Identification and validation of novel human pregnane X receptor activators among prescribed drugs via ligand-based virtual screening

Development and Experimental Validation of Regularized Machine Learning Models Detecting New, Structurally Distinct Activators of PXR

The pregnane X receptor (PXR) regulates the metabolism of many xenobiotic and endobiotic substances. In consequence, PXR decreases the efficacy of many small-molecule drugs and induces drug-drug interactions. The prediction of PXR activators with theoretical approaches such as machine learning (ML) proves challenging due to the ligand promiscuity of PXR, which is related to its large and flexible binding pocket. In this work we demonstrate, by the example of random forest models and support vector machines, that classifiers generated following classical training procedures often fail to predict PXR activity for compounds that are dissimilar from those in the training set. We present a novel regularization technique that penalizes the gap between a model’s training and validation performance. On a challenging test set, this technique led to improvements in Matthew correlation coefficients (MCCs) by up to 0.21. Using these regularized ML models, we selected 31 compounds that are structurally distinct from known PXR ligands for experimental validation. Twelve of them were confirmed as active in the cellular PXR ligand-binding domain assembly assay and more hits were identified during follow-up studies. Comprehensive analysis of key features of PXR biology conducted for three representative hits confirmed their ability to activate the PXR.

Nuclear Receptor PXR in Drug-Induced Hypercholesterolemia

Atherosclerosis is a major global health concern. The central modifiable risk factors and causative agents of the disease are high total and low-density lipoprotein (LDL) cholesterol. To reduce morbidity and mortality, a thorough understanding of the factors that influence an individual’s cholesterol status during the decades when the arteria-narrowing arteriosclerotic plaques are forming is critical. Several drugs are known to increase cholesterol levels; however, the mechanisms are poorly understood. Activation of pregnane X receptor (PXR), the major regulator of drug metabolism and molecular mediator of clinically significant drug–drug interactions, has been shown to induce hypercholesterolemia. As a major sensor of the chemical environment, PXR may in part mediate hypercholesterolemic effects of drug treatment. This review compiles the current knowledge of PXR in cholesterol homeostasis and discusses the role of PXR in drug-induced hypercholesterolemia.

Designing Out PXR Activity on Drug Discovery Projects: A Review of Structure-Based Methods, Empirical and Computational Approaches

This perspective discusses the role of pregnane xenobiotic receptor (PXR) in drug discovery and the impact of its activation on CYP3A4 induction. The use of structural biology to reduce PXR activity on drug discovery projects has become more common in recent years. Analysis of this work highlights several important molecular interactions, and the resultant structural modifications to reduce PXR activity are summarized. The computational approaches undertaken to support the design of new drugs devoid of PXR activation potential are also discussed. Finally, the SAR of empirical design strategies to reduce PXR activity is reviewed, and the key SAR transformations are discussed and summarized. In conclusion, this perspective demonstrates that PXR activity can be greatly diminished or negated on active drug discovery projects with the knowledge now available. This perspective should be useful to anyone who seeks to reduce PXR activity on a drug discovery project.

Vitamin E acetate as linactant in the pathophysiology of EVALI

The recent identification of Vitamin E acetate as one of the causal agents for the e-cigarette, or vaping, product use associated lung injury (EVALI) is a major milestone. In membrane biophysics, Vitamin E is a linactant and a potent modulator of lateral phase separation that effectively reduces the line tension at the two-dimensional phase boundaries and thereby exponentially increases the surface viscosity of the pulmonary surfactant. Disrupted dynamics of respiratory compression-expansion cycling may result in an extensive hypoxemia, leading to an acute respiratory distress entailing the formation of intraalveolar lipid-laden macrophages. Supplementation of pulmonary surfactants which retain moderate level of cholesterol and controlled hypothermia for patients are recommended when the hypothesis that the line-active property of the vitamin derivative drives the pathogenesis of EVALI holds.

Computational prediction of cytochrome P450 inhibition and induction

Cytochrome P450 (CYP) enzymes play an important role in the phase I metabolism of many xenobiotics. Most drug-drug interactions (DDIs) associated with CYP are caused by either CYP inhibition or induction. The early detection of potential DDIs is highly desirable in the pharmaceutical industry because DDIs can cause serious adverse events, which can lead to poor patient health and drug development failures. Recently, many computational studies predicting CYP inhibition and induction have been reported. The current computational modeling approaches for CYP metabolism are classified as ligand- and structure-based; various techniques, such as quantitative structure-activity relationships, machine learning, docking, and molecular dynamic simulation, are involved in both the approaches. Recently, combining these two approaches have resulted in improvements in the prediction accuracy of DDIs. In this review, we present important, recent developments in the computational prediction of the inhibition of four clinically crucial CYP isoforms (CYP1A2, 2C9, 2D6, and 3A4) and three nuclear receptors (aryl hydrocarbon receptor, constitutive androstane receptor, and pregnane X receptor) involved in the induction of CYP1A2, 2B6, and 3A4, respectively.

Repurposing Approved Drugs as Inhibitors of Kv7.1 and Nav1.8 to Treat Pitt Hopkins Syndrome

PURPOSE

Pitt Hopkins Syndrome (PTHS) is a rare genetic disorder caused by mutations of a specific gene, transcription factor 4 (TCF4), located on chromosome 18. PTHS results in individuals that have moderate to severe intellectual disability, with most exhibiting psychomotor delay. PTHS also exhibits features of autistic spectrum disorders, which are characterized by the impaired ability to communicate and socialize. PTHS is comorbid with a higher prevalence of epileptic seizures which can be present from birth or which commonly develop in childhood. Attenuated or absent TCF4 expression results in increased translation of peripheral ion channels K_v7.1 and Na_v1.8 which triggers an increase in after-hyperpolarization and altered firing properties.

METHODS

We now describe a high throughput screen (HTS) of 1280 approved drugs and machine learning models developed from this data. The ion channels were expressed in either CHO (K_V7.1) or HEK293 (Na_v1.8) cells and the HTS used either ⁸⁶Rb⁺ efflux (K_V7.1) or a FLIPR assay (Na_v1.8).

RESULTS

The HTS delivered 55 inhibitors of K_v7.1 (4.2% hit rate) and 93 inhibitors of Na_v1.8 (7.2% hit rate) at a screening concentration of 10 μM. These datasets also enabled us to generate and validate Bayesian machine learning models for these ion channels. We also describe a structure activity relationship for several dihydropyridine compounds as inhibitors of Na_v1.8.

CONCLUSIONS

This work could lead to the potential repurposing of nicardipine or other dihydropyridine calcium channel antagonists as potential treatments for PTHS acting via Na_v1.8, as there are currently no approved treatments for this rare disorder.

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.

Using TR-FRET to Investigate Protein-Protein Interactions: A Case Study of PXR-Coregulator Interaction

Time-resolved fluorescence resonance energy transfer (TR-FRET) protein-protein interaction assays, especially in the format of receptor coregulator (coactivator and corepressor) recruitment/repression assays, have been widely used in nuclear receptor research to characterize the modes of action, efficacies, and binding affinities of ligands (including their properties as agonists, antagonists, and inverse agonists). However, there has been only limited progress in using this assay format for pregnane X receptor (PXR). In this chapter, we discuss TR-FRET protein-protein interaction assays and focus on a novel PXR TR-FRET coactivator interaction assay that we have developed based on a PXR coactivator cocrystal study. This new PXR TR-FRET coactivator interaction assay can characterize the binding affinities of PXR ligands and also differentiate antagonists from agonists. This assay is very robust, with the signal remaining stable over a long incubation time (up to 300min has been tested). It can tolerate high concentrations of DMSO (up to 5%) and has a high signal-to-noise ratio (six under typical assay conditions). This newly developed PXR TR-FRET coactivator interaction assay has potential application in high-throughput screening to identify and characterize novel PXR agonists and antagonists.

Predictive models for identifying the binding activity of structurally diverse chemicals to human pregnane X receptor

Toxic chemicals entered into human body would undergo a series of metabolism, transport and excretion, and the key roles played in there processes were metabolizing enzymes, which was regulated by the pregnane X receptor (PXR). However, some chemicals in environment could activate or antagonize human pregnane X receptor, thereby leading to a disturbance of normal physiological systems. In this study, based on a larger number of 2724 structurally diverse chemicals, we developed qualitative classification models by the k-nearest neighbor method. Moreover, the logarithm of 20 and 50% effective concentrations (log EC ₂₀ and log EC ₅₀) was used to establish quantitative structure-activity relationship (QSAR) models. With the classification model, two descriptors were enough to establish acceptable models, with the sensitivity, specificity, and accuracy being larger than 0.7, highlighting a high classification performance of the models. With two QSAR models, the statistics parameters with the correlation coefficient (R ²) of 0.702-0.749 and the cross-validation and external validation coefficient (Q ²) of 0.643-0.712, this indicated that the models complied with the criteria proposed in previous studies, i.e., R ² > 0.6, Q ² > 0.5. The small root mean square error (RMSE) of 0.254-0.414 and the good consistency between observed and predicted values proved satisfactory goodness of fit, robustness, and predictive ability of the developed QSAR models. Additionally, the applicability domains were characterized by the Euclidean distance-based approach and Williams plot, and results indicated that the current models had a wide applicability domain, which especially included a few classes of environmental contaminant, those that were not included in the previous models.

Building a virtual ligand screening pipeline using free software: a survey

Virtual screening, the search for bioactive compounds via computational methods, provides a wide range of opportunities to speed up drug development and reduce the associated risks and costs. While virtual screening is already a standard practice in pharmaceutical companies, its applications in preclinical academic research still remain under-exploited, in spite of an increasing availability of dedicated free databases and software tools. In this survey, an overview of recent developments in this field is presented, focusing on free software and data repositories for screening as alternatives to their commercial counterparts, and outlining how available resources can be interlinked into a comprehensive virtual screening pipeline using typical academic computing facilities. Finally, to facilitate the set-up of corresponding pipelines, a downloadable software system is provided, using platform virtualization to integrate pre-installed screening tools and scripts for reproducible application across different operating systems.

Open Source Bayesian Models. 3. Composite Models for Prediction of Binned Responses

Bayesian models constructed from structure-derived fingerprints have been a popular and useful method for drug discovery research when applied to bioactivity measurements that can be effectively classified as active or inactive. The results can be used to rank candidate structures according to their probability of activity, and this ranking benefits from the high degree of interpretability when structure-based fingerprints are used, making the results chemically intuitive. Besides selecting an activity threshold, building a Bayesian model is fast and requires few or no parameters or user intervention. The method also does not suffer from such acute overtraining problems as quantitative structure–activity relationships or quantitative structure–property relationships (QSAR/QSPR). This makes it an approach highly suitable for automated workflows that are independent of user expertise or prior knowledge of the training data. We now describe a new method for creating a composite group of Bayesian models to extend the method to work with multiple states, rather than just binary. Incoming activities are divided into bins, each covering a mutually exclusive range of activities. For each of these bins, a Bayesian model is created to model whether or not the compound belongs in the bin. Analyzing putative molecules using the composite model involves making a prediction for each bin and examining the relative likelihood for each assignment, for example, highest value wins. The method has been evaluated on a collection of hundreds of data sets extracted from ChEMBL v20 and validated data sets for ADME/Tox and bioactivity.

Potent and Selective Inhibition of Plasma Membrane Monoamine Transporter by HIV Protease Inhibitors

Plasma membrane monoamine transporter (PMAT) is a major uptake-2 monoamine transporter that shares extensive substrate and inhibitor overlap with organic cation transporters 1-3 (OCT1-3). Currently, there are no PMAT-specific inhibitors available that can be used in in vitro and in vivo studies to differentiate between PMAT and OCT activities. In this study, we showed that IDT307 (4-(4-(dimethylamino)phenyl)-1-methylpyridinium iodide), a fluorescent analog of 1-methyl-4-phenylpyridinium (MPP+), is a transportable substrate for PMAT and that IDT307-based fluorescence assay can be used to rapidly identify and characterize PMAT inhibitors. Using the fluorescent substrate-based assays, we analyzed the interactions of eight human immunodeficiency virus (HIV) protease inhibitors (PIs) with human PMAT and OCT1-3 in human embryonic kidney 293 (HEK293) cells stably transfected with individual transporters. Our data revealed that PMAT and OCTs exhibit distinct sensitivity and inhibition patterns toward HIV PIs. PMAT is most sensitive to PI inhibition whereas OCT2 and OCT3 are resistant. OCT1 showed an intermediate sensitivity and a distinct inhibition profile from PMAT. Importantly, lopinavir is a potent PMAT inhibitor and exhibited >120 fold selectivity toward PMAT (IC₅₀ = 1.4 ± 0.2 µM) over OCT1 (IC₅₀ = 174 ± 40 µM). Lopinavir has no inhibitory effect on OCT2 or OCT3 at maximal tested concentrations. Lopinavir also exhibited no or much weaker interactions with uptake-1 monoamine transporters. Together, our results reveal that PMAT and OCTs have distinct specificity exemplified by their differential interaction with HIV PIs. Further, we demonstrate that lopinavir can be used as a selective PMAT inhibitor to differentiate PMAT-mediated monoamine and organic cation transport from those mediated by OCT1-3.

Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets

On the order of hundreds of absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) models have been described in the literature in the past decade which are more often than not inaccessible to anyone but their authors. Public accessibility is also an issue with computational models for bioactivity, and the ability to share such models still remains a major challenge limiting drug discovery. We describe the creation of a reference implementation of a Bayesian model-building software module, which we have released as an open source component that is now included in the Chemistry Development Kit (CDK) project, as well as implemented in the CDD Vault and in several mobile apps. We use this implementation to build an array of Bayesian models for ADME/Tox, in vitro and in vivo bioactivity, and other physicochemical properties. We show that these models possess cross-validation receiver operator curve values comparable to those generated previously in prior publications using alternative tools. We have now described how the implementation of Bayesian models with FCFP6 descriptors generated in the CDD Vault enables the rapid production of robust machine learning models from public data or the user’s own datasets. The current study sets the stage for generating models in proprietary software (such as CDD) and exporting these models in a format that could be run in open source software using CDK components. This work also demonstrates that we can enable biocomputation across distributed private or public datasets to enhance drug discovery.

Open Source Bayesian Models. 2. Mining a "Big Dataset" To Create and Validate Models with ChEMBL

In an associated paper, we have described a reference implementation of Laplacian-corrected naïve Bayesian model building using extended connectivity (ECFP)- and molecular function class fingerprints of maximum diameter 6 (FCFP)-type fingerprints. As a follow-up, we have now undertaken a large-scale validation study in order to ensure that the technique generalizes to a broad variety of drug discovery datasets. To achieve this, we have used the ChEMBL (version 20) database and split it into more than 2000 separate datasets, each of which consists of compounds and measurements with the same target and activity measurement. In order to test these datasets with the two-state Bayesian classification, we developed an automated algorithm for detecting a suitable threshold for active/inactive designation, which we applied to all collections. With these datasets, we were able to establish that our Bayesian model implementation is effective for the large majority of cases, and we were able to quantify the impact of fingerprint folding on the receiver operator curve cross-validation metrics. We were also able to study the impact that the choice of training/testing set partitioning has on the resulting recall rates. The datasets have been made publicly available to be downloaded, along with the corresponding model data files, which can be used in conjunction with the CDK and several mobile apps. We have also explored some novel visualization methods which leverage the structural origins of the ECFP/FCFP fingerprints to attribute regions of a molecule responsible for positive and negative contributions to activity. The ability to score molecules across thousands of relevant datasets across organisms also may help to access desirable and undesirable off-target effects as well as suggest potential targets for compounds derived from phenotypic screens.

Quantitative high-throughput identification of drugs as modulators of human constitutive androstane receptor

The constitutive androstane receptor (CAR, NR1I3) plays a key role in governing the transcription of numerous hepatic genes that involve xenobiotic metabolism/clearance, energy homeostasis, and cell proliferation. Thus, identification of novel human CAR (hCAR) modulators may not only enhance early prediction of drug-drug interactions but also offer potentially novel therapeutics for diseases such as metabolic disorders and cancer. In this study, we have generated a double stable cell line expressing both hCAR and a CYP2B6-driven luciferase reporter for quantitative high-throughput screening (qHTS) of hCAR modulators. Approximately 2800 compounds from the NIH Chemical Genomics Center Pharmaceutical Collection were screened employing both the activation and deactivation modes of the qHTS. Activators (115) and deactivators (152) of hCAR were identified from the primary qHTS, among which 10 agonists and 10 antagonists were further validated in the physiologically relevant human primary hepatocytes for compound-mediated hCAR nuclear translocation and target gene expression. Collectively, our results reveal that hCAR modulators can be efficiently identified through this newly established qHTS assay. Profiling drug collections for hCAR activity would facilitate the prediction of metabolism-based drug-drug interactions, and may lead to the identification of potential novel therapeutics.

Thiazide-like diuretic drug metolazone activates human pregnane X receptor to induce cytochrome 3A4 and multidrug-resistance protein 1

Human pregnane X receptor (hPXR) regulates the expression of drug-metabolizing enzyme cytochrome P450 3A4 (CYP3A4) and drug transporters such as multidrug-resistance protein 1 (MDR1). PXR can be modulated by small molecules, including Federal Drug Administration (FDA)-approved drugs, thus altering drug metabolism and causing drug-drug interactions. To determine the role of FDA-approved drugs in PXR-mediated regulation of drug metabolism and clearance, we screened 1481 FDA-approved small-molecule drugs by using a luciferase reporter assay in HEK293T cells and identified the diuretic drug metolazone as an activator of hPXR. Our data showed that metolazone activated hPXR-mediated expression of CYP3A4 and MDR1 in human hepatocytes and intestine cells and increased CYP3A4 promoter activity in various cell lines. Mammalian two-hybrid assays showed that hPXR recruits its co-activator SRC-1 upon metolazone binding in HepG2 cells, explaining the mechanism of hPXR activation. To understand the role of other commonly-used diuretics in hPXR activation and the structure-activity relationship of metolazone, thiazide and non-thiazide diuretics drugs were also tested but only metolazone activates hPXR. To understand the molecular mechanism, docking studies and mutational analysis were carried out and showed that metolazone binds in the ligand-binding pocket and interacts with mostly hydrophobic amino acid residues. This is the first report showing that metolazone activates hPXR. Because activation of hPXR might cause drug-drug interactions, metolazone should be used with caution for drug treatment in patients undergoing combination therapy.

Activation of the constitutive androstane receptor inhibits gluconeogenesis without affecting lipogenesis or fatty acid synthesis in human hepatocytes

OBJECTIVE

Accumulating evidence suggests that activation of mouse constitutive androstane receptor (mCAR) alleviates type 2 diabetes and obesity by inhibiting hepatic gluconeogenesis, lipogenesis, and fatty acid synthesis. However, the role of human (h) CAR in energy metabolism is largely unknown. The present study aims to investigate the effects of selective hCAR activators on hepatic energy metabolism in human primary hepatocytes (HPH).

METHODS

Ligand-based structure-activity models were used for virtual screening of the Specs database (www.specs.net) followed by biological validation in cell-based luciferase assays. The effects of two novel hCAR activators (UM104 and UM145) on hepatic energy metabolism were evaluated in HPH.

RESULTS

Real-time PCR and Western blotting analyses reveal that activation of hCAR by UM104 and UM145 significantly repressed the expression of glucose-6-phosphatase and phosphoenolpyruvate carboxykinase, two pivotal gluconeogenic enzymes, while exerting negligible effects on the expression of genes associated with lipogenesis and fatty acid synthesis. Functional experiments show that UM104 and UM145 markedly inhibit hepatic synthesis of glucose but not triglycerides in HPH. In contrast, activation of mCAR by 1,4-bis[2-(3,5-dichloropyridyloxy)]benzene, a selective mCAR activator, repressed the expression of genes associated with gluconeogenesis, lipogenesis, and fatty acid synthesis in mouse primary hepatocytes, which were consistent with previous observations in mouse model in vivo.

CONCLUSION

Our findings uncover an important species difference between hCAR and mCAR in hepatic energy metabolism, where hCAR selectively inhibits gluconeogenesis without suppressing fatty acid synthesis.

IMPLICATIONS

Such species selectivity should be considered when exploring CAR as a potential therapeutic target for metabolic disorders.

Pathway analysis for drug repositioning based on public database mining

Sixteen FDA-approved drugs were investigated to elucidate their mechanisms of action (MOAs) and clinical functions by pathway analysis based on retrieved drug targets interacting with or affected by the investigated drugs. Protein and gene targets and associated pathways were obtained by data-mining of public databases including the MMDB, PubChem BioAssay, GEO DataSets, and the BioSystems databases. Entrez E-Utilities were applied, and in-house Ruby scripts were developed for data retrieval and pathway analysis to identify and evaluate relevant pathways common to the retrieved drug targets. Pathways pertinent to clinical uses or MOAs were obtained for most drugs. Interestingly, some drugs identified pathways responsible for other diseases than their current therapeutic uses, and these pathways were verified retrospectively by in vitro tests, in vivo tests, or clinical trials. The pathway enrichment analysis based on drug target information from public databases could provide a novel approach for elucidating drug MOAs and repositioning, therefore benefiting the discovery of new therapeutic treatments for diseases.

Progress in computational toxicology

INTRODUCTION

Computational methods have been widely applied to toxicology across pharmaceutical, consumer product and environmental fields over the past decade. Progress in computational toxicology is now reviewed.

METHODS

A literature review was performed on computational models for hepatotoxicity (e.g. for drug-induced liver injury (DILI)), cardiotoxicity, renal toxicity and genotoxicity. In addition various publications have been highlighted that use machine learning methods. Several computational toxicology model datasets from past publications were used to compare Bayesian and Support Vector Machine (SVM) learning methods.

RESULTS

The increasing amounts of data for defined toxicology endpoints have enabled machine learning models that have been increasingly used for predictions. It is shown that across many different models Bayesian and SVM perform similarly based on cross validation data.

DISCUSSION

Considerable progress has been made in computational toxicology in a decade in both model development and availability of larger scale or 'big data' models. The future efforts in toxicology data generation will likely provide us with hundreds of thousands of compounds that are readily accessible for machine learning models. These models will cover relevant chemistry space for pharmaceutical, consumer product and environmental applications.

Fusing dual-event data sets for Mycobacterium tuberculosis machine learning models and their evaluation

The search for new tuberculosis treatments continues as we need to find molecules that can act more quickly, be accommodated in multidrug regimens, and overcome ever increasing levels of drug resistance. Multiple large scale phenotypic high-throughput screens against Mycobacterium tuberculosis (Mtb) have generated dose response data, enabling the generation of machine learning models. These models also incorporated cytotoxicity data and were recently validated with a large external data set. A cheminformatics data-fusion approach followed by Bayesian machine learning, Support Vector Machine, or Recursive Partitioning model development (based on publicly available Mtb screening data) was used to compare individual data sets and subsequent combined models. A set of 1924 commercially available molecules with promising antitubercular activity (and lack of relative cytotoxicity to Vero cells) were used to evaluate the predictive nature of the models. We demonstrate that combining three data sets incorporating antitubercular and cytotoxicity data in Vero cells from our previous screens results in external validation receiver operator curve (ROC) of 0.83 (Bayesian or RP Forest). Models that do not have the highest 5-fold cross-validation ROC scores can outperform other models in a test set dependent manner. We demonstrate with predictions for a recently published set of Mtb leads from GlaxoSmithKline that no single machine learning model may be enough to identify compounds of interest. Data set fusion represents a further useful strategy for machine learning construction as illustrated with Mtb. Coverage of chemistry and Mtb target spaces may also be limiting factors for the whole-cell screening data generated to date.

Combining computational methods for hit to lead optimization in Mycobacterium tuberculosis drug discovery

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.

Modulation of xenobiotic receptors by steroids

Nuclear receptors (NRs) are ligand-activated transcription factors that regulate the expression of their target genes. NRs play important roles in many human diseases, including metabolic diseases and cancer, and are therefore a key class of therapeutic targets. Steroids play important roles in regulating nuclear receptors; in addition to being ligands of steroid receptors, steroids (and their metabolites) also regulate other NRs, such as the pregnane X receptor and constitutive androstane receptor (termed xenobiotic receptors), which participate in steroid metabolism. Xenobiotic receptors have promiscuous ligand-binding properties, and their structurally diverse ligands include steroids and their metabolites. Therefore, steroids, their metabolism and metabolites, xenobiotic receptors, steroid receptors, and the respective signaling pathways they regulate have functional interactions. This review discusses these functional interactions and their implications for activities mediated by steroid receptors and xenobiotic receptors, focusing on steroids that modulate pathways involving the pregnane X receptor and constitutive androstane receptor. The emphasis of the review is on structure-function studies of xenobiotic receptors bound to steroid ligands.

Development of time resolved fluorescence resonance energy transfer-based assay for FXR antagonist discovery

FXR (farnesoid X receptor, NRIH4), a nuclear receptor, plays a major role in the control of cholesterol metabolism. FXR ligands have been investigated in preclinical studies for targeted therapy against metabolic diseases, but have shown limitations. Therefore, there is a need for new agonist or antagonist ligands of FXR, both for potential clinical applications, as well as to further elucidate its biological functions. Here we describe the use of the X-ray crystal structure of FXR complexed with the potent small molecule agonist GW4064 to design and synthesize a novel fluorescent, high-affinity probe (DY246) for time resolved fluorescence resonance energy transfer (TR-FRET) assays. We then used the TR-FRET assay for high throughput screening of a library of over 5000 bioactive compounds. From this library, we identified 13 compounds that act as putative FXR transcriptional antagonists.

Pharmacophore and 3D-QSAR characterization of 6-arylquinazolin-4-amines as Cdc2-like kinase 4 (Clk4) and dual specificity tyrosine-phosphorylation-regulated kinase 1A (Dyrk1A) inhibitors

Cdc2-like kinase 4 (Clk4) and dual specificity tyrosine-phosphorylation-regulated kinase 1A (Dyrk1A) are protein kinases that are promising targets for treatment of diseases caused by abnormal gene splicing. 6-Arylquinazolin-4-amines have been recently identified as potent Clk4 and Dyrk1A inhibitors. In order to understand the structure-activity correlation of these analogs, we have applied ligand-based pharmacophore and 3D-QSAR modeling combined with structure-based homology modeling and docking. The high R(2) and Q(2) (0.88 and 0.79 for Clk4, 0.85 and 0.82 for Dyrk1A, respectively) based on validation with training and test set compounds suggested that the generated 3D-QSAR models are reliable in predicting novel ligand activities against Clk4 and Dyrk1A. The binding mode identified through docking ligands to the ATP binding domain of Clk4 was consistent with the structural properties and energy field contour maps characterized by pharmacophore and 3D-QSAR models and gave valuable insights into the structure-activity profile of 6-arylquinazolin-4-amine analogs. The obtained 3D-QSAR and pharmacophore models in combination with the binding mode between inhibitor and residues of Clk4 will be helpful for future lead compound identification and optimization to design potent and selective Clk4 and Dyrk1A inhibitors.

Pharmacophore and 3D-QSAR characterization of 6-arylquinazolin-4-amines as Cdc2-like kinase 4 (Clk4) and dual specificity tyrosine-phosphorylation-regulated kinase 1A (Dyrk1A) inhibitors

Cdc2-like kinase 4 (Clk4) and dual specificity tyrosine-phosphorylation-regulated kinase 1A (Dyrk1A) are protein kinases that are promising targets for treatment of diseases caused by abnormal gene splicing. 6-Arylquinazolin-4-amines have been recently identified as potent Clk4 and Dyrk1A inhibitors. In order to understand the structure-activity correlation of these analogs, we have applied ligand-based pharmacophore and 3D-QSAR modeling combined with structure-based homology modeling and docking. The high R(2) and Q(2) (0.88 and 0.79 for Clk4, 0.85 and 0.82 for Dyrk1A, respectively) based on validation with training and test set compounds suggested that the generated 3D-QSAR models are reliable in predicting novel ligand activities against Clk4 and Dyrk1A. The binding mode identified through docking ligands to the ATP binding domain of Clk4 was consistent with the structural properties and energy field contour maps characterized by pharmacophore and 3D-QSAR models and gave valuable insights into the structure-activity profile of 6-arylquinazolin-4-amine analogs. The obtained 3D-QSAR and pharmacophore models in combination with the binding mode between inhibitor and residues of Clk4 will be helpful for future lead compound identification and optimization to design potent and selective Clk4 and Dyrk1A inhibitors.

Identification of novel breast cancer resistance protein (BCRP) inhibitors by virtual screening

Breast cancer resistance protein (BCRP; ABCG2) is an efflux transporter that plays an important role in multidrug resistance to antineoplastic drugs. The identification of drugs as BCRP inhibitors could aid in designing better therapeutic strategies for cancer treatment and will be critical for identifying potential drug-drug interactions. In the present study, we applied ligand-based virtual screening combined with experimental testing for the identification of novel drugs that can possibly interact with BCRP. Bayesian and pharmacophore models generated with known BCRP inhibitors were validated with an external test set. The resulting models were applied to predict new potential drug candidates from a database with more than 2000 FDA-approved drugs. Thirty-three drugs were tested in vitro for their inhibitory effects on BCRP-mediated transport of [(3)H]-mitoxantrone in MCF-7/AdrVp cells. Nineteen drugs were identified with significant inhibitory effect on BCRP transport function. The combined strategy of computational and experimental approaches in this paper has suggested potential drug candidates and thus represents an effective tool for rational identification of modulators of other proteins.

Identification of novel activators of constitutive androstane receptor from FDA-approved drugs by integrated computational and biological approaches

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.

Development of in silico filters to predict activation of the pregnane X receptor (PXR) by structurally diverse drug-like molecules

The pregnane X receptor (PXR), a member of the nuclear hormone superfamily, regulates the expression of several enzymes and transporters involved in metabolically relevant processes. The significant induction of CYP450 enzymes by PXR, in particular CYP3A4, might significantly alter the metabolism of prescribed drugs. In order to early identify molecules in drug discovery with a potential to activate PXR as antitarget, we developed fast and reliable in silico filters by ligand-based QSAR techniques. Two classification models were established on a diverse dataset of 434 drug-like molecules. A second augmented set allowed focusing on interesting regions in chemical space. These classifiers are based on decision trees combined with a genetic algorithm based variable selection to arrive at predictive models. The classifier for the first dataset on 29 descriptors showed good performance on a test set with a correct classification of both 100% for PXR activators and non-activators plus 87% for activators and 83% for non-activators in an external dataset. The second classifier then correctly predicts 97% activators and 91% non-activators in a test set and 94% for activators and 64% non-activators in an external set of 50 molecules, which still qualifies for application as a filter focusing on PXR activators. Finally a quantitative model for PXR activation for a subset of these molecules was derived using a regression-tree approach combined with GA variable selection. This final model shows a predictive r(2) of 0.774 for the test set and 0.452 for an external set of 33 molecules. Thus, the combination of these filters consistently provide guidelines for lowering PXR activation in novel candidate molecules.

Drug-nutrient interactions: a broad view with implications for practice

The relevance of drug?nutrient interactions in daily practice continues to grow with the widespread use of medication. Interactions can involve a single nutrient, multiple nutrients, food in general, or nutrition status. Mechanistically, drug?nutrient interactions occur because of altered intestinal transport and metabolism, or systemic distribution, metabolism and excretion, as well as additive or antagonistic effects. Optimal patient care includes identifying, evaluating, and managing these interactions. This task can be supported by a systematic approach for categorizing interactions and rating their clinical significance. This review provides such a broad framework using recent examples, as well as some classic drug?nutrient interactions. Pertinent definitions are presented, as is a suggested approach for clinicians. This important and expanding subject will benefit tremendously from further clinician involvement.

Polypharmacy and food-drug interactions among older persons: a review

Polypharmacy is generally defined as the use of 5 or more prescription medications on a regular basis. The average number of prescribed and over-the-counter medications used by community-dwelling older adults per day in the United States is 6 medications, and the number used by institutionalized older persons is 9 medications. Almost all medications affect nutriture, either directly or indirectly, and nutriture affects drug disposition and effect. This review will highlight the issues surrounding polypharmacy, food-drug interactions, and the consequences of these interactions for the older adult.

Ligand- and structure-based pregnane X receptor models

The human pregnane X receptor (PXR) is a ligand dependent transcription factor that can be activated by structurally diverse agonists including steroid hormones, bile acids, herbal drugs, and prescription medications. PXR regulates the transcription of several genes involved in xenobiotic detoxification and apoptosis. Activation of PXR has the potential to initiate adverse effects by altering drug pharmacokinetics or perturbing physiological processes. Hence, more reliable prediction of PXR activators would be valuable for pharmaceutical drug discovery to avoid potential toxic effects. Ligand- and protein structure-based computational models for PXR activation have been developed in several studies. There has been limited success with structure-based modeling approaches to predict human PXR activators, which can be attributed to the large and promiscuous site of this protein. Slightly better success has been achieved with ligand-based modeling methods including quantitative structure-activity relationship (QSAR) analysis, pharmacophore modeling and machine learning that use appropriate descriptors to account for the diversity of the ligand classes that bind to PXR. These combined computational approaches using molecular shape information may assist scientists to more confidently identify PXR activators. This chapter reviews the various ligand and structure based methods undertaken to date and their results.