docking. Using this method, it is possible to generate a lot of highly diverse molecules and find virtual active lead compounds. The method was validated by the study of a set of anti-tumor drugs. With the constraints of pharmacophore obtained by DISCO implemented in SYBYL 6.8, 97 virtual bioactive compounds were generated, and their anti-tumor activities were predicted by CoMFA. Eight structures with high activity were selected and screened by the 3D-QSAR model. The most active generated structure was further investigated by modifying its structure in order to increase the activity. A comparative docking study with telomeric receptor was carried out, and the results showed that the generated structures could form more stable complexes with receptor than the reference compound selected from experimental data. This investigation showed that the proposed method was a feasible way for rational drug design with high screening efficiency.

A potential model for drug screening by simulating the effect of shear stress in vivo on endothelium.

PubMed

Xu, Yingqian; Wang, Bochu; Deng, Jia; Liu, Zerong; Zhu, Liancai

2013-01-01

The purpose of this paper was to research the potential of a dynamic cell model in drug screening by studying the influence of microvascular wall shear stress on the drug absorption of endothelial cells compared to that in the static state. The cells were grown and seeded on gelatin-coated glass slides and were pretreated with extracts of Salviae miltiorrhizae (200 μg/ml) for 1 h. Then oxidative stress damage was produced by H2O2 (300 μmol/l) for 0.5 h under the 1.5 dyn/cm2 shear stress incorporated in a parallel plate flow chamber. Morphological analysis was conducted with an inverted microscope and image analysis software, and high performance liquid chromatography-mass spectrometry was used for the detection of active compounds. We compared the drug absorption in the dynamic group with that in the static group. In the dynamic model, five compounds and two new metabolite peaks were detected. However, in the static model, four compounds were absorbed by cells, and one metabolite peak was found. This study indicated that there were some effects on the absorption and metabolism of drugs under the microvascular shear stress compared to that under stasis. We infer that shear stress in the microcirculation situation in vivo played a role in causing the differences between drug screening in vitro and in vivo.

Computational approaches for drug discovery.

PubMed

Hung, Che-Lun; Chen, Chi-Chun

2014-09-01

Cellular proteins are the mediators of multiple organism functions being involved in physiological mechanisms and disease. By discovering lead compounds that affect the function of target proteins, the target diseases or physiological mechanisms can be modulated. Based on knowledge of the ligand-receptor interaction, the chemical structures of leads can be modified to improve efficacy, selectivity and reduce side effects. One rational drug design technology, which enables drug discovery based on knowledge of target structures, functional properties and mechanisms, is computer-aided drug design (CADD). The application of CADD can be cost-effective using experiments to compare predicted and actual drug activity, the results from which can used iteratively to improve compound properties. The two major CADD-based approaches are structure-based drug design, where protein structures are required, and ligand-based drug design, where ligand and ligand activities can be used to design compounds interacting with the protein structure. Approaches in structure-based drug design include docking, de novo design, fragment-based drug discovery and structure-based pharmacophore modeling. Approaches in ligand-based drug design include quantitative structure-affinity relationship and pharmacophore modeling based on ligand properties. Based on whether the structure of the receptor and its interaction with the ligand are known, different design strategies can be seed. After lead compounds are generated, the rule of five can be used to assess whether these have drug-like properties. Several quality validation methods, such as cost function analysis, Fisher's cross-validation analysis and goodness of hit test, can be used to estimate the metrics of different drug design strategies. To further improve CADD performance, multi-computers and graphics processing units may be applied to reduce costs. © 2014 Wiley Periodicals, Inc.

Development and validation of in silico models for estimating drug preformulation risk in PEG400/water and Tween80/water systems.

PubMed

Crivori, Patrizia; Morelli, Amedea; Pezzetta, Daniele; Rocchetti, Maurizio; Poggesi, Italo

2007-11-01

Solubility is one of the most important properties of drug candidates for achieving the targeted plasma concentrations following oral dosing. Furthermore, the formulations adopted in the in vivo preclinical studies, for both oral and intravenous administrations, are usually solutions. To formulate compounds sparingly soluble in water, pharmaceutically acceptable cosolvents or surfactants are typically employed to increase solubility. Compounds poorly soluble also in these systems will likely show severe formulation issues. In such cases, relatively high amount of compounds, rarely available in the early preclinical phases, are needed to identify the most appropriate dosing vehicles. Hence, the purpose of this study was to build two computational models which, on the basis of the molecular structure, are able to predict the compound solubility in two vehicle systems (40% PEG400/water and 10% Tween80/water) used in our company as screening tools for anticipating potential formulation issues. The two models were developed using the solubility data obtained from the analysis of approximately 2000 chemically diverse compounds. The structural diversity and the drug-like space covered by these molecules were investigated using the ChemGPS methodology. The compounds were classified (high/low preformulation risk) based on the experimental solubility value range. A combination of descriptors (i.e. logD at two different pH, E-state indices and other 2D structural descriptors) was correlated to these classes using partial least squares discriminant (PLSD) analysis. The overall accuracy of each PLSD model applied to independent sets of compounds was approximately 78%. The accuracy reached when the models were used in combination to identify molecules with low preformulation risk in both systems was 83%. The models appeared a valuable tool for predicting the preformulation risk of drug candidates and consequently for identifying the most appropriate dosing vehicles to be further investigated before the first in vivo preclinical studies. Since only a small number of 2D descriptors is need to evaluate the preformulation risk classes, the models resulted easy to use and characterized by high throughput.

Target-mediated drug disposition model and its approximations for antibody-drug conjugates.

PubMed

Gibiansky, Leonid; Gibiansky, Ekaterina

2014-02-01

Antibody-drug conjugate (ADC) is a complex structure composed of an antibody linked to several molecules of a biologically active cytotoxic drug. The number of ADC compounds in clinical development now exceeds 30, with two of them already on the market. However, there is no rigorous mechanistic model that describes pharmacokinetic (PK) properties of these compounds. PK modeling of ADCs is even more complicated than that of other biologics as the model should describe distribution, binding, and elimination of antibodies with different toxin load, and also the deconjugation process and PK of the released toxin. This work extends the target-mediated drug disposition (TMDD) model to describe ADCs, derives the rapid binding (quasi-equilibrium), quasi-steady-state, and Michaelis-Menten approximations of the TMDD model as applied to ADCs, derives the TMDD model and its approximations for ADCs with load-independent properties, and discusses further simplifications of the system under various assumptions. The developed models are shown to describe data simulated from the available clinical population PK models of trastuzumab emtansine (T-DM1), one of the two currently approved ADCs. Identifiability of model parameters is also discussed and illustrated on the simulated T-DM1 examples.

Statistical molecular design of balanced compound libraries for QSAR modeling.

PubMed

Linusson, A; Elofsson, M; Andersson, I E; Dahlgren, M K

2010-01-01

A fundamental step in preclinical drug development is the computation of quantitative structure-activity relationship (QSAR) models, i.e. models that link chemical features of compounds with activities towards a target macromolecule associated with the initiation or progression of a disease. QSAR models are computed by combining information on the physicochemical and structural features of a library of congeneric compounds, typically assembled from two or more building blocks, and biological data from one or more in vitro assays. Since the models provide information on features affecting the compounds' biological activity they can be used as guides for further optimization. However, in order for a QSAR model to be relevant to the targeted disease, and drug development in general, the compound library used must contain molecules with balanced variation of the features spanning the chemical space believed to be important for interaction with the biological target. In addition, the assays used must be robust and deliver high quality data that are directly related to the function of the biological target and the associated disease state. In this review, we discuss and exemplify the concept of statistical molecular design (SMD) in the selection of building blocks and final synthetic targets (i.e. compounds to synthesize) to generate information-rich, balanced libraries for biological testing and computation of QSAR models.

Perspectives on zebrafish models of hallucinogenic drugs and related psychotropic compounds.

PubMed

Neelkantan, Nikhil; Mikhaylova, Alina; Stewart, Adam Michael; Arnold, Raymond; Gjeloshi, Visar; Kondaveeti, Divya; Poudel, Manoj K; Kalueff, Allan V

2013-08-21

Among different classes of psychotropic drugs, hallucinogenic agents exert one of the most prominent effects on human and animal behaviors, markedly altering sensory, motor, affective, and cognitive responses. The growing clinical and preclinical interest in psychedelic, dissociative, and deliriant hallucinogens necessitates novel translational, sensitive, and high-throughput in vivo models and screens. Primate and rodent models have been traditionally used to study cellular mechanisms and neural circuits of hallucinogenic drugs' action. The utility of zebrafish ( Danio rerio ) in neuroscience research is rapidly growing due to their high physiological and genetic homology to humans, ease of genetic manipulation, robust behaviors, and cost effectiveness. Possessing a fully characterized genome, both adult and larval zebrafish are currently widely used for in vivo screening of various psychotropic compounds, including hallucinogens and related drugs. Recognizing the growing importance of hallucinogens in biological psychiatry, here we discuss hallucinogenic-induced phenotypes in zebrafish and evaluate their potential as efficient preclinical models of drug-induced states in humans.

Data Mining and Machine Learning Models for Predicting Drug Likeness and their Disease or Organ Category

NASA Astrophysics Data System (ADS)

Yosipof, Abraham; Guedes, Rita C.; García-Sosa, Alfonso T.

2018-05-01

Data mining approaches can uncover underlying patterns in chemical and pharmacological property space decisive for drug discovery and development. Two of the most common approaches are visualization and machine learning methods. Visualization methods use dimensionality reduction techniques in order to reduce multi-dimension data into 2D or 3D representations with a minimal loss of information. Machine learning attempts to find correlations between specific activities or classifications for a set of compounds and their features by means of recurring mathematical models. Both models take advantage of the different and deep relationships that can exist between features of compounds, and helpfully provide classification of compounds based on such features. Drug-likeness has been studied from several viewpoints, but here we provide the first implementation in chemoinformatics of the t-Distributed Stochastic Neighbor Embedding (t-SNE) method for the visualization and the representation of chemical space, and the use of different machine learning methods separately and together to form a new ensemble learning method called AL Boost. The models obtained from AL Boost synergistically combine decision tree, random forests (RF), support vector machine (SVM), artificial neuronal network (ANN), k nearest neighbors (kNN), and logistic regression models. In this work, we show that together they form a predictive model that not only improves the predictive force but also decreases bias. This resulted in a corrected classification rate of over 0.81, as well as higher sensitivity and specificity rates for the models. In addition, separation and good models were also achieved for disease categories such as antineoplastic compounds and nervous system diseases, among others. Such models can be used to guide decision on the feature landscape of compounds and their likeness to either drugs or other characteristics, such as specific or multiple disease-category(ies) or organ(s) of action of a molecule.

Data Mining and Machine Learning Models for Predicting Drug Likeness and Their Disease or Organ Category.

PubMed

Yosipof, Abraham; Guedes, Rita C; García-Sosa, Alfonso T

2018-01-01

Data mining approaches can uncover underlying patterns in chemical and pharmacological property space decisive for drug discovery and development. Two of the most common approaches are visualization and machine learning methods. Visualization methods use dimensionality reduction techniques in order to reduce multi-dimension data into 2D or 3D representations with a minimal loss of information. Machine learning attempts to find correlations between specific activities or classifications for a set of compounds and their features by means of recurring mathematical models. Both models take advantage of the different and deep relationships that can exist between features of compounds, and helpfully provide classification of compounds based on such features or in case of visualization methods uncover underlying patterns in the feature space. Drug-likeness has been studied from several viewpoints, but here we provide the first implementation in chemoinformatics of the t-Distributed Stochastic Neighbor Embedding (t-SNE) method for the visualization and the representation of chemical space, and the use of different machine learning methods separately and together to form a new ensemble learning method called AL Boost. The models obtained from AL Boost synergistically combine decision tree, random forests (RF), support vector machine (SVM), artificial neural network (ANN), k nearest neighbors (kNN), and logistic regression models. In this work, we show that together they form a predictive model that not only improves the predictive force but also decreases bias. This resulted in a corrected classification rate of over 0.81, as well as higher sensitivity and specificity rates for the models. In addition, separation and good models were also achieved for disease categories such as antineoplastic compounds and nervous system diseases, among others. Such models can be used to guide decision on the feature landscape of compounds and their likeness to either drugs or other characteristics, such as specific or multiple disease-category(ies) or organ(s) of action of a molecule.

Analysis of pharmacology data and the prediction of adverse drug reactions and off-target effects from chemical structure.

PubMed

Bender, Andreas; Scheiber, Josef; Glick, Meir; Davies, John W; Azzaoui, Kamal; Hamon, Jacques; Urban, Laszlo; Whitebread, Steven; Jenkins, Jeremy L

2007-06-01

Preclinical Safety Pharmacology (PSP) attempts to anticipate adverse drug reactions (ADRs) during early phases of drug discovery by testing compounds in simple, in vitro binding assays (that is, preclinical profiling). The selection of PSP targets is based largely on circumstantial evidence of their contribution to known clinical ADRs, inferred from findings in clinical trials, animal experiments, and molecular studies going back more than forty years. In this work we explore PSP chemical space and its relevance for the prediction of adverse drug reactions. Firstly, in silico (computational) Bayesian models for 70 PSP-related targets were built, which are able to detect 93% of the ligands binding at IC(50) < or = 10 microM at an overall correct classification rate of about 94%. Secondly, employing the World Drug Index (WDI), a model for adverse drug reactions was built directly based on normalized side-effect annotations in the WDI, which does not require any underlying functional knowledge. This is, to our knowledge, the first attempt to predict adverse drug reactions across hundreds of categories from chemical structure alone. On average 90% of the adverse drug reactions observed with known, clinically used compounds were detected, an overall correct classification rate of 92%. Drugs withdrawn from the market (Rapacuronium, Suprofen) were tested in the model and their predicted ADRs align well with known ADRs. The analysis was repeated for acetylsalicylic acid and Benperidol which are still on the market. Importantly, features of the models are interpretable and back-projectable to chemical structure, raising the possibility of rationally engineering out adverse effects. By combining PSP and ADR models new hypotheses linking targets and adverse effects can be proposed and examples for the opioid mu and the muscarinic M2 receptors, as well as for cyclooxygenase-1 are presented. It is hoped that the generation of predictive models for adverse drug reactions is able to help support early SAR to accelerate drug discovery and decrease late stage attrition in drug discovery projects. In addition, models such as the ones presented here can be used for compound profiling in all development stages.

Aglycone solanidine and solasodine derivatives: A natural approach towards cancer.

PubMed

Hameed, Abdul; Ijaz, Shakeel; Mohammad, Imran Shair; Muhammad, Kiran Sher; Akhtar, Naveed; Khan, Haji Muhammad Shoaib

2017-10-01

Over the past few years, it was suggested that a rational approach to treat cancer in clinical settings requires a multipronged approach that augments improvement in systemic efficiency along with modification in cellular phenotype leads to more efficient cell death response. Recently, the combinatory delivery of traditional chemotherapeutic drugs with natural compounds proved to be astonishing to deal with a variety of cancers, especially that are resistant to chemotherapeutic drugs. The natural compounds not only synergize the effects of chemotherapeutics but also minimize drug associated systemic toxicity. In this review, our primary focus was on antitumor effects of natural compounds. Previously, the drugs from natural sources are highly precise and safer than drugs of synthetic origins. Many natural compounds exhibit anti-cancer potentials by inducing apoptosis in different tumor models, in-vitro and in-vivo. Furthermore, natural compounds are also found equally useful in chemotherapeutic drug resistant tumors. Moreover, these Phyto-compounds also possess numerous other pharmacological properties such as antifungal, antimicrobial, antiprotozoal, and hepatoprotection. Aglycone solasodine and solanidine derivatives are the utmost important steroidal glycoalkaloids that are present in various Solanum species, are discussed here. These natural compounds are highly cytotoxic against different tumor cell lines. As the molecular weight is concerned; these are smaller molecular weight chemotherapeutic agents that induce cell death response by initiating apoptosis through both extrinsic and intrinsic pathways. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery.

PubMed

Thomford, Nicholas Ekow; Senthebane, Dimakatso Alice; Rowe, Arielle; Munro, Daniella; Seele, Palesa; Maroyi, Alfred; Dzobo, Kevin

2018-05-25

The therapeutic properties of plants have been recognised since time immemorial. Many pathological conditions have been treated using plant-derived medicines. These medicines are used as concoctions or concentrated plant extracts without isolation of active compounds. Modern medicine however, requires the isolation and purification of one or two active compounds. There are however a lot of global health challenges with diseases such as cancer, degenerative diseases, HIV/AIDS and diabetes, of which modern medicine is struggling to provide cures. Many times the isolation of "active compound" has made the compound ineffective. Drug discovery is a multidimensional problem requiring several parameters of both natural and synthetic compounds such as safety, pharmacokinetics and efficacy to be evaluated during drug candidate selection. The advent of latest technologies that enhance drug design hypotheses such as Artificial Intelligence, the use of 'organ-on chip' and microfluidics technologies, means that automation has become part of drug discovery. This has resulted in increased speed in drug discovery and evaluation of the safety, pharmacokinetics and efficacy of candidate compounds whilst allowing novel ways of drug design and synthesis based on natural compounds. Recent advances in analytical and computational techniques have opened new avenues to process complex natural products and to use their structures to derive new and innovative drugs. Indeed, we are in the era of computational molecular design, as applied to natural products. Predictive computational softwares have contributed to the discovery of molecular targets of natural products and their derivatives. In future the use of quantum computing, computational softwares and databases in modelling molecular interactions and predicting features and parameters needed for drug development, such as pharmacokinetic and pharmacodynamics, will result in few false positive leads in drug development. This review discusses plant-based natural product drug discovery and how innovative technologies play a role in next-generation drug discovery.

Machine learning algorithms for the prediction of hERG and CYP450 binding in drug development.

PubMed

Klon, Anthony E

2010-07-01

The cost of developing new drugs is estimated at approximately $1 billion; the withdrawal of a marketed compound due to toxicity can result in serious financial loss for a pharmaceutical company. There has been a greater interest in the development of in silico tools that can identify compounds with metabolic liabilities before they are brought to market. The two largest classes of machine learning (ML) models, which will be discussed in this review, have been developed to predict binding to the human ether-a-go-go related gene (hERG) ion channel protein and the various CYP isoforms. Being able to identify potentially toxic compounds before they are made would greatly reduce the number of compound failures and the costs associated with drug development. This review summarizes the state of modeling hERG and CYP binding towards this goal since 2003 using ML algorithms. A wide variety of ML algorithms that are comparable in their overall performance are available. These ML methods may be applied regularly in discovery projects to flag compounds with potential metabolic liabilities.

Identification of potential hit compounds for Dengue virus NS2B/NS3 protease inhibitors by combining virtual screening and binding free energy calculations.

PubMed

Wichapong, K; Nueangaudom, A; Pianwanit, S; Sippl, W; Kokpol, S

2013-09-01

Dengue virus (DV) infections are a serious public health problem and there is currently no vaccine or drug treatment. NS2B/NS3 protease, an essential enzyme for viral replication, is one of the promising targets in the search for drugs against DV. In this research work, virtual screening (VS) was carried out on four multi-conformational databases using several criteria. Firstly, molecular dynamics simulations of the NS2B/NS3 protease and four known inhibitors, which reveal an importance of both electrostatic and van der Waals interactions in stabilizing the ligand-enzyme interaction, were used to generate three different pharmacophore models (a structure-based, a static and a dynamic). Subsequently, these three models were employed for pharmacophore search in the VS. Secondly, compounds passing the first criterion were further reduced using the Lipinski's rule of five to keep only compounds with drug-like properties. Thirdly, molecular docking calculations were performed to remove compounds with unsuitable ligand-enzyme interactions. Finally, binding free energy of each compound was calculated. Compounds having better energy than the known inhibitors were selected and thus 20 potential hits were obtained.

The bleomycin animal model: a useful tool to investigate treatment options for idiopathic pulmonary fibrosis?

PubMed Central

Moeller, Antje; Ask, Kjetil; Warburton, David; Gauldie, Jack; Kolb, Martin

2008-01-01

Different animal models of pulmonary fibrosis have been developed to investigate potential therapies for idiopathic pulmonary fibrosis (IPF). The most common is the bleomycin model in rodents (mouse, rat and hamster). Over the years, numerous agents have been shown to inhibit fibrosis in this model. However, to date none of these compounds are used in the clinical management of IPF and none has shown a comparable antifibrotic effect in humans. We performed a systematic review of publications on drug efficacy studies in the bleomycin model to evaluate the value of this model regarding transferability to clinical use. Between 1980 and 2006 we identified 246 experimental studies describing beneficial antifibrotic compounds in the bleomycin model. In 221 of the studies we found enough details about the timing of drug application to allow inter-study comparison. 211 of those used a preventive regimen (drug given ≤ day 7 after last bleomycin application), only 10 were therapeutic trials (> 7 days after last bleomycin application). It is critical to distinguish between drugs interfering with the inflammatory and early fibrogenic response from those preventing progression of fibrosis, the latter likely much more meaningful for clinical application. All potential antifibrotic compounds should be evaluated in the phase of established fibrosis rather than in the early period of bleomycin-induced inflammation for assessment of its antifibrotic properties. Further care should be taken in extrapolation of drugs successfully tested in the bleomycin model due to partial reversibility of bleomycin induced fibrosis over time. The use of alternative and more robust animal models, which better reflect human IPF, is warranted. PMID:17936056

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

PubMed

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

2016-01-01

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

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

PubMed

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

2017-01-01

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

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.

Thermodynamics of Highly Supersaturated Aqueous Solutions of Poorly Water-Soluble Drugs-Impact of a Second Drug on the Solution Phase Behavior and Implications for Combination Products.

PubMed

Trasi, Niraj S; Taylor, Lynne S

2015-08-01

There is increasing interest in formulating combination products that contain two or more drugs. Furthermore, it is also common for different drug products to be taken simultaneously. This raises the possibility of interactions between different drugs that may impact formulation performance. For poorly water-soluble compounds, the supersaturation behavior may be a critical factor in determining the extent of oral absorption. The goal of the current study was to evaluate the maximum achievable supersaturation for several poorly water-soluble compounds alone, and in combination. Model compounds included ritonavir, lopinavir, paclitaxel, felodipine, and diclofenac. The "amorphous solubility" for the pure drugs was determined using different techniques and the change in this solubility was then measured in the presence of differing amounts of a second drug. The results showed that "amorphous solubility" of each component in aqueous solution is substantially decreased by the second component, as long as the two drugs are miscible in the amorphous state. A simple thermodynamic model could be used to predict the changes in solubility as a function of composition. This information is of great value when developing co-amorphous or other supersaturating formulations and should contribute to a broader understanding of drug-drug physicochemical interactions in in vitro assays as well as in the gastrointestinal tract. © 2015 Wiley Periodicals, Inc. and the American Pharmacists Association.

Hit identification of novel heparanase inhibitors by structure- and ligand-based approaches.

PubMed

Gozalbes, Rafael; Mosulén, Silvia; Ortí, Leticia; Rodríguez-Díaz, Jesús; Carbajo, Rodrigo J; Melnyk, Patricia; Pineda-Lucena, Antonio

2013-04-01

Heparanase is a key enzyme involved in the dissemination of metastatic cancer cells. In this study a combination of in silico techniques and experimental methods was used to identify new potential inhibitors against this target. A 3D model of heparanase was built from sequence homology and applied to the virtual screening of a library composed of 27 known heparanase inhibitors and a commercial collection of drugs and drug-like compounds. The docking results from this campaign were combined with those obtained from a pharmacophore model recently published based in the same set of chemicals. Compounds were then ranked according to their theoretical binding affinity, and the top-rated commercial drugs were selected for further experimental evaluation. Biophysical methods (NMR and SPR) were applied to assess experimentally the interaction of the selected compounds with heparanase. The binding site was evaluated via competition experiments, using a known inhibitor of heparanase. Three of the selected drugs were found to bind to the active site of the protein and their KD values were determined. Among them, the antimalarial drug amodiaquine presented affinity towards the protein in the low-micromolar range, and was singled out for a SAR study based on its chemical scaffold. A subset of fourteen 4-arylaminoquinolines from a global set of 249 analogues of amodiaquine was selected based on the application of in silico models, a QSAR solubility prediction model and a chemical diversity analysis. Some of these compounds displayed binding affinities in the micromolar range. Copyright © 2013 Elsevier Ltd. All rights reserved.

Understanding the polypharmacological anticancer effects of Xiao Chai Hu Tang via a computational pharmacological model

PubMed Central

ZHENG, CHUN-SONG; WU, YIN-SHENG; BAO, HONG-JUAN; XU, XIAO-JIE; CHEN, XING-QIANG; YE, HONG-ZHI; WU, GUANG-WEN; XU, HUI-FENG; LI, XI-HAI; CHEN, JIA-SHOU; LIU, XIAN-XIANG

2014-01-01

Xiao Chai Hu Tang (XCHT), a traditional herbal formula, is widely administered as a cancer treatment. However, the underlying molecular mechanisms of its anticancer effects are not fully understood. In the present study, a computational pharmacological model that combined chemical space mapping, molecular docking and network analysis was employed to predict which chemical compounds in XCHT are potential inhibitors of cancer-associated targets, and to establish a compound-target (C-T) network and compound-compound (C-C) association network. The identified compounds from XCHT demonstrated diversity in chemical space. Furthermore, they occupied regions of chemical space that were the same, or close to, those occupied by drug or drug-like compounds that are associated with cancer, according to the Therapeutic Targets Database. The analysis of the molecular docking and the C-T network demonstrated that the potential inhibitors possessed the properties of promiscuous drugs and combination therapies. The C-C network was classified into four clusters and the different clusters contained various multi-compound combinations that acted on different targets. The study indicated that XCHT has a polypharmacological role in treating cancer and the potential inhibitory components of XCHT require further investigation as potential therapeutic strategies for cancer patients. PMID:24926384

Improved BM212 MmpL3 Inhibitor Analogue Shows Efficacy in Acute Murine Model of Tuberculosis Infection

PubMed Central

Alfonso, Salvatore; Cocozza, Martina; Porretta, Giulio Cesare; Ballell, Lluís; Rullas, Joaquin; Ortega, Fátima; De Logu, Alessandro; Agus, Emanuela; La Rosa, Valentina; Pasca, Maria Rosalia; De Rossi, Edda; Wae, Baojie; Franzblau, Scott G.; Manetti, Fabrizio; Botta, Maurizio; Biava, Mariangela

2013-01-01

1,5-Diphenyl pyrroles were previously identified as a class of compounds endowed with high in vitro efficacy against M. tuberculosis. To improve the physical chemical properties and drug-like parameters of this class of compounds, a medicinal chemistry effort was undertaken. By selecting the optimal substitution patterns for the phenyl rings at N1 and C5 and by replacing the thiomorpholine moiety with a morpholine one, a new series of compounds was produced. The replacement of the sulfur with oxygen gave compounds with lower lipophilicity and improved in vitro microsomal stability. Moreover, since the parent compound of this family has been shown to target MmpL3, mycobacterial mutants resistant to two compounds have been isolated and characterized by sequencing the mmpL3 gene; all the mutants showed point mutations in this gene. The best compound identified to date was progressed to dose-response studies in an acute murine TB infection model. The resulting ED99 of 49 mg/Kg is within the range of commonly employed tuberculosis drugs, demonstrating the potential of this chemical series. The in vitro and in vivo target validation evidence presented here adds further weight to MmpL3 as a druggable target of interest for anti-tubercular drug discovery. PMID:23437287

Synthesis, in-vitro antibacterial, antifungal, and molecular modeling of potent anti-microbial agents with a combined pyrazole and thiophene pharmacophore.

PubMed

Mabkhot, Yahia Nasser; Kaal, Nahed Ahmed; Alterary, Seham; Al-Showiman, Salim S; Barakat, Assem; Ghabbour, Hazem A; Frey, Wolfgang

2015-05-14

Ethyl 5-acetyl-4-methyl-2-(phenylamino)thiophene-3-carboxylate (2) and there derivatives 3a-c, 4, 6a-c and 9a-f were synthesized. The structure of compound 2 was deduced by 1H-NMR, 13C-NMR, FT-IR, MS, microanalysis, and single-crystal X-ray crystallography. The compound crystallized in the monoclinic system, with space group P21/c and cell coordinates a = 8.5752(16) Å, b = 21.046(4) Å, c = 8.2941(12) Å, β = 101.131(6)°, V = 1468.7(4) Å3, and Z = 4. Compounds 2, 3a-c, 4, 5a-c and 9a-f were subjected into in vitro antimicrobial activity tests. Compounds 3a and 3c were more potent than standard drug amphotericin B, showing MIC values of 23.8 ± 0.42 and 24.3 ± 0.68, respectively, against Aspergillus fumigatus while the standard drug MIC was 23.7 ± 0.1. Compound 3c was also more potent (MIC 24.8 ± 0.64) than the standard drug amphotericin B (MIC 19.7 ± 0.2) against Syncephalastrum racemosum. Compounds 4 and 9f also showed promising anti-microbial activity. Molecular modeling was performed for the most active compounds.

Bioactive Molecule from Streptomyces sp. Mitigates MDR Klebsiella pneumoniae in Zebrafish Infection Model

PubMed Central

Cheepurupalli, Lalitha; Raman, Thiagarajan; Rathore, Sudarshan S.; Ramakrishnan, Jayapradha

2017-01-01

The emergence and spread of multi-drug resistant (MDR) especially carbapenem-resistant Klebsiella pneumoniae is a major emerging threat to public health, leading to excess in mortality rate as high as 50–86%. MDR K. pneumoniae manifests all broad mechanisms of drug resistance, hence development of new drugs to treat MDR K. pneumoniae infection has become a more relevant question in the scientific community. In the present study a potential Streptomyces sp. ASK2 was isolated from rhizosphere soil of medicinal plant. The multistep HPLC purification identified the active principle exhibiting antagonistic activity against MDR K. pneumoniae. The purified compound was found to be an aromatic compound with aliphatic side chain molecule having a molecular weight of 444.43 Da. FT-IR showed the presence of OH and C=O as functional groups. The bioactive compound was further evaluated for drug induced toxicity and efficacy in adult zebrafish infection model. As this is the first study on K. pneumoniae – zebrafish model, the infectious doses to manifest sub-clinical and clinical infection were optimized. Furthermore, the virulence of K. pneumoniae in planktonic and biofilm state was studied in zebrafish. The MTT assay of ex vivo culture of zebrafish liver reveals non-toxic nature of the proposed ASK2 compound at an effective dose. Moreover, significant increase in survival rate of infected zebrafish suggests that ASK2 compound from a new strain of Streptomyces sp. was potent in mitigating MDR K. pneumoniae infection. PMID:28446900

Parallel screening of drug-like natural compounds using Caco-2 cell permeability QSAR model with applicability domain, lipophilic ligand efficiency index and shape property: A case study of HIV-1 reverse transcriptase inhibitors

NASA Astrophysics Data System (ADS)

Patel, Rikin D.; Kumar, Sivakumar Prasanth; Patel, Chirag N.; Shankar, Shetty Shilpa; Pandya, Himanshu A.; Solanki, Hitesh A.

2017-10-01

The traditional drug design strategy centrally focuses on optimizing binding affinity with the receptor target and evaluates pharmacokinetic properties at a later stage which causes high rate of attrition in clinical trials. Alternatively, parallel screening allows evaluation of these properties and affinity simultaneously. In a case study to identify leads from natural compounds with experimental HIV-1 reverse transcriptase (RT) inhibition, we integrated various computational approaches including Caco-2 cell permeability QSAR model with applicability domain (AD) to recognize drug-like natural compounds, molecular docking to study HIV-1 RT interactions and shape similarity analysis with known crystal inhibitors having characteristic butterfly-like model. Further, the lipophilic properties of the compounds refined from the process with best scores were examined using lipophilic ligand efficiency (LLE) index. Seven natural compound hits viz. baicalien, (+)-calanolide A, mniopetal F, fagaronine chloride, 3,5,8-trihydroxy-4-quinolone methyl ether derivative, nitidine chloride and palmatine, were prioritized based on LLE score which demonstrated Caco-2 well absorption labeling, encompassment in AD structural coverage, better receptor affinity, shape adaptation and permissible AlogP value. We showed that this integrative approach is successful in lead exploration of natural compounds targeted against HIV-1 RT enzyme.

Longitudinal trends and subgroup analysis in publication patterns for preclinical data of newly approved drugs.

PubMed

Köster, Ursula; Nolte, Ingo; Michel, Martin C

2016-02-01

Having observed a large variation in the number and type of original preclinical publications for newly registered drugs, we have explored whether longitudinal trends and/or factors specific for certain drugs or their manufacturers may explain such variation. Our analysis is based on 1954 articles related to 170 newly approved drugs. The number of preclinical publications per compound declined from a median of 10.5 in 1991 to 3 in 2011. A similar trend was observed for the number of in vivo studies in general, but not in the subset of in vivo studies in animal models of disease. The percentage of compounds with studies using isolated human cells or cell lines almost doubled over time from 37 to 72%. Number of publications did not exhibit major differences between compounds intended for human versus veterinary use, therapeutic areas, small molecules versus biologicals, or innovator versus follow-up compounds; however, some companies may publish fewer studies per compound than others. However, there were qualitative differences in the types of models being used depending on the therapeutic area; specifically, compounds for use in oncology very often used isolated cells and cell lines, often from human origin. We conclude that the large variation in number and type of reported preclinical data is not easily explained. We propose that pharmaceutical companies should consistently provide a comprehensive documentation of the preclinical data they generate as part of their development programs in the public domain to enable a better understanding of the drugs they intend to market.

[Screen potential CYP450 2E1 inhibitors from Chinese herbal medicine based on support vector regression and molecular docking method].

PubMed

Chen, Xi; Lu, Fang; Jiang, Lu-di; Cai, Yi-Lian; Li, Gong-Yu; Zhang, Yan-Ling

2016-07-01

Inhibition of cytochrome P450 (CYP450) enzymes is the most common reasons for drug interactions, so the study on early prediction of CYPs inhibitors can help to decrease the incidence of adverse reactions caused by drug interactions.CYP450 2E1(CYP2E1), as a key role in drug metabolism process, has broad spectrum of drug metabolism substrate. In this study, 32 CYP2E1 inhibitors were collected for the construction of support vector regression (SVR) model. The test set data were used to verify CYP2E1 quantitative models and obtain the optimal prediction model of CYP2E1 inhibitor. Meanwhile, one molecular docking program, CDOCKER, was utilized to analyze the interaction pattern between positive compounds and active pocket to establish the optimal screening model of CYP2E1 inhibitors.SVR model and molecular docking prediction model were combined to screen traditional Chinese medicine database (TCMD), which could improve the calculation efficiency and prediction accuracy. 6 376 traditional Chinese medicine (TCM) compounds predicted by SVR model were obtained, and in further verification by using molecular docking model, 247 TCM compounds with potential inhibitory activities against CYP2E1 were finally retained. Some of them have been verified by experiments. The results demonstrated that this study could provide guidance for the virtual screening of CYP450 inhibitors and the prediction of CYPs-mediated DDIs, and also provide references for clinical rational drug use. Copyright© by the Chinese Pharmaceutical Association.

Shifting from the single- to the multitarget paradigm in drug discovery

PubMed Central

Medina-Franco, José L.; Giulianotti, Marc A.; Welmaker, Gregory S.; Houghten, Richard A.

2013-01-01

Increasing evidence that several drug compounds exert their effects through interactions with multiple targets is boosting the development of research fields that challenge the data reductionism approach. In this article, we review and discuss the concepts of drug repurposing, polypharmacology, chemogenomics, phenotypic screening and highthroughput in vivo testing of mixture-based libraries in an integrated manner. These research fields offer alternatives to the current paradigm of drug discovery, from a one target–one drug model to a multiple-target approach. Furthermore, the goals of lead identification are being expanded accordingly to identify not only ‘key’ compounds that fit with a single-target ‘lock’, but also ‘master key’ compounds that favorably interact with multiple targets (i.e. operate a set of desired locks to gain access to the expected clinical effects). PMID:23340113

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.

High throughput ADME screening: practical considerations, impact on the portfolio and enabler of in silico ADME models.

PubMed

Hop, Cornelis E C A; Cole, Mark J; Davidson, Ralph E; Duignan, David B; Federico, James; Janiszewski, John S; Jenkins, Kelly; Krueger, Suzanne; Lebowitz, Rebecca; Liston, Theodore E; Mitchell, Walter; Snyder, Mark; Steyn, Stefan J; Soglia, John R; Taylor, Christine; Troutman, Matt D; Umland, John; West, Michael; Whalen, Kevin M; Zelesky, Veronica; Zhao, Sabrina X

2008-11-01

Evaluation and optimization of drug metabolism and pharmacokinetic data plays an important role in drug discovery and development and several reliable in vitro ADME models are available. Recently higher throughput in vitro ADME screening facilities have been established in order to be able to evaluate an appreciable fraction of synthesized compounds. The ADME screening process can be dissected in five distinct steps: (1) plate management of compounds in need of in vitro ADME data, (2) optimization of the MS/MS method for the compounds, (3) in vitro ADME experiments and sample clean up, (4) collection and reduction of the raw LC-MS/MS data and (5) archival of the processed ADME data. All steps will be described in detail and the value of the data on drug discovery projects will be discussed as well. Finally, in vitro ADME screening can generate large quantities of data obtained under identical conditions to allow building of reliable in silico models.

Zebrafish Heart Failure Models for the Evaluation of Chemical Probes and Drugs

PubMed Central

Monte, Aaron; Cook, James M.; Kabir, Mohd Shahjahan; Peterson, Karl P.

2013-01-01

Abstract Heart failure is a complex disease that involves genetic, environmental, and physiological factors. As a result, current medication and treatment for heart failure produces limited efficacy, and better medication is in demand. Although mammalian models exist, simple and low-cost models will be more beneficial for drug discovery and mechanistic studies of heart failure. We previously reported that aristolochic acid (AA) caused cardiac defects in zebrafish embryos that resemble heart failure. Here, we showed that cardiac troponin T and atrial natriuretic peptide were expressed at significantly higher levels in AA-treated embryos, presumably due to cardiac hypertrophy. In addition, several human heart failure drugs could moderately attenuate the AA-induced heart failure by 10%–40%, further verifying the model for drug discovery. We then developed a drug screening assay using the AA-treated zebrafish embryos and identified three compounds. Mitogen-activated protein kinase kinase inhibitor (MEK-I), an inhibitor for the MEK-1/2 known to be involved in cardiac hypertrophy and heart failure, showed nearly 60% heart failure attenuation. C25, a chalcone derivative, and A11, a phenolic compound, showed around 80% and 90% attenuation, respectively. Time course experiments revealed that, to obtain 50% efficacy, these compounds were required within different hours of AA treatment. Furthermore, quantitative polymerase chain reaction showed that C25, not MEK-I or A11, strongly suppressed inflammation. Finally, C25 and MEK-I, but not A11, could also rescue the doxorubicin-induced heart failure in zebrafish embryos. In summary, we have established two tractable heart failure models for drug discovery and three potential drugs have been identified that seem to attenuate heart failure by different mechanisms. PMID:24351044

Rapid Countermeasure Discovery against Francisella tularensis Based on a Metabolic Network Reconstruction

PubMed Central

Chaudhury, Sidhartha; Abdulhameed, Mohamed Diwan M.; Singh, Narender; Tawa, Gregory J.; D’haeseleer, Patrik M.; Zemla, Adam T.; Navid, Ali; Zhou, Carol E.; Franklin, Matthew C.; Cheung, Jonah; Rudolph, Michael J.; Love, James; Graf, John F.; Rozak, David A.; Dankmeyer, Jennifer L.; Amemiya, Kei; Daefler, Simon; Wallqvist, Anders

2013-01-01

In the future, we may be faced with the need to provide treatment for an emergent biological threat against which existing vaccines and drugs have limited efficacy or availability. To prepare for this eventuality, our objective was to use a metabolic network-based approach to rapidly identify potential drug targets and prospectively screen and validate novel small-molecule antimicrobials. Our target organism was the fully virulent Francisella tularensis subspecies tularensis Schu S4 strain, a highly infectious intracellular pathogen that is the causative agent of tularemia and is classified as a category A biological agent by the Centers for Disease Control and Prevention. We proceeded with a staggered computational and experimental workflow that used a strain-specific metabolic network model, homology modeling and X-ray crystallography of protein targets, and ligand- and structure-based drug design. Selected compounds were subsequently filtered based on physiological-based pharmacokinetic modeling, and we selected a final set of 40 compounds for experimental validation of antimicrobial activity. We began screening these compounds in whole bacterial cell-based assays in biosafety level 3 facilities in the 20th week of the study and completed the screens within 12 weeks. Six compounds showed significant growth inhibition of F. tularensis, and we determined their respective minimum inhibitory concentrations and mammalian cell cytotoxicities. The most promising compound had a low molecular weight, was non-toxic, and abolished bacterial growth at 13 µM, with putative activity against pantetheine-phosphate adenylyltransferase, an enzyme involved in the biosynthesis of coenzyme A, encoded by gene coaD. The novel antimicrobial compounds identified in this study serve as starting points for lead optimization, animal testing, and drug development against tularemia. Our integrated in silico/in vitro approach had an overall 15% success rate in terms of active versus tested compounds over an elapsed time period of 32 weeks, from pathogen strain identification to selection and validation of novel antimicrobial compounds. PMID:23704901

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

PubMed

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

2017-10-02

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

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.

Predicting targets of compounds against neurological diseases using cheminformatic methodology

NASA Astrophysics Data System (ADS)

Nikolic, Katarina; Mavridis, Lazaros; Bautista-Aguilera, Oscar M.; Marco-Contelles, José; Stark, Holger; do Carmo Carreiras, Maria; Rossi, Ilaria; Massarelli, Paola; Agbaba, Danica; Ramsay, Rona R.; Mitchell, John B. O.

2015-02-01

Recently developed multi-targeted ligands are novel drug candidates able to interact with monoamine oxidase A and B; acetylcholinesterase and butyrylcholinesterase; or with histamine N-methyltransferase and histamine H3-receptor (H3R). These proteins are drug targets in the treatment of depression, Alzheimer's disease, obsessive disorders, and Parkinson's disease. A probabilistic method, the Parzen-Rosenblatt window approach, was used to build a "predictor" model using data collected from the ChEMBL database. The model can be used to predict both the primary pharmaceutical target and off-targets of a compound based on its structure. Molecular structures were represented based on the circular fingerprint methodology. The same approach was used to build a "predictor" model from the DrugBank dataset to determine the main pharmacological groups of the compound. The study of off-target interactions is now recognised as crucial to the understanding of both drug action and toxicology. Primary pharmaceutical targets and off-targets for the novel multi-target ligands were examined by use of the developed cheminformatic method. Several multi-target ligands were selected for further study, as compounds with possible additional beneficial pharmacological activities. The cheminformatic targets identifications were in agreement with four 3D-QSAR (H3R/D1R/D2R/5-HT2aR) models and by in vitro assays for serotonin 5-HT1a and 5-HT2a receptor binding of the most promising ligand ( 71/MBA-VEG8).

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

PubMed

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

2016-07-06

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

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

PubMed Central

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

2016-01-01

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

A review of QSAR studies to discover new drug-like compounds actives against leishmaniasis and trypanosomiasis.

PubMed

Castillo-Garit, Juan Alberto; Abad, Concepción; Rodríguez-Borges, J Enrique; Marrero-Ponce, Yovani; Torrens, Francisco

2012-01-01

The neglected tropical diseases (NTDs) affect more than one billion people (one-sixth of the world's population) and occur primarily in undeveloped countries in sub-Saharan Africa, Asia, and Latin America. Available drugs for these diseases are decades old and present an important number of limitations, especially high toxicity and, more recently, the emergence of drug resistance. In the last decade several Quantitative Structure-Activity Relationship (QSAR) studies have been developed in order to identify new organic compounds with activity against the parasites responsible for these diseases, which are reviewed in this paper. The topics summarized in this work are: 1) QSAR studies to identify new organic compounds actives against Chaga's disease; 2) Development of QSAR studies to discover new antileishmanial drusg; 3) Computational studies to identify new drug-like compounds against human African trypanosomiasis. Each topic include the general characteristics, epidemiology and chemotherapy of the disease as well as the main QSAR approaches to discovery/identification of new actives compounds for the corresponding neglected disease. The last section is devoted to a new approach know as multi-target QSAR models developed for antiparasitic drugs specifically those actives against trypanosomatid parasites. At present, as a result of these QSAR studies several promising compounds, active against these parasites, are been indentify. However, more efforts will be required in the future to develop more selective (specific) useful drugs.

Compartmental transport model of microbicide delivery by an intravaginal ring

PubMed Central

Geonnotti, Anthony R.; Katz, David F.

2010-01-01

Topical antimicrobials, or microbicides, are being developed to prevent HIV transmission through local, mucosal delivery of antiviral compounds. While hydrogel vehicles deliver the majority of current microbicide products, intravaginal rings (IVRs) are an alternative microbicide modality in preclinical development. IVRs provide a long-term dosing alternative to hydrogel use, and might provide improved user adherence. IVR efficacy requires sustained delivery of antiviral compounds to the entire vaginal compartment. A two-dimensional, compartmental vaginal drug transport model was created to evaluate the delivery of drugs from an intravaginal ring. The model utilized MRI-derived ring geometry and location, experimentally defined ring fluxes and vaginal fluid velocities, and biophysically relevant transport theory. Model outputs indicated the presence of potentially inhibitory concentrations of antiviral compounds along the entire vaginal canal within 24 hours following IVR insertion. Distributions of inhibitory concentrations of antiviral compounds were substantially influenced by vaginal fluid flow and production, while showing little change due to changes in diffusion coefficients or ring fluxes. Additionally, model results were predictive of in vivo concentrations obtained in clinical trials. Overall, this analysis initiates a mechanistic computational framework, heretofore missing, to understand and evaluate the potential of IVRs for effective delivery of antiviral compounds. PMID:20222027

Effective screening strategy using ensembled pharmacophore models combined with cascade docking: application to p53-MDM2 interaction inhibitors.

PubMed

Xue, Xin; Wei, Jin-Lian; Xu, Li-Li; Xi, Mei-Yang; Xu, Xiao-Li; Liu, Fang; Guo, Xiao-Ke; Wang, Lei; Zhang, Xiao-Jin; Zhang, Ming-Ye; Lu, Meng-Chen; Sun, Hao-Peng; You, Qi-Dong

2013-10-28

Protein-protein interactions (PPIs) play a crucial role in cellular function and form the backbone of almost all biochemical processes. In recent years, protein-protein interaction inhibitors (PPIIs) have represented a treasure trove of potential new drug targets. Unfortunately, there are few successful drugs of PPIIs on the market. Structure-based pharmacophore (SBP) combined with docking has been demonstrated as a useful Virtual Screening (VS) strategy in drug development projects. However, the combination of target complexity and poor binding affinity prediction has thwarted the application of this strategy in the discovery of PPIIs. Here we report an effective VS strategy on p53-MDM2 PPI. First, we built a SBP model based on p53-MDM2 complex cocrystal structures. The model was then simplified by using a Receptor-Ligand complex-based pharmacophore model considering the critical binding features between MDM2 and its small molecular inhibitors. Cascade docking was subsequently applied to improve the hit rate. Based on this strategy, we performed VS on NCI and SPECS databases and successfully discovered 6 novel compounds from 15 hits with the best, compound 1 (NSC 5359), K(i) = 180 ± 50 nM. These compounds can serve as lead compounds for further optimization.

Identification of Ideal Multi-targeting Bioactive Compounds Against Mur Ligases of Enterobacter aerogenes and Its Binding Mechanism in Comparison with Chemical Inhibitors.

PubMed

Chakkyarath, Vijina; Natarajan, Jeyakumar

2017-10-31

Enterobacter aerogenes have been reported as important opportunistic and multi-resistant bacterial pathogens for humans during the last three decades in hospital wards. The emergence of drug-resistant E. aerogenes demands the need for developing new drugs. Peptidoglycan is an important component of the cell wall of bacteria and the peptidoglycan biochemical pathway is considered as the best source of antibacterial targets. Within this pathway, four Mur ligases MurC, MurD, MurE, and MurF are responsible for the successive additions of L-alanine and suitable targets for developing novel antibacterial drugs. As an inference from this fact, we modeled the three-dimensional structure of above Mur ligases using best template structures available in PDB and analyzed its common binding features. Structural refinement and energy minimization of the predicted Mur ligases models is also being done using molecular dynamics studies. The models of Mur ligases were further investigated for in silico docking studies using bioactive plant compounds from the literature. Interestingly, these results indicate that four plant compounds Isojuripidine, Atroviolacegenin, Porrigenin B, and Nummularogenin showing better docking results in terms of binding energy and number of hydrogen bonds. All these four compounds are spirostan-based compounds with differences in side chains and the amino acid such as ASN, LYS, THR, HIS, ARG (polar) and PHE, GLY, VAL, ALA, MET (non-polar) playing active role in binding site of all four Mur ligases. Overall, in the predicted model, the four plant compounds with its binding features could pave way to design novel multi-targeted antibacterial plant-based bioactive compounds specific to Mur ligases for the treatment of Enterobacter infections.

Adverse drug reaction prediction using scores produced by large-scale drug-protein target docking on high-performance computing machines.

PubMed

LaBute, Montiago X; Zhang, Xiaohua; Lenderman, Jason; Bennion, Brian J; Wong, Sergio E; Lightstone, Felice C

2014-01-01

Late-stage or post-market identification of adverse drug reactions (ADRs) is a significant public health issue and a source of major economic liability for drug development. Thus, reliable in silico screening of drug candidates for possible ADRs would be advantageous. In this work, we introduce a computational approach that predicts ADRs by combining the results of molecular docking and leverages known ADR information from DrugBank and SIDER. We employed a recently parallelized version of AutoDock Vina (VinaLC) to dock 906 small molecule drugs to a virtual panel of 409 DrugBank protein targets. L1-regularized logistic regression models were trained on the resulting docking scores of a 560 compound subset from the initial 906 compounds to predict 85 side effects, grouped into 10 ADR phenotype groups. Only 21% (87 out of 409) of the drug-protein binding features involve known targets of the drug subset, providing a significant probe of off-target effects. As a control, associations of this drug subset with the 555 annotated targets of these compounds, as reported in DrugBank, were used as features to train a separate group of models. The Vina off-target models and the DrugBank on-target models yielded comparable median area-under-the-receiver-operating-characteristic-curves (AUCs) during 10-fold cross-validation (0.60-0.69 and 0.61-0.74, respectively). Evidence was found in the PubMed literature to support several putative ADR-protein associations identified by our analysis. Among them, several associations between neoplasm-related ADRs and known tumor suppressor and tumor invasiveness marker proteins were found. A dual role for interstitial collagenase in both neoplasms and aneurysm formation was also identified. These associations all involve off-target proteins and could not have been found using available drug/on-target interaction data. This study illustrates a path forward to comprehensive ADR virtual screening that can potentially scale with increasing number of CPUs to tens of thousands of protein targets and millions of potential drug candidates.

The Microphysiology Systems Database for Analyzing and Modeling Compound Interactions with Human and Animal Organ Models

PubMed Central

Vernetti, Lawrence; Bergenthal, Luke; Shun, Tong Ying; Taylor, D. Lansing

2016-01-01

Abstract Microfluidic human organ models, microphysiology systems (MPS), are currently being developed as predictive models of drug safety and efficacy in humans. To design and validate MPS as predictive of human safety liabilities requires safety data for a reference set of compounds, combined with in vitro data from the human organ models. To address this need, we have developed an internet database, the MPS database (MPS-Db), as a powerful platform for experimental design, data management, and analysis, and to combine experimental data with reference data, to enable computational modeling. The present study demonstrates the capability of the MPS-Db in early safety testing using a human liver MPS to relate the effects of tolcapone and entacapone in the in vitro model to human in vivo effects. These two compounds were chosen to be evaluated as a representative pair of marketed drugs because they are structurally similar, have the same target, and were found safe or had an acceptable risk in preclinical and clinical trials, yet tolcapone induced unacceptable levels of hepatotoxicity while entacapone was found to be safe. Results demonstrate the utility of the MPS-Db as an essential resource for relating in vitro organ model data to the multiple biochemical, preclinical, and clinical data sources on in vivo drug effects. PMID:28781990

A Chemoinformatics Approach to the Discovery of Lead-Like Molecules from Marine and Microbial Sources En Route to Antitumor and Antibiotic Drugs

PubMed Central

Pereira, Florbela; Latino, Diogo A. R. S.; Gaudêncio, Susana P.

2014-01-01

The comprehensive information of small molecules and their biological activities in the PubChem database allows chemoinformatic researchers to access and make use of large-scale biological activity data to improve the precision of drug profiling. A Quantitative Structure–Activity Relationship approach, for classification, was used for the prediction of active/inactive compounds relatively to overall biological activity, antitumor and antibiotic activities using a data set of 1804 compounds from PubChem. Using the best classification models for antibiotic and antitumor activities a data set of marine and microbial natural products from the AntiMarin database were screened—57 and 16 new lead compounds for antibiotic and antitumor drug design were proposed, respectively. All compounds proposed by our approach are classified as non-antibiotic and non-antitumor compounds in the AntiMarin database. Recently several of the lead-like compounds proposed by us were reported as being active in the literature. PMID:24473174

Perspectives on Zebrafish Models of Hallucinogenic Drugs and Related Psychotropic Compounds

PubMed Central

2013-01-01

Among different classes of psychotropic drugs, hallucinogenic agents exert one of the most prominent effects on human and animal behaviors, markedly altering sensory, motor, affective, and cognitive responses. The growing clinical and preclinical interest in psychedelic, dissociative, and deliriant hallucinogens necessitates novel translational, sensitive, and high-throughput in vivo models and screens. Primate and rodent models have been traditionally used to study cellular mechanisms and neural circuits of hallucinogenic drugs’ action. The utility of zebrafish (Danio rerio) in neuroscience research is rapidly growing due to their high physiological and genetic homology to humans, ease of genetic manipulation, robust behaviors, and cost effectiveness. Possessing a fully characterized genome, both adult and larval zebrafish are currently widely used for in vivo screening of various psychotropic compounds, including hallucinogens and related drugs. Recognizing the growing importance of hallucinogens in biological psychiatry, here we discuss hallucinogenic-induced phenotypes in zebrafish and evaluate their potential as efficient preclinical models of drug-induced states in humans. PMID:23883191

Formulation Studies of InhA inhibitors and Combination therapy to improve efficacy against Mycobacterium tuberculosis

PubMed Central

Knudson, Susan E.; Cummings, Jason E.; Bommineni, Gopal R.; Pan, Pan; Tonge, Peter J.; Slayden, Richard A.

2016-01-01

Summary Previously, structure-based drug design was used to develop substituted diphenyl ethers with potency against the Mycobacterium tuberculosis (Mtb) enoyl-ACP reductase (InhA), however, the highly lipophilic centroid compound, SB-PT004, lacked sufficient efficacy in the acute murine Mtb infection model. A next generation series of compounds were designed with improved specificity, potency against InhA, and reduced cytotoxicity in vitro, but these compounds also had limited solubility. Accordingly, solubility and pharmacokinetics studies were performed to develop formulations for this class and other experimental drug candidates with high logP values often encountered in drug discovery. Lead diphenyl ethers were formulated in co-solvent and Self-Dispersing Lipid Formulations (SDLFs) and evaluated in a rapid murine Mtb infection model that assesses dissemination to and bacterial burden in the spleen. In vitro synergy studies were performed with the lead diphenyl ether compounds, SB-PT070 and SB-PT091, and rifampin (RIF), which demonstrated an additive effect, and that guided the in vivo studies. Combinatorial therapy in vivo studies with these compounds delivered in our Self-Micro Emulsifying Drug Delivery System (SMEDDS) resulted in an additional 1.4 log10 CFU reduction in the spleen of animals co-treated with SB-PT091 and RIF and an additional 1.7 log10 reduction in the spleen with animals treated with both SB-PT070 and RIF. PMID:27865404

POTENT INHIBITORS OF HUMAN ORGANIC ANION TRANSPORTERS 1 AND 3 FROM CLINICAL DRUG LIBRARIES: DISCOVERY AND MOLECULAR CHARACTERIZATION

PubMed Central

Duan, Peng; Li, Shanshan; Ai, Ni; Hu, Longqin; Welsh, William J.; You, Guofeng

2012-01-01

Transporter-mediated drug-drug interactions in the kidney dramatically influence the pharmacokinetics and other clinical effects of drugs. Human organic anion transporters 1 (hOAT1) and 3 (hOAT3) are the major transporters in the basolateral membrane of kidney proximal tubules, mediating the rate-limiting step in the elimination of a broad spectrum of drugs. In the present study, we screened two clinical drug libraries against hOAT1 and hOAT3. Of the 727 compounds screened, 92 compounds inhibited hOAT1 and 262 compounds inhibited hOAT3. When prioritized based on the peak unbound plasma concentrations of these compounds, three inhibitors for hOAT1 and seven inhibitors for hOAT3 were subsequently identified with high inhibitory potency (>95%). Computational analyses revealed that inhibitors and non-inhibitors can be differentiated from each other on the basis of several physico-chemical features, including: number of hydrogen-bond donors, number of rotatable bonds, and topological polar surface area (TPSA) for hOAT1; and molecular weight, number of hydrogen-bond donors and acceptors, TPSA, partition coefficient (Log P7.4), and polarizability for hOAT3. Pharmacophore modeling identified two common structural features associated with inhibitors for hOAT1 and hOAT3, viz., an anionic hydrogen-bond acceptor atom, and an aromatic center separated by ~5.7 Å. Such model provides mechanistic insights for predicting new OAT inhibitors. PMID:22973893

Potent inhibitors of human organic anion transporters 1 and 3 from clinical drug libraries: discovery and molecular characterization.

PubMed

Duan, Peng; Li, Shanshan; Ai, Ni; Hu, Longqin; Welsh, William J; You, Guofeng

2012-11-05

Transporter-mediated drug-drug interactions in the kidney dramatically influence the pharmacokinetics and other clinical effects of drugs. Human organic anion transporters 1 (hOAT1) and 3 (hOAT3) are the major transporters in the basolateral membrane of kidney proximal tubules, mediating the rate-limiting step in the elimination of a broad spectrum of drugs. In the present study, we screened two clinical drug libraries against hOAT1 and hOAT3. Of the 727 compounds screened, 92 compounds inhibited hOAT1 and 262 compounds inhibited hOAT3. When prioritized based on the peak unbound plasma concentrations of these compounds, three inhibitors for hOAT1 and seven inhibitors for hOAT3 were subsequently identified with high inhibitory potency (>95%). Computational analyses revealed that inhibitors and noninhibitors can be differentiated from each other on the basis of several physicochemical features, including number of hydrogen-bond donors, number of rotatable bonds, and topological polar surface area (TPSA) for hOAT1; and molecular weight, number of hydrogen-bond donors and acceptors, TPSA, partition coefficient (log P(7.4)), and polarizability for hOAT3. Pharmacophore modeling identified two common structural features associated with inhibitors for hOAT1 and hOAT3, viz., an anionic hydrogen-bond acceptor atom, and an aromatic center separated by ∼5.7 Å. Such model provides mechanistic insights for predicting new OAT inhibitors.

Antitumor activity of a novel and orally available inhibitor of serine palmitoyltransferase

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

Yaguchi, Masahiro; Shibata, Sachio; Satomi, Yoshinori

Metabolic reprogramming is an essential hallmark of neoplasia. Therefore, targeting cancer metabolism, including lipid synthesis, has attracted much interest in recent years. Serine palmitoyltransferase (SPT) plays a key role in the initial and rate-limiting step of de novo sphingolipid biosynthesis, and inhibiting SPT activity prevents the proliferation of certain cancer cells. Here, we identified a novel and orally available SPT inhibitor, compound-2. Compound-2 showed an anti-proliferative effect in several cancer cell models, reducing the levels of the sphingolipids ceramide and sphingomyelin. In the presence of compound-2, exogenously added S1P partially compensated the intracellular sphingolipid levels through the salvage pathway bymore » partially rescuing compound-2-induced cytotoxicity. This suggested that the mechanism underlying the anti-proliferative effect of compound-2 involved the reduction of sphingolipid levels. Indeed, compound-2 promoted multinuclear formation with reduced endogenous sphingomyelin levels specifically in a compound-2-sensitive cell line, indicating that the effect was induced by sphingolipid reduction. Furthermore, compound-2 showed potent antitumor activity without causing significant body weight loss in the PL-21 acute myeloid leukemia mouse xenograft model. Therefore, SPT may be an attractive therapeutic anti-cancer drug target for which compound-2 may be a promising new drug. - Highlights: • We discovered compound-2, a novel and orally available SPT inhibitor. • Compound-2 was cytotoxic against PL-21 acute myeloid leukemia cells. • Compound-2 showed antitumor activity in the PL-21 mouse xenograft model.« less

Applied metabolomics in drug discovery.

PubMed

Cuperlovic-Culf, M; Culf, A S

2016-08-01

The metabolic profile is a direct signature of phenotype and biochemical activity following any perturbation. Metabolites are small molecules present in a biological system including natural products as well as drugs and their metabolism by-products depending on the biological system studied. Metabolomics can provide activity information about possible novel drugs and drug scaffolds, indicate interesting targets for drug development and suggest binding partners of compounds. Furthermore, metabolomics can be used for the discovery of novel natural products and in drug development. Metabolomics can enhance the discovery and testing of new drugs and provide insight into the on- and off-target effects of drugs. This review focuses primarily on the application of metabolomics in the discovery of active drugs from natural products and the analysis of chemical libraries and the computational analysis of metabolic networks. Metabolomics methodology, both experimental and analytical is fast developing. At the same time, databases of compounds are ever growing with the inclusion of more molecular and spectral information. An increasing number of systems are being represented by very detailed metabolic network models. Combining these experimental and computational tools with high throughput drug testing and drug discovery techniques can provide new promising compounds and leads.

Evidence-based selection of training compounds for use in the mechanism-based integrated prediction of drug-induced liver injury in man.

PubMed

Dragovic, Sanja; Vermeulen, Nico P E; Gerets, Helga H; Hewitt, Philip G; Ingelman-Sundberg, Magnus; Park, B Kevin; Juhila, Satu; Snoeys, Jan; Weaver, Richard J

2016-12-01

The current test systems employed by pharmaceutical industry are poorly predictive for drug-induced liver injury (DILI). The 'MIP-DILI' project addresses this situation by the development of innovative preclinical test systems which are both mechanism-based and of physiological, pharmacological and pathological relevance to DILI in humans. An iterative, tiered approach with respect to test compounds, test systems, bioanalysis and systems analysis is adopted to evaluate existing models and develop new models that can provide validated test systems with respect to the prediction of specific forms of DILI and further elucidation of mechanisms. An essential component of this effort is the choice of compound training set that will be used to inform refinement and/or development of new model systems that allow prediction based on knowledge of mechanisms, in a tiered fashion. In this review, we focus on the selection of MIP-DILI training compounds for mechanism-based evaluation of non-clinical prediction of DILI. The selected compounds address both hepatocellular and cholestatic DILI patterns in man, covering a broad range of pharmacologies and chemistries, and taking into account available data on potential DILI mechanisms (e.g. mitochondrial injury, reactive metabolites, biliary transport inhibition, and immune responses). Known mechanisms by which these compounds are believed to cause liver injury have been described, where many if not all drugs in this review appear to exhibit multiple toxicological mechanisms. Thus, the training compounds selection offered a valuable tool to profile DILI mechanisms and to interrogate existing and novel in vitro systems for the prediction of human DILI.

A combined Fisher and Laplacian score for feature selection in QSAR based drug design using compounds with known and unknown activities.

PubMed

Valizade Hasanloei, Mohammad Amin; Sheikhpour, Razieh; Sarram, Mehdi Agha; Sheikhpour, Elnaz; Sharifi, Hamdollah

2018-02-01

Quantitative structure-activity relationship (QSAR) is an effective computational technique for drug design that relates the chemical structures of compounds to their biological activities. Feature selection is an important step in QSAR based drug design to select the most relevant descriptors. One of the most popular feature selection methods for classification problems is Fisher score which aim is to minimize the within-class distance and maximize the between-class distance. In this study, the properties of Fisher criterion were extended for QSAR models to define the new distance metrics based on the continuous activity values of compounds with known activities. Then, a semi-supervised feature selection method was proposed based on the combination of Fisher and Laplacian criteria which exploits both compounds with known and unknown activities to select the relevant descriptors. To demonstrate the efficiency of the proposed semi-supervised feature selection method in selecting the relevant descriptors, we applied the method and other feature selection methods on three QSAR data sets such as serine/threonine-protein kinase PLK3 inhibitors, ROCK inhibitors and phenol compounds. The results demonstrated that the QSAR models built on the selected descriptors by the proposed semi-supervised method have better performance than other models. This indicates the efficiency of the proposed method in selecting the relevant descriptors using the compounds with known and unknown activities. The results of this study showed that the compounds with known and unknown activities can be helpful to improve the performance of the combined Fisher and Laplacian based feature selection methods.

A combined Fisher and Laplacian score for feature selection in QSAR based drug design using compounds with known and unknown activities

NASA Astrophysics Data System (ADS)

Valizade Hasanloei, Mohammad Amin; Sheikhpour, Razieh; Sarram, Mehdi Agha; Sheikhpour, Elnaz; Sharifi, Hamdollah

2018-02-01

Quantitative structure-activity relationship (QSAR) is an effective computational technique for drug design that relates the chemical structures of compounds to their biological activities. Feature selection is an important step in QSAR based drug design to select the most relevant descriptors. One of the most popular feature selection methods for classification problems is Fisher score which aim is to minimize the within-class distance and maximize the between-class distance. In this study, the properties of Fisher criterion were extended for QSAR models to define the new distance metrics based on the continuous activity values of compounds with known activities. Then, a semi-supervised feature selection method was proposed based on the combination of Fisher and Laplacian criteria which exploits both compounds with known and unknown activities to select the relevant descriptors. To demonstrate the efficiency of the proposed semi-supervised feature selection method in selecting the relevant descriptors, we applied the method and other feature selection methods on three QSAR data sets such as serine/threonine-protein kinase PLK3 inhibitors, ROCK inhibitors and phenol compounds. The results demonstrated that the QSAR models built on the selected descriptors by the proposed semi-supervised method have better performance than other models. This indicates the efficiency of the proposed method in selecting the relevant descriptors using the compounds with known and unknown activities. The results of this study showed that the compounds with known and unknown activities can be helpful to improve the performance of the combined Fisher and Laplacian based feature selection methods.

Developing Enhanced Blood–Brain Barrier Permeability Models: Integrating External Bio-Assay Data in QSAR Modeling

PubMed Central

Wang, Wenyi; Kim, Marlene T.; Sedykh, Alexander

2015-01-01

Purpose Experimental Blood–Brain Barrier (BBB) permeability models for drug molecules are expensive and time-consuming. As alternative methods, several traditional Quantitative Structure-Activity Relationship (QSAR) models have been developed previously. In this study, we aimed to improve the predictivity of traditional QSAR BBB permeability models by employing relevant public bio-assay data in the modeling process. Methods We compiled a BBB permeability database consisting of 439 unique compounds from various resources. The database was split into a modeling set of 341 compounds and a validation set of 98 compounds. Consensus QSAR modeling workflow was employed on the modeling set to develop various QSAR models. A five-fold cross-validation approach was used to validate the developed models, and the resulting models were used to predict the external validation set compounds. Furthermore, we used previously published membrane transporter models to generate relevant transporter profiles for target compounds. The transporter profiles were used as additional biological descriptors to develop hybrid QSAR BBB models. Results The consensus QSAR models have R2=0.638 for fivefold cross-validation and R2=0.504 for external validation. The consensus model developed by pooling chemical and transporter descriptors showed better predictivity (R2=0.646 for five-fold cross-validation and R2=0.526 for external validation). Moreover, several external bio-assays that correlate with BBB permeability were identified using our automatic profiling tool. Conclusions The BBB permeability models developed in this study can be useful for early evaluation of new compounds (e.g., new drug candidates). The combination of chemical and biological descriptors shows a promising direction to improve the current traditional QSAR models. PMID:25862462

Human intestinal transporter database: QSAR modeling and virtual profiling of drug uptake, efflux and interactions.

PubMed

Sedykh, Alexander; Fourches, Denis; Duan, Jianmin; Hucke, Oliver; Garneau, Michel; Zhu, Hao; Bonneau, Pierre; Tropsha, Alexander

2013-04-01

Membrane transporters mediate many biological effects of chemicals and play a major role in pharmacokinetics and drug resistance. The selection of viable drug candidates among biologically active compounds requires the assessment of their transporter interaction profiles. Using public sources, we have assembled and curated the largest, to our knowledge, human intestinal transporter database (>5,000 interaction entries for >3,700 molecules). This data was used to develop thoroughly validated classification Quantitative Structure-Activity Relationship (QSAR) models of transport and/or inhibition of several major transporters including MDR1, BCRP, MRP1-4, PEPT1, ASBT, OATP2B1, OCT1, and MCT1. QSAR models have been developed with advanced machine learning techniques such as Support Vector Machines, Random Forest, and k Nearest Neighbors using Dragon and MOE chemical descriptors. These models afforded high external prediction accuracies of 71-100% estimated by 5-fold external validation, and showed hit retrieval rates with up to 20-fold enrichment in the virtual screening of DrugBank compounds. The compendium of predictive QSAR models developed in this study can be used for virtual profiling of drug candidates and/or environmental agents with the optimal transporter profiles.

Trainable structure-activity relationship model for virtual screening of CYP3A4 inhibition.

PubMed

Didziapetris, Remigijus; Dapkunas, Justas; Sazonovas, Andrius; Japertas, Pranas

2010-11-01

A new structure-activity relationship model predicting the probability for a compound to inhibit human cytochrome P450 3A4 has been developed using data for >800 compounds from various literature sources and tested on PubChem screening data. Novel GALAS (Global, Adjusted Locally According to Similarity) modeling methodology has been used, which is a combination of baseline global QSAR model and local similarity based corrections. GALAS modeling method allows forecasting the reliability of prediction thus defining the model applicability domain. For compounds within this domain the statistical results of the final model approach the data consistency between experimental data from literature and PubChem datasets with the overall accuracy of 89%. However, the original model is applicable only for less than a half of PubChem database. Since the similarity correction procedure of GALAS modeling method allows straightforward model training, the possibility to expand the applicability domain has been investigated. Experimental data from PubChem dataset served as an example of in-house high-throughput screening data. The model successfully adapted itself to both data classified using the same and different IC₅₀ threshold compared with the training set. In addition, adjustment of the CYP3A4 inhibition model to compounds with a novel chemical scaffold has been demonstrated. The reported GALAS model is proposed as a useful tool for virtual screening of compounds for possible drug-drug interactions even prior to the actual synthesis.

Defense Technology Area Plan.

DTIC Science & Technology

1996-05-01

Nonpolluting fouling-resistant or fouling-release hull coatings, which exploit low- surface-energy and surface-oriented perfluorinated alkyl compounds ... compounds Novel radioprotective drugs Fieldable biodosimetry capability Modeling for casualties in NBC environments Combined injury treatment protocols...Viral Agents, including encephalomyelitis viruses, variola (smallpox), and filoviridae (e.g., Ebola virus). • Neuroactive Compounds , including

Pharmacokinetic parameters explain the therapeutic activity of antimicrobial agents in a silkworm infection model.

PubMed

Paudel, Atmika; Panthee, Suresh; Urai, Makoto; Hamamoto, Hiroshi; Ohwada, Tomohiko; Sekimizu, Kazuhisa

2018-01-25

Poor pharmacokinetic parameters are a major reason for the lack of therapeutic activity of some drug candidates. Determining the pharmacokinetic parameters of drug candidates at an early stage of development requires an inexpensive animal model with few associated ethical issues. In this study, we used the silkworm infection model to perform structure-activity relationship studies of an antimicrobial agent, GPI0039, a novel nitrofuran dichloro-benzyl ester, and successfully identified compound 5, a nitrothiophene dichloro-benzyl ester, as a potent antimicrobial agent with superior therapeutic activity in the silkworm infection model. Further, we compared the pharmacokinetic parameters of compound 5 with a nitrothiophene benzyl ester lacking chlorine, compound 7, that exerted similar antimicrobial activity but had less therapeutic activity in silkworms, and examined the metabolism of these antimicrobial agents in human liver fractions in vitro. Compound 5 had appropriate pharmacokinetic parameters, such as an adequate half-life, slow clearance, large area under the curve, low volume of distribution, and long mean residence time, compared with compound 7, and was slowly metabolized by human liver fractions. These findings suggest that the therapeutic effectiveness of an antimicrobial agent in the silkworms reflects appropriate pharmacokinetic properties.

CANDO and the infinite drug discovery frontier

PubMed Central

Minie, Mark; Chopra, Gaurav; Sethi, Geetika; Horst, Jeremy; White, George; Roy, Ambrish; Hatti, Kaushik; Samudrala, Ram

2014-01-01

The Computational Analysis of Novel Drug Opportunities (CANDO) platform (http://protinfo.org/cando) uses similarity of compound–proteome interaction signatures to infer homology of compound/drug behavior. We constructed interaction signatures for 3733 human ingestible compounds covering 48,278 protein structures mapping to 2030 indications based on basic science methodologies to predict and analyze protein structure, function, and interactions developed by us and others. Our signature comparison and ranking approach yielded benchmarking accuracies of 12–25% for 1439 indications with at least two approved compounds. We prospectively validated 49/82 ‘high value’ predictions from nine studies covering seven indications, with comparable or better activity to existing drugs, which serve as novel repurposed therapeutics. Our approach may be generalized to compounds beyond those approved by the FDA, and can also consider mutations in protein structures to enable personalization. Our platform provides a holistic multiscale modeling framework of complex atomic, molecular, and physiological systems with broader applications in medicine and engineering. PMID:24980786

An Improved Small-Molecule Inhibitor of FtsZ with Superior In Vitro Potency, Drug-Like Properties, and In Vivo Efficacy

PubMed Central

Baker, Nicola; Bennett, James M.; Berry, Joanne; Collins, Ian; Czaplewski, Lloyd G.; Logan, Alastair; Macdonald, Rebecca; MacLeod, Leanne; Peasley, Hilary; Mitchell, Jeffrey P.; Nayal, Narendra; Yadav, Anju; Srivastava, Anil; Haydon, David J.

2013-01-01

The bacterial cell division protein FtsZ is an attractive target for small-molecule antibacterial drug discovery. Derivatives of 3-methoxybenzamide, including compound PC190723, have been reported to be potent and selective antistaphylococcal agents which exert their effects through the disruption of intracellular FtsZ function. Here, we report the further optimization of 3-methoxybenzamide derivatives towards a drug candidate. The in vitro and in vivo characterization of a more advanced lead compound, designated compound 1, is described. Compound 1 was potently antibacterial, with an average MIC of 0.12 μg/ml against all staphylococcal species, including methicillin- and multidrug-resistant Staphylococcus aureus and Staphylococcus epidermidis. Compound 1 inhibited an S. aureus strain carrying the G196A mutation in FtsZ, which confers resistance to PC190723. Like PC190723, compound 1 acted on whole bacterial cells by blocking cytokinesis. No interactions between compound 1 and a diverse panel of antibiotics were measured in checkerboard experiments. Compound 1 displayed suitable in vitro pharmaceutical properties and a favorable in vivo pharmacokinetic profile following intravenous and oral administration, with a calculated bioavailability of 82.0% in mice. Compound 1 demonstrated efficacy in a murine model of systemic S. aureus infection and caused a significant decrease in the bacterial load in the thigh infection model. A greater reduction in the number of S. aureus cells recovered from infected thighs, equivalent to 3.68 log units, than in those recovered from controls was achieved using a succinate prodrug of compound 1, which was designated compound 2. In summary, optimized derivatives of 3-methoxybenzamide may yield a first-in-class FtsZ inhibitor for the treatment of antibiotic-resistant staphylococcal infections. PMID:23114779

[Discussion on efficacy evaluation thought and method for innovation medicine of Chinese herbal compound formula based on clinical application characteristics].

PubMed

Sun, Jian-Ning; Sun, Wen-Yan; Dong, Shi-Fen

2017-03-01

The Chinese herbal compound formula preparation was made based on theory of Chinese medicine, which was confirmed by long period clinical application, and with multi-compound and multi-target characteristics. During the exploitation process of innovation medicine of Chinese herbal compound formula, selecting and speeding up the research development of drugs with clinical value shall be paid more attention, and as request of rules involved in new drug research and development, the whole process management should be carried out, including project evaluation, manufacturing process determination, establishment of quality control standards, evaluation for pharmacological and toxic effect, as well as new drug application process. This reviews was aimed to give some proposals for pharmacodynamics research methods involved in exploration of Chinese herbal compound formula preparation, including： ①the endpoint criteria should meet the clinical attribution of new drugs; ②the pre-clinical pharmacodynamics evaluation should be carried on appropriate animal models according to the characteristics of diagnosis and therapy of Chinese medicine and observation indexes; ③during the innovation of drug for infants and children, information on drug action conforming to physiological characteristics of infants and children should be supplied, and the pharmacodynamics and toxicology research shall be conducted in immature rats according to the body weight of children. In a summary, the clinical application characteristics are the important criteria for evaluation of pharmacological effect of innovation medicine of Chinese herbal compound formula. Copyright© by the Chinese Pharmaceutical Association.

Human stem cells and drug screening: opportunities and challenges.

PubMed

Ebert, Allison D; Svendsen, Clive N

2010-05-01

High-throughput screening technologies are widely used in the early stages of drug discovery to rapidly evaluate the properties of thousands of compounds. However, they generally rely on testing compound libraries on highly proliferative immortalized or cancerous cell lines, which do not necessarily provide an accurate indication of the effects of compounds in normal human cells or the specific cell type under study. Recent advances in stem cell technology have the potential to allow production of a virtually limitless supply of normal human cells that can be differentiated into any specific cell type. Moreover, using induced pluripotent stem cell technology, they can also be generated from patients with specific disease traits, enabling more relevant modelling and drug screens. This article discusses the opportunities and challenges for the use of stem cells in drug screening with a focus on induced pluripotent stem cells.

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.

Improving compound-protein interaction prediction by building up highly credible negative samples.

PubMed

Liu, Hui; Sun, Jianjiang; Guan, Jihong; Zheng, Jie; Zhou, Shuigeng

2015-06-15

Computational prediction of compound-protein interactions (CPIs) is of great importance for drug design and development, as genome-scale experimental validation of CPIs is not only time-consuming but also prohibitively expensive. With the availability of an increasing number of validated interactions, the performance of computational prediction approaches is severely impended by the lack of reliable negative CPI samples. A systematic method of screening reliable negative sample becomes critical to improving the performance of in silico prediction methods. This article aims at building up a set of highly credible negative samples of CPIs via an in silico screening method. As most existing computational models assume that similar compounds are likely to interact with similar target proteins and achieve remarkable performance, it is rational to identify potential negative samples based on the converse negative proposition that the proteins dissimilar to every known/predicted target of a compound are not much likely to be targeted by the compound and vice versa. We integrated various resources, including chemical structures, chemical expression profiles and side effects of compounds, amino acid sequences, protein-protein interaction network and functional annotations of proteins, into a systematic screening framework. We first tested the screened negative samples on six classical classifiers, and all these classifiers achieved remarkably higher performance on our negative samples than on randomly generated negative samples for both human and Caenorhabditis elegans. We then verified the negative samples on three existing prediction models, including bipartite local model, Gaussian kernel profile and Bayesian matrix factorization, and found that the performances of these models are also significantly improved on the screened negative samples. Moreover, we validated the screened negative samples on a drug bioactivity dataset. Finally, we derived two sets of new interactions by training an support vector machine classifier on the positive interactions annotated in DrugBank and our screened negative interactions. The screened negative samples and the predicted interactions provide the research community with a useful resource for identifying new drug targets and a helpful supplement to the current curated compound-protein databases. Supplementary files are available at: http://admis.fudan.edu.cn/negative-cpi/. © The Author 2015. Published by Oxford University Press.

Integrating in Silico and in Vitro Approaches To Predict Drug Accessibility to the Central Nervous System.

PubMed

Zhang, Yan-Yan; Liu, Houfu; Summerfield, Scott G; Luscombe, Christopher N; Sahi, Jasminder

2016-05-02

Estimation of uptake across the blood-brain barrier (BBB) is key to designing central nervous system (CNS) therapeutics. In silico approaches ranging from physicochemical rules to quantitative structure-activity relationship (QSAR) models are utilized to predict potential for CNS penetration of new chemical entities. However, there are still gaps in our knowledge of (1) the relationship between marketed human drug derived CNS-accessible chemical space and preclinical neuropharmacokinetic (neuroPK) data, (2) interpretability of the selected physicochemical descriptors, and (3) correlation of the in vitro human P-glycoprotein (P-gp) efflux ratio (ER) and in vivo rodent unbound brain-to-blood ratio (Kp,uu), as these are assays routinely used to predict clinical CNS exposure, during drug discovery. To close these gaps, we explored the CNS druglike property boundaries of 920 market oral drugs (315 CNS and 605 non-CNS) and 846 compounds (54 CNS drugs and 792 proprietary GlaxoSmithKline compounds) with available rat Kp,uu data. The exact permeability coefficient (Pexact) and P-gp ER were determined for 176 compounds from the rat Kp,uu data set. Receiver operating characteristic curves were performed to evaluate the predictive power of human P-gp ER for rat Kp,uu. Our data demonstrates that simple physicochemical rules (most acidic pKa ≥ 9.5 and TPSA < 100) in combination with P-gp ER < 1.5 provide mechanistic insights for filtering BBB permeable compounds. For comparison, six classification modeling methods were investigated using multiple sets of in silico molecular descriptors. We present a random forest model with excellent predictive power (∼0.75 overall accuracy) using the rat neuroPK data set. We also observed good concordance between the structural interpretation results and physicochemical descriptor importance from the Kp,uu classification QSAR model. In summary, we propose a novel, hybrid in silico/in vitro approach and an in silico screening model for the effective development of chemical series with the potential to achieve optimal CNS exposure.

Methodologies Related to Computational models in View of Developing Anti-Alzheimer Drugs: An Overview.

PubMed

Baheti, Kirtee; Kale, Mayura Ajay

2018-04-17

Since last two decades, there has been more focus on the development strategies related to Anti-Alzheimer's drug research. This may be attributed to the fact that most of the Alzheimer's cases are still mostly unknown except for a few cases, where genetic differences have been identified. With the progress of the disease, the symptoms involve intellectual deterioration, memory impairment, abnormal personality and behavioural patterns, confusion, aggression, mood swings, irritability Current therapies available for this disease give only symptomatic relief and do not focus on manipulations of biololecular processes. Nearly all the therapies to treat Alzheimer's disease, target to change the amyloid cascade which is considered to be an important in AD pathogenesis. New drug regimens are not able to keep pace with the ever-increasing understanding about dementia at molecular level. Looking into these aggravated problems, we though to put forth molecular modeling as a drug discovery approach for developing novel drugs to treat Alzheimer disease. The disease is incurable and it gets worst as it advances and finally causes death. Due to this, the design of drugs to treat this disease has become an utmost priority for research. One of the most important emerging technologies applied for this has been Computer-assisted drug design (CADD). It is a research tool that employs large scale computing strategies in an attempt to develop a model receptor site which can be used for designing of an anti-Alzheimer drug. The various models of amyloid-based calcium channels have been computationally optimized. Docking and De novo evolution are used to design the compounds. These are further subjected to absorption, distribution, metabolism, excretion and toxicity (ADMET) studies to finally bring about active compounds that are able to cross BBB. Many novel compounds have been designed which might be promising ones for the treatment of AD. The present review describes the research carried out on various heterocyclic scaffolds that can serve as lead compounds to design Anti-Alzheimer's drugs in future. The molecular modeling methods can thus become a better alternative for discovery of newer Anti-Alzheimer agents. This methodology is extremely useful to design drugs in minimum time, with enhanced activity keeping balanced ethical considerations. Thus, the researchers are opting for this improved process over the conventional methods hoping to achieve a sure shot way out for the sufferings of people affected by Alzheimer besides other diseases. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

HIVprotI: an integrated web based platform for prediction and design of HIV proteins inhibitors.

PubMed

Qureshi, Abid; Rajput, Akanksha; Kaur, Gazaldeep; Kumar, Manoj

2018-03-09

A number of anti-retroviral drugs are being used for treating Human Immunodeficiency Virus (HIV) infection. Due to emergence of drug resistant strains, there is a constant quest to discover more effective anti-HIV compounds. In this endeavor, computational tools have proven useful in accelerating drug discovery. Although methods were published to design a class of compounds against a specific HIV protein, but an integrated web server for the same is lacking. Therefore, we have developed support vector machine based regression models using experimentally validated data from ChEMBL repository. Quantitative structure activity relationship based features were selected for predicting inhibition activity of a compound against HIV proteins namely protease (PR), reverse transcriptase (RT) and integrase (IN). The models presented a maximum Pearson correlation coefficient of 0.78, 0.76, 0.74 and 0.76, 0.68, 0.72 during tenfold cross-validation on IC 50 and percent inhibition datasets of PR, RT, IN respectively. These models performed equally well on the independent datasets. Chemical space mapping, applicability domain analyses and other statistical tests further support robustness of the predictive models. Currently, we have identified a number of chemical descriptors that are imperative in predicting the compound inhibition potential. HIVprotI platform ( http://bioinfo.imtech.res.in/manojk/hivproti ) would be useful in virtual screening of inhibitors as well as designing of new molecules against the important HIV proteins for therapeutics development.

Evaluation of a series of 2-napthamide derivatives as inhibitors of the drug efflux pump AcrB for the reversal of antimicrobial resistance.

PubMed

Wang, Yinhu; Mowla, Rumana; Guo, Liwei; Ogunniyi, Abiodun D; Rahman, Taufiq; De Barros Lopes, Miguel A; Ma, Shutao; Venter, Henrietta

2017-02-15

Drug efflux pumps confer multidrug resistance to dangerous pathogens which makes these pumps important drug targets. We have synthesised a novel series of compounds based on a 2-naphthamide pharmacore aimed at inhibiting the efflux pumps from Gram-negative bacteria. The archeatypical transporter AcrB from Escherichia coli was used as model efflux pump as AcrB is widely conserved throughout Gram-negative organisms. The compounds were tested for their antibacterial action, ability to potentiate the action of antibiotics and for their ability to inhibit Nile Red efflux by AcrB. None of the compounds were antimicrobial against E. coli wild type cells. Most of the compounds were able to inhibit Nile Red efflux indicating that they are substrates of the AcrB efflux pump. Three compounds were able to synergise with antibiotics and reverse resistance in the resistant phenotype. Compound A3, 4-(isopentyloxy)-2-naphthamide, reduced the MICs of erythromycin and chloramphenicol to the MIC levels of the drug sensitive strain that lacks an efflux pump. A3 had no effect on the MIC of the non-substrate rifampicin indicating that this compound acts specifically through the AcrB efflux pump. A3 also does not act through non-specific mechanisms such as outer membrane or inner membrane permeabilisation and is not cytotoxic against mammalian cell lines. Therefore, we have designed and synthesised a novel chemical compound with great potential to further optimisation as inhibitor of drug efflux pumps. Copyright © 2017 Elsevier Ltd. All rights reserved.

Spectroscopic, structural characterizations and antioxidant capacity of the chromium (III) niacinamide compound as a diabetes mellitus drug model

NASA Astrophysics Data System (ADS)

Refat, Moamen S.; El-Megharbel, Samy M.; Hussien, M. A.; Hamza, Reham Z.; Al-Omar, Mohamed A.; Naglah, Ahmed M.; Afifi, Walid M.; Kobeasy, Mohamed I.

2017-02-01

New binuclear chromium (III) niacinamide compound with chemical formula [Cr2(Nic)(Cl)6(H2O)4]·H2O was obtained upon the reaction of chromium (III) chloride with niacinamide (Nic) in methanol solvent at 60 °C. The proposed structure was discussed with the help of microanalytical analyses, conductivity, spectroscopic (FT-IR and UV-vis.), magnetic calculations, thermogravimetric analyses (TG/TGA), and morphological studies (X-ray of solid powder and scan electron microscopy. The infrared spectrum of free niacinamide in comparison with its chromium (III) compound indicated that the chelation mode occurs via both nitrogen atoms of pyridine ring and primary -NH2 group. The efficiency of chromium (III) niacinamide compound in decreasing of glucose level of blood and HbA1c in case of diabetic rats was checked. The ameliorating gluconeogenic enzymes, lipid profile and antioxidant defense capacities are considered as an indicator of the efficiency of new chromium (III) compound as antidiabetic drug model.

Characterization and validation of an in silico toxicology model to predict the mutagenic potential of drug impurities*

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

Valerio, Luis G., E-mail: luis.valerio@fda.hhs.gov; Cross, Kevin P.

Control and minimization of human exposure to potential genotoxic impurities found in drug substances and products is an important part of preclinical safety assessments of new drug products. The FDA's 2008 draft guidance on genotoxic and carcinogenic impurities in drug substances and products allows use of computational quantitative structure–activity relationships (QSAR) to identify structural alerts for known and expected impurities present at levels below qualified thresholds. This study provides the information necessary to establish the practical use of a new in silico toxicology model for predicting Salmonella t. mutagenicity (Ames assay outcome) of drug impurities and other chemicals. We describemore » the model's chemical content and toxicity fingerprint in terms of compound space, molecular and structural toxicophores, and have rigorously tested its predictive power using both cross-validation and external validation experiments, as well as case studies. Consistent with desired regulatory use, the model performs with high sensitivity (81%) and high negative predictivity (81%) based on external validation with 2368 compounds foreign to the model and having known mutagenicity. A database of drug impurities was created from proprietary FDA submissions and the public literature which found significant overlap between the structural features of drug impurities and training set chemicals in the QSAR model. Overall, the model's predictive performance was found to be acceptable for screening drug impurities for Salmonella mutagenicity. -- Highlights: ► We characterize a new in silico model to predict mutagenicity of drug impurities. ► The model predicts Salmonella mutagenicity and will be useful for safety assessment. ► We examine toxicity fingerprints and toxicophores of this Ames assay model. ► We compare these attributes to those found in drug impurities known to FDA/CDER. ► We validate the model and find it has a desired predictive performance.« less

The preclinical testing strategy for the development of novel chemical entities for the treatment of asthma.

PubMed

Hahn, Christian; Erb, Klaus Joseph

2008-06-01

Identifying and developing novel chemical entities (NCE) for the treatment of asthma is a time-consuming process and liabilities that endanger the successful progression of a compound from research into the patient are found throughout all phases of drug discovery. In particular the failure of advanced compounds in clinical studies due to lack of efficacy and/or safety concerns is tremendously costly. Therefore, in order to try and reduce the failure rate in clinical trials various in vitro and in vivo tests are performed during preclinical development, to rapidly identify liabilities, eliminate high risk compounds and promote promising potential drug candidates. To achieve this objective, numerous prerequisites have to be met regarding the physico-chemical properties of the compound, and bioactivity or model systems are needed to rate the therapeutic potential of new compounds. Drug liabilities such as target and species specificity, formulation issues, pharmacokinetics as well as pharmacodynamics and the toxic potential of the compound have to be analyzed in great detail before a compound can enter a clinical trial. A particularly challenging aspect of developing novel NCEs for the treatment of asthma is choosing and setting up in vivo models believed to be predictive for human disease. Numerous companies have in the past and are currently developing NCEs targeting many different pathways and cells with the aim to treat asthma. However, currently the only NCE having a significant market share are long-acting beta-agonists (LABA), inhaled and orally active steroids and leukotriene receptor antagonists. In the past many novel NCE for the treatment of asthma were effective in animal models but failed in the clinic. In this review we outline the prerequisites of novel NCE needed for clinical development.

Formulation studies of InhA inhibitors and combination therapy to improve efficacy against Mycobacterium tuberculosis.

PubMed

Knudson, Susan E; Cummings, Jason E; Bommineni, Gopal R; Pan, Pan; Tonge, Peter J; Slayden, Richard A

2016-12-01

Previously, structure-based drug design was used to develop substituted diphenyl ethers with potency against the Mycobacterium tuberculosis (Mtb) enoyl-ACP reductase (InhA), however, the highly lipophilic centroid compound, SB-PT004, lacked sufficient efficacy in the acute murine Mtb infection model. A next generation series of compounds were designed with improved specificity, potency against InhA, and reduced cytotoxicity in vitro, but these compounds also had limited solubility. Accordingly, solubility and pharmacokinetics studies were performed to develop formulations for this class and other experimental drug candidates with high logP values often encountered in drug discovery. Lead diphenyl ethers were formulated in co-solvent and Self-Dispersing Lipid Formulations (SDLFs) and evaluated in a rapid murine Mtb infection model that assesses dissemination to and bacterial burden in the spleen. In vitro synergy studies were performed with the lead diphenyl ether compounds, SB-PT070 and SB-PT091, and rifampin (RIF), which demonstrated an additive effect, and that guided the in vivo studies. Combinatorial therapy in vivo studies with these compounds delivered in our Self-Micro Emulsifying Drug Delivery System (SMEDDS) resulted in an additional 1.4 log 10  CFU reduction in the spleen of animals co-treated with SB-PT091 and RIF and an additional 1.7 log 10 reduction in the spleen with animals treated with both SB-PT070 and RIF. Copyright © 2016 Elsevier Ltd. All rights reserved.

Design of inhibitors of thymidylate kinase from Variola virus as new selective drugs against smallpox.

PubMed

Guimarães, Ana P; de Souza, Felipe R; Oliveira, Aline A; Gonçalves, Arlan S; de Alencastro, Ricardo B; Ramalho, Teodorico C; França, Tanos C C

2015-02-16

Recently we constructed a homology model of the enzyme thymidylate kinase from Variola virus (VarTMPK) and proposed it as a new target to the drug design against smallpox. In the present work, we used the antivirals cidofovir and acyclovir as reference compounds to choose eleven compounds as leads to the drug design of inhibitors for VarTMPK. Docking and molecular dynamics (MD) studies of the interactions of these compounds inside VarTMPK and human TMPK (HssTMPK) suggest that they compete for the binding region of the substrate and were used to propose the structures of ten new inhibitors for VarTMPK. Further docking and MD simulations of these compounds, inside VarTMPK and HssTMPK, suggest that nine among ten are potential selective inhibitors of VarTMPK. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

Computational prediction of formulation strategies for beyond-rule-of-5 compounds.

PubMed

Bergström, Christel A S; Charman, William N; Porter, Christopher J H

2016-06-01

The physicochemical properties of some contemporary drug candidates are moving towards higher molecular weight, and coincidentally also higher lipophilicity in the quest for biological selectivity and specificity. These physicochemical properties move the compounds towards beyond rule-of-5 (B-r-o-5) chemical space and often result in lower water solubility. For such B-r-o-5 compounds non-traditional delivery strategies (i.e. those other than conventional tablet and capsule formulations) typically are required to achieve adequate exposure after oral administration. In this review, we present the current status of computational tools for prediction of intestinal drug absorption, models for prediction of the most suitable formulation strategies for B-r-o-5 compounds and models to obtain an enhanced understanding of the interplay between drug, formulation and physiological environment. In silico models are able to identify the likely molecular basis for low solubility in physiologically relevant fluids such as gastric and intestinal fluids. With this baseline information, a formulation scientist can, at an early stage, evaluate different orally administered, enabling formulation strategies. Recent computational models have emerged that predict glass-forming ability and crystallisation tendency and therefore the potential utility of amorphous solid dispersion formulations. Further, computational models of loading capacity in lipids, and therefore the potential for formulation as a lipid-based formulation, are now available. Whilst such tools are useful for rapid identification of suitable formulation strategies, they do not reveal drug localisation and molecular interaction patterns between drug and excipients. For the latter, Molecular Dynamics simulations provide an insight into the interplay between drug, formulation and intestinal fluid. These different computational approaches are reviewed. Additionally, we analyse the molecular requirements of different targets, since these can provide an early signal that enabling formulation strategies will be required. Based on the analysis we conclude that computational biopharmaceutical profiling can be used to identify where non-conventional gateways, such as prediction of 'formulate-ability' during lead optimisation and early development stages, are important and may ultimately increase the number of orally tractable contemporary targets. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Interpreting linear support vector machine models with heat map molecule coloring

PubMed Central

2011-01-01

Background Model-based virtual screening plays an important role in the early drug discovery stage. The outcomes of high-throughput screenings are a valuable source for machine learning algorithms to infer such models. Besides a strong performance, the interpretability of a machine learning model is a desired property to guide the optimization of a compound in later drug discovery stages. Linear support vector machines showed to have a convincing performance on large-scale data sets. The goal of this study is to present a heat map molecule coloring technique to interpret linear support vector machine models. Based on the weights of a linear model, the visualization approach colors each atom and bond of a compound according to its importance for activity. Results We evaluated our approach on a toxicity data set, a chromosome aberration data set, and the maximum unbiased validation data sets. The experiments show that our method sensibly visualizes structure-property and structure-activity relationships of a linear support vector machine model. The coloring of ligands in the binding pocket of several crystal structures of a maximum unbiased validation data set target indicates that our approach assists to determine the correct ligand orientation in the binding pocket. Additionally, the heat map coloring enables the identification of substructures important for the binding of an inhibitor. Conclusions In combination with heat map coloring, linear support vector machine models can help to guide the modification of a compound in later stages of drug discovery. Particularly substructures identified as important by our method might be a starting point for optimization of a lead compound. The heat map coloring should be considered as complementary to structure based modeling approaches. As such, it helps to get a better understanding of the binding mode of an inhibitor. PMID:21439031

ARQiv-HTS, a versatile whole-organism screening platform enabling in vivo drug discovery at high-throughput rates

PubMed Central

White, David T; Eroglu, Arife Unal; Wang, Guohua; Zhang, Liyun; Sengupta, Sumitra; Ding, Ding; Rajpurohit, Surendra K; Walker, Steven L; Ji, Hongkai; Qian, Jiang; Mumm, Jeff S

2017-01-01

The zebrafish has emerged as an important model for whole-organism small-molecule screening. However, most zebrafish-based chemical screens have achieved only mid-throughput rates. Here we describe a versatile whole-organism drug discovery platform that can achieve true high-throughput screening (HTS) capacities. This system combines our automated reporter quantification in vivo (ARQiv) system with customized robotics, and is termed ‘ARQiv-HTS’. We detail the process of establishing and implementing ARQiv-HTS: (i) assay design and optimization, (ii) calculation of sample size and hit criteria, (iii) large-scale egg production, (iv) automated compound titration, (v) dispensing of embryos into microtiter plates, and (vi) reporter quantification. We also outline what we see as best practice strategies for leveraging the power of ARQiv-HTS for zebrafish-based drug discovery, and address technical challenges of applying zebrafish to large-scale chemical screens. Finally, we provide a detailed protocol for a recently completed inaugural ARQiv-HTS effort, which involved the identification of compounds that elevate insulin reporter activity. Compounds that increased the number of insulin-producing pancreatic beta cells represent potential new therapeutics for diabetic patients. For this effort, individual screening sessions took 1 week to conclude, and sessions were performed iteratively approximately every other day to increase throughput. At the conclusion of the screen, more than a half million drug-treated larvae had been evaluated. Beyond this initial example, however, the ARQiv-HTS platform is adaptable to almost any reporter-based assay designed to evaluate the effects of chemical compounds in living small-animal models. ARQiv-HTS thus enables large-scale whole-organism drug discovery for a variety of model species and from numerous disease-oriented perspectives. PMID:27831568

Preventing Drug-Induced Liver Injury: How Useful Are Animal Models?

PubMed

Ballet, François

2015-01-01

Drug-induced liver injury (DILI) is the most common organ toxicity encountered in regulatory animal toxicology studies required prior to the clinical development of new drug candidates. Very few reports have evaluated the value of these studies for predicting DILI in humans. Indeed, compounds inducing liver toxicity in regulatory toxicology studies are not always correlated with a risk of DILI in humans. Conversely, compounds associated with the occurrence of DILI in phase 3 studies or after market release are often tested negative in regulatory toxicology studies. Idiosyncratic DILI is a rare event that is precipitated in an individual by the simultaneous occurrence of several critical factors. These factors may relate to the host (e.g. human leukocyte antigen polymorphism, inflammation), the drug (e.g. reactive metabolites) or the environment (e.g. diet/microbiota). This type of toxicity therefore cannot be detected in conventional animal toxicology studies. Several animal models have recently been proposed for the identification of drugs with the potential to cause idiosyncratic DILI: rats treated with lipopolysaccharide, Sod2(+/-) mice, panels of inbred mouse strains or chimeric mice with humanized livers. These models are not suitable for use in the prospective screening of new drug candidates. Humans therefore constitute the best model for predicting and assessing idiopathic DILI. © 2015 S. Karger AG, Basel.

Hit Generation in TB Drug Discovery: From Genome to Granuloma

PubMed Central

2018-01-01

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

Effect of hydrotalcite-like compounds on the aqueous solubility of some poorly water-soluble drugs.

PubMed

Ambrogi, Valeria; Fardella, Giuseppe; Grandolini, Giuliano; Nocchetti, Morena; Perioli, Luana

2003-07-01

A new approach of improving drug dissolution properties is described. This method exploits the property of a carrier owing to the hydrotalcite-type anionic clays (HTlc). HTlc is an inorganic layered solid that lodges anionic compounds among its layers. As HTlc dissolves at acidic pH values (pH < 4), the anions intercalated among the layers are promptly released in the medium. In this article some nonsteroidal antiinflammatory drugs were chosen as models of poorly water-soluble drugs. They were intercalated in HTlc and solubility measurements in acidic medium were performed. A remarkable improvement of drug solubility was observed especially in the case of indomethacin. Copyright 2003 Wiley-Liss, Inc. and the American Pharmacists Association

Molecular Modeling in Drug Design for the Development of Organophosphorus Antidotes/Prophylactics.

DTIC Science & Technology

1986-06-01

multidimensional statistical QSAR analysis techniques to suggest new structures for synthesis and evaluation. C. Application of quantum chemical techniques to...compounds for synthesis and testing for antidotal potency. E. Use of computer-assisted methods to determine the steric constraints at the active site...modeling techniques to model the enzyme acetylcholinester-se. H. Suggestion of some novel compounds for synthesis and testing for reactivating

Physicochemical characteristics of structurally determined metabolite-protein and drug-protein binding events with respect to binding specificity.

PubMed

Korkuć, Paula; Walther, Dirk

2015-01-01

To better understand and ultimately predict both the metabolic activities as well as the signaling functions of metabolites, a detailed understanding of the physical interactions of metabolites with proteins is highly desirable. Focusing in particular on protein binding specificity vs. promiscuity, we performed a comprehensive analysis of the physicochemical properties of compound-protein binding events as reported in the Protein Data Bank (PDB). We compared the molecular and structural characteristics obtained for metabolites to those of the well-studied interactions of drug compounds with proteins. Promiscuously binding metabolites and drugs are characterized by low molecular weight and high structural flexibility. Unlike reported for drug compounds, low rather than high hydrophobicity appears associated, albeit weakly, with promiscuous binding for the metabolite set investigated in this study. Across several physicochemical properties, drug compounds exhibit characteristic binding propensities that are distinguishable from those associated with metabolites. Prediction of target diversity and compound promiscuity using physicochemical properties was possible at modest accuracy levels only, but was consistently better for drugs than for metabolites. Compound properties capturing structural flexibility and hydrogen-bond formation descriptors proved most informative in PLS-based prediction models. With regard to diversity of enzymatic activities of the respective metabolite target enzymes, the metabolites benzylsuccinate, hypoxanthine, trimethylamine N-oxide, oleoylglycerol, and resorcinol showed very narrow process involvement, while glycine, imidazole, tryptophan, succinate, and glutathione were identified to possess broad enzymatic reaction scopes. Promiscuous metabolites were found to mainly serve as general energy currency compounds, but were identified to also be involved in signaling processes and to appear in diverse organismal systems (digestive and nervous system) suggesting specific molecular and physiological roles of promiscuous metabolites.

Physicochemical characteristics of structurally determined metabolite-protein and drug-protein binding events with respect to binding specificity

PubMed Central

Korkuć, Paula; Walther, Dirk

2015-01-01

To better understand and ultimately predict both the metabolic activities as well as the signaling functions of metabolites, a detailed understanding of the physical interactions of metabolites with proteins is highly desirable. Focusing in particular on protein binding specificity vs. promiscuity, we performed a comprehensive analysis of the physicochemical properties of compound-protein binding events as reported in the Protein Data Bank (PDB). We compared the molecular and structural characteristics obtained for metabolites to those of the well-studied interactions of drug compounds with proteins. Promiscuously binding metabolites and drugs are characterized by low molecular weight and high structural flexibility. Unlike reported for drug compounds, low rather than high hydrophobicity appears associated, albeit weakly, with promiscuous binding for the metabolite set investigated in this study. Across several physicochemical properties, drug compounds exhibit characteristic binding propensities that are distinguishable from those associated with metabolites. Prediction of target diversity and compound promiscuity using physicochemical properties was possible at modest accuracy levels only, but was consistently better for drugs than for metabolites. Compound properties capturing structural flexibility and hydrogen-bond formation descriptors proved most informative in PLS-based prediction models. With regard to diversity of enzymatic activities of the respective metabolite target enzymes, the metabolites benzylsuccinate, hypoxanthine, trimethylamine N-oxide, oleoylglycerol, and resorcinol showed very narrow process involvement, while glycine, imidazole, tryptophan, succinate, and glutathione were identified to possess broad enzymatic reaction scopes. Promiscuous metabolites were found to mainly serve as general energy currency compounds, but were identified to also be involved in signaling processes and to appear in diverse organismal systems (digestive and nervous system) suggesting specific molecular and physiological roles of promiscuous metabolites. PMID:26442281

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

Optimization of the Caco-2 permeability assay to screen drug compounds for intestinal absorption and efflux.

PubMed

Press, Barry

2011-01-01

In vitro permeability assays are a valuable tool for scientists during lead compound optimization. As a majority of discovery projects are focused on the development of orally bioavailable drugs, correlation of in vitro permeability data to in vivo absorption results is critical for understanding the structural-physicochemical relationship (SPR) of drugs exhibiting low levels of absorption. For more than a decade, the Caco-2 screening assay has remained a popular, in vitro system to test compounds for both intestinal permeability and efflux liability. Despite advances in artificial membrane technology and in silico modeling systems, drug compounds still benefit from testing in cell-based epithelial monolayer assays for lead optimization. This chapter provides technical information for performing and optimizing the Caco-2 assay. In addition, techniques are discussed for dealing with some of the most pressing issues surrounding in vitro permeability assays (i.e., low aqueous solubility of test compounds and low postassay recovery). Insights are offered to help researchers avoid common pitfalls in the interpretation of in vitro permeability data, which can often lead to the perception of misleading results for correlation to in vivo data.

System-level multi-target drug discovery from natural products with applications to cardiovascular diseases.

PubMed

Zheng, Chunli; Wang, Jinan; Liu, Jianling; Pei, Mengjie; Huang, Chao; Wang, Yonghua

2014-08-01

The term systems pharmacology describes a field of study that uses computational and experimental approaches to broaden the view of drug actions rooted in molecular interactions and advance the process of drug discovery. The aim of this work is to stick out the role that the systems pharmacology plays across the multi-target drug discovery from natural products for cardiovascular diseases (CVDs). Firstly, based on network pharmacology methods, we reconstructed the drug-target and target-target networks to determine the putative protein target set of multi-target drugs for CVDs treatment. Secondly, we reintegrated a compound dataset of natural products and then obtained a multi-target compounds subset by virtual-screening process. Thirdly, a drug-likeness evaluation was applied to find the ADME-favorable compounds in this subset. Finally, we conducted in vitro experiments to evaluate the reliability of the selected chemicals and targets. We found that four of the five randomly selected natural molecules can effectively act on the target set for CVDs, indicating the reasonability of our systems-based method. This strategy may serve as a new model for multi-target drug discovery of complex diseases.

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.

Open-source approaches for the repurposing of existing or failed candidate drugs: learning from and applying the lessons across diseases

PubMed Central

Allarakhia, Minna

2013-01-01

Repurposing has the objective of targeting existing drugs and failed, abandoned, or yet-to-be-pursued clinical candidates to new disease areas. The open-source model permits for the sharing of data, resources, compounds, clinical molecules, small libraries, and screening platforms to cost-effectively advance old drugs and/or candidates into clinical re-development. Clearly, at the core of drug-repurposing activities is collaboration, in many cases progressing beyond the open sharing of resources, technology, and intellectual property, to the sharing of facilities and joint program development to foster drug-repurposing human-capacity development. A variety of initiatives under way for drug repurposing, including those targeting rare and neglected diseases, are discussed in this review and provide insight into the stakeholders engaged in drug-repurposing discovery, the models of collaboration used, the intellectual property-management policies crafted, and human capacity developed. In the case of neglected tropical diseases, it is suggested that the development of human capital be a central aspect of drug-repurposing programs. Open-source models can support human-capital development through collaborative data generation, open compound access, open and collaborative screening, preclinical and possibly clinical studies. Given the urgency of drug development for neglected tropical diseases, the review suggests elements from current repurposing programs be extended to the neglected tropical diseases arena. PMID:23966771

Open-source approaches for the repurposing of existing or failed candidate drugs: learning from and applying the lessons across diseases.

PubMed

Allarakhia, Minna

2013-01-01

Repurposing has the objective of targeting existing drugs and failed, abandoned, or yet-to-be-pursued clinical candidates to new disease areas. The open-source model permits for the sharing of data, resources, compounds, clinical molecules, small libraries, and screening platforms to cost-effectively advance old drugs and/or candidates into clinical re-development. Clearly, at the core of drug-repurposing activities is collaboration, in many cases progressing beyond the open sharing of resources, technology, and intellectual property, to the sharing of facilities and joint program development to foster drug-repurposing human-capacity development. A variety of initiatives under way for drug repurposing, including those targeting rare and neglected diseases, are discussed in this review and provide insight into the stakeholders engaged in drug-repurposing discovery, the models of collaboration used, the intellectual property-management policies crafted, and human capacity developed. In the case of neglected tropical diseases, it is suggested that the development of human capital be a central aspect of drug-repurposing programs. Open-source models can support human-capital development through collaborative data generation, open compound access, open and collaborative screening, preclinical and possibly clinical studies. Given the urgency of drug development for neglected tropical diseases, the review suggests elements from current repurposing programs be extended to the neglected tropical diseases arena.

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

PubMed

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

2018-06-13

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

Pharmacokinetics, brain distribution and plasma protein binding of carbamazepine and nine derivatives: new set of data for predictive in silico ADME models.

PubMed

Fortuna, Ana; Alves, Gilberto; Soares-da-Silva, Patrício; Falcão, Amílcar

2013-11-01

In silico approaches to predict absorption, distribution, metabolism and excretion (ADME) of new drug candidates are gaining a relevant importance in drug discovery programmes. When considering particularly the pharmacokinetics during the development of oral antiepileptic drugs (AEDs), one of the most prominent goals is designing compounds with good bioavailability and brain penetration. Thus, it is expected that in silico models able to predict these features may be applied during the early stages of AEDs discovery. The present investigation was mainly carried out in order to generate in vivo pharmacokinetic data that can be utilized for development and validation of in silico models. For this purpose, a single dose of each compound (1.4mmol/kg) was orally administered to male CD-1 mice. After quantifying the parent compound and main metabolites in plasma and brain up to 12h post-dosing, a non-compartmental pharmacokinetic analysis was performed and the corresponding brain/plasma ratios were calculated. Moreover the plasma protein binding was estimated in vitro applying the ultrafiltration procedure. The present in vivo pharmacokinetic characterization of the test compounds and corresponding metabolites demonstrated that the metabolism extensively compromised the in vivo activity of CBZ derivatives and their toxicity. Furthermore, it was clearly evidenced that the time to reach maximum peak concentration, bioavailability (given by the area under the curve) and metabolic stability (given by the AUC0-12h ratio of the parent compound and total systemic drug) influenced the in vivo pharmacological activities and must be considered as primary parameters to be investigated. All the test compounds presented brain/plasma ratios lower than 1.0, suggesting that the blood-brain barrier restricts drug entry into the brain. In agreement with in vitro studies already performed within our research group, CBZ, CBZ-10,11-epoxide and oxcarbazepine exhibited the highest brain/plasma ratios (>0.50), followed by eslicarbazepine, R-licarbazepine, trans-diol and BIA 2-024 (ratios within 0.05-0.50). BIA 2-265 was not found in the biophase, probably due to its high plasma-protein bound fraction (>90%) herein revealed for the first time. The comparative in vivo pharmacokinetic data obtained in the present work might be usefully applied in the context of discovery of new antiepileptic drugs that are derivatives of CBZ. Copyright © 2013 Elsevier B.V. All rights reserved.

A novel prediction approach for antimalarial activities of Trimethoprim, Pyrimethamine, and Cycloguanil analogues using extremely randomized trees.

PubMed

Nattee, Cholwich; Khamsemanan, Nirattaya; Lawtrakul, Luckhana; Toochinda, Pisanu; Hannongbua, Supa

2017-01-01

Malaria is still one of the most serious diseases in tropical regions. This is due in part to the high resistance against available drugs for the inhibition of parasites, Plasmodium, the cause of the disease. New potent compounds with high clinical utility are urgently needed. In this work, we created a novel model using a regression tree to study structure-activity relationships and predict the inhibition constant, K i of three different antimalarial analogues (Trimethoprim, Pyrimethamine, and Cycloguanil) based on their molecular descriptors. To the best of our knowledge, this work is the first attempt to study the structure-activity relationships of all three analogues combined. The most relevant descriptors and appropriate parameters of the regression tree are harvested using extremely randomized trees. These descriptors are water accessible surface area, Log of the aqueous solubility, total hydrophobic van der Waals surface area, and molecular refractivity. Out of all possible combinations of these selected parameters and descriptors, the tree with the strongest coefficient of determination is selected to be our prediction model. Predicted K i values from the proposed model show a strong coefficient of determination, R 2 =0.996, to experimental K i values. From the structure of the regression tree, compounds with high accessible surface area of all hydrophobic atoms (ASA_H) and low aqueous solubility of inhibitors (Log S) generally possess low K i values. Our prediction model can also be utilized as a screening test for new antimalarial drug compounds which may reduce the time and expenses for new drug development. New compounds with high predicted K i should be excluded from further drug development. It is also our inference that a threshold of ASA_H greater than 575.80 and Log S less than or equal to -4.36 is a sufficient condition for a new compound to possess a low K i . Copyright © 2016 Elsevier Inc. All rights reserved.

Development of pharmacophore models for small molecules targeting RNA: Application to the RNA repeat expansion in myotonic dystrophy type 1.

PubMed

Angelbello, Alicia J; González, Àlex L; Rzuczek, Suzanne G; Disney, Matthew D

2016-12-01

RNA is an important drug target, but current approaches to identify bioactive small molecules have been engineered primarily for protein targets. Moreover, the identification of small molecules that bind a specific RNA target with sufficient potency remains a challenge. Computer-aided drug design (CADD) and, in particular, ligand-based drug design provide a myriad of tools to identify rapidly new chemical entities for modulating a target based on previous knowledge of active compounds without relying on a ligand complex. Herein we describe pharmacophore virtual screening based on previously reported active molecules that target the toxic RNA that causes myotonic dystrophy type 1 (DM1). DM1-associated defects are caused by sequestration of muscleblind-like 1 protein (MBNL1), an alternative splicing regulator, by expanded CUG repeats (r(CUG) exp ). Several small molecules have been found to disrupt the MBNL1-r(CUG) exp complex, ameliorating DM1 defects. Our pharmacophore model identified a number of potential lead compounds from which we selected 11 compounds to evaluate. Of the 11 compounds, several improved DM1 defects both in vitro and in cells. Copyright © 2016 Elsevier Ltd. All rights reserved.

Biotechnology and genetic engineering in the new drug development. Part III. Biocatalysis, metabolic engineering and molecular modelling.

PubMed

Stryjewska, Agnieszka; Kiepura, Katarzyna; Librowski, Tadeusz; Lochyński, Stanisław

2013-01-01

Industrial biotechnology has been defined as the use and application of biotechnology for the sustainable processing and production of chemicals, materials and fuels. It makes use of biocatalysts such as microbial communities, whole-cell microorganisms or purified enzymes. In the review these processes are described. Drug design is an iterative process which begins when a chemist identifies a compound that displays an interesting biological profile and ends when both the activity profile and the chemical synthesis of the new chemical entity are optimized. Traditional approaches to drug discovery rely on a stepwise synthesis and screening program for large numbers of compounds to optimize activity profiles. Over the past ten to twenty years, scientists have used computer models of new chemical entities to help define activity profiles, geometries and relativities. This article introduces inter alia the concepts of molecular modelling and contains references for further reading.

Pharmaceutical compounding or pharmaceutical manufacturing? A regulatory perspective.

PubMed

Timko, Robert J; Crooker, Philip E M

2014-01-01

At one time, nearly all prescriptions were compounded preparations. There is an ongoing demand for compounded prescription medications because manufacturers cannot fulfill the needs of all individual patients. Compounding pharmacies are a long standing yet less frequently discussed element in the complex matrix of prescription drug manufacturing, distribution, and patient use. The drug shortage situation for many necessary and life-saving drug products is a complicating factor that has led to the numerous quality issues that currently plague large-scale compounding pharmacies. The states are the primary regulator of pharmacies, including community drug stores, large chains, and specialty pharmacies. Pharmacies making and distributing drugs in a way that is outside the bounds of traditional pharmacy compounding are of great concern to the U.S. Food and Drug Administration. The U.S. Congress has recently passed the Drug Quality and Security Act. This legislation establishes a clear boundary between traditional compounders and compounding manufacturers. It clarifies a national, uniform set of rules for compounding manufacturers while preserving the states' primary role in traditional pharmacy regulation. It clarifies the U.S. Food and Drug Administration's authority over the compounding of human drugs while requiring the Agency to engage and coordinate with states to ensure the safety of compounded drugs.

Efficient Modeling and Active Learning Discovery of Biological Responses

PubMed Central

Naik, Armaghan W.; Kangas, Joshua D.; Langmead, Christopher J.; Murphy, Robert F.

2013-01-01

High throughput and high content screening involve determination of the effect of many compounds on a given target. As currently practiced, screening for each new target typically makes little use of information from screens of prior targets. Further, choices of compounds to advance to drug development are made without significant screening against off-target effects. The overall drug development process could be made more effective, as well as less expensive and time consuming, if potential effects of all compounds on all possible targets could be considered, yet the cost of such full experimentation would be prohibitive. In this paper, we describe a potential solution: probabilistic models that can be used to predict results for unmeasured combinations, and active learning algorithms for efficiently selecting which experiments to perform in order to build those models and determining when to stop. Using simulated and experimental data, we show that our approaches can produce powerful predictive models without exhaustive experimentation and can learn them much faster than by selecting experiments at random. PMID:24358322

Compound stimulus presentation and the norepinephrine reuptake inhibitor atomoxetine enhance long-term extinction of cocaine-seeking behavior.

PubMed

Janak, Patricia H; Bowers, M Scott; Corbit, Laura H

2012-03-01

Drug abstinence is frequently compromised when addicted individuals are re-exposed to environmental stimuli previously associated with drug use. Research with human addicts and in animal models has demonstrated that extinction learning (non-reinforced cue-exposure) can reduce the capacity of such stimuli to induce relapse, yet extinction therapies have limited long-term success under real-world conditions (Bouton, 2002; O'Brien, 2008). We hypothesized that enhancing extinction would reduce the later ability of drug-predictive cues to precipitate drug-seeking behavior. We, therefore, tested whether compound stimulus presentation and pharmacological treatments that augment noradrenergic activity (atomoxetine; norepinephrine reuptake inhibitor) during extinction training would facilitate the extinction of drug-seeking behaviors, thus reducing relapse. Rats were trained that the presentation of a discrete cue signaled that a lever press response would result in cocaine reinforcement. Rats were subsequently extinguished and spontaneous recovery of drug-seeking behavior following presentation of previously drug-predictive cues was tested 4 weeks later. We find that compound stimulus presentations or pharmacologically increasing noradrenergic activity during extinction training results in less future recovery of responding, whereas propranolol treatment reduced the benefit seen with compound stimulus presentation. These data may have important implications for understanding the biological basis of extinction learning, as well as for improving the outcome of extinction-based therapies.

Structure-activity models of oral clearance, cytotoxicity, and LD50: a screen for promising anticancer compounds

PubMed Central

Boik, John C; Newman, Robert A

2008-01-01

Background Quantitative structure-activity relationship (QSAR) models have become popular tools to help identify promising lead compounds in anticancer drug development. Few QSAR studies have investigated multitask learning, however. Multitask learning is an approach that allows distinct but related data sets to be used in training. In this paper, a suite of three QSAR models is developed to identify compounds that are likely to (a) exhibit cytotoxic behavior against cancer cells, (b) exhibit high rat LD50 values (low systemic toxicity), and (c) exhibit low to modest human oral clearance (favorable pharmacokinetic characteristics). Models were constructed using Kernel Multitask Latent Analysis (KMLA), an approach that can effectively handle a large number of correlated data features, nonlinear relationships between features and responses, and multitask learning. Multitask learning is particularly useful when the number of available training records is small relative to the number of features, as was the case with the oral clearance data. Results Multitask learning modestly but significantly improved the classification precision for the oral clearance model. For the cytotoxicity model, which was constructed using a large number of records, multitask learning did not affect precision but did reduce computation time. The models developed here were used to predict activities for 115,000 natural compounds. Hundreds of natural compounds, particularly in the anthraquinone and flavonoids groups, were predicted to be cytotoxic, have high LD50 values, and have low to moderate oral clearance. Conclusion Multitask learning can be useful in some QSAR models. A suite of QSAR models was constructed and used to screen a large drug library for compounds likely to be cytotoxic to multiple cancer cell lines in vitro, have low systemic toxicity in rats, and have favorable pharmacokinetic properties in humans. PMID:18554402

Structure-activity models of oral clearance, cytotoxicity, and LD50: a screen for promising anticancer compounds.

PubMed

Boik, John C; Newman, Robert A

2008-06-13

Quantitative structure-activity relationship (QSAR) models have become popular tools to help identify promising lead compounds in anticancer drug development. Few QSAR studies have investigated multitask learning, however. Multitask learning is an approach that allows distinct but related data sets to be used in training. In this paper, a suite of three QSAR models is developed to identify compounds that are likely to (a) exhibit cytotoxic behavior against cancer cells, (b) exhibit high rat LD50 values (low systemic toxicity), and (c) exhibit low to modest human oral clearance (favorable pharmacokinetic characteristics). Models were constructed using Kernel Multitask Latent Analysis (KMLA), an approach that can effectively handle a large number of correlated data features, nonlinear relationships between features and responses, and multitask learning. Multitask learning is particularly useful when the number of available training records is small relative to the number of features, as was the case with the oral clearance data. Multitask learning modestly but significantly improved the classification precision for the oral clearance model. For the cytotoxicity model, which was constructed using a large number of records, multitask learning did not affect precision but did reduce computation time. The models developed here were used to predict activities for 115,000 natural compounds. Hundreds of natural compounds, particularly in the anthraquinone and flavonoids groups, were predicted to be cytotoxic, have high LD50 values, and have low to moderate oral clearance. Multitask learning can be useful in some QSAR models. A suite of QSAR models was constructed and used to screen a large drug library for compounds likely to be cytotoxic to multiple cancer cell lines in vitro, have low systemic toxicity in rats, and have favorable pharmacokinetic properties in humans.

Immunomodulatory effect of ethanolic extract of Shirishadi compound

PubMed Central

Kajaria, Divya; Tripathi, Jyoti Shankar; Tiwari, Shri Kant; Pandey, Bajrangi Lal

2013-01-01

Immunomodulators are substances that helps to regulate the immune system. The basic mechanisms by which the herbs defend the body against infection have two probable ways- one by destroying pathogens and other by enhancing the body immunity. Shirishadi compound is a polyherbal drug used in Ayurvedic system of medicine for the management of allergic disorders such as allergic rhinitis, allergic asthma etc., The present study was carried out to evaluate the immunomodulatory activity of ethanolic extract of polyherbal compound “Shirishadi” on Swiss albino mice. Cyclophosphamide (CP) induced immunosuppression model was used to assess the activity of drug. CP was given in the dose of 30 mg/kg body weight through i.p route. 500 mg/kg body weight of Shirishadi polyherbal drug was given through oral route. The extent of protection against immunosuppression caused by CP was evaluated after 14 days of drug administration, by estimating hematological parameters and neutrophil adhesion test. Ethanolic extracts of Shirishadi compound showed pronounced immunoprotective activity by increasing the depleted levels of total WBC count and RBC, % Hb, and % neutrophils adhesion. The extract was found to be an effective immunomodulatory agent. PMID:24501532

1-[(2-arylthiazol-4-yl)methyl]azoles as a new class of anticonvulsants: design, synthesis, in vivo screening, and in silico drug-like properties.

PubMed

Ahangar, Nematollah; Ayati, Adile; Alipour, Eskandar; Pashapour, Arsalan; Foroumadi, Alireza; Emami, Saeed

2011-11-01

A series of novel thiazole incorporated (arylalkyl)azoles were synthesized and screened for their anticonvulsant properties using maximal electroshock and pentylenetetrazole models in mice. Among target compounds, 1-[(2-(4-chlorophenyl)thiazol-4-yl)methyl]-1H-imidazole (compound 4b), 1-[(2-phenylthiazol-4-yl)methyl]-1H-1,2,4-tria-zole (8a), and its 4-chlorophenyl analog (compound 8b) were able to display noticeable anticonvulsant activity in both pentylenetetrazole and maximal electroshock tests with percentage protection range of 33-100%. A computational study was carried out for prediction of pharmacokinetics properties and drug-likeness. The structure-activity relationship and in silico drug relevant properties (molecular weight, topological polar surface area, clog P, hydrogen bond donors, hydrogen bond acceptors, and log BB) confirmed that the compounds were within the range set by Lipinski's rule-of-five, and possessing favorable physicochemical properties for acting as CNS-drugs, making them potentially promising agents for epilepsy therapy. © 2011 John Wiley & Sons A/S.

Efficient discovery of responses of proteins to compounds using active learning

PubMed Central

2014-01-01

Background Drug discovery and development has been aided by high throughput screening methods that detect compound effects on a single target. However, when using focused initial screening, undesirable secondary effects are often detected late in the development process after significant investment has been made. An alternative approach would be to screen against undesired effects early in the process, but the number of possible secondary targets makes this prohibitively expensive. Results This paper describes methods for making this global approach practical by constructing predictive models for many target responses to many compounds and using them to guide experimentation. We demonstrate for the first time that by jointly modeling targets and compounds using descriptive features and using active machine learning methods, accurate models can be built by doing only a small fraction of possible experiments. The methods were evaluated by computational experiments using a dataset of 177 assays and 20,000 compounds constructed from the PubChem database. Conclusions An average of nearly 60% of all hits in the dataset were found after exploring only 3% of the experimental space which suggests that active learning can be used to enable more complete characterization of compound effects than otherwise affordable. The methods described are also likely to find widespread application outside drug discovery, such as for characterizing the effects of a large number of compounds or inhibitory RNAs on a large number of cell or tissue phenotypes. PMID:24884564

Physical stability of drugs after storage above and below the glass transition temperature: Relationship to glass-forming ability.

PubMed

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

2015-11-10

Amorphous materials are inherently unstable and tend to crystallize upon storage. In this study, we investigated the extent to which the physical stability and inherent crystallization tendency of drugs are related to their glass-forming ability (GFA), the glass transition temperature (Tg) and thermodynamic factors. Differential scanning calorimetry was used to produce the amorphous state of 52 drugs [18 compounds crystallized upon heating (Class II) and 34 remained in the amorphous state (Class III)] and to perform in situ storage for the amorphous material for 12h at temperatures 20°C above or below the Tg. A computational model based on the support vector machine (SVM) algorithm was developed to predict the structure-property relationships. All drugs maintained their Class when stored at 20°C below the Tg. Fourteen of the Class II compounds crystallized when stored above the Tg whereas all except one of the Class III compounds remained amorphous. These results were only related to the glass-forming ability and no relationship to e.g. thermodynamic factors was found. The experimental data were used for computational modeling and a classification model was developed that correctly predicted the physical stability above the Tg. The use of a large dataset revealed that molecular features related to aromaticity and π-π interactions reduce the inherent physical stability of amorphous drugs. Copyright © 2015 Elsevier B.V. All rights reserved.

ADME evaluation in drug discovery. 1. Applications of genetic algorithms to the prediction of blood-brain partitioning of a large set of drugs.

PubMed

Hou, Tingjun; Xu, Xiaojie

2002-12-01

In this study, the relationships between the brain-blood concentration ratio of 96 structurally diverse compounds with a large number of structurally derived descriptors were investigated. The linear models were based on molecular descriptors that can be calculated for any compound simply from a knowledge of its molecular structure. The linear correlation coefficients of the models were optimized by genetic algorithms (GAs), and the descriptors used in the linear models were automatically selected from 27 structurally derived descriptors. The GA optimizations resulted in a group of linear models with three or four molecular descriptors with good statistical significance. The change of descriptor use as the evolution proceeds demonstrates that the octane/water partition coefficient and the partial negative solvent-accessible surface area multiplied by the negative charge are crucial to brain-blood barrier permeability. Moreover, we found that the predictions using multiple QSPR models from GA optimization gave quite good results in spite of the diversity of structures, which was better than the predictions using the best single model. The predictions for the two external sets with 37 diverse compounds using multiple QSPR models indicate that the best linear models with four descriptors are sufficiently effective for predictive use. Considering the ease of computation of the descriptors, the linear models may be used as general utilities to screen the blood-brain barrier partitioning of drugs in a high-throughput fashion.

Induced Pluripotent Stem Cells for Disease Modeling and Evaluation of Therapeutics for Niemann-Pick Disease Type A.

PubMed

Long, Yan; Xu, Miao; Li, Rong; Dai, Sheng; Beers, Jeanette; Chen, Guokai; Soheilian, Ferri; Baxa, Ulrich; Wang, Mengqiao; Marugan, Juan J; Muro, Silvia; Li, Zhiyuan; Brady, Roscoe; Zheng, Wei

2016-12-01

: Niemann-Pick disease type A (NPA) is a lysosomal storage disease caused by mutations in the SMPD1 gene that encodes acid sphingomyelinase (ASM). Deficiency in ASM function results in lysosomal accumulation of sphingomyelin and neurodegeneration. Currently, there is no effective treatment for NPA. To accelerate drug discovery for treatment of NPA, we generated induced pluripotent stem cells from two patient dermal fibroblast lines and differentiated them into neural stem cells. The NPA neural stem cells exhibit a disease phenotype of lysosomal sphingomyelin accumulation and enlarged lysosomes. By using this disease model, we also evaluated three compounds that reportedly reduced lysosomal lipid accumulation in Niemann-Pick disease type C as well as enzyme replacement therapy with ASM. We found that α-tocopherol, δ-tocopherol, hydroxypropyl-β-cyclodextrin, and ASM reduced sphingomyelin accumulation and enlarged lysosomes in NPA neural stem cells. Therefore, the NPA neural stem cells possess the characteristic NPA disease phenotype that can be ameliorated by tocopherols, cyclodextrin, and ASM. Our results demonstrate the efficacies of cyclodextrin and tocopherols in the NPA cell-based model. Our data also indicate that the NPA neural stem cells can be used as a new cell-based disease model for further study of disease pathophysiology and for high-throughput screening to identify new lead compounds for drug development. Currently, there is no effective treatment for Niemann-Pick disease type A (NPA). To accelerate drug discovery for treatment of NPA, NPA-induced pluripotent stem cells were generated from patient dermal fibroblasts and differentiated into neural stem cells. By using the differentiated NPA neuronal cells as a cell-based disease model system, α-tocopherol, δ-tocopherol, and hydroxypropyl-β-cyclodextrin significantly reduced sphingomyelin accumulation in these NPA neuronal cells. Therefore, this cell-based NPA model can be used for further study of disease pathophysiology and for high-throughput screening of compound libraries to identify lead compounds for drug development. ©AlphaMed Press.

Compound 49b Reduces Inflammatory Markers and Apoptosis after Ocular Blast Injury

DTIC Science & Technology

2014-09-01

drug, Compound 49b, have anti-apoptotic and anti-inflammatory properties in retinal endothelial cells and in a diabetic retinopathy model [7, 10...plays an important role in the development of early diabetic retinopathy and long-term histopathological alterations. Mol Vis, 2009; 15: 1418-28. 12...in other retinal damage models, specifically the streptozotocin- induced type 1 diabetic retinopathy model and retinal endothelial cells cultured in

Involvement of CYP4F2 in the Metabolism of a Novel Monophosphate Ester Prodrug of Gemcitabine and Its Interaction Potential In Vitro.

PubMed

Wang, Yedong; Li, Yuan; Lu, Jia; Qi, Huixin; Cheng, Isabel; Zhang, Hongjian

2018-05-16

Compound- 3 is an oral monophosphate prodrug of gemcitabine. Previous data showed that Compound- 3 was more potent than gemcitabine and it was orally active in a tumor xenograft model. In the present study, the metabolism of Compound- 3 was investigated in several well-known in vitro matrices. While relatively stable in human and rat plasma, Compound- 3 demonstrated noticeable metabolism in liver and intestinal microsomes in the presence of NADPH and human hepatocytes. Compound- 3 could also be hydrolyzed by alkaline phosphatase, leading to gemcitabine formation. Metabolite identification using accurate mass- and information-based scan techniques revealed that Compound- 3 was subjected to sequential metabolism, forming alcohol, aldehyde and carboxylic acid metabolites, respectively. Results from reaction phenotyping studies indicated that cytochrome P450 4F2 (CYP4F2) was a key CYP isozyme involved in Compound- 3 metabolism. Interaction assays suggested that CYP4F2 activity could be inhibited by Compound- 3 or an antiparasitic prodrug pafuramidine. Because CYP4F2 is a key CYP isozyme involved in the metabolism of eicosanoids and therapeutic drugs, clinical relevance of drug-drug interactions mediated via CYP4F2 inhibition warrants further investigation.

Cheaper faster drug development validated by the repositioning of drugs against neglected tropical diseases.

PubMed

Williams, Kevin; Bilsland, Elizabeth; Sparkes, Andrew; Aubrey, Wayne; Young, Michael; Soldatova, Larisa N; De Grave, Kurt; Ramon, Jan; de Clare, Michaela; Sirawaraporn, Worachart; Oliver, Stephen G; King, Ross D

2015-03-06

There is an urgent need to make drug discovery cheaper and faster. This will enable the development of treatments for diseases currently neglected for economic reasons, such as tropical and orphan diseases, and generally increase the supply of new drugs. Here, we report the Robot Scientist 'Eve' designed to make drug discovery more economical. A Robot Scientist is a laboratory automation system that uses artificial intelligence (AI) techniques to discover scientific knowledge through cycles of experimentation. Eve integrates and automates library-screening, hit-confirmation, and lead generation through cycles of quantitative structure activity relationship learning and testing. Using econometric modelling we demonstrate that the use of AI to select compounds economically outperforms standard drug screening. For further efficiency Eve uses a standardized form of assay to compute Boolean functions of compound properties. These assays can be quickly and cheaply engineered using synthetic biology, enabling more targets to be assayed for a given budget. Eve has repositioned several drugs against specific targets in parasites that cause tropical diseases. One validated discovery is that the anti-cancer compound TNP-470 is a potent inhibitor of dihydrofolate reductase from the malaria-causing parasite Plasmodium vivax.

Cheaper faster drug development validated by the repositioning of drugs against neglected tropical diseases

PubMed Central

Williams, Kevin; Bilsland, Elizabeth; Sparkes, Andrew; Aubrey, Wayne; Young, Michael; Soldatova, Larisa N.; De Grave, Kurt; Ramon, Jan; de Clare, Michaela; Sirawaraporn, Worachart; Oliver, Stephen G.; King, Ross D.

2015-01-01

There is an urgent need to make drug discovery cheaper and faster. This will enable the development of treatments for diseases currently neglected for economic reasons, such as tropical and orphan diseases, and generally increase the supply of new drugs. Here, we report the Robot Scientist ‘Eve’ designed to make drug discovery more economical. A Robot Scientist is a laboratory automation system that uses artificial intelligence (AI) techniques to discover scientific knowledge through cycles of experimentation. Eve integrates and automates library-screening, hit-confirmation, and lead generation through cycles of quantitative structure activity relationship learning and testing. Using econometric modelling we demonstrate that the use of AI to select compounds economically outperforms standard drug screening. For further efficiency Eve uses a standardized form of assay to compute Boolean functions of compound properties. These assays can be quickly and cheaply engineered using synthetic biology, enabling more targets to be assayed for a given budget. Eve has repositioned several drugs against specific targets in parasites that cause tropical diseases. One validated discovery is that the anti-cancer compound TNP-470 is a potent inhibitor of dihydrofolate reductase from the malaria-causing parasite Plasmodium vivax. PMID:25652463

New consensus multivariate models based on PLS and ANN studies of sigma-1 receptor antagonists.

PubMed

Oliveira, Aline A; Lipinski, Célio F; Pereira, Estevão B; Honorio, Kathia M; Oliveira, Patrícia R; Weber, Karen C; Romero, Roseli A F; de Sousa, Alexsandro G; da Silva, Albérico B F

2017-10-02

The treatment of neuropathic pain is very complex and there are few drugs approved for this purpose. Among the studied compounds in the literature, sigma-1 receptor antagonists have shown to be promising. In order to develop QSAR studies applied to the compounds of 1-arylpyrazole derivatives, multivariate analyses have been performed in this work using partial least square (PLS) and artificial neural network (ANN) methods. A PLS model has been obtained and validated with 45 compounds in the training set and 13 compounds in the test set (r 2 training = 0.761, q 2 = 0.656, r 2 test = 0.746, MSE test = 0.132 and MAE test = 0.258). Additionally, multi-layer perceptron ANNs (MLP-ANNs) were employed in order to propose non-linear models trained by gradient descent with momentum backpropagation function. Based on MSE test values, the best MLP-ANN models were combined in a MLP-ANN consensus model (MLP-ANN-CM; r 2 test = 0.824, MSE test = 0.088 and MAE test = 0.197). In the end, a general consensus model (GCM) has been obtained using PLS and MLP-ANN-CM models (r 2 test = 0.811, MSE test = 0.100 and MAE test = 0.218). Besides, the selected descriptors (GGI6, Mor23m, SRW06, H7m, MLOGP, and μ) revealed important features that should be considered when one is planning new compounds of the 1-arylpyrazole class. The multivariate models proposed in this work are definitely a powerful tool for the rational drug design of new compounds for neuropathic pain treatment. Graphical abstract Main scaffold of the 1-arylpyrazole derivatives and the selected descriptors.

Modeling and design of challenge tests: Inflammatory and metabolic biomarker study examples.

PubMed

Gabrielsson, Johan; Hjorth, Stephan; Vogg, Barbara; Harlfinger, Stephanie; Gutierrez, Pablo Morentin; Peletier, Lambertus; Pehrson, Rikard; Davidsson, Pia

2015-01-25

Given the complexity of pharmacological challenge experiments, it is perhaps not surprising that design and analysis, and in turn interpretation and communication of results from a quantitative point of view, is often suboptimal. Here we report an inventory of common designs sampled from anti-inflammatory, respiratory and metabolic disease drug discovery studies, all of which are based on animal models of disease involving pharmacological and/or patho/physiological interaction challenges. The corresponding data are modeled and analyzed quantitatively, the merits of the respective approach discussed and inferences made with respect to future design improvements. Although our analysis is limited to these disease model examples, the challenge approach is generally applicable to the vast majority of pharmacological intervention studies. In the present five Case Studies results from pharmacodynamic effect models from different therapeutic areas were explored and analyzed according to five typical designs. Plasma exposures of test compounds were assayed by either liquid chromatography/mass spectrometry or ligand binding assays. To describe how drug intervention can regulate diverse processes, turnover models of test compound-challenger interaction, transduction processes, and biophase time courses were applied for biomarker response in eosinophil count, IL6 response, paw-swelling, TNFα response and glucose turnover in vivo. Case Study 1 shows results from intratracheal administration of Sephadex, which is a glucocorticoid-sensitive model of airway inflammation in rats. Eosinophils in bronchoalveolar fluid were obtained at different time points via destructive sampling and then regressed by the mixed-effects modeling. A biophase function of the Sephadex time course was inferred from the modeled eosinophil time courses. In Case Study 2, a mouse model showed that the time course of cytokine-induced IL1β challenge was altered with or without drug intervention. Anakinra reversed the IL1β induced cytokine IL6 response in a dose-dependent manner. This Case Study contained time courses of test compound (drug), challenger (IL1β) and cytokine response (IL6), which resulted in high parameter precision. Case Study 3 illustrates collagen-induced arthritis progression in the rat. Swelling scores (based on severity of hind paw swelling) were used to describe arthritis progression after the challenge and the inhibitory effect of two doses of an orally administered test compound. In Case Study 4, a cynomolgus monkey model for lipopolysaccharide LPS-induced TNFα synthesis and/or release was investigated. This model provides integrated information on pharmacokinetics and in vivo potency of the test compounds. Case Study 5 contains data from an oral glucose tolerance test in rats, where the challenger is the same as the pharmacodynamic response biomarker (glucose). It is therefore convenient to model the extra input of glucose simultaneously with baseline data and during intervention of a glucose-lowering compound at different dose levels. Typically time-series analyses of challenger- and biomarker-time data are necessary if an accurate and precise estimate of the pharmacodynamic properties of a test compound is sought. Erosion of data, resulting in the single-point assessment of drug action after a challenge test, should generally be avoided. This is particularly relevant for situations where one expects time-curve shifts, tolerance/rebound, impact of disease, or hormetic concentration-response relationships to occur. Copyright © 2014 Elsevier B.V. All rights reserved.

Permeability and diffusion in vitreous humor: implications for drug delivery.

PubMed

Xu, J; Heys, J J; Barocas, V H; Randolph, T W

2000-06-01

Previous experimental work suggests that convection may be important in determining the biodistribution of drugs implanted or injected in the vitreous humor. To develop accurate biodistribution models, the relative importance of diffusion and convection in intravitreal transport must be assessed. This requires knowledge of both the diffusivity of candidate drugs and the hydraulic conductivity of the vitreous humor. Hydraulic conductivity of cadaveric bovine vitreous humor was measured by confined compression tests at constant loads of 0.15 and 0.2 N and analyzed numerically using a two-phase model. Diffusion coefficient of acid orange 8, a model compound, in the same medium was measured in a custom-built diffusion cell. Acid orange 8 diffusivity within vitreous humor is about half that in free solution. When viscous effects are properly accounted for, the hydraulic conductivity of bovine vitreous humor is 8.4+/-4.5 x 10(-7) cm2/Pa x s. We predict that convection does not contribute significantly to transport in the mouse eye, particularly for low-molecular-weight compounds. For delivery to larger animals, such as humans we conclude that convection accounts for roughly 30% of the total intravitreal drug transport. This effect should be magnified for higher-molecular-weight compounds, which diffuse more slowly, and in glaucoma, which involves higher intraocular pressure and thus potentially faster convective flow. Thus, caution should be exercised in the extrapolation of small-animal-model biodistribution data to human scale.

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.

QSAR modeling based on structure-information for properties of interest in human health.

PubMed

Hall, L H; Hall, L M

2005-01-01

The development of QSAR models based on topological structure description is presented for problems in human health. These models are based on the structure-information approach to quantitative biological modeling and prediction, in contrast to the mechanism-based approach. The structure-information approach is outlined, starting with basic structure information developed from the chemical graph (connection table). Information explicit in the connection table (element identity and skeletal connections) leads to significant (implicit) structure information that is useful for establishing sound models of a wide range of properties of interest in drug design. Valence state definition leads to relationships for valence state electronegativity and atom/group molar volume. Based on these important aspects of molecules, together with skeletal branching patterns, both the electrotopological state (E-state) and molecular connectivity (chi indices) structure descriptors are developed and described. A summary of four QSAR models indicates the wide range of applicability of these structure descriptors and the predictive quality of QSAR models based on them: aqueous solubility (5535 chemically diverse compounds, 938 in external validation), percent oral absorption (%OA, 417 therapeutic drugs, 195 drugs in external validation testing), AMES mutagenicity (2963 compounds including 290 therapeutic drugs, 400 in external validation), fish toxicity (92 substituted phenols, anilines and substituted aromatics). These models are established independent of explicit three-dimensional (3-D) structure information and are directly interpretable in terms of the implicit structure information useful to the drug design process.

Design, synthesis, biological evaluation, and molecular modeling studies of chalcone-rivastigmine hybrids as cholinesterase inhibitors.

PubMed

Wang, Ling; Wang, Yu; Tian, Yiguang; Shang, Jinling; Sun, Xiaoou; Chen, Hongzhuan; Wang, Hao; Tan, Wen

2017-01-01

A series of novel chalcone-rivastigmine hybrids were designed, synthesized, and tested in vitro for their ability to inhibit human acetylcholinesterase and butyrylcholinesterase. Most of the target compounds showed hBChE selective activity in the micro- and submicromolar ranges. The most potent compound 3 exhibited comparable IC 50 to the commercially available drug (rivastigmine). To better understand their structure activity relationships (SAR) and mechanisms of enzyme-inhibitor interactions, kinetic and molecular modeling studies including molecular docking and molecular dynamics (MD) simulations were carried out. Furthermore, compound 3 blocks the formation of reactive oxygen species (ROS) in SH-SY5Y cells and shows the required druggability and low cytotoxicity, suggesting this hybrid is a promising multifunctional drug candidate for Alzheimer's disease (AD) treatment. Copyright © 2016 Elsevier Ltd. All rights reserved.

Inside the Mind of a Medicinal Chemist: The Role of Human Bias in Compound Prioritization during Drug Discovery

PubMed Central

Kutchukian, Peter S.; Vasilyeva, Nadya Y.; Xu, Jordan; Lindvall, Mika K.; Dillon, Michael P.; Glick, Meir; Coley, John D.; Brooijmans, Natasja

2012-01-01

Medicinal chemists’ “intuition” is critical for success in modern drug discovery. Early in the discovery process, chemists select a subset of compounds for further research, often from many viable candidates. These decisions determine the success of a discovery campaign, and ultimately what kind of drugs are developed and marketed to the public. Surprisingly little is known about the cognitive aspects of chemists’ decision-making when they prioritize compounds. We investigate 1) how and to what extent chemists simplify the problem of identifying promising compounds, 2) whether chemists agree with each other about the criteria used for such decisions, and 3) how accurately chemists report the criteria they use for these decisions. Chemists were surveyed and asked to select chemical fragments that they would be willing to develop into a lead compound from a set of ∼4,000 available fragments. Based on each chemist’s selections, computational classifiers were built to model each chemist’s selection strategy. Results suggest that chemists greatly simplified the problem, typically using only 1–2 of many possible parameters when making their selections. Although chemists tended to use the same parameters to select compounds, differing value preferences for these parameters led to an overall lack of consensus in compound selections. Moreover, what little agreement there was among the chemists was largely in what fragments were undesirable. Furthermore, chemists were often unaware of the parameters (such as compound size) which were statistically significant in their selections, and overestimated the number of parameters they employed. A critical evaluation of the problem space faced by medicinal chemists and cognitive models of categorization were especially useful in understanding the low consensus between chemists. PMID:23185259

In vitro pharmacokinetic/pharmacodynamic models in anti-infective drug development: focus on TB

PubMed Central

Vaddady, Pavan K; Lee, Richard E; Meibohm, Bernd

2011-01-01

For rapid anti-tuberculosis (TB) drug development in vitro pharmacokinetic/pharmacodynamic (PK/PD) models are useful in evaluating the direct interaction between the drug and the bacteria, thereby guiding the selection of candidate compounds and the optimization of their dosing regimens. Utilizing in vivo drug-clearance profiles from animal and/or human studies and simulating them in an in vitro PK/PD model allows the in-depth characterization of antibiotic activity of new and existing antibacterials by generating time–kill data. These data capture the dynamic interplay between mycobacterial growth and changing drug concentration as encountered during prolonged drug therapy. This review focuses on important PK/PD parameters relevant to anti-TB drug development, provides an overview of in vitro PK/PD models used to evaluate the efficacy of agents against mycobacteria and discusses the related mathematical modeling approaches of time–kill data. Overall, it provides an introduction to in vitro PK/PD models and their application as critical tools in evaluating anti-TB drugs. PMID:21359155

Identification of New Antifungal Compounds Targeting Thioredoxin Reductase of Paracoccidioides Genus

PubMed Central

Abadio, Ana Karina Rodrigues; Kioshima, Erika Seki; Leroux, Vincent; Martins, Natalia Florêncio; Maigret, Bernard; Felipe, Maria Sueli Soares

2015-01-01

The prevalence of invasive fungal infections worldwide has increased in the last decades. The development of specific drugs targeting pathogenic fungi without producing collateral damage to mammalian cells is a daunting pharmacological challenge. Indeed, many of the toxicities and drug interactions observed with contemporary antifungal therapies can be attributed to “nonselective” interactions with enzymes or cell membrane systems found in mammalian host cells. A computer-aided screening strategy against the TRR1 protein of Paracoccidioides lutzii is presented here. Initially, a bank of commercially available compounds from Life Chemicals provider was docked to model by virtual screening simulations. The small molecules that interact with the model were ranked and, among the best hits, twelve compounds out of 3,000 commercially-available candidates were selected. These molecules were synthesized for validation and in vitro antifungal activity assays for Paracoccidioides lutzii and P. brasiliensis were performed. From 12 molecules tested, 3 harbor inhibitory activity in antifungal assays against the two pathogenic fungi. Corroborating these findings, the molecules have inhibitory activity against the purified recombinant enzyme TRR1 in biochemical assays. Therefore, a rational combination of molecular modeling simulations and virtual screening of new drugs has provided a cost-effective solution to an early-stage medicinal challenge. These results provide a promising technique to the development of new and innovative drugs. PMID:26569405

Synergy testing of FDA-approved drugs identifies potent drug combinations against Trypanosoma cruzi.

PubMed

Planer, Joseph D; Hulverson, Matthew A; Arif, Jennifer A; Ranade, Ranae M; Don, Robert; Buckner, Frederick S

2014-07-01

An estimated 8 million persons, mainly in Latin America, are infected with Trypanosoma cruzi, the etiologic agent of Chagas disease. Existing antiparasitic drugs for Chagas disease have significant toxicities and suboptimal effectiveness, hence new therapeutic strategies need to be devised to address this neglected tropical disease. Due to the high research and development costs of bringing new chemical entities to the clinic, we and others have investigated the strategy of repurposing existing drugs for Chagas disease. Screens of FDA-approved drugs (described in this paper) have revealed a variety of chemical classes that have growth inhibitory activity against mammalian stage Trypanosoma cruzi parasites. Aside from azole antifungal drugs that have low or sub-nanomolar activity, most of the active compounds revealed in these screens have effective concentrations causing 50% inhibition (EC50's) in the low micromolar or high nanomolar range. For example, we have identified an antihistamine (clemastine, EC50 of 0.4 µM), a selective serotonin reuptake inhibitor (fluoxetine, EC50 of 4.4 µM), and an antifolate drug (pyrimethamine, EC50 of 3.8 µM) and others. When tested alone in the murine model of Trypanosoma cruzi infection, most compounds had insufficient efficacy to lower parasitemia thus we investigated using combinations of compounds for additive or synergistic activity. Twenty-four active compounds were screened in vitro in all possible combinations. Follow up isobologram studies showed at least 8 drug pairs to have synergistic activity on T. cruzi growth. The combination of the calcium channel blocker, amlodipine, plus the antifungal drug, posaconazole, was found to be more effective at lowering parasitemia in mice than either drug alone, as was the combination of clemastine and posaconazole. Using combinations of FDA-approved drugs is a promising strategy for developing new treatments for Chagas disease.

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

Effects of halogenated aromatics/aliphatics and nitrogen(N)-heterocyclic aromatics on estimating the persistence of future pharmaceutical compounds using a modified QSAR model.

PubMed

Lim, Seung Joo; Fox, Peter

2014-02-01

The effects of halogenated aromatics/aliphatics and nitrogen(N)-heterocyclic aromatics on estimating the persistence of future pharmaceutical compounds were investigated using a modified half life equation. The potential future pharmaceutical compounds investigated were approximately 2000 pharmaceutical drugs currently undergoing the United States Food and Drug Administration (US FDA) testing. EPI Suite (BIOWIN) model estimates the fates of compounds based on the biodegradability under aerobic conditions. While BIOWIN considered the biodegradability of a compound only, the half life equation used in this study was modified by biodegradability, sorption and cometabolic oxidation. It was possible that the potential future pharmaceutical compounds were more accurately estimated using the modified half life equation. The modified half life equation considered sorption and cometabolic oxidation of halogenated aromatic/aliphatics and nitrogen(N)-heterocyclic aromatics in the sub-surface, while EPI Suite (BIOWIN) did not. Halogenated aliphatics in chemicals were more persistent than halogenated aromatics in the sub-surface. In addition, in the sub-surface environment, the fates of organic chemicals were much more affected by halogenation in chemicals than by nitrogen(N)-heterocyclic aromatics. © 2013.

Behavior of platinum(iv) complexes in models of tumor hypoxia: cytotoxicity, compound distribution and accumulation.

PubMed

Schreiber-Brynzak, Ekaterina; Pichler, Verena; Heffeter, Petra; Hanson, Buck; Theiner, Sarah; Lichtscheidl-Schultz, Irene; Kornauth, Christoph; Bamonti, Luca; Dhery, Vineet; Groza, Diana; Berry, David; Berger, Walter; Galanski, Markus; Jakupec, Michael A; Keppler, Bernhard K

2016-04-01

Hypoxia in solid tumors remains a challenge for conventional cancer therapeutics. As a source for resistance, metastasis development and drug bioprocessing, it influences treatment results and disease outcome. Bioreductive platinum(iv) prodrugs might be advantageous over conventional metal-based therapeutics, as biotransformation in a reductive milieu, such as under hypoxia, is required for drug activation. This study deals with a two-step screening of experimental platinum(iv) prodrugs with different rates of reduction and lipophilicity with the aim of identifying the most appropriate compounds for further investigations. In the first step, the cytotoxicity of all compounds was compared in hypoxic multicellular spheroids and monolayer culture using a set of cancer cell lines with different sensitivities to platinum(ii) compounds. Secondly, two selected compounds were tested in hypoxic xenografts in SCID mouse models in comparison to satraplatin, and, additionally, (LA)-ICP-MS-based accumulation and distribution studies were performed for these compounds in hypoxic spheroids and xenografts. Our findings suggest that, while cellular uptake and cytotoxicity strongly correlate with lipophilicity, cytotoxicity under hypoxia compared to non-hypoxic conditions and antitumor activity of platinum(iv) prodrugs are dependent on their rate of reduction.

Computational modeling-based discovery of novel classes of anti-inflammatory drugs that target lanthionine synthetase C-like protein 2.

PubMed

Lu, Pinyi; Hontecillas, Raquel; Horne, William T; Carbo, Adria; Viladomiu, Monica; Pedragosa, Mireia; Bevan, David R; Lewis, Stephanie N; Bassaganya-Riera, Josep

2012-01-01

Lanthionine synthetase component C-like protein 2 (LANCL2) is a member of the eukaryotic lanthionine synthetase component C-Like protein family involved in signal transduction and insulin sensitization. Recently, LANCL2 is a target for the binding and signaling of abscisic acid (ABA), a plant hormone with anti-diabetic and anti-inflammatory effects. The goal of this study was to determine the role of LANCL2 as a potential therapeutic target for developing novel drugs and nutraceuticals against inflammatory diseases. Previously, we performed homology modeling to construct a three-dimensional structure of LANCL2 using the crystal structure of lanthionine synthetase component C-like protein 1 (LANCL1) as a template. Using this model, structure-based virtual screening was performed using compounds from NCI (National Cancer Institute) Diversity Set II, ChemBridge, ZINC natural products, and FDA-approved drugs databases. Several potential ligands were identified using molecular docking. In order to validate the anti-inflammatory efficacy of the top ranked compound (NSC61610) in the NCI Diversity Set II, a series of in vitro and pre-clinical efficacy studies were performed using a mouse model of dextran sodium sulfate (DSS)-induced colitis. Our findings showed that the lead compound, NSC61610, activated peroxisome proliferator-activated receptor gamma in a LANCL2- and adenylate cyclase/cAMP dependent manner in vitro and ameliorated experimental colitis by down-modulating colonic inflammatory gene expression and favoring regulatory T cell responses. LANCL2 is a novel therapeutic target for inflammatory diseases. High-throughput, structure-based virtual screening is an effective computational-based drug design method for discovering anti-inflammatory LANCL2-based drug candidates.

Computational Modeling-Based Discovery of Novel Classes of Anti-Inflammatory Drugs That Target Lanthionine Synthetase C-Like Protein 2

PubMed Central

Lu, Pinyi; Hontecillas, Raquel; Horne, William T.; Carbo, Adria; Viladomiu, Monica; Pedragosa, Mireia; Bevan, David R.; Lewis, Stephanie N.; Bassaganya-Riera, Josep

2012-01-01

Background Lanthionine synthetase component C-like protein 2 (LANCL2) is a member of the eukaryotic lanthionine synthetase component C-Like protein family involved in signal transduction and insulin sensitization. Recently, LANCL2 is a target for the binding and signaling of abscisic acid (ABA), a plant hormone with anti-diabetic and anti-inflammatory effects. Methodology/Principal Findings The goal of this study was to determine the role of LANCL2 as a potential therapeutic target for developing novel drugs and nutraceuticals against inflammatory diseases. Previously, we performed homology modeling to construct a three-dimensional structure of LANCL2 using the crystal structure of lanthionine synthetase component C-like protein 1 (LANCL1) as a template. Using this model, structure-based virtual screening was performed using compounds from NCI (National Cancer Institute) Diversity Set II, ChemBridge, ZINC natural products, and FDA-approved drugs databases. Several potential ligands were identified using molecular docking. In order to validate the anti-inflammatory efficacy of the top ranked compound (NSC61610) in the NCI Diversity Set II, a series of in vitro and pre-clinical efficacy studies were performed using a mouse model of dextran sodium sulfate (DSS)-induced colitis. Our findings showed that the lead compound, NSC61610, activated peroxisome proliferator-activated receptor gamma in a LANCL2- and adenylate cyclase/cAMP dependent manner in vitro and ameliorated experimental colitis by down-modulating colonic inflammatory gene expression and favoring regulatory T cell responses. Conclusions/Significance LANCL2 is a novel therapeutic target for inflammatory diseases. High-throughput, structure-based virtual screening is an effective computational-based drug design method for discovering anti-inflammatory LANCL2-based drug candidates. PMID:22509338

In silico-based combinatorial pharmacophore modelling and docking studies of GSK-3β and GK inhibitors of Hippophae.

PubMed

Middha, Sushil Kumar; Goyal, Arvind Kumar; Faizan, Syed Ahmed; Sanghamitra, Nethramurthy; Basistha, Bharat Chandra; Usha, Talambedu

2013-11-01

Type 2 diabetes is an inevitably progressive disease, with irreversible beta cell failure. Glycogen synthase kinase and Glukokinase, two important enzymes with diverse biological actions in carbohydrate metabolism, are promising targets for developing novel antidiabetic drugs. A combinatorial structure-based molecular docking and pharmacophore modelling study was performed with the compounds of Hippophae salicifolia and H. rhamnoides as inhibitors. Docking with Discovery Studio 3.5 revealed that two compounds from H. salicifolia, viz Lutein D and an analogue of Zeaxanthin, and two compounds from H. rhamnoides, viz Isorhamnetin-3-rhamnoside and Isorhamnetin-7-glucoside, bind significantly to the GSK-3 beta receptor and play a role in its inhibition; whereas in the case of Glucokinase, only one compound from both the plants, i.e. vitamin C, had good binding characteristics capable of activation. The results help to understand the type of interactions that occur between the ligands and the receptors. Toxicity predictions revealed that none of the compounds had hepatotoxic effects and had good absorption as well as solubility characteristics. The compounds did not possess plasma protein-binding, crossing blood-brain barrier ability. Further, in vivo and in vitro studies need to be performed to prove that these compounds can be used effectively as antidiabetic drugs.

Discovery of 2-aminothiazolyl berberine derivatives as effectively antibacterial agents toward clinically drug-resistant Gram-negative Acinetobacter baumanii.

PubMed

Gao, Wei-Wei; Gopala, Lavanya; Bheemanaboina, Rammohan R Yadav; Zhang, Guo-Biao; Li, Shuo; Zhou, Cheng-He

2018-02-25

Aminothiazolyl berberine derivatives as potentially antimicrobial agents were designed and synthesized in an effort to overcome drug resistance. The antimicrobial assay revealed that some target compounds exhibited significantly inhibitory efficiencies toward bacteria and fungi including drug-resistant pathogens, and the aminothiazole and Schiff base moieties were helpful structural fragments for aqueous solubility and antibacterial activity. Especially, aminothiazolyl 9-hexyl berberine 9c and 2,4-dichlorobenzyl derivative 18a exhibited good activities (MIC = 2 nmol/mL) against clinically drug-resistant Gram-negative Acinetobacter baumanii with low cytotoxicity to hepatocyte LO2 cells, rapidly bactericidal effects and quite slow development of bacterial resistance toward A. baumanii. Molecular modeling indicated that compounds 9c and 18a could bind with GLY-102, ARG-136 and/or ALA-100 residues of DNA gyrase through hydrogen bonds. It was found that compounds 9c and 18a were able to disturb the drug-resistant A. baumanii membrane effectively, and molecule 9c could not only intercalate but also cleave bacterial DNA isolated from resistant A. baumanii, which might be the preliminary antibacterial action mechanism of inhibiting the growth of A. baumanii strain. In particular, the combination use of compound 9c with norfloxacin could enhance the antibacterial activity, broaden antibacterial spectrum and overcome the drug resistance. Copyright © 2018 Elsevier Masson SAS. All rights reserved.

Hydrophobin-nanofibrillated cellulose stabilized emulsions for encapsulation and release of BCS class II drugs.

PubMed

Paukkonen, Heli; Ukkonen, Anni; Szilvay, Geza; Yliperttula, Marjo; Laaksonen, Timo

2017-03-30

The purpose of this study was to construct biopolymer-based oil-in-water emulsion formulations for encapsulation and release of poorly water soluble model compounds naproxen and ibuprofen. Class II hydrophobin protein HFBII from Trichoderma reesei was used as a surfactant to stabilize the oil/water interfaces of the emulsion droplets in the continuous aqueous phase. Nanofibrillated cellulose (NFC) was used as a viscosity modifier to further stabilize the emulsions and encapsulate protein coated oil droplets in NFC fiber network. The potential of both native and oxidized NFC were studied for this purpose. Various emulsion formulations were prepared and the abilities of different formulations to control the drug release rate of naproxen and ibuprofen, used as model compounds, were evaluated. The optimal formulation for sustained drug release consisted of 0.01% of drug, 0.1% HFBII, 0.15% oxidized NFC, 10% soybean oil and 90% water phase. By comparison, the use of native NFC in combination with HFBII resulted in an immediate drug release for both of the compounds. The results indicate that these NFC originated biopolymers are suitable for pharmaceutical emulsion formulations. The native and oxidized NFC grades can be used as emulsion stabilizers in sustained and immediate drug release applications. Furthermore, stabilization of the emulsions was achieved with low concentrations of both HFBII and NFC, which may be an advantage when compared to surfactant concentrations of conventional excipients traditionally used in pharmaceutical emulsion formulations. Copyright © 2017 Elsevier B.V. All rights reserved.

A Novel Spirooxindole Derivative Inhibits the Growth of Leishmania donovani Parasites both In Vitro and In Vivo by Targeting Type IB Topoisomerase

PubMed Central

Saha, Sourav; Acharya, Chiranjit; Pal, Uttam; Chowdhury, Somenath Roy; Sarkar, Kahini; Maiti, Nakul C.

2016-01-01

Visceral leishmaniasis is a fatal parasitic disease, and there is an emergent need for development of effective drugs against this neglected tropical disease. We report here the development of a novel spirooxindole derivative, N-benzyl-2,2′α-3,3′,5′,6′,7′,7α,α′-octahydro-2methoxycarbonyl-spiro[indole-3,3′-pyrrolizidine]-2-one (compound 4c), which inhibits Leishmania donovani topoisomerase IB (LdTopIB) and kills the wild type as well as drug-resistant parasite strains. This compound inhibits catalytic activity of LdTopIB in a competitive manner. Unlike camptothecin (CPT), the compound does not stabilize the DNA-topoisomerase IB cleavage complex; rather, it hinders drug-DNA-enzyme covalent complex formation. Fluorescence studies show that the stoichiometry of this compound binding to LdTopIB is 2:1 (mole/mole), with a dissociation constant of 6.65 μM. Molecular docking with LdTopIB using the stereoisomers of compound 4c produced two probable hits for the binding site, one in the small subunit and the other in the hinge region of the large subunit of LdTopIB. This spirooxindole is highly cytotoxic to promastigotes of L. donovani and also induces apoptosis-like cell death in the parasite. Treatment with compound 4c causes depolarization of mitochondrial membrane potential, formation of reactive oxygen species inside parasites, and ultimately fragmentation of nuclear DNA. Compound 4c also effectively clears amastigote forms of wild-type and drug-resistant parasites from infected mouse peritoneal macrophages but has less of an effect on host macrophages. Moreover, compound 4c showed strong antileishmanial efficacies in the BALB/c mouse model of leishmaniasis. This compound potentially can be used as a lead for developing excellent antileishmanial agents against emerging drug-resistant strains of the parasite. PMID:27503653

Hepatoprotective and Anti-fibrotic Agents: It's Time to Take the Next Step

PubMed Central

Weiskirchen, Ralf

2016-01-01

Hepatic fibrosis and cirrhosis cause strong human suffering and necessitate a monetary burden worldwide. Therefore, there is an urgent need for the development of therapies. Pre-clinical animal models are indispensable in the drug discovery and development of new anti-fibrotic compounds and are immensely valuable for understanding and proofing the mode of their proposed action. In fibrosis research, inbreed mice and rats are by far the most used species for testing drug efficacy. During the last decades, several hundred or even a thousand different drugs that reproducibly evolve beneficial effects on liver health in respective disease models were identified. However, there are only a few compounds (e.g., GR-MD-02, GM-CT-01) that were translated from bench to bedside. In contrast, the large number of drugs successfully tested in animal studies is repeatedly tested over and over engender findings with similar or identical outcome. This circumstance undermines the 3R (Replacement, Refinement, Reduction) principle of Russell and Burch that was introduced to minimize the suffering of laboratory animals. This ethical framework, however, represents the basis of the new animal welfare regulations in the member states of the European Union. Consequently, the legal authorities in the different countries are halted to foreclose testing of drugs in animals that were successfully tested before. This review provides a synopsis on anti-fibrotic compounds that were tested in classical rodent models. Their mode of action, potential sources and the observed beneficial effects on liver health are discussed. This review attempts to provide a reference compilation for all those involved in the testing of drugs or in the design of new clinical trials targeting hepatic fibrosis. PMID:26779021

Zebrafish-Based Discovery of Antiseizure Compounds from the Red Sea: Pseurotin A2 and Azaspirofuran A.

PubMed

Copmans, Daniëlle; Rateb, Mostafa; Tabudravu, Jioji N; Pérez-Bonilla, Mercedes; Dirkx, Nina; Vallorani, Riccardo; Diaz, Caridad; Pérez Del Palacio, José; Smith, Alan J; Ebel, Rainer; Reyes, Fernando; Jaspars, Marcel; de Witte, Peter A M

2018-04-19

In search for novel antiseizure drugs (ASDs), the European FP7-funded PharmaSea project used zebrafish embryos and larvae as a drug discovery platform to screen marine natural products to identify promising antiseizure hits in vivo for further development. Within the framework of this project, seven known heterospirocyclic γ-lactams, namely, pseurotin A, pseurotin A 2 , pseurotin F1, 11- O-methylpseurotin A, pseurotin D, azaspirofuran A, and azaspirofuran B, were isolated from the bioactive marine fungus Aspergillus fumigatus, and their antiseizure activity was evaluated in the larval zebrafish pentylenetetrazole (PTZ) seizure model. Pseurotin A 2 and azaspirofuran A were identified as antiseizure hits, while their close chemical analogues were inactive. Besides, electrophysiological analysis from the zebrafish midbrain demonstrated that pseurotin A 2 and azaspirofuran A also ameliorate PTZ-induced epileptiform discharges. Next, to determine whether these findings translate to mammalians, both compounds were analyzed in the mouse 6 Hz (44 mA) psychomotor seizure model. They lowered the seizure duration dose-dependently, thereby confirming their antiseizure properties and suggesting activity against drug-resistant seizures. Finally, in a thorough ADMET assessment, pseurotin A 2 and azaspirofuran A were found to be drug-like. Based on the prominent antiseizure activity in both species and the drug-likeness, we propose pseurotin A 2 and azaspirofuran A as lead compounds that are worth further investigation for the treatment of epileptic seizures. This study not only provides the first evidence of antiseizure activity of pseurotins and azaspirofurans, but also demonstrates the value of the zebrafish model in (marine) natural product drug discovery in general, and for ASD discovery in particular.

Cathepsin B is a New Drug Target for Traumatic Brain Injury Therapeutics: Evidence for E64d as a Promising Lead Drug Candidate

PubMed Central

Hook, Gregory; Jacobsen, J. Steven; Grabstein, Kenneth; Kindy, Mark; Hook, Vivian

2015-01-01

There is currently no therapeutic drug treatment for traumatic brain injury (TBI) despite decades of experimental clinical trials. This may be because the mechanistic pathways for improving TBI outcomes have yet to be identified and exploited. As such, there remains a need to seek out new molecular targets and their drug candidates to find new treatments for TBI. This review presents supporting evidence for cathepsin B, a cysteine protease, as a potentially important drug target for TBI. Cathepsin B expression is greatly up-regulated in TBI animal models, as well as in trauma patients. Importantly, knockout of the cathepsin B gene in TBI mice results in substantial improvements of TBI-caused deficits in behavior, pathology, and biomarkers, as well as improvements in related injury models. During the process of TBI-induced injury, cathepsin B likely escapes the lysosome, its normal subcellular location, into the cytoplasm or extracellular matrix (ECM) where the unleashed proteolytic power causes destruction via necrotic, apoptotic, autophagic, and activated glia-induced cell death, together with ECM breakdown and inflammation. Significantly, chemical inhibitors of cathepsin B are effective for improving deficits in TBI and related injuries including ischemia, cerebral bleeding, cerebral aneurysm, edema, pain, infection, rheumatoid arthritis, epilepsy, Huntington’s disease, multiple sclerosis, and Alzheimer’s disease. The inhibitor E64d is unique among cathepsin B inhibitors in being the only compound to have demonstrated oral efficacy in a TBI model and prior safe use in man and as such it is an excellent tool compound for preclinical testing and clinical compound development. These data support the conclusion that drug development of cathepsin B inhibitors for TBI treatment should be accelerated. PMID:26388830

A magnetic anti-cancer compound for magnet-guided delivery and magnetic resonance imaging

PubMed Central

Eguchi, Haruki; Umemura, Masanari; Kurotani, Reiko; Fukumura, Hidenobu; Sato, Itaru; Kim, Jeong-Hwan; Hoshino, Yujiro; Lee, Jin; Amemiya, Naoyuki; Sato, Motohiko; Hirata, Kunio; Singh, David J.; Masuda, Takatsugu; Yamamoto, Masahiro; Urano, Tsutomu; Yoshida, Keiichiro; Tanigaki, Katsumi; Yamamoto, Masaki; Sato, Mamoru; Inoue, Seiichi; Aoki, Ichio; Ishikawa, Yoshihiro

2015-01-01

Research on controlled drug delivery for cancer chemotherapy has focused mainly on ways to deliver existing anti-cancer drug compounds to specified targets, e.g., by conjugating them with magnetic particles or encapsulating them in micelles. Here, we show that an iron-salen, i.e., μ-oxo N,N'- bis(salicylidene)ethylenediamine iron (Fe(Salen)), but not other metal salen derivatives, intrinsically exhibits both magnetic character and anti-cancer activity. X-Ray crystallographic analysis and first principles calculations based on the measured structure support this. It promoted apoptosis of various cancer cell lines, likely, via production of reactive oxygen species. In mouse leg tumor and tail melanoma models, Fe(Salen) delivery with magnet caused a robust decrease in tumor size, and the accumulation of Fe(Salen) was visualized by magnetic resonance imaging. Fe(Salen) is an anti-cancer compound with magnetic property, which is suitable for drug delivery and imaging. We believe such magnetic anti-cancer drugs have the potential to greatly advance cancer chemotherapy for new theranostics and drug-delivery strategies. PMID:25779357

A Molecular-Level View of the Physical Stability of Amorphous Solid Dispersions

NASA Astrophysics Data System (ADS)

Yuan, Xiaoda

Many pharmaceutical compounds being developed in recent years are poorly soluble in water. This has led to insufficient oral bioavailability of many compounds in vitro. The amorphous formulation is one of the promising techniques to increase the oral bioavailability of these poorly water-soluble compounds. However, an amorphous drug substance is inherently unstable because it is a high energy form. In order to increase the physical stability, the amorphous drug is often formulated with a suitable polymer to form an amorphous solid dispersion. Previous research has suggested that the formation of an intimately mixed drug-polymer mixture contributes to the stabilization of the amorphous drug compound. The goal of this research is to better understand the role of miscibility, molecular interactions and mobility on the physical stability of amorphous solid dispersions. Methods were developed to detect different degrees of miscibility on nanometer scale and to quantify the extent of hydrogen-bonding interactions between the drug and the polymer. Miscibility, hydrogen-bonding interactions and molecular mobility were correlated with physical stability during a six-month period using three model systems. Overall, this research provides molecular-level insights into many factors that govern the physical stability of amorphous solid dispersions which can lead to a more effective design of stable amorphous formulations.

BitterSweetForest: A random forest based binary classifier to predict bitterness and sweetness of chemical compounds

NASA Astrophysics Data System (ADS)

Banerjee, Priyanka; Preissner, Robert

2018-04-01

Taste of a chemical compounds present in food stimulates us to take in nutrients and avoid poisons. However, the perception of taste greatly depends on the genetic as well as evolutionary perspectives. The aim of this work was the development and validation of a machine learning model based on molecular fingerprints to discriminate between sweet and bitter taste of molecules. BitterSweetForest is the first open access model based on KNIME workflow that provides platform for prediction of bitter and sweet taste of chemical compounds using molecular fingerprints and Random Forest based classifier. The constructed model yielded an accuracy of 95% and an AUC of 0.98 in cross-validation. In independent test set, BitterSweetForest achieved an accuracy of 96 % and an AUC of 0.98 for bitter and sweet taste prediction. The constructed model was further applied to predict the bitter and sweet taste of natural compounds, approved drugs as well as on an acute toxicity compound data set. BitterSweetForest suggests 70% of the natural product space, as bitter and 10 % of the natural product space as sweet with confidence score of 0.60 and above. 77 % of the approved drug set was predicted as bitter and 2% as sweet with a confidence scores of 0.75 and above. Similarly, 75% of the total compounds from acute oral toxicity class were predicted only as bitter with a minimum confidence score of 0.75, revealing toxic compounds are mostly bitter. Furthermore, we applied a Bayesian based feature analysis method to discriminate the most occurring chemical features between sweet and bitter compounds from the feature space of a circular fingerprint.

BitterSweetForest: A Random Forest Based Binary Classifier to Predict Bitterness and Sweetness of Chemical Compounds

PubMed Central

Banerjee, Priyanka; Preissner, Robert

2018-01-01

Taste of a chemical compound present in food stimulates us to take in nutrients and avoid poisons. However, the perception of taste greatly depends on the genetic as well as evolutionary perspectives. The aim of this work was the development and validation of a machine learning model based on molecular fingerprints to discriminate between sweet and bitter taste of molecules. BitterSweetForest is the first open access model based on KNIME workflow that provides platform for prediction of bitter and sweet taste of chemical compounds using molecular fingerprints and Random Forest based classifier. The constructed model yielded an accuracy of 95% and an AUC of 0.98 in cross-validation. In independent test set, BitterSweetForest achieved an accuracy of 96% and an AUC of 0.98 for bitter and sweet taste prediction. The constructed model was further applied to predict the bitter and sweet taste of natural compounds, approved drugs as well as on an acute toxicity compound data set. BitterSweetForest suggests 70% of the natural product space, as bitter and 10% of the natural product space as sweet with confidence score of 0.60 and above. 77% of the approved drug set was predicted as bitter and 2% as sweet with a confidence score of 0.75 and above. Similarly, 75% of the total compounds from acute oral toxicity class were predicted only as bitter with a minimum confidence score of 0.75, revealing toxic compounds are mostly bitter. Furthermore, we applied a Bayesian based feature analysis method to discriminate the most occurring chemical features between sweet and bitter compounds using the feature space of a circular fingerprint. PMID:29696137

Predicting cytotoxicity from heterogeneous data sources with Bayesian learning.

PubMed

Langdon, Sarah R; Mulgrew, Joanna; Paolini, Gaia V; van Hoorn, Willem P

2010-12-09

We collected data from over 80 different cytotoxicity assays from Pfizer in-house work as well as from public sources and investigated the feasibility of using these datasets, which come from a variety of assay formats (having for instance different measured endpoints, incubation times and cell types) to derive a general cytotoxicity model. Our main aim was to derive a computational model based on this data that can highlight potentially cytotoxic series early in the drug discovery process. We developed Bayesian models for each assay using Scitegic FCFP_6 fingerprints together with the default physical property descriptors. Pairs of assays that are mutually predictive were identified by calculating the ROC score of the model derived from one predicting the experimental outcome of the other, and vice versa. The prediction pairs were visualised in a network where nodes are assays and edges are drawn for ROC scores >0.60 in both directions. We observed that, if assay pairs (A, B) and (B, C) were mutually predictive, this was often not the case for the pair (A, C). The results from 48 assays connected to each other were merged in one training set of 145590 compounds and a general cytotoxicity model was derived. The model has been cross-validated as well as being validated with a set of 89 FDA approved drug compounds. We have generated a predictive model for general cytotoxicity which could speed up the drug discovery process in multiple ways. Firstly, this analysis has shown that the outcomes of different assay formats can be mutually predictive, thus removing the need to submit a potentially toxic compound to multiple assays. Furthermore, this analysis enables selection of (a) the easiest-to-run assay as corporate standard, or (b) the most descriptive panel of assays by including assays whose outcomes are not mutually predictive. The model is no replacement for a cytotoxicity assay but opens the opportunity to be more selective about which compounds are to be submitted to it. On a more mundane level, having data from more than 80 assays in one dataset answers, for the first time, the question - "what are the known cytotoxic compounds from the Pfizer compound collection?" Finally, having a predictive cytotoxicity model will assist the design of new compounds with a desired cytotoxicity profile, since comparison of the model output with data from an in vitro safety/toxicology assay suggests one is predictive of the other.

Finite element modeling of drug distribution in the vitreous humor of the rabbit eye.

PubMed

Friedrich, S; Cheng, Y L; Saville, B

1997-01-01

Direct intravitreal injection of drug is a common method for treating diseases of the retina or vitreous. The stagnant nature of the vitreous humor and surrounding tissue barriers creates concentration gradients within the vitreous that must be accounted for when developing drug therapy. The objective of this research was to study drug distribution in the vitreous humor of the rabbit eye after an intravitreal injection, using a finite element model. Fluorescein and fluorescein glucuronide were selected as model compounds due to available experimental data. All required model parameters were known except for the permeability of these compounds through the retina, which was determined by fitting model predictions to experimental data. The location of the intravitreal injection in the experimental studies was not precisely known; therefore, several injection locations were considered, and best-fit retinal permeability was determined for each case. Retinal permeability of fluorescein and fluorescein glucuronide estimated by the model ranged from 1.94 x 10(-5) to 3.5 x 10(-5) cm s(-1) and from 0 to 7.62 x 10(-7) cm s(-1), respectively, depending on the assumed site of the injection. These permeability values were compared with values previously calculated from other models, and the limitations of the models are discussed. Intravitreal injection position was found to be an important variable that must be controlled in both experimental and clinical settings.

Approved oncology drugs lack in vivo activity against Trichuris muris despite in vitro activity.

PubMed

Cowan, Noemi; Raimondo, Alessia; Keiser, Jennifer

2016-11-01

Infections with soil-transmitted helminths (STHs) are considered among the most persistent global health problems. The few available drugs have limitations including low efficacy against Trichuris trichiura infections. As a starting point toward drug repositioning, we studied a set of FDA-approved oncology drugs for activity against Trichuris muris since targets relevant to cancer therapy might have a function in helminth biology. Drugs were tested in vitro on the larval and adult stage of T. muris. Compounds active in vitro were tested in the T. muris mouse model at single oral dosages of 200-400 mg/kg. Of the 114 drugs tested in vitro, 12 showed activity against T. muris larvae (>80 % drug effect at 50 μM). Ten of these drugs were also active on the adult worm stage (>80 % drug effect at 50 μM), of which six revealed IC 50 values between 1.8 and 5.0 μM. Except for tamoxifen citrate, all in vitro active drugs were protein kinase inhibitors. None of the drugs tested in vivo showed efficacy, revealing worm burden reductions of 0-24 % and worm expulsion rates of 0-7.9 %. The promising in vitro activities of protein kinases could not be confirmed in vivo. Drug discovery against STH should be strengthened including the definition of compound progression criteria. Follow-up structure-activity relationship studies with modified compounds might be considered.

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

PubMed

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

2015-01-01

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

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

PubMed Central

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

2015-01-01

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

Disease-in-a-dish: the contribution of patient-specific induced pluripotent stem cell technology to regenerative rehabilitation.

PubMed

Mack, David L; Guan, Xuan; Wagoner, Ashley; Walker, Stephen J; Childers, Martin K

2014-11-01

Advances in regenerative medicine technologies will lead to dramatic changes in how patients in rehabilitation medicine clinics are treated in the upcoming decades. The multidisciplinary field of regenerative medicine is developing new tools for disease modeling and drug discovery based on induced pluripotent stem cells. This approach capitalizes on the idea of personalized medicine by using the patient's own cells to discover new drugs, increasing the likelihood of a favorable outcome. The search for compounds that can correct disease defects in the culture dish is a conceptual departure from how drug screens were done in the past. This system proposes a closed loop from sample collection from the diseased patient, to in vitro disease model, to drug discovery and Food and Drug Administration approval, to delivering that drug back to the same patient. Here, recent progress in patient-specific induced pluripotent stem cell derivation, directed differentiation toward diseased cell types, and how those cells can be used for high-throughput drug screens are reviewed. Given that restoration of normal function is a driving force in rehabilitation medicine, the authors believe that this drug discovery platform focusing on phenotypic rescue will become a key contributor to therapeutic compounds in regenerative rehabilitation.

Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosis.

PubMed

Ekins, Sean; Kaneko, Takushi; Lipinski, Christopher A; Bradford, Justin; Dole, Krishna; Spektor, Anna; Gregory, Kellan; Blondeau, David; Ernst, Sylvia; Yang, Jeremy; Goncharoff, Nicko; Hohman, Moses M; Bunin, Barry A

2010-11-01

There is an urgent need for new drugs against tuberculosis which annually claims 1.7-1.8 million lives. One approach to identify potential leads is to screen in vitro small molecules against Mycobacterium tuberculosis (Mtb). Until recently there was no central repository to collect information on compounds screened. Consequently, it has been difficult to analyze molecular properties of compounds that inhibit the growth of Mtb in vitro. We have collected data from publically available sources on over 300 000 small molecules deposited in the Collaborative Drug Discovery TB Database. A cheminformatics analysis on these compounds indicates that inhibitors of the growth of Mtb have statistically higher mean logP, rule of 5 alerts, while also having lower HBD count, atom count and lower PSA (ChemAxon descriptors), compared to compounds that are classed as inactive. Additionally, Bayesian models for selecting Mtb active compounds were evaluated with over 100 000 compounds and, they demonstrated 10 fold enrichment over random for the top ranked 600 compounds. This represents a promising approach for finding compounds active against Mtb in whole cells screened under the same in vitro conditions. Various sets of Mtb hit molecules were also examined by various filtering rules used widely in the pharmaceutical industry to identify compounds with potentially reactive moieties. We found differences between the number of compounds flagged by these rules in Mtb datasets, malaria hits, FDA approved drugs and antibiotics. Combining these approaches may enable selection of compounds with increased probability of inhibition of whole cell Mtb activity.

Eyelid skin as a potential site for drug delivery to conjunctiva and ocular tissues.

PubMed

See, Gerard Lee; Sagesaka, Ayano; Sugasawa, Satoko; Todo, Hiroaki; Sugibayashi, Kenji

2017-11-25

The feasibility of topical application onto the (lower) eyelid skin to deliver hydrophilic and lipophilic compounds into the conjunctiva and ocular tissues was evaluated by comparing with conventional eye drop application. Skin permeation and the concentration of several model compounds, and skin impedance were determined utilizing eyelid skin from hairless rats, as well as abdominal skin in the same animals for comparison. In vitro static diffusion cells were used to assess the skin permeation in order to provide key insights into the relationship between the skin sites and drugs. The obtained results revealed that drug permeation through the eyelid skin was much higher than that through abdominal skin regardless of the drug lipophilicity. Specifically, diclofenac sodium salt and tranilast exhibited approximately 6-fold and 11-fold higher permeability coefficients, respectively, through eyelid skin compared with abdominal skin. Histomorphological evaluation and in vivo distribution of model fluorescent dyes were also examined in the conjunctiva and skin after eyelid administration by conventional microscope and confocal laser scanning microscope analyses. The result revealed that eyelid skin has a thinner stratum corneum, thereby showing lower impedance, which could be the reason for the higher drug permeation through eyelid skin. Comparative evaluation of lipophilic and hydrophilic model compounds administered via the eyelid skin over 8h revealed stronger fluorescence intensity in the skin and surrounding tissues compared with eye drop administration. These results suggested that the (lower) eyelid skin is valuable as a prospective site for ophthalmic medicines. Copyright © 2017 Elsevier B.V. All rights reserved.

Combining Computational Methods for Hit to Lead Optimization in Mycobacterium tuberculosis Drug Discovery

PubMed Central

Ekins, Sean; Freundlich, Joel S.; Hobrath, Judith V.; White, E. Lucile; Reynolds, Robert C

2013-01-01

Purpose Tuberculosis treatments need to be shorter and overcome drug resistance. Our previous large scale phenotypic high-throughput screening against Mycobacterium tuberculosis (Mtb) has identified 737 active compounds and thousands that are inactive. We have used this data for building computational models as an approach to minimize the number of compounds tested. Methods A cheminformatics clustering approach followed by Bayesian machine learning models (based on publicly available Mtb screening data) was used to illustrate that application of these models for screening set selections can enrich the hit rate. Results In order to explore chemical diversity around active cluster scaffolds of the dose-response hits obtained from our previous Mtb screens a set of 1924 commercially available molecules have been selected and evaluated for antitubercular activity and cytotoxicity using Vero, THP-1 and HepG2 cell lines with 4.3%, 4.2% and 2.7% hit rates, respectively. We demonstrate that models incorporating antitubercular and cytotoxicity data in Vero cells can significantly enrich the selection of non-toxic actives compared to random selection. Across all cell lines, the Molecular Libraries Small Molecule Repository (MLSMR) and cytotoxicity model identified ~10% of the hits in the top 1% screened (>10 fold enrichment). We also showed that seven out of nine Mtb active compounds from different academic published studies and eight out of eleven Mtb active compounds from a pharmaceutical screen (GSK) would have been identified by these Bayesian models. Conclusion Combining clustering and Bayesian models represents a useful strategy for compound prioritization and hit-to lead optimization of antitubercular agents. PMID:24132686

Drug-drug interaction predictions with PBPK models and optimal multiresponse sampling time designs: application to midazolam and a phase I compound. Part 1: comparison of uniresponse and multiresponse designs using PopDes.

PubMed

Chenel, Marylore; Bouzom, François; Aarons, Leon; Ogungbenro, Kayode

2008-12-01

To determine the optimal sampling time design of a drug-drug interaction (DDI) study for the estimation of apparent clearances (CL/F) of two co-administered drugs (SX, a phase I compound, potentially a CYP3A4 inhibitor, and MDZ, a reference CYP3A4 substrate) without any in vivo data using physiologically based pharmacokinetic (PBPK) predictions, population PK modelling and multiresponse optimal design. PBPK models were developed with AcslXtreme using only in vitro data to simulate PK profiles of both drugs when they were co-administered. Then, using simulated data, population PK models were developed with NONMEM and optimal sampling times were determined by optimizing the determinant of the population Fisher information matrix with PopDes using either two uniresponse designs (UD) or a multiresponse design (MD) with joint sampling times for both drugs. Finally, the D-optimal sampling time designs were evaluated by simulation and re-estimation with NONMEM by computing the relative root mean squared error (RMSE) and empirical relative standard errors (RSE) of CL/F. There were four and five optimal sampling times (=nine different sampling times) in the UDs for SX and MDZ, respectively, whereas there were only five sampling times in the MD. Whatever design and compound, CL/F was well estimated (RSE < 20% for MDZ and <25% for SX) and expected RSEs from PopDes were in the same range as empirical RSEs. Moreover, there was no bias in CL/F estimation. Since MD required only five sampling times compared to the two UDs, D-optimal sampling times of the MD were included into a full empirical design for the proposed clinical trial. A joint paper compares the designs with real data. This global approach including PBPK simulations, population PK modelling and multiresponse optimal design allowed, without any in vivo data, the design of a clinical trial, using sparse sampling, capable of estimating CL/F of the CYP3A4 substrate and potential inhibitor when co-administered together.

Intact blood-brain barrier transport of small molecular drugs in animal models of amyloid beta and alpha-synuclein pathology.

PubMed

Gustafsson, Sofia; Lindström, Veronica; Ingelsson, Martin; Hammarlund-Udenaes, Margareta; Syvänen, Stina

2018-01-01

Pathophysiological impairment of the neurovascular unit, including the integrity and dynamics of the blood-brain barrier (BBB), has been denoted both a cause and consequence of neurodegenerative diseases. Pathological impact on BBB drug delivery has also been debated. The aim of the present study was to investigate BBB drug transport, by determining the unbound brain-to-plasma concentration ratio (K p,uu,brain ), in aged AβPP-transgenic mice, α-synuclein transgenic mice, and wild type mice. Mice were dosed with a cassette of five compounds, including digoxin, levofloxacin (1 mg/kg, s.c.), paliperidone, oxycodone, and diazepam (0.25 mg/kg, s.c.). Brain and blood were collected at 0.5, 1, or 3 h after dosage. Drug concentrations were measured using LC-MS/MS. The total brain-to-plasma concentration ratio was calculated and equilibrium dialysis was used to determine the fraction of unbound drug in brain and plasma for all compounds. Together, these three measures were used to determine the K p,uu,brain value. Despite Aβ or α-synuclein pathology in the current animal models, no difference was observed in the extent of drug transport across the BBB compared to wild type animals for any of the compounds investigated. Hence, the present study shows that the concept of a leaking barrier within neurodegenerative conditions has to be interpreted with caution when estimating drug transport into the brain. The capability of the highly dynamic BBB to regulate brain drug exposure still seems to be intact despite the presence of pathology. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Synthesis, pharmacology and molecular docking on multifunctional tacrine-ferulic acid hybrids as cholinesterase inhibitors against Alzheimer's disease.

PubMed

Zhu, Jie; Yang, Hongyu; Chen, Yao; Lin, Hongzhi; Li, Qi; Mo, Jun; Bian, Yaoyao; Pei, Yuqiong; Sun, Haopeng

2018-12-01

The cholinergic hypothesis has long been a "polar star" in drug discovery for Alzheimer's disease (AD), resulting in many small molecules and biological drug candidates. Most of the drugs marketed for AD are cholinergic. Herein, we report our efforts in the discovery of cholinesterases inhibitors (ChEIs) as multi-target-directed ligands. A series of tacrine-ferulic acid hybrids have been designed and synthesised. All these compounds showed potent acetyl-(AChE) and butyryl cholinesterase(BuChE) inhibition. Among them, the optimal compound 10g, was the most potent inhibitor against AChE (electrophorus electricus (eeAChE) half maximal inhibitory concentration (IC 50 ) = 37.02 nM), it was also a strong inhibitor against BuChE (equine serum (eqBuChE) IC 50  = 101.40 nM). Besides, it inhibited amyloid β-protein self-aggregation by 65.49% at 25 μM. In subsequent in vivo scopolamine-induced AD models, compound 10g obviously ameliorated the cognition impairment and showed preliminary safety in hepatotoxicity evaluation. These data suggest compound 10g as a promising multifunctional agent in the drug discovery process against AD.

A method to predict different mechanisms for blood-brain barrier permeability of CNS activity compounds in Chinese herbs using support vector machine.

PubMed

Jiang, Ludi; Chen, Jiahua; He, Yusu; Zhang, Yanling; Li, Gongyu

2016-02-01

The blood-brain barrier (BBB), a highly selective barrier between central nervous system (CNS) and the blood stream, restricts and regulates the penetration of compounds from the blood into the brain. Drugs that affect the CNS interact with the BBB prior to their target site, so the prediction research on BBB permeability is a fundamental and significant research direction in neuropharmacology. In this study, we combed through the available data and then with the help of support vector machine (SVM), we established an experiment process for discovering potential CNS compounds and investigating the mechanisms of BBB permeability of them to advance the research in this field four types of prediction models, referring to CNS activity, BBB permeability, passive diffusion and efflux transport, were obtained in the experiment process. The first two models were used to discover compounds which may have CNS activity and also cross the BBB at the same time; the latter two were used to elucidate the mechanism of BBB permeability of those compounds. Three optimization parameter methods, Grid Search, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), were used to optimize the SVM models. Then, four optimal models were selected with excellent evaluation indexes (the accuracy, sensitivity and specificity of each model were all above 85%). Furthermore, discrimination models were utilized to study the BBB properties of the known CNS activity compounds in Chinese herbs and this may guide the CNS drug development. With the relatively systematic and quick approach, the application rationality of traditional Chinese medicines for treating nervous system disease in the clinical practice will be improved.

Microarray analysis in rat liver slices correctly predicts in vivo hepatotoxicity.

PubMed

Elferink, M G L; Olinga, P; Draaisma, A L; Merema, M T; Bauerschmidt, S; Polman, J; Schoonen, W G; Groothuis, G M M

2008-06-15

The microarray technology, developed for the simultaneous analysis of a large number of genes, may be useful for the detection of toxicity in an early stage of the development of new drugs. The effect of different hepatotoxins was analyzed at the gene expression level in the rat liver both in vivo and in vitro. As in vitro model system the precision-cut liver slice model was used, in which all liver cell types are present in their natural architecture. This is important since drug-induced toxicity often is a multi-cellular process involving not only hepatocytes but also other cell types such as Kupffer and stellate cells. As model toxic compounds lipopolysaccharide (LPS, inducing inflammation), paracetamol (necrosis), carbon tetrachloride (CCl(4), fibrosis and necrosis) and gliotoxin (apoptosis) were used. The aim of this study was to validate the rat liver slice system as in vitro model system for drug-induced toxicity studies. The results of the microarray studies show that the in vitro profiles of gene expression cluster per compound and incubation time, and when analyzed in a commercial gene expression database, can predict the toxicity and pathology observed in vivo. Each toxic compound induces a specific pattern of gene expression changes. In addition, some common genes were up- or down-regulated with all toxic compounds. These data show that the rat liver slice system can be an appropriate tool for the prediction of multi-cellular liver toxicity. The same experiments and analyses are currently performed for the prediction of human specific toxicity using human liver slices.

Microarray analysis in rat liver slices correctly predicts in vivo hepatotoxicity

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

Elferink, M.G.L.; Olinga, P.; Draaisma, A.L.

2008-06-15

The microarray technology, developed for the simultaneous analysis of a large number of genes, may be useful for the detection of toxicity in an early stage of the development of new drugs. The effect of different hepatotoxins was analyzed at the gene expression level in the rat liver both in vivo and in vitro. As in vitro model system the precision-cut liver slice model was used, in which all liver cell types are present in their natural architecture. This is important since drug-induced toxicity often is a multi-cellular process involving not only hepatocytes but also other cell types such asmore » Kupffer and stellate cells. As model toxic compounds lipopolysaccharide (LPS, inducing inflammation), paracetamol (necrosis), carbon tetrachloride (CCl{sub 4}, fibrosis and necrosis) and gliotoxin (apoptosis) were used. The aim of this study was to validate the rat liver slice system as in vitro model system for drug-induced toxicity studies. The results of the microarray studies show that the in vitro profiles of gene expression cluster per compound and incubation time, and when analyzed in a commercial gene expression database, can predict the toxicity and pathology observed in vivo. Each toxic compound induces a specific pattern of gene expression changes. In addition, some common genes were up- or down-regulated with all toxic compounds. These data show that the rat liver slice system can be an appropriate tool for the prediction of multi-cellular liver toxicity. The same experiments and analyses are currently performed for the prediction of human specific toxicity using human liver slices.« less

General Pharmacokinetic Model for Topically Administered Ocular Drug Dosage Forms.

PubMed

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

2016-11-01

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

Prediction of passive blood-brain partitioning: straightforward and effective classification models based on in silico derived physicochemical descriptors

PubMed Central

Vilar, Santiago; Chakrabarti, Mayukh; Costanzi, Stefano

2010-01-01

The distribution of compounds between blood and brain is a very important consideration for new candidate drug molecules. In this paper, we describe the derivation of two linear discriminant analysis (LDA) models for the prediction of passive blood-brain partitioning, expressed in terms of log BB values. The models are based on computationally derived physicochemical descriptors, namely the octanol/water partition coefficient (log P), the topological polar surface area (TPSA) and the total number of acidic and basic atoms, and were obtained using a homogeneous training set of 307 compounds, for all of which the published experimental log BB data had been determined in vivo. In particular, since molecules with log BB > 0.3 cross the blood-brain barrier (BBB) readily while molecules with log BB < −1 are poorly distributed to the brain, on the basis of these thresholds we derived two distinct models, both of which show a percentage of good classification of about 80%. Notably, the predictive power of our models was confirmed by the analysis of a large external dataset of compounds with reported activity on the central nervous system (CNS) or lack thereof. The calculation of straightforward physicochemical descriptors is the only requirement for the prediction of the log BB of novel compounds through our models, which can be conveniently applied in conjunction with drug design and virtual screenings. PMID:20427217

Prediction of passive blood-brain partitioning: straightforward and effective classification models based on in silico derived physicochemical descriptors.

PubMed

Vilar, Santiago; Chakrabarti, Mayukh; Costanzi, Stefano

2010-06-01

The distribution of compounds between blood and brain is a very important consideration for new candidate drug molecules. In this paper, we describe the derivation of two linear discriminant analysis (LDA) models for the prediction of passive blood-brain partitioning, expressed in terms of logBB values. The models are based on computationally derived physicochemical descriptors, namely the octanol/water partition coefficient (logP), the topological polar surface area (TPSA) and the total number of acidic and basic atoms, and were obtained using a homogeneous training set of 307 compounds, for all of which the published experimental logBB data had been determined in vivo. In particular, since molecules with logBB>0.3 cross the blood-brain barrier (BBB) readily while molecules with logBB<-1 are poorly distributed to the brain, on the basis of these thresholds we derived two distinct models, both of which show a percentage of good classification of about 80%. Notably, the predictive power of our models was confirmed by the analysis of a large external dataset of compounds with reported activity on the central nervous system (CNS) or lack thereof. The calculation of straightforward physicochemical descriptors is the only requirement for the prediction of the logBB of novel compounds through our models, which can be conveniently applied in conjunction with drug design and virtual screenings. Published by Elsevier Inc.

In Vitro Evaluation of Antileishmanial Activity of Computationally Screened Compounds against Ascorbate Peroxidase To Combat Amphotericin B Drug Resistance

PubMed Central

Mansuri, Rani; Kumar, Ashish; Rana, Sindhuprava; Panthi, Bhavana; Ansari, M. Yousuf; Das, Sushmita; Dikhit, Manas Ranjan

2017-01-01

ABSTRACT In visceral leishmaniasis (VL), the host macrophages generate oxidative stress to destroy the pathogen, while Leishmania combats the harmful effect of radicals by redox homeostasis through its unique trypanothione cascade. Leishmania donovani ascorbate peroxidase (LdAPx) is a redox enzyme that regulates the trypanothione cascade and detoxifies the effect of H2O2. The absence of an LdAPx homologue in humans makes it an excellent drug target. In this study, the homology model of LdAPx was built, including heme, and diverse compounds were prefiltered (PAINS, ADMET, and Lipinski's rule of five) and thereafter screened against the LdAPx model. Compounds having good affinity in terms of the Glide XP (extra precision) score were clustered to select diverse compounds for experimental validation. A total of 26 cluster representatives were procured and tested on promastigote culture, yielding 12 compounds with good antileishmanial activity. Out of them, six compounds were safer on the BALB/c peritoneal macrophages and were also effective against disease-causing intracellular amastigotes. Three out of six compounds inhibited recombinant LdAPx in a noncompetitive manner and also demonstrated partial reversion of the resistance property in an amphotericin B (AmB)-resistant strain, which may be due to an increased level of reactive oxygen species (ROS) and decrease of glutathione (GSH) content. However, inhibition of LdAPx in resistant parasites enhanced annexin V staining and activation of metacaspase-like protease activity, which may help in DNA fragmentation and apoptosis-like cell death. Thus, the present study will help in the search for specific hits and templates of potential therapeutic interest and therefore may facilitate the development of new drugs for combination therapy against VL. PMID:28461317

Production of a compound against methicillin resistant Staphylococcus aureus (MRSA) from Streptomyces rubrolavendulae ICN3 & its evaluation in zebrafish embryos

PubMed Central

Kannan, Rajaretinam Rajesh; Iniyan, Appadurai Muthamil; Vincent, Samuel Gnana Prakash

2014-01-01

Background & objectives: Antibiotic resistance in pathogens has become a serious problem worldwide. Therefore, the search for new antibiotics for drug resistanct pathogens is an important endeavor. The present study deals with the production of anti-methicillin resistant Staphylococcus aureus (MRSA) potential of Streptomyces rubrolavendulae ICN3 and evaluation of anti-MRSA compound in zebrafish embryos. Methods: The antibiotic production from S. rubrolavendulae ICN3 was optimized in solid state fermentation and extracted. The antagonistic activity was confirmed against MRSA and purified in silica gel column and reverse phase - HPLC with an absorption maximum at 215 nm. Minimal inhibitory concentration of the compound was determined by broth microdilution method. Zebrafish embryos were used to evaluate the extract/compound for its minimal inhibition studies, influences on heart beat rates, haematopoietic blood cell count and lethal dose values. Results: Streptomyces rubrolavendulae ICN3 showed potent antagonistic activity against MRSA with a zone of 42 mm. The minimum inhibitory concentration was calculated as 500 μg/ml of the crude extract and the purified C23 exhibited 2.5 μg/ml in in vitro assay. The LC50 value of the anti MRSA compound C23 was calculated as 60.49 μg/ml and the MRSA treated embryos survived in the presence of purified compound C23 at a dose of 10 μg/ml. Interpretation & conclusions: Our results suggested that the compound was potent with less toxic effects in zebrafish embryonic model system for MRSA infection. Further structural evaluation and analysis in higher mammalian model system may lead to a novel drug candidate for drug resistant Staphylococcus aureus. PMID:25109726

78 FR 72897 - Draft Guidance for Industry on Interim Product Reporting for Human Drug Compounding Outsourcing...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-04

...] Draft Guidance for Industry on Interim Product Reporting for Human Drug Compounding Outsourcing... Compounding Outsourcing Facilities Under Section 503B of the Federal Food, Drug, and Cosmetic Act.'' The draft... human drug compounders that choose to register as outsourcing facilities (outsourcing facilities). DATES...

Antibacterial Drug Discovery: Some Assembly Required.

PubMed

Tommasi, Rubén; Iyer, Ramkumar; Miller, Alita A

2018-05-11

Our limited understanding of the molecular basis for compound entry into and efflux out of Gram-negative bacteria is now recognized as a key bottleneck for the rational discovery of novel antibacterial compounds. Traditional, large-scale biochemical or target-agnostic phenotypic antibacterial screening efforts have, as a result, not been very fruitful. A main driver of this knowledge gap has been the historical lack of predictive cellular assays, tools, and models that provide structure-activity relationships to inform optimization of compound accumulation. A variety of recent approaches has recently been described to address this conundrum. This Perspective explores these approaches and considers ways in which their integration could successfully redirect antibacterial drug discovery efforts.

Zebrafish is a predictive model for identifying compounds that protect against brain toxicity in severe acute organophosphorus intoxication.

PubMed

Faria, Melissa; Prats, Eva; Padrós, Francesc; Soares, Amadeu M V M; Raldúa, Demetrio

2017-04-01

Acute organophosphorus (OP) intoxication is a worldwide clinical and public health problem. In addition to cholinergic crisis, neurodegeneration and brain damage are hallmarks of the severe form of this toxidrome. Recently, we generated a chemical model of severe acute OP intoxication in zebrafish that is characterized by altered head morphology and brain degeneration. The pathophysiological pathways resulting in brain toxicity in this model are similar to those described in humans. The aim of this study was to assess the predictive power of this zebrafish model by testing the effect of a panel of drugs that provide protection in mammalian models. The selected drugs included "standard therapy" drugs (atropine and pralidoxime), reversible acetylcholinesterase inhibitors (huperzine A, galantamine, physostigmine and pyridostigmine), N-methyl-D-aspartate (NMDA) receptor antagonists (MK-801 and memantine), dual-function NMDA receptor and acetylcholine receptor antagonists (caramiphen and benactyzine) and anti-inflammatory drugs (dexamethasone and ibuprofen). The effects of these drugs on zebrafish survival and the prevalence of abnormal head morphology in the larvae exposed to 4 µM chlorpyrifos oxon [1 × median lethal concentration (LC 50 )] were determined. Moreover, the neuroprotective effects of pralidoxime, memantine, caramiphen and dexamethasone at the gross morphological level were confirmed by histopathological and transcriptional analyses. Our results demonstrated that the zebrafish model for severe acute OP intoxication has a high predictive value and can be used to identify new compounds that provide neuroprotection against severe acute OP intoxication.

Deposition and transport of linezolid mediated by a synthetic surfactant Synsurf® within a pressurized metered dose inhaler: a Calu-3 model

PubMed Central

van Rensburg, Lyné; van Zyl, Johann M; Smith, Johan

2018-01-01

Background Previous studies in our laboratory demonstrated that a synthetic peptide containing lung surfactant enhances the permeability of chemical compounds through bronchial epithelium. The purpose of this study was to test two formulations of Synsurf® combined with linezolid as respirable compounds using a pressurized metered dose inhaler (pMDI). Methods Aerosolization efficiency of the surfactant-drug microparticles onto Calu-3 monolayers as an air interface culture was analyzed using a Next Generation Impactor™. Results The delivered particles and drug dose showed a high dependency from the preparation that was aerosolized. Scanning electron microscopy imaging allowed for visualization of the deposited particles, establishing them as liposomal-type structures (diameter 500 nm to 2 μm) with filamentous features. Conclusion The different surfactant drug combinations allow for an evaluation of the significance of the experimental model system, as well as assessment of the formulations providing a possible noninvasive, site-specific, delivery model via pMDI. PMID:29765201

Design, synthesis, molecular modeling and biological evaluation of novel 2,3-dihydrophthalazine-1,4-dione derivatives as potential anticonvulsant agents

NASA Astrophysics Data System (ADS)

El-Helby, Abdel Ghany A.; Ayyad, Rezk R.; Sakr, Helmy M.; Abdelrahim, Adel S.; El-Adl, K.; Sherbiny, Farag S.; Eissa, Ibrahim H.; Khalifa, Mohamed M.

2017-02-01

In view of their expected anticonvulsant activity, some novel derivatives of 2,3-dihydrophthalazine-1,4-dione 4-22 were designed, synthesized and evaluated using pentylenetetrazole (PTZ) and picrotoxin as convulsion-inducing models. Moreover, the most active compounds were tested against electrical induced convulsion using maximal electroshock (MES) models of seizures. Most of the tested compounds showed considerable anticonvulsant activity in at least one of the anticonvulsant tests. Compounds 13 and 14g were proved to be the most potent compounds of this series with relatively low toxicity in the median lethal dose test when compared with the reference drug. Molecular modeling studies were done to verify the biological activity. The obtained results showed that the most potent compounds could be useful as a template for future design, optimization, and investigation to produce more active analogues.

Use of Natural Products as Chemical Library for Drug Discovery and Network Pharmacology

PubMed Central

Gu, Jiangyong; Gui, Yuanshen; Chen, Lirong; Yuan, Gu; Lu, Hui-Zhe; Xu, Xiaojie

2013-01-01

Background Natural products have been an important source of lead compounds for drug discovery. How to find and evaluate bioactive natural products is critical to the achievement of drug/lead discovery from natural products. Methodology We collected 19,7201 natural products structures, reported biological activities and virtual screening results. Principal component analysis was employed to explore the chemical space, and we found that there was a large portion of overlap between natural products and FDA-approved drugs in the chemical space, which indicated that natural products had large quantity of potential lead compounds. We also explored the network properties of natural product-target networks and found that polypharmacology was greatly enriched to those compounds with large degree and high betweenness centrality. In order to make up for a lack of experimental data, high throughput virtual screening was employed. All natural products were docked to 332 target proteins of FDA-approved drugs. The most potential natural products for drug discovery and their indications were predicted based on a docking score-weighted prediction model. Conclusions Analysis of molecular descriptors, distribution in chemical space and biological activities of natural products was conducted in this article. Natural products have vast chemical diversity, good drug-like properties and can interact with multiple cellular target proteins. PMID:23638153

Probability Based hERG Blocker Classifiers.

PubMed

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

2012-09-01

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

Advantageous use of HepaRG cells for the screening and mechanistic study of drug-induced steatosis

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

Tolosa, Laia

Only a few in vitro assays have been proposed to evaluate the steatotic potential of new drugs. The present study examines the utility of HepaRG cells as a cell-based assay system for screening drug-induced liver steatosis. A high-content screening assay was run to evaluate multiple toxicity-related cell parameters in HepaRG cells exposed to 28 compounds, including drugs reported to cause steatosis through different mechanisms and non-steatotic compounds. Lipid content was the most sensitive parameter for all the steatotic drugs, whereas no effects on lipid levels were produced by non-steatotic compounds. Apart from fat accumulation, increased ROS production and altered mitochondrialmore » membrane potential were also found in the cells exposed to steatotic drugs, which indicates that all these cellular events contributed to drug-induced hepatotoxicity. These findings are of clinical relevance as most effects were observed at drug concentrations under 100-fold of the therapeutic peak plasmatic concentration. HepaRG cells showed increased lipid overaccumulation vs. HepG2 cells, which suggests greater sensitivity to drug-induced steatosis. An altered expression profile of transcription factors and the genes that code key proteins in lipid metabolism was also found in the cells exposed to drugs capable of inducing liver steatosis. Our results generally indicate the value of HepaRG cells for assessing the risk of liver damage associated with steatogenic compounds and for investigating the molecular mechanisms involved in drug-induced steatosis. - Highlights: • HepaRG cells were explored as an in vitro model to detect steatogenic potential. • Multiple toxicity-related endpoints were analysed by HCS. • HepaRG showed a greater sensitivity to drug-induced steatosis than HepG2 cells. • Changes in the expression of genes related to lipid metabolism were revealed. • HepaRG allow mechanistic understanding of liver damage induced by steatogenic drugs.« less

Simple animal models for amyotrophic lateral sclerosis drug discovery.

PubMed

Patten, Shunmoogum A; Parker, J Alex; Wen, Xiao-Yan; Drapeau, Pierre

2016-08-01

Simple animal models have enabled great progress in uncovering the disease mechanisms of amyotrophic lateral sclerosis (ALS) and are helping in the selection of therapeutic compounds through chemical genetic approaches. Within this article, the authors provide a concise overview of simple model organisms, C. elegans, Drosophila and zebrafish, which have been employed to study ALS and discuss their value to ALS drug discovery. In particular, the authors focus on innovative chemical screens that have established simple organisms as important models for ALS drug discovery. There are several advantages of using simple animal model organisms to accelerate drug discovery for ALS. It is the authors' particular belief that the amenability of simple animal models to various genetic manipulations, the availability of a wide range of transgenic strains for labelling motoneurons and other cell types, combined with live imaging and chemical screens should allow for new detailed studies elucidating early pathological processes in ALS and subsequent drug and target discovery.

Molecular Phenotyping Combines Molecular Information, Biological Relevance, and Patient Data to Improve Productivity of Early Drug Discovery.

PubMed

Drawnel, Faye Marie; Zhang, Jitao David; Küng, Erich; Aoyama, Natsuyo; Benmansour, Fethallah; Araujo Del Rosario, Andrea; Jensen Zoffmann, Sannah; Delobel, Frédéric; Prummer, Michael; Weibel, Franziska; Carlson, Coby; Anson, Blake; Iacone, Roberto; Certa, Ulrich; Singer, Thomas; Ebeling, Martin; Prunotto, Marco

2017-05-18

Today, novel therapeutics are identified in an environment which is intrinsically different from the clinical context in which they are ultimately evaluated. Using molecular phenotyping and an in vitro model of diabetic cardiomyopathy, we show that by quantifying pathway reporter gene expression, molecular phenotyping can cluster compounds based on pathway profiles and dissect associations between pathway activities and disease phenotypes simultaneously. Molecular phenotyping was applicable to compounds with a range of binding specificities and triaged false positives derived from high-content screening assays. The technique identified a class of calcium-signaling modulators that can reverse disease-regulated pathways and phenotypes, which was validated by structurally distinct compounds of relevant classes. Our results advocate for application of molecular phenotyping in early drug discovery, promoting biological relevance as a key selection criterion early in the drug development cascade. Copyright © 2017 Elsevier Ltd. All rights reserved.

Development of a convenient ex vivo model for the study of the transcorneal permeation of drugs: histological and permeability evaluation.

PubMed

Pescina, Silvia; Govoni, Paolo; Potenza, Arianna; Padula, Cristina; Santi, Patrizia; Nicoli, Sara

2015-01-01

In this paper, an ex vivo model for the study of the transcorneal permeation of drugs, based on porcine tissues, was evaluated. The setup is characterized by ease of realization, absence of O₂ and CO₂ bubbling and low cost; additionally, the large availability of porcine tissue permits a high throughput. Histological images showed the comparability between porcine and human corneas and confirmed the effectiveness of the isolation procedure. A new de-epithelization procedure based on a thermal approach was also set up to simulate cornea permeability in pathological conditions. The procedure did not affect the integrity of the underlying layers and allowed the characterization of the barrier properties of epithelium and stroma. Six compounds with different physicochemical properties were tested: fluorescein, atenolol, propranolol, diclofenac, ganciclovir and lidocaine. The model highlighted the barrier function played by epithelium toward the diffusion of hydrophilic compounds and the permselectivity with regard to more lipophilic molecules. In particular, positively charged compounds showed a significantly higher transcorneal permeability than negatively charged compounds. The comparability of results with literature data supports the goodness and the robustness of the model, especially taking into account the behavior of fluorescein, which is generally considered a marker of tissue integrity. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.

A systematic study of chemogenomics of carbohydrates.

PubMed

Gu, Jiangyong; Luo, Fang; Chen, Lirong; Yuan, Gu; Xu, Xiaojie

2014-03-04

Chemogenomics focuses on the interactions between biologically active molecules and protein targets for drug discovery. Carbohydrates are the most abundant compounds in natural products. Compared with other drugs, the carbohydrate drugs show weaker side effects. Searching for multi-target carbohydrate drugs can be regarded as a solution to improve therapeutic efficacy and safety. In this work, we collected 60 344 carbohydrates from the Universal Natural Products Database (UNPD) and explored the chemical space of carbohydrates by principal component analysis. We found that there is a large quantity of potential lead compounds among carbohydrates. Then we explored the potential of carbohydrates in drug discovery by using a network-based multi-target computational approach. All carbohydrates were docked to 2389 target proteins. The most potential carbohydrates for drug discovery and their indications were predicted based on a docking score-weighted prediction model. We also explored the interactions between carbohydrates and target proteins to find the pathological networks, potential drug candidates and new indications.

Application of PBPK modelling in drug discovery and development at Pfizer.

PubMed

Jones, Hannah M; Dickins, Maurice; Youdim, Kuresh; Gosset, James R; Attkins, Neil J; Hay, Tanya L; Gurrell, Ian K; Logan, Y Raj; Bungay, Peter J; Jones, Barry C; Gardner, Iain B

2012-01-01

Early prediction of human pharmacokinetics (PK) and drug-drug interactions (DDI) in drug discovery and development allows for more informed decision making. Physiologically based pharmacokinetic (PBPK) modelling can be used to answer a number of questions throughout the process of drug discovery and development and is thus becoming a very popular tool. PBPK models provide the opportunity to integrate key input parameters from different sources to not only estimate PK parameters and plasma concentration-time profiles, but also to gain mechanistic insight into compound properties. Using examples from the literature and our own company, we have shown how PBPK techniques can be utilized through the stages of drug discovery and development to increase efficiency, reduce the need for animal studies, replace clinical trials and to increase PK understanding. Given the mechanistic nature of these models, the future use of PBPK modelling in drug discovery and development is promising, however, some limitations need to be addressed to realize its application and utility more broadly.

Developing new drugs for ovarian cancer: a challenging task in a changing reality.

PubMed

Canetta, R M; Carter, S K

1984-01-01

Recent therapeutic and technological advances have profoundly modified the parameters of new drug testing in ovarian cancer. The potential of compounds tested today in this disease therefore needs to be assessed according to this changing reality. Previous treatment with or without cisplatin is the criterion we have applied in our review of the single agent clinical data. Results obtained with older compounds have also been, when possible, reassessed in order to facilitate a comparative interpretation of recent trials. A brief overview of the most recently developed laboratory screening models has been conducted in order to stress their close relationship and their crucial role in future new drug development.

Personalized drug discovery: HCA approach optimized for rare diseases at Tel Aviv University.

PubMed

Solmesky, Leonardo J; Weil, Miguel

2014-03-01

The Cell screening facility for personalized medicine (CSFPM) at Tel Aviv University in Israel is devoted to screening small molecules libraries for finding new drugs for rare diseases using human cell based models. The main strategy of the facility is based on smartly reducing the size of the compounds collection in similarity clusters and at the same time keeping high diversity of pharmacophores. This strategy allows parallel screening of several patient derived - cells in a personalized screening approach. The tested compounds are repositioned drugs derived from collections of phase III and FDA approved small molecules. In addition, the facility carries screenings using other chemical libraries and toxicological characterizations of nanomaterials.

Combinatorially-generated library of 6-fluoroquinolone analogs as potential novel antitubercular agents: a chemometric and molecular modeling assessment.

PubMed

Minovski, Nikola; Perdih, Andrej; Solmajer, Tom

2012-05-01

The virtual combinatorial chemistry approach as a methodology for generating chemical libraries of structurally-similar analogs in a virtual environment was employed for building a general mixed virtual combinatorial library with a total of 53.871 6-FQ structural analogs, introducing the real synthetic pathways of three well known 6-FQ inhibitors. The druggability properties of the generated combinatorial 6-FQs were assessed using an in-house developed drug-likeness filter integrating the Lipinski/Veber rule-sets. The compounds recognized as drug-like were used as an external set for prediction of the biological activity values using a neural-networks (NN) model based on an experimentally-determined set of active 6-FQs. Furthermore, a subset of compounds was extracted from the pool of drug-like 6-FQs, with predicted biological activity, and subsequently used in virtual screening (VS) campaign combining pharmacophore modeling and molecular docking studies. This complex scheme, a powerful combination of chemometric and molecular modeling approaches provided novel QSAR guidelines that could aid in the further lead development of 6-FQs agents.

Mechanism and kinetics of the loss of poorly soluble drugs from liposomal carriers studied by a novel flow field-flow fractionation-based drug release-/transfer-assay.

PubMed

Hinna, Askell Hvid; Hupfeld, Stefan; Kuntsche, Judith; Bauer-Brandl, Annette; Brandl, Martin

2016-06-28

Liposomes represent a versatile drug formulation approach e.g. for improving the water-solubility of poorly soluble drugs but also to achieve drug targeting and controlled release. For the latter applications it is essential that the drug remains associated with the liposomal carrier during transit in the vascular bed. A range of in vitro test methods has been suggested over the years for prediction of the release of drug from liposomal carriers. The majority of these fail to give a realistic prediction for poorly water-soluble drugs due to the intrinsic tendency of such compounds to remain associated with liposome bilayers even upon extensive dilution. Upon i.v. injection, in contrast, rapid drug loss often occurs due to drug transfer from the liposomal carriers to endogenous lipophilic sinks such as lipoproteins, plasma proteins or membranes of red blood cells and endothelial cells. Here we report on the application of a recently introduced in vitro predictive drug transfer assay based on incubation of the liposomal drug carrier with large multilamellar liposomes, the latter serving as a biomimetic model sink, using flow field-flow fractionation as a tool to separate the two types of liposomes. By quantifying the amount of drug remaining associated with the liposomal drug carrier as well as that transferred to the acceptor liposomes at distinct times of incubation, both the kinetics of drug transfer and release to the water phase could be established for the model drug p-THPP (5,10,15,20-tetrakis(4-hydroxyphenyl)21H,23H-porphine). p-THPP is structurally similar to temoporfin, a photosensitizer which is under clinical evaluation in a liposomal formulation. Mechanistic insights were gained by varying the donor-to-acceptor lipid mass ratio, size and lamellarity of the liposomes. Drug transfer kinetics from one liposome to another was found rate determining as compared to redistribution from the outermost to the inner concentric bilayers, such that the overall process could be adequately described by a single 1st order kinetic model. By varying the donor-to-acceptor lipid mass ratio in the range 1:1 to 1:10, a correlation was established between donor-to-acceptor-lipid mass ratio and transfer kinetics, which is regarded essential for scaling to physiological lipid mass ratios. By applying the assay to a series of structurally related model compounds of different bilayer affinity, transfer and release kinetics were established over the whole expected range of liposome bilayer associated drugs in terms of water solubility and lipophilicity. A very rapid transfer and considerable release from liposomes to the water phase was observed for the more water-soluble compounds Sudan II (clogP 5.45) and Sudan III (clogP 6.83). For the more lipophilic compounds, the rate of transfer from the donor liposomes followed the rank order Sudan IV (fastest)>Oil Red O>Sudan Black>p-THPP (slowest). For an equimolar donor-to-acceptor lipid mass ratio, half-lifes of transfer in the range of 12min (Sudan IV) up to 1.5h (p-THPP) were determined. In essence, the results presented here allow for both, mechanistic insights and predictions of drug loss from liposomal carriers upon exposure to biological sinks, which appear more realistic than the commonly employed in vitro release tests. Copyright © 2016 Elsevier B.V. All rights reserved.

Porcine sclera as a model of human sclera for in vitro transport experiments: histology, SEM, and comparative permeability

PubMed Central

Ferrari, G.; Quarta, M.; Macaluso, C.; Govoni, P.; Dallatana, D.; Santi, P.

2009-01-01

Purpose To evaluate porcine sclera as a model of human sclera for in vitro studies of transscleral drug delivery of both low and high molecular weight compounds. Methods Human and porcine scleras were characterized for thickness and water content. The tissue surface was examined by scanning electron microscopy (SEM), and the histology was studied with hematoxylin-eosin staining. Comparative permeation experiments were performed using three model molecules, acetaminophen as the model compound for small molecules; a linear dextran with a molecular weight of 120 kDa as the model compound for high molecular weight drugs; and insulin, which was chosen as the model protein. Permeation parameters such as flux, lag time, and permeability coefficient were determined and compared. Results Human and porcine scleras have a similar histology and collagen bundle organization. The water content is approx 70% for both tissues while a statistically significant difference was found for the thickness, porcine sclera being approximately twofold thicker than human sclera. Differences in thickness produced differences in the permeability coefficient. In fact, human sclera was found to be two to threefold more permeable toward the three molecules studied than porcine sclera. Conclusions The results obtained in the present paper prove that porcine sclera can be considered a good model for human sclera for in vitro permeation experiments of both low and high molecular weight compounds. In fact, if the different tissue thickness is taken into account, comparable permeability was demonstrated. This suggests a possible use of this model in the evaluation of the transscleral permeation of new biotech compounds, which currently represent the most innovative and efficient therapeutic options for the treatment of ocular diseases. PMID:19190734

Porcine sclera as a model of human sclera for in vitro transport experiments: histology, SEM, and comparative permeability.

PubMed

Nicoli, S; Ferrari, G; Quarta, M; Macaluso, C; Govoni, P; Dallatana, D; Santi, P

2009-01-01

To evaluate porcine sclera as a model of human sclera for in vitro studies of transscleral drug delivery of both low and high molecular weight compounds. Human and porcine scleras were characterized for thickness and water content. The tissue surface was examined by scanning electron microscopy (SEM), and the histology was studied with hematoxylin-eosin staining. Comparative permeation experiments were performed using three model molecules, acetaminophen as the model compound for small molecules; a linear dextran with a molecular weight of 120 kDa as the model compound for high molecular weight drugs; and insulin, which was chosen as the model protein. Permeation parameters such as flux, lag time, and permeability coefficient were determined and compared. Human and porcine scleras have a similar histology and collagen bundle organization. The water content is approx 70% for both tissues while a statistically significant difference was found for the thickness, porcine sclera being approximately twofold thicker than human sclera. Differences in thickness produced differences in the permeability coefficient. In fact, human sclera was found to be two to threefold more permeable toward the three molecules studied than porcine sclera. The results obtained in the present paper prove that porcine sclera can be considered a good model for human sclera for in vitro permeation experiments of both low and high molecular weight compounds. In fact, if the different tissue thickness is taken into account, comparable permeability was demonstrated. This suggests a possible use of this model in the evaluation of the transscleral permeation of new biotech compounds, which currently represent the most innovative and efficient therapeutic options for the treatment of ocular diseases.

Molecular Dynamics Studies of Liposomes as Carriers for Photosensitizing Drugs: Development, Validation, and Simulations with a Coarse-Grained Model.

PubMed

Jämbeck, Joakim P M; Eriksson, Emma S E; Laaksonen, Aatto; Lyubartsev, Alexander P; Eriksson, Leif A

2014-01-14

Liposomes are proposed as drug delivery systems and can in principle be designed so as to cohere with specific tissue types or local environments. However, little detail is known about the exact mechanisms for drug delivery and the distributions of drug molecules inside the lipid carrier. In the current work, a coarse-grained (CG) liposome model is developed, consisting of over 2500 lipids, with varying degrees of drug loading. For the drug molecule, we chose hypericin, a natural compound proposed for use in photodynamic therapy, for which a CG model was derived and benchmarked against corresponding atomistic membrane bilayer model simulations. Liposomes with 21-84 hypericin molecules were generated and subjected to 10 microsecond simulations. Distribution of the hypericins, their orientations within the lipid bilayer, and the potential of mean force for transferring a hypericin molecule from the interior aqueous "droplet" through the liposome bilayer are reported herein.

Consensus QSAR model for identifying novel H5N1 inhibitors.

PubMed

Sharma, Nitin; Yap, Chun Wei

2012-08-01

Due to the importance of neuraminidase in the pathogenesis of influenza virus infection, it has been regarded as the most important drug target for the treatment of influenza. Resistance to currently available drugs and new findings related to structure of the protein requires novel neuraminidase 1 (N1) inhibitors. In this study, a consensus QSAR model with defined applicability domain (AD) was developed using published N1 inhibitors. The consensus model was validated using an external validation set. The model achieved high sensitivity, specificity, and overall accuracy along with low false positive rate (FPR) and false discovery rate (FDR). The performance of model on the external validation set and training set were comparable, thus it was unlikely to be overfitted. The low FPR and low FDR will increase its accuracy in screening large chemical libraries. Screening of ZINC library resulted in 64,772 compounds as probable N1 inhibitors, while 173,674 compounds were defined to be outside the AD of the consensus model. The advantage of the current model is that it was developed using a large and diverse dataset and has a defined AD which prevents its use on compounds that it is not capable of predicting. The consensus model developed in this study is made available via the free software, PaDEL-DDPredictor.

CASE STUDY ON AN IPILIMUMAB COST-CONTAINMENT STRATEGY IN AN ITALIAN HOSPITAL.

PubMed

Russi, Alberto; Chiarion-Sileni, Vanna; Damuzzo, Vera; Di Sarra, Francesca; Pigozzo, Jacopo; Palozzo, Angelo Claudio

2017-01-01

Ipilimumab is the first licensed immune checkpoint inhibitor for treatment of melanoma. The promising results of the registration clinical study need confirmation in real practice and its clinical success comes together with a relevant budget impact due to the high price of this drug. The aim of this work is to describe a new model of economical sustainability of ipilimumab developed in an Italian reference center for melanoma treatment. This retrospective, observational, and monocentric study was carried out at the Veneto Institute of Oncology. Ipilimumab was administered to fifty-seven patients with advanced melanoma. Overall survival, progression free survival, and toxicity were evaluated. A local management procedure was evaluated together with the cost-saving strategies implemented by the Italian Medicines Agency (AIFA). We demonstrated that the use of ipilimumab for metastatic melanoma in real practice had an efficacy and toxicity similar to that reported in the literature. In this scenario, our management model (centralization of compounding + drug-day) permitted savings up to the 11.1 percent of the gross cost for the drug (calculated assuming that no cost saving procedures were applied) while the policy of cost containment designed by AIFA produced an additional 6.2 percent of savings. In real practice conditions, the centralized administration of ipilimumab allows to replicate the results of clinical studies and in the meantime to contain the cost associated with this drug. The local strategy of management can be readily applied to most of the high cost drugs compounded in the hospital pharmacy. Impact of findings on practice: (i) We describe a new model of economic sustainability (drug-day, centralization of compounding, payback systems) of an expensive and innovative drug, ipilimumab, for treatment of melanoma within an Italian cancer center. (ii) This pivotal study demonstrated that a cost containment strategy is feasible and it needs the cooperation of all healthcare providers (oncologists, pharmacists, nurses, and technicians) to guarantee the full efficiency of the process.

Open innovation for phenotypic drug discovery: The PD2 assay panel.

PubMed

Lee, Jonathan A; Chu, Shaoyou; Willard, Francis S; Cox, Karen L; Sells Galvin, Rachelle J; Peery, Robert B; Oliver, Sarah E; Oler, Jennifer; Meredith, Tamika D; Heidler, Steven A; Gough, Wendy H; Husain, Saba; Palkowitz, Alan D; Moxham, Christopher M

2011-07-01

Phenotypic lead generation strategies seek to identify compounds that modulate complex, physiologically relevant systems, an approach that is complementary to traditional, target-directed strategies. Unlike gene-specific assays, phenotypic assays interrogate multiple molecular targets and signaling pathways in a target "agnostic" fashion, which may reveal novel functions for well-studied proteins and discover new pathways of therapeutic value. Significantly, existing compound libraries may not have sufficient chemical diversity to fully leverage a phenotypic strategy. To address this issue, Eli Lilly and Company launched the Phenotypic Drug Discovery Initiative (PD(2)), a model of open innovation whereby external research groups can submit compounds for testing in a panel of Lilly phenotypic assays. This communication describes the statistical validation, operations, and initial screening results from the first PD(2) assay panel. Analysis of PD(2) submissions indicates that chemical diversity from open source collaborations complements internal sources. Screening results for the first 4691 compounds submitted to PD(2) have confirmed hit rates from 1.6% to 10%, with the majority of active compounds exhibiting acceptable potency and selectivity. Phenotypic lead generation strategies, in conjunction with novel chemical diversity obtained via open-source initiatives such as PD(2), may provide a means to identify compounds that modulate biology by novel mechanisms and expand the innovation potential of drug discovery.

Zebrafish Embryo as an In Vivo Model for Behavioral and Pharmacological Characterization of Methylxanthine Drugs.

PubMed

Basnet, Ram Manohar; Guarienti, Michela; Memo, Maurizio

2017-03-09

Zebrafish embryo is emerging as an important tool for behavior analysis as well as toxicity testing. In this study, we compared the effect of nine different methylxanthine drugs using zebrafish embryo as a model. We performed behavioral analysis, biochemical assay and Fish Embryo Toxicity (FET) test in zebrafish embryos after treatment with methylxanthines. Each drug appeared to behave in different ways and showed a distinct pattern of results. Embryos treated with seven out of nine methylxanthines exhibited epileptic-like pattern of movements, the severity of which varied with drugs and doses used. Cyclic AMP measurement showed that, despite of a significant increase in cAMP with some compounds, it was unrelated to the observed movement behavior changes. FET test showed a different pattern of toxicity with different methylxanthines. Each drug could be distinguished from the other based on its effect on mortality, morphological defects and teratogenic effects. In addition, there was a strong positive correlation between the toxic doses (TC 50 ) calculated in zebrafish embryos and lethal doses (LD 50 ) in rodents obtained from TOXNET database. Taken together, all these findings elucidate the potentiality of zebrafish embryos as an in vivo model for behavioral and toxicity testing of methylxanthines and other related compounds.

Phytochemical analysis and docking study of compounds present in a polyherbal preparation used in the treatment of dermatophytosis

PubMed Central

Simhadri VSDNA, Nagesh; Muniappan, Muthuchamy; Kannan, Iyanar; Viswanathan, Subramanyam

2017-01-01

Background and Purpose: Soleshine is a polyherbal preparation established in the market for the treatment of cracks and tinea pedis, which is applied externally. This preparation is composed of the extracts of indigenous plants, namely Azadirachta indica, Lawsonia alba, and Shorea robusta, mixed with castor oil and sesame oil. In the present study, an attempt was made to identify the constituents of soleshine and identify some potential drug-like molecules that can inhibit important drug targets of the dermatophytes using molecular docking method. Materials and Methods: The active ingredients of polyherbal preparation were identified with the aid of gas chromatography-mass spectrometry (GC-MS). Two major compounds were selected based on the retention time and percentage of the area covered in the graph for docking study. The three-dimensional structures of 1,3-β-glucan synthase, chitinase, fungalysin, and lumazine synthase were derived by homology modelling using MODELLER software, version 9.0. The docking of the ligand and receptor was performed using iGEMDOCK and AutodockVina software. The physicochemical properties, lipophilicity, hydrophilicity, and drug likeness properties were obtained from the Swiss ADME online server tool. Results: The GC-MS analysis demonstrated the presence of different phytochemical compounds in the extract of polyherbal preparation. A total of 20 compounds were identified, among which 3,7-dimethyl-2,6-octadienaland 2-pentene-2-methyl were the major compounds. Regarding 3,7-dimethyl-2,6-octadienal, the covered area and height were 40.15% and 46.17%, respectively. These values were 31.90% and 23.33% for 2-pentene-2-methyl, respectively. These two major compounds had an excellent binding affinity and obeyed the rules for the drug likeness and lead likeness. Conclusion: As the findings indicated, the two major ingredients present in soleshine showed a good antifungal activity as they inhibited the enzymes responsible for the survival of fungal organism; furthermore, they were appropriate for the lead molecules. PMID:29707673

Phytochemical analysis and docking study of compounds present in a polyherbal preparation used in the treatment of dermatophytosis.

PubMed

Simhadri Vsdna, Nagesh; Muniappan, Muthuchamy; Kannan, Iyanar; Viswanathan, Subramanyam

2017-12-01

Soleshine is a polyherbal preparation established in the market for the treatment of cracks and tinea pedis, which is applied externally. This preparation is composed of the extracts of indigenous plants, namely Azadirachta indica, Lawsonia alba, and Shorea robusta , mixed with castor oil and sesame oil. In the present study, an attempt was made to identify the constituents of soleshine and identify some potential drug-like molecules that can inhibit important drug targets of the dermatophytes using molecular docking method. The active ingredients of polyherbal preparation were identified with the aid of gas chromatography-mass spectrometry (GC-MS). Two major compounds were selected based on the retention time and percentage of the area covered in the graph for docking study. The three-dimensional structures of 1,3-β-glucan synthase, chitinase, fungalysin, and lumazine synthase were derived by homology modelling using MODELLER software, version 9.0. The docking of the ligand and receptor was performed using iGEMDOCK and AutodockVina software. The physicochemical properties, lipophilicity, hydrophilicity, and drug likeness properties were obtained from the Swiss ADME online server tool. The GC-MS analysis demonstrated the presence of different phytochemical compounds in the extract of polyherbal preparation. A total of 20 compounds were identified, among which 3,7-dimethyl-2,6-octadienaland 2-pentene-2-methyl were the major compounds. Regarding 3,7-dimethyl-2,6-octadienal, the covered area and height were 40.15% and 46.17%, respectively. These values were 31.90% and 23.33% for 2-pentene-2-methyl, respectively. These two major compounds had an excellent binding affinity and obeyed the rules for the drug likeness and lead likeness. As the findings indicated, the two major ingredients present in soleshine showed a good antifungal activity as they inhibited the enzymes responsible for the survival of fungal organism; furthermore, they were appropriate for the lead molecules.

A strategy to find novel candidate anti-Alzheimer's disease drugs by constructing interaction networks between drug targets and natural compounds in medical plants.

PubMed

Chen, Bi-Wen; Li, Wen-Xing; Wang, Guang-Hui; Li, Gong-Hua; Liu, Jia-Qian; Zheng, Jun-Juan; Wang, Qian; Li, Hui-Juan; Dai, Shao-Xing; Huang, Jing-Fei

2018-01-01

Alzheimer' disease (AD) is an ultimately fatal degenerative brain disorder that has an increasingly large burden on health and social care systems. There are only five drugs for AD on the market, and no new effective medicines have been discovered for many years. Chinese medicinal plants have been used to treat diseases for thousands of years, and screening herbal remedies is a way to develop new drugs. We used molecular docking to screen 30,438 compounds from Traditional Chinese Medicine (TCM) against a comprehensive list of AD target proteins. TCM compounds in the top 0.5% of binding affinity scores for each target protein were selected as our research objects. Structural similarities between existing drugs from DrugBank database and selected TCM compounds as well as the druggability of our candidate compounds were studied. Finally, we searched the CNKI database to obtain studies on anti-AD Chinese plants from 2007 to 2017, and only clinical studies were included. A total of 1,476 compounds (top 0.5%) were selected as drug candidates. Most of these compounds are abundantly found in plants used for treating AD in China, especially the plants from two genera Panax and Morus. We classified the compounds by single target and multiple targets and analyzed the interactions between target proteins and compounds. Analysis of structural similarity revealed that 17 candidate anti-AD compounds were structurally identical to 14 existing approved drugs. Most of them have been reported to have a positive effect in AD. After filtering for compound druggability, we identified 11 anti-AD compounds with favorable properties, seven of which are found in anti-AD Chinese plants. Of 11 anti-AD compounds, four compounds 5,862, 5,863, 5,868, 5,869 have anti-inflammatory activity. The compound 28,814 mainly has immunoregulatory activity. The other six compounds have not yet been reported for any biology activity at present. Natural compounds from TCM provide a broad prospect for the screening of anti-AD drugs. In this work, we established networks to systematically study the connections among natural compounds, approved drugs, TCM plants and AD target proteins with the goal of identifying promising drug candidates. We hope that our study will facilitate in-depth research for the treatment of AD in Chinese medicine.

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

PubMed

Bian, Yuemin; Xie, Xiang-Qun Sean

2018-04-09

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

ADMET Evaluation in Drug Discovery. 16. Predicting hERG Blockers by Combining Multiple Pharmacophores and Machine Learning Approaches.

PubMed

Wang, Shuangquan; Sun, Huiyong; Liu, Hui; Li, Dan; Li, Youyong; Hou, Tingjun

2016-08-01

Blockade of human ether-à-go-go related gene (hERG) channel by compounds may lead to drug-induced QT prolongation, arrhythmia, and Torsades de Pointes (TdP), and therefore reliable prediction of hERG liability in the early stages of drug design is quite important to reduce the risk of cardiotoxicity-related attritions in the later development stages. In this study, pharmacophore modeling and machine learning approaches were combined to construct classification models to distinguish hERG active from inactive compounds based on a diverse data set. First, an optimal ensemble of pharmacophore hypotheses that had good capability to differentiate hERG active from inactive compounds was identified by the recursive partitioning (RP) approach. Then, the naive Bayesian classification (NBC) and support vector machine (SVM) approaches were employed to construct classification models by integrating multiple important pharmacophore hypotheses. The integrated classification models showed improved predictive capability over any single pharmacophore hypothesis, suggesting that the broad binding polyspecificity of hERG can only be well characterized by multiple pharmacophores. The best SVM model achieved the prediction accuracies of 84.7% for the training set and 82.1% for the external test set. Notably, the accuracies for the hERG blockers and nonblockers in the test set reached 83.6% and 78.2%, respectively. Analysis of significant pharmacophores helps to understand the multimechanisms of action of hERG blockers. We believe that the combination of pharmacophore modeling and SVM is a powerful strategy to develop reliable theoretical models for the prediction of potential hERG liability.

Inhibitors of Trypanosoma cruzi Sir2 related protein 1 as potential drugs against Chagas disease

PubMed Central

KongThoo Lin, Paul; Costa, David M.; Perez-Cabezas, Begoña; Tavares, Joana; Roura-Ferrer, Meritxell; Ramos, Isbaal; Ronin, Céline; Major, Louise L.; Ciesielski, Fabrice; Pemberton, Iain K.; MacDougall, Jane; Ciapetti, Paola; Cordeiro-da-Silva, Anabela

2018-01-01

Chagas disease remains one of the most neglected diseases in the world despite being the most important parasitic disease in Latin America. The characteristic chronic manifestation of chagasic cardiomyopathy is the region’s leading cause of heart-related illness, causing significant mortality and morbidity. Due to the limited available therapeutic options, new drugs are urgently needed to control the disease. Sirtuins, also called Silent information regulator 2 (Sir2) proteins have long been suggested as interesting targets to treat different diseases, including parasitic infections. Recent studies on Trypanosoma cruzi sirtuins have hinted at the possibility to exploit these enzymes as a possible drug targets. In the present work, the T. cruzi Sir2 related protein 1 (TcSir2rp1) is genetically validated as a drug target and biochemically characterized for its NAD+-dependent deacetylase activity and its inhibition by the classic sirtuin inhibitor nicotinamide, as well as by bisnaphthalimidopropyl (BNIP) derivatives, a class of parasite sirtuin inhibitors. BNIPs ability to inhibit TcSir2rp1, and anti-parasitic activity against T. cruzi amastigotes in vitro were investigated. The compound BNIP Spermidine (BNIPSpd) (9), was found to be the most potent inhibitor of TcSir2rp1. Moreover, this compound showed altered trypanocidal activity against TcSir2rp1 overexpressing epimastigotes and anti-parasitic activity similar to the reference drug benznidazole against the medically important amastigotes, while having the highest selectivity index amongst the compounds tested. Unfortunately, BNIPSpd failed to treat a mouse model of Chagas disease, possibly due to its pharmacokinetic profile. Medicinal chemistry modifications of the compound, as well as alternative formulations may improve activity and pharmacokinetics in the future. Additionally, an initial TcSIR2rp1 model in complex with p53 peptide substrate was obtained from low resolution X-ray data (3.5 Å) to gain insight into the potential specificity of the interaction with the BNIP compounds. In conclusion, the search for TcSir2rp1 specific inhibitors may represent a valuable strategy for drug discovery against T. cruzi. PMID:29357372

Inhibitors of Trypanosoma cruzi Sir2 related protein 1 as potential drugs against Chagas disease.

PubMed

Gaspar, Luís; Coron, Ross P; KongThoo Lin, Paul; Costa, David M; Perez-Cabezas, Begoña; Tavares, Joana; Roura-Ferrer, Meritxell; Ramos, Isbaal; Ronin, Céline; Major, Louise L; Ciesielski, Fabrice; Pemberton, Iain K; MacDougall, Jane; Ciapetti, Paola; Smith, Terry K; Cordeiro-da-Silva, Anabela

2018-01-01

Chagas disease remains one of the most neglected diseases in the world despite being the most important parasitic disease in Latin America. The characteristic chronic manifestation of chagasic cardiomyopathy is the region's leading cause of heart-related illness, causing significant mortality and morbidity. Due to the limited available therapeutic options, new drugs are urgently needed to control the disease. Sirtuins, also called Silent information regulator 2 (Sir2) proteins have long been suggested as interesting targets to treat different diseases, including parasitic infections. Recent studies on Trypanosoma cruzi sirtuins have hinted at the possibility to exploit these enzymes as a possible drug targets. In the present work, the T. cruzi Sir2 related protein 1 (TcSir2rp1) is genetically validated as a drug target and biochemically characterized for its NAD+-dependent deacetylase activity and its inhibition by the classic sirtuin inhibitor nicotinamide, as well as by bisnaphthalimidopropyl (BNIP) derivatives, a class of parasite sirtuin inhibitors. BNIPs ability to inhibit TcSir2rp1, and anti-parasitic activity against T. cruzi amastigotes in vitro were investigated. The compound BNIP Spermidine (BNIPSpd) (9), was found to be the most potent inhibitor of TcSir2rp1. Moreover, this compound showed altered trypanocidal activity against TcSir2rp1 overexpressing epimastigotes and anti-parasitic activity similar to the reference drug benznidazole against the medically important amastigotes, while having the highest selectivity index amongst the compounds tested. Unfortunately, BNIPSpd failed to treat a mouse model of Chagas disease, possibly due to its pharmacokinetic profile. Medicinal chemistry modifications of the compound, as well as alternative formulations may improve activity and pharmacokinetics in the future. Additionally, an initial TcSIR2rp1 model in complex with p53 peptide substrate was obtained from low resolution X-ray data (3.5 Å) to gain insight into the potential specificity of the interaction with the BNIP compounds. In conclusion, the search for TcSir2rp1 specific inhibitors may represent a valuable strategy for drug discovery against T. cruzi.

Recent trends in specialty pharma business model.

PubMed

Ku, Mannching Sherry

2015-12-01

The recent rise of specialty pharma is attributed to its flexible, versatile, and open business model while the traditional big pharma is facing a challenging time with patent cliff, generic threat, and low research and development (R&D) productivity. These multinational pharmaceutical companies, facing a difficult time, have been systematically externalizing R&D and some even establish their own corporate venture capital so as to diversify with more shots on goal, with the hope of achieving a higher success rate in their compound pipeline. Biologics and clinical Phase II proof-of-concept (POC) compounds are the preferred licensing and collaboration targets. Biologics enjoys a high success rate with a low generic biosimilar threat, while the need is high for clinical Phase II POC compounds, due to its high attrition/low success rate. Repurposing of big pharma leftover compounds is a popular strategy but with limitations. Most old compounds come with baggage either in lackluster clinical performance or short in patent life. Orphan drugs is another area which has gained popularity in recent years. The shorter and less costly regulatory pathway provides incentives, especially for smaller specialty pharma. However, clinical studies on orphan drugs require a large network of clinical operations in many countries in order to recruit enough patients. Big pharma is also working on orphan drugs starting with a small indication, with the hope of expanding the indication into a blockbuster status. Specialty medicine, including orphan drugs, has become the growth engine in the pharmaceutical industry worldwide. Big pharma is also keen on in-licensing technology or projects from specialty pharma to extend product life cycles, in order to protect their blockbuster drug franchises. Ample opportunities exist for smaller players, even in the emerging countries, to collaborate with multinational pharmaceutical companies provided that the technology platforms or specialty medicinal products are what the big pharma wants. The understanding of intellectual properties and international drug regulations are the key for specialty pharma to have a workable strategy for product registration worldwide. Copyright © 2015. Published by Elsevier B.V.

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.

Designer drugs: the evolving science of drug discovery.

PubMed

Wanke, L A; DuBose, R F

1998-07-01

Drug discovery and design are fundamental to drug development. Until recently, most drugs were discovered through random screening or developed through molecular modification. New technologies are revolutionizing this phase of drug development. Rational drug design, using powerful computers and computational chemistry and employing X-ray crystallography, nuclear magnetic resonance spectroscopy, and three-dimensional quantitative structure activity relationship analysis, is creating highly specific, biologically active molecules by virtual reality modeling. Sophisticated screening technologies are eliminating all but the most active lead compounds. These new technologies promise more efficacious, safe, and cost-effective medications, while minimizing drug development time and maximizing profits.

Update on medical and regulatory issues pertaining to compounded and FDA-approved drugs, including hormone therapy

PubMed Central

Pinkerton, JoAnn V.; Pickar, James H.

2016-01-01

Abstract Objective: We review the historical regulation of drug compounding, concerns about widespread use of non-Food and Drug Admiistration (FDA)-approved compounded bioidentical hormone therapies (CBHTs), which do not have proper labeling and warnings, and anticipated impact of the 2013 Drug Quality and Security Act (DQSA) on compounding. Methods: US government websites were searched for documents concerning drug compounding regulation and oversight from 1938 (passage of Federal Food, Drug, and Cosmetic Act [FDCA]) through 2014, including chronologies, Congressional testimony, FDA guidelines and enforcements, and reports. The FDCA and DQSA were reviewed. PubMed and Google were searched for articles on compounded drugs, including CBHT. Results: Congress explicitly granted the FDA limited oversight of compounded drugs in a 1997 amendment to the FDCA, but the FDA has encountered obstacles in exercising that authority. After 64 patient deaths and 750 adversely affected patients from the 2012 meningitis outbreak due to contaminated compounded steroid injections, Congress passed the DQSA, authorizing the FDA to create a voluntary registration for facilities that manufacture and distribute sterile compounded drugs in bulk and reinforcing FDCA regulations for traditional compounding. Given history and current environment, concerns remain about CBHT product regulation and their lack of safety and efficacy data. Conclusions: The DQSA and its reinforcement of §503A of the FDCA solidifies FDA authority to enforce FDCA provisions against compounders of CBHT. The new law may improve compliance and accreditation by the compounding industry; support state and FDA oversight; and prevent the distribution of misbranded, adulterated, or inconsistently compounded medications, and false and misleading claims, thus reducing public health risk. PMID:26418479

Exoproteome and Secretome Derived Broad Spectrum Novel Drug and Vaccine Candidates in Vibrio cholerae Targeted by Piper betel Derived Compounds

PubMed Central

Barh, Debmalya; Barve, Neha; Gupta, Krishnakant; Chandra, Sudha; Jain, Neha; Tiwari, Sandeep; Leon-Sicairos, Nidia; Canizalez-Roman, Adrian; Rodrigues dos Santos, Anderson; Hassan, Syed Shah; Almeida, Síntia; Thiago Jucá Ramos, Rommel; Augusto Carvalho de Abreu, Vinicius; Ribeiro Carneiro, Adriana; de Castro Soares, Siomar; Luiz de Paula Castro, Thiago; Miyoshi, Anderson; Silva, Artur; Kumar, Anil; Narayan Misra, Amarendra; Blum, Kenneth; Braverman, Eric R.; Azevedo, Vasco

2013-01-01

Vibrio cholerae is the causal organism of the cholera epidemic, which is mostly prevalent in developing and underdeveloped countries. However, incidences of cholera in developed countries are also alarming. Because of the emergence of new drug-resistant strains, even though several generic drugs and vaccines have been developed over time, Vibrio infections remain a global health problem that appeals for the development of novel drugs and vaccines against the pathogen. Here, applying comparative proteomic and reverse vaccinology approaches to the exoproteome and secretome of the pathogen, we have identified three candidate targets (ompU, uppP and yajC) for most of the pathogenic Vibrio strains. Two targets (uppP and yajC) are novel to Vibrio, and two targets (uppP and ompU) can be used to develop both drugs and vaccines (dual targets) against broad spectrum Vibrio serotypes. Using our novel computational approach, we have identified three peptide vaccine candidates that have high potential to induce both B- and T-cell-mediated immune responses from our identified two dual targets. These two targets were modeled and subjected to virtual screening against natural compounds derived from Piper betel. Seven compounds were identified first time from Piper betel to be highly effective to render the function of these targets to identify them as emerging potential drugs against Vibrio. Our preliminary validation suggests that these identified peptide vaccines and betel compounds are highly effective against Vibrio cholerae. Currently we are exhaustively validating these targets, candidate peptide vaccines, and betel derived lead compounds against a number of Vibrio species. PMID:23382822

Drug discrimination: A versatile tool for characterization of CNS safety pharmacology and potential for drug abuse.

PubMed

Swedberg, Michael D B

2016-01-01

Drug discrimination studies for assessment of psychoactive properties of drugs in safety pharmacology and drug abuse and drug dependence potential evaluation have traditionally been focused on testing novel compounds against standard drugs for which drug abuse has been documented, e.g. opioids, CNS stimulants, cannabinoids etc. (e.g. Swedberg & Giarola, 2015), and results are interpreted such that the extent to which the test drug causes discriminative effects similar to those of the standard training drug, the test drug would be further characterized as a potential drug of abuse. Regulatory guidance for preclinical assessment of abuse liability by the European Medicines Agency (EMA, 2006), the U.S. Food and Drug Administration (FDA, 2010), the International Conference of Harmonization (ICH, 2009), and the Japanese Ministry of Health Education and Welfare (MHLW, 1994) detail that compounds with central nervous system (CNS) activity, whether by design or not, need abuse and dependence liability assessment. Therefore, drugs with peripheral targets and a potential to enter the CNS, as parent or metabolite, are also within scope (see Swedberg, 2013, for a recent review and strategy). Compounds with novel mechanisms of action present a special challenge due to unknown abuse potential, and should be carefully assessed against defined risk criteria. Apart from compounds sharing mechanisms of action with known drugs of abuse, compounds intended for indications currently treated with drugs with potential for abuse and or dependence are also within scope, regardless of mechanism of action. Examples of such compounds are analgesics, anxiolytics, cognition enhancers, appetite control drugs, sleep control drugs and drugs for psychiatric indications. Recent results (Swedberg et al., 2014; Swedberg & Raboisson, 2014; Swedberg, 2015) on the metabotropic glutamate receptor type 5 (mGluR5) antagonists demonstrate that compounds causing hallucinatory effects in humans did not exhibit clear discriminative effects when tested against classical drugs of abuse in drug discrimination studies, and were not self-administered by rats. However, these compounds did cause salient discriminative effects of their own in animals trained to discriminate them from no drug. Therefore, from a safety pharmacology perspective, novel compounds that do not cause discriminative effects similar to classical drugs of abuse, may still cause psychoactive effects in humans and carry the potential to maintain drug abuse, suggesting that proactive investigation of drug abuse potential is warranted (Swedberg, 2013). These and other findings will be discussed, and the application of drug discrimination procedures beyond the typical standard application of testing novel compounds against known and well characterized reference drugs will be addressed. Copyright © 2016 Elsevier Inc. All rights reserved.

Short communication: cheminformatics analysis to identify predictors of antiviral drug penetration into the female genital tract.

PubMed

Thompson, Corbin G; Sedykh, Alexander; Nicol, Melanie R; Muratov, Eugene; Fourches, Denis; Tropsha, Alexander; Kashuba, Angela D M

2014-11-01

The exposure of oral antiretroviral (ARV) drugs in the female genital tract (FGT) is variable and almost unpredictable. Identifying an efficient method to find compounds with high tissue penetration would streamline the development of regimens for both HIV preexposure prophylaxis and viral reservoir targeting. Here we describe the cheminformatics investigation of diverse drugs with known FGT penetration using cluster analysis and quantitative structure-activity relationships (QSAR) modeling. A literature search over the 1950-2012 period identified 58 compounds (including 21 ARVs and representing 13 drug classes) associated with their actual concentration data for cervical or vaginal tissue, or cervicovaginal fluid. Cluster analysis revealed significant trends in the penetrative ability for certain chemotypes. QSAR models to predict genital tract concentrations normalized to blood plasma concentrations were developed with two machine learning techniques utilizing drugs' molecular descriptors and pharmacokinetic parameters as inputs. The QSAR model with the highest predictive accuracy had R(2)test=0.47. High volume of distribution, high MRP1 substrate probability, and low MRP4 substrate probability were associated with FGT concentrations ≥1.5-fold plasma concentrations. However, due to the limited FGT data available, prediction performances of all models were low. Despite this limitation, we were able to support our findings by correctly predicting the penetration class of rilpivirine and dolutegravir. With more data to enrich the models, we believe these methods could potentially enhance the current approach of clinical testing.

The Kinetic Mechanism for Cytochrome P450 Metabolism of Type II Binding Compounds: Evidence Supporting Direct Reduction

PubMed Central

Pearson, Joshua; Dahal, Upendra P.; Rock, Daniel; Peng, Chi-Chi; Schenk, James O.; Joswig-Jones, Carolyn; Jones, Jeffrey P.

2011-01-01

The metabolic stability of a drug is an important property that should be optimized during drug design and development. Nitrogen incorporation is hypothesized to increase the stability by coordination of nitrogen to the heme iron of cytochrome P450, a binding mode that is referred to as type II binding. However, we noticed that the type II binding compound 1 has less metabolic stability at subsaturating conditions than a closely related type I binding compound 3. Three kinetic models will be presented for type II binder metabolism; 1) Dead-end type II binding, 2) a rapid equilibrium between type I and II binding modes before reduction, and 3) a direct reduction of the type II coordinated heme. Data will be presented on reduction rates of iron, the off rates of substrate (using surface plasmon resonance) and the catalytic rate constants. These data argue against the dead-end, and rapid equilibrium models, leaving the direct reduction kinetic mechanism for metabolism of the type II binding compound 1. PMID:21530484

Bayesian Models Leveraging Bioactivity and Cytotoxicity Information for Drug Discovery

PubMed Central

Ekins, Sean; Reynolds, Robert C.; Kim, Hiyun; Koo, Mi-Sun; Ekonomidis, Marilyn; Talaue, Meliza; Paget, Steve D.; Woolhiser, Lisa K.; Lenaerts, Anne J.; Bunin, Barry A.; Connell, Nancy; Freundlich, Joel S.

2013-01-01

SUMMARY Identification of unique leads represents a significant challenge in drug discovery. This hurdle is magnified in neglected diseases such as tuberculosis. We have leveraged public high-throughput screening (HTS) data, to experimentally validate virtual screening approach employing Bayesian models built with bioactivity information (single-event model) as well as bioactivity and cytotoxicity information (dual-event model). We virtually screen a commercial library and experimentally confirm actives with hit rates exceeding typical HTS results by 1-2 orders of magnitude. The first dual-event Bayesian model identified compounds with antitubercular whole-cell activity and low mammalian cell cytotoxicity from a published set of antimalarials. The most potent hit exhibits the in vitro activity and in vitro/in vivo safety profile of a drug lead. These Bayesian models offer significant economies in time and cost to drug discovery. PMID:23521795

Bitter tastant responses in the amoeba Dictyostelium correlate with rat and human taste assays.

PubMed

Cocorocchio, Marco; Ives, Robert; Clapham, David; Andrews, Paul L R; Williams, Robin S B

2016-01-01

Treatment compliance is reduced when pharmaceutical compounds have a bitter taste and this is particularly marked for paediatric medications. Identification of bitter taste liability during drug discovery utilises the rat in vivo brief access taste aversion (BATA) test which apart from animal use is time consuming with limited throughput. We investigated the suitability of using a simple, non-animal model, the amoeba Dictyostelium discoideum to investigate taste-related responses and particularly identification of compounds with a bitter taste liability. The effect of taste-related compounds on Dictyostelium behaviour following acute exposure (15 minutes) was monitored. Dictyostelium did not respond to salty, sour, umami or sweet tasting compounds, however, cells rapidly responded to bitter tastants. Using time-lapse photography and computer-generated quantification to monitor changes in cell membrane movement, we developed an assay to assess the response of Dictyostelium to a wide range of structurally diverse known bitter compounds and blinded compounds. Dictyostelium showed varying responses to the bitter tastants, with IC50 values providing a rank order of potency. Comparison of Dictyostelium IC50 values to those observed in response to a similar range of compounds in the rat in vivo brief access taste aversion test showed a significant (p = 0.0172) positive correlation between the two models, and additionally a similar response to that provided by a human sensory panel assessment test. These experiments demonstrate that Dictyostelium may provide a suitable model for early prediction of bitterness for novel tastants and drugs. Interestingly, a response to bitter tastants appears conserved from single-celled amoebae to humans.

High-Throughput Phenotypic Screening of Human Astrocytes to Identify Compounds That Protect Against Oxidative Stress.

PubMed

Thorne, Natasha; Malik, Nasir; Shah, Sonia; Zhao, Jean; Class, Bradley; Aguisanda, Francis; Southall, Noel; Xia, Menghang; McKew, John C; Rao, Mahendra; Zheng, Wei

2016-05-01

Astrocytes are the predominant cell type in the nervous system and play a significant role in maintaining neuronal health and homeostasis. Recently, astrocyte dysfunction has been implicated in the pathogenesis of many neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis. Astrocytes are thus an attractive new target for drug discovery for neurological disorders. Using astrocytes differentiated from human embryonic stem cells, we have developed an assay to identify compounds that protect against oxidative stress, a condition associated with many neurodegenerative diseases. This phenotypic oxidative stress assay has been optimized for high-throughput screening in a 1,536-well plate format. From a screen of approximately 4,100 bioactive tool compounds and approved drugs, we identified a set of 22 that acutely protect human astrocytes from the consequences of hydrogen peroxide-induced oxidative stress. Nine of these compounds were also found to be protective of induced pluripotent stem cell-differentiated astrocytes in a related assay. These compounds are thought to confer protection through hormesis, activating stress-response pathways and preconditioning astrocytes to handle subsequent exposure to hydrogen peroxide. In fact, four of these compounds were found to activate the antioxidant response element/nuclear factor-E2-related factor 2 pathway, a protective pathway induced by toxic insults. Our results demonstrate the relevancy and utility of using astrocytes differentiated from human stem cells as a disease model for drug discovery and development. Astrocytes play a key role in neurological diseases. Drug discovery efforts that target astrocytes can identify novel therapeutics. Human astrocytes are difficult to obtain and thus are challenging to use for high-throughput screening, which requires large numbers of cells. Using human embryonic stem cell-derived astrocytes and an optimized astrocyte differentiation protocol, it was possible to screen approximately 4,100 compounds in titration to identify 22 that are cytoprotective of astrocytes. This study is the largest-scale high-throughput screen conducted using human astrocytes, with a total of 17,536 data points collected in the primary screen. The results demonstrate the relevancy and utility of using astrocytes differentiated from human stem cells as a disease model for drug discovery and development. ©AlphaMed Press.

Discovery of novel polyamine analogs with anti-protozoal activity by computer guided drug repositioning

NASA Astrophysics Data System (ADS)

Alberca, Lucas N.; Sbaraglini, María L.; Balcazar, Darío; Fraccaroli, Laura; Carrillo, Carolina; Medeiros, Andrea; Benitez, Diego; Comini, Marcelo; Talevi, Alan

2016-04-01

Chagas disease is a parasitic infection caused by the protozoa Trypanosoma cruzi that affects about 6 million people in Latin America. Despite its sanitary importance, there are currently only two drugs available for treatment: benznidazole and nifurtimox, both exhibiting serious adverse effects and limited efficacy in the chronic stage of the disease. Polyamines are ubiquitous to all living organisms where they participate in multiple basic functions such as biosynthesis of nucleic acids and proteins, proliferation and cell differentiation. T. cruzi is auxotroph for polyamines, which are taken up from the extracellular medium by efficient transporters and, to a large extent, incorporated into trypanothione (bis-glutathionylspermidine), the major redox cosubstrate of trypanosomatids. From a 268-compound database containing polyamine analogs with and without inhibitory effect on T. cruzi we have inferred classificatory models that were later applied in a virtual screening campaign to identify anti-trypanosomal compounds among drugs already used for other therapeutic indications (i.e. computer-guided drug repositioning) compiled in the DrugBank and Sweetlead databases. Five of the candidates identified with this strategy were evaluated in cellular models from different pathogenic trypanosomatids ( T. cruzi wt, T. cruzi PAT12, T. brucei and Leishmania infantum), and in vitro models of aminoacid/polyamine transport assays and trypanothione synthetase inhibition assay. Triclabendazole, sertaconazole and paroxetine displayed inhibitory effects on the proliferation of T. cruzi (epimastigotes) and the uptake of putrescine by the parasite. They also interfered with the uptake of others aminoacids and the proliferation of infective T. brucei and L. infantum (promastigotes). Trypanothione synthetase was ruled out as molecular target for the anti-parasitic activity of these compounds.

Metabolic stability for drug discovery and development: pharmacokinetic and biochemical challenges.

PubMed

Masimirembwa, Collen M; Bredberg, Ulf; Andersson, Tommy B

2003-01-01

Metabolic stability refers to the susceptibility of compounds to biotransformation in the context of selecting and/or designing drugs with favourable pharmacokinetic properties. Metabolic stability results are usually reported as measures of intrinsic clearance, from which secondary pharmacokinetic parameters such as bioavailability and half-life can be calculated when other data on volume of distribution and fraction absorbed are available. Since these parameters are very important in defining the pharmacological and toxicological profile of drugs as well as patient compliance, the pharmaceutical industry has a particular interest in optimising for metabolic stability during the drug discovery and development process. In the early phases of drug discovery, new chemical entities cannot be administered to humans; hence, predictions of these properties have to be made from in vivo animal, in vitro cellular/subcellular and computational systems. The utility of these systems to define the metabolic stability of compounds that is predictive of the human situation will be reviewed here. The timing of performing the studies in the discovery process and the impact of recent advances in research on drug absorption, distribution, metabolism and excretion (ADME) will be evaluated with respect to the scope and depth of metabolic stability issues. Quantitative prediction of in vivo clearance from in vitro metabolism data has, for many compounds, been shown to be poor in retrospective studies. One explanation for this may be that there are components used in the equations for scaling that are missing or uncertain and should be an area of more research. For example, as a result of increased biochemical understanding of drug metabolism, old assumptions (e.g. that the liver is the principal site of first-pass metabolism) need revision and new knowledge (e.g. the relationship between transporters and drug metabolising enzymes) needs to be incorporated into in vitro-in vivo correlation models. With ADME parameters increasingly being determined on automated platforms, instead of using results from high throughput screening (HTS) campaigns as simple go/no-go filters, the time saved and the many compounds analysed using the robots should be invested in careful processing of the data. A logical step would be to investigate the potential to construct computational models to understand the factors governing metabolic stability. A rational approach to the use of HTS assays should aim to screen for many properties (e.g. physicochemical parameters, absorption, metabolism, protein binding, pharmacokinetics in animals and pharmacology) in an integrated manner rather than screen against one property on many compounds, since it is likely that the final drug will represent a global average of these properties.

Developing a novel dual PI3K–mTOR inhibitor from the prodrug of a metabolite

PubMed Central

Zhou, Yan; Zhang, Genyan; Wang, Feng; Wang, Jin; Ding, Yanwei; Li, Xinyu; Shi, Chongtie; Li, Jiakui; Shih, Chengkon; You, Song

2017-01-01

This study presents a process of developing a novel PI3K–mTOR inhibitor through the prodrug of a metabolite. The lead compound (compound 1) was identified with similar efficacy as that of NVP-BEZ235 in a tumor xenograft model, but the exposure of compound 1 was much lower than that of NVP-BEZ235. After reanalysis of the blood sample, a major metabolite (compound 2) was identified. Compound 2 exerted similar in vitro activity as compound 1, which indicated that compound 2 was an active metabolite and that the in vivo efficacy in the animal model came from compound 2 instead of compound 1. However, compound 1 was metabolized into compound 2 predominantly in the liver microsomes of mouse, but not in the liver microsomes of rat, dog, or human. In order to translate the efficacy in the animal model into clinical development or predict the pharmacokinetic/pharmacodynamic parameters in the clinical study using a preclinical model, we developed the metabolite (compound 2) instead of compound 1. Due to the low bioavailability of compound 2, its prodrug (compound 3) was designed and synthesized to improve the solubility. The prodrug was quickly converted to compound 2 through both intravenous and oral administrations. Because the prodrug (compound 3) did not improve the oral exposure of compound 2, developing compound 3 as an intravenous drug was considered by our team, and the latest results will be reported in the future. PMID:29118584

Synergy Testing of FDA-Approved Drugs Identifies Potent Drug Combinations against Trypanosoma cruzi

PubMed Central

Ranade, Ranae M.; Don, Robert; Buckner, Frederick S.

2014-01-01

An estimated 8 million persons, mainly in Latin America, are infected with Trypanosoma cruzi, the etiologic agent of Chagas disease. Existing antiparasitic drugs for Chagas disease have significant toxicities and suboptimal effectiveness, hence new therapeutic strategies need to be devised to address this neglected tropical disease. Due to the high research and development costs of bringing new chemical entities to the clinic, we and others have investigated the strategy of repurposing existing drugs for Chagas disease. Screens of FDA-approved drugs (described in this paper) have revealed a variety of chemical classes that have growth inhibitory activity against mammalian stage Trypanosoma cruzi parasites. Aside from azole antifungal drugs that have low or sub-nanomolar activity, most of the active compounds revealed in these screens have effective concentrations causing 50% inhibition (EC50's) in the low micromolar or high nanomolar range. For example, we have identified an antihistamine (clemastine, EC50 of 0.4 µM), a selective serotonin reuptake inhibitor (fluoxetine, EC50 of 4.4 µM), and an antifolate drug (pyrimethamine, EC50 of 3.8 µM) and others. When tested alone in the murine model of Trypanosoma cruzi infection, most compounds had insufficient efficacy to lower parasitemia thus we investigated using combinations of compounds for additive or synergistic activity. Twenty-four active compounds were screened in vitro in all possible combinations. Follow up isobologram studies showed at least 8 drug pairs to have synergistic activity on T. cruzi growth. The combination of the calcium channel blocker, amlodipine, plus the antifungal drug, posaconazole, was found to be more effective at lowering parasitemia in mice than either drug alone, as was the combination of clemastine and posaconazole. Using combinations of FDA-approved drugs is a promising strategy for developing new treatments for Chagas disease. PMID:25033456

Computer aided drug design

NASA Astrophysics Data System (ADS)

Jain, A.

2017-08-01

Computer based method can help in discovery of leads and can potentially eliminate chemical synthesis and screening of many irrelevant compounds, and in this way, it save time as well as cost. Molecular modeling systems are powerful tools for building, visualizing, analyzing and storing models of complex molecular structure that can help to interpretate structure activity relationship. The use of various techniques of molecular mechanics and dynamics and software in Computer aided drug design along with statistics analysis is powerful tool for the medicinal chemistry to synthesis therapeutic and effective drugs with minimum side effect.

Predicting A Drug'S Membrane Permeability: Evolution of a Computational Model Validated with in Vitro Permeability Assay Data

DOE PAGES

Carpenter, Timothy S.; McNerney, M. Windy; Be, Nicholas A.; ...

2016-02-16

Membrane permeability is a key property to consider in drug design, especially when the drugs in question need to cross the blood-brain barrier (BBB). A comprehensive in vivo assessment of the BBB permeability of a drug takes considerable time and financial resources. A current, simplified in vitro model to investigate drug permeability is a Parallel Artificial Membrane Permeability Assay (PAMPA) that generally provides higher throughput and initial quantification of a drug's passive permeability. Computational methods can also be used to predict drug permeability. Our methods are highly advantageous as they do not require the synthesis of the desired drug, andmore » can be implemented rapidly using high-performance computing. In this study, we have used umbrella sampling Molecular Dynamics (MD) methods to assess the passive permeability of a range of compounds through a lipid bilayer. Furthermore, the permeability of these compounds was comprehensively quantified using the PAMPA assay to calibrate and validate the MD methodology. And after demonstrating a firm correlation between the two approaches, we then implemented our MD method to quantitatively predict the most permeable potential drug from a series of potential scaffolds. This permeability was then confirmed by the in vitro PAMPA methodology. Therefore, in this work we have illustrated the potential that these computational methods hold as useful tools to help predict a drug's permeability in a faster and more cost-effective manner. Release number: LLNL-ABS-677757.« less

Predicting A Drug'S Membrane Permeability: Evolution of a Computational Model Validated with in Vitro Permeability Assay Data

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

Carpenter, Timothy S.; McNerney, M. Windy; Be, Nicholas A.

Membrane permeability is a key property to consider in drug design, especially when the drugs in question need to cross the blood-brain barrier (BBB). A comprehensive in vivo assessment of the BBB permeability of a drug takes considerable time and financial resources. A current, simplified in vitro model to investigate drug permeability is a Parallel Artificial Membrane Permeability Assay (PAMPA) that generally provides higher throughput and initial quantification of a drug's passive permeability. Computational methods can also be used to predict drug permeability. Our methods are highly advantageous as they do not require the synthesis of the desired drug, andmore » can be implemented rapidly using high-performance computing. In this study, we have used umbrella sampling Molecular Dynamics (MD) methods to assess the passive permeability of a range of compounds through a lipid bilayer. Furthermore, the permeability of these compounds was comprehensively quantified using the PAMPA assay to calibrate and validate the MD methodology. And after demonstrating a firm correlation between the two approaches, we then implemented our MD method to quantitatively predict the most permeable potential drug from a series of potential scaffolds. This permeability was then confirmed by the in vitro PAMPA methodology. Therefore, in this work we have illustrated the potential that these computational methods hold as useful tools to help predict a drug's permeability in a faster and more cost-effective manner. Release number: LLNL-ABS-677757.« less

Development of a pharmacokinetic/pharmacodynamic/disease progression model in NC/Nga mice for development of novel anti-atopic dermatitis drugs.

PubMed

Baek, In-Hwan; Lee, Byung-Yo; Chae, Jung-Woo; Song, Gyu Yong; Kang, Wonku; Kwon, Kwang-Il

2014-11-01

1. JHL45, a novel immune modulator against atopic dermatitis (AD), was synthesized from decursin isolated from Angelica gigas. The goal is to evaluate the lead compound using quantitative modeling approaches to novel anti-AD drug development. 2. We tested the anti-inflammatory effect of JHL45 by in vitro screening, characterized its in vitro pharmacokinetic (PK) properties. The dose-dependent efficacy of JHL45 was developed using a pharmacokinetics/pharmacodynamics/disease progression (PK/PD/DIS) model in NC/Nga mice. 3. JHL45 has drug-like properties and pharmacological effects when administered orally to treat atopic dermatitis. The developed PK/PD/DIS model described well the rapid metabolism of JHL45, double-peak phenomenon in the PK of decursinol and inhibition of IgE generation by compounds in NC/Nga mice. Also, a quantitative model was developed and used to elucidate the complex interactions between serum IgE concentration and atopic dermatitis symptoms. 4. Our findings indicate that JHL45 has good physicochemical properties and powerful pharmacological effects when administered orally for treatment of AD in rodents.

Drug-binding energetics of human α-1-acid glycoprotein assessed by isothermal titration calorimetry and molecular docking simulations

PubMed Central

Huang, Johnny X.; Cooper, Matthew A.; Baker, Mark A.; Azad, Mohammad A.K.; Nation, Roger L.; Li, Jian; Velkov, Tony

2012-01-01

This study utilizes sensitive, modern isothermal titration calorimetric (ITC) methods to characterize the microscopic thermodynamic parameters that drive the binding of basic drugs to α-1-acid glycoprotein (AGP) and thereby rationalize the thermodynamic data in relation to docking models and crystallographic structures of the drug-AGP complexes. The binding of basic compounds from the tricyclic antidepressant series, together with miaserine, chlorpromazine, disopyramide and cimetidine all displayed an exothermically driven binding interaction with AGP. The impact of protonation/deprotonation events, ionic strength, temperature and the individual selectivity of the A and F1*S AGP variants on drug-binding thermodynamics were characterized. A correlation plot of the thermodynamic parameters for all of the test compounds revealed enthalpy-entropy compensation is in effect. The exothermic binding energetics of the test compounds were driven by a combination of favorable (negative) enthalpic (ΔH°) and favorable (positive) entropic (ΔS°) contributions to the Gibbs free energy (ΔG°). Collectively, the data imply that the free energies that drive drug binding to AGP and its relationship to drug-serum residency evolve from the complex interplay of enthalpic and entropic forces from interactions with explicit combinations of hydrophobic and polar side-chain sub-domains within the multi-lobed AGP ligand binding cavity. PMID:23192962

Historeceptomic Fingerprints for Drug-Like Compounds.

PubMed

Shmelkov, Evgeny; Grigoryan, Arsen; Swetnam, James; Xin, Junyang; Tivon, Doreen; Shmelkov, Sergey V; Cardozo, Timothy

2015-01-01

Most drugs exert their beneficial and adverse effects through their combined action on several different molecular targets (polypharmacology). The true molecular fingerprint of the direct action of a drug has two components: the ensemble of all the receptors upon which a drug acts and their level of expression in organs/tissues. Conversely, the fingerprint of the adverse effects of a drug may derive from its action in bystander tissues. The ensemble of targets is almost always only partially known. Here we describe an approach improving upon and integrating both components: in silico identification of a more comprehensive ensemble of targets for any drug weighted by the expression of those receptors in relevant tissues. Our system combines more than 300,000 experimentally determined bioactivity values from the ChEMBL database and 4.2 billion molecular docking scores. We integrated these scores with gene expression data for human receptors across a panel of human tissues to produce drug-specific tissue-receptor (historeceptomics) scores. A statistical model was designed to identify significant scores, which define an improved fingerprint representing the unique activity of any drug. These multi-dimensional historeceptomic fingerprints describe, in a novel, intuitive, and easy to interpret style, the holistic, in vivo picture of the mechanism of any drug's action. Valuable applications in drug discovery and personalized medicine, including the identification of molecular signatures for drugs with polypharmacologic modes of action, detection of tissue-specific adverse effects of drugs, matching molecular signatures of a disease to drugs, target identification for bioactive compounds with unknown receptors, and hypothesis generation for drug/compound phenotypes may be enabled by this approach. The system has been deployed at drugable.org for access through a user-friendly web site.

Identification of small molecule lead compounds for visceral leishmaniasis using a novel ex vivo splenic explant model system.

PubMed

Osorio, Yaneth; Travi, Bruno L; Renslo, Adam R; Peniche, Alex G; Melby, Peter C

2011-02-15

New drugs are needed to treat visceral leishmaniasis (VL) because the current therapies are toxic, expensive, and parasite resistance may weaken drug efficacy. We established a novel ex vivo splenic explant culture system from hamsters infected with luciferase-transfected Leishmania donovani to screen chemical compounds for anti-leishmanial activity. THIS MODEL HAS ADVANTAGES OVER IN VITRO SYSTEMS IN THAT IT: 1) includes the whole cellular population involved in the host-parasite interaction; 2) is initiated at a stage of infection when the immunosuppressive mechanisms that lead to progressive VL are evident; 3) involves the intracellular form of Leishmania; 4) supports parasite replication that can be easily quantified by detection of parasite-expressed luciferase; 5) is adaptable to a high-throughput screening format; and 6) can be used to identify compounds that have both direct and indirect anti-parasitic activity. The assay showed excellent discrimination between positive (amphotericin B) and negative (vehicle) controls with a Z' Factor >0.8. A duplicate screen of 4 chemical libraries containing 4,035 compounds identified 202 hits (5.0%) with a Z score of <-1.96 (p<0.05). Eighty-four (2.1%) of the hits were classified as lead compounds based on the in vitro therapeutic index (ratio of the compound concentration causing 50% cytotoxicity in the HepG(2) cell line to the concentration that caused 50% reduction in the parasite load). Sixty-nine (82%) of the lead compounds were previously unknown to have anti-leishmanial activity. The most frequently identified lead compounds were classified as quinoline-containing compounds (14%), alkaloids (10%), aromatics (11%), terpenes (8%), phenothiazines (7%) and furans (5%). The ex vivo splenic explant model provides a powerful approach to identify new compounds active against L. donovani within the pathophysiologic environment of the infected spleen. Further in vivo evaluation and chemical optimization of these lead compounds may generate new candidates for preclinical studies of treatment for VL.

Effect of composition of simulated intestinal media on the solubility of poorly soluble compounds investigated by design of experiments.

PubMed

Madsen, Cecilie Maria; Feng, Kung-I; Leithead, Andrew; Canfield, Nicole; Jørgensen, Søren Astrup; Müllertz, Anette; Rades, Thomas

2018-01-01

The composition of the human intestinal fluids varies both intra- and inter-individually. This will influence the solubility of orally administered drug compounds, and hence, the absorption and efficacy of compounds displaying solubility limited absorption. The purpose of this study was to assess the influence of simulated intestinal fluid (SIF) composition on the solubility of poorly soluble compounds. Using a Design of Experiments (DoE) approach, a set of 24 SIF was defined within the known compositions of human fasted state intestinal fluid. The SIF were composed of phospholipid, bile salt, and different pH, buffer capacities and osmolarities. On a small scale semi-robotic system, the solubility of 6 compounds (aprepitant, carvedilol, felodipine, fenofibrate, probucol, and zafirlukast) was determined in the 24 SIF. Compound specific models, describing key factors influencing the solubility of each compound, were identified. Although all models were different, the level of phospholipid and bile salt, the pH, and the interactions between these, had the biggest influences on solubility overall. Thus, a reduction of the DoE from five to three factors was possible (11-13 media), making DoE solubility studies feasible compared to single SIF solubility studies. Applying this DoE approach will lead to a better understanding of the impact of intestinal fluid composition on the solubility of a given drug compound. Copyright © 2017 Elsevier B.V. All rights reserved.

Identification of Novel Activators of Constitutive Androstane Receptor from FDA-approved Drugs by Integrated Computational and Biological Approaches

PubMed Central

Lynch, Caitlin; Pan, Yongmei; Li, Linhao; Ferguson, Stephen S.; Xia, Menghang; Swaan, Peter W.; Wang, Hongbing

2012-01-01

Purpose The constitutive androstane receptor (CAR, NR1I3) is a xenobiotic sensor governing the transcription of numerous hepatic genes associated with drug metabolism and clearance. Recent evidence suggests that CAR also modulates energy homeostasis and cancer development. Thus, identification of novel human (h) CAR activators is of both clinical importance and scientific interest. Methods Docking and ligand-based structure-activity models were used for virtual screening of a database containing over 2000 FDA-approved drugs. Identified lead compounds were evaluated in cell-based reporter assays to determine hCAR activation. Potential activators were further tested in human primary hepatocytes (HPHs) for the expression of the prototypical hCAR target gene CYP2B6. Results Nineteen lead compounds with optimal modeling parameters were selected for biological evaluation. Seven of the 19 leads exhibited moderate to potent activation of hCAR. Five out of the seven compounds translocated hCAR from the cytoplasm to the nucleus of HPHs in a concentration-dependent manner. These compounds also induce the expression of CYP2B6 in HPHs with rank-order of efficacies closely resembling that of hCAR activation. Conclusion These results indicate that our strategically integrated approaches are effective in the identification of novel hCAR modulators, which may function as valuable research tools or potential therapeutic molecules. PMID:23090669

High-Throughput Screening Using iPSC-Derived Neuronal Progenitors to Identify Compounds Counteracting Epigenetic Gene Silencing in Fragile X Syndrome.

PubMed

Kaufmann, Markus; Schuffenhauer, Ansgar; Fruh, Isabelle; Klein, Jessica; Thiemeyer, Anke; Rigo, Pierre; Gomez-Mancilla, Baltazar; Heidinger-Millot, Valerie; Bouwmeester, Tewis; Schopfer, Ulrich; Mueller, Matthias; Fodor, Barna D; Cobos-Correa, Amanda

2015-10-01

Fragile X syndrome (FXS) is the most common form of inherited mental retardation, and it is caused in most of cases by epigenetic silencing of the Fmr1 gene. Today, no specific therapy exists for FXS, and current treatments are only directed to improve behavioral symptoms. Neuronal progenitors derived from FXS patient induced pluripotent stem cells (iPSCs) represent a unique model to study the disease and develop assays for large-scale drug discovery screens since they conserve the Fmr1 gene silenced within the disease context. We have established a high-content imaging assay to run a large-scale phenotypic screen aimed to identify compounds that reactivate the silenced Fmr1 gene. A set of 50,000 compounds was tested, including modulators of several epigenetic targets. We describe an integrated drug discovery model comprising iPSC generation, culture scale-up, and quality control and screening with a very sensitive high-content imaging assay assisted by single-cell image analysis and multiparametric data analysis based on machine learning algorithms. The screening identified several compounds that induced a weak expression of fragile X mental retardation protein (FMRP) and thus sets the basis for further large-scale screens to find candidate drugs or targets tackling the underlying mechanism of FXS with potential for therapeutic intervention. © 2015 Society for Laboratory Automation and Screening.

Neuroprotective effects of compounds with antioxidant and anti-inflammatory properties in a Drosophila model of Parkinson's disease

PubMed Central

2009-01-01

Background Parkinson's disease (PD) is the most common movement disorder. Extrapyramidal motor symptoms stem from the degeneration of the dopaminergic pathways in patient brain. Current treatments for PD are symptomatic, alleviating disease symptoms without reversing or retarding disease progression. Although the cause of PD remains unknown, several pathogenic factors have been identified, which cause dopaminergic neuron (DN) death in the substantia nigra (SN). These include oxidative stress, mitochondrial dysfunction, inflammation and excitotoxicity. Manipulation of these factors may allow the development of disease-modifying treatment strategies to slow neuronal death. Inhibition of DJ-1A, the Drosophila homologue of the familial PD gene DJ-1, leads to oxidative stress, mitochondrial dysfunction, and DN loss, making fly DJ-1A model an excellent in vivo system to test for compounds with therapeutic potential. Results In the present study, a Drosophila DJ-1A model of PD was used to test potential neuroprotective drugs. The drugs applied are the Chinese herb celastrol, the antibiotic minocycline, the bioenergetic amine coenzyme Q10 (coQ10), and the glutamate antagonist 2,3-dihydroxy-6-nitro-7-sulphamoylbenzo[f]-quinoxaline (NBQX). All of these drugs target pathogenic processes implicated in PD, thus constitute mechanism-based treatment strategies. We show that celastrol and minocycline, both having antioxidant and anti-inflammatory properties, confer potent dopaminergic neuroprotection in Drosophila DJ-1A model, while coQ10 shows no protective effect. NBQX exerts differential effects on cell survival and brain dopamine content: it protects against DN loss but fails to restore brain dopamine level. Conclusion The present study further validates Drosophila as a valuable model for preclinical testing of drugs with therapeutic potential for neurodegenerative diseases. The lower cost and amenability to high throughput testing make Drosophila PD models effective in vivo tools for screening novel therapeutic compounds. If our findings can be further validated in mammalian PD models, they would implicate drugs combining antioxidant and anti-inflammatory properties as strong therapeutic candidates for mechanism-based PD treatment. PMID:19723328

Compound annotation with real time cellular activity profiles to improve drug discovery.

PubMed

Fang, Ye

2016-01-01

In the past decade, a range of innovative strategies have been developed to improve the productivity of pharmaceutical research and development. In particular, compound annotation, combined with informatics, has provided unprecedented opportunities for drug discovery. In this review, a literature search from 2000 to 2015 was conducted to provide an overview of the compound annotation approaches currently used in drug discovery. Based on this, a framework related to a compound annotation approach using real-time cellular activity profiles for probe, drug, and biology discovery is proposed. Compound annotation with chemical structure, drug-like properties, bioactivities, genome-wide effects, clinical phenotypes, and textural abstracts has received significant attention in early drug discovery. However, these annotations are mostly associated with endpoint results. Advances in assay techniques have made it possible to obtain real-time cellular activity profiles of drug molecules under different phenotypes, so it is possible to generate compound annotation with real-time cellular activity profiles. Combining compound annotation with informatics, such as similarity analysis, presents a good opportunity to improve the rate of discovery of novel drugs and probes, and enhance our understanding of the underlying biology.

Development of a New Generation of 4-Aminoquinoline Antimalarial Compounds Using Predictive Pharmacokinetic and Toxicology Models

PubMed Central

Ray, Sunetra; Madrid, Peter B.; Catz, Paul; LeValley, Susanna E.; Furniss, Michael J.; Rausch, Linda L.; Guy, R. Kiplin; DeRisi, Joseph L.; Iyer, Lalitha V.; Green, Carol E.; Mirsalis, Jon C.

2010-01-01

Among the known antimalarial drugs, chloroquine (CQ) and other 4-aminoquinolines have shown high potency and good bioavailability, yet complications associated with drug resistance necessitate the discovery of effective new antimalarial agents. ADMETa prediction studies were employed to evaluate a library of new molecules based on the 4-aminoquinolone-related structure of CQ. Extensive in vitro screening and in vivo pharmacokinetic studies in mice helped to identify two lead molecules, 18 and 4, with promising in vitro therapeutic efficacy, improved ADMET properties, low risk for drug-drug interactions, and desirable pharmacokinetic profiles. Both 18 and 4 are highly potent antimalarial compounds, with IC50 values = 5.6 nM and 17.3 nM, respectively, against the W2 (CQ-resistant) strain of Plasmodium falciparum (IC50 for CQ = 382 nM). When tested in mice, these compounds were found to have biological half-lives and plasma exposure values similar to or higher than those of CQ; they are therefore desirable candidates to pursue in future clinical trials. PMID:20361799

Nature Bank and the Queensland Compound Library: unique international resources at the Eskitis Institute for Drug Discovery.

PubMed

Camp, David; Newman, Stuart; Pham, Ngoc B; Quinn, Ronald J

2014-03-01

The Eskitis Institute for Drug Discovery is home to two unique resources, Nature Bank and the Queensland Compound Library (QCL), that differentiate it from many other academic institutes pursuing chemical biology or early phase drug discovery. Nature Bank is a comprehensive collection of plants and marine invertebrates that have been subjected to a process which aligns downstream extracts and fractions with lead- and drug-like physicochemical properties. Considerable expertise in screening natural product extracts/fractions was developed at Eskitis over the last two decades. Importantly, biodiscovery activities have been conducted from the beginning in accordance with the UN Convention on Biological Diversity (CBD) to ensure compliance with all international and national legislative requirements. The QCL is a compound management and logistics facility that was established from public funds to augment previous investments in high throughput and phenotypic screening in the region. A unique intellectual property (IP) model has been developed in the case of the QCL to stimulate applied, basic and translational research in the chemical and life sciences by industry, non-profit, and academic organizations.

Cytochrome P450 humanised mice

PubMed Central

2004-01-01

Humans are exposed to countless foreign compounds, typically referred to as xenobiotics. These can include clinically used drugs, environmental pollutants, food additives, pesticides, herbicides and even natural plant compounds. Xenobiotics are metabolised primarily in the liver, but also in the gut and other organs, to derivatives that are more easily eliminated from the body. In some cases, however, a compound is converted to an electrophile that can cause cell toxicity and transformation leading to cancer. Among the most important xenobiotic-metabolising enzymes are the cytochromes P450 (P450s). These enzymes represent a superfamily of multiple forms that exhibit marked species differences in their expression and catalytic activities. To predict how humans will metabolise xenobiotics, including drugs, human liver extracts and recombinant P450s have been used. New humanised mouse models are being developed which will be of great value in the study of drug metabolism, pharmacokinetics and pharmacodynamics in vivo, and in carrying out human risk assessment of xenobiotics. Humanised mice expressing CYP2D6 and CYP3A4, two major drug-metabolising P450s, have revealed the feasibility of this approach. PMID:15588489

Computational ligand-based rational design: Role of conformational sampling and force fields in model development.

PubMed

Shim, Jihyun; Mackerell, Alexander D

2011-05-01

A significant number of drug discovery efforts are based on natural products or high throughput screens from which compounds showing potential therapeutic effects are identified without knowledge of the target molecule or its 3D structure. In such cases computational ligand-based drug design (LBDD) can accelerate the drug discovery processes. LBDD is a general approach to elucidate the relationship of a compound's structure and physicochemical attributes to its biological activity. The resulting structure-activity relationship (SAR) may then act as the basis for the prediction of compounds with improved biological attributes. LBDD methods range from pharmacophore models identifying essential features of ligands responsible for their activity, quantitative structure-activity relationships (QSAR) yielding quantitative estimates of activities based on physiochemical properties, and to similarity searching, which explores compounds with similar properties as well as various combinations of the above. A number of recent LBDD approaches involve the use of multiple conformations of the ligands being studied. One of the basic components to generate multiple conformations in LBDD is molecular mechanics (MM), which apply an empirical energy function to relate conformation to energies and forces. The collection of conformations for ligands is then combined with functional data using methods ranging from regression analysis to neural networks, from which the SAR is determined. Accordingly, for effective application of LBDD for SAR determinations it is important that the compounds be accurately modelled such that the appropriate range of conformations accessible to the ligands is identified. Such accurate modelling is largely based on use of the appropriate empirical force field for the molecules being investigated and the approaches used to generate the conformations. The present chapter includes a brief overview of currently used SAR methods in LBDD followed by a more detailed presentation of issues and limitations associated with empirical energy functions and conformational sampling methods.

Pain: Systematic Review of Pharmacy Compounding of Pain Medication.

PubMed

Shawaqfeh, Mohammad S; Harrington, Catherine

2018-01-01

There are limited resources available for pharmacists and doctors to reference proper compounded formulas for pain medications. The systematic review discussed within this article provides the foundation for a searchable database, allowing users to find various compounded formulations. It also provides data about the safety and efficacy of the preparations. Compounding information about several drug classes was reviewed. Those drug classes included, but were not limited to, opioids, non-steroidal anti-inflammatory drugs, central nervous system agents, and anesthetics, with evidence that of the various drugs that could be compounded for pain, anesthetics, non-steroidal anti-inflammatory drugs, and opioids ranked highest within the articles researched. Copyright© by International Journal of Pharmaceutical Compounding, Inc.

Muscular dystrophy in a dish: engineered human skeletal muscle mimetics for disease modeling and drug discovery

PubMed Central

Smith, Alec S.T.; Davis, Jennifer; Lee, Gabsang; Mack, David L.

2016-01-01

Engineered in vitro models using human cells, particularly patient-derived induced pluripotent stem cells (iPSCs), offer a potential solution to issues associated with the use of animals for studying disease pathology and drug efficacy. Given the prevalence of muscle diseases in human populations, an engineered tissue model of human skeletal muscle could provide a biologically accurate platform to study basic muscle physiology, disease progression, and drug efficacy and/or toxicity. Such platforms could be used as phenotypic drug screens to identify compounds capable of alleviating or reversing congenital myopathies, such as Duchene muscular dystrophy (DMD). Here, we review current skeletal muscle modeling technologies with a specific focus on efforts to generate biomimetic systems for investigating the pathophysiology of dystrophic muscle. PMID:27109386

An update on the use of C. elegans for preclinical drug discovery: screening and identifying anti-infective drugs.

PubMed

Kim, Wooseong; Hendricks, Gabriel Lambert; Lee, Kiho; Mylonakis, Eleftherios

2017-06-01

The emergence of antibiotic-resistant and -tolerant bacteria is a major threat to human health. Although efforts for drug discovery are ongoing, conventional bacteria-centered screening strategies have thus far failed to yield new classes of effective antibiotics. Therefore, new paradigms for discovering novel antibiotics are of critical importance. Caenorhabditis elegans, a model organism used for in vivo, offers a promising solution for identification of anti-infective compounds. Areas covered: This review examines the advantages of C. elegans-based high-throughput screening over conventional, bacteria-centered in vitro screens. It discusses major anti-infective compounds identified from large-scale C. elegans-based screens and presents the first clinically-approved drugs, then known bioactive compounds, and finally novel small molecules. Expert opinion: There are clear advantages of using a C. elegans-infection based screening method. A C. elegans-based screen produces an enriched pool of non-toxic, efficacious, potential anti-infectives, covering: conventional antimicrobial agents, immunomodulators, and anti-virulence agents. Although C. elegans-based screens do not denote the mode of action of hit compounds, this can be elucidated in secondary studies by comparing the results to target-based screens, or conducting subsequent target-based screens, including the genetic knock-down of host or bacterial genes.

Role of Chemical Reactivity and Transition State Modeling for Virtual Screening.

PubMed

Karthikeyan, Muthukumarasamy; Vyas, Renu; Tambe, Sanjeev S; Radhamohan, Deepthi; Kulkarni, Bhaskar D

2015-01-01

Every drug discovery research program involves synthesis of a novel and potential drug molecule utilizing atom efficient, economical and environment friendly synthetic strategies. The current work focuses on the role of the reactivity based fingerprints of compounds as filters for virtual screening using a tool ChemScore. A reactant-like (RLS) and a product- like (PLS) score can be predicted for a given compound using the binary fingerprints derived from the numerous known organic reactions which capture the molecule-molecule interactions in the form of addition, substitution, rearrangement, elimination and isomerization reactions. The reaction fingerprints were applied to large databases in biology and chemistry, namely ChEMBL, KEGG, HMDB, DSSTox, and the Drug Bank database. A large network of 1113 synthetic reactions was constructed to visualize and ascertain the reactant product mappings in the chemical reaction space. The cumulative reaction fingerprints were computed for 4000 molecules belonging to 29 therapeutic classes of compounds, and these were found capable of discriminating between the cognition disorder related and anti-allergy compounds with reasonable accuracy of 75% and AUC 0.8. In this study, the transition state based fingerprints were also developed and used effectively for virtual screening in drug related databases. The methodology presented here provides an efficient handle for the rapid scoring of molecular libraries for virtual screening.

High-throughput screening of PLGA thin films utilizing hydrophobic fluorescent dyes for hydrophobic drug compounds.

PubMed

Steele, Terry W J; Huang, Charlotte L; Kumar, Saranya; Widjaja, Effendi; Chiang Boey, Freddy Yin; Loo, Joachim S C; Venkatraman, Subbu S

2011-10-01

Hydrophobic, antirestenotic drugs such as paclitaxel (PCTX) and rapamycin are often incorporated into thin film coatings for local delivery using implantable medical devices and polymers such as drug-eluting stents and balloons. Selecting the optimum coating formulation through screening the release profile of these drugs in thin films is time consuming and labor intensive. We describe here a high-throughput assay utilizing three model hydrophobic fluorescent compounds: fluorescein diacetate (FDAc), coumarin-6, and rhodamine 6G that were incorporated into poly(d,l-lactide-co-glycolide) (PLGA) and PLGA-polyethylene glycol films. Raman microscopy determined the hydrophobic fluorescent dye distribution within the PLGA thin films in comparison with that of PCTX. Their subsequent release was screened in a high-throughput assay and directly compared with HPLC quantification of PCTX release. It was observed that PCTX controlled-release kinetics could be mimicked by a hydrophobic dye that had similar octanol-water partition coefficient values and homogeneous dissolution in a PLGA matrix as the drug. In particular, FDAc was found to be the optimal hydrophobic dye at modeling the burst release as well as the total amount of PCTX released over a period of 30 days. Copyright © 2011 Wiley-Liss, Inc.

The interaction of an antiparasitic peptide active against African sleeping sickness with cell membrane models.

PubMed

Pascholati, Cauê P; Lopera, Esteban Parra; Pavinatto, Felippe J; Caseli, Luciano; Nobre, Thatyane M; Zaniquelli, Maria E D; Viitala, Tapani; D'Silva, Claudius; Oliveira, Osvaldo N

2009-12-01

Zwitterionic peptides with trypanocidal activity are promising lead compounds for the treatment of African Sleeping Sickness, and have motivated research into the design of compounds capable of disrupting the protozoan membrane. In this study, we use the Langmuir monolayer technique to investigate the surface properties of an antiparasitic peptide, namely S-(2,4-dinitrophenyl)glutathione di-2-propyl ester, and its interaction with a model membrane comprising a phospholipid monolayer. The drug formed stable Langmuir monolayers, whose main feature was a phase transition accompanied by a negative surface elasticity. This was attributed to aggregation upon compression due to intermolecular bond associations of the molecules, inferred from surface pressure and surface potential isotherms, Brewster angle microscopy (BAM) images, infrared spectroscopy and dynamic elasticity measurements. When co-spread with dipalmitoyl phosphatidyl choline (DPPC), the drug affected both the surface pressure and the monolayer morphology, even at high surface pressures and with low amounts of the drug. The results were interpreted by assuming a repulsive, cooperative interaction between the drug and DPPC molecules. Such repulsive interaction and the large changes in fluidity arising from drug aggregation may be related to the disruption of the membrane, which is key for the parasite killing property.

Ligand Biological Activity Predictions Using Fingerprint-Based Artificial Neural Networks (FANN-QSAR)

PubMed Central

Myint, Kyaw Z.; Xie, Xiang-Qun

2015-01-01

This chapter focuses on the fingerprint-based artificial neural networks QSAR (FANN-QSAR) approach to predict biological activities of structurally diverse compounds. Three types of fingerprints, namely ECFP6, FP2, and MACCS, were used as inputs to train the FANN-QSAR models. The results were benchmarked against known 2D and 3D QSAR methods, and the derived models were used to predict cannabinoid (CB) ligand binding activities as a case study. In addition, the FANN-QSAR model was used as a virtual screening tool to search a large NCI compound database for lead cannabinoid compounds. We discovered several compounds with good CB2 binding affinities ranging from 6.70 nM to 3.75 μM. The studies proved that the FANN-QSAR method is a useful approach to predict bioactivities or properties of ligands and to find novel lead compounds for drug discovery research. PMID:25502380

The dispersion releaser technology is an effective method for testing drug release from nanosized drug carriers.

PubMed

Janas, Christine; Mast, Marc-Phillip; Kirsamer, Li; Angioni, Carlo; Gao, Fiona; Mäntele, Werner; Dressman, Jennifer; Wacker, Matthias G

2017-06-01

The dispersion releaser (DR) is a dialysis-based setup for the analysis of the drug release from nanosized drug carriers. It is mounted into dissolution apparatus2 of the United States Pharmacopoeia. The present study evaluated the DR technique investigating the drug release of the model compound flurbiprofen from drug solution and from nanoformulations composed of the drug and the polymer materials poly (lactic acid), poly (lactic-co-glycolic acid) or Eudragit®RSPO. The drug loaded nanocarriers ranged in size between 185.9 and 273.6nm and were characterized by a monomodal size distribution (PDI<0.1). The membrane permeability constants of flurbiprofen were calculated and mathematical modeling was applied to obtain the normalized drug release profiles. For comparing the sensitivities of the DR and the dialysis bag technique, the differences in the membrane permeation rates were calculated. Finally, different formulation designs of flurbiprofen were sensitively discriminated using the DR technology. The mechanism of drug release from the nanosized carriers was analyzed by applying two mathematical models described previously, the reciprocal powered time model and the three parameter model. Copyright © 2017 Elsevier B.V. All rights reserved.

Drugs and Drug-Like Compounds: Discriminating Approved Pharmaceuticals from Screening-Library Compounds

NASA Astrophysics Data System (ADS)

Schierz, Amanda C.; King, Ross D.

Compounds in drug screening-libraries should resemble pharmaceuticals. To operationally test this, we analysed the compounds in terms of known drug-like filters and developed a novel machine learning method to discriminate approved pharmaceuticals from “drug-like” compounds. This method uses both structural features and molecular properties for discrimination. The method has an estimated accuracy of 91% in discriminating between the Maybridge HitFinder library and approved pharmaceuticals, and 99% between the NATDiverse collection (from Analyticon Discovery) and approved pharmaceuticals. These results show that Lipinski’s Rule of 5 for oral absorption is not sufficient to describe “drug-likeness” and be the main basis of screening-library design.

A simplified PBPK modeling approach for prediction of pharmacokinetics of four primarily renally excreted and CYP3A metabolized compounds during pregnancy.

PubMed

Xia, Binfeng; Heimbach, Tycho; Gollen, Rakesh; Nanavati, Charvi; He, Handan

2013-10-01

During pregnancy, a drug's pharmacokinetics may be altered and hence anticipation of potential systemic exposure changes is highly desirable. Physiologically based pharmacokinetics (PBPK) models have recently been used to influence clinical trial design or to facilitate regulatory interactions. Ideally, whole-body PBPK models can be used to predict a drug's systemic exposure in pregnant women based on major physiological changes which can impact drug clearance (i.e., in the kidney and liver) and distribution (i.e., adipose and fetoplacental unit). We described a simple and readily implementable multitissue/organ whole-body PBPK model with key pregnancy-related physiological parameters to characterize the PK of reference drugs (metformin, digoxin, midazolam, and emtricitabine) in pregnant women compared with the PK in nonpregnant or postpartum (PP) women. Physiological data related to changes in maternal body weight, tissue volume, cardiac output, renal function, blood flows, and cytochrome P450 activity were collected from the literature and incorporated into the structural PBPK model that describes HV or PP women PK data. Subsequently, the changes in exposure (area under the curve (AUC) and maximum concentration (C max)) in pregnant women were simulated. Model-simulated PK profiles were overall in agreement with observed data. The prediction fold error for C max and AUC ratio (pregnant vs. nonpregnant) was less than 1.3-fold, indicating that the pregnant PBPK model is useful. The utilization of this simplified model in drug development may aid in designing clinical studies to identify potential exposure changes in pregnant women a priori for compounds which are mainly eliminated renally or metabolized by CYP3A4.

Targeting Dengue Virus NS-3 Helicase by Ligand based Pharmacophore Modeling and Structure based Virtual Screening

NASA Astrophysics Data System (ADS)

Halim, Sobia A.; Khan, Shanza; Khan, Ajmal; Wadood, Abdul; Mabood, Fazal; Hussain, Javid; Al-Harrasi, Ahmed

2017-10-01

Dengue fever is an emerging public health concern, with several million viral infections occur annually, for which no effective therapy currently exist. Non-structural protein 3 (NS-3) Helicase encoded by the dengue virus (DENV) is considered as a potential drug target to design new and effective drugs against dengue. Helicase is involved in unwinding of dengue RNA. This study was conducted to design new NS-3 Helicase inhibitor by in silico ligand- and structure based approaches. Initially ligand-based pharmacophore model was generated that was used to screen a set of 1201474 compounds collected from ZINC Database. The compounds matched with the pharmacophore model were docked into the active site of NS-3 helicase. Based on docking scores and binding interactions, twenty five compounds are suggested to be potential inhibitors of NS3 Helicase. The pharmacokinetic properties of these hits were predicted. The selected hits revealed acceptable ADMET properties. This study identified potential inhibitors of NS-3 Helicase in silico, and can be helpful in the treatment of Dengue.

Effects of senolytic drugs on human mesenchymal stromal cells.

PubMed

Grezella, Clara; Fernandez-Rebollo, Eduardo; Franzen, Julia; Ventura Ferreira, Mónica Sofia; Beier, Fabian; Wagner, Wolfgang

2018-04-18

Senolytic drugs are thought to target senescent cells and might thereby rejuvenate tissues. In fact, such compounds were suggested to increase health and lifespan in various murine aging models. So far, effects of senolytic drugs have not been analysed during replicative senescence of human mesenchymal stromal cells (MSCs). In this study, we tested four potentially senolytic drugs: ABT-263 (navitoclax), quercetin, nicotinamide riboside, and danazol. The effects of these compounds were analysed during long-term expansion of MSCs, until replicative senescence. Furthermore, we determined the effect on molecular markers for replicative senescence, such as senescence-associated beta-galactosidase staining (SA-β-gal), telomere attrition, and senescence-associated DNA methylation changes. Co-culture experiments of fluorescently labelled early and late passages revealed that particularly ABT-263 had a significant but moderate senolytic effect. This was in line with reduced SA-β-gal staining in senescent MSCs upon treatment with ABT-263. However, none of the drugs had significant effects on the maximum number of population doublings, telomere length, or epigenetic senescence predictions. Of the four tested drugs, only ABT-263 revealed a senolytic effect in human MSCs-and even treatment with this compound did not rejuvenate MSCs with regard to telomere length or epigenetic senescence signature. It will be important to identify more potent senolytic drugs to meet the high hopes for regenerative medicine.

EMA401: an old antagonist of the AT2R for a new indication in neuropathic pain.

PubMed

Keppel Hesselink, Jan M; Schatman, Michael E

2017-01-01

EMA401 is an old molecule, synthesized by Parke-Davis in the last century and characterized at that time as an AT2R antagonist. Professor Maree Smith and her group from the University of Queensland (Australia) patented the drug and many related derivatives and other compounds with high affinity for the AT2R for the indication neuropathic pain in 2004, an example of drug repositioning. After some years of university work, the Australian biotech company Spinifex Pharmaceuticals took over further research and development and characterized the S-enantiomer, code name EMA401, and related compounds in a variety of animal models for neuropathic and cancer pain. EMA401 was selected as the lead compound, based on high selectivity for the AT2R and good oral bioavailability (33%). EMA401, however, was only administered once in a chronic neuropathic pain model, and no data have been published in other pain models, or during steady state, although such data were available for the racemate EMA400 and some related compounds (EMA200, EMA400). A pilot phase IIa study demonstrated the efficacy and safety of the drug taken twice daily as two capsules of 50 mg (400 mg/day). In 2015, Novartis took over the clinical development. Two phase IIb studies designed by Spinifex Pharmaceuticals were put on hold, probably because Novartis wanted to improve the clinical design or collect additional preclinical data. Further data are eagerly awaited, especially since EMA401 is first-in-class in neuropathic pain.

EMA401: an old antagonist of the AT2R for a new indication in neuropathic pain

PubMed Central

Keppel Hesselink, Jan M; Schatman, Michael E

2017-01-01

EMA401 is an old molecule, synthesized by Parke-Davis in the last century and characterized at that time as an AT2R antagonist. Professor Maree Smith and her group from the University of Queensland (Australia) patented the drug and many related derivatives and other compounds with high affinity for the AT2R for the indication neuropathic pain in 2004, an example of drug repositioning. After some years of university work, the Australian biotech company Spinifex Pharmaceuticals took over further research and development and characterized the S-enantiomer, code name EMA401, and related compounds in a variety of animal models for neuropathic and cancer pain. EMA401 was selected as the lead compound, based on high selectivity for the AT2R and good oral bioavailability (33%). EMA401, however, was only administered once in a chronic neuropathic pain model, and no data have been published in other pain models, or during steady state, although such data were available for the racemate EMA400 and some related compounds (EMA200, EMA400). A pilot phase IIa study demonstrated the efficacy and safety of the drug taken twice daily as two capsules of 50 mg (400 mg/day). In 2015, Novartis took over the clinical development. Two phase IIb studies designed by Spinifex Pharmaceuticals were put on hold, probably because Novartis wanted to improve the clinical design or collect additional preclinical data. Further data are eagerly awaited, especially since EMA401 is first-in-class in neuropathic pain. PMID:28255254

Generative Recurrent Networks for De Novo Drug Design.

PubMed

Gupta, Anvita; Müller, Alex T; Huisman, Berend J H; Fuchs, Jens A; Schneider, Petra; Schneider, Gisbert

2018-01-01

Generative artificial intelligence models present a fresh approach to chemogenomics and de novo drug design, as they provide researchers with the ability to narrow down their search of the chemical space and focus on regions of interest. We present a method for molecular de novo design that utilizes generative recurrent neural networks (RNN) containing long short-term memory (LSTM) cells. This computational model captured the syntax of molecular representation in terms of SMILES strings with close to perfect accuracy. The learned pattern probabilities can be used for de novo SMILES generation. This molecular design concept eliminates the need for virtual compound library enumeration. By employing transfer learning, we fine-tuned the RNN's predictions for specific molecular targets. This approach enables virtual compound design without requiring secondary or external activity prediction, which could introduce error or unwanted bias. The results obtained advocate this generative RNN-LSTM system for high-impact use cases, such as low-data drug discovery, fragment based molecular design, and hit-to-lead optimization for diverse drug targets. © 2017 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.

Ligand-based virtual screening and in silico design of new antimalarial compounds using nonstochastic and stochastic total and atom-type quadratic maps.

PubMed

Marrero-Ponce, Yovani; Iyarreta-Veitía, Maité; Montero-Torres, Alina; Romero-Zaldivar, Carlos; Brandt, Carlos A; Avila, Priscilla E; Kirchgatter, Karin; Machado, Yanetsy

2005-01-01

Malaria has been one of the most significant public health problems for centuries. It affects many tropical and subtropical regions of the world. The increasing resistance of Plasmodium spp. to existing therapies has heightened alarms about malaria in the international health community. Nowadays, there is a pressing need for identifying and developing new drug-based antimalarial therapies. In an effort to overcome this problem, the main purpose of this study is to develop simple linear discriminant-based quantitative structure-activity relationship (QSAR) models for the classification and prediction of antimalarial activity using some of the TOMOCOMD-CARDD (TOpological MOlecular COMputer Design-Computer Aided "Rational" Drug Design) fingerprints, so as to enable computational screening from virtual combinatorial datasets. In this sense, a database of 1562 organic chemicals having great structural variability, 597 of them antimalarial agents and 965 compounds having other clinical uses, was analyzed and presented as a helpful tool, not only for theoretical chemists but also for other researchers in this area. This series of compounds was processed by a k-means cluster analysis in order to design training and predicting sets. Afterward, two linear classification functions were derived in order to discriminate between antimalarial and nonantimalarial compounds. The models (including nonstochastic and stochastic indices) correctly classify more than 93% of the compound set, in both training and external prediction datasets. They showed high Matthews' correlation coefficients, 0.889 and 0.866 for the training set and 0.855 and 0.857 for the test one. The models' predictivity was also assessed and validated by the random removal of 10% of the compounds to form a new test set, for which predictions were made using the models. The overall means of the correct classification for this process (leave group 10% full-out cross validation) using the equations with nonstochastic and stochastic atom-based quadratic fingerprints were 93.93% and 92.77%, respectively. The quadratic maps-based TOMOCOMD-CARDD approach implemented in this work was successfully compared with four of the most useful models for antimalarials selection reported to date. The developed models were then used in a simulation of a virtual search for Ras FTase (FTase = farnesyltransferase) inhibitors with antimalarial activity; 70% and 100% of the 10 inhibitors used in this virtual search were correctly classified, showing the ability of the models to identify new lead antimalarials. Finally, these two QSAR models were used in the identification of previously unknown antimalarials. In this sense, three synthetic intermediaries of quinolinic compounds were evaluated as active/inactive ones using the developed models. The synthesis and biological evaluation of these chemicals against two malaria strains, using chloroquine as a reference, was performed. An accuracy of 100% with the theoretical predictions was observed. Compound 3 showed antimalarial activity, being the first report of an arylaminomethylenemalonate having such behavior. This result opens a door to a virtual study considering a higher variability of the structural core already evaluated, as well as of other chemicals not included in this study. We conclude that the approach described here seems to be a promising QSAR tool for the molecular discovery of novel classes of antimalarial drugs, which may meet the dual challenges posed by drug-resistant parasites and the rapid progression of malaria illnesses.

Hepatic cells derived from human skin progenitors show a typical phospholipidotic response upon exposure to amiodarone.

PubMed

Natale, Alessandra; Boeckmans, Joost; Desmae, Terry; De Boe, Veerle; De Kock, Joery; Vanhaecke, Tamara; Rogiers, Vera; Rodrigues, Robim M

2018-03-01

Phospholipidosis is a metabolic disorder characterized by intracellular accumulation of phospholipids. It can be caused by short-term or chronic exposure to cationic amphiphilic drugs (CADs). These compounds bind to phospholipids, leading to inhibition of their degradation and consequently to their accumulation in lysosomes. Drug-induced phospholipidosis (DIPL) is frequently at the basis of discontinuation of drug development and post-market drug withdrawal. Therefore, reliable human-relevant in vitro models must be developed to speed up the identification of compounds that are potential inducers of phospholipidosis. Here, hepatic cells derived from human skin (hSKP-HPC) were evaluated as an in vitro model for DIPL. These cells were exposed over time to amiodarone, a CAD known to induce phospholipidosis in humans. Transmission electron microscopy revealed the formation of the typical lamellar inclusions in the cell cytoplasm. Increase of phospholipids was already detected after 24 h exposure to amiodarone, whereas a significant increase of neutral lipid vesicles could be observed after 72 h. At the transcriptional level, the modulation of genes involved in DIPL was detected. These results provide a valuable indication of the applicability of hSKP-HPC for the quick assessment of drug-induced phospholipidosis in vitro, early in the drug development process. Copyright © 2017 Elsevier B.V. All rights reserved.

Effects of formulation variables and post-compression curing on drug release from a new sustained-release matrix material: polyvinylacetate-povidone.

PubMed

Shao, Z J; Farooqi, M I; Diaz, S; Krishna, A K; Muhammad, N A

2001-01-01

A new commercially available sustained-release matrix material, Kollidon SR, composed of polyvinylacetate and povidone, was evaluated with respect to its ability to modulate the in vitro release of a highly water-soluble model compound, diphenhydramine HCl. Kollidon SR was found to provide a sustained-release effect for the model compound, with certain formulation and processing variables playing an important role in controlling its release kinetics. Formulation variables affecting the release include the level of the polymeric material in the matrix, excipient level, as well as the nature of the excipients (water soluble vs. water insoluble). Increasing the ratio of a water-insoluble excipient, Emcompress, to Kollidon SR enhanced drug release. The incorporation of a water-soluble excipient, lactose, accelerated its release rate in a more pronounced manner. Stability studies conducted at 40 degrees C/75% RH revealed a slow-down in dissolution rate for the drug-Kollidon SR formulation, as a result of polyvinylacetate relaxation. Further studies demonstrated that a post-compression curing step effectively stabilized the release pattern of formulations containing > or = 47% Kollidon SR. The release mechanism of Kollidon-drug and drug-Kollidon-Emcompress formulations appears to be diffusion controlled, while that of the drug-Kollidon-lactose formulation appears to be controlled predominantly by diffusion along with erosion.

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

Small molecule compound logistics outsourcing--going beyond the "thought experiment".

PubMed

Ramsay, Devon L; Kwasnoski, Joseph D; Caldwell, Gary W

2012-01-01

Increasing pressure on the pharmaceutical industry to reduce cost and focus internal resources on "high value" activities is driving a trend to outsource traditionally "in-house" drug discovery activities. Compound collections are typically viewed as drug discovery's "crown jewels"; however, in late 2007, Johnson & Johnson Pharmaceutical Research & Development (J PRD) took a bold step to move their entire North American compound inventory and processing capability to an external third party vendor. The authors discuss the combination model implemented, that of local compound logistics site support with an outsourced centralized processing center. Some of the lessons learned over the past five years were predictable while others were unexpected. The substantial cost savings, improved local service response and flexible platform to adjust to changing business needs resulted. Continued sustainable success relies heavily upon maintaining internal headcount dedicated to vendor management, an open collaboration approach and a solid information technology infrastructure with complete transparency and visibility.

A practical drug discovery project at the undergraduate level.

PubMed

Fray, M Jonathan; Macdonald, Simon J F; Baldwin, Ian R; Barton, Nick; Brown, Jack; Campbell, Ian B; Churcher, Ian; Coe, Diane M; Cooper, Anthony W J; Craven, Andrew P; Fisher, Gail; Inglis, Graham G A; Kelly, Henry A; Liddle, John; Maxwell, Aoife C; Patel, Vipulkumar K; Swanson, Stephen; Wellaway, Natalie

2013-12-01

In this article, we describe a practical drug discovery project for third-year undergraduates. No previous knowledge of medicinal chemistry is assumed. Initial lecture workshops cover the basic principles; then students, in teams, seek to improve the profile of a weakly potent, insoluble phosphatidylinositide 3-kinase delta (PI3Kδ) inhibitor (1) through compound array design, molecular modelling, screening data analysis and the synthesis of target compounds in the laboratory. The project benefits from significant industrial support, including lectures, student mentoring and consumables. The aim is to make the learning experience as close as possible to real-life industrial situations. In total, 48 target compounds were prepared, the best of which (5b, 5j, 6b and 6ap) improved the potency and aqueous solubility of the lead compound (1) by 100-1000 fold and ≥tenfold, respectively. Copyright © 2013 Elsevier Ltd. All rights reserved.

DEGAS: sharing and tracking target compound ideas with external collaborators.

PubMed

Lee, Man-Ling; Aliagas, Ignacio; Dotson, Jennafer; Feng, Jianwen A; Gobbi, Alberto; Heffron, Timothy

2012-02-27

To minimize the risk of failure in clinical trials, drug discovery teams must propose active and selective clinical candidates with good physicochemical properties. An additional challenge is that today drug discovery is often conducted by teams at different geographical locations. To improve the collaborative decision making on which compounds to synthesize, we have implemented DEGAS, an application which enables scientists from Genentech and from collaborating external partners to instantly access the same data. DEGAS was implemented to ensure that only the best target compounds are made and that they are made without duplicate effort. Physicochemical properties and DMPK model predictions are computed for each compound to allow the team to make informed decisions when prioritizing. The synthesis progress can be easily tracked. While developing DEGAS, ease of use was a particular goal in order to minimize the difficulty of training and supporting remote users.

A chemical family-based strategy for uncovering hidden bioactive molecules and multicomponent interactions in herbal medicines.

PubMed

Song, Hui-Peng; Wu, Si-Qi; Hao, Haiping; Chen, Jun; Lu, Jun; Xu, Xiaojun; Li, Ping; Yang, Hua

2016-03-30

Two concepts involving natural products were proposed and demonstrated in this paper. (1) Natural product libraries (e.g. herbal extract) are not perfect for bioactivity screening because of the vast complexity of compound compositions, and thus a library reconstruction procedure is necessary before screening. (2) The traditional mode of "screening single compound" could be improved to "screening single compound, drug combination and multicomponent interaction" due to the fact that herbal medicines work by integrative effects of multi-components rather than single effective constituents. Based on the two concepts, we established a novel strategy aiming to make screening easier and deeper. Using thrombin as the model enzyme, we firstly uncovered the minor lead compounds, potential drug combinations and multicomponent interactions in an herbal medicine of Dan-Qi pair, showing a significant advantage over previous methods. This strategy was expected to be a new and promising mode for investigation of herbal medicines.

Discovery of potent cytotoxic ortho-aryl chalcones as new scaffold targeting tubulin and mitosis with affinity-based fluorescence.

PubMed

Zhu, Cuige; Zuo, Yinglin; Wang, Ruimin; Liang, Baoxia; Yue, Xin; Wen, Gesi; Shang, Nana; Huang, Lei; Chen, Yu; Du, Jun; Bu, Xianzhang

2014-08-14

A series of new ortho-aryl chalcones have been designed and synthesized. Many of these compounds were found to exhibit significant antiproliferation activity toward a panel of cancer cell lines. Selected compounds show potent cytotoxicity against several drug resistant cell lines including paclitaxel (Taxol) resistant human ovarian carcinoma cells, vincristine resistant human ileocecum carcinoma cells, and doxorubicin resistant human breast carcinoma cells. Further investigation revealed that active analogues could inhibit the microtubule polymerization by binding to colchicine site and thus induce multipolar mitosis, G2/M phase arrest, and apoptosis of cancer cells. Furthermore, affinity-based fluorescence enhancement was observed during the binding of active compounds with tubulin, which greatly facilitated the determination of tubulin binding site of the compounds. Finally, selected compound 26 was found to exhibit obvious in vivo antitumor activity in A549 tumor xenografts model. Our systematic studies implied a new scaffold targeting tubulin and mitosis for novel antitumor drug discovery.

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

PubMed

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

2014-03-01

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

The fruit fly Drosophila melanogaster as an innovative preclinical ADME model for solute carrier membrane transporters, with consequences for pharmacology and drug therapy.

PubMed

Wang, Yiwen; Moussian, Bernard; Schaeffeler, Elke; Schwab, Matthias; Nies, Anne T

2018-06-08

Solute carrier membrane transporters (SLCs) control cell exposure to small-molecule drugs, thereby contributing to drug efficacy and failure and/or adverse effects. Moreover, SLCs are genetically linked to various diseases. Hence, in-depth knowledge of SLC function is fundamental for a better understanding of disease pathophysiology and the drug development process. Given that the model organism Drosophila melanogaster (fruit fly) expresses SLCs, such as for the excretion of endogenous and toxic compounds by the hindgut and Malpighian tubules, equivalent to human intestine and kidney, this system appears to be a promising preclinical model to use to study human SLCs. Here, we systematically compare current knowledge of SLCs in Drosophila and humans and describe the Drosophila model as an innovative tool for drug development. Copyright © 2018 Elsevier Ltd. All rights reserved.

Evaluation of the physicochemical and biopharmaceutical properties of fluoro-indomethacin.

PubMed

Mori, Michela M; Airaksinen, Anu J; Hirvonen, Jouni T; Santos, Hélder A; Caramella, Carla M

2013-01-01

Drug nanocarriers have shown great potential in therapy and as diagnostic probes, e.g. in imaging of cancer and inflammation. Imaging can be applied to localize the carrier or the drug itself in the body and/or tissues. In this particular case it is important that drug molecules have the characteristics for possible detection, e.g. after modification with positron emission tomography compliant radioisotopes, without affecting their pharmacological behavior. In order to easily and efficiently follow the ADME profile of the drug after loaded into nanocarriers, the drug can be radiolabelled with, e.g. 18F-label, in order to assess its biodistribution after enteral and parenteral administration in rats. However, this is only possible if the derivative compound behaves similarly to the parent drug compound. In this study, indomethacin (a poorly water-soluble drug) was chosen as a model compound and aimed to evaluate the physicochemical and biopharmaceutical properties of an analog of indomethacin (IMC), fluoro-indomethacin (F-IMC). Although some of the physicochemical and biopharmaceutical properties of IMC are already known, in order to establish a feasible comparison between IMC and F-IMC, the behavior of the former was also investigated in the same conditions as for F-IMC. In this context, both IMC and F-IMC were thermally and morphologically studied. Furthermore, the following properties were also studied for both compounds: pKa and logP, solubility and dissolution profiles at physiological pH values, and toxicity at different concentrations in Caco-2 cells. Finally, the transport across Caco- 2 monolayers of the IMC and F-IMC at physiological pH range was also investigated. The results obtained showed similar values in pKalogP, solubility, dissolution, cytotoxicity, and permeability for both compounds. Thus, there might be strong evidence that both IMC and F-IMC should have a similar ADME behavior and profiles in vivo. The results provide fundamental tools and ideas for further research with nanocarriers of 18F-IMC.

In Silico QT and APD Prolongation Assay for Early Screening of Drug-Induced Proarrhythmic Risk.

PubMed

Romero, Lucia; Cano, Jordi; Gomis-Tena, Julio; Trenor, Beatriz; Sanz, Ferran; Pastor, Manuel; Saiz, Javier

2018-04-23

Drug-induced proarrhythmicity is a major concern for regulators and pharmaceutical companies. For novel drug candidates, the standard assessment involves the evaluation of the potassium hERG channels block and the in vivo prolongation of the QT interval. However, this method is known to be too restrictive and to stop the development of potentially valuable therapeutic drugs. The aim of this work is to create an in silico tool for early detection of drug-induced proarrhythmic risk. The system is based on simulations of how different compounds affect the action potential duration (APD) of isolated endocardial, midmyocardial, and epicardial cells as well as the QT prolongation in a virtual tissue. Multiple channel-drug interactions and state-of-the-art human ventricular action potential models ( O'Hara , T. , PLos Comput. Biol. 2011 , 7 , e1002061 ) were used in our simulations. Specifically, 206.766 cellular and 7072 tissue simulations were performed by blocking the slow and the fast components of the delayed rectifier current ( I Ks and I Kr , respectively) and the L-type calcium current ( I CaL ) at different levels. The performance of our system was validated by classifying the proarrhythmic risk of 84 compounds, 40 of which present torsadogenic properties. On the basis of these results, we propose the use of a new index (Tx) for discriminating torsadogenic compounds, defined as the ratio of the drug concentrations producing 10% prolongation of the cellular endocardial, midmyocardial, and epicardial APDs and the QT interval, over the maximum effective free therapeutic plasma concentration (EFTPC). Our results show that the Tx index outperforms standard methods for early identification of torsadogenic compounds. Indeed, for the analyzed compounds, the Tx tests accuracy was in the range of 87-88% compared with a 73% accuracy of the hERG IC 50 based test.

Topological pattern for the search of new active drugs against methicillin resistant Staphylococcus aureus.

PubMed

Bueso-Bordils, Jose I; Perez-Gracia, Maria T; Suay-Garcia, Beatriz; Duart, Maria J; Martin Algarra, Rafael V; Lahuerta Zamora, Luis; Anton-Fos, Gerardo M; Aleman Lopez, Pedro A

2017-09-29

Molecular topology was used to develop a mathematical model capable of classifying compounds according to antimicrobial activity against methicillin resistant Staphylococcus aureus (MRSA). Topological indices were used as structural descriptors and their relation to antimicrobial activity was determined by using linear discriminant analysis. This topological model establishes new structure activity relationships which show that the presence of cyclopropyl, chlorine and ramification pairs at a distance of two bonds favor this activity, while the presence of tertiary amines decreases it. This model was applied to a combinatorial library of a thousand and one 6-fluoroquinolones, from which 117 theoretical active molecules were obtained. The compound 10 and five new quinolones were tested against MRSA. They all showed some activity against MRSA, although compounds 6, 8 and 9 showed anti-MRSA activity similar to ciprofloxacin. This model was also applied to 263 theoretical antibacterial agents described by us in a previous work, from which 34 were predicted as theoretically active. Anti-MRSA activity was found bibliographically in 9 of them (ensuring at least 26% of success), and from the rest, 3 compounds were randomly chosen and tested, finding mitomycin C to be more active than ciprofloxacin. The results demonstrate the utility of the molecular topology approaches for identifying new drugs active against MRSA. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Mimicking brain tissue binding in an in vitro model of the blood-brain barrier illustrates differences between in vitro and in vivo methods for assessing the rate of brain penetration.

PubMed

Heymans, Marjolein; Sevin, Emmanuel; Gosselet, Fabien; Lundquist, Stefan; Culot, Maxime

2018-06-01

Assessing the rate of drug delivery to the central nervous system (CNS) in vitro has been used for decades to predict whether CNS drug candidates are likely to attain their pharmacological targets, located within the brain parenchyma, at an effective dose. The predictive value of in vitro blood-brain barrier (BBB) models is therefore frequently assessed by comparing in vitro BBB permeability, usually quoted as the endothelial permeability coefficient (P e ) or apparent permeability (P app ), to their rate of BBB permeation measured in vivo, the latter being commonly assessed in rodents. In collaboration with AstraZeneca (DMPK department, Södertälje, Sweden), the in vitro BBB permeability (P app and P e ) of 27 marketed CNS drugs has been determined using a bovine in vitro BBB model and compared to their in vivo permeability (P vivo ), obtained by rat in-situ brain perfusion. The latter was taken from published data from Summerfield et al. (2007). This comparison confirmed previous reports, showing a strong in vitro/in vivo correlation for hydrophilic compounds, characterized by low brain tissue binding and a weak correlation for lipophilic compounds, characterized by high brain tissue binding. This observation can be explained by the influence of brain tissue binding on the uptake of drugs into the CNS in vivo and the absence of possible brain tissue binding in vitro. The use of glial cells (GC) in the in vitro BBB model to mimic brain tissue binding and the introduction of a new calculation method for in vitro BBB permeability (P vitro ) resulted in a strong correlation between the in vitro and in vivo rate of BBB permeation for the whole set of compounds. These findings might facilitate further in vitro to in vivo extrapolation for CNS drug candidates. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Application of an Artificial Stomach-Duodenum Reduced Gastric pH Dog Model for Formulation Principle Assessment and Mechanistic Performance Understanding.

PubMed

Lee, Chen-Ming; Luner, Paul E; Locke, Karen; Briggs, Katherine

2017-08-01

The objective of this study was to develop an artificial stomach-duodenum (ASD) dissolution model as an in vitro evaluation tool that would simulate the gastrointestinal physiology of gastric pH-reduced dogs as a method to assess formulations for a poorly soluble free acid compound with ng/mL solubility. After establishing the ASD model with well-controlled duodenum pH, 5 formulations each applying different solubilization principles were developed and their performance in the ASD model and in vivo in dogs was evaluated. Excellent correlations were obtained between dog area under the curve (AUC) and ASD AUC of 5 formulations evaluated with simulated intestinal fluid (r 2  = 0.987) and fasted-state simulated intestinal fluid (r 2  = 0.989) as the duodenum dissolution medium, indicating that the approach of infusing NaOH into duodenum compartment to maintain duodenum pH of an ASD worked properly in simulating gastric pH-reduced dog. Raman spectroscopy was used to study drug dissolution kinetics associated with different solubilization principles and the results suggested that the solubilization principles performed as designed. Spectroscopic results also identified that the compound formed a gel during dissolution and hypromellose maintained the drug-gelled state to avoid further solid form conversion. The implication of the compound physical gelation to drug dissolution kinetics and in vivo exposure are discussed. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

A simplified protocol employing elacridar in rodents: a screening model in drug discovery to assess P-gp mediated efflux at the blood brain barrier.

PubMed

Kallem, Rajareddy; Kulkarni, Chetan P; Patel, Dakshay; Thakur, Megha; Sinz, Michael; Singh, Sheelendra P; Mahammad, S Shahe; Mandlekar, Sandhya

2012-06-01

In the present study we have developed a simple, time, and cost effective in vivo rodent protocol to screen the susceptibility of a test compound for P-glycoprotein (P-gp) mediated efflux at the blood brain barrier (BBB) during early drug discovery. We used known P-gp substrates as test compounds (quinidine, digoxin, and talinolol) and elacridar (GF120918) as a chemical inhibitor to establish the model. The studies were carried out in both mice and rats. Elacridar was dosed intravenously at 5 mg/kg, 0.5 h prior to probe substrate administration. Plasma and brain samples were collected and analyzed using UPLC-MS/MS. In the presence of elacridar, the ratio of brain to plasma area under the curve (B/P) in mouse increased 2, 4, and 38-fold, respectively, for talinolol, digoxin, and quinidine; whereas in rat, a 70-fold increase was observed for quinidine. Atenolol, a non P-gp substrate, exhibited poor brain penetration in the presence or absence of elacridar in both species (B/P ratio ~ 0.1). Elacridar had no significant effect on the systemic clearance of digoxin or quinidine; however, a trend towards increasing volume of distribution and half life was observed. Our results support the utility of elacridar in evaluation of the influence of P-gp mediated efflux on drug distribution to the brain. Our protocol employing a single intravenous dose of elacridar and test compound provides a cost effective alternative to expensive P-gp knockout mice models during early drug discovery.

Identifying Medication Targets for Psychostimulant Addiction: Unraveling the Dopamine D3 Receptor Hypothesis

PubMed Central

2016-01-01

The dopamine D3 receptor (D3R) is a target for developing medications to treat substance use disorders. D3R-selective compounds with high affinity and varying efficacies have been discovered, providing critical research tools for cell-based studies that have been translated to in vivo models of drug abuse. D3R antagonists and partial agonists have shown especially promising results in rodent models of relapse-like behavior, including stress-, drug-, and cue-induced reinstatement of drug seeking. However, to date, translation to human studies has been limited. Herein, we present an overview and illustrate some of the pitfalls and challenges of developing novel D3R-selective compounds toward clinical utility, especially for treatment of cocaine abuse. Future research and development of D3R-selective antagonists and partial agonists for substance abuse remains critically important but will also require further evaluation and development of translational animal models to determine the best time in the addiction cycle to target D3Rs for optimal therapeutic efficacy. PMID:25826710

Model System to Define Pharmacokinetic Requirements for Antimalarial Drug Efficacy

PubMed Central

Bakshi, Rahul P.; Nenortas, Elizabeth; Tripathi, Abhai K.; Sullivan, David J.; Shapiro, Theresa A.

2013-01-01

Malaria presents a tremendous public health burden and new therapies are needed. Massive compound libraries screened against Plasmodium falciparum have yielded thousands of lead compounds, resulting in an acute need for rational criteria to select the best candidates for development. We reasoned that, akin to antibacterials, antimalarials might have an essential pharmacokinetic requirement for efficacy: action governed either by total exposure or peak concentration (AUC/CMAX), or by duration above a defined minimum concentration (Time above Minimum Inhibitory Concentration, TMIC). We devised an in vitro system for P. falciparum, capable of mimicking the dynamic fluctuations of a drug in vivo. Utilizing this apparatus, we find that chloroquine is TMIC-dependent while the efficacy of artemisinin is driven by CMAX. The latter was confirmed in a mouse model of malaria. These characteristics can explain the clinical success of two antimalarial drugs with widely different kinetics in humans. Chloroquine, which persists for weeks, is ideally suited for its TMIC mechanism, whereas great efficacy despite short exposure (t1/2 in blood 3 h or less) is attained by CMAX-driven artemisinins. This validated preclinical model system can be used to select those antimalarial lead compounds whose CMAX or TMIC requirement for efficacy match pharmacokinetics obtained in vivo. The apparatus can also be used to explore the kinetic-dependence of other pharmacodynamic endpoints in parasites. PMID:24089407

Retro-2 and its dihydroquinazolinone derivatives inhibit filovirus infection.

PubMed

Shtanko, Olena; Sakurai, Yasuteru; Reyes, Ann N; Noël, Romain; Cintrat, Jean-Christophe; Gillet, Daniel; Barbier, Julien; Davey, Robert A

2018-01-01

Members of the family Filoviridae cause severe, often fatal disease in humans, for which there are no approved vaccines and only a few experimental drugs tested in animal models. Retro-2, a small molecule that inhibits retrograde trafficking of bacterial and plant toxins inside host cells, has been demonstrated to be effective against a range of bacterial and virus pathogens, both in vitro and in animal models. Here, we demonstrated that Retro-2 and its derivatives, Retro-2.1 and compound 25, blocked infection by Ebola virus and Marburg virus in vitro. We show that the derivatives were more potent inhibitors of infection as compared to the parent compound. Pseudotyped virus assays indicated that the compounds affected virus entry into cells while virus particle localization to Niemann-Pick C1-positive compartments showed that they acted at a late step in virus entry. Our work demonstrates a potential for Retro-type drugs to be developed into anti-filoviral therapeutics. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Nanomedicine Drug Delivery across Mucous Membranes

NASA Astrophysics Data System (ADS)

Lancina, Michael George, III

Control over the distribution of therapeutic compounds is a complex and somewhat overlooked field of pharmaceutical research. When swallowing a pill or receiving an injection, it is commonly assumed that drug will spread throughout the body in a more or less uniform concentration and find its way to wherever it is needed. In truth, drug biodistribuition is highly non-uniform and dependent on a large number of factors. The development of advanced drug delivery systems to control biodistribution can produce significant advances in clinical treatments without the need to discover new therapeutic compounds. This work focuses on a number of nanostructured materials designed to improve drug delivery by direct and efficient transfer of drugs across one of the body's external mucous membranes. Chapter 1 outlines the central concept that unites these studies: nanomaterials and cationic particles can be used to delivery therapeutic compounds across mucous membranes. Special attention is given to dendritic nanoparticles. In chapter 2, uses for dendrimers in ocular drug delivery are presented. The studies are divided into two main groups: topical and injectable formulations. Chapter 3 does not involve dendrimers but instead another cationic particle used in transmembrane drug delivery, chitosan. Next, a dendrimer based nanofiber mat was used to deliver anti-glaucoma drugs in chapter 4. A three week in vivo efficacy trial showed dendrimer nanofiber mats outperformed traditional eye drops in terms of intra-ocular pressure decrease in a normotensive rat model. Finally, we have developed a new dendrimer based anti-glaucoma drug in chapter 5. Collectively, these studies demonstrate some of the potential applications for nanotechnology to improve transmembrane drug delivery. These particles and fibers are able to readily adhere and penetrate across epithelial cell lays. Utilizing these materials to improve drug absorption through these portals has the potential to improve the clinical treatment of wide variety of diseases.

Current advances in mathematical modeling of anti-cancer drug penetration into tumor tissues.

PubMed

Kim, Munju; Gillies, Robert J; Rejniak, Katarzyna A

2013-11-18

Delivery of anti-cancer drugs to tumor tissues, including their interstitial transport and cellular uptake, is a complex process involving various biochemical, mechanical, and biophysical factors. Mathematical modeling provides a means through which to understand this complexity better, as well as to examine interactions between contributing components in a systematic way via computational simulations and quantitative analyses. In this review, we present the current state of mathematical modeling approaches that address phenomena related to drug delivery. We describe how various types of models were used to predict spatio-temporal distributions of drugs within the tumor tissue, to simulate different ways to overcome barriers to drug transport, or to optimize treatment schedules. Finally, we discuss how integration of mathematical modeling with experimental or clinical data can provide better tools to understand the drug delivery process, in particular to examine the specific tissue- or compound-related factors that limit drug penetration through tumors. Such tools will be important in designing new chemotherapy targets and optimal treatment strategies, as well as in developing non-invasive diagnosis to monitor treatment response and detect tumor recurrence.

Prime-, Stress- and Cue-Induced Reinstatement of Extinguished Drug-Reinforced Responding in Rats: Cocaine as the Prototypical Drug of Abuse

PubMed Central

Beardsley, Patrick M.; Shelton, Keith L.

2012-01-01

This unit describes the testing of rats in prime-, footshock- and cue-induced reinstatement procedures. Evaluating rats in these procedures enables the assessment of treatments on behavior thought to model drug relapse precipitated by re-contact with an abused drug (prime-induced), induced by stress (footshock-induced), or by stimuli previously associated with drug administration (cue-induced). For instance, levels of reinstatement under the effects of test compound administration could be compared to levels under vehicle administration to help identify potential treatments for drug relapse, or reinstatement levels of different rat strains could be compared to identify potential genetic determinants of perseverative drug-seeking behavior. Cocaine is used as a prototypical drug of abuse, and relapse to its use serves as the model in this unit, but other self-administered drugs could readily be substituted with little modification to the procedures. PMID:23093352

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.

Multiscale Modelling of Relationships between Protein Classes and Drug Behavior Across all Diseases Using the CANDO Platform

PubMed Central

Samudrala, Ram

2015-01-01

We have examined the effect of eight different protein classes (channels, GPCRs, kinases, ligases, nuclear receptors, proteases, phosphatases, transporters) on the benchmarking performance of the CANDO drug discovery and repurposing platform (http://protinfo.org/cando). The first version of the CANDO platform utilizes a matrix of predicted interactions between 48278 proteins and 3733 human ingestible compounds (including FDA approved drugs and supplements) that map to 2030 indications/diseases using a hierarchical chem and bio-informatic fragment based docking with dynamics protocol (> one billion predicted interactions considered). The platform uses similarity of compound-proteome interaction signatures as indicative of similar functional behavior and benchmarking accuracy is calculated across 1439 indications/diseases with more than one approved drug. The CANDO platform yields a significant correlation (0.99, p-value < 0.0001) between the number of proteins considered and benchmarking accuracy obtained indicating the importance of multitargeting for drug discovery. Average benchmarking accuracies range from 6.2 % to 7.6 % for the eight classes when the top 10 ranked compounds are considered, in contrast to a range of 5.5 % to 11.7 % obtained for the comparison/control sets consisting of 10, 100, 1000, and 10000 single best performing proteins. These results are generally two orders of magnitude better than the average accuracy of 0.2% obtained when randomly generated (fully scrambled) matrices are used. Different indications perform well when different classes are used but the best accuracies (up to 11.7% for the top 10 ranked compounds) are achieved when a combination of classes are used containing the broadest distribution of protein folds. Our results illustrate the utility of the CANDO approach and the consideration of different protein classes for devising indication specific protocols for drug repurposing as well as drug discovery. PMID:25694071

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.

Fraction of a dose absorbed estimation for structurally diverse low solubility compounds.

PubMed

Sugano, Kiyohiko

2011-02-28

The purpose of the present study was to investigate the prediction accuracy of the fully mechanistic gastrointestinal unified theoretical (GUT) framework for in vivo oral absorption of low solubility drugs. Solubility in biorelevant media, molecular weight, logP(oct), pK(a), Caco-2 permeability, dose and particle size were used as the input parameters. To neglect the effect of the low stomach pH on dissolution of a drug, the fraction of a dose absorbed (Fa%) of undissociable and free acids were used. In addition, Fa% of free base drugs with the high pH stomach was also included to increase the number of model drugs. In total twenty nine structurally diverse compounds were used as the model drugs. Fa% data at several doses and particle sizes in humans and dogs were collated from the literature (total 110 Fa% data). In approximately 80% cases, the prediction error was within 2 fold, suggesting that the GUT framework has practical predictability for drug discovery, but not for drug development. The GUT framework appropriately captured the dose and particle size dependency of Fa% as the particle drifting effect was taken into account. It should be noted that the present validation results cannot be applied for salt form cases and other special formulations such as solid dispersions and emulsion formulations. Copyright © 2010 Elsevier B.V. All rights reserved.

Statistical investigation of simulated intestinal fluid composition on the equilibrium solubility of biopharmaceutics classification system class II drugs.

PubMed

Khadra, Ibrahim; Zhou, Zhou; Dunn, Claire; Wilson, Clive G; Halbert, Gavin

2015-01-25

A drug's solubility and dissolution behaviour within the gastrointestinal tract is a key property for successful administration by the oral route and one of the key factors in the biopharmaceutics classification system. This property can be determined by investigating drug solubility in human intestinal fluid (HIF) but this is difficult to obtain and highly variable, which has led to the development of multiple simulated intestinal fluid (SIF) recipes. Using a statistical design of experiment (DoE) technique this paper has investigated the effects and interactions on equilibrium drug solubility of seven typical SIF components (sodium taurocholate, lecithin, sodium phosphate, sodium chloride, pH, pancreatin and sodium oleate) within concentration ranges relevant to human intestinal fluid values. A range of poorly soluble drugs with acidic (naproxen, indomethacin, phenytoin, and piroxicam), basic (aprepitant, carvedilol, zafirlukast, tadalafil) or neutral (fenofibrate, griseofulvin, felodipine and probucol) properties have been investigated. The equilibrium solubility results determined are comparable with literature studies of the drugs in either HIF or SIF indicating that the DoE is operating in the correct space. With the exception of pancreatin, all of the factors individually had a statistically significant influence on equilibrium solubility with variations in magnitude of effect between the acidic and basic or neutral compounds and drug specific interactions were evident. Interestingly for the neutral compounds pH was the factor with the second largest solubility effect. Around one third of all the possible factor combinations showed a significant influence on equilibrium solubility with variations in interaction significance and magnitude of effect between the acidic and basic or neutral compounds. The least number of significant media component interactions were noted for the acidic compounds with three and the greatest for the neutral compounds at seven, with again drug specific effects evident. This indicates that a drug's equilibrium solubility in SIF is influenced depending upon drug type by between eight to fourteen individual or combinations of media components with some of these drug specific. This illustrates the complex nature of these fluids and provides for individual drugs a visualisation of the possible solubility envelope within the gastrointestinal tract, which may be of importance for modelling in vivo behaviour. In addition the results indicate that the design of experiment approach can be employed to provide greater detail of drug solubility behaviour, possible drug specific interactions and influence of variations in gastrointestinal media components due to disease. The approach is also feasible and amenable to adaptation for high throughput screening of drug candidates. Copyright © 2014 Elsevier B.V. All rights reserved.

Inhibition of protein synthesis and malaria parasite development by drug targeting of methionyl-tRNA synthetases.

PubMed

Hussain, Tahir; Yogavel, Manickam; Sharma, Amit

2015-04-01

Aminoacyl-tRNA synthetases (aaRSs) are housekeeping enzymes that couple cognate tRNAs with amino acids to transmit genomic information for protein translation. The Plasmodium falciparum nuclear genome encodes two P. falciparum methionyl-tRNA synthetases (PfMRS), termed PfMRS(cyt) and PfMRS(api). Phylogenetic analyses revealed that the two proteins are of primitive origin and are related to heterokonts (PfMRS(cyt)) or proteobacteria/primitive bacteria (PfMRS(api)). We show that PfMRS(cyt) localizes in parasite cytoplasm, while PfMRS(api) localizes to apicoplasts in asexual stages of malaria parasites. Two known bacterial MRS inhibitors, REP3123 and REP8839, hampered Plasmodium growth very effectively in the early and late stages of parasite development. Small-molecule drug-like libraries were screened against modeled PfMRS structures, and several "hit" compounds showed significant effects on parasite growth. We then tested the effects of the hit compounds on protein translation by labeling nascent proteins with (35)S-labeled cysteine and methionine. Three of the tested compounds reduced protein synthesis and also blocked parasite growth progression from the ring stage to the trophozoite stage. Drug docking studies suggested distinct modes of binding for the three compounds, compared with the enzyme product methionyl adenylate. Therefore, this study provides new targets (PfMRSs) and hit compounds that can be explored for development as antimalarial drugs. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

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

PubMed

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

2016-11-01

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

In Silico Chemogenomics Drug Repositioning Strategies for Neglected Tropical Diseases.

PubMed

Andrade, Carolina Horta; Neves, Bruno Junior; Melo-Filho, Cleber Camilo; Rodrigues, Juliana; Silva, Diego Cabral; Braga, Rodolpho Campos; Cravo, Pedro Vitor Lemos

2018-03-08

Only ~1% of all drug candidates against Neglected Tropical Diseases (NTDs) have reached clinical trials in the last decades, underscoring the need for new, safe and effective treatments. In such context, drug repositioning, which allows finding novel indications for approved drugs whose pharmacokinetic and safety profiles are already known, is emerging as a promising strategy for tackling NTDs. Chemogenomics is a direct descendent of the typical drug discovery process that involves the systematic screening of chemical compounds against drug targets in high-throughput screening (HTS) efforts, for the identification of lead compounds. However, different to the one-drug-one-target paradigm, chemogenomics attempts to identify all potential ligands for all possible targets and diseases. In this review, we summarize current methodological development efforts in drug repositioning that use state-of-the-art computational ligand- and structure-based chemogenomics approaches. Furthermore, we highlighted the recent progress in computational drug repositioning for some NTDs, based on curation and modeling of genomic, biological, and chemical data. Additionally, we also present in-house and other successful examples and suggest possible solutions to existing pitfalls. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

QSAR-driven design, synthesis and discovery of potent chalcone derivatives with antitubercular activity.

PubMed

Gomes, Marcelo N; Braga, Rodolpho C; Grzelak, Edyta M; Neves, Bruno J; Muratov, Eugene; Ma, Rui; Klein, Larry L; Cho, Sanghyun; Oliveira, Guilherme R; Franzblau, Scott G; Andrade, Carolina Horta

2017-09-08

New anti-tuberculosis (anti-TB) drugs are urgently needed to battle drug-resistant Mycobacterium tuberculosis strains and to shorten the current 6-12-month treatment regimen. In this work, we have continued the efforts to develop chalcone-based anti-TB compounds by using an in silico design and QSAR-driven approach. Initially, we developed SAR rules and binary QSAR models using literature data for targeted design of new heteroaryl chalcone compounds with anti-TB activity. Using these models, we prioritized 33 compounds for synthesis and biological evaluation. As a result, 10 heteroaryl chalcone compounds (4, 8, 9, 11, 13, 17-20, and 23) were found to exhibit nanomolar activity against replicating mycobacteria, low micromolar activity against nonreplicating bacteria, and nanomolar and micromolar against rifampin (RMP) and isoniazid (INH) monoresistant strains (rRMP and rINH) (<1 μM and <10 μM, respectively). The series also show low activity against commensal bacteria and generally show good selectivity toward M. tuberculosis, with very low cytotoxicity against Vero cells (SI = 11-545). Our results suggest that our designed heteroaryl chalcone compounds, due to their high potency and selectivity, are promising anti-TB agents. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Composite multi-parameter ranking of real and virtual compounds for design of MC4R agonists: renaissance of the Free-Wilson methodology.

PubMed

Nilsson, Ingemar; Polla, Magnus O

2012-10-01

Drug design is a multi-parameter task present in the analysis of experimental data for synthesized compounds and in the prediction of new compounds with desired properties. This article describes the implementation of a binned scoring and composite ranking scheme for 11 experimental parameters that were identified as key drivers in the MC4R project. The composite ranking scheme was implemented in an AstraZeneca tool for analysis of project data, thereby providing an immediate re-ranking as new experimental data was added. The automated ranking also highlighted compounds overlooked by the project team. The successful implementation of a composite ranking on experimental data led to the development of an equivalent virtual score, which was based on Free-Wilson models of the parameters from the experimental ranking. The individual Free-Wilson models showed good to high predictive power with a correlation coefficient between 0.45 and 0.97 based on the external test set. The virtual ranking adds value to the selection of compounds for synthesis but error propagation must be controlled. The experimental ranking approach adds significant value, is parameter independent and can be tuned and applied to any drug discovery project.

Use of the Aspergillus oryzae actin gene promoter in a novel reporter system for exploring antifungal compounds and their target genes.

PubMed

Marui, Junichiro; Yoshimi, Akira; Hagiwara, Daisuke; Fujii-Watanabe, Yoshimi; Oda, Ken; Koike, Hideaki; Tamano, Koichi; Ishii, Tomoko; Sano, Motoaki; Machida, Masayuki; Abe, Keietsu

2010-08-01

Demand for novel antifungal drugs for medical and agricultural uses has been increasing because of the diversity of pathogenic fungi and the emergence of drug-resistant strains. Genomic resources for various living species, including pathogenic fungi, can be utilized to develop novel and effective antifungal compounds. We used Aspergillus oryzae as a model to construct a reporter system for exploring novel antifungal compounds and their target genes. The comprehensive gene expression analysis showed that the actin-encoding actB gene was transcriptionally highly induced by benomyl treatment. We therefore used the actB gene to construct a novel reporter system for monitoring responses to cytoskeletal stress in A. oryzae by introducing the actB promoter::EGFP fusion gene. Distinct fluorescence was observed in the reporter strain with minimum background noise in response to not only benomyl but also compounds inhibiting lipid metabolism that is closely related to cell membrane integrity. The fluorescent responses indicated that the reporter strain can be used to screen for lead compounds affecting fungal microtubule and cell membrane integrity, both of which are attractive antifungal targets. Furthermore, the reporter strain was shown to be technically applicable for identifying novel target genes of antifungal drugs triggering perturbation of fungal microtubules or membrane integrity.

Fractional derivatives in the transport of drugs across biological materials and human skin

NASA Astrophysics Data System (ADS)

Caputo, Michele; Cametti, Cesare

2016-11-01

The diffusion of drugs across a composite structure such as a biological membrane is a rather complex phenomenon, because of its inhomogeneous nature, yielding a diffusion rate and a drug solubility strongly dependent on the local position across the membrane itself. These problems are particularly strengthened in composite structures of a considerable thickness like, for example, the human skin, where the high heterogeneity provokes the transport through different simultaneous pathways. In this note, we propose a generalization of the diffusion model based on Fick's 2nd equation by substituting a diffusion constant by means of the memory formalism approach (diffusion with memory). In particular, we employ two different definitions of the fractional derivative, i.e., the usual Caputo fractional derivative and a new definition recently proposed by Caputo and Fabrizio. The model predictions have been compared to experimental results concerning the permeation of two different compounds through human skin in vivo, such as piroxicam, an anti-inflammatory drug, and 4-cyanophenol, a test chemical model compound. Moreover, we have also considered water penetration across human stratum corneum and the diffusion of an antiviral agent employed as model drugs across the skin of male hairless rats. In all cases, a satisfactory good agreement based on the diffusion with memory has been found. However, the model based on the new definition of fractional derivative gives a better description of the experimental data, on the basis of the residuals analysis. The use of the new definition widens the applicability of the fractional derivative to diffusion processes in highly heterogeneous systems.

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

PubMed

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

2013-03-01

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

Drugs as habitable planets in the space of dark chemical matter.

PubMed

Siramshetty, Vishal B; Preissner, Robert

2018-03-01

A recent study demonstrated antifungal activity of dark chemical matter (DCM) compounds that were otherwise inactive in more than 100 HTS assays. These compounds were proposed to possess unique activity and 'clean' safety profiles. Here, we present an outlook of the promiscuity and safety of these compounds by retrospectively comparing their chemical and biological spaces with those of drugs. Significant amounts of marketed drugs (16%), withdrawn drugs (16.5%) and natural compounds (3.5%) share structural identity with DCM. Compound promiscuity assessment indicates that dark matter compounds could potentially interact with multiple biological targets. Further, thousands of DCM compounds showed presence of frequent-hitting pan-assay interference compound (PAINS) substructures. In light of these observations, filtering these compounds from screening libraries can be an irrevocable loss. Copyright © 2017 Elsevier Ltd. All rights reserved.

Click chemistry, 3D-printing, and omics: the future of drug development.

PubMed

Kurzrock, Razelle; Stewart, David J

2016-01-19

Genomics is a disruptive technology, having revealed that cancers are tremendously complex and differ from patient to patient. Therefore, conventional treatment approaches fit poorly with genomic reality. Furthermore, it is likely that this type of complexity will also be observed in other illnesses. Precision medicine has been posited as a way to better target disease-related aberrations, but developing drugs and tailoring therapy to each patient's complicated problem is a major challenge. One solution would be to match patients to existing compounds based on in silico modeling. However, optimization of complex therapy will eventually require designing compounds for patients using computer modeling and just-in-time production, perhaps achievable in the future by three-dimensional (3D) printing. Indeed, 3D printing is potentially transformative by virtue of its ability to rapidly generate almost limitless numbers of objects that previously required manufacturing facilities. Companies are already endeavoring to develop affordable 3D printers for home use. An attractive, but as yet scantily explored, application is to place chemical design and production under digital control. This could be accomplished by utilizing a 3D printer to initiate chemical reactions, and print the reagents and/or the final compounds directly. Of interest, the Food and Drug Administration (FDA) has recently approved a 3D printed drug-levetiracetam-indicated for seizures. Further, it is now increasingly clear that biologic materials-tissues, and eventually organs-can also be "printed." In the near future, it is plausible that high-throughput computing may be deployed to design customized drugs, which will reshape medicine.

Prediction of Human Cytochrome P450 Inhibition Using a Multitask Deep Autoencoder Neural Network.

PubMed

Li, Xiang; Xu, Youjun; Lai, Luhua; Pei, Jianfeng

2018-05-30

Adverse side effects of drug-drug interactions induced by human cytochrome P450 (CYP450) inhibition is an important consideration in drug discovery. It is highly desirable to develop computational models that can predict the inhibitive effect of a compound against a specific CYP450 isoform. In this study, we developed a multitask model for concurrent inhibition prediction of five major CYP450 isoforms, namely, 1A2, 2C9, 2C19, 2D6, and 3A4. The model was built by training a multitask autoencoder deep neural network (DNN) on a large dataset containing more than 13 000 compounds, extracted from the PubChem BioAssay Database. We demonstrate that the multitask model gave better prediction results than that of single-task models, previous reported classifiers, and traditional machine learning methods on an average of five prediction tasks. Our multitask DNN model gave average prediction accuracies of 86.4% for the 10-fold cross-validation and 88.7% for the external test datasets. In addition, we built linear regression models to quantify how the other tasks contributed to the prediction difference of a given task between single-task and multitask models, and we explained under what conditions the multitask model will outperform the single-task model, which suggested how to use multitask DNN models more effectively. We applied sensitivity analysis to extract useful knowledge about CYP450 inhibition, which may shed light on the structural features of these isoforms and give hints about how to avoid side effects during drug development. Our models are freely available at http://repharma.pku.edu.cn/deepcyp/home.php or http://www.pkumdl.cn/deepcyp/home.php .

Drug-likeness analysis of traditional Chinese medicines: 2. Characterization of scaffold architectures for drug-like compounds, non-drug-like compounds, and natural compounds from traditional Chinese medicines.

PubMed

Tian, Sheng; Li, Youyong; Wang, Junmei; Xu, Xiaojie; Xu, Lei; Wang, Xiaohong; Chen, Lei; Hou, Tingjun

2013-01-21

In order to better understand the structural features of natural compounds from traditional Chinese medicines, the scaffold architectures of drug-like compounds in MACCS-II Drug Data Report (MDDR), non-drug-like compounds in Available Chemical Directory (ACD), and natural compounds in Traditional Chinese Medicine Compound Database (TCMCD) were explored and compared. First, the different scaffolds were extracted from ACD, MDDR and TCMCD by using three scaffold representations, including Murcko frameworks, Scaffold Tree, and ring systems with different complexity and side chains. Then, by examining the accumulative frequency of the scaffolds in each dataset, we observed that the Level 1 scaffolds of the Scaffold Tree offer advantages over the other scaffold architectures to represent the scaffold diversity of the compound libraries. By comparing the similarity of the scaffold architectures presented in MDDR, ACD and TCMCD, structural overlaps were observed not only between MDDR and TCMCD but also between MDDR and ACD. Finally, Tree Maps were used to cluster the Level 1 scaffolds of the Scaffold Tree and visualize the scaffold space of the three datasets. The analysis of the scaffold architectures of MDDR, ACD and TCMCD shows that, on average, drug-like molecules in MDDR have the highest diversity while natural compounds in TCMCD have the highest complexity. According to the Tree Maps, it can be observed that the Level 1 scaffolds present in MDDR have higher diversity than those presented in TCMCD and ACD. However, some representative scaffolds in MDDR with high frequency show structural similarities to those in TCMCD and ACD, suggesting that some scaffolds in TCMCD and ACD may be potentially drug-like fragments for fragment-based and de novo drug design.

Drug-likeness analysis of traditional Chinese medicines: 2. Characterization of scaffold architectures for drug-like compounds, non-drug-like compounds, and natural compounds from traditional Chinese medicines

PubMed Central

2013-01-01

Background In order to better understand the structural features of natural compounds from traditional Chinese medicines, the scaffold architectures of drug-like compounds in MACCS-II Drug Data Report (MDDR), non-drug-like compounds in Available Chemical Directory (ACD), and natural compounds in Traditional Chinese Medicine Compound Database (TCMCD) were explored and compared. Results First, the different scaffolds were extracted from ACD, MDDR and TCMCD by using three scaffold representations, including Murcko frameworks, Scaffold Tree, and ring systems with different complexity and side chains. Then, by examining the accumulative frequency of the scaffolds in each dataset, we observed that the Level 1 scaffolds of the Scaffold Tree offer advantages over the other scaffold architectures to represent the scaffold diversity of the compound libraries. By comparing the similarity of the scaffold architectures presented in MDDR, ACD and TCMCD, structural overlaps were observed not only between MDDR and TCMCD but also between MDDR and ACD. Finally, Tree Maps were used to cluster the Level 1 scaffolds of the Scaffold Tree and visualize the scaffold space of the three datasets. Conclusion The analysis of the scaffold architectures of MDDR, ACD and TCMCD shows that, on average, drug-like molecules in MDDR have the highest diversity while natural compounds in TCMCD have the highest complexity. According to the Tree Maps, it can be observed that the Level 1 scaffolds present in MDDR have higher diversity than those presented in TCMCD and ACD. However, some representative scaffolds in MDDR with high frequency show structural similarities to those in TCMCD and ACD, suggesting that some scaffolds in TCMCD and ACD may be potentially drug-like fragments for fragment-based and de novo drug design. PMID:23336706

21 CFR 500.84 - Conditions for approval of the sponsored compound.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 6 2013-04-01 2013-04-01 false Conditions for approval of the sponsored compound. 500.84 Section 500.84 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) ANIMAL DRUGS, FEEDS, AND RELATED PRODUCTS GENERAL Regulation of Carcinogenic Compounds...

21 CFR 500.84 - Conditions for approval of the sponsored compound.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 6 2010-04-01 2010-04-01 false Conditions for approval of the sponsored compound. 500.84 Section 500.84 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) ANIMAL DRUGS, FEEDS, AND RELATED PRODUCTS GENERAL Regulation of Carcinogenic Compounds...

21 CFR 500.84 - Conditions for approval of the sponsored compound.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 6 2011-04-01 2011-04-01 false Conditions for approval of the sponsored compound. 500.84 Section 500.84 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) ANIMAL DRUGS, FEEDS, AND RELATED PRODUCTS GENERAL Regulation of Carcinogenic Compounds...

21 CFR 500.84 - Conditions for approval of the sponsored compound.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 6 2012-04-01 2012-04-01 false Conditions for approval of the sponsored compound. 500.84 Section 500.84 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) ANIMAL DRUGS, FEEDS, AND RELATED PRODUCTS GENERAL Regulation of Carcinogenic Compounds...

21 CFR 500.84 - Conditions for approval of the sponsored compound.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 6 2014-04-01 2014-04-01 false Conditions for approval of the sponsored compound. 500.84 Section 500.84 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) ANIMAL DRUGS, FEEDS, AND RELATED PRODUCTS GENERAL Regulation of Carcinogenic Compounds...

Development of a Sigma-2 Receptor affinity filter through a Monte Carlo based QSAR analysis.

PubMed

Rescifina, Antonio; Floresta, Giuseppe; Marrazzo, Agostino; Parenti, Carmela; Prezzavento, Orazio; Nastasi, Giovanni; Dichiara, Maria; Amata, Emanuele

2017-08-30

For the first time in sigma-2 (σ 2 ) receptor field, a quantitative structure-activity relationship (QSAR) model has been built using pK i values of the whole set of known selective σ 2 receptor ligands (548 compounds), taken from the Sigma-2 Receptor Selective Ligands Database (S2RSLDB) (http://www.researchdsf.unict.it/S2RSLDB/), through the Monte Carlo technique and employing the software CORAL. The model has been developed by using a large and structurally diverse set of compounds, allowing for a prediction of different populations of chemical compounds endpoint (σ 2 receptor pK i ). The statistical quality reached, suggested that model for pK i determination is robust and possesses a satisfactory predictive potential. The statistical quality is high for both visible and invisible sets. The screening of the FDA approved drugs, external to our dataset, suggested that sixteen compounds might be repositioned as σ 2 receptor ligands (predicted pK i ≥8). A literature check showed that six of these compounds have already been tested for affinity at σ 2 receptor and, of these, two (Flunarizine and Terbinafine) have shown an experimental σ 2 receptor pK i >7. This suggests that this QSAR model may be used as focusing screening filter in order to prospectively find or repurpose new drugs with high affinity for the σ 2 receptor, and overall allowing for an enhanced hit rate respect to a random screening. Copyright © 2017 Elsevier B.V. All rights reserved.

Development of Ciprofloxacin-loaded contact lenses using fluorous chemistry

PubMed Central

Zhu, Zhiling; Li, Siheng; McDermott, Alison M.

2017-01-01

In this work, we developed a simple method to load drugs into commercially available contact lenses utilizing fluorous chemistry. We demonstrated this method using model compounds including fluorous-tagged fluorescein and antibiotic ciprofloxacin. We showed that fluorous interactions facilitated the loading of model molecules into fluorocarbon-containing contact lenses, and that the release profiles exhibited sustained release. Contact lenses loaded with fluorous-tagged ciprofloxacin exhibited antimicrobial activity against Pseudomonas aeruginosa in vitro, while no cytotoxicity towards human corneal epithelial cells was observed. To mimic the tear turnover, we designed a porcine eye infection model under flow conditions. Significantly, the modified lenses also exhibited antimicrobial efficacy against Pseudomonas aeruginosa in the ex vivo infection model. Overall, utilizing fluorous chemistry, we can construct a drug delivery system that exhibits high drug loading capacity, sustained drug release, and robust biological activity. PMID:28188995

Development of a high-throughput brain slice method for studying drug distribution in the central nervous system.

PubMed

Fridén, Markus; Ducrozet, Frederic; Middleton, Brian; Antonsson, Madeleine; Bredberg, Ulf; Hammarlund-Udenaes, Margareta

2009-06-01

New, more efficient methods of estimating unbound drug concentrations in the central nervous system (CNS) combine the amount of drug in whole brain tissue samples measured by conventional methods with in vitro estimates of the unbound brain volume of distribution (V(u,brain)). Although the brain slice method is the most reliable in vitro method for measuring V(u,brain), it has not previously been adapted for the needs of drug discovery research. The aim of this study was to increase the throughput and optimize the experimental conditions of this method. Equilibrium of drug between the buffer and the brain slice within the 4 to 5 h of incubation is a fundamental requirement. However, it is difficult to meet this requirement for many of the extensively binding, lipophilic compounds in drug discovery programs. In this study, the dimensions of the incubation vessel and mode of stirring influenced the equilibration time, as did the amount of brain tissue per unit of buffer volume. The use of cassette experiments for investigating V(u,brain) in a linear drug concentration range increased the throughput of the method. The V(u,brain) for the model compounds ranged from 4 to 3000 ml . g brain(-1), and the sources of variability are discussed. The optimized setup of the brain slice method allows precise, robust estimation of V(u,brain) for drugs with diverse properties, including highly lipophilic compounds. This is a critical step forward for the implementation of relevant measurements of CNS exposure in the drug discovery setting.

Molecular diversity management strategies for building and enhancement of diverse and focused lead discovery compound screening collections.

PubMed

Schuffenhauer, A; Popov, M; Schopfer, U; Acklin, P; Stanek, J; Jacoby, E

2004-12-01

This publication describes processes for the selection of chemical compounds for the building of a high-throughput screening (HTS) collection for drug discovery, using the currently implemented process in the Discovery Technologies Unit of the Novartis Institute for Biomedical Research, Basel Switzerland as reference. More generally, the currently existing compound acquisition models and practices are discussed. Our informatics, chemistry and biology-driven compound selection consists of two steps: 1) The individual compounds are filtered and grouped into three priority classes on the basis of their individual structural properties. Substructure filters are used to eliminate or penalize compounds based on unwanted structural properties. The similarity of the structures to reference ligands of the main proven druggable target families is computed, and drug-similar compounds are prioritized for the following diversity analysis. 2) The compounds are compared to the archive compounds and a diversity analysis is performed. This is done separately for the prioritized, regular and penalized compounds with increasingly stringent dissimilarity criterion. The process includes collecting vendor catalogues and monitoring the availability of samples together with the selection and purchase decision points. The development of a corporate vendor catalogue database is described. In addition to the selection methods on a per single molecule basis, selection criteria for scaffold and combinatorial chemistry projects in collaboration with compound vendors are discussed.

QSAR of phytochemicals for the design of better drugs.

PubMed

Kar, Supratik; Roy, Kunal

2012-10-01

Phytochemicals have been the single most prolific source of leads for the development of new drug entities from the dawn of the drug discovery. They cover a wide range of therapeutic indications with a great diversity of chemical structures. The research fraternity still believes in exploring the phytochemicals for new drug discovery. Application of molecular biological techniques has increased the availability of novel compounds that can be conveniently isolated from natural sources. Combinatorial chemistry approaches are being applied based on phytochemical scaffolds to create screening libraries that closely resemble drug-like compounds. In silico techniques like quantitative structure-activity relationships (QSAR), pharmacophore and virtual screening are playing crucial and rate accelerating steps for the better drug design in modern era. QSAR models of different classes of phytochemicals covering different therapeutic areas are thoroughly discussed in the review. Further, the authors have enlisted all the available phytochemical databases for the convenience of researchers working in the area. This review justifies the need to develop more QSAR models for the design of better drugs from phytochemicals. Technical drawbacks associated with phytochemical research have been lessened, and there are better opportunities to explore the biological activity of previously inaccessible sources of phytochemicals although there is still the need to reduce the time and cost involvement in such exercise. The future possibilities for the integration of ethnopharmacology with QSAR, place us at an exciting stage that will allow us to explore plant sources worldwide and design better drugs.

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

PubMed

Lan, Shih-Feng; Starly, Binil

2011-10-01

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

Understanding the lipid-digestion processes in the GI tract before designing lipid-based drug-delivery systems.

PubMed

Bakala N'Goma, Jean-Claude; Amara, Sawsan; Dridi, Kaouthar; Jannin, Vincent; Carrière, Frédéric

2012-01-01

Many of the compounds present in lipid-based drug-delivery systems are esters, such as acylglycerols, phospholipids, polyethyleneglycol mono- and di-esters and polysorbate, which can be hydrolyzed by the various lipolytic enzymes present in the GI tract. Lipolysis of these compounds, along with dietary fats, affects the solubility, dispersion and bioavailibity of poorly water-soluble drugs. Pharmaceutical scientists have been taking a new interest in fat digestion in this context, and several studies presenting in vitro gastrointestinal lipolysis models have been published. In most models, it is generally assumed that pancreatic lipase is the main enzyme involved in the gastrointestinal lipolysis of lipid formulations. It was established, however, that gastric lipase, pancreatic carboxyl ester hydrolaze and pancreatic lipase-related protein 2 are the major players involved in the lipolysis of lipid excipients containing acylglycerols and polyethyleneglycol esters. These findings have shown that the lipolysis of lipid excipients may actually start in the stomach and involve several lipolytic enzymes. These findings should therefore be taken into account when testing in vitro the dispersion and bioavailability of poorly water-soluble drugs formulated with lipids. In this review, we present the latest data available about the lipolytic enzymes involved in gastrointestinal lipolysis and suggest tracks for designing physiologically relevant in vitro digestion models.

Photo-affinity labelling and biochemical analyses identify the target of trypanocidal simplified natural product analogues

PubMed Central

Tulloch, Lindsay B.; Menzies, Stefanie K.; Fraser, Andrew L.; Gould, Eoin R.; King, Elizabeth F.; Zacharova, Marija K.; Florence, Gordon J.

2017-01-01

Current drugs to treat African sleeping sickness are inadequate and new therapies are urgently required. As part of a medicinal chemistry programme based upon the simplification of acetogenin-type ether scaffolds, we previously reported the promising trypanocidal activity of compound 1, a bis-tetrahydropyran 1,4-triazole (B-THP-T) inhibitor. This study aims to identify the protein target(s) of this class of compound in Trypanosoma brucei to understand its mode of action and aid further structural optimisation. We used compound 3, a diazirine- and alkyne-containing bi-functional photo-affinity probe analogue of our lead B-THP-T, compound 1, to identify potential targets of our lead compound in the procyclic form T. brucei. Bi-functional compound 3 was UV cross-linked to its target(s) in vivo and biotin affinity or Cy5.5 reporter tags were subsequently appended by Cu(II)-catalysed azide-alkyne cycloaddition. The biotinylated protein adducts were isolated with streptavidin affinity beads and subsequent LC-MSMS identified the FoF1-ATP synthase (mitochondrial complex V) as a potential target. This target identification was confirmed using various different approaches. We show that (i) compound 1 decreases cellular ATP levels (ii) by inhibiting oxidative phosphorylation (iii) at the FoF1-ATP synthase. Furthermore, the use of GFP-PTP-tagged subunits of the FoF1-ATP synthase, shows that our compounds bind specifically to both the α- and β-subunits of the ATP synthase. The FoF1-ATP synthase is a target of our simplified acetogenin-type analogues. This mitochondrial complex is essential in both procyclic and bloodstream forms of T. brucei and its identification as our target will enable further inhibitor optimisation towards future drug discovery. Furthermore, the photo-affinity labeling technique described here can be readily applied to other drugs of unknown targets to identify their modes of action and facilitate more broadly therapeutic drug design in any pathogen or disease model. PMID:28873407

Antimycobacterial drug discovery using Mycobacteria-infected amoebae identifies anti-infectives and new molecular targets.

PubMed

Trofimov, Valentin; Kicka, Sébastien; Mucaria, Sabrina; Hanna, Nabil; Ramon-Olayo, Fernando; Del Peral, Laura Vela-Gonzalez; Lelièvre, Joël; Ballell, Lluís; Scapozza, Leonardo; Besra, Gurdyal S; Cox, Jonathan A G; Soldati, Thierry

2018-03-02

Tuberculosis remains a serious threat to human health world-wide, and improved efficiency of medical treatment requires a better understanding of the pathogenesis and the discovery of new drugs. In the present study, we performed a whole-cell based screen in order to complete the characterization of 168 compounds from the GlaxoSmithKline TB-set. We have established and utilized novel previously unexplored host-model systems to characterize the GSK compounds, i.e. the amoeboid organisms D. discoideum and A. castellanii, as well as a microglial phagocytic cell line, BV2. We infected these host cells with Mycobacterium marinum to monitor and characterize the anti-infective activity of the compounds with quantitative fluorescence measurements and high-content microscopy. In summary, 88.1% of the compounds were confirmed as antibiotics against M. marinum, 11.3% and 4.8% displayed strong anti-infective activity in, respectively, the mammalian and protozoan infection models. Additionally, in the two systems, 13-14% of the compounds displayed pro-infective activity. Our studies underline the relevance of using evolutionarily distant pathogen and host models in order to reveal conserved mechanisms of virulence and defence, respectively, which are potential "universal" targets for intervention. Subsequent mechanism of action studies based on generation of over-expresser M. bovis BCG strains, generation of spontaneous resistant mutants and whole genome sequencing revealed four new molecular targets, including FbpA, MurC, MmpL3 and GlpK.

Graph theoretical analysis, insilico modeling, design, and synthesis of compounds containing benzimidazole skeleton as antidepressant agents.

PubMed

Theivendren, Panneerselvam; Subramanian, Arumugam; Murugan, Indhumathy; Joshi, Shrinivas D; More, Uttam A

2017-05-01

In this study, drug target was identified using KEGG database and network analysis through Cytoscape software. Designed series of novel benzimidazoles were taken along with reference standard Flibanserin for insilico modeling. The novel 4-(1H-benzo[d]imidazol-2-yl)-N-(substituted phenyl)-4-oxobutanamide (3a-j) analogs were synthesized and evaluated for their antidepressant activity. Reaction of 4-(1H-benzo[d]imidazol-2-yl)-4-oxobutanoic acid (1) with 4-(1H-benzo [d] imidazol-2-yl)-4-oxobutanoyl chloride (2) furnished novel 4-(1H-benzo [d] imidazol-2-yl)-N-(substituted phenyl)-4-oxobutanamide (3a-j). All the newly synthesized compounds were characterized by IR, 1 H-NMR, and mass spectral analysis. The antidepressant activities of synthesized derivatives were compared with standard drug clomipramine at a dose level of 20 mg/kg. Among the derivatives tested, most of the compounds were found to have potent activity against depression. The high level of activity was shown by the compounds 3d, 3e, 3i, and it significantly reduced the duration of immobility time at the dose level of 50 mg/kg. © 2016 John Wiley & Sons A/S.

Modeling nasopharyngeal carcinoma in three dimensions

PubMed Central

Siva Sankar, Prabu; Che Mat, Mohd Firdaus; Muniandy, Kalaivani; Xiang, Benedict Lian Shi; Ling, Phang Su; Hoe, Susan Ling Ling; Khoo, Alan Soo-Beng; Mohana-Kumaran, Nethia

2017-01-01

Nasopharyngeal carcinoma (NPC) is a type of cancer endemic in Asia, including Malaysia, Southern China, Hong Kong and Taiwan. Treatment resistance, particularly in recurring cases, remains a challenge. Thus, studies to develop novel therapeutic agents are important. Potential therapeutic compounds may be effectively examined using two-dimensional (2D) cell culture models, three-dimensional (3D) spheroid models or in vivo animal models. The majority of drug assessments for cancers, including for NPC, are currently performed with 2D cell culture models. This model offers economical and high-throughput screening advantages. However, 2D cell culture models cannot recapitulate the architecture and the microenvironment of a tumor. In vivo models may recapitulate certain architectural and microenvironmental conditions of a tumor, however, these are not feasible for the screening of large numbers of compounds. By contrast, 3D spheroid models may be able to recapitulate a physiological microenvironment not observed in 2D cell culture models, in addition to avoiding the impediments of in vivo animal models. Thus, the 3D spheroid model offers a more representative model for the study of NPC growth, invasion and drug response, which may be cost-effective without forgoing quality. PMID:28454359

Predictive teratology: teratogenic risk-hazard identification partnered in the discovery process.

PubMed

Augustine-Rauch, K A

2008-11-01

Unexpected teratogenicity is ranked as one of the most prevalent causes for toxicity-related attrition of drug candidates. Without proactive assessment, the liability tends to be identified relatively late in drug development, following significant investment in compound and engagement in pre clinical and clinical studies. When unexpected teratogenicity occurs in pre-clinical development, three principle questions arise: Can clinical trials that include women of child bearing populations be initiated? Will all compounds in this pharmacological class produce the same liability? Could this effect be related to the chemical structure resulting in undesirable off-target adverse effects? The first question is typically addressed at the time of the unexpected finding and involves considering the nature of the teratogenicity, whether or not maternal toxicity could have had a role in onset, human exposure margins and therapeutic indication. The latter two questions can be addressed proactively, earlier in the discovery process as drug target profiling and lead compound optimization is taking place. Such proactive approaches include thorough assessment of the literature for identification of potential liabilities and follow-up work that can be conducted on the level of target expression and functional characterization using molecular biology and developmental model systems. Developmental model systems can also be applied in the form of in vitro teratogenicity screens, and show potential for effective hazard identification or issue resolution on the level of characterizing teratogenic mechanism. This review discusses approaches that can be applied for proactive assessment of compounds for teratogenic liability.

Mouse Models Applied to the Research of Pharmacological Treatments in Asthma.

PubMed

Marqués-García, Fernando; Marcos-Vadillo, Elena

2016-01-01

Models developed for the study of asthma mechanisms can be used to investigate new compounds with pharmacological activity against this disease. The increasing number of compounds requires a preclinical evaluation before starting the application in humans. Preclinical evaluation in animal models reduces the number of clinical trials positively impacting in the cost and in safety. In this chapter, three protocols for the study of drugs are shown: a model to investigate corticoids as a classical treatment of asthma; a protocol to test the effects of retinoic acid (RA) on asthma; and a mouse model to test new therapies in asthma as monoclonal antibodies.

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

PubMed

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

2014-01-10

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

Novel Vitamin K analogs suppress seizures in zebrafish and mouse models of epilepsy.

PubMed

Rahn, J J; Bestman, J E; Josey, B J; Inks, E S; Stackley, K D; Rogers, C E; Chou, C J; Chan, S S L

2014-02-14

Epilepsy is a debilitating disease affecting 1-2% of the world's population. Despite this high prevalence, 30% of patients suffering from epilepsy are not successfully managed by current medication suggesting a critical need for new anti-epileptic drugs (AEDs). In an effort to discover new therapeutics for the management of epilepsy, we began our study by screening drugs that, like some currently used AEDs, inhibit histone deacetylases (HDACs) using a well-established larval zebrafish model. In this model, 7-day post fertilization (dpf) larvae are treated with the widely used seizure-inducing compound pentylenetetrazol (PTZ) which stimulates a rapid increase in swimming behavior previously determined to be a measurable manifestation of seizures. In our first screen, we tested a number of different HDAC inhibitors and found that one, 2-benzamido-1 4-naphthoquinone (NQN1), significantly decreased swim activity to levels equal to that of valproic acid, 2-n-propylpentanoic acid (VPA). We continued to screen structurally related compounds including Vitamin K3 (VK3) and a number of novel Vitamin K (VK) analogs. We found that VK3 was a robust inhibitor of the PTZ-induced swim activity, as were several of our novel compounds. Three of these compounds were subsequently tested on mouse seizure models at the National Institute of Neurological Disorders and Stroke (NINDS) Anticonvulsant Screening Program. Compound 2h reduced seizures particularly well in the minimal clonic seizure (6Hz) and corneal-kindled mouse models of epilepsy, with no observable toxicity. As VK3 affects mitochondrial function, we tested the effects of our compounds on mitochondrial respiration and ATP production in a mouse hippocampal cell line. We demonstrate that these compounds affect ATP metabolism and increase total cellular ATP. Our data indicate the potential utility of these and other VK analogs for the prevention of seizures and suggest the potential mechanism for this protection may lie in the ability of these compounds to affect energy production. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

Predictive Modeling of Estrogen Receptor Binding Agents Using Advanced Cheminformatics Tools and Massive Public Data.

PubMed

Ribay, Kathryn; Kim, Marlene T; Wang, Wenyi; Pinolini, Daniel; Zhu, Hao

2016-03-01

Estrogen receptors (ERα) are a critical target for drug design as well as a potential source of toxicity when activated unintentionally. Thus, evaluating potential ERα binding agents is critical in both drug discovery and chemical toxicity areas. Using computational tools, e.g., Quantitative Structure-Activity Relationship (QSAR) models, can predict potential ERα binding agents before chemical synthesis. The purpose of this project was to develop enhanced predictive models of ERα binding agents by utilizing advanced cheminformatics tools that can integrate publicly available bioassay data. The initial ERα binding agent data set, consisting of 446 binders and 8307 non-binders, was obtained from the Tox21 Challenge project organized by the NIH Chemical Genomics Center (NCGC). After removing the duplicates and inorganic compounds, this data set was used to create a training set (259 binders and 259 non-binders). This training set was used to develop QSAR models using chemical descriptors. The resulting models were then used to predict the binding activity of 264 external compounds, which were available to us after the models were developed. The cross-validation results of training set [Correct Classification Rate (CCR) = 0.72] were much higher than the external predictivity of the unknown compounds (CCR = 0.59). To improve the conventional QSAR models, all compounds in the training set were used to search PubChem and generate a profile of their biological responses across thousands of bioassays. The most important bioassays were prioritized to generate a similarity index that was used to calculate the biosimilarity score between each two compounds. The nearest neighbors for each compound within the set were then identified and its ERα binding potential was predicted by its nearest neighbors in the training set. The hybrid model performance (CCR = 0.94 for cross validation; CCR = 0.68 for external prediction) showed significant improvement over the original QSAR models, particularly for the activity cliffs that induce prediction errors. The results of this study indicate that the response profile of chemicals from public data provides useful information for modeling and evaluation purposes. The public big data resources should be considered along with chemical structure information when predicting new compounds, such as unknown ERα binding agents.

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

PubMed

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

2012-12-01

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

ECG telemetry in conscious guinea pigs.

PubMed

Ruppert, Sabine; Vormberge, Thomas; Igl, Bernd-Wolfgang; Hoffmann, Michael

2016-01-01

During preclinical drug development, monitoring of the electrocardiogram (ECG) is an important part of cardiac safety assessment. To detect potential pro-arrhythmic liabilities of a drug candidate and for internal decision-making during early stage drug development an in vivo model in small animals with translatability to human cardiac function is required. Over the last years, modifications/improvements regarding animal housing, ECG electrode placement, and data evaluation have been introduced into an established model for ECG recordings using telemetry in conscious, freely moving guinea pigs. Pharmacological validation using selected reference compounds affecting different mechanisms relevant for cardiac electrophysiology (quinidine, flecainide, atenolol, dl-sotalol, dofetilide, nifedipine, moxifloxacin) was conducted and findings were compared with results obtained in telemetered Beagle dogs. Under standardized conditions, reliable ECG data with low variability allowing largely automated evaluation were obtained from the telemetered guinea pig model. The model is sensitive to compounds blocking cardiac sodium channels, hERG K(+) channels and calcium channels, and appears to be even more sensitive to β-blockers as observed in dogs at rest. QT interval correction according to Bazett and Sarma appears to be appropriate methods in conscious guinea pigs. Overall, the telemetered guinea pig is a suitable model for the conduct of early stage preclinical ECG assessment. Copyright © 2016 Elsevier Inc. All rights reserved.

Virtual Screening Techniques to Probe the Antimalarial Activity of some Traditionally Used Phytochemicals.

PubMed

Shibi, Indira G; Aswathy, Lilly; Jisha, Radhakrishnan S; Masand, Vijay H; Gajbhiye, Jayant M

2016-01-01

Malaria parasites show resistance to most of the antimalarial drugs and hence developing antimalarials which can act on multitargets rather than a single target will be a promising strategy of drug design. Here we report a new approach by which virtual screening of 292 unique phytochemicals present in 72 traditionally important herbs is used for finding out inhibitors of plasmepsin-2 and falcipain-2 for antimalarial activity against P. falciparum. Initial screenings of the selected molecules by Random Forest algorithm model of Weka using the bioassay datasets AID 504850 and AID 2302 screened 120 out of the total 292 phytochemicals to be active against the targets. Toxtree scan cautioned 21 compounds to be either carcinogenic or mutagenic and were thus removed for further analysis. Out of the remaining 99 compounds, only 46 compounds offered drug-likeness as per the 'rule of five' criteria. Out of ten antimalarial drug targets, only two target proteins such as 3BPF and 3PNR of falcipain-2 and 1PFZ and 2BJU of plasmepsin-2 are selected as targets. The potential binding of the selected 46 compounds to the active sites of these four targets was analyzed using MOE software. The docked conformations and the interactions with the binding pocket residues of the target proteins were understood by 'Ligplot' analysis. It has been found that 8 compounds are dual inhibitors of falcipain-2 and plasmepsin-2, with the best binding energies. Compound 117 (6aR, 12aS)-12a-Hydroxy-9-methoxy-2,3-dimethylenedioxy-8-prenylrotenone (Usaratenoid C) present in the plant Millettia usaramensis showed maximum molecular docking score.

Comprehensive analog synthesis of (S)-valine thiazole peptidomimetic TTT-28 to understand enigmatic drug-binding sites of P-glycoprotein

NASA Astrophysics Data System (ADS)

Patel, Bhargav A.

P-glycoprotein (P-gp) is considered an important therapeutic target for reversal of multidrug resistance (MDR) in cancer. It recognizes a diverse range of chemically and mechanistically dissimilar drugs. It has been postulated that the efflux by P-gp plays a major role in failure of chemotherapy. Hence, researchers have been trying to obtain a potent inhibitor of P-gp with specificity to tumor sites. In this pursuit, we previously were able to obtain a novel (S)-valine thiazole-derived peptidomimetic compound 1 ( TTT-28), which showed potent reversal of MDR in vitro as well as in vivo compared to verapamil, a well-known MDR modulator. We have also found that compound 1 triggers ATPase stimulation when incubated with P-gp alike verapamil, which implies its mechanism of action as competitive in nature. In this study, we attempted to understand structural requirements of ligands binding to a perplexing drug-binding site of P-gp and affecting its ATPase function. Toward this goal, we prepared a novel set of 64 analogues by fine tuning lead compound 1. These synthesized analogues were tested using ATPase activity assay. During the course of the study, a potent stimulator (1) of ATPase activity was transformed into an ATPase inhibitory leads such as compounds 43 , 57 and 113. The ATPase inhibitory activity of these compounds is predominantly contributed by the presence of a cyclohexyl group in place of the 2-aminobenzophenone moiety of ATPase activity stimulatory lead compound 1. Molecular modeling studies suggested a need for specific interactions with the drug-binding site of P-gp to induce different conformational states of P-gp to produce either stimulation or inhibition of ATPase activity. Collectively, this comprehensive synthesis work will facilitate further research towards P-gp inhibitor development.

Prediction of poly(ethylene) glycol-drug eutectic compositions using an index based on the van't Hoff equation.

PubMed

Law, Devalina; Wang, Weili; Schmitt, Eric A; Long, Michelle A

2002-03-01

To define an index based on the van't Hoff equation that can be used as a screening tool for predicting poly(ethylene) glycol (PEG)-drug eutectic composition. Phase diagrams of PEG with ritonavir, ibuprofen, fenofibrate. naproxen, and griseofulvin were constructed using differential scanning calorimetry, hot stage microscopy and powder X-ray diftractometry. Previously reported phase diagrams were also used to test the predictive capability of the index. This work shows that a modified van't Hoff equation can be used to model the drug liquidus line of these phase diagrams. The slope of the liquidus line depends on the melting point (T(f)d) and heat of fusion (deltaH(f)d) of the drug and describes the initial rate at which the eutectic or monotectic point is approached. Based on this finding, a dimensionless index Ic was defined. The index can be calculated from the melting points of the pure components and heat of fusion of the drug. In addition to the compounds listed above, the index was found to predict the eutectic composition for flurbiprofen, temazepam and indomethacin. These compounds range over 150 degrees C in T(f)d, and from 25-65 kJ/mole in deltaH(f)d. Using Ic the approximate eutectic composition for eight different compounds was predicted. The index provides a useful screening tool for assessing the maximum drug loading in a drug-polymer eutectic/monotectic formulation.

Leishmania donovani pteridine reductase 1: comparative protein modeling and protein-ligand interaction studies of the leishmanicidal constituents isolated from the fruits of Piper longum.

PubMed

Sahi, Shakti; Tewatia, Parul; Ghosal, Sabari

2012-12-01

Visceral leishmaniasis or kala-azar is caused by the dimorphic parasite Leishmania donovani in the Indian subcontinent. Treatment options for kala-azar are currently inadequate due to various limitations. Currently, drug discovery for leishmaniases is oriented towards rational drug design; the aim is to identify specific inhibitors that target particular metabolic activities as a possible means of controlling the parasites without affecting the host. Leishmania salvages pteridin from its host and reduces it using pteridine reductase 1 (PTR1, EC 1.5.1.33), which makes this reductase an excellent drug target. Recently, we identified six alkamides and one benzenoid compound from the n-hexane fraction of the fruit of Piper longum that possess potent leishmanicidal activity against promastigotes as well as axenic amastigotes. Based on a homology model derived for recombinant pteridine reductase isolated from a clinical isolate of L. donovani, we carried out molecular modeling and docking studies with these compounds to evaluate their binding affinity. A fairly good agreement between experimental data and the results of molecular modeling investigation of the bioactive and inactive compounds was observed. The amide group in the conjugated alkamides and the 3,4-methylenedioxystyrene moiety in the benzenoid compound acts as heads and the long aliphatic chain acts as a tail, thus playing important roles in the binding of the inhibitor to the appropriate position at the active site. The remarkably high activity of a component containing piperine and piperine isomers (3.36:1) as observed by our group prompted us to study the activities of all four isomers of piperine-piperine (2E,4E), isopiperine (2Z,4E), isochavicine (2E,4Z), and chavicine (2Z,4Z)-against LdPTR1. The maximum inhibitory effect was demonstrated by isochavicine. The identification of these predicted inhibitors of LdPTR1 allowed us to build up a stereoview of the structure of the binding site in relation to activity, affording significant information that should prove useful during the structure-based design of leishmanicidal drugs.

Predicting when biliary excretion of parent drug is a major route of elimination in humans.

PubMed

Hosey, Chelsea M; Broccatelli, Fabio; Benet, Leslie Z

2014-09-01

Biliary excretion is an important route of elimination for many drugs, yet measuring the extent of biliary elimination is difficult, invasive, and variable. Biliary elimination has been quantified for few drugs with a limited number of subjects, who are often diseased patients. An accurate prediction of which drugs or new molecular entities are significantly eliminated in the bile may predict potential drug-drug interactions, pharmacokinetics, and toxicities. The Biopharmaceutics Drug Disposition Classification System (BDDCS) characterizes significant routes of drug elimination, identifies potential transporter effects, and is useful in understanding drug-drug interactions. Class 1 and 2 drugs are primarily eliminated in humans via metabolism and will not exhibit significant biliary excretion of parent compound. In contrast, class 3 and 4 drugs are primarily excreted unchanged in the urine or bile. Here, we characterize the significant elimination route of 105 orally administered class 3 and 4 drugs. We introduce and validate a novel model, predicting significant biliary elimination using a simple classification scheme. The model is accurate for 83% of 30 drugs collected after model development. The model corroborates the observation that biliarily eliminated drugs have high molecular weights, while demonstrating the necessity of considering route of administration and extent of metabolism when predicting biliary excretion. Interestingly, a predictor of potential metabolism significantly improves predictions of major elimination routes of poorly metabolized drugs. This model successfully predicts the major elimination route for poorly permeable/poorly metabolized drugs and may be applied prior to human dosing.

Pharmacological Evaluation and Preparation of Nonsteroidal Anti-Inflammatory Drugs Containing an N-Acyl Hydrazone Subunit

PubMed Central

de Melo, Thais Regina Ferreira; Chelucci, Rafael Consolin; Pires, Maria Elisa Lopes; Dutra, Luiz Antonio; Barbieri, Karina Pereira; Bosquesi, Priscila Longhin; Trossini, Gustavo Henrique Goulart; Chung, Man Chin; dos Santos, Jean Leandro

2014-01-01

A series of anti-inflammatory derivatives containing an N-acyl hydrazone subunit (4a–e) were synthesized and characterized. Docking studies were performed that suggest that compounds 4a–e bind to cyclooxygenase (COX)-1 and COX-2 isoforms, but with higher affinity for COX-2. The compounds display similar anti-inflammatory activities in vivo, although compound 4c is the most effective compound for inhibiting rat paw edema, with a reduction in the extent of inflammation of 35.9% and 52.8% at 2 and 4 h, respectively. The anti-inflammatory activity of N-acyl hydrazone derivatives was inferior to their respective parent drugs, except for compound 4c after 5 h. Ulcerogenic studies revealed that compounds 4a–e are less gastrotoxic than the respective parent drug. Compounds 4b–e demonstrated mucosal damage comparable to celecoxib. The in vivo analgesic activities of the compounds are higher than the respective parent drug for compounds 4a–b and 4d–e. Compound 4a was more active than dipyrone in reducing acetic-acid-induced abdominal constrictions. Our results indicate that compounds 4a–e are anti-inflammatory and analgesic compounds with reduced gastrotoxicity compared to their respective parent non-steroidal anti-inflammatory drugs. PMID:24714090

Selected approaches for rational drug design and high throughput screening to identify anti-cancer molecules.

PubMed

Hedvat, Michael; Emdad, Luni; Das, Swadesh K; Kim, Keetae; Dasgupta, Santanu; Thomas, Shibu; Hu, Bin; Zhu, Shan; Dash, Rupesh; Quinn, Bridget A; Oyesanya, Regina A; Kegelman, Timothy P; Sokhi, Upneet K; Sarkar, Siddik; Erdogan, Eda; Menezes, Mitchell E; Bhoopathi, Praveen; Wang, Xiang-Yang; Pomper, Martin G; Wei, Jun; Wu, Bainan; Stebbins, John L; Diaz, Paul W; Reed, John C; Pellecchia, Maurizio; Sarkar, Devanand; Fisher, Paul B

2012-11-01

Structure-based modeling combined with rational drug design, and high throughput screening approaches offer significant potential for identifying and developing lead compounds with therapeutic potential. The present review focuses on these two approaches using explicit examples based on specific derivatives of Gossypol generated through rational design and applications of a cancer-specificpromoter derived from Progression Elevated Gene-3. The Gossypol derivative Sabutoclax (BI-97C1) displays potent anti-tumor activity against a diverse spectrum of human tumors. The model of the docked structure of Gossypol bound to Bcl-XL provided a virtual structure-activity-relationship where appropriate modifications were predicted on a rational basis. These structure-based studies led to the isolation of Sabutoclax, an optically pure isomer of Apogossypol displaying superior efficacy and reduced toxicity. These studies illustrate the power of combining structure-based modeling with rational design to predict appropriate derivatives of lead compounds to be empirically tested and evaluated for bioactivity. Another approach to cancer drug discovery utilizes a cancer-specific promoter as readouts of the transformed state. The promoter region of Progression Elevated Gene-3 is such a promoter with cancer-specific activity. The specificity of this promoter has been exploited as a means of constructing cancer terminator viruses that selectively kill cancer cells and as a systemic imaging modality that specifically visualizes in vivo cancer growth with no background from normal tissues. Screening of small molecule inhibitors that suppress the Progression Elevated Gene-3-promoter may provide relevant lead compounds for cancer therapy that can be combined with further structure-based approaches leading to the development of novel compounds for cancer therapy.

The simcyp population based simulator: architecture, implementation, and quality assurance.

PubMed

Jamei, Masoud; Marciniak, Steve; Edwards, Duncan; Wragg, Kris; Feng, Kairui; Barnett, Adrian; Rostami-Hodjegan, Amin

2013-01-01

Developing a user-friendly platform that can handle a vast number of complex physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) models both for conventional small molecules and larger biologic drugs is a substantial challenge. Over the last decade the Simcyp Population Based Simulator has gained popularity in major pharmaceutical companies (70% of top 40 - in term of R&D spending). Under the Simcyp Consortium guidance, it has evolved from a simple drug-drug interaction tool to a sophisticated and comprehensive Model Based Drug Development (MBDD) platform that covers a broad range of applications spanning from early drug discovery to late drug development. This article provides an update on the latest architectural and implementation developments within the Simulator. Interconnection between peripheral modules, the dynamic model building process and compound and population data handling are all described. The Simcyp Data Management (SDM) system, which contains the system and drug databases, can help with implementing quality standards by seamless integration and tracking of any changes. This also helps with internal approval procedures, validation and auto-testing of the new implemented models and algorithms, an area of high interest to regulatory bodies.

Chromaffin cells as a model to evaluate mechanisms of cell death and neuroprotective compounds.

PubMed

de Los Rios, Cristobal; Cano-Abad, Maria F; Villarroya, Mercedes; López, Manuela G

2018-01-01

In this review, we show how chromaffin cells have contributed to evaluate neuroprotective compounds with diverse mechanisms of action. Chromaffin cells are considered paraneurons, as they share many common features with neurons: (i) they synthesize, store, and release neurotransmitters upon stimulation and (ii) they express voltage-dependent calcium, sodium, and potassium channels, in addition to a wide variety of receptors. All these characteristics, together with the fact that primary cultures from bovine adrenal glands or chromaffin cells from the tumor pheochromocytoma cell line PC12 are easy to culture, make them an ideal model to study neurotoxic mechanisms and neuroprotective drugs. In the first part of this review, we will analyze the different cytotoxicity models related to calcium dyshomeostasis and neurodegenerative disorders like Alzheimer's or Parkinson's. Along the second part of the review, we describe how different classes of drugs have been evaluated in chromaffin cells to determine their neuroprotective profile in different neurodegenerative-related models.

Predicting Error Bars for QSAR Models

NASA Astrophysics Data System (ADS)

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

2007-09-01

Unfavorable physicochemical properties often cause drug failures. It is therefore important to take lipophilicity and water solubility into account early on in lead discovery. This study presents log D7 models built using Gaussian Process regression, Support Vector Machines, decision trees and ridge regression algorithms based on 14556 drug discovery compounds of Bayer Schering Pharma. A blind test was conducted using 7013 new measurements from the last months. We also present independent evaluations using public data. Apart from accuracy, we discuss the quality of error bars that can be computed by Gaussian Process models, and ensemble and distance based techniques for the other modelling approaches.

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.

Screening strategies to identify new chemical diversity for drug development to treat kinetoplastid infections.

PubMed

Don, Rob; Ioset, Jean-Robert

2014-01-01

The Drugs for Neglected Diseases initiative (DNDi) has defined and implemented an early discovery strategy over the last few years, in fitting with its virtual R&D business model. This strategy relies on a medium- to high-throughput phenotypic assay platform to expedite the screening of compound libraries accessed through its collaborations with partners from the pharmaceutical industry. We review the pragmatic approaches used to select compound libraries for screening against kinetoplastids, taking into account screening capacity. The advantages, limitations and current achievements in identifying new quality series for further development into preclinical candidates are critically discussed, together with attractive new approaches currently under investigation.

Antiprotozoal activity of proton-pump inhibitors.

PubMed

Pérez-Villanueva, Jaime; Romo-Mancillas, Antonio; Hernández-Campos, Alicia; Yépez-Mulia, Lilián; Hernández-Luis, Francisco; Castillo, Rafael

2011-12-15

Parasitic diseases are still a major health problem in developing countries. In our effort to find new antiparasitic agents, in this Letter we report the in vitro antiprotozoal activity of omeprazole, lansoprazole, rabeprazole and pantoprazole against Trichomonas vaginalis, Giardia intestinalis and Entamoeba histolytica. Molecular modeling studies were an important tool to highlight the potential antiprotozoal activity of these drugs. Experimental evaluations revealed a strong activity for all compounds tested. Rabeprazole and pantoprazole were the most active compounds, having IC(50) values in the nanomolar range, which were even better than metronidazole, the drug of choice for these parasites. Copyright © 2011 Elsevier Ltd. All rights reserved.

Predicting Drug-Target Interactions for New Drug Compounds Using a Weighted Nearest Neighbor Profile.

PubMed

van Laarhoven, Twan; Marchiori, Elena

2013-01-01

In silico discovery of interactions between drug compounds and target proteins is of core importance for improving the efficiency of the laborious and costly experimental determination of drug-target interaction. Drug-target interaction data are available for many classes of pharmaceutically useful target proteins including enzymes, ion channels, GPCRs and nuclear receptors. However, current drug-target interaction databases contain a small number of drug-target pairs which are experimentally validated interactions. In particular, for some drug compounds (or targets) there is no available interaction. This motivates the need for developing methods that predict interacting pairs with high accuracy also for these 'new' drug compounds (or targets). We show that a simple weighted nearest neighbor procedure is highly effective for this task. We integrate this procedure into a recent machine learning method for drug-target interaction we developed in previous work. Results of experiments indicate that the resulting method predicts true interactions with high accuracy also for new drug compounds and achieves results comparable or better than those of recent state-of-the-art algorithms. Software is publicly available at http://cs.ru.nl/~tvanlaarhoven/drugtarget2013/.

Inkjet Printing of Drug-Loaded Mesoporous Silica Nanoparticles-A Platform for Drug Development.

PubMed

Wickström, Henrika; Hilgert, Ellen; Nyman, Johan O; Desai, Diti; Şen Karaman, Didem; de Beer, Thomas; Sandler, Niklas; Rosenholm, Jessica M

2017-11-21

Mesoporous silica nanoparticles (MSNs) have shown great potential in improving drug delivery of poorly water soluble (BCS class II, IV) and poorly permeable (BCS class III, IV) drugs, as well as facilitating successful delivery of unstable compounds. The nanoparticle technology would allow improved treatment by reducing adverse reactions of currently approved drugs and possibly reintroducing previously discarded compounds from the drug development pipeline. This study aims to highlight important aspects in mesoporous silica nanoparticle (MSN) ink formulation development for digital inkjet printing technology and to advice on choosing a method (2D/3D) for nanoparticle print deposit characterization. The results show that both unfunctionalized and polyethyeleneimine (PEI) surface functionalized MSNs, as well as drug-free and drug-loaded MSN-PEI suspensions, can be successfully inkjet-printed. Furthermore, the model BCS class IV drug remained incorporated in the MSNs and the suspension remained physically stable during the processing time and steps. This proof-of-concept study suggests that inkjet printing technology would be a flexible deposition method of pharmaceutical MSN suspensions to generate patterns according to predefined designs. The concept could be utilized as a versatile drug screening platform in the future due to the possibility of accurately depositing controlled volumes of MSN suspensions on various materials.

Nature as a source of metabolites with cholinesterase-inhibitory activity: an approach to Alzheimer's disease treatment.

PubMed

Pinho, Brígida R; Ferreres, Federico; Valentão, Patrícia; Andrade, Paula B

2013-12-01

Alzheimer's disease (AD) is the most common cause of dementia, being responsible for high healthcare costs and familial hardships. Despite the efforts of researchers, no treatment able to delay or stop AD progress exists. Currently, the available treatments are only symptomatic, cholinesterase inhibitors being the most widely used drugs. Here we describe several natural compounds with anticholinesterase (acetylcholinesterase and butyrylcholinesterase) activity and also some synthetic compounds whose structures are based on those of natural compounds. Galantamine and rivastigmine are two cholinesterase inhibitors used in therapeutics: galantamine is a natural alkaloid that was extracted for the first time from Galanthus nivalis L., while rivastigmine is a synthetic alkaloid, the structure of which is modelled on that of natural physostigmine. Alkaloids include a high number of compounds with anticholinesterases activity at the submicromolar range. Quinones and stilbenes are less well studied regarding cholinesterase inhibition, although some of them, such as sargaquinoic acid or (+)-α-viniferin, show promising activity. Among flavonoids, flavones and isoflavones are the most potent compounds. Xanthones and monoterpenes are generally weak cholinesterase inhibitors. Nature is an almost endless source of bioactive compounds. Several natural compounds have anticholinesterase activity and others can be used as leader compounds for the synthesis of new drugs. © 2013 Royal Pharmaceutical Society.

Targeting Lysine Deacetylases (KDACs) in Parasites

PubMed Central

Wang, Qi; Rosa, Bruce A.; Nare, Bakela; Powell, Kerrie; Valente, Sergio; Rotili, Dante; Mai, Antonello; Marshall, Garland R.; Mitreva, Makedonka

2015-01-01

Due to an increasing problem of drug resistance among almost all parasites species ranging from protists to worms, there is an urgent need to explore new drug targets and their inhibitors to provide new and effective parasitic therapeutics. In this regard, there is growing interest in exploring known drug leads of human epigenetic enzymes as potential starting points to develop novel treatments for parasitic diseases. This approach of repurposing (starting with validated targets and inhibitors) is quite attractive since it has the potential to reduce the expense of drug development and accelerate the process of developing novel drug candidates for parasite control. Lysine deacetylases (KDACs) are among the most studied epigenetic drug targets of humans, and a broad range of small-molecule inhibitors for these enzymes have been reported. In this work, we identify the KDAC protein families in representative species across important classes of parasites, screen a compound library of 23 hydroxamate- or benzamide-based small molecules KDAC inhibitors, and report their activities against a range of parasitic species, including the pathogen of malaria (Plasmodium falciparum), kinetoplastids (Trypanosoma brucei and Leishmania donovani), and nematodes (Brugia malayi, Dirofilaria immitis and Haemonchus contortus). Compound activity against parasites is compared to that observed against the mammalian cell line (L929 mouse fibroblast) in order to determine potential parasite-versus-host selectivity). The compounds showed nanomolar to sub-nanomolar potency against various parasites, and some selectivity was observed within the small panel of compounds tested. The possible binding modes of the active compounds at the different protein target sites within different species were explored by docking to homology models to help guide the discovery of more selective, parasite-specific inhibitors. This current work supports previous studies that explored the use of KDAC inhibitors in targeting Plasmodium to develop new anti-malarial treatments, and also pioneers experiments with these KDAC inhibitors as potential new anthelminthics. The selectivity observed begins to address the challenges of targeting specific parasitic diseases while limiting host toxicity. PMID:26402733

Cell and small animal models for phenotypic drug discovery.

PubMed

Szabo, Mihaly; Svensson Akusjärvi, Sara; Saxena, Ankur; Liu, Jianping; Chandrasekar, Gayathri; Kitambi, Satish S

2017-01-01

The phenotype-based drug discovery (PDD) approach is re-emerging as an alternative platform for drug discovery. This review provides an overview of the various model systems and technical advances in imaging and image analyses that strengthen the PDD platform. In PDD screens, compounds of therapeutic value are identified based on the phenotypic perturbations produced irrespective of target(s) or mechanism of action. In this article, examples of phenotypic changes that can be detected and quantified with relative ease in a cell-based setup are discussed. In addition, a higher order of PDD screening setup using small animal models is also explored. As PDD screens integrate physiology and multiple signaling mechanisms during the screening process, the identified hits have higher biomedical applicability. Taken together, this review highlights the advantages gained by adopting a PDD approach in drug discovery. Such a PDD platform can complement target-based systems that are currently in practice to accelerate drug discovery.

Blocking Protein kinase C signaling pathway: mechanistic insights into the anti-leishmanial activity of prospective herbal drugs from Withania somnifera

PubMed Central

2012-01-01

Background Leishmaniasis is caused by several species of leishmania protozoan and is one of the major vector-born diseases after malaria and sleeping sickness. Toxicity of available drugs and drug resistance development by protozoa in recent years has made Leishmaniasis cure difficult and challenging. This urges the need to discover new antileishmanial-drug targets and antileishmanial-drug development. Results Tertiary structure of leishmanial protein kinase C was predicted and found stable with a RMSD of 5.8Å during MD simulations. Natural compound withaferin A inhibited the predicted protein at its active site with -28.47 kcal/mol binding free energy. Withanone was also found to inhibit LPKC with good binding affinity of -22.57 kcal/mol. Both withaferin A and withanone were found stable within the binding pocket of predicted protein when MD simulations of ligand-bound protein complexes were carried out to examine the consistency of interactions between the two. Conclusions Leishmanial protein kinase C (LPKC) has been identified as a potential target to develop drugs against Leishmaniasis. We modelled and refined the tertiary structure of LPKC using computational methods such as homology modelling and molecular dynamics simulations. This structure of LPKC was used to reveal mode of inhibition of two previous experimentally reported natural compounds from Withania somnifera - withaferin A and withanone. PMID:23281834

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

PubMed

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

2009-11-01

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

A photochemical kinetic model for solid dosage forms.

PubMed

Carvalho, Thiago C; La Cruz, Thomas E; Tábora, Jose E

2017-11-01

Photochemical kinetic models to describe the solution phase degradation of pharmaceutical compounds have been extensively reported, but formalisms applicable to the solid phase under polychromatic light have not received as much attention. The objective of this study was to develop a mathematical model to describe the solid state photodegradation of pharmaceutical powder materials under different area/volumetric scales and light exposure conditions. The model considered the previous formalism presented for photodegradation kinetics in solution phase with important elements applied to static powder material being irradiated with a polychromatic light source. The model also included the influence of optical phenomena (i.e. reflectance, scattering factors, etc.) by applying Beer-Lambert law to light attenuation, including effects of powder density. Drug substance and drug product intermediates (blends and tablet cores) were exposed to different light sources and intensities. The model reasonably predicted the photodegradation levels of powder beds of drug substance and drug product intermediates under white and yellow lights with intensities around 5-11kLux. Importantly, the model estimates demonstrated that the reciprocity law for photoreactions was held. Further model evaluation showed that, due to light attenuation, the powder bed is in virtual darkness at cake depths greater than 500μm. At 100μm, the photodegradation of the investigated compound is expected to be close to 100% in 10days under white fluorescent halophosphate light at 9.5kLux. For tablets, defining the volume over exposed surface area ratio is more challenging. Nevertheless, the model can consider a bracket between worst and best cases to provide a reasonable photodegradation estimate. This tool can be significantly leveraged to simulate different light exposure scenarios while assessing photostability risk in order to define appropriate control strategy in manufacturing. Copyright © 2017 Elsevier B.V. All rights reserved.

21 CFR 216.23 - [Reserved

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 4 2014-04-01 2014-04-01 false [Reserved] 216.23 Section 216.23 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) DRUGS: GENERAL PHARMACY COMPOUNDING Compounded Drug Products § 216.23 [Reserved] ...

21 CFR 216.23 - [Reserved

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 4 2012-04-01 2012-04-01 false [Reserved] 216.23 Section 216.23 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) DRUGS: GENERAL PHARMACY COMPOUNDING Compounded Drug Products § 216.23 [Reserved] ...

21 CFR 216.23 - [Reserved

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 4 2010-04-01 2010-04-01 false [Reserved] 216.23 Section 216.23 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) DRUGS: GENERAL PHARMACY COMPOUNDING Compounded Drug Products § 216.23 [Reserved] ...

21 CFR 216.23 - [Reserved

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 4 2011-04-01 2011-04-01 false [Reserved] 216.23 Section 216.23 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) DRUGS: GENERAL PHARMACY COMPOUNDING Compounded Drug Products § 216.23 [Reserved] ...

21 CFR 216.23 - [Reserved

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 4 2013-04-01 2013-04-01 false [Reserved] 216.23 Section 216.23 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) DRUGS: GENERAL PHARMACY COMPOUNDING Compounded Drug Products § 216.23 [Reserved] ...

Prediction of solubility parameters and miscibility of pharmaceutical compounds by molecular dynamics simulations.

PubMed

Gupta, Jasmine; Nunes, Cletus; Vyas, Shyam; Jonnalagadda, Sriramakamal

2011-03-10

The objectives of this study were (i) to develop a computational model based on molecular dynamics technique to predict the miscibility of indomethacin in carriers (polyethylene oxide, glucose, and sucrose) and (ii) to experimentally verify the in silico predictions by characterizing the drug-carrier mixtures using thermoanalytical techniques. Molecular dynamics (MD) simulations were performed using the COMPASS force field, and the cohesive energy density and the solubility parameters were determined for the model compounds. The magnitude of difference in the solubility parameters of drug and carrier is indicative of their miscibility. The MD simulations predicted indomethacin to be miscible with polyethylene oxide and to be borderline miscible with sucrose and immiscible with glucose. The solubility parameter values obtained using the MD simulations values were in reasonable agreement with those calculated using group contribution methods. Differential scanning calorimetry showed melting point depression of polyethylene oxide with increasing levels of indomethacin accompanied by peak broadening, confirming miscibility. In contrast, thermal analysis of blends of indomethacin with sucrose and glucose verified general immiscibility. The findings demonstrate that molecular modeling is a powerful technique for determining the solubility parameters and predicting miscibility of pharmaceutical compounds. © 2011 American Chemical Society

Understanding of Relationship between Phospholipid Membrane Permeability and Self-Diffusion Coefficients of Some Drugs and Biologically Active Compounds in Model Solvents.

PubMed

Blokhina, Svetlana V; Volkova, Tatyana V; Golubev, Vasiliy A; Perlovich, German L

2017-10-02

In this work we measured self-diffusion coefficients of 5 drugs (aspirin, caffeine, ethionamide, salicylic acid, and paracetamol) and 11 biologically active compounds of similar structure in deuterated water and 1-octanol by NMR. It has been found that an increase in the van der Waals volume of the molecules of the studied substances result in reduction of their diffusion mobility in both solvents. The analysis of the experimental data showed the influence of chemical nature and structural isomerization of the molecules on the diffusion mobility. Apparent permeability coefficients of the studied compounds were determined using an artificial phospholipid membrane made of egg lecithin as a model of in vivo absorption. Distribution coefficients in 1-octanol/buffer pH 7.4 system were measured. For the first time the model of the passive diffusion through the phospholipid membrane was validated based on the experimental data. To this end, the passive diffusion was considered as an additive process of molecule passage through the aqueous boundary layer before the membrane and 1-octanol barrier simulating the lipid layer of the membrane.

Discovery of novel InhA reductase inhibitors: application of pharmacophore- and shape-based screening approach.

PubMed

Kumar, Uday Chandra; Bvs, Suneel Kumar; Mahmood, Shaik; D, Sriram; Kumar-Sahu, Prashanta; Pulakanam, Sridevi; Ballell, Lluís; Alvarez-Gomez, Daniel; Malik, Siddharth; Jarp, Sarma

2013-03-01

InhA is a promising and attractive target in antimycobacterial drug development. InhA is involved in the reduction of long-chain trans-2-enoyl-ACP in the type II fatty acid biosynthesis pathway of Mycobacterium tuberculosis. Recent studies have demonstrated that InhA is one of the targets for the second line antitubercular drug ethionamide. In the current study, we have generated quantitative pharmacophore models using known InhA inhibitors and validated using a large test set. The validated pharmacophore model was used as a query to screen an in-house database of 400,000 compounds and retrieved 25,000 hits. These hits were further ranked based on its shape and feature similarity with potent InhA inhibitor using rapid overlay of chemical structures (OpenEye™) and subsequent hits were subjected for docking. Based on the pharmacophore, rapid overlay of chemical structures model and docking interactions, 32 compounds with more than eight chemotypes were selected, purchased and assayed for InhA inhibitory activity. Out of the 32 compounds, 28 demonstrated 10-38% inhibition against InhA at 10 µM. Further optimization of these analogues is in progress and will update in due course.

Hepatic 3D spheroid models for the detection and study of compounds with cholestatic liability

PubMed Central

Hendriks, Delilah F. G.; Fredriksson Puigvert, Lisa; Messner, Simon; Mortiz, Wolfgang; Ingelman-Sundberg, Magnus

2016-01-01

Drug-induced cholestasis (DIC) is poorly understood and its preclinical prediction is mainly limited to assessing the compound’s potential to inhibit the bile salt export pump (BSEP). Here, we evaluated two 3D spheroid models, one from primary human hepatocytes (PHH) and one from HepaRG cells, for the detection of compounds with cholestatic liability. By repeatedly co-exposing both models to a set of compounds with different mechanisms of hepatotoxicity and a non-toxic concentrated bile acid (BA) mixture for 8 days we observed a selective synergistic toxicity of compounds known to cause cholestatic or mixed cholestatic/hepatocellular toxicity and the BA mixture compared to exposure to the compounds alone, a phenomenon that was more pronounced after extending the exposure time to 14 days. In contrast, no such synergism was observed after both 8 and 14 days of exposure to the BA mixture for compounds that cause non-cholestatic hepatotoxicity. Mechanisms behind the toxicity of the cholestatic compound chlorpromazine were accurately detected in both spheroid models, including intracellular BA accumulation, inhibition of ABCB11 expression and disruption of the F-actin cytoskeleton. Furthermore, the observed synergistic toxicity of chlorpromazine and BA was associated with increased oxidative stress and modulation of death receptor signalling. Combined, our results demonstrate that the hepatic spheroid models presented here can be used to detect and study compounds with cholestatic liability. PMID:27759057

Science of the science, drug discovery and artificial neural networks.

PubMed

Patel, Jigneshkumar

2013-03-01

Drug discovery process many times encounters complex problems, which may be difficult to solve by human intelligence. Artificial Neural Networks (ANNs) are one of the Artificial Intelligence (AI) technologies used for solving such complex problems. ANNs are widely used for primary virtual screening of compounds, quantitative structure activity relationship studies, receptor modeling, formulation development, pharmacokinetics and in all other processes involving complex mathematical modeling. Despite having such advanced technologies and enough understanding of biological systems, drug discovery is still a lengthy, expensive, difficult and inefficient process with low rate of new successful therapeutic discovery. In this paper, author has discussed the drug discovery science and ANN from very basic angle, which may be helpful to understand the application of ANN for drug discovery to improve efficiency.

Integrated Computational Approach for Virtual Hit Identification against Ebola Viral Proteins VP35 and VP40.

PubMed

Mirza, Muhammad Usman; Ikram, Nazia

2016-10-26

The Ebola virus (EBOV) has been recognised for nearly 40 years, with the most recent EBOV outbreak being in West Africa, where it created a humanitarian crisis. Mortalities reported up to 30 March 2016 totalled 11,307. However, up until now, EBOV drugs have been far from achieving regulatory (FDA) approval. It is therefore essential to identify parent compounds that have the potential to be developed into effective drugs. Studies on Ebola viral proteins have shown that some can elicit an immunological response in mice, and these are now considered essential components of a vaccine designed to protect against Ebola haemorrhagic fever. The current study focuses on chemoinformatic approaches to identify virtual hits against Ebola viral proteins (VP35 and VP40), including protein binding site prediction, drug-likeness, pharmacokinetic and pharmacodynamic properties, metabolic site prediction, and molecular docking. Retrospective validation was performed using a database of non-active compounds, and early enrichment of EBOV actives at different false positive rates was calculated. Homology modelling and subsequent superimposition of binding site residues on other strains of EBOV were carried out to check residual conformations, and hence to confirm the efficacy of potential compounds. As a mechanism for artefactual inhibition of proteins through non-specific compounds, virtual hits were assessed for their aggregator potential compared with previously reported aggregators. These systematic studies have indicated that a few compounds may be effective inhibitors of EBOV replication and therefore might have the potential to be developed as anti-EBOV drugs after subsequent testing and validation in experiments in vivo.

Investigation of the Anti-Prostate Cancer Properties of Marine-Derived Compounds

PubMed Central

Fan, Meiqi; Nath, Amit Kumar; Tang, Yujiao; Choi, Young-Jin; Debnath, Trishna; Choi, Eun-Ju

2018-01-01

This review focuses on marine compounds with anti-prostate cancer properties. Marine species are unique and have great potential for the discovery of anticancer drugs. Marine sources are taxonomically diverse and include bacteria, cyanobacteria, fungi, algae, and mangroves. Marine-derived compounds, including nucleotides, amides, quinones, polyethers, and peptides are biologically active compounds isolated from marine organisms such as sponges, ascidians, gorgonians, soft corals, and bryozoans, including those mentioned above. Several compound classes such as macrolides and alkaloids include drugs with anti-cancer mechanisms, such as antioxidants, anti-angiogenics, antiproliferatives, and apoptosis-inducing drugs. Despite the diversity of marine species, most marine-derived bioactive compounds have not yet been evaluated. Our objective is to explore marine compounds to identify new treatment strategies for prostate cancer. This review discusses chemically and pharmacologically diverse marine natural compounds and their sources in the context of prostate cancer drug treatment. PMID:29757237

Structure⁻Activity Relationship and Molecular Docking of Natural Product Library Reveal Chrysin as a Novel Dipeptidyl Peptidase-4 (DPP-4) Inhibitor: An Integrated In Silico and In Vitro Study.

PubMed

Kalhotra, Poonam; Chittepu, Veera C S R; Osorio-Revilla, Guillermo; Gallardo-Velázquez, Tzayhri

2018-06-06

Numerous studies indicate that diets with a variety of fruits and vegetables decrease the incidence of severe diseases, like diabetes, obesity, and cancer. Diets contain a variety of bioactive compounds, and their features, like diverge scaffolds, and structural complexity make them the most successful source of potential leads or hits in the process of drug discovery and drug development. Recently, novel serine protease dipeptidyl peptidase-4 (DPP-4) inhibitors played a role in the management of diabetes, obesity, and cancer. This study describes the development of field template, field-based qualitative structure⁻activity relationship (SAR) model demonstrating DPP-4 inhibitors of natural origin, and the same model is used to screen virtually focused food database composed of polyphenols as potential DPP-4 inhibitors. Compounds’ similarity to field template, and novelty score “high and very high”, were used as primary criteria to identify novel DPP-4 inhibitors. Molecular docking simulations were performed on the resulting natural compounds using FlexX algorithm. Finally, one natural compound, chrysin, was chosen to be evaluated experimentally to demonstrate the applicability of constructed SAR model. This study provides the molecular insights necessary in the discovery of new leads as DPP-4 inhibitors, to improve the potency of existing DPP-4 natural inhibitors.

21 CFR 170.45 - Fluorine-containing compounds.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 3 2014-04-01 2014-04-01 false Fluorine-containing compounds. 170.45 Section 170.45 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) FOOD ADDITIVES Specific Administrative Rulings and Decisions § 170.45 Fluorine-containing compounds...

Analyzing compound and project progress through multi-objective-based compound quality assessment.

PubMed

Nissink, J Willem M; Degorce, Sébastien

2013-05-01

Compound-quality scoring methods designed to evaluate multiple drug properties concurrently are useful to analyze and prioritize output from drug-design efforts. However, formalized multiparameter optimization approaches are not widely used in drug design. We rank molecules synthesized in drug-discovery projects using simple and aggregated desirability functions reflecting medicinal chemistry 'rules'. Our quality score deals transparently with missing data, a key requirement in drug-hunting projects where data availability is often limited. We further estimate confidence in the interpretation of such a compound-quality measure. Scores and associated confidences provide systematic insight in the quality of emerging chemical equity. Tracking quality of synthetic output over time yields valuable insight into the progress of drug-design teams, with potential applications in risk and resource management of a drug portfolio.

Guanidino-containing drugs in cancer chemotherapy: biochemical and clinical pharmacology.

PubMed

Ekelund, S; Nygren, P; Larsson, R

2001-05-15

The pharmacology and clinical application of three guanidino-containing compounds are reviewed in this commentary with special focus on a new member of this group of drugs, CHS 828 [N-(6-(4-chlorophenoxy)hexyl)-N'-cyano-N"-4-pyridylguanidine]. m-Iodobenzylguanidine (MIBG) and methylglyoxal bis(guanylhydrazone) (MGBG) have been extensively studied, preclinically as well as clinically, and have established use as anticancer agents. MIBG has structural similarities to the neurotransmitter, norepinephrine, and MGBG is a structural analog of the natural polyamine spermidine. CHS 828 is a pyridyl cyanoguanidine newly recognized as having cytotoxic effects when screening antihypertensive compounds. Apart from having the guanidino groups in common, there are many differences between these drugs in both structure and their mechanisms of action. However, they all inhibit mitochondrial function, a seemingly unique feature among chemotherapeutic drugs. In vitro in various cell lines and primary cultures of patient tumor cells and in vivo in various tumor models, CHS 828 has cytotoxic properties unlike any of the standard cytotoxic drugs with which it has been compared. Among these are non-cross-resistance to standard drugs and pronounced activity in tumor models acknowledged to be highly drug-resistant. Similar to MIBG, CHS 828 induces an early increase in extracellular acidification, due to stimulation of the glycolytic flux. Furthermore, ATP levels decrease, and the syntheses of DNA and protein are shut off after approximately 30 hr of exposure, indicating active cell death. CHS 828 is now in early clinical trials, the results of which are eagerly awaited.

Molecular Modeling and Evaluation of Novel Dibenzopyrrole Derivatives as Telomerase Inhibitors and Potential Drug for Cancer Therapy.

PubMed

Kalathiya, Umesh; Padariya, Monikaben; Baginski, Maciej

2014-01-01

During previous years, many studies on synthesis, as well as on anti-tumor, anti-inflammatory and anti-bacterial activities of the pyrazole derivatives have been described. Certain pyrazole derivatives exhibit important pharmacological activities and have proved to be useful template in drug research. Considering importance of pyrazole template, in current work the series of novel inhibitors were designed by replacing central ring of acridine with pyrazole ring. These heterocyclic compounds were proposed as a new potential base for telomerase inhibitors. Obtained dibenzopyrrole structure was used as a novel scaffold structure and extension of inhibitors was done by different functional groups. Docking of newly designed compounds in the telomerase active site (telomerase catalytic subunit TERT) was carried out. All dibenzopyrrole derivatives were evaluated by three docking programs: CDOCKER, Ligandfit docking (Scoring Functions) and AutoDock. Compound C_9g, C_9k and C_9l performed best in comparison to all designed inhibitors during the docking in all methods and in interaction analysis. Introduction of pyrazole and extension of dibenzopyrrole in compounds confirm that such compound may act as potential telomerase inhibitors.

The influence of alcohol, propylene glycol and 1,2-pentanediol on the permeability of hydrophilic model drug through excised pig skin.

PubMed

Duracher, Lucie; Blasco, Laurent; Hubaud, Jean-Claude; Vian, Laurence; Marti-Mestres, Gilberte

2009-06-05

Alcohol and glycol including 1,2-pentanediol, a new product in this field, were examined for their transdermal penetration enhancing in vitro properties using pig skin and caffeine as a model drug. In order to investigate a possible influence of these compounds, we followed diffusion from an aqueous solution with caffeine followed by a series of different vehicles, their compositions were: (1) in water as a control; (2) in propylene glycol/ethanol/water (25:25:48; v/v/v); (3) in 1,2-pentanediol/water (2.5:95.5, v/v); (4) in 1,2-pentanediol/water (5:93, v/v); in propylene glycol/water (5:93; v/v); and in ethanol/water (5:93; v/v). The stratum corneum/vehicle partition coefficients (K(m)), maximum flux (J), enhancement factor (EF), 24-h receptor concentration (Q(24h)) were determined and compared to control values (caffeine in water). Permeation was also expressed in percentage of the applied dose absorbed in the different compartments. In all test models, caffeine was released and penetrated into pig skin. The 1,2-pentanediol was presented as the most effective enhancer; with a low proportion of this compound (only 5%), caffeine penetrated the skin quicker and in a greater extent. While this compound showed promise as penetration enhancer, further study was required to determine its effectiveness with others drugs and its irritation potential.

Human neuron-astrocyte 3D co-culture-based assay for evaluation of neuroprotective compounds.

PubMed

Terrasso, Ana Paula; Silva, Ana Carina; Filipe, Augusto; Pedroso, Pedro; Ferreira, Ana Lúcia; Alves, Paula Marques; Brito, Catarina

Central nervous system drug development has registered high attrition rates, mainly due to the lack of efficacy of drug candidates, highlighting the low reliability of the models used in early-stage drug development and the need for new in vitro human cell-based models and assays to accurately identify and validate drug candidates. 3D human cell models can include different tissue cell types and represent the spatiotemporal context of the original tissue (co-cultures), allowing the establishment of biologically-relevant cell-cell and cell-extracellular matrix interactions. Nevertheless, exploitation of these 3D models for neuroprotection assessment has been limited due to the lack of data to validate such 3D co-culture approaches. In this work we combined a 3D human neuron-astrocyte co-culture with a cell viability endpoint for the implementation of a novel in vitro neuroprotection assay, over an oxidative insult. Neuroprotection assay robustness and specificity, and the applicability of Presto Blue, MTT and CytoTox-Glo viability assays to the 3D co-culture were evaluated. Presto Blue was the adequate endpoint as it is non-destructive and is a simpler and reliable assay. Semi-automation of the cell viability endpoint was performed, indicating that the assay setup is amenable to be transferred to automated screening platforms. Finally, the neuroprotection assay setup was applied to a series of 36 test compounds and several candidates with higher neuroprotective effect than the positive control, Idebenone, were identified. The robustness and simplicity of the implemented neuroprotection assay with the cell viability endpoint enables the use of more complex and reliable 3D in vitro cell models to identify and validate drug candidates. Copyright © 2016 Elsevier Inc. All rights reserved.

Animal models to guide clinical drug development in ADHD: lost in translation?

PubMed Central

Wickens, Jeffery R; Hyland, Brian I; Tripp, Gail

2011-01-01

We review strategies for developing animal models for examining and selecting compounds with potential therapeutic benefit in attention-deficit hyperactivity disorder (ADHD). ADHD is a behavioural disorder of unknown aetiology and pathophysiology. Current understanding suggests that genetic factors play an important role in the aetiology of ADHD. The involvement of dopaminergic and noradrenergic systems in the pathophysiology of ADHD is probable. We review the clinical features of ADHD including inattention, hyperactivity and impulsivity and how these are operationalized for laboratory study. Measures of temporal discounting (but not premature responding) appear to predict known drug effects well (treatment validity). Open-field measures of overactivity commonly used do not have treatment validity in human populations. A number of animal models have been proposed that simulate the symptoms of ADHD. The most commonly used are the spontaneously hypertensive rat (SHR) and the 6-hydroxydopamine-lesioned (6-OHDA) animals. To date, however, the SHR lacks treatment validity, and the effects of drugs on symptoms of impulsivity and inattention have not been studied extensively in 6-OHDA-lesioned animals. At the present stage of development, there are no in vivo models of proven effectiveness for examining and selecting compounds with potential therapeutic benefit in ADHD. However, temporal discounting is an emerging theme in theories of ADHD, and there is good evidence of increased value of delayed reward following treatment with stimulant drugs. Therefore, operant behaviour paradigms that measure the effects of drugs in situations of delayed reinforcement, whether in normal rats or selected models, show promise for the future. LINKED ARTICLES This article is part of a themed issue on Translational Neuropharmacology. To view the other articles in this issue visit http://dx.doi.org/10.1111/bph.2011.164.issue-4 PMID:21480864

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

PubMed

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

2009-01-01

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

Evaluation of the interaction and drug release from alpha,beta-polyaspartamide derivatives to a biomembrane model.

PubMed

Castelli, F; Messina, C; Craparo, E F; Mandracchia, D; Pitarresi, G

2005-01-01

This article reports on a comparative study on the ability of various polymers, containing hydrophilic and/or hydrophobic groups, to interact with a biomembrane model using the differential scanning calorimetry (DSC) technique. Multilamellar vesicles of mixed dimyristoylphosphatidylcholine (DMPC) and dimyristoylphosphatidic acid (DMPA) were chosen as a model of cell membranes. The investigated samples were a water soluble polymer, the alpha,beta-poly(N-2-hydroxyethyl)-DL-aspartamide (PHEA) and its derivatives partially functionalized with polyethylene glycol (PEG2000) to obtain PHEA-PEG2000, with hexadecylamine (C16) to obtain PHEA-C16, and with both compounds to obtain PHEA-PEG2000-C16. These polymers are potential candidates to prepare drug delivery systems. In particular, some samples give rise to polymeric micelles able to entrap hydrophobic drugs in an aqueous medium. The migration of drug molecules from these micelles to DMPC/DMPA vesicles also has been evaluated by DSC analysis, by using ketoprofen as a model drug.

Generation of ligand-based pharmacophore model and virtual screening for identification of novel tubulin inhibitors with potent anticancer activity.

PubMed

Chiang, Yi-Kun; Kuo, Ching-Chuan; Wu, Yu-Shan; Chen, Chung-Tong; Coumar, Mohane Selvaraj; Wu, Jian-Sung; Hsieh, Hsing-Pang; Chang, Chi-Yen; Jseng, Huan-Yi; Wu, Ming-Hsine; Leou, Jiun-Shyang; Song, Jen-Shin; Chang, Jang-Yang; Lyu, Ping-Chiang; Chao, Yu-Sheng; Wu, Su-Ying

2009-07-23

A pharmacophore model, Hypo1, was built on the basis of 21 training-set indole compounds with varying levels of antiproliferative activity. Hypo1 possessed important chemical features required for the inhibitors and demonstrated good predictive ability for biological activity, with high correlation coefficients of 0.96 and 0.89 for the training-set and test-set compounds, respectively. Further utilization of the Hypo1 pharmacophore model to screen chemical database in silico led to the identification of four compounds with antiproliferative activity. Among these four compounds, 43 showed potent antiproliferative activity against various cancer cell lines with the strongest inhibition on the proliferation of KB cells (IC(50) = 187 nM). Further biological characterization revealed that 43 effectively inhibited tubulin polymerization and significantly induced cell cycle arrest in G(2)-M phase. In addition, 43 also showed the in vivo-like anticancer effects. To our knowledge, 43 is the most potent antiproliferative compound with antitubulin activity discovered by computer-aided drug design. The chemical novelty of 43 and its anticancer activities make this compound worthy of further lead optimization.

Chromatographic behaviour predicts the ability of potential nootropics to permeate the blood-brain barrier.

PubMed

Farsa, Oldřich

2013-01-01

The log BB parameter is the logarithm of the ratio of a compound's equilibrium concentrations in the brain tissue versus the blood plasma. This parameter is a useful descriptor in assessing the ability of a compound to permeate the blood-brain barrier. The aim of this study was to develop a Hansch-type linear regression QSAR model that correlates the parameter log BB and the retention time of drugs and other organic compounds on a reversed-phase HPLC containing an embedded amide moiety. The retention time was expressed by the capacity factor log k'. The second aim was to estimate the brain's absorption of 2-(azacycloalkyl)acetamidophenoxyacetic acids, which are analogues of piracetam, nefiracetam, and meclofenoxate. Notably, these acids may be novel nootropics. Two simple regression models that relate log BB and log k' were developed from an assay performed using a reversed-phase HPLC that contained an embedded amide moiety. Both the quadratic and linear models yielded statistical parameters comparable to previously published models of log BB dependence on various structural characteristics. The models predict that four members of the substituted phenoxyacetic acid series have a strong chance of permeating the barrier and being absorbed in the brain. The results of this study show that a reversed-phase HPLC system containing an embedded amide moiety is a functional in vitro surrogate of the blood-brain barrier. These results suggest that racetam-type nootropic drugs containing a carboxylic moiety could be more poorly absorbed than analogues devoid of the carboxyl group, especially if the compounds penetrate the barrier by a simple diffusion mechanism.

From the Test Tube to the Treatment Room: Fundamentals of Boron-containing Compounds and their Relevance to Dermatology.

PubMed

Del Rosso, James Q; Plattner, Jacob J

2014-02-01

The development of new drug classes and novel molecules that are brought to the marketplace has been a formidable challenge, especially for dermatologic drugs. The relative absence of new classes of antimicrobial agents is also readily apparent. Several barriers account for slow drug development, including regulatory changes, added study requirements, commercial pressures to bring drugs to market quickly by developing new generations of established compounds, and the greater potential for failure and higher financial risk when researching new drug classes. In addition, the return on investment is usually much lower with dermatologic drugs as compared to the potential revenue from "blockbuster" drugs for cardiovascular or gastrointestinal disease, hypercholesterolemia, and mood disorders. Nevertheless, some researchers are investigating new therapeutic platforms, one of which is boron-containing compounds. Boron-containing compounds offer a wide variety of potential applications in dermatology due to their unique physical and chemical properties, with several in formal phases of development. Tavaborole, a benzoxaborole compound, has been submitted to the United States Food and Drug Administration for approval for treatment of onychomycosis. This article provides a thorough overview of the history of boron-based compounds in medicine, their scientific rationale, physiochemical and pharmacologic properties, and modes of actions including therapeutic targets. A section dedicated to boron-based compounds in development for treatment of various skin disorders is also included.

Design and synthesis of some new 2,3'-bipyridine-5-carbonitriles as potential anti-inflammatory/antimicrobial agents.

PubMed

Elzahhar, Perihan A; Elkazaz, Salwa; Soliman, Raafat; El-Tombary, Alaa A; Shaltout, Hossam A; El-Ashmawy, Ibrahim M; Abdel Wahab, Abeer E; El-Hawash, Soad A

2017-08-01

Inflammation may cause accumulation of fluid in the injured area, which may promote bacterial growth. Other reports disclosed that non-steroidal anti-inflammatory drugs may enhance progression of bacterial infection. This work describes synthesis of new series of 2,3'-bipyridine-5-carbonitriles as structural analogs of etoricoxib, linked at position-6 to variously substituted thio or oxo moieties. Biological screening results revealed that compounds 2b, 4b, 7e and 8 showed significant acute and chronic AI activities and broad spectrum of antimicrobial activity. In addition, similarity ensemble approach was applied to predict potential biological targets of the tested compounds. Then, pharmacophore modeling study was employed to determine the most important structural parameters controlling bioactivity. Moreover, title compounds showed physicochemical properties within those considered adequate for drug candidates. This study explored the potential of such series of compounds as structural leads for further modification to develop a new class of dual AI-antimicrobial agents.

Modern drug discovery technologies: opportunities and challenges in lead discovery.

PubMed

Guido, Rafael V C; Oliva, Glaucius; Andricopulo, Adriano D

2011-12-01

The identification of promising hits and the generation of high quality leads are crucial steps in the early stages of drug discovery projects. The definition and assessment of both chemical and biological space have revitalized the screening process model and emphasized the importance of exploring the intrinsic complementary nature of classical and modern methods in drug research. In this context, the widespread use of combinatorial chemistry and sophisticated screening methods for the discovery of lead compounds has created a large demand for small organic molecules that act on specific drug targets. Modern drug discovery involves the employment of a wide variety of technologies and expertise in multidisciplinary research teams. The synergistic effects between experimental and computational approaches on the selection and optimization of bioactive compounds emphasize the importance of the integration of advanced technologies in drug discovery programs. These technologies (VS, HTS, SBDD, LBDD, QSAR, and so on) are complementary in the sense that they have mutual goals, thereby the combination of both empirical and in silico efforts is feasible at many different levels of lead optimization and new chemical entity (NCE) discovery. This paper provides a brief perspective on the evolution and use of key drug design technologies, highlighting opportunities and challenges.

Synthesis, biological assessment and molecular modeling of new multipotent MAO and cholinesterase inhibitors as potential drugs for the treatment of Alzheimer's disease.

PubMed

Samadi, Abdelouahid; Chioua, Mourad; Bolea, Irene; de Los Ríos, Cristóbal; Iriepa, Isabel; Moraleda, Ignacio; Bastida, Agatha; Esteban, Gerard; Unzeta, Mercedes; Gálvez, Enrique; Marco-Contelles, José

2011-09-01

The synthesis, biological evaluation and molecular modeling of new multipotent inhibitors of type I and type II, able to simultaneously inhibit monoamine oxidases (MAO) as well as acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE), is described. Compounds of type I were prepared by sequential reaction of 2,6-dichloro-4-phenylpyridine-3,5-dicarbonitrile (14) [or 2,6-dichloropyridine-3,5-dicarbonitrile (15)] with prop-2-yn-1-amine (or N-methylprop-2-yn-1-amine) and 2-(1-benzyl-piperidin-4-yl)alkylamines 22-25. Compounds of type II were prepared by Friedländer type reaction of 6-amino-5-formyl-2-(methyl(prop-2-yn-1-yl)amino)nicotinonitriles 32 and 33 with 4-(1-benzylpiperidin-4-yl)butan-2-one (31). The biological evaluation of molecules 1-11 showed that most of these compounds are potent, in the nanomolar range, and selective AChEI, with moderate and equipotent selectivity for MAO-A and MAO-B inhibition. Kinetic studies of compound 8 proved that this is a EeAChE mixed type inhibitor (IC(50) = 16 ± 2; Ki = 12 ± 3 nM). Molecular modeling investigation on compound 8 confirmed its dual AChE inhibitory profile, binding simultaneously at the catalytic active site (CAS) and at the peripheric anionic site (PAS). In overall, compound 11, as a potent and selective dual AChEI, showing a moderate and selective MAO-A inhibitory profile, can be considered as an attractive multipotent drug for further development on two key pharmacological targets playing key roles in the therapy of Alzheimer's disease. Copyright © 2011 Elsevier Masson SAS. All rights reserved.

Tricyclic antidepressants, quinacrine and a novel, synthetic chimera thereof clear prions by destabilizing detergent-resistant membrane compartments.

PubMed

Klingenstein, Ralf; Löber, Stefan; Kujala, Pekka; Godsave, Susan; Leliveld, S Rutger; Gmeiner, Peter; Peters, Peter J; Korth, Carsten

2006-08-01

Prion diseases are invariably fatal, neurodegenerative diseases transmitted by an infectious agent, PrPSc, a pathogenic, conformational isoform of the normal prion protein (PrPC). Heterocyclic compounds such as acridine derivatives like quinacrine abolish prion infectivity in a cell culture model of prion disease. Here, we report that these compounds execute their antiprion activity by redistributing cholesterol from the plasma membrane to intracellular compartments, thereby destabilizing membrane domains. Our findings are supported by the fact that structurally unrelated compounds with known cholesterol-redistributing effects - U18666A, amiodarone, and progesterone - also possessed high antiprion potency. We show that tricyclic antidepressants (e.g. desipramine), another class of heterocyclic compounds, displayed structure-dependent antiprion effects and enhanced the antiprion effects of quinacrine, allowing lower doses of both drugs to be used in combination. Treatment of ScN2a cells with quinacrine or desipramine induced different ultrastructural and morphological changes in endosomal compartments. We synthesized a novel drug from quinacrine and desipramine, termed quinpramine, that led to a fivefold increase in antiprion activity compared to quinacrine with an EC50 of 85 nm. Furthermore, simvastatin, an inhibitor of cholesterol biosynthesis, acted synergistically with both heterocyclic compounds to clear PrPSc. Our data suggest that a cocktail of drugs targeting the lipid metabolism that controls PrP conversion may be the most efficient in treating Creutzfeldt-Jakob disease.

Identification of potential therapeutic compounds for Parkinson's disease using Drosophila and human cell models.

PubMed

Sanz, Francisco José; Solana-Manrique, Cristina; Muñoz-Soriano, Verónica; Calap-Quintana, Pablo; Moltó, María Dolores; Paricio, Nuria

2017-07-01

Parkinson's disease (PD) is the second most common neurodegenerative disorder after Alzheimer's disease. It is caused by a loss of dopaminergic neurons in the substantia nigra pars compacta, leading to a decrease in dopamine levels in the striatum and thus producing movement impairment. Major physiological causes of neurodegeneration in PD are oxidative stress (OS) and mitochondrial dysfunction; these pathophysiological changes can be caused by both genetic and environmental factors. Although most PD cases are sporadic, it has been shown that 5-10% of them are familial forms caused by mutations in certain genes. One of these genes is the DJ-1 oncogene, which is involved in an early-onset recessive PD form. Currently, PD is an incurable disease for which existing therapies are not sufficiently effective to counteract or delay the progression of the disease. Therefore, the discovery of alternative drugs for the treatment of PD is essential. In this study we used a Drosophila PD model to identify candidate compounds with therapeutic potential for this disease. These flies carry a loss-of-function mutation in the DJ-1β gene, the Drosophila ortholog of human DJ-1, and show locomotor defects reflected by a reduced climbing ability. A pilot modifier chemical screen was performed, and several candidate compounds were identified based on their ability to improve locomotor activity of PD model flies. We demonstrated that some of them were also able to reduce OS levels in these flies. To validate the compounds identified in the Drosophila screen, a human cell PD model was generated by knocking down DJ-1 function in SH-SY5Y neuroblastoma cells. Our results showed that some of the compounds were also able to increase the viability of the DJ-1-deficient cells subjected to OS, thus supporting the use of Drosophila for PD drug discovery. Interestingly, some of them have been previously proposed as alternative therapies for PD or tested in clinical trials and others are first suggested in this study as potential drugs for the treatment of this disease. Copyright © 2017 Elsevier Inc. All rights reserved.

Identification of anthranilamide derivatives as potential factor Xa inhibitors: drug design, synthesis and biological evaluation.

PubMed

Xing, Junhao; Yang, Lingyun; Li, Hui; Li, Qing; Zhao, Leilei; Wang, Xinning; Zhang, Yuan; Zhou, Muxing; Zhou, Jinpei; Zhang, Huibin

2015-05-05

The coagulation enzyme factor Xa (fXa) plays a crucial role in the blood coagulation cascade. In this study, three-dimensional fragment based drug design (FBDD) combined with structure-based pharmacophore (SBP) model and structural consensus docking were employed to identify novel fXa inhibitors. After a multi-stage virtual screening (VS) workflow, two hit compounds 3780 and 319 having persistent high performance were identified. Then, these two hit compounds and several analogs were synthesized and screened for in-vitro inhibition of fXa. The experimental data showed that most of the designed compounds displayed significant in vitro potency against fXa. Among them, compound 9b displayed the greatest in vitro potency against fXa with the IC50 value of 23 nM and excellent selectivity versus thrombin (IC50 = 40 μM). Moreover, the prolongation of the prothrombin time (PT) was measured for compound 9b to evaluate its in vitro anticoagulant activity. As a result, compound 9b exhibited pronounced anticoagulant activity with the 2 × PT value of 8.7 μM. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Quantum probability ranking principle for ligand-based virtual screening.

PubMed

Al-Dabbagh, Mohammed Mumtaz; Salim, Naomie; Himmat, Mubarak; Ahmed, Ali; Saeed, Faisal

2017-04-01

Chemical libraries contain thousands of compounds that need screening, which increases the need for computational methods that can rank or prioritize compounds. The tools of virtual screening are widely exploited to enhance the cost effectiveness of lead drug discovery programs by ranking chemical compounds databases in decreasing probability of biological activity based upon probability ranking principle (PRP). In this paper, we developed a novel ranking approach for molecular compounds inspired by quantum mechanics, called quantum probability ranking principle (QPRP). The QPRP ranking criteria would make an attempt to draw an analogy between the physical experiment and molecular structure ranking process for 2D fingerprints in ligand based virtual screening (LBVS). The development of QPRP criteria in LBVS has employed the concepts of quantum at three different levels, firstly at representation level, this model makes an effort to develop a new framework of molecular representation by connecting the molecular compounds with mathematical quantum space. Secondly, estimate the similarity between chemical libraries and references based on quantum-based similarity searching method. Finally, rank the molecules using QPRP approach. Simulated virtual screening experiments with MDL drug data report (MDDR) data sets showed that QPRP outperformed the classical ranking principle (PRP) for molecular chemical compounds.

Quantum probability ranking principle for ligand-based virtual screening

NASA Astrophysics Data System (ADS)

Al-Dabbagh, Mohammed Mumtaz; Salim, Naomie; Himmat, Mubarak; Ahmed, Ali; Saeed, Faisal

2017-04-01

Chemical libraries contain thousands of compounds that need screening, which increases the need for computational methods that can rank or prioritize compounds. The tools of virtual screening are widely exploited to enhance the cost effectiveness of lead drug discovery programs by ranking chemical compounds databases in decreasing probability of biological activity based upon probability ranking principle (PRP). In this paper, we developed a novel ranking approach for molecular compounds inspired by quantum mechanics, called quantum probability ranking principle (QPRP). The QPRP ranking criteria would make an attempt to draw an analogy between the physical experiment and molecular structure ranking process for 2D fingerprints in ligand based virtual screening (LBVS). The development of QPRP criteria in LBVS has employed the concepts of quantum at three different levels, firstly at representation level, this model makes an effort to develop a new framework of molecular representation by connecting the molecular compounds with mathematical quantum space. Secondly, estimate the similarity between chemical libraries and references based on quantum-based similarity searching method. Finally, rank the molecules using QPRP approach. Simulated virtual screening experiments with MDL drug data report (MDDR) data sets showed that QPRP outperformed the classical ranking principle (PRP) for molecular chemical compounds.

Therapeutic Phytogenic Compounds for Obesity and Diabetes

PubMed Central

Jung, Hee Soong; Lim, Yun; Kim, Eun-Kyoung

2014-01-01

Natural compounds have been used to develop drugs for many decades. Vast diversities and minimum side effects make natural compounds a good source for drug development. However, the composition and concentrations of natural compounds can vary. Despite this inconsistency, half of the Food and Drug Administration (FDA)-approved pharmaceuticals are natural compounds or their derivatives. Therefore, it is essential to continuously investigate natural compounds as sources of new pharmaceuticals. This review provides comprehensive information and analysis on natural compounds from plants (phytogenic compounds) that may serve as anti-obesity and/or anti-diabetes therapeutics. Our growing understanding and further exploration of the mechanisms of action of the phytogenic compounds may afford opportunities for development of therapeutic interventions in metabolic diseases. PMID:25421245

Illicit and pharmaceutical drug consumption estimated via wastewater analysis. Part A: chemical analysis and drug use estimates.

PubMed

Baker, David R; Barron, Leon; Kasprzyk-Hordern, Barbara

2014-07-15

This paper presents, for the first time, community-wide estimation of drug and pharmaceuticals consumption in England using wastewater analysis and a large number of compounds. Among groups of compounds studied were: stimulants, hallucinogens and their metabolites, opioids, morphine derivatives, benzodiazepines, antidepressants and others. Obtained results showed the usefulness of wastewater analysis in order to provide estimates of local community drug consumption. It is noticeable that where target compounds could be compared to NHS prescription statistics, good comparisons were apparent between the two sets of data. These compounds include oxycodone, dihydrocodeine, methadone, tramadol, temazepam and diazepam. Whereas, discrepancies were observed for propoxyphene, codeine, dosulepin and venlafaxine (over-estimations in each case except codeine). Potential reasons for discrepancies include: sales of drugs sold without prescription and not included within NHS data, abuse of a drug with the compound trafficked through illegal sources, different consumption patterns in different areas, direct disposal leading to over estimations when using parent compound as the drug target residue and excretion factors not being representative of the local community. It is noticeable that using a metabolite (and not a parent drug) as a biomarker leads to higher certainty of obtained estimates. With regard to illicit drugs, consistent and logical results were reported. Monitoring of these compounds over a one week period highlighted the expected recreational use of many of these drugs (e.g. cocaine and MDMA) and the more consistent use of others (e.g. methadone). Copyright © 2014 Elsevier B.V. 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.

FAF-Drugs3: a web server for compound property calculation and chemical library design

PubMed Central

Lagorce, David; Sperandio, Olivier; Baell, Jonathan B.; Miteva, Maria A.; Villoutreix, Bruno O.

2015-01-01

Drug attrition late in preclinical or clinical development is a serious economic problem in the field of drug discovery. These problems can be linked, in part, to the quality of the compound collections used during the hit generation stage and to the selection of compounds undergoing optimization. Here, we present FAF-Drugs3, a web server that can be used for drug discovery and chemical biology projects to help in preparing compound libraries and to assist decision-making during the hit selection/lead optimization phase. Since it was first described in 2006, FAF-Drugs has been significantly modified. The tool now applies an enhanced structure curation procedure, can filter or analyze molecules with user-defined or eight predefined physicochemical filters as well as with several simple ADMET (absorption, distribution, metabolism, excretion and toxicity) rules. In addition, compounds can be filtered using an updated list of 154 hand-curated structural alerts while Pan Assay Interference compounds (PAINS) and other, generally unwanted groups are also investigated. FAF-Drugs3 offers access to user-friendly html result pages and the possibility to download all computed data. The server requires as input an SDF file of the compounds; it is open to all users and can be accessed without registration at http://fafdrugs3.mti.univ-paris-diderot.fr. PMID:25883137

Advanced Cell Culture Techniques for Cancer Drug Discovery

PubMed Central

Lovitt, Carrie J.; Shelper, Todd B.; Avery, Vicky M.

2014-01-01

Human cancer cell lines are an integral part of drug discovery practices. However, modeling the complexity of cancer utilizing these cell lines on standard plastic substrata, does not accurately represent the tumor microenvironment. Research into developing advanced tumor cell culture models in a three-dimensional (3D) architecture that more prescisely characterizes the disease state have been undertaken by a number of laboratories around the world. These 3D cell culture models are particularly beneficial for investigating mechanistic processes and drug resistance in tumor cells. In addition, a range of molecular mechanisms deconstructed by studying cancer cells in 3D models suggest that tumor cells cultured in two-dimensional monolayer conditions do not respond to cancer therapeutics/compounds in a similar manner. Recent studies have demonstrated the potential of utilizing 3D cell culture models in drug discovery programs; however, it is evident that further research is required for the development of more complex models that incorporate the majority of the cellular and physical properties of a tumor. PMID:24887773

Advanced cell culture techniques for cancer drug discovery.

PubMed

Lovitt, Carrie J; Shelper, Todd B; Avery, Vicky M

2014-05-30

Human cancer cell lines are an integral part of drug discovery practices. However, modeling the complexity of cancer utilizing these cell lines on standard plastic substrata, does not accurately represent the tumor microenvironment. Research into developing advanced tumor cell culture models in a three-dimensional (3D) architecture that more prescisely characterizes the disease state have been undertaken by a number of laboratories around the world. These 3D cell culture models are particularly beneficial for investigating mechanistic processes and drug resistance in tumor cells. In addition, a range of molecular mechanisms deconstructed by studying cancer cells in 3D models suggest that tumor cells cultured in two-dimensional monolayer conditions do not respond to cancer therapeutics/compounds in a similar manner. Recent studies have demonstrated the potential of utilizing 3D cell culture models in drug discovery programs; however, it is evident that further research is required for the development of more complex models that incorporate the majority of the cellular and physical properties of a tumor.

Aromatase inhibitory activity of 1,4-naphthoquinone derivatives and QSAR study

PubMed Central

Prachayasittikul, Veda; Pingaew, Ratchanok; Worachartcheewan, Apilak; Sitthimonchai, Somkid; Nantasenamat, Chanin; Prachayasittikul, Supaluk; Ruchirawat, Somsak; Prachayasittikul, Virapong

2017-01-01

A series of 2-amino(chloro)-3-chloro-1,4-naphthoquinone derivatives (1-11) were investigated for their aromatase inhibitory activities. 1,4-Naphthoquinones 1 and 4 were found to be the most potent compounds affording IC50 values 5.2 times lower than the reference drug, ketoconazole. A quantitative structure-activity relationship (QSAR) model provided good predictive performance (R2CV = 0.9783 and RMSECV = 0.0748) and indicated mass (Mor04m and H8m), electronegativity (Mor08e), van der Waals volume (G1v) and structural information content index (SIC2) descriptors as key descriptors governing the activity. To investigate the effects of structural modifications on aromatase inhibitory activity, the model was employed to predict the activities of an additional set of 39 structurally modified compounds constructed in silico. The prediction suggested that the 2,3-disubstitution of 1,4-naphthoquinone ring with halogen atoms (i.e., Br, I and F) is the most effective modification for potent activity (1a, 1b and 1c). Importantly, compound 1b was predicted to be more potent than its parent compound 1 (11.90-fold) and the reference drug, letrozole (1.03-fold). The study suggests the 1,4-naphthoquinone derivatives as promising compounds to be further developed as a novel class of aromatase inhibitors. PMID:28827987

Immune Homeostasis: Effects of Chinese Herbal Formulae and Herb-Derived Compounds on Allergic Asthma in Different Experimental Models.

PubMed

Liu, Lu; Wang, Lin-Peng; He, Shan; Ma, Yan

2018-05-01

Allergic asthma is thought to arise from an imbalance of immune regulation, which is characterized by the production of large quantities of IgE antibodies by B cells and a decrease of the interferon-γ/interleukin-4 (Th1/Th2) ratio. Certain immunomodulatory components and Chinese herbal formulae have been used in traditional herbal medicine for thousands of years. However, there are few studies performing evidence-based Chinese medicine (CM) research on the mechanisms and effificacy of these drugs in allergic asthma. This review aims to explore the roles of Chinese herbal formulae and herb-derived compounds in experimental research models of allergic asthma. We screened published modern CM research results on the experimental effects of Chinese herbal formulae and herb-derived bioactive compounds for allergic asthma and their possible underlying mechanisms in English language articles from the PubMed and the Google Scholar databases with the keywords allergic asthma, experimental model and Chinese herbal medicine. We found 22 Chinese herb species and 31 herb-derived anti-asthmatic compounds as well as 12 Chinese herbal formulae which showed a reduction of airway hyperresponsiveness, allergen-specifific immunoglobulin E, inflflammatory cell infifiltration and a regulation of Th1 and Th2 cytokines in vivo, in vitro and ex vivo, respectively. Chinese herbal formulae and herbderived bioactive compounds exhibit immunomodulatory, anti-inflflammatory and anti-asthma activities in different experimental models and their various mechanisms of action are being investigated in modern CM research with genomics, proteomics and metabolomics technologies, which will lead to a new era in the development of new drug discovery for allergic asthma in CM.

Novel platinum compounds and nanoparticles as anticancer agents.

PubMed

Sarkar, Arindam

2018-01-01

Since the approval of cisplatin in 1979, platinum-based drugs have been regularly used in cancer chemotherapy as a first-line treatment or with the combination of other nonplatinum drugs. Subsequent approval of second- and third-generation drugs such as carboplatin and oxaliplatin respectively, has widened the therapeutic achievement of platinum compounds. There are few other platinum drugs approved recently and many other new drugs as well as the formulations of the old ones are going through clinical trials now. Considering the astonishing achievement of these drugs, analyses on the overall scenario of the patent applications on platinum compounds have become the priority to the scientific community. This review summarizes the published patent applications on the novel platinum anticancer compounds from 2012 to 2017 (August).

Antimalarial drug discovery: screening of Brazilian medicinal plants and purified compounds.

PubMed

Krettli, Antoniana Ursine

2009-02-01

Malaria is the most important parasitic disease and its control depends on specific chemotherapy, now complicated by Plasmodium falciparum that has become resistant to most commonly available antimalarials. Treatment of the disease requires quinine or drug combinations of artemisinin derivatives and other antimalarials. Further drug resistance is expected. New active compounds need to be discovered. To find new antimalarials from medicinal and randomly collected plants, crude extracts are screened against P. falciparum in cultures and in malaria animal models, following bioassays of purified fractions, and cytotoxicity tests. For antimalarial research, screening medicinal plants is more efficient than screening randomly chosen plants. Biomonitored fractionation allows selection of new active molecules identified as potential antimalarials in multidisciplinary projects in Brazil; no new molecule is available for human testing. The advantages of projects based on ethnopharmacology are discussed.

Prediction of blood-brain barrier permeation of α-adrenergic and imidazoline receptor ligands using PAMPA technique and quantitative-structure permeability relationship analysis.

PubMed

Vucicevic, Jelica; Nikolic, Katarina; Dobričić, Vladimir; Agbaba, Danica

2015-02-20

Imidazoline receptor ligands are a numerous family of biologically active compounds known to produce central hypotensive effect by interaction with both α2-adrenoreceptors (α2-AR) and imidazoline receptors (IRs). Recent hypotheses connect those ligands with several neurological disorders. Therefore some IRs ligands are examined as novel centrally acting antihypertensives and drug candidates for treatment of various neurological diseases. Effective Blood-Brain Barrier (BBB) permeability (P(e)) of 18 IRs/α-ARs ligands and 22 Central Nervous System (CNS) drugs was experimentally determined using Parallel Artificial Membrane Permeability Assay (PAMPA) and studied by the Quantitative-Structure-Permeability Relationship (QSPR) methodology. The dominant molecules/cations species of compounds have been calculated at pH = 7.4. The analyzed ligands were optimized using Density Functional Theory (B3LYP/6-31G(d,p)) included in ChemBio3D Ultra 13.0 program and molecule descriptors for optimized compounds were calculated using ChemBio3D Ultra 13.0, Dragon 6.0 and ADMET predictor 6.5 software. Effective permeability of compounds was used as dependent variable (Y), while calculated molecular parametres were used as independent variables (X) in the QSPR study. SIMCA P+ 12.0 was used for Partial Least Square (PLS) analysis, while the stepwise Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) modeling were performed using STASTICA Neural Networks 4.0. Predictive potential of the formed models was confirmed by Leave-One-Out Cross- and external-validation and the most reliable models were selected. The descriptors that are important for model building are identified as well as their influence on BBB permeability. Results of the QSPR studies could be used as time and cost efficient screening tools for evaluation of BBB permeation of novel α-adrenergic/imidazoline receptor ligands, as promising drug candidates for treatment of hypertension or neurological diseases. Copyright © 2014 Elsevier B.V. All rights reserved.

Identification of new candidate drugs for lung cancer using chemical-chemical interactions, chemical-protein interactions and a K-means clustering algorithm.

PubMed

Lu, Jing; Chen, Lei; Yin, Jun; Huang, Tao; Bi, Yi; Kong, Xiangyin; Zheng, Mingyue; Cai, Yu-Dong

2016-01-01

Lung cancer, characterized by uncontrolled cell growth in the lung tissue, is the leading cause of global cancer deaths. Until now, effective treatment of this disease is limited. Many synthetic compounds have emerged with the advancement of combinatorial chemistry. Identification of effective lung cancer candidate drug compounds among them is a great challenge. Thus, it is necessary to build effective computational methods that can assist us in selecting for potential lung cancer drug compounds. In this study, a computational method was proposed to tackle this problem. The chemical-chemical interactions and chemical-protein interactions were utilized to select candidate drug compounds that have close associations with approved lung cancer drugs and lung cancer-related genes. A permutation test and K-means clustering algorithm were employed to exclude candidate drugs with low possibilities to treat lung cancer. The final analysis suggests that the remaining drug compounds have potential anti-lung cancer activities and most of them have structural dissimilarity with approved drugs for lung cancer.

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

PubMed Central

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

2014-01-01

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

The use of compound topical anesthetics: a review.

PubMed

Kravitz, Neal D

2007-10-01

The author reviewed the history of, federal regulations regarding, risks of and adverse drug reactions of five compound topical anesthetics: tetracaine, adrenaline/epinephrine and cocaine (TAC); lidocaine, adrenaline/epinephrine and tetracaine (LET); lidocaine, tetracaine and phenylephrine (TAC 20 percent Alternate); lidocaine, prilocaine and tetracaine (Profound); and lidocaine, prilocaine, tetracaine and phenylephrine with thickeners (Profound PET). The author reviewed clinical trials, case reports, descriptive articles, and U.S. Food and Drug Administration (FDA) regulations and recent public advisory warnings regarding the federal approval of and risks associated with the use of compound topical anesthetics. Compound topical anesthetics are neither FDA-regulated nor -unregulated. Some compounding pharmacies bypass the new FDA drug approval process, which is based on reliable scientific data and ensures that a marketed drug is safe, effective, properly manufactured and accurately labeled. Two deaths have been attributed to the lay use of compound topical anesthetics. In response, the FDA has announced the strengthening of its efforts against unapproved drug products. Compound topical anesthetics may be an effective alternative to local infiltration for some minimally invasive dental procedures; however, legitimate concerns exist in regard to their safety. Until they become federally regulated, compound topical anesthetics remain unapproved drug products whose benefits may not outweigh their risks for dental patients.

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.

Functional Coupling of Human Microphysiology Systems: Intestine, Liver, Kidney Proximal Tubule, Blood-Brain Barrier and Skeletal Muscle

PubMed Central

Vernetti, Lawrence; Gough, Albert; Baetz, Nicholas; Blutt, Sarah; Broughman, James R.; Brown, Jacquelyn A.; Foulke-Abel, Jennifer; Hasan, Nesrin; In, Julie; Kelly, Edward; Kovbasnjuk, Olga; Repper, Jonathan; Senutovitch, Nina; Stabb, Janet; Yeung, Catherine; Zachos, Nick C.; Donowitz, Mark; Estes, Mary; Himmelfarb, Jonathan; Truskey, George; Wikswo, John P.; Taylor, D. Lansing

2017-01-01

Organ interactions resulting from drug, metabolite or xenobiotic transport between organs are key components of human metabolism that impact therapeutic action and toxic side effects. Preclinical animal testing often fails to predict adverse outcomes arising from sequential, multi-organ metabolism of drugs and xenobiotics. Human microphysiological systems (MPS) can model these interactions and are predicted to dramatically improve the efficiency of the drug development process. In this study, five human MPS models were evaluated for functional coupling, defined as the determination of organ interactions via an in vivo-like sequential, organ-to-organ transfer of media. MPS models representing the major absorption, metabolism and clearance organs (the jejunum, liver and kidney) were evaluated, along with skeletal muscle and neurovascular models. Three compounds were evaluated for organ-specific processing: terfenadine for pharmacokinetics (PK) and toxicity; trimethylamine (TMA) as a potentially toxic microbiome metabolite; and vitamin D3. We show that the organ-specific processing of these compounds was consistent with clinical data, and discovered that trimethylamine-N-oxide (TMAO) crosses the blood-brain barrier. These studies demonstrate the potential of human MPS for multi-organ toxicity and absorption, distribution, metabolism and excretion (ADME), provide guidance for physically coupling MPS, and offer an approach to coupling MPS with distinct media and perfusion requirements. PMID:28176881

Experimental and computational prediction of glass transition temperature of drugs.

PubMed

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

2014-12-22

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

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

PubMed Central

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

2016-01-01

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

Human topoisomerase I poisoning: docking protoberberines into a structure-based binding site model

NASA Astrophysics Data System (ADS)

Kettmann, Viktor; Košt'álová, Daniela; Höltje, Hans-Dieter

2004-12-01

Using the X-ray crystal structure of the human topoisomerase I (top1) - DNA cleavable complex and the Sybyl software package, we have developed a general model for the ternary cleavable complex formed with four protoberberine alkaloids differing in the substitution on the terminal phenyl rings and covering a broad range of the top1-poisoning activities. This model has the drug intercalated with its planar chromophore between the -1 and +1 base pairs flanking the cleavage site, with the nonplanar portion pointing into the minor groove. The ternary complexes were geometry-optimized and relative interaction energies, computed by using the Tripos force field, were found to rank in correct order the biological potency of the compounds; in addition, the model is also consistent with the top1-poisoning inactivity of berberine, a major prototype of the protoberberine alkaloids. The model might serve as a rational basis for elaboration of the most active compound as a lead structure, in order to develop more potent top1 poisons as next generation anti-cancer drugs.

Bond-based bilinear indices for computational discovery of novel trypanosomicidal drug-like compounds through virtual screening.

PubMed

Castillo-Garit, Juan Alberto; del Toro-Cortés, Oremia; Vega, Maria C; Rolón, Miriam; Rojas de Arias, Antonieta; Casañola-Martin, Gerardo M; Escario, José A; Gómez-Barrio, Alicia; Marrero-Ponce, Yovani; Torrens, Francisco; Abad, Concepción

2015-01-01

Two-dimensional bond-based bilinear indices and linear discriminant analysis are used in this report to perform a quantitative structure-activity relationship study to identify new trypanosomicidal compounds. A data set of 440 organic chemicals, 143 with antitrypanosomal activity and 297 having other clinical uses, is used to develop the theoretical models. Two discriminant models, computed using bond-based bilinear indices, are developed and both show accuracies higher than 86% for training and test sets. The stochastic model correctly indentifies nine out of eleven compounds of a set of organic chemicals obtained from our synthetic collaborators. The in vitro antitrypanosomal activity of this set against epimastigote forms of Trypanosoma cruzi is assayed. Both models show a good agreement between theoretical predictions and experimental results. Three compounds showed IC50 values for epimastigote elimination (AE) lower than 50 μM, while for the benznidazole the IC50 = 54.7 μM which was used as reference compound. The value of IC50 for cytotoxicity of these compounds is at least 5 times greater than their value of IC50 for AE. Finally, we can say that, the present algorithm constitutes a step forward in the search for efficient ways of discovering new antitrypanosomal compounds. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Time-course measurements of drug concentrations in hair and toenails after single administrations of pharmaceutical products.

PubMed

Kuwayama, Kenji; Miyaguchi, Hajime; Iwata, Yuko T; Kanamori, Tatsuyuki; Tsujikawa, Kenji; Yamamuro, Tadashi; Segawa, Hiroki; Inoue, Hiroyuki

2017-04-01

Hair and nails are often used to prove long-term intake of drugs in forensic drug testing. The aim of this study was to evaluate the effectiveness of drug testing using hair and nails and the feasibility of determining when drugs were ingested by measuring the time-courses of drug concentrations in hair and toenails after single administrations of various drugs. Healthy subjects ingested four pharmaceutical products containing eight active ingredients in single doses. Hair and toenails were collected at predetermined intervals, and drug concentrations in hair and nails were measured for 12 months. The administered drugs and their main metabolites were extracted using micropulverized extraction with a stainless steel bullet and were analyzed using liquid chromatography/tandem mass spectrometry. Acidic compounds such as ibuprofen and its metabolites were not detected in both specimens. Acetaminophen, a weakly acidic compound, was detected in nails more frequently than in hair. The maximum concentration of allyl isopropyl acetylurea, a neutral compound, in nails was significantly higher than in hair. Nails are an effective specimen to detect neutral and weakly acidic compounds. For fexofenadine, a zwitterionic compound, and for most basic compounds, the maximum concentrations in hair segments tended to be higher than those in nails. The hair segments showing the maximum concentrations varied between drugs, samples, and subjects. Drug concentrations in hair segments greatly depended on the selection of the hair. Careful interpretation of analytical results is required to predict the time of drug intake. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Biological activity and molecular docking studies of curcumin-related α,β-unsaturated carbonyl-based synthetic compounds as anticancer agents and mushroom tyrosinase inhibitors.

PubMed

Bukhari, Syed Nasir Abbas; Jantan, Ibrahim; Unsal Tan, Oya; Sher, Muhammad; Naeem-Ul-Hassan, M; Qin, Hua-Li

2014-06-18

Hyperpigmentation in human skin and enzymatic browning in fruits, which are caused by tyrosinase enzyme, are not desirable. Investigations in the discovery of tyrosinase enzyme inhibitors and search for improved cytotoxic agents continue to be an important line in drug discovery and development. In present work, a new series of 30 compounds bearing α,β-unsaturated carbonyl moiety was designed and synthesized following curcumin as model. All compounds were evaluated for their effects on human cancer cell lines and mushroom tyrosinase enzyme. Moreover, the structure-activity relationships of these compounds are also explained. Molecular modeling studies of these new compounds were carried out to explore interactions with tyrosinase enzyme. Synthetic curcumin-like compounds (2a-b) were identified as potent anticancer agents with 81-82% cytotoxicity. Five of these newly synthesized compounds (1a, 8a-b, 10a-b) emerged to be the potent inhibitors of mushroom tyrosinase, providing further insight into designing compounds useful in fields of food, health, and agriculture.

In silico assessment of the acute toxicity of chemicals: recent advances and new model for multitasking prediction of toxic effect.

PubMed

Kleandrova, Valeria V; Luan, Feng; Speck-Planche, Alejandro; Cordeiro, M Natália D S

2015-01-01

The assessment of acute toxicity is one of the most important stages to ensure the safety of chemicals with potential applications in pharmaceutical sciences, biomedical research, or any other industrial branch. A huge and indiscriminate number of toxicity assays have been carried out on laboratory animals. In this sense, computational approaches involving models based on quantitative-structure activity/toxicity relationships (QSAR/QSTR) can help to rationalize time and financial costs. Here, we discuss the most significant advances in the last 6 years focused on the use of QSAR/QSTR models to predict acute toxicity of drugs/chemicals in laboratory animals, employing large and heterogeneous datasets. The advantages and drawbacks of the different QSAR/QSTR models are analyzed. As a contribution to the field, we introduce the first multitasking (mtk) QSTR model for simultaneous prediction of acute toxicity of compounds by considering different routes of administration, diverse breeds of laboratory animals, and the reliability of the experimental conditions. The mtk-QSTR model was based on artificial neural networks (ANN), allowing the classification of compounds as toxic or non-toxic. This model correctly classified more than 94% of the 1646 cases present in the whole dataset, and its applicability was demonstrated by performing predictions of different chemicals such as drugs, dietary supplements, and molecules which could serve as nanocarriers for drug delivery. The predictions given by the mtk-QSTR model are in very good agreement with the experimental results.

Computational toxicology: Its essential role in reducing drug attrition.

PubMed

Naven, R T; Louise-May, S

2015-12-01

Predictive toxicology plays a critical role in reducing the failure rate of new drugs in pharmaceutical research and development. Despite recent gains in our understanding of drug-induced toxicity, however, it is urgent that the utility and limitations of our current predictive tools be determined in order to identify gaps in our understanding of mechanistic and chemical toxicology. Using recently published computational regression analyses of in vitro and in vivo toxicology data, it will be demonstrated that significant gaps remain in early safety screening paradigms. More strategic analyses of these data sets will allow for a better understanding of their domain of applicability and help identify those compounds that cause significant in vivo toxicity but which are currently mis-predicted by in silico and in vitro models. These 'outliers' and falsely predicted compounds are metaphorical lighthouses that shine light on existing toxicological knowledge gaps, and it is essential that these compounds are investigated if attrition is to be reduced significantly in the future. As such, the modern computational toxicologist is more productively engaged in understanding these gaps and driving investigative toxicology towards addressing them. © The Author(s) 2015.

A Reprofiled Drug, Auranofin, Is Effective against Metronidazole-Resistant Giardia lamblia

PubMed Central

Tejman-Yarden, Noa; Miyamoto, Yukiko; Leitsch, David; Santini, Jennifer; Debnath, Anjan; Gut, Jiri; McKerrow, James H.; Reed, Sharon L.

2013-01-01

Giardiasis is one of the most common causes of diarrheal disease worldwide. Treatment is primarily with 5-nitro antimicrobials, particularly metronidazole. Resistance to metronidazole has been described, and treatment failures can occur in up to 20% of cases, making development of alternative antigiardials an important goal. To this end, we have screened a chemical library of 746 approved human drugs and 164 additional bioactive compounds for activity against Giardia lamblia. We identified 56 compounds that caused significant inhibition of G. lamblia growth and attachment. Of these, 15 were previously reported to have antigiardial activity, 20 were bioactive but not approved for human use, and 21 were drugs approved for human use for other indications. One notable compound of the last group was the antirheumatic drug auranofin. Further testing revealed that auranofin was active in the low (4 to 6)-micromolar range against a range of divergent G. lamblia isolates representing both human-pathogenic assemblages A and B. Most importantly, auranofin was active against multiple metronidazole-resistant strains. Mechanistically, auranofin blocked the activity of giardial thioredoxin oxidoreductase, a critical enzyme involved in maintaining normal protein function and combating oxidative damage, suggesting that this inhibition contributes to the antigiardial activity. Furthermore, auranofin was efficacious in vivo, as it eradicated infection with different G. lamblia isolates in different rodent models. These results indicate that the approved human drug auranofin could be developed as a novel agent in the armamentarium of antigiardial drugs, particularly against metronidazole-resistant strains. PMID:23403423

Screening a repurposing library for potentiators of antibiotics against Staphylococcus aureus biofilms.

PubMed

Van den Driessche, Freija; Brackman, Gilles; Swimberghe, Rosalie; Rigole, Petra; Coenye, Tom

2017-03-01

Staphylococcus aureus biofilms are involved in a wide range of infections that are extremely difficult to treat with conventional antibiotic therapy. We aimed to identify potentiators of antibiotics against mature biofilms of S. aureus Mu50, a methicillin-resistant and vancomycin-intermediate-resistant strain. Over 700 off-patent drugs from a repurposing library were screened in combination with vancomycin in a microtitre plate (MTP)-based biofilm model system. This led to the identification of 25 hit compounds, including four phenothiazines among which thioridazine was the most potent. Their activity was evaluated in combination with other antibiotics both against planktonic and biofilm-grown S. aureus cells. The most promising combinations were subsequently tested in an in vitro chronic wound biofilm infection model. Although no synergistic activity was observed against planktonic cells, thioridazine potentiated the activity of tobramycin, linezolid and flucloxacillin against S. aureus biofilm cells. However, this effect was only observed in a general biofilm model and not in a chronic wound model of biofilm infection. Several drug compounds were identified that potentiated the activity of vancomycin against biofilms formed in a MTP-based biofilm model. A selected hit compound lost its potentiating activity in a model that mimics specific aspects of wound biofilms. This study provides a platform for discovering and evaluating potentiators against bacterial biofilms and highlights the necessity of using relevant in vitro biofilm model systems. Copyright © 2017 Elsevier B.V. and International Society of Chemotherapy. All rights reserved.

Analysis of A Drug Target-based Classification System using Molecular Descriptors.

PubMed

Lu, Jing; Zhang, Pin; Bi, Yi; Luo, Xiaomin

2016-01-01

Drug-target interaction is an important topic in drug discovery and drug repositioning. KEGG database offers a drug annotation and classification using a target-based classification system. In this study, we gave an investigation on five target-based classes: (I) G protein-coupled receptors; (II) Nuclear receptors; (III) Ion channels; (IV) Enzymes; (V) Pathogens, using molecular descriptors to represent each drug compound. Two popular feature selection methods, maximum relevance minimum redundancy and incremental feature selection, were adopted to extract the important descriptors. Meanwhile, an optimal prediction model based on nearest neighbor algorithm was constructed, which got the best result in identifying drug target-based classes. Finally, some key descriptors were discussed to uncover their important roles in the identification of drug-target classes.

In silico prediction of multiple-category classification model for cytochrome P450 inhibitors and non-inhibitors using machine-learning method.

PubMed

Lee, J H; Basith, S; Cui, M; Kim, B; Choi, S

2017-10-01

The cytochrome P450 (CYP) enzyme superfamily is involved in phase I metabolism which chemically modifies a variety of substrates via oxidative reactions to make them more water-soluble and easier to eliminate. Inhibition of these enzymes leads to undesirable effects, including toxic drug accumulations and adverse drug-drug interactions. Hence, it is necessary to develop in silico models that can predict the inhibition potential of compounds for different CYP isoforms. This study focused on five major CYP isoforms, including CYP1A2, 2C9, 2C19, 2D6 and 3A4, that are responsible for more than 90% of the metabolism of clinical drugs. The main aim of this study is to develop a multiple-category classification model (MCM) for the major CYP isoforms using a Laplacian-modified naïve Bayesian method. The dataset composed of more than 4500 compounds was collected from the PubChem Bioassay database. VolSurf+ descriptors and FCFP_8 fingerprint were used as input features to build classification models. The results demonstrated that the developed MCM using Laplacian-modified naïve Bayesian method was successful in classifying inhibitors and non-inhibitors for each CYP isoform. Moreover, the accuracy, sensitivity and specificity values for both training and test sets were above 80% and also yielded satisfactory area under the receiver operating characteristic curve and Matthews correlation coefficient values.

Modeling Liver-Related Adverse Effects of Drugs Using kNN QSAR Method

PubMed Central

Rodgers, Amie D.; Zhu, Hao; Fourches, Dennis; Rusyn, Ivan; Tropsha, Alexander

2010-01-01

Adverse effects of drugs (AEDs) continue to be a major cause of drug withdrawals both in development and post-marketing. While liver-related AEDs are a major concern for drug safety, there are few in silico models for predicting human liver toxicity for drug candidates. We have applied the Quantitative Structure Activity Relationship (QSAR) approach to model liver AEDs. In this study, we aimed to construct a QSAR model capable of binary classification (active vs. inactive) of drugs for liver AEDs based on chemical structure. To build QSAR models, we have employed an FDA spontaneous reporting database of human liver AEDs (elevations in activity of serum liver enzymes), which contains data on approximately 500 approved drugs. Approximately 200 compounds with wide clinical data coverage, structural similarity and balanced (40/60) active/inactive ratio were selected for modeling and divided into multiple training/test and external validation sets. QSAR models were developed using the k nearest neighbor method and validated using external datasets. Models with high sensitivity (>73%) and specificity (>94%) for prediction of liver AEDs in external validation sets were developed. To test applicability of the models, three chemical databases (World Drug Index, Prestwick Chemical Library, and Biowisdom Liver Intelligence Module) were screened in silico and the validity of predictions was determined, where possible, by comparing model-based classification with assertions in publicly available literature. Validated QSAR models of liver AEDs based on the data from the FDA spontaneous reporting system can be employed as sensitive and specific predictors of AEDs in pre-clinical screening of drug candidates for potential hepatotoxicity in humans. PMID:20192250

Subtoxic Concentrations of Hepatotoxic Drugs Lead to Kupffer Cell Activation in a Human In Vitro Liver Model: An Approach to Study DILI

PubMed Central

Kegel, Victoria; Pfeiffer, Elisa; Burkhardt, Britta; Liu, Jia L.; Zeilinger, Katrin; Nüssler, Andreas K.; Seehofer, Daniel; Damm, Georg

2015-01-01

Drug induced liver injury (DILI) is an idiosyncratic adverse drug reaction leading to severe liver damage. Kupffer cells (KC) sense hepatic tissue stress/damage and therefore could be a tool for the estimation of consequent effects associated with DILI. Aim of the present study was to establish a human in vitro liver model for the investigation of immune-mediated signaling in the pathogenesis of DILI. Hepatocytes and KC were isolated from human liver specimens. The isolated KC yield was 1.2 ± 0.9 × 106 cells/g liver tissue with a purity of >80%. KC activation was investigated by the measurement of reactive oxygen intermediates (ROI, DCF assay) and cell activity (XTT assay). The initial KC activation levels showed broad donor variability. Additional activation of KC using supernatants of hepatocytes treated with hepatotoxic drugs increased KC activity and led to donor-dependent changes in the formation of ROI compared to KC incubated with supernatants from untreated hepatocytes. Additionally, a compound- and donor-dependent increase in proinflammatory cytokines or in anti-inflammatory cytokines was detected. In conclusion, KC related immune signaling in hepatotoxicity was successfully determined in a newly established in vitro liver model. KC were able to detect hepatocyte stress/damage and to transmit a donor- and compound-dependent immune response via cytokine production. PMID:26491234

Temperature Treatment of Highly Porous Zirconium-Containing Metal-Organic Frameworks Extends Drug Delivery Release.

PubMed

Teplensky, Michelle H; Fantham, Marcus; Li, Peng; Wang, Timothy C; Mehta, Joshua P; Young, Laurence J; Moghadam, Peyman Z; Hupp, Joseph T; Farha, Omar K; Kaminski, Clemens F; Fairen-Jimenez, David

2017-06-07

Utilizing metal-organic frameworks (MOFs) as a biological carrier can lower the amount of the active pharmaceutical ingredient (API) required in cancer treatments to provide a more efficacious therapy. In this work, we have developed a temperature treatment process for delaying the release of a model drug compound from the pores of NU-1000 and NU-901, while taking care to utilize these MOFs' large pore volume and size to achieve exceptional model drug loading percentages over 35 wt %. Video-rate super-resolution microscopy reveals movement of MOF particles when located outside of the cell boundary, and their subsequent immobilization when taken up by the cell. Through the use of optical sectioning structured illumination microscopy (SIM), we have captured high-resolution 3D images showing MOF uptake by HeLa cells over a 24 h period. We found that addition of a model drug compound into the MOF and the subsequent temperature treatment process does not affect the rate of MOF uptake by the cell. Endocytosis analysis revealed that MOFs are internalized by active transport and that inhibiting the caveolae-mediated pathway significantly reduced cellular uptake of MOFs. Encapsulation of an anticancer therapeutic, alpha-cyano-4-hydroxycinnamic acid (α-CHC), and subsequent temperature treatment produced loadings of up to 81 wt % and demonstrated efficacy at killing cells beyond the burst release effect.

Key aspects of the Novartis compound collection enhancement project for the compilation of a comprehensive chemogenomics drug discovery screening collection.

PubMed

Jacoby, Edgar; Schuffenhauer, Ansgar; Popov, Maxim; Azzaoui, Kamal; Havill, Benjamin; Schopfer, Ulrich; Engeloch, Caroline; Stanek, Jaroslav; Acklin, Pierre; Rigollier, Pascal; Stoll, Friederike; Koch, Guido; Meier, Peter; Orain, David; Giger, Rudolph; Hinrichs, Jürgen; Malagu, Karine; Zimmermann, Jürg; Roth, Hans-Joerg

2005-01-01

The NIBR (Novartis Institutes for BioMedical Research) compound collection enrichment and enhancement project integrates corporate internal combinatorial compound synthesis and external compound acquisition activities in order to build up a comprehensive screening collection for a modern drug discovery organization. The main purpose of the screening collection is to supply the Novartis drug discovery pipeline with hit-to-lead compounds for today's and the future's portfolio of drug discovery programs, and to provide tool compounds for the chemogenomics investigation of novel biological pathways and circuits. As such, it integrates designed focused and diversity-based compound sets from the synthetic and natural paradigms able to cope with druggable and currently deemed undruggable targets and molecular interaction modes. Herein, we will summarize together with new trends published in the literature, scientific challenges faced and key approaches taken at NIBR to match the chemical and biological spaces.

Behavioral Studies on the Mechanism of Buspirone, an Atypical Anti-Anxiety Drug

DTIC Science & Technology

1986-06-17

D. P., Hyslop , D. K., & Riblet, L. AA Buspirone: A model for anxioselective drug action. Neuroscie~ce 8bstracts, 1980 , ~, 791. Temple, D. L...shows few other similarites to conventional anxiolytics. While benzodiazepines (BZs) are potent anticonvulsants and sedatives (Harvey, 1980 ), buspirone...anxiolytics, and is considered an effective predictor of their effectiveness (Sepinwall & Cook, 1980 ). However, compounds that stimulate GABA receptors

Compounding and Extralabel Use of Drugs in Exotic Animal Medicine.

PubMed

Powers, Lauren V; Davidson, Gigi

2018-05-01

Extralabel drug use is the use of a Food and Drug Administration (FDA)-approved drug in a manner different from what is stipulated on the approved label. Compounding is the process of preparing a medication in a manner not indicated on the label to create a formulation specifically tailored to the needs of an individual patient. Extralabel drug use and compounding are vital aspects of safe and effective drug delivery to patients in exotic animal practice. There are few FDA-approved drugs for exotic animal species, and many approved drugs for other species are not available in suitable formulations for use in exotic animals. Copyright © 2018 Elsevier Inc. All rights reserved.

Automatically updating predictive modeling workflows support decision-making in drug design.

PubMed

Muegge, Ingo; Bentzien, Jörg; Mukherjee, Prasenjit; Hughes, Robert O

2016-09-01

Using predictive models for early decision-making in drug discovery has become standard practice. We suggest that model building needs to be automated with minimum input and low technical maintenance requirements. Models perform best when tailored to answering specific compound optimization related questions. If qualitative answers are required, 2-bin classification models are preferred. Integrating predictive modeling results with structural information stimulates better decision making. For in silico models supporting rapid structure-activity relationship cycles the performance deteriorates within weeks. Frequent automated updates of predictive models ensure best predictions. Consensus between multiple modeling approaches increases the prediction confidence. Combining qualified and nonqualified data optimally uses all available information. Dose predictions provide a holistic alternative to multiple individual property predictions for reaching complex decisions.

Identification of Compounds with Efficacy against Malaria Parasites from Common North American Plants.

PubMed

Cai, Shengxin; Risinger, April L; Nair, Shalini; Peng, Jiangnan; Anderson, Timothy J C; Du, Lin; Powell, Douglas R; Mooberry, Susan L; Cichewicz, Robert H

2016-03-25

Some of the most valuable antimalarial compounds, including quinine and artemisinin, originated from plants. While these drugs have served important roles over many years for the treatment of malaria, drug resistance has become a widespread problem. Therefore, a critical need exists to identify new compounds that have efficacy against drug-resistant malaria strains. In the current study, extracts prepared from plants readily obtained from local sources were screened for activity against Plasmodium falciparum. Bioassay-guided fractionation was used to identify 18 compounds from five plant species. These compounds included eight lupane triterpenes (1-8), four kaempferol 3-O-rhamnosides (10-13), four kaempferol 3-O-glucosides (14-17), and the known compounds amentoflavone and knipholone. These compounds were tested for their efficacy against multi-drug-resistant malaria parasites and counterscreened against HeLa cells to measure their antimalarial selectivity. Most notably, one of the new lupane triterpenes (3) isolated from the supercritical extract of Buxus sempervirens, the common boxwood, showed activity against both drug-sensitive and -resistant malaria strains at a concentration that was 75-fold more selective for the drug-resistant malaria parasites as compared to HeLa cells. This study demonstrates that new antimalarial compounds with efficacy against drug-resistant strains can be identified from native and introduced plant species in the United States, which traditionally have received scant investigation compared to more heavily explored tropical and semitropical botanical resources from around the world.

Animal models of ulcerative colitis and their application in drug research

PubMed Central

Low, Daren; Nguyen, Deanna D; Mizoguchi, Emiko

2013-01-01

The specific pathogenesis underlying inflammatory bowel disease is complex, and it is even more difficult to decipher the pathophysiology to explain for the similarities and differences between two of its major subtypes, Crohn’s disease and ulcerative colitis (UC). Animal models are indispensable to pry into mechanistic details that will facilitate better preclinical drug/therapy design to target specific components involved in the disease pathogenesis. This review focuses on common animal models that are particularly useful for the study of UC and its therapeutic strategy. Recent reports of the latest compounds, therapeutic strategies, and approaches tested on UC animal models are also discussed. PMID:24250223

Predicting Error Bars for QSAR Models

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

Schroeter, Timon; Technische Universitaet Berlin, Department of Computer Science, Franklinstrasse 28/29, 10587 Berlin; Schwaighofer, Anton

2007-09-18

Unfavorable physicochemical properties often cause drug failures. It is therefore important to take lipophilicity and water solubility into account early on in lead discovery. This study presents log D{sub 7} models built using Gaussian Process regression, Support Vector Machines, decision trees and ridge regression algorithms based on 14556 drug discovery compounds of Bayer Schering Pharma. A blind test was conducted using 7013 new measurements from the last months. We also present independent evaluations using public data. Apart from accuracy, we discuss the quality of error bars that can be computed by Gaussian Process models, and ensemble and distance based techniquesmore » for the other modelling approaches.« less

Systematic integration of biomedical knowledge prioritizes drugs for repurposing

PubMed Central

Himmelstein, Daniel Scott; Lizee, Antoine; Hessler, Christine; Brueggeman, Leo; Chen, Sabrina L; Hadley, Dexter; Green, Ari; Khankhanian, Pouya

2017-01-01

The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes Project Rephetio to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data were integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then, we predicted the probability of treatment for 209,168 compound–disease pairs (het.io/repurpose). Our predictions validated on two external sets of treatment and provided pharmacological insights on epilepsy, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 40 community members. PMID:28936969

Artifacts Generated During Azoalkane Peroxy Radical Oxidative Stress Testing of Pharmaceuticals Containing Primary and Secondary Amines.

PubMed

Nefliu, Marcela; Zelesky, Todd; Jansen, Patrick; Sluggett, Gregory W; Foti, Christopher; Baertschi, Steven W; Harmon, Paul A

2015-12-01

We report artifactual degradation of pharmaceutical compounds containing primary and secondary amines during peroxy radical-mediated oxidative stress carried out using azoalkane initiators. Two degradation products were detected when model drug compounds dissolved in methanol/water were heated to 40°C with radical initiators such as 2,2'-azobis(2-methylpropionitrile) (AIBN). The primary artifact was identified as an α-aminonitrile generated from the reaction of the amine group of the model drug with formaldehyde and hydrogen cyanide, generated as byproducts of the stress reaction. A minor artifact was generated from the reaction between the amine group and isocyanic acid, also a byproduct of the stress reaction. We report the effects of pH, initiator/drug molar ratio, and type of azoalkane initiator on the formation of these artifacts. Mass spectrometry and nuclear magnetic resonance were used for structure elucidation, whereas mechanistic studies, including stable isotope labeling experiments, cyanide analysis, and experiments exploring the effects of butylated hydroxyanisole addition, were employed to support the degradation pathways. © 2015 Wiley Periodicals, Inc. and the American Pharmacists Association.

Developing Hypothetical Inhibition Mechanism of Novel Urea Transporter B Inhibitor

NASA Astrophysics Data System (ADS)

Li, Min; Tou, Weng Ieong; Zhou, Hong; Li, Fei; Ren, Huiwen; Chen, Calvin Yu-Chian; Yang, Baoxue

2014-07-01

Urea transporter B (UT-B) is a membrane channel protein that specifically transports urea. UT-B null mouse exhibited urea selective urine concentrating ability deficiency, which suggests the potential clinical applications of the UT-B inhibitors as novel diuretics. Primary high-throughput virtual screening (HTVS) of 50000 small-molecular drug-like compounds identified 2319 hit compounds. These 2319 compounds were screened by high-throughput screening using an erythrocyte osmotic lysis assay. Based on the pharmacological data, putative UT-B binding sites were identified by structure-based drug design and validated by ligand-based and QSAR model. Additionally, UT-B structural and functional characteristics under inhibitors treated and untreated conditions were simulated by molecular dynamics (MD). As the result, we identified four classes of compounds with UT-B inhibitory activity and predicted a human UT-B model, based on which computative binding sites were identified and validated. A novel potential mechanism of UT-B inhibitory activity was discovered by comparing UT-B from different species. Results suggest residue PHE198 in rat and mouse UT-B might block the inhibitor migration pathway. Inhibitory mechanisms of UT-B inhibitors and the functions of key residues in UT-B were proposed. The binding site analysis provides a structural basis for lead identification and optimization of UT-B inhibitors.

Using Environment-Sensitive Fluorescent Probes to Characterize Liquid-Liquid Phase Separation in Supersaturated Solutions of Poorly Water Soluble Compounds.

PubMed

Raina, Shweta A; Alonzo, David E; Zhang, Geoff G Z; Gao, Yi; Taylor, Lynne S

2015-11-01

Highly supersaturated aqueous solutions of poorly soluble compounds can undergo liquid-liquid phase separation (LLPS) when the concentration exceeds the "amorphous solubility". This phenomenon has been widely observed during high throughput screening of new molecular entities as well as during the dissolution of amorphous solid dispersions. In this study, we have evaluated the use of environment-sensitive fluorescence probes to investigate the formation and properties of the non-crystalline drug-rich aggregates formed in aqueous solutions as a result of LLPS. Six different environment-sensitive fluorophores were employed to study LLPS in highly supersaturated solutions of several model compounds, all dihydropyridine derivatives. Each fluoroprobe exhibited a large hypsochromic shift with decreasing environment polarity. Upon drug aggregate formation, the probes partitioned into the drug-rich phase and exhibited changes in emission wavelength and intensity consistent with sensing a lower polarity environment. The LLPS onset concentrations determined using the fluorescence measurements were in good agreement with light scattering measurements as well as theoretically estimated amorphous solubility values. Environment-sensitive fluorescence probes are useful to help understand the phase behavior of highly supersaturated aqueous solutions, which in turn is important in the context of developing enabling formulations for poorly soluble compounds.

The importance of employing computational resources for the automation of drug discovery.

PubMed

Rosales-Hernández, Martha Cecilia; Correa-Basurto, José

2015-03-01

The application of computational tools to drug discovery helps researchers to design and evaluate new drugs swiftly with a reduce economic resources. To discover new potential drugs, computational chemistry incorporates automatization for obtaining biological data such as adsorption, distribution, metabolism, excretion and toxicity (ADMET), as well as drug mechanisms of action. This editorial looks at examples of these computational tools, including docking, molecular dynamics simulation, virtual screening, quantum chemistry, quantitative structural activity relationship, principal component analysis and drug screening workflow systems. The authors then provide their perspectives on the importance of these techniques for drug discovery. Computational tools help researchers to design and discover new drugs for the treatment of several human diseases without side effects, thus allowing for the evaluation of millions of compounds with a reduced cost in both time and economic resources. The problem is that operating each program is difficult; one is required to use several programs and understand each of the properties being tested. In the future, it is possible that a single computer and software program will be capable of evaluating the complete properties (mechanisms of action and ADMET properties) of ligands. It is also possible that after submitting one target, this computer-software will be capable of suggesting potential compounds along with ways to synthesize them, and presenting biological models for testing.

Accelerating early anti-tuberculosis drug discovery by creating mycobacterial indicator strains that predict mode of action.

PubMed

Boot, Maikel; Commandeur, Susanna; Subudhi, Amit K; Bahira, Meriem; Smith, Trever C; Abdallah, Abdallah M; van Gemert, Mae; Lelièvre, Joël; Ballell, Lluís; Aldridge, Bree B; Pain, Arnab; Speer, Alexander; Bitter, Wilbert

2018-04-16

Due to the rise of drug resistant forms of tuberculosis there is an urgent need for novel antibiotics to effectively combat these cases and shorten treatment regimens. Recently, drug screens using whole cell analyses have been shown to be successful. However, current high-throughput screens focus mostly on stricto sensu life-death screening that give little qualitative information. In doing so, promising compound scaffolds or non-optimized compounds that fail to reach inhibitory concentrations are missed. To accelerate early TB drug discovery, we performed RNA sequencing on Mycobacterium tuberculosis and Mycobacterium marinum to map the stress responses that follow upon exposure to sub-inhibitory concentrations of antibiotics with known targets: ciprofloxacin, ethambutol, isoniazid, streptomycin and rifampicin. The resulting dataset comprises the first overview of transcriptional stress responses of mycobacteria to different antibiotics. We show that antibiotics can be distinguished based on their specific transcriptional stress fingerprint. Notably, this fingerprint was more distinctive in M. marinum. We decided to use this to our advantage and continue with this model organism. A selection of diverse antibiotic stress genes was used to construct stress reporters. In total, three functional reporters were constructed to respond to DNA damage, cell wall damage and ribosomal inhibition. Subsequently, these reporter strains were used to screen a small anti-TB compound library to predict the mode of action. In doing so, we could identify the putative mode of action for three novel compounds, which confirms our approach. Copyright © 2018 American Society for Microbiology.

Concepts and applications of "natural computing" techniques in de novo drug and peptide design.

PubMed

Hiss, Jan A; Hartenfeller, Markus; Schneider, Gisbert

2010-05-01

Evolutionary algorithms, particle swarm optimization, and ant colony optimization have emerged as robust optimization methods for molecular modeling and peptide design. Such algorithms mimic combinatorial molecule assembly by using molecular fragments as building-blocks for compound construction, and relying on adaptation and emergence of desired pharmacological properties in a population of virtual molecules. Nature-inspired algorithms might be particularly suited for bioisosteric replacement or scaffold-hopping from complex natural products to synthetically more easily accessible compounds that are amenable to optimization by medicinal chemistry. The theory and applications of selected nature-inspired algorithms for drug design are reviewed, together with practical applications and a discussion of their advantages and limitations.

Hollow Fiber Methodology for Pharmacokinetic/Pharmacodynamic Studies of Antimalarial Compounds

PubMed Central

Caton, Emily; Nenortas, Elizabeth; Bakshi, Rahul P.; Shapiro, Theresa A.

2016-01-01

Knowledge of pharmacokinetic/pharmacodynamic (PK/PD) relationships can enhance the speed and economy of drug development by enabling informed and rational decisions at every step, from lead selection to clinical dosing. For anti-infective agents in particular, dynamic in vitro hollow fiber cartridge experiments permit exquisite control of kinetic parameters and the study of their consequent impact on pharmacodynamic efficacy. Such information is of great interest for the cost-restricted but much-needed development of new antimalarial drugs, especially since major human pathogen Plasmodium falciparum can be cultivated in vitro but is not readily available in animal models. This protocol describes the materials and procedures for determining the PK/PD relationships of antimalarial compounds. PMID:26995353

In vitro and In vivo Antimalarial Activity of Amphiphilic Naphthothiazolium Salts with Amine-Bearing Side Chains

PubMed Central

Ulrich, Peter; Gipson, Gregory R.; Clark, Martha A.; Tripathi, Abhai; Sullivan, David J.; Cerami, Carla

2014-01-01

Because of emerging resistance to existing drugs, new chemical classes of antimalarial drugs are urgently needed. We have rationally designed a library of compounds that were predicted to accumulate in the digestive vacuole and then decrystallize hemozoin by breaking the iron carboxylate bond in hemozoin. We report the synthesis of 16 naphthothiazolium salts with amine-bearing side chains and their activities against the erythrocytic stage of Plasmodium falciparum in vitro. KSWI-855, the compound with the highest efficacy against the asexual stages of P. falciparum in vitro, also had in vitro activity against P. falciparum gametocytes and in vivo activity against P. berghei in a murine malaria model. PMID:25184829

Dihydroagarofuranoid Sesquiterpenes as Acetylcholinesterase Inhibitors from Celastraceae Plants: Maytenus disticha and Euonymus japonicus.

PubMed

Alarcón, Julio; Cespedes, Carlos L; Muñoz, Evelyn; Balbontin, Cristian; Valdes, Francisco; Gutierrez, Margarita; Astudillo, Luis; Seigler, David S

2015-12-02

Natural cholinesterase inhibitors have been found in many biological sources. Nine compounds with agarofuran (epoxyeudesmane) skeletons were isolated from seeds and aerial parts of Maytenus disticha and Euonymus japonicus. The identification and structural elucidation of compounds were based on spectroscopic data analyses. All compounds had inhibitory acetylcholinesterase (AChE) activity. These natural compounds, which possessed mixed or uncompetitive mechanisms of inhibitory activity against AChE, may be considered as models for the design and development of new naturally occurring drugs for management strategies for neurodegenerative diseases. This is the first report of these chemical structures for seeds of M. disticha.

Translational neuropharmacology and the appropriate and effective use of animal models.

PubMed

Green, A R; Gabrielsson, J; Fone, K C F

2011-10-01

This issue of the British Journal of Pharmacology is dedicated to reviews of the major animal models used in neuropharmacology to examine drugs for both neurological and psychiatric conditions. Almost all major conditions are reviewed. In general, regulatory authorities require evidence for the efficacy of novel compounds in appropriate animal models. However, the failure of many compounds in clinical trials following clear demonstration of efficacy in animal models has called into question both the value of the models and the discovery process in general. These matters are expertly reviewed in this issue and proposals for better models outlined. In this editorial, we further suggest that more attention be made to incorporate pharmacokinetic knowledge into the studies (quantitative pharmacology). We also suggest that more attention be made to ensure that full methodological details are published and recommend that journals should be more amenable to publishing negative data. Finally, we propose that new approaches must be used in drug discovery so that preclinical studies become more reflective of the clinical situation, and studies using animal models mimic the anticipated design of studies to be performed in humans, as closely as possible. © 2011 The Authors. British Journal of Pharmacology © 2011 The British Pharmacological Society.

A comprehensive pharmacokinetic/pharmacodynamics analysis of the novel IGF1R/INSR inhibitor BI 893923 applying in vitro, in vivo and in silico modeling techniques.

PubMed

Titze, Melanie I; Schaaf, Otmar; Hofmann, Marco H; Sanderson, Michael P; Zahn, Stephan K; Quant, Jens; Lehr, Thorsten

2016-06-01

BI 893923 is a novel IGF1R/INSR tyrosine kinase inhibitor demonstrating anti-tumor efficacy and good tolerability. We aimed to characterize the relationship between BI 893923 plasma concentration, tumor biomarker modulation, tumor growth and hyperglycemia in mice using in silico modeling analyses. In vitro molecular and cellular assays were used to demonstrate the potency and selectivity of BI 893923. Diverse in vitro DMPK assays were used to characterize the compound's drug-like properties. Mice xenografted with human GEO tumors were treated with different doses of BI 893923 to demonstrate the compound's efficacy, biomarker modulation and tolerability. PK/PD analyses were performed using nonlinear mixed-effects modeling. BI 893923 demonstrated potent and selective molecular inhibition of the IGF1R and INSR and demonstrated attractive drug-like properties (permeability, bioavailability). BI 893923 dose-dependently reduced GEO tumor growth and demonstrated good tolerability, characterized by transient hyperglycemia and normal body weight gain. A population PK/PD model was developed, which established relationships between BI 893923 pharmacokinetics, hyperglycemia, pIGF1R reduction and tumor growth. BI 893923 demonstrates molecular properties consistent with a highly attractive inhibitor of the IGF1R/INSR. A generic PK/PD model was developed to support preclinical drug development and dose finding in mice.

Computational modeling for cardiac safety pharmacology analysis: Contribution of fibroblasts.

PubMed

Gao, Xin; Engel, Tyler; Carlson, Brian E; Wakatsuki, Tetsuro

2017-09-01

Drug-induced proarrhythmic potential is an important regulatory criterion in safety pharmacology. The application of in silico approaches to predict proarrhythmic potential of new compounds is under consideration as part of future guidelines. Current approaches simulate the electrophysiology of a single human adult ventricular cardiomyocyte. However, drug-induced proarrhythmic potential can be different when cardiomyocytes are surrounded by non-muscle cells. Incorporating fibroblasts in models of myocardium is important particularly for predicting a drugs cardiac liability in the aging population - a growing population who take more medications and exhibit increased cardiac fibrosis. In this study, we used computational models to investigate the effects of fibroblast coupling on the electrophysiology and response to drugs of cardiomyocytes. A computational model of cardiomyocyte electrophysiology and ion handling (O'Hara, Virag, Varro, & Rudy, 2011) is coupled to a passive model of fibroblast electrophysiology to test the effects of three compounds that block cardiomyocyte ion channels. Results are compared to model results without fibroblast coupling to see how fibroblasts affect cardiomyocyte action potential duration at 90% repolarization (APD 90 ) and propensity for early after depolarization (EAD). Simulation results show changes in cardiomyocyte APD 90 with increasing concentration of three drugs that affect cardiac function (dofetilide, vardenafil and nebivolol) when no fibroblasts are coupled to the cardiomyocyte. Coupling fibroblasts to cardiomyocytes markedly shortens APD 90 . Moreover, increasing the number of fibroblasts can augment the shortening effect. Coupling cardiomyocytes and fibroblasts are predicted to decrease proarrhythmic susceptibility under dofetilide, vardenafil and nebivolol block. However, this result is sensitive to parameters which define the electrophysiological function of the fibroblast. Fibroblast membrane capacitance and conductance (C FB and G FB ) have less of an effect on APD 90 than the fibroblast resting membrane potential (E FB ). This study suggests that in both theoretical models and experimental tissue constructs that represent cardiac tissue, both cardiomyocytes and non-muscle cells should be considered when testing cardiac pharmacological agents. Copyright © 2017 Elsevier Inc. All rights reserved.

Click chemistry, 3D-printing, and omics: the future of drug development

PubMed Central

Kurzrock, Razelle; Stewart, David J.

2016-01-01

Genomics is a disruptive technology, having revealed that cancers are tremendously complex and differ from patient to patient. Therefore, conventional treatment approaches fit poorly with genomic reality. Furthermore, it is likely that this type of complexity will also be observed in other illnesses. Precision medicine has been posited as a way to better target disease-related aberrations, but developing drugs and tailoring therapy to each patient's complicated problem is a major challenge. One solution would be to match patients to existing compounds based on in silico modeling. However, optimization of complex therapy will eventually require designing compounds for patients using computer modeling and just-in-time production, perhaps achievable in the future by three-dimensional (3D) printing. Indeed, 3D printing is potentially transformative by virtue of its ability to rapidly generate almost limitless numbers of objects that previously required manufacturing facilities. Companies are already endeavoring to develop affordable 3D printers for home use. An attractive, but as yet scantily explored, application is to place chemical design and production under digital control. This could be accomplished by utilizing a 3D printer to initiate chemical reactions, and print the reagents and/or the final compounds directly. Of interest, the Food and Drug Administration (FDA) has recently approved a 3D printed drug—levetiracetam—indicated for seizures. Further, it is now increasingly clear that biologic materials—tissues, and eventually organs—can also be “printed.” In the near future, it is plausible that high-throughput computing may be deployed to design customized drugs, which will reshape medicine. PMID:26734837

Diversity-oriented synthesis yields novel multistage antimalarial inhibitors.

PubMed

Kato, Nobutaka; Comer, Eamon; Sakata-Kato, Tomoyo; Sharma, Arvind; Sharma, Manmohan; Maetani, Micah; Bastien, Jessica; Brancucci, Nicolas M; Bittker, Joshua A; Corey, Victoria; Clarke, David; Derbyshire, Emily R; Dornan, Gillian L; Duffy, Sandra; Eckley, Sean; Itoe, Maurice A; Koolen, Karin M J; Lewis, Timothy A; Lui, Ping S; Lukens, Amanda K; Lund, Emily; March, Sandra; Meibalan, Elamaran; Meier, Bennett C; McPhail, Jacob A; Mitasev, Branko; Moss, Eli L; Sayes, Morgane; Van Gessel, Yvonne; Wawer, Mathias J; Yoshinaga, Takashi; Zeeman, Anne-Marie; Avery, Vicky M; Bhatia, Sangeeta N; Burke, John E; Catteruccia, Flaminia; Clardy, Jon C; Clemons, Paul A; Dechering, Koen J; Duvall, Jeremy R; Foley, Michael A; Gusovsky, Fabian; Kocken, Clemens H M; Marti, Matthias; Morningstar, Marshall L; Munoz, Benito; Neafsey, Daniel E; Sharma, Amit; Winzeler, Elizabeth A; Wirth, Dyann F; Scherer, Christina A; Schreiber, Stuart L

2016-10-20

Antimalarial drugs have thus far been chiefly derived from two sources-natural products and synthetic drug-like compounds. Here we investigate whether antimalarial agents with novel mechanisms of action could be discovered using a diverse collection of synthetic compounds that have three-dimensional features reminiscent of natural products and are underrepresented in typical screening collections. We report the identification of such compounds with both previously reported and undescribed mechanisms of action, including a series of bicyclic azetidines that inhibit a new antimalarial target, phenylalanyl-tRNA synthetase. These molecules are curative in mice at a single, low dose and show activity against all parasite life stages in multiple in vivo efficacy models. Our findings identify bicyclic azetidines with the potential to both cure and prevent transmission of the disease as well as protect at-risk populations with a single oral dose, highlighting the strength of diversity-oriented synthesis in revealing promising therapeutic targets.

Naringenin and quercetin--potential anti-HCV agents for NS2 protease targets.

PubMed

Lulu, S Sajitha; Thabitha, A; Vino, S; Priya, A Mohana; Rout, Madhusmita

2016-01-01

Nonstructural proteins of hepatitis C virus had drawn much attention for the scientific fraternity in drug discovery due to its important role in the disease. 3D structure of the protein was predicted using molecular modelling protocol. Docking studies of 10 medicinal plant compounds and three drugs available in the market (control) with NS2 protease were employed by using rigid docking approach of AutoDock 4.2. Among the molecules tested for docking study, naringenin and quercetin revealed minimum binding energy of - 7.97 and - 7.95 kcal/mol with NS2 protease. All the ligands were docked deeply within the binding pocket region of the protein. The docking study results showed that these compounds are potential inhibitors of the target; and also all these docked compounds have good inhibition constant, vdW+Hbond+desolv energy with best RMSD value.

4-Nitro styrylquinoline is an antimalarial inhibiting multiple stages of Plasmodium falciparum asexual life cycle.

PubMed

Roberts, Bracken F; Zheng, Yongsheng; Cleaveleand, Jacob; Lee, Sukjun; Lee, Eunyoung; Ayong, Lawrence; Yuan, Yu; Chakrabarti, Debopam

2017-04-01

Drugs against malaria are losing their effectiveness because of emerging drug resistance. This underscores the need for novel therapeutic options for malaria with mechanism of actions distinct from current antimalarials. To identify novel pharmacophores against malaria we have screened compounds containing structural features of natural products that are pharmacologically relevant. This screening has identified a 4-nitro styrylquinoline (SQ) compound with submicromolar antiplasmodial activity and excellent selectivity. SQ exhibits a cellular action distinct from current antimalarials, acting early on malaria parasite's intraerythrocytic life cycle including merozoite invasion. The compound is a fast-acting parasitocidal agent and also exhibits curative property in the rodent malaria model when administered orally. In this report, we describe the synthesis, preliminary structure-function analysis, and the parasite developmental stage specific action of the SQ scaffold. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Selection and optimization of hits from a high-throughput phenotypic screen against Trypanosoma cruzi.

PubMed

Keenan, Martine; Alexander, Paul W; Chaplin, Jason H; Abbott, Michael J; Diao, Hugo; Wang, Zhisen; Best, Wayne M; Perez, Catherine J; Cornwall, Scott M J; Keatley, Sarah K; Thompson, R C Andrew; Charman, Susan A; White, Karen L; Ryan, Eileen; Chen, Gong; Ioset, Jean-Robert; von Geldern, Thomas W; Chatelain, Eric

2013-10-01

Inhibitors of Trypanosoma cruzi with novel mechanisms of action are urgently required to diversify the current clinical and preclinical pipelines. Increasing the number and diversity of hits available for assessment at the beginning of the discovery process will help to achieve this aim. We report the evaluation of multiple hits generated from a high-throughput screen to identify inhibitors of T. cruzi and from these studies the discovery of two novel series currently in lead optimization. Lead compounds from these series potently and selectively inhibit growth of T. cruzi in vitro and the most advanced compound is orally active in a subchronic mouse model of T. cruzi infection. High-throughput screening of novel compound collections has an important role to play in diversifying the trypanosomatid drug discovery portfolio. A new T. cruzi inhibitor series with good drug-like properties and promising in vivo efficacy has been identified through this process.

Neural stem cells for disease modeling of Wolman disease and evaluation of therapeutics.

PubMed

Aguisanda, Francis; Yeh, Charles D; Chen, Catherine Z; Li, Rong; Beers, Jeanette; Zou, Jizhong; Thorne, Natasha; Zheng, Wei

2017-06-28

Wolman disease (WD) is a rare lysosomal storage disorder that is caused by mutations in the LIPA gene encoding lysosomal acid lipase (LAL). Deficiency in LAL function causes accumulation of cholesteryl esters and triglycerides in lysosomes. Fatality usually occurs within the first year of life. While an enzyme replacement therapy has recently become available, there is currently no small-molecule drug treatment for WD. We have generated induced pluripotent stem cells (iPSCs) from two WD patient dermal fibroblast lines and subsequently differentiated them into neural stem cells (NSCs). The WD NSCs exhibited the hallmark disease phenotypes of neutral lipid accumulation, severely deficient LAL activity, and increased LysoTracker dye staining. Enzyme replacement treatment dramatically reduced the WD phenotype in these cells. In addition, δ-tocopherol (DT) and hydroxypropyl-beta-cyclodextrin (HPBCD) significantly reduced lysosomal size in WD NSCs, and an enhanced effect was observed in DT/HPBCD combination therapy. The results demonstrate that these WD NSCs are valid cell-based disease models with characteristic disease phenotypes that can be used to evaluate drug efficacy and screen compounds. DT and HPBCD both reduce LysoTracker dye staining in WD cells. The cells may be used to further dissect the pathology of WD, evaluate compound efficacy, and serve as a platform for high-throughput drug screening to identify new compounds for therapeutic development.

Cannabinoids as Anticancer Drugs.

PubMed

Ramer, Robert; Hinz, Burkhard

2017-01-01

The endocannabinoid system encompassing cannabinoid receptors, endogenous receptor ligands (endocannabinoids), as well as enzymes conferring the synthesis and degradation of endocannabinoids has emerged as a considerable target for pharmacotherapeutical approaches of numerous diseases. Besides palliative effects of cannabinoids used in cancer treatment, phytocannabinoids, synthetic agonists, as well as substances that increase endogenous endocannabinoid levels have gained interest as potential agents for systemic cancer treatment. Accordingly, cannabinoid compounds have been reported to inhibit tumor growth and spreading in numerous rodent models. The underlying mechanisms include induction of apoptosis, autophagy, and cell cycle arrest in tumor cells as well as inhibition of tumor cell invasion and angiogenic features of endothelial cells. In addition, cannabinoids have been shown to suppress epithelial-to-mesenchymal transition, to enhance tumor immune surveillance, and to support chemotherapeutics' effects on drug-resistant cancer cells. However, unwanted side effects include psychoactivity and possibly pathogenic effects on liver health. Other cannabinoids such as the nonpsychoactive cannabidiol exert a comparatively good safety profile while exhibiting considerable anticancer properties. So far experience with anticarcinogenic effects of cannabinoids is confined to in vitro studies and animal models. Although a bench-to-bedside conversion remains to be established, the current knowledge suggests cannabinoid compounds to serve as a group of drugs that may offer significant advantages for patients suffering from cancer diseases. The present review summarizes the role of the endocannabinoid system and cannabinoid compounds in tumor progression. © 2017 Elsevier Inc. All rights reserved.

Reflection on design and testing of pancreatic alpha-amylase inhibitors: an in silico comparison between rat and rabbit enzyme models

PubMed Central

2012-01-01

Background Inhibitors of pancreatic alpha-amylase are potential drugs to treat diabetes and obesity. In order to find compounds that would be effective amylase inhibitors, in vitro and in vivo models are usually used. The accuracy of models is limited, but these tools are nonetheless valuable. In vitro models could be used in large screenings involving thousands of chemicals that are tested to find potential lead compounds. In vivo models are still used as preliminary mean of testing compounds behavior in the whole organism. In the case of alpha-amylase inhibitors, both rats and rabbits could be chosen as in vivo models. The question was which animal could present more accuracy with regard to its pancreatic alpha-amylase. Results As there is no crystal structure of these enzymes, a molecular modeling study was done in order to compare the rabbit and rat enzymes with the human one. The overall result is that rabbit enzyme could probably be a better choice in this regard, but in the case of large ligands, which could make putative interactions with the −4 subsite of pancreatic alpha-amylase, interpretation of results should be made cautiously. Conclusion Molecular modeling tools could be used to choose the most suitable model enzyme that would help to identify new enzyme inhibitors. In the case of alpha-amylase, three-dimensional structures of animal enzymes show differences with the human one which should be taken into account when testing potential new drugs. PMID:23352052

Screen of FDA-approved drug library reveals compounds that protect hair cells from aminoglycosides and cisplatin

PubMed Central

Vlasits, Anna L.; Simon, Julian A.; Raible, David W.; Rubel, Edwin W; Owens, Kelly N.

2012-01-01

Loss of mechanosensory hair cells in the inner ear accounts for many hearing loss and balance disorders. Several beneficial pharmaceutical drugs cause hair cell death as a side effect. These include aminoglycoside antibiotics, such as neomycin, kanamycin and gentamicin, and several cancer chemotherapy drugs, such as cisplatin. Discovering new compounds that protect mammalian hair cells from toxic insults is experimentally difficult because of the inaccessibility of the inner ear. We used the zebrafish lateral line sensory system as an in vivo screening platform to survey a library of FDA-approved pharmaceuticals for compounds that protect hair cells from neomycin, gentamicin, kanamycin and cisplatin. Ten compounds were identified that provide protection from at least two of the four toxins. The resulting compounds fall into several drug classes, including serotonin and dopamine-modulating drugs, adrenergic receptor ligands, and estrogen receptor modulators. The protective compounds show different effects against the different toxins, supporting the idea that each toxin causes hair cell death by distinct, but partially overlapping, mechanisms. Furthermore, some compounds from the same drug classes had different protective properties, suggesting that they might not prevent hair cell death by their known target mechanisms. Some protective compounds blocked gentamicin uptake into hair cells, suggesting that they may block mechanotransduction or other routes of entry. The protective compounds identified in our screen will provide a starting point for studies in mammals as well as further research discovering the cellular signaling pathways that trigger hair cell death. PMID:22967486

Multifunctional Hybrid Compounds Derived from 2-(2,5-Dioxopyrrolidin-1-yl)-3-methoxypropanamides with Anticonvulsant and Antinociceptive Properties.

PubMed

Abram, Michał; Zagaja, Mirosław; Mogilski, Szczepan; Andres-Mach, Marta; Latacz, Gniewomir; Baś, Sebastian; Łuszczki, Jarogniew J; Kieć-Kononowicz, Katarzyna; Kamiński, Krzysztof

2017-10-26

The focused set of new pyrrolidine-2,5-diones as potential broad-spectrum hybrid anticonvulsants was described. These derivatives integrate on the common structural scaffold the chemical fragments of well-known antiepileptic drugs such as ethosuximide, levetiracetam, and lacosamide. Such hybrids demonstrated effectiveness in two of the most widely used animal seizure models, namely, the maximal electroshock (MES) test and the psychomotor 6 Hz (32 mA) seizure models. Compound 33 showed the highest anticonvulsant activity in these models (ED 50 MES = 79.5 mg/kg, ED 50 6 Hz = 22.4 mg/kg). Compound 33 was also found to be effective in pentylenetetrazole-induced seizure model (ED 50 PTZ = 123.2 mg/kg). In addition, 33 demonstrated effectiveness by decreasing pain responses in formalin-induced tonic pain, in capsaicin-induced neurogenic pain, and notably in oxaliplatin-induced neuropathic pain in mice. The pharmacological data of stereoisomers of compound 33 revealed greater anticonvulsant activity by R(+)-33 enantiomer in both MES and 6 Hz seizure models.

Comparative study of binding interactions between porphyrin systems and aromatic compounds of biological importance by multiple spectroscopic techniques: A review

NASA Astrophysics Data System (ADS)

Makarska-Bialokoz, Magdalena

2018-07-01

The specific spectroscopic and redox properties of porphyrins predestine them to fulfill the role of sensors during interacting with different biologically active substances. Monitoring of binding interactions in the systems porphyrin-biologically active compound is a key question not only in the field of physiological functions of living organisms, but also in environmental protection, notably in the light of the rapidly growing drug consumption and concurrently the production of drug effluents. Not always beneficial action of drugs on natural porphyrin systems induces to further studies, with commercially available porphyrins as the model systems. Therefore the binding process between several water-soluble porphyrins and a series of biologically active compounds (e.g. caffeine, guanine, theophylline, theobromine, xanthine, uric acid) has been studied in different aqueous solutions analyzing their absorption and steady-state fluorescence spectra, the porphyrin fluorescence lifetimes and their quantum yields. The magnitude of the binding and fluorescence quenching constants values for particular quenchers decreases in a series: uric acid > guanine > caffeine > theophylline > theobromine > xanthine. In all the systems studied there are characters of static quenching, as a consequence of the π-π-stacked non-covalent and non-fluorescent complexes formation between porphyrins and interacting compounds, accompanied simultaneously by the additional specific binding interactions. The porphyrin fluorescence quenching can be explain by the photoinduced intermolecular electron transfer from aromatic compound to the center of the porphyrin molecule, playing the role of the binding site. Presented results can be valuable for designing of new fluorescent porphyrin chemosensors or monitoring of drug traces in aqueous solutions. The obtained outcomes have also the toxicological and medical importance, providing insight into the interactions of the water-soluble porphyrins with biologically active substances.

Development of an ex vivo lymph node explant model for identification of novel molecules active against Leishmania major.

PubMed

Peniche, Alex G; Osorio, Yaneth; Renslo, Adam R; Frantz, Doug E; Melby, Peter C; Travi, Bruno L

2014-01-01

Leishmaniasis is a vector-borne zoonotic infection affecting people in tropical and subtropical regions of the world. Current treatments for cutaneous leishmaniasis are difficult to administer, toxic, expensive, and limited in effectiveness and availability. Here we describe the development and application of a medium-throughput screening approach to identify new drug candidates for cutaneous leishmaniasis using an ex vivo lymph node explant culture (ELEC) derived from the draining lymph nodes of Leishmania major-infected mice. The ELEC supported intracellular amastigote proliferation and contained lymph node cell populations (and their secreted products) that enabled the testing of compounds within a system that mimicked the immunopathological environment of the infected host, which is known to profoundly influence parasite replication, killing, and drug efficacy. The activity of known antileishmanial drugs in the ELEC system was similar to the activity measured in peritoneal macrophages infected in vitro with L. major. Using the ELEC system, we screened a collection of 334 compounds, some of which we had demonstrated previously to be active against L. donovani, and identified 119 hits, 85% of which were confirmed to be active by determination of the 50% effective concentration (EC50). We found 24 compounds (7%) that had an in vitro therapeutic index (IVTI; 50% cytotoxic/effective concentration [CC50]/EC50) > 100; 19 of the compounds had an EC50 below 1 μM. According to PubChem searchs, 17 of those compounds had not previously been reported to be active against Leishmania. We expect that this novel method will help to accelerate discovery of new drug candidates for treatment of cutaneous leishmaniasis.

Development of an Ex Vivo Lymph Node Explant Model for Identification of Novel Molecules Active against Leishmania major

PubMed Central

Peniche, Alex G.; Osorio, Yaneth; Renslo, Adam R.; Frantz, Doug E.; Melby, Peter C.

2014-01-01

Leishmaniasis is a vector-borne zoonotic infection affecting people in tropical and subtropical regions of the world. Current treatments for cutaneous leishmaniasis are difficult to administer, toxic, expensive, and limited in effectiveness and availability. Here we describe the development and application of a medium-throughput screening approach to identify new drug candidates for cutaneous leishmaniasis using an ex vivo lymph node explant culture (ELEC) derived from the draining lymph nodes of Leishmania major-infected mice. The ELEC supported intracellular amastigote proliferation and contained lymph node cell populations (and their secreted products) that enabled the testing of compounds within a system that mimicked the immunopathological environment of the infected host, which is known to profoundly influence parasite replication, killing, and drug efficacy. The activity of known antileishmanial drugs in the ELEC system was similar to the activity measured in peritoneal macrophages infected in vitro with L. major. Using the ELEC system, we screened a collection of 334 compounds, some of which we had demonstrated previously to be active against L. donovani, and identified 119 hits, 85% of which were confirmed to be active by determination of the 50% effective concentration (EC50). We found 24 compounds (7%) that had an in vitro therapeutic index (IVTI; 50% cytotoxic/effective concentration [CC50]/EC50) > 100; 19 of the compounds had an EC50 below 1 μM. According to PubChem searchs, 17 of those compounds had not previously been reported to be active against Leishmania. We expect that this novel method will help to accelerate discovery of new drug candidates for treatment of cutaneous leishmaniasis. PMID:24126577

Prediction of metabolism-induced hepatotoxicity on three-dimensional hepatic cell culture and enzyme microarrays.

PubMed

Yu, Kyeong-Nam; Nadanaciva, Sashi; Rana, Payal; Lee, Dong Woo; Ku, Bosung; Roth, Alexander D; Dordick, Jonathan S; Will, Yvonne; Lee, Moo-Yeal

2018-03-01

Human liver contains various oxidative and conjugative enzymes that can convert nontoxic parent compounds to toxic metabolites or, conversely, toxic parent compounds to nontoxic metabolites. Unlike primary hepatocytes, which contain myriad drug-metabolizing enzymes (DMEs), but are difficult to culture and maintain physiological levels of DMEs, immortalized hepatic cell lines used in predictive toxicity assays are easy to culture, but lack the ability to metabolize compounds. To address this limitation and predict metabolism-induced hepatotoxicity in high-throughput, we developed an advanced miniaturized three-dimensional (3D) cell culture array (DataChip 2.0) and an advanced metabolizing enzyme microarray (MetaChip 2.0). The DataChip is a functionalized micropillar chip that supports the Hep3B human hepatoma cell line in a 3D microarray format. The MetaChip is a microwell chip containing immobilized DMEs found in the human liver. As a proof of concept for generating compound metabolites in situ on the chip and rapidly assessing their toxicity, 22 model compounds were dispensed into the MetaChip and sandwiched with the DataChip. The IC 50 values obtained from the chip platform were correlated with rat LD 50 values, human C max values, and drug-induced liver injury categories to predict adverse drug reactions in vivo. As a result, the platform had 100% sensitivity, 86% specificity, and 93% overall predictivity at optimum cutoffs of IC 50 and C max values. Therefore, the DataChip/MetaChip platform could be used as a high-throughput, early stage, microscale alternative to conventional in vitro multi-well plate platforms and provide a rapid and inexpensive assessment of metabolism-induced toxicity at early phases of drug development.

Radiation protective effects of baclofen predicted by a computational drug repurposing strategy.

PubMed

Ren, Lei; Xie, Dafei; Li, Peng; Qu, Xinyan; Zhang, Xiujuan; Xing, Yaling; Zhou, Pingkun; Bo, Xiaochen; Zhou, Zhe; Wang, Shengqi

2016-11-01

Exposure to ionizing radiation causes damage to living tissues; however, only a small number of agents have been approved for use in radiation injuries. Radioprotector is the primary countermeasure to radiation injury and none radioprotector has indeed reached the drug development stage. Repurposing the long list of approved, non-radioprotective drugs is an attractive strategy to find new radioprotective agents. Here, we applied a computational approach to discover new radioprotectors in silico by comparing publicly available gene expression data of ionizing radiation-treated samples from the Gene Expression Omnibus (GEO) database with gene expression signatures of more than 1309 small-molecule compounds from the Connectivity Map (cmap) dataset. Among the best compounds predicted to be therapeutic for ionizing radiation damage by this approach were some previously reported radioprotectors and baclofen (P<0.01), a chemical that was not previously used as radioprotector. Validation using a cell-based model and a rodent in vivo model demonstrated that treatment with baclofen reduced radiation-induced cytotoxicity in vitro (P<0.01), attenuated bone marrow damage and increased survival in vivo (P<0.05). These findings suggest that baclofen might serve as a radioprotector. The drug repurposing strategy by connecting the GEO data and cmap can be used to identify known drugs as potential radioprotective agents. Copyright © 2016 Elsevier Ltd. All rights reserved.

In vivo assessment of parenteral formulations of oligo(3-hydroxybutyric Acid) conjugates with the model compound Ibuprofen.

PubMed

Stasiak, Pawel; Sznitowska, Malgorzata; Ehrhardt, Carsten; Luczyk-Juzwa, Maria; Grieb, Pawel

2010-12-01

Polymer-drug conjugates have gained significant attention as pro-drugs releasing an active substance as a result of enzymatic hydrolysis in physiological environment. In this study, a conjugate of 3-hydroxybutyric acid oligomers with a carboxylic acid group-bearing model drug (ibuprofen) was evaluated in vivo as a potential pro-drug for parenteral administration. Two different formulations, an oily solution and an o/w emulsion were prepared and administered intramuscularly (IM) to rabbits in a dose corresponding to 40 mg of ibuprofen/kilogramme. The concentration of ibuprofen in blood plasma was analysed by HPLC, following solid-phase extraction and using indometacin as internal standard (detection limit, 0.05 microg/ml). No significant differences in the pharmacokinetic parameters (C (max), T (max), AUC) were observed between the two tested formulations of the 3-hydroxybutyric acid conjugate. In comparison to the non-conjugated drug in oily solution, the relative bioavailability of ibuprofen conjugates from oily solution, and o/w emulsion was reduced to 17% and 10%, respectively. The 3-hydroxybutyric acid formulations released the active substance over a significantly extended period of time with ibuprofen still being detectable 24 h post-injection, whereas the free compound was almost completely eliminated as early as 6 h after administration. The conjugates remained in a muscle tissue for a prolonged time and can hence be considered as sustained release systems for carboxylic acid derivatives.

Screening the Medicines for Malaria Venture Pathogen Box across Multiple Pathogens Reclassifies Starting Points for Open-Source Drug Discovery

PubMed Central

Sykes, Melissa L.; Jones, Amy J.; Shelper, Todd B.; Simpson, Moana; Lang, Rebecca; Poulsen, Sally-Ann; Sleebs, Brad E.

2017-01-01

ABSTRACT Open-access drug discovery provides a substantial resource for diseases primarily affecting the poor and disadvantaged. The open-access Pathogen Box collection is comprised of compounds with demonstrated biological activity against specific pathogenic organisms. The supply of this resource by the Medicines for Malaria Venture has the potential to provide new chemical starting points for a number of tropical and neglected diseases, through repurposing of these compounds for use in drug discovery campaigns for these additional pathogens. We tested the Pathogen Box against kinetoplastid parasites and malaria life cycle stages in vitro. Consequently, chemical starting points for malaria, human African trypanosomiasis, Chagas disease, and leishmaniasis drug discovery efforts have been identified. Inclusive of this in vitro biological evaluation, outcomes from extensive literature reviews and database searches are provided. This information encompasses commercial availability, literature reference citations, other aliases and ChEMBL number with associated biological activity, where available. The release of this new data for the Pathogen Box collection into the public domain will aid the open-source model of drug discovery. Importantly, this will provide novel chemical starting points for drug discovery and target identification in tropical disease research. PMID:28674055

Screening the Medicines for Malaria Venture Pathogen Box across Multiple Pathogens Reclassifies Starting Points for Open-Source Drug Discovery.

PubMed

Duffy, Sandra; Sykes, Melissa L; Jones, Amy J; Shelper, Todd B; Simpson, Moana; Lang, Rebecca; Poulsen, Sally-Ann; Sleebs, Brad E; Avery, Vicky M

2017-09-01

Open-access drug discovery provides a substantial resource for diseases primarily affecting the poor and disadvantaged. The open-access Pathogen Box collection is comprised of compounds with demonstrated biological activity against specific pathogenic organisms. The supply of this resource by the Medicines for Malaria Venture has the potential to provide new chemical starting points for a number of tropical and neglected diseases, through repurposing of these compounds for use in drug discovery campaigns for these additional pathogens. We tested the Pathogen Box against kinetoplastid parasites and malaria life cycle stages in vitro Consequently, chemical starting points for malaria, human African trypanosomiasis, Chagas disease, and leishmaniasis drug discovery efforts have been identified. Inclusive of this in vitro biological evaluation, outcomes from extensive literature reviews and database searches are provided. This information encompasses commercial availability, literature reference citations, other aliases and ChEMBL number with associated biological activity, where available. The release of this new data for the Pathogen Box collection into the public domain will aid the open-source model of drug discovery. Importantly, this will provide novel chemical starting points for drug discovery and target identification in tropical disease research. Copyright © 2017 Duffy et al.

Machine learning and docking models for Mycobacterium tuberculosis topoisomerase I.

PubMed

Ekins, Sean; Godbole, Adwait Anand; Kéri, György; Orfi, Lászlo; Pato, János; Bhat, Rajeshwari Subray; Verma, Rinkee; Bradley, Erin K; Nagaraja, Valakunja

2017-03-01

There is a shortage of compounds that are directed towards new targets apart from those targeted by the FDA approved drugs used against Mycobacterium tuberculosis. Topoisomerase I (Mttopo I) is an essential mycobacterial enzyme and a promising target in this regard. However, it suffers from a shortage of known inhibitors. We have previously used computational approaches such as homology modeling and docking to propose 38 FDA approved drugs for testing and identified several active molecules. To follow on from this, we now describe the in vitro testing of a library of 639 compounds. These data were used to create machine learning models for Mttopo I which were further validated. The combined Mttopo I Bayesian model had a 5 fold cross validation receiver operator characteristic of 0.74 and sensitivity, specificity and concordance values above 0.76 and was used to select commercially available compounds for testing in vitro. The recently described crystal structure of Mttopo I was also compared with the previously described homology model and then used to dock the Mttopo I actives norclomipramine and imipramine. In summary, we describe our efforts to identify small molecule inhibitors of Mttopo I using a combination of machine learning modeling and docking studies in conjunction with screening of the selected molecules for enzyme inhibition. We demonstrate the experimental inhibition of Mttopo I by small molecule inhibitors and show that the enzyme can be readily targeted for lead molecule development. Copyright © 2017 Elsevier Ltd. All rights reserved.

GDA, a web-based tool for Genomics and Drugs integrated analysis.

PubMed

Caroli, Jimmy; Sorrentino, Giovanni; Forcato, Mattia; Del Sal, Giannino; Bicciato, Silvio

2018-05-25

Several major screenings of genetic profiling and drug testing in cancer cell lines proved that the integration of genomic portraits and compound activities is effective in discovering new genetic markers of drug sensitivity and clinically relevant anticancer compounds. Despite most genetic and drug response data are publicly available, the availability of user-friendly tools for their integrative analysis remains limited, thus hampering an effective exploitation of this information. Here, we present GDA, a web-based tool for Genomics and Drugs integrated Analysis that combines drug response data for >50 800 compounds with mutations and gene expression profiles across 73 cancer cell lines. Genomic and pharmacological data are integrated through a modular architecture that allows users to identify compounds active towards cancer cell lines bearing a specific genomic background and, conversely, the mutational or transcriptional status of cells responding or not-responding to a specific compound. Results are presented through intuitive graphical representations and supplemented with information obtained from public repositories. As both personalized targeted therapies and drug-repurposing are gaining increasing attention, GDA represents a resource to formulate hypotheses on the interplay between genomic traits and drug response in cancer. GDA is freely available at http://gda.unimore.it/.

A Chemogenomic Analysis of Ionization Constants - Implications for Drug Discovery

PubMed Central

Manallack, David T.; Prankerd, Richard J.; Nassta, Gemma C.; Ursu, Oleg; Oprea, Tudor I.; Chalmers, David K.

2013-01-01

Chemogenomics methods seek to characterize the interaction between drugs and biological systems and are an important guide for the selection of screening compounds. The acid/base character of drugs has a profound influence on their affinity for the receptor, on their absorption, distribution, metabolism, excretion and toxicity (ADMET) profile and the way the drug can be formulated. In particular, the charge state of a molecule greatly influences its lipophilicity and biopharmaceutical characteristics. This study investigates the acid/base profile of human small molecule drugs, chemogenomics datasets and screening compounds including a natural products set. We estimate the ionization constants (pKa values) of these compounds and determine the identity of the ionizable functional groups in each set. We find substantial differences in acid/base profiles of the chemogenomic classes. In many cases, these differences can be linked to the nature of the target binding site and the corresponding functional groups needed for recognition of the ligand. Clear differences are also observed between the acid/base characteristics of drugs and screening compounds. For example, the proportion of drugs containing a carboxylic acid was 20%, in stark contrast to a value of 2.4% for the screening set sample. The proportion of aliphatic amines was 27% for drugs and only 3.4% for screening compounds. This suggests that there is a mismatch between commercially available screening compounds and the compounds that are likely to interact with a given chemogenomic target family. Our analysis provides a guide for the selection of screening compounds to better target specific chemogenomic families with regard to the overall balance of acids, bases and pKa distributions. PMID:23303535

Antinociceptive activity of novel amide derivatives of imidazolidine-2,4-dione in a mouse model of acute pain.

PubMed

Czopek, Anna; Sałat, Kinga; Byrtus, Hanna; Rychtyk, Joanna; Pawłowski, Maciej; Siwek, Agata; Soluch, Joanna; Mureddu, Valentina; Filipek, Barbara

2016-06-01

Antiepileptic drugs are commonly used in non-epileptic disorders. For example, phenytoin and levetiracetam demonstrate analgesic properties in rodent models of pain. In order to enhance their antinociceptive activity, structural features of phenytoin and levetiracetam, such as imidazolidine-2,4-dione and amide bond in alkyl chain, were combined in one molecule. Furthermore, in preliminary studies, methoxyphenylpiperazinpropyl derivatives of imidazolidine-2,4-dione acted as antinociceptive agents in several rodent models of acute pain. The final compounds and the reference drugs - levetiracetam and phenytoin were evaluated in the hot plate test to assess their antinociceptive activity in this acute pain model. Furthermore, for the analgesic active compounds the impact on animals' locomotor activity and motor performance were estimated and the affinity to serotonergic (5-HT1A, 5-HT7) and adrenergic (α1) receptors was determined. Three of the tested compounds: 7, 15 and 18 showed statistically significant antinociceptive properties at the dose of 30mg/kg. Among them, compound 18, 1-methyl-3-[1-(morpholin-4-yl)-1-oxobutan-2-yl]imidazolidine-2,4-dione, exhibited the most significant and long-lasting antinociceptive activity. Noteworthy, this activity was not associated with a negative effect on animals' motor functions. Serotonergic or adrenergic neurotransmission is not involved in this antinociceptive effect. Some amide derivatives of imidazolidine-2,4-diones possess antinociceptive properties in mice but further studies are needed to explain their mechanism of action and assess their toxicity. Copyright © 2016 Institute of Pharmacology, Polish Academy of Sciences. Published by Elsevier Urban & Partner Sp. z o.o. All rights reserved.

Predicting the activity of drugs for a group of imidazopyridine anticoccidial compounds.

PubMed

Si, Hongzong; Lian, Ning; Yuan, Shuping; Fu, Aiping; Duan, Yun-Bo; Zhang, Kejun; Yao, Xiaojun

2009-10-01

Gene expression programming (GEP) is a novel machine learning technique. The GEP is used to build nonlinear quantitative structure-activity relationship model for the prediction of the IC(50) for the imidazopyridine anticoccidial compounds. This model is based on descriptors which are calculated from the molecular structure. Four descriptors are selected from the descriptors' pool by heuristic method (HM) to build multivariable linear model. The GEP method produced a nonlinear quantitative model with a correlation coefficient and a mean error of 0.96 and 0.24 for the training set, 0.91 and 0.52 for the test set, respectively. It is shown that the GEP predicted results are in good agreement with experimental ones.

Biohybrid Membrane Systems and Bioreactors as Tools for In Vitro Drug Testing.

PubMed

Salerno, Simona; Bartolo, Loredana De

2017-01-01

In drug development, in vitro human model systems are absolutely essential prior to the clinical trials, considering the increasing number of chemical compounds in need of testing, and, keeping in mind that animals cannot predict all the adverse human health effects and reactions, due to the species-specific differences in metabolic pathways. The liver plays a central role in the clearance and biotransformation of chemicals and xenobiotics. In vitro liver model systems by using highly differentiated human cells could have a great impact in preclinical trials. Membrane biohybrid systems constituted of human hepatocytes and micro- and nano-structured membranes, represent valuable tools for studying drug metabolism and toxicity. Membranes act as an extracellular matrix for the adhesion of hepatocytes, and compartmentalise them in a well-defined physical and chemical microenvironment with high selectivity. Advanced 3-D tissue cultures are furthermore achieved by using membrane bioreactors (MBR), which ensure the continuous perfusion of cells protecting them from shear stress. MBRs with different configurations allow the culturing of cells at high density and under closely monitored high perfusion, similarly to the natural liver. These devices that promote the long-term maintenance and differentiation of primary human hepatocytes with preserved liver specific functions can be employed in drug testing for prolonged exposure to chemical compounds and for assessing repeated-dose toxicity. The use of primary human hepatocytes in MBRs is the only system providing a faster and more cost-effective method of analysis for the prediction of in vitro human drug metabolism and enzyme induction alternative and/or complementary to the animal experimentation. In this paper, in vitro models for studying drug metabolism and toxicity as advanced biohybrid membrane systems and MBRs will be reviewed. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Active pharmaceutical ingredients available as substances for extemporaneous preparation in veterinary medicine in the Czech Republic.

PubMed

Sklenář, Zbyněk; Horáčková, Kateřina; Bakhouche, Hana

2014-04-01

In veterinary medicine, extemporaneously prepared drugs can be also used in therapy. In the recent four years the selection of suitable compounds for extemporaneous (magistral) preparation has been expanded and new possibilities for the creation of formulas have appeared. The paper reports on the substances available for compounding that can be used in veterinary medicine, in the pharmacotherapeutic classes antibiotics, antimycotics, antiseptics, corticosteroids, emollients and epithelizing agents, anti-inflammatory drugs, local anesthetics, decongestives, beta-blockers and calcium channel blockers, antiemetics and prokinetics, sedatives and hypnotics. The emphasis has been placed on newly available substances. Examples of suitable magistral formulas are presented that can replace mass-produced drug products which are not readily obtainable. The aim of the paper is to inform pharmacists and veterinarians about new possibilities of drug compounding. compounded preparations extemporaneous preparation compounding of drugs possibilities magistral formulas in veterinary medicine.

Use of Optical Imaging Technology in the Validation of a New, Rapid, Cost-Effective Drug Screen as Part of a Tiered In Vivo Screening Paradigm for Development of Drugs To Treat Cutaneous Leishmaniasis

PubMed Central

Parriot, Sandi; Hudson, Thomas H.; Lang, Thierry; Ngundam, Franklyn; Leed, Susan; Sena, Jenell; Harris, Michael; O'Neil, Michael; Sciotti, Richard; Read, Lisa; Lecoeur, Herve; Grogl, Max

2017-01-01

ABSTRACT In any drug discovery and development effort, a reduction in the time of the lead optimization cycle is critical to decrease the time to license and reduce costs. In addition, ethical guidelines call for the more ethical use of animals to minimize the number of animals used and decrease their suffering. Therefore, any effort to develop drugs to treat cutaneous leishmaniasis requires multiple tiers of in vivo testing that start with higher-throughput efficacy assessments and progress to lower-throughput models with the most clinical relevance. Here, we describe the validation of a high-throughput, first-tier, noninvasive model of lesion suppression that uses an in vivo optical imaging technology for the initial screening of compounds. A strong correlation between luciferase activity and the parasite load at up to 18 days postinfection was found. This correlation allows the direct assessment of the effects of drug treatment on parasite burden. We demonstrate that there is a strong correlation between drug efficacy measured on day 18 postinfection and the suppression of lesion size by day 60 postinfection, which allows us to reach an accurate conclusion on drug efficacy in only 18 days. Compounds demonstrating a significant reduction in the bioluminescence signal compared to that in control animals can be tested in lower-throughput, more definitive tests of lesion cure in BALB/c mice and Golden Syrian hamsters (GSH) using Old World and New World parasites. PMID:28137819

From the Test Tube to the Treatment Room

PubMed Central

Del Rosso, James Q.; Plattner, Jacob J.

2014-01-01

The development of new drug classes and novel molecules that are brought to the marketplace has been a formidable challenge, especially for dermatologic drugs. The relative absence of new classes of antimicrobial agents is also readily apparent. Several barriers account for slow drug development, including regulatory changes, added study requirements, commercial pressures to bring drugs to market quickly by developing new generations of established compounds, and the greater potential for failure and higher financial risk when researching new drug classes. In addition, the return on investment is usually much lower with dermatologic drugs as compared to the potential revenue from “blockbuster” drugs for cardiovascular or gastrointestinal disease, hypercholesterolemia, and mood disorders. Nevertheless, some researchers are investigating new therapeutic platforms, one of which is boron-containing compounds. Boron-containing compounds offer a wide variety of potential applications in dermatology due to their unique physical and chemical properties, with several in formal phases of development. Tavaborole, a benzoxaborole compound, has been submitted to the United States Food and Drug Administration for approval for treatment of onychomycosis. This article provides a thorough overview of the history of boron-based compounds in medicine, their scientific rationale, physiochemical and pharmacologic properties, and modes of actions including therapeutic targets. A section dedicated to boron-based compounds in development for treatment of various skin disorders is also included. PMID:24578778

Sodium deoxycholate-decorated zein nanoparticles for a stable colloidal drug delivery system

PubMed Central

Gagliardi, Agnese; Paolino, Donatella; Iannone, Michelangelo; Palma, Ernesto

2018-01-01

Background The use of biopolymers is increasing in drug delivery, thanks to the peculiar properties of these compounds such as their biodegradability, availability, and the possibility of modulating their physico-chemical characteristics. In particular, protein-based systems such as albumin are able to interact with many active compounds, modulating their biopharmaceutical properties. Zein is a protein of 20–40 kDa made up of many hydrophobic amino acids, generally regarded as safe (GRAS) and used as a coating material. Methods In this investigation, zein was combined with various surfactants in order to obtain stable nanosystems by means of the nanoprecipitation technique. Specific parameters, eg, temperature, pH value, Turbiscan Stability Index, serum stability, in vitro cytotoxicity and entrapment efficiency of various model compounds were investigated, in order to identify the nanoformulation most useful for a systemic drug delivery application. Results The use of non-ionic and ionic surfactants such as Tween 80, poloxamer 188, and sodium deoxycholate allowed us to obtain nanoparticles characterized by a mean diameter of 100–200 nm when a protein concentration of 2 mg/mL was used. The surface charge was modulated by means of the protein concentration and the nature of the stabilizer. The most suitable nanoparticle formulation to be proposed as a colloidal drug delivery system was obtained using sodium deoxycholate (1.25% w/v) because it was characterized by a narrow size distribution, a good storage stability after freeze-drying and significant feature of retaining lipophilic and hydrophilic compounds. Conclusion The sodium deoxycholate-coated zein nanoparticles are stable biocompatible colloidal carriers to be used as useful drug delivery systems. PMID:29430179

In vitro screening techniques for reactive metabolites for minimizing bioactivation potential in drug discovery.

PubMed

Prakash, Chandra; Sharma, Raman; Gleave, Michelle; Nedderman, Angus

2008-11-01

Drug induced toxicity remains one of the major reasons for failures of new pharmaceuticals, and for the withdrawal of approved drugs from the market. Efforts are being made to reduce attrition of drug candidates, and to minimize their bioactivation potential in the early stages of drug discovery in order to bring safer compounds to the market. Therefore, in addition to potency and selectivity; drug candidates are now selected on the basis of acceptable metabolism/toxicology profiles in preclinical species. To support this, new approaches have been developed, which include extensive in vitro methods using human and animal hepatic cellular and subcellular systems, recombinant human drug metabolizing enzymes, increased automation for higher-throughput screens, sensitive analytical technologies and in silico computational models to assess the metabolism aspects of the new chemical entities. By using these approaches many compounds that might have serious adverse reactions associated with them are effectively eliminated before reaching clinical trials, however some toxicities such as those caused by idiosyncratic responses, are not detected until a drug is in late stages of clinical trials or has become available to the market. One of the proposed mechanisms for the development of idiosyncratic drug toxicity is the bioactivation of drugs to form reactive metabolites by drug metabolizing enzymes. This review discusses the different approaches to, and benefits of using existing in vitro techniques, for the detection of reactive intermediates in order to minimize bioactivation potential in drug discovery.

Predicting hydration free energies of amphetamine-type stimulants with a customized molecular model

NASA Astrophysics Data System (ADS)

Li, Jipeng; Fu, Jia; Huang, Xing; Lu, Diannan; Wu, Jianzhong

2016-09-01

Amphetamine-type stimulants (ATS) are a group of incitation and psychedelic drugs affecting the central nervous system. Physicochemical data for these compounds are essential for understanding the stimulating mechanism, for assessing their environmental impacts, and for developing new drug detection methods. However, experimental data are scarce due to tight regulation of such illicit drugs, yet conventional methods to estimate their properties are often unreliable. Here we introduce a tailor-made multiscale procedure for predicting the hydration free energies and the solvation structures of ATS molecules by a combination of first principles calculations and the classical density functional theory. We demonstrate that the multiscale procedure performs well for a training set with similar molecular characteristics and yields good agreement with a testing set not used in the training. The theoretical predictions serve as a benchmark for the missing experimental data and, importantly, provide microscopic insights into manipulating the hydrophobicity of ATS compounds by chemical modifications.

Preclinical investigation of tolerance and antitumour activity of new fluorodeoxyglucose-coupled chlorambucil alkylating agents.

PubMed

Miot-Noirault, Elisabeth; Reux, Bastien; Debiton, Eric; Madelmont, Jean-Claude; Chezal, Jean-Michel; Coudert, Pascal; Weber, Valérie

2011-06-01

Our strategy is to increase drug accumulation in target tumour cells using specific "vectors" tailored to neoplastic tissue characteristics, which ideally are not found in healthy tissues. The aim of this work was to use 2-fluoro-2-deoxyglucose (FDG) as a drug carrier, in view of its well-known accumulation by most primary and disseminated human tumours. We had previously selected two FDG-cytotoxic conjugates of chlorambucil (CLB), i.e. compounds 21a and 40a, on the basis of their in vitro profiles. Here we investigated the antitumour profile and tolerance of these compounds in vitro and in vivo in two murine cell lines of solid tumours. In vitro, we found that micromolar concentrations of compounds 21a and 40a inhibited proliferation of B16F0 and CT-26 cell lines. Interestingly, compounds 21a and 40a were found to act at different levels in the cell cycle: S and subG1 accumulation for 21a and G2 accumulation for 40a. In vivo, a single-dose-finding study to select the Maximum Tolerated Dose (MTD) by the intraperitoneal route (IP) showed that the two peracetylated glucoconjugates of CLB were less toxic than CLB itself. When given to tumour-bearing mice (melanoma and colon carcinoma models), according to a "q4d × 3" schedule (i.e., three doses at 4-day intervals) both compounds demonstrated a promising antitumour activity, with Log Cell Kill (LCK) values higher than 1.3 in both B16F0 and CT-26 models. Hence compounds 21a and 40a are good candidates for further works to develop new highly active antineoplastic compounds.

Mechanistic systems modeling to guide drug discovery and development

PubMed Central

Schmidt, Brian J.; Papin, Jason A.; Musante, Cynthia J.

2013-01-01

A crucial question that must be addressed in the drug development process is whether the proposed therapeutic target will yield the desired effect in the clinical population. Pharmaceutical and biotechnology companies place a large investment on research and development, long before confirmatory data are available from human trials. Basic science has greatly expanded the computable knowledge of disease processes, both through the generation of large omics data sets and a compendium of studies assessing cellular and systemic responses to physiologic and pathophysiologic stimuli. Given inherent uncertainties in drug development, mechanistic systems models can better inform target selection and the decision process for advancing compounds through preclinical and clinical research. PMID:22999913

Mechanistic systems modeling to guide drug discovery and development.

PubMed

Schmidt, Brian J; Papin, Jason A; Musante, Cynthia J

2013-02-01

A crucial question that must be addressed in the drug development process is whether the proposed therapeutic target will yield the desired effect in the clinical population. Pharmaceutical and biotechnology companies place a large investment on research and development, long before confirmatory data are available from human trials. Basic science has greatly expanded the computable knowledge of disease processes, both through the generation of large omics data sets and a compendium of studies assessing cellular and systemic responses to physiologic and pathophysiologic stimuli. Given inherent uncertainties in drug development, mechanistic systems models can better inform target selection and the decision process for advancing compounds through preclinical and clinical research. Copyright © 2012 Elsevier Ltd. All rights reserved.

Three-dimensional quantitative structure-activity relationship analysis for human pregnane X receptor for the prediction of CYP3A4 induction in human hepatocytes: structure-based comparative molecular field analysis.

PubMed

Handa, Koichi; Nakagome, Izumi; Yamaotsu, Noriyuki; Gouda, Hiroaki; Hirono, Shuichi

2015-01-01

The pregnane X receptor [PXR (NR1I2)] induces the expression of xenobiotic metabolic genes and transporter genes. In this study, we aimed to establish a computational method for quantifying the enzyme-inducing potencies of different compounds via their ability to activate PXR, for the application in drug discovery and development. To achieve this purpose, we developed a three-dimensional quantitative structure-activity relationship (3D-QSAR) model using comparative molecular field analysis (CoMFA) for predicting enzyme-inducing potencies, based on computer-ligand docking to multiple PXR protein structures sampled from the trajectory of a molecular dynamics simulation. Molecular mechanics-generalized born/surface area scores representing the ligand-protein-binding free energies were calculated for each ligand. As a result, the predicted enzyme-inducing potencies for compounds generated by the CoMFA model were in good agreement with the experimental values. Finally, we concluded that this 3D-QSAR model has the potential to predict the enzyme-inducing potencies of novel compounds with high precision and therefore has valuable applications in the early stages of the drug discovery process. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.

[Moderation-integrated-balance presupposition of Chinese medicine compound and pharmacological problems in traditional Chinese drug research].

PubMed

Zhao, Jun-Ning

2017-03-01

The moderation-integrated-balance presupposition (MIBP) of Chinese medicine compound was first proposed in this paper based on the review of function characteristics and action principles of Chinese medicine compound. Furthermore, the pharmacological problems of traditional Chinese drug research were discussed in details. The results were of important value in accelerating the transformation of traditional Chinese medicine compound, and constructing the new drug innovation and review system for traditional Chinese medicine. Copyright© by the Chinese Pharmaceutical Association.

Design and characterization of submicron formulation for a poorly soluble drug: the effect of Vitamin E TPGS and other solubilizers on skin permeability enhancement.

PubMed

Ghosh, Indrajit; Michniak-Kohn, Bozena

2012-09-15

In transdermal drug delivery systems (TDDS), it is a challenge to achieve stable and prolonged high permeation rates across the skin since the concentrations of the drug dissolved in the matrix have to be high in order to maintain zero order release kinetics. Several attempts have been reported to improve the permeability of poorly soluble drug compounds using supersaturated systems, however, due to thermodynamic challenges, there was a high tendency for the drug to nucleate immediately after formulating or even during storage. The present study focuses on the efficiency of drug crystals at the submicron/nano range in presence of different solubilizers to improve the permeation rate. Effect of several solubilizers, e.g. Pluronic F-127, Vitamin E TPGS, propylene glycol were studied on the submicron suspension systems of ibuprofen as a model drug. Various stabilizers such as hydroxylpropyl methylcellulose (HPMC) and polyvinylpyrrolidone (PVP) were examined to evaluate their crystal inhibitory effects on particle growth of the drug compound at submicron range. The overall permeation enhancement process through the skin seems to be influenced by the presence of solubilizers and also the presence of submicron drug crystal. The most promising stable formulation was developed with Vitamin E TPGS+HPMC submicron suspension, which produced higher permeation rate compared to other vehicles. Copyright © 2012 Elsevier B.V. All rights reserved.

A predictive pharmacokinetic-pharmacodynamic model of tumor growth kinetics in xenograft mice after administration of anticancer agents given in combination.

PubMed

Terranova, Nadia; Germani, Massimiliano; Del Bene, Francesca; Magni, Paolo

2013-08-01

In clinical oncology, combination treatments are widely used and increasingly preferred over single drug administrations. A better characterization of the interaction between drug effects and the selection of synergistic combinations represent an open challenge in drug development process. To this aim, preclinical studies are routinely performed, even if they are only qualitatively analyzed due to the lack of generally applicable mathematical models. This paper presents a new pharmacokinetic-pharmacodynamic model that, starting from the well-known single agent Simeoni TGI model, is able to describe tumor growth in xenograft mice after the co-administration of two anticancer agents. Due to the drug action, tumor cells are divided in two groups: damaged and not damaged ones. The damaging rate has two terms proportional to drug concentrations (as in the single drug administration model) and one interaction term proportional to their product. Six of the eight pharmacodynamic parameters assume the same value as in the corresponding single drug models. Only one parameter summarizes the interaction, and it can be used to compute two important indexes that are a clear way to score the synergistic/antagonistic interaction among drug effects. The model was successfully applied to four new compounds co-administered with four drugs already available on the market for the treatment of three different tumor cell lines. It also provided reliable predictions of different combination regimens in which the same drugs were administered at different doses/schedules. A good and quantitative measurement of the intensity and nature of interaction between drug effects, as well as the capability to correctly predict new combination arms, suggest the use of this generally applicable model for supporting the experiment optimal design and the prioritization of different therapies.

Capture compound mass spectrometry sheds light on the molecular mechanisms of liver toxicity of two Parkinson drugs.

PubMed

Fischer, Jenny J; Michaelis, Simon; Schrey, Anna K; Graebner, Olivia Graebner nee; Glinski, Mirko; Dreger, Mathias; Kroll, Friedrich; Koester, Hubert

2010-01-01

Capture compound mass spectrometry (CCMS) is a novel technology that helps understand the molecular mechanism of the mode of action of small molecules. The Capture Compounds are trifunctional probes: A selectivity function (the drug) interacts with the proteins in a biological sample, a reactivity function (phenylazide) irreversibly forms a covalent bond, and a sorting function (biotin) allows the captured protein(s) to be isolated for mass spectrometric analysis. Tolcapone and entacapone are potent inhibitors of catechol-O-methyltransferase (COMT) for the treatment of Parkinson's disease. We aimed to understand the molecular basis of the difference of both drugs with respect to side effects. Using Capture Compounds with these drugs as selectivity functions, we were able to unambiguously and reproducibly isolate and identify their known target COMT. Tolcapone Capture Compounds captured five times more proteins than entacapone Capture Compounds. Moreover, tolcapone Capture Compounds isolated mitochondrial and peroxisomal proteins. The major tolcapone-protein interactions occurred with components of the respiratory chain and of the fatty acid beta-oxidation. Previously reported symptoms in tolcapone-treated rats suggested that tolcapone might act as decoupling reagent of the respiratory chain (Haasio et al., 2002b). Our results demonstrate that CCMS is an effective tool for the identification of a drug's potential off targets. It fills a gap in currently used in vitro screens for drug profiling that do not contain all the toxicologically relevant proteins. Thereby, CCMS has the potential to fill a technological need in drug safety assessment and helps reengineer or to reject drugs at an early preclinical stage.

Combined computational-experimental approach to predict blood-brain barrier (BBB) permeation based on "green" salting-out thin layer chromatography supported by simple molecular descriptors.

PubMed

Ciura, Krzesimir; Belka, Mariusz; Kawczak, Piotr; Bączek, Tomasz; Markuszewski, Michał J; Nowakowska, Joanna

2017-09-05

The objective of this paper is to build QSRR/QSAR model for predicting the blood-brain barrier (BBB) permeability. The obtained models are based on salting-out thin layer chromatography (SOTLC) constants and calculated molecular descriptors. Among chromatographic methods SOTLC was chosen, since the mobile phases are free of organic solvent. As consequences, there are less toxic, and have lower environmental impact compared to classical reserved phases liquid chromatography (RPLC). During the study three stationary phase silica gel, cellulose plates and neutral aluminum oxide were examined. The model set of solutes presents a wide range of log BB values, containing compounds which cross the BBB readily and molecules poorly distributed to the brain including drugs acting on the nervous system as well as peripheral acting drugs. Additionally, the comparison of three regression models: multiple linear regression (MLR), partial least-squares (PLS) and orthogonal partial least squares (OPLS) were performed. The designed QSRR/QSAR models could be useful to predict BBB of systematically synthesized newly compounds in the drug development pipeline and are attractive alternatives of time-consuming and demanding directed methods for log BB measurement. The study also shown that among several regression techniques, significant differences can be obtained in models performance, measured by R 2 and Q 2 , hence it is strongly suggested to evaluate all available options as MLR, PLS and OPLS. Copyright © 2017 Elsevier B.V. All rights reserved.

Current perspectives on selective dopamine D3 receptor antagonists as pharmacotherapeutics for addictions and related disorders

PubMed Central

Heidbreder, Christian A.; Newman, Amy H.

2011-01-01

Repeated exposure to drugs of abuse produces long-term molecular and neurochemical changes that may explain the core features of addiction, such as the compulsive seeking and taking of the drug, as well as the risk of relapse. A growing number of new molecular and cellular targets of addictive drugs have been identified, and rapid advances are being made in relating those targets to specific behavioral phenotypes in animal models of addiction. In this context, the pattern of expression of the dopamine (DA) D3 receptor in the rodent and human brain and changes in this pattern in response to drugs of abuse have contributed primarily to direct research efforts toward the development of selective DA D3 receptor antagonists. Growing preclinical evidence indicates that these compounds may actually regulate the motivation to self-administer drugs and disrupt drug-associated cue-induced craving. This report will be divided into three parts. First, preclinical evidence in support of the efficacy of selective DA D3 receptor antagonists in animal models of drug addiction will be reviewed. The effects of mixed DA D2/D3 receptor antagonists will not be discussed here because most of these compounds have low selectivity at the D3 versus D2 receptor, and their efficacy profile is related primarily to functional antagonism at D2 receptors and possibly interactions with other neurotransmitter systems. Second, major advances in medicinal chemistry for the identification and optimization of selective DA D3 receptor antagonists and partial agonists will be analyzed. Third, translational research from preclinical efficacy studies to so-called proof-of-concept studies for drug addiction indications will be discussed. PMID:20201845

Current perspectives on selective dopamine D(3) receptor antagonists as pharmacotherapeutics for addictions and related disorders.

PubMed

Heidbreder, Christian A; Newman, Amy H

2010-02-01

Repeated exposure to drugs of abuse produces long-term molecular and neurochemical changes that may explain the core features of addiction, such as the compulsive seeking and taking of the drug, as well as the risk of relapse. A growing number of new molecular and cellular targets of addictive drugs have been identified, and rapid advances are being made in relating those targets to specific behavioral phenotypes in animal models of addiction. In this context, the pattern of expression of the dopamine (DA) D(3) receptor in the rodent and human brain and changes in this pattern in response to drugs of abuse have contributed primarily to direct research efforts toward the development of selective DA D(3) receptor antagonists. Growing preclinical evidence indicates that these compounds may actually regulate the motivation to self-administer drugs and disrupt drug-associated cue-induced craving. This report will be divided into three parts. First, preclinical evidence in support of the efficacy of selective DA D(3) receptor antagonists in animal models of drug addiction will be reviewed. The effects of mixed DA D(2)/D(3) receptor antagonists will not be discussed here because most of these compounds have low selectivity at the D(3) versus D(2) receptor, and their efficacy profile is related primarily to functional antagonism at D(2) receptors and possibly interactions with other neurotransmitter systems. Second, major advances in medicinal chemistry for the identification and optimization of selective DA D(3) receptor antagonists and partial agonists will be analyzed. Third, translational research from preclinical efficacy studies to so-called proof-of-concept studies for drug addiction indications will be discussed.

Effect of menthone and related compounds on skin permeation of drugs with different lipophilicity and molecular organization of stratum corneum lipids.

PubMed

Lan, Yi; Wang, Jingyan; Li, Hui; Zhang, Yewen; Chen, Yanyan; Zhao, Bochen; Wu, Qing

2016-01-01

The objective of this article was to investigate the enhancing effect of menthone, menthol and pulegone on the transdermal absorption of drugs with different lipophilicity and probe their mechanisms of action at molecular level. Five model drugs, namely osthole, tetramethylpyrazine, ferulic acid, puerarin and geniposide, which were selected based on their lipophilicity denoted by logKo/w, were tested using in vitro permeation studies in which Franz diffusion cells and rat skin were employed. Infrared spectroscopy and molecular dynamic simulation were used to investigate the effect of these enhancers on the stratum corneum (SC) lipids, respectively. Three compounds could effectively promote the transdermal absorption of drugs with different lipophilicity, and the overall promoting capacities were in the following increasing order: pulegone < menthol < menthone. The penetration enhancement ratio was roughly in parabolic curve relationships with the drug lipophilicity after treatment with menthol or menthone, while the penetration enhancement effect of pulegone hardly changed with the alteration of the drug lipophilicity. The molecular mechanism studies suggested that menthone and menthol enhanced the skin permeability by disordering the ordered organization of SC lipids and extracted part of SC lipids, while pulegone appeared to promote drug transport across the skin only by extracting part of SC lipids.

Drug2Gene: an exhaustive resource to explore effectively the drug-target relation network.

PubMed

Roider, Helge G; Pavlova, Nadia; Kirov, Ivaylo; Slavov, Stoyan; Slavov, Todor; Uzunov, Zlatyo; Weiss, Bertram

2014-03-11

Information about drug-target relations is at the heart of drug discovery. There are now dozens of databases providing drug-target interaction data with varying scope, and focus. Therefore, and due to the large chemical space, the overlap of the different data sets is surprisingly small. As searching through these sources manually is cumbersome, time-consuming and error-prone, integrating all the data is highly desirable. Despite a few attempts, integration has been hampered by the diversity of descriptions of compounds, and by the fact that the reported activity values, coming from different data sets, are not always directly comparable due to usage of different metrics or data formats. We have built Drug2Gene, a knowledge base, which combines the compound/drug-gene/protein information from 19 publicly available databases. A key feature is our rigorous unification and standardization process which makes the data truly comparable on a large scale, allowing for the first time effective data mining in such a large knowledge corpus. As of version 3.2, Drug2Gene contains 4,372,290 unified relations between compounds and their targets most of which include reported bioactivity data. We extend this set with putative (i.e. homology-inferred) relations where sufficient sequence homology between proteins suggests they may bind to similar compounds. Drug2Gene provides powerful search functionalities, very flexible export procedures, and a user-friendly web interface. Drug2Gene v3.2 has become a mature and comprehensive knowledge base providing unified, standardized drug-target related information gathered from publicly available data sources. It can be used to integrate proprietary data sets with publicly available data sets. Its main goal is to be a 'one-stop shop' to identify tool compounds targeting a given gene product or for finding all known targets of a drug. Drug2Gene with its integrated data set of public compound-target relations is freely accessible without restrictions at http://www.drug2gene.com.

Identifying relationships between unrelated pharmaceutical target proteins on the basis of shared active compounds.

PubMed

Miljković, Filip; Kunimoto, Ryo; Bajorath, Jürgen

2017-08-01

Computational exploration of small-molecule-based relationships between target proteins from different families. Target annotations of drugs and other bioactive compounds were systematically analyzed on the basis of high-confidence activity data. A total of 286 novel chemical links were established between distantly related or unrelated target proteins. These relationships involved a total of 1859 bioactive compounds including 147 drugs and 141 targets. Computational analysis of large amounts of compounds and activity data has revealed unexpected relationships between diverse target proteins on the basis of compounds they share. These relationships are relevant for drug discovery efforts. Target pairs that we have identified and associated compound information are made freely available.

Permeation of Therapeutic Drugs in Different Formulations across the Airway Epithelium In Vitro

PubMed Central

Meindl, Claudia; Stranzinger, Sandra; Dzidic, Neira; Salar-Behzadi, Sharareh; Mohr, Stefan; Zimmer, Andreas; Fröhlich, Eleonore

2015-01-01

Background Pulmonary drug delivery is characterized by short onset times of the effects and an increased therapeutic ratio compared to oral drug delivery. This delivery route can be used for local as well as for systemic absorption applying drugs as single substance or as a fixed dose combination. Drugs can be delivered as nebulized aerosols or as dry powders. A screening system able to mimic delivery by the different devices might help to assess the drug effect in the different formulations and to identify potential interference between drugs in fixed dose combinations. The present study evaluates manual devices used in animal studies for their suitability for cellular studies. Methods Calu-3 cells were cultured submersed and in air-liquid interface culture and characterized regarding mucus production and transepithelial electrical resistance. The influence of pore size and material of the transwell membranes and of the duration of air-liquid interface culture was assessed. Compounds were applied in solution and as aerosols generated by MicroSprayer IA-1C Aerosolizer or by DP-4 Dry Powder Insufflator using fluorescein and rhodamine 123 as model compounds. Budesonide and formoterol, singly and in combination, served as examples for drugs relevant in pulmonary delivery. Results and Conclusions Membrane material and duration of air-liquid interface culture had no marked effect on mucus production and tightness of the cell monolayer. Co-application of budesonide and formoterol, applied in solution or as aerosol, increased permeation of formoterol across cells in air-liquid interface culture. Problems with the DP-4 Dry Powder Insufflator included compound-specific delivery rates and influence on the tightness of the cell monolayer. These problems were not encountered with the MicroSprayer IA-1C Aerosolizer. The combination of Calu-3 cells and manual aerosol generation devices appears suitable to identify interactions of drugs in fixed drug combination products on permeation. PMID:26274590

Compound Libraries: Recent Advances and Their Applications in Drug Discovery.

PubMed

Gong, Zhen; Hu, Guoping; Li, Qiang; Liu, Zhiguo; Wang, Fei; Zhang, Xuejin; Xiong, Jian; Li, Peng; Xu, Yan; Ma, Rujian; Chen, Shuhui; Li, Jian

2017-01-01

Hit identification is the starting point of small-molecule drug discovery and is therefore very important to the pharmaceutical industry. One of the most important approaches to identify a new hit is to screen a compound library using an in vitro assay. High-throughput screening has made great contributions to drug discovery since the 1990s but requires expensive equipment and facilities, and its success depends on the size of the compound library. Recent progress in the development of compound libraries has provided more efficient ways to identify new hits for novel drug targets, thereby helping to promote the development of the pharmaceutical industry, especially for firstin- class drugs. A multistage and systematic research of articles published between 1986 and 2017 has been performed, which was organized into 5 sections and discussed in detail. In this review, the sources and classification of compound libraries are summarized. The progress made in combinatorial libraries and DNA-encoded libraries is reviewed. Library design methods, especially for focused libraries, are introduced in detail. In the final part, the status of the compound libraries at WuXi is reported. The progress related to compound libraries, especially drug template libraries, DELs, and focused libraries, will help to identify better hits for novel drug targets and promote the development of the pharmaceutical industry. Moreover, these libraries can facilitate hit identification, which benefits most research organizations, including academics and small companies. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Biomedical Applications Of Aromatic Azo Compounds: From Chromophore To Pharmacophore.

PubMed

Ali, Yousaf; Hamid, Shafida Abd; Rashid, Umer

2018-05-23

Azo dyes are widely used in textile, fiber, cosmetic, leather, paint and printing industries. Besides their characteristic coloring function, biological properties of certain azo compounds including antibacterial, antiviral, antifungal and cytotoxic are also reported. Azo compounds can be used as drug carriers, either by acting as a 'cargo' that entrap therapeutic agents or by prodrug approach. The drug is released by internal or external stimuli in the region of interest, as observed in colon-targeted drug delivery. Besides drug-like and drug carrier properties, a number of azo dyes are used in cellular staining to visualize cellular components and metabolic processes. However, the biological significance of azo compounds, especially in cancer chemotherapy, is still in its infancy. This may be linked to early findings that declared azo compounds as one of the possible causes of cancer and mutagenesis. Currently, researchers are screening the aromatic azo compounds for their potential biomedical use, including cancer diagnosis and therapy. The medical applications of azo compounds, particularly in cancer research are discussed. The biomedical significance of cis-trans interchange and negative implications of azo compounds are also highlighted in brief. This review may provide the researchers a platform in the quest of more potent therapeutic agents of this class. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Extemporaneous compounding in a sample of New Zealand hospitals: a retrospective survey.

PubMed

Kairuz, Therése; Chhim, Srey; Hasan, Fhazeel; Kumar, Karishma; Lal, Aarti; Patel, Roshni; Singh, Ranjani; Dogra, Mridula; Garg, Sanjay

2007-03-23

To determine the extent and nature of extemporaneous compounding of liquid preparations in a sample of New Zealand hospitals. Retrospective data were collected from eight hospitals known to provide compounding services during the period 1 June 2004 to 31 December 2004; including dosage form, volume, and quantity prepared. Data were collected on site from compounding logbooks and batch sheets. Demographic patient data was limited to age and was only collected from pharmacy departments where this information was readily available. Off-label use was analysed where appropriate data were available. 2015 products were compounded over the 7-month period; an average of 251.9 per month. More oral dosage forms were compounded (n=152) compared to topical dosage forms (n=100); 74 drugs required extemporaneous preparation for oral use. There were 16 drugs used in an off-label manner on 144 occasions for paediatric patients. Most off-label drugs were reformulated as suspensions; omeprazole suspension was compounded at all of the hospitals. Off-label use of four drugs (sotalol, labetalol, diazoxide, and clonidine) was analysed for different paediatric age groups. Suspensions are the most frequently compounded dosage form and omeprazole is the drug that is most frequently reformulated. Off-label medicines form a small but integral role in the supply of medicinal products.

A non-human primate model for investigating drug-induced risk of orthostatic hypotension and sympathetic dysfunction: Preclinical correlate to a clinical test.

PubMed

Bhatt, Siddhartha; Foote, Stephen; Smith, Andrew; Butler, Paul; Steidl-Nichols, Jill

2015-01-01

Drug induced orthostatic hypotension (OH) is an important clinical concern and can be an unexpected hurdle during drug development. OH is defined as an abnormal decrease in blood pressure (BP) triggered by a rapid postural change. The sympathetic nervous system is critical for controlling normal cardiovascular function and compensatory responses to changes in posture. Thus, OH can also serve as a surrogate indicator of sympathetic dysfunction. However, preclinical conscious models for investigating risk of OH and/or sympathetic dysfunction are lacking. Herein, we describe a conscious nonhuman primate (NHP) model which mimics the widely used clinical tilt table test for OH. Male, Cynomolgus NHPs (n = 7-8) implanted with radio-telemetry transmitters were placed in modified tilt chairs in a supine position. Subsequently, a 90° head up tilt was performed for 3 min followed by return to the supine position. BP and heart rate were continuously monitored. Test compounds were administered either intravenously or via oral gavage in a crossover design, with blood samples collected at the end of the each tilt to assess total drug concentrations. Tilt responses were assessed following treatment with positive control compounds that cause sympathetic dysfunction; hexamethonium (ganglionic blocker) and prazosin (alpha-1 adrenergic receptor antagonist). Both compounds induced marked OH as evidenced by robust and sustained BP reduction in response to a head up tilt (decrease of 25-35 mmHg for hexamethonium, decrease of 21-44 mmHg for prazosin). OH incidence rates increased in a dose-dependent manner. OH incidences following treatment with minoxidil (vasodilator) were markedly lower to those observed with hexamethonium and prazosin indicating the role of sympathetic dysfunction in causing OH. These data demonstrate that the NHP tilt test is a valuable model for investigating OH risk. This model fills an important preclinical gap for assessing such a safety concern and can be applied to programs where a sympathetic deficit and/or OH are anticipated or clinically observed. Copyright © 2015 Elsevier Inc. All rights reserved.

Using Pareto points for model identification in predictive toxicology

PubMed Central

2013-01-01

Predictive toxicology is concerned with the development of models that are able to predict the toxicity of chemicals. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in cosmetics, drug design or food protection to speed up the process of chemical compound discovery while reducing the need for lab tests. There is an extensive literature associated with the best practice of model generation and data integration but management and automated identification of relevant models from available collections of models is still an open problem. Currently, the decision on which model should be used for a new chemical compound is left to users. This paper intends to initiate the discussion on automated model identification. We present an algorithm, based on Pareto optimality, which mines model collections and identifies a model that offers a reliable prediction for a new chemical compound. The performance of this new approach is verified for two endpoints: IGC50 and LogP. The results show a great potential for automated model identification methods in predictive toxicology. PMID:23517649

[Application of fuzzy mathematics on modifying taste of oral solution of traditional Chinese drug].

PubMed

Wang, Youjie; Feng, Yi; Zhang, Bo

2009-01-01

To apply Fuzzy mathematical methods to choose the best taste modifying prescription of oral solution of traditional Chinese drug. Jin-Fukang oral solution was used as a model drug. The oral solution was prepared in different taste modifying prescriptions, whose tastes were evaluated by the fuzzy quality synthetic evaluation system. Compound-sweeteners with Sucralose and Erythritol was the best choice. Fuzzy integrated evaluation can be used to evaluate the taste of traditional Chinese medicinal pharmaceuticals, which overcame the artificial factors and achieve more objective conclusion.

Overcoming Multidrug Resistance in Human Cancer Cells by Natural Compounds

PubMed Central

Nabekura, Tomohiro

2010-01-01

Multidrug resistance is a phenomenon whereby tumors become resistant to structurally unrelated anticancer drugs. P-glycoprotein belongs to the large ATP-binding cassette (ABC) transporter superfamily of membrane transport proteins. P-glycoprotein mediates resistance to various classes of anticancer drugs including vinblastine, daunorubicin, and paclitaxel, by actively extruding the drugs from the cells. The quest for inhibitors of anticancer drug efflux transporters has uncovered natural compounds, including (-)-epigallocatechin gallate, curcumin, capsaicin, and guggulsterone, as promising candidates. In this review, studies on the effects of natural compounds on P-glycoprotein and anticancer drug efflux transporters are summarized. PMID:22069634

21 CFR 170.45 - Fluorine-containing compounds.

Code of Federal Regulations, 2010 CFR

2010-04-01

... § 170.45 Fluorine-containing compounds. The Commissioner of Food and Drugs has concluded that it is in the interest of the public health to limit the addition of fluorine compounds to foods (a) to that... 21 Food and Drugs 3 2010-04-01 2009-04-01 true Fluorine-containing compounds. 170.45 Section 170...

Post processing of protein-compound docking for fragment-based drug discovery (FBDD): in-silico structure-based drug screening and ligand-binding pose prediction.

PubMed

Fukunishi, Yoshifumi

2010-01-01

For fragment-based drug development, both hit (active) compound prediction and docking-pose (protein-ligand complex structure) prediction of the hit compound are important, since chemical modification (fragment linking, fragment evolution) subsequent to the hit discovery must be performed based on the protein-ligand complex structure. However, the naïve protein-compound docking calculation shows poor accuracy in terms of docking-pose prediction. Thus, post-processing of the protein-compound docking is necessary. Recently, several methods for the post-processing of protein-compound docking have been proposed. In FBDD, the compounds are smaller than those for conventional drug screening. This makes it difficult to perform the protein-compound docking calculation. A method to avoid this problem has been reported. Protein-ligand binding free energy estimation is useful to reduce the procedures involved in the chemical modification of the hit fragment. Several prediction methods have been proposed for high-accuracy estimation of protein-ligand binding free energy. This paper summarizes the various computational methods proposed for docking-pose prediction and their usefulness in FBDD.

Synthesis and structure-activity relationships of varied ether linker analogues of the antitubercular drug (6S)-2-nitro-6-{[4-(trifluoromethoxy)benzyl]oxy}-6,7-dihydro-5h-imidazo[2,1-b][1,3]oxazine (PA-824).

PubMed

Thompson, Andrew M; Sutherland, Hamish S; Palmer, Brian D; Kmentova, Iveta; Blaser, Adrian; Franzblau, Scott G; Wan, Baojie; Wang, Yuehong; Ma, Zhenkun; Denny, William A

2011-10-13

New analogues of antitubercular drug PA-824 were synthesized, featuring alternative side chain ether linkers of varying size and flexibility, seeking drug candidates with enhanced metabolic stability and high efficacy. Both α-methyl substitution and removal of the benzylic methylene were broadly tolerated in vitro, with a biaryl example of the latter class exhibiting an 8-fold better efficacy than the parent drug in a mouse model of acute Mycobacterium tuberculosis infection and negligible fragmentation to an alcohol metabolite in liver microsomes. Extended linkers (notably propenyloxy, propynyloxy, and pentynyloxy) provided greater potencies against replicating M. tb (monoaryl analogues), with propynyl ethers being most effective under anaerobic (nonreplicating) conditions (mono/biaryl analogues). For benzyloxybenzyl and biaryl derivatives, aerobic activity was maximal with the original (OCH(2)) linker. One propynyloxy-linked compound displayed an 89-fold higher efficacy than the parent drug in the acute model, and it was slightly superior to antitubercular drug OPC-67683 in a chronic infection model.

Machine Learning-based Virtual Screening and Its Applications to Alzheimer's Drug Discovery: A Review.

PubMed

Carpenter, Kristy A; Huang, Xudong

2018-06-07

Virtual Screening (VS) has emerged as an important tool in the drug development process, as it conducts efficient in silico searches over millions of compounds, ultimately increasing yields of potential drug leads. As a subset of Artificial Intelligence (AI), Machine Learning (ML) is a powerful way of conducting VS for drug leads. ML for VS generally involves assembling a filtered training set of compounds, comprised of known actives and inactives. After training the model, it is validated and, if sufficiently accurate, used on previously unseen databases to screen for novel compounds with desired drug target binding activity. The study aims to review ML-based methods used for VS and applications to Alzheimer's disease (AD) drug discovery. To update the current knowledge on ML for VS, we review thorough backgrounds, explanations, and VS applications of the following ML techniques: Naïve Bayes (NB), k-Nearest Neighbors (kNN), Support Vector Machines (SVM), Random Forests (RF), and Artificial Neural Networks (ANN). All techniques have found success in VS, but the future of VS is likely to lean more heavily toward the use of neural networks - and more specifically, Convolutional Neural Networks (CNN), which are a subset of ANN that utilize convolution. We additionally conceptualize a work flow for conducting ML-based VS for potential therapeutics of for AD, a complex neurodegenerative disease with no known cure and prevention. This both serves as an example of how to apply the concepts introduced earlier in the review and as a potential workflow for future implementation. Different ML techniques are powerful tools for VS, and they have advantages and disadvantages albeit. ML-based VS can be applied to AD drug development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Development of classification models for identifying "true" P-glycoprotein (P-gp) inhibitors through inhibition, ATPase activation and monolayer efflux assays.

PubMed

Rapposelli, Simona; Coi, Alessio; Imbriani, Marcello; Bianucci, Anna Maria

2012-01-01

P-glycoprotein (P-gp) is an efflux pump involved in the protection of tissues of several organs by influencing xenobiotic disposition. P-gp plays a key role in multidrug resistance and in the progression of many neurodegenerative diseases. The development of new and more effective therapeutics targeting P-gp thus represents an intriguing challenge in drug discovery. P-gp inhibition may be considered as a valid approach to improve drug bioavailability as well as to overcome drug resistance to many kinds of tumours characterized by the over-expression of this protein. This study aims to develop classification models from a unique dataset of 59 compounds for which there were homogeneous experimental data on P-gp inhibition, ATPase activation and monolayer efflux. For each experiment, the dataset was split into a training and a test set comprising 39 and 20 molecules, respectively. Rational splitting was accomplished using a sphere-exclusion type algorithm. After a two-step (internal/external) validation, the best-performing classification models were used in a consensus predicting task for the identification of compounds named as "true" P-gp inhibitors, i.e., molecules able to inhibit P-gp without being effluxed by P-gp itself and simultaneously unable to activate the ATPase function.