In Silico Prediction of Pregnane X Receptor Activators by Machine Learning Approache

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.

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.

Identification of novel pregnane X receptor (PXR) agonists by In silico and biological activity analyses and reversal of cigarette smoke-induced PXR downregulation

Cigarette smoke (CS) contains many toxins that collectively harm nearly every organ in the body, and smoking is a key risk factor for many chronic diseases. Aside from its toxic actions, CS may alter expression of the drug- and steroid-binding pregnane X receptor (PXR), which when activated upregulates expression of cytochrome P450 (CYP) enzymes, glutathione transferases (GSTs), and multidrug resistance protein 1 (MDR1), an adaptive metabolic array that mediates clearance of CS component toxins. We sought to identify new PXR agonists that may be useful for restoring PXR activity in conditions wherein it is suppressed, and their mechanisms of PXR binding and activation. PXR has a uniquely larger, hydrophobic, and highly flexible ligand-binding domain (LBD) vs. other nuclear receptors, enabling it to interact with structurally diverse molecules. We tested certain calcium channel blockers (CCBs) as a pharmacological subset of potential PXR ligands, analyzing by molecular docking methods, and identified a putative active site in the PXR LBD, along with the relevant bonds and bonding energies. We analyzed felodipine binding and agonist activity in detail, as it showed the lowest binding energy among CCBs tested. We found felodipine was a potent PXR agonist as measured by luciferase reporter assay, whereas CCBs with higher binding energies were less potent (amlodipine) or nearly inactive (manidipine), and it induced CYP3A4 expression in HepG2 cells, a known target of PXR agonism. Felodipine also both induced PXR mRNA in HepG2 hepatocytes and reduced CS extract-induced diminution of PXR levels, indicating it modulates PXR expression. The results illuminate mechanisms of ligand-induced PXR activation and identify felodipine as a novel PXR agonist.

An in vitro and in silico investigation of human pregnane X receptor agonistic activity of poly- and perfluorinated compounds using the heuristic method-best subset and comparative similarity indices analysis

Poly- and perfluorinated compounds (PFCs) may induce potential endocrine-disrupting hormonal effects. However, the molecular mechanism of the toxicology of PFCs remains unclear, and the insufficient information is available on the biological activities of PFCs at present. In this study, the cell-based reporter gene assays were used to determine the agonistic activity of PFCs on the human pregnane X receptor (hPXR). The heuristic method combined with best subset modeling (HM-BSM) based on Dragon descriptors and comparative similarity indices analysis (CoMSIA) were employed to build classical quantitative structure-activity relationship (QSAR) and three-dimensional QSAR models, respectively. The applicability domain (AD) of the classical QSAR model was assessed. Both the HM-BSM and CoMSIA approaches demonstrated good robustness, predictive ability, and mechanistic interpretability. The r² and leave-one-out cross-validation squared correlated coefficient (q²_LOO) values were 0.872 and 0.759 for the HM-BSM, and 0.976 and 0.751 for the CoMSIA model, respectively. The hPXR agonistic activity of the PFCs predicted by the built HM-BSM and CoMSIA agreed well with experimental activity, with root mean square error (RMSE) values of 0.0803 and 0.117, respectively, and external validation squared correlated coefficients (q²_EXT) of 0.972 and 0.932, respectively. The hPXR agonistic activity of PFCs was related to their molecular polarizability, charge and atomic mass. Hydrogen bonding and hydrophobic interactions constituted the primary intermolecular forces between PFCs and the hPXR. The developed models were used to screen the PFCs with high hPXR agonistic activity.

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.

Prediction of Drug-Drug Interactions Related to Inhibition or Induction of Drug-Metabolizing Enzymes

Drug-drug interaction (DDI) is the phenomenon of alteration of the pharmacological activity of a drug(s) when another drug(s) is co-administered in cases of so-called polypharmacy. There are three types of DDIs: pharmacokinetic (PK), pharmacodynamic, and pharmaceutical. PK is the most frequent type of DDI, which often appears as a result of the inhibition or induction of drug-metabolising enzymes (DME). In this review, we summarise in silico methods that may be applied for the prediction of the inhibition or induction of DMEs and describe appropriate computational methods for DDI prediction, showing the current situation and perspectives of these approaches in medicinal and pharmaceutical chemistry. We review sources of information on DDI, which can be used in pharmaceutical investigations and medicinal practice and/or for the creation of computational models. The problem of the inaccuracy and redundancy of these data are discussed. We provide information on the state-of-the-art physiologically- based pharmacokinetic modelling (PBPK) approaches and DME-based in silico methods. In the section on ligand-based methods, we describe pharmacophore models, molecular field analysis, quantitative structure-activity relationships (QSAR), and similarity analysis applied to the prediction of DDI related to the inhibition or induction of DME. In conclusion, we discuss the problems of DDI severity assessment, mention factors that influence severity, and highlight the issues, perspectives and practical using of in silico methods.

Machine Learning Consensus To Predict the Binding to the Androgen Receptor within the CoMPARA Project

The nuclear androgen receptor (AR) is one of the most relevant biological targets of Endocrine Disrupting Chemicals (EDCs), which produce adverse effects by interfering with hormonal regulation and endocrine system functioning. This paper describes novel in silico models to identify organic AR modulators in the context of the Collaborative Modeling Project of Androgen Receptor Activity (CoMPARA), coordinated by the National Center of Computational Toxicology (U.S. Environmental Protection Agency). The collaborative project involved 35 international research groups to prioritize the experimental tests of approximatively 40k compounds, based on the predictions provided by each participant. In this paper, we describe our machine learning approach to predict the binding to AR, which is based on a consensus of a multivariate Bernoulli Naive Bayes, a Random Forest, and N-Nearest Neighbor classification models. The approach was developed in compliance with the Organization of Economic Cooperation and Development (OECD) principles, trained on 1687 ToxCast molecules classified according to 11 in vitro assays, and further validated on a set of 3,882 external compounds. The models provided robust and reliable predictions and were used to gather novel data-driven insights on the structural features related to AR binding, agonism, and antagonism.

Machine Learning-Assisted Network Inference Approach to Identify a New Class of Genes that Coordinate the Functionality of Cancer Networks

Emerging evidence indicates the existence of a new class of cancer genes that act as "signal linkers" coordinating oncogenic signals between mutated and differentially expressed genes. While frequently mutated oncogenes and differentially expressed genes, which we term Class I cancer genes, are readily detected by most analytical tools, the new class of cancer-related genes, i.e., Class II, escape detection because they are neither mutated nor differentially expressed. Given this hypothesis, we developed a Machine Learning-Assisted Network Inference (MALANI) algorithm, which assesses all genes regardless of expression or mutational status in the context of cancer etiology. We used 8807 expression arrays, corresponding to 9 cancer types, to build more than 2 × 108 Support Vector Machine (SVM) models for reconstructing a cancer network. We found that ~3% of ~19,000 not differentially expressed genes are Class II cancer gene candidates. Some Class II genes that we found, such as SLC19A1 and ATAD3B, have been recently reported to associate with cancer outcomes. To our knowledge, this is the first study that utilizes both machine learning and network biology approaches to uncover Class II cancer genes in coordinating functionality in cancer networks and will illuminate our understanding of how genes are modulated in a tissue-specific network contribute to tumorigenesis and therapy development.

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.

Evaluation of a novel PXR-knockout in HepaRG™ cells

The nuclear pregnane X receptor (PXR) regulates the expression of genes involved in the metabolism, hepatobiliary disposition, and toxicity of drugs and endogenous compounds. PXR is a promiscuous nuclear hormone receptor (NHR) with significant ligand and DNA‐binding crosstalk with the constitutive androstane receptor (CAR); hence, defining the precise role of PXR in gene regulation is challenging. Here, utilising a novel PXR‐knockout (KO) HepaRG cell line, real‐time PCR analysis was conducted to determine PXR involvement for a range of inducers. The selective PXR agonist rifampicin, a selective CAR activator, 6‐(4‐chlorophenyl)imidazo[2,1‐b][1,3]thiazole‐5‐carbaldehyde O‐(3,4‐dichlorobenzyl)oxime (CITCO), and dual activators of CAR and PXR including phenobarbital (PB) were analyzed. HepaRG control cells (5F clone) were responsive to prototypical inducers of CYP2B6 and CYP3A4. No response was observed in the PXR‐KO cells treated with rifampicin. Induction of CYP3A4 by PB, artemisinin, and phenytoin was also much reduced in PXR‐KO cells, while the response to CITCO was maintained. This finding is in agreement with the abolition of functional PXR expression. The apparent EC₅₀ values for PB were in agreement between the cell lines; however, CITCO was ~threefold (0.3 μmol/L vs. 1 μmol/L) lower in the PXR‐KO cells compared with the 5F cells for CYP2B6 induction. Results presented support the application of the novel PXR‐KO cells in the definitive assignment of PXR‐mediated CYP2B6 and CYP3A4 induction. Utilization of such cell lines will allow advancement in composing structure activity relationships rather than relying predominantly on pharmacological manipulations and provide in‐depth mechanistic evaluation.

Establishment of In Silico Prediction Models for CYP3A4 and CYP2B6 Induction in Human Hepatocytes by Multiple Regression Analysis Using Azole Compounds

Drug-drug interactions (DDIs) via cytochrome P450 (P450) induction are one clinical problem leading to increased risk of adverse effects and the need for dosage adjustments and additional therapeutic monitoring. In silico models for predicting P450 induction are useful for avoiding DDI risk. In this study, we have established regression models for CYP3A4 and CYP2B6 induction in human hepatocytes using several physicochemical parameters for a set of azole compounds with different P450 induction as characteristics as model compounds. To obtain a well-correlated regression model, the compounds for CYP3A4 or CYP2B6 induction were independently selected from the tested azole compounds using principal component analysis with fold-induction data. Both of the multiple linear regression models obtained for CYP3A4 and CYP2B6 induction are represented by different sets of physicochemical parameters. The adjusted coefficients of determination for these models were of 0.8 and 0.9, respectively. The fold-induction of the validation compounds, another set of 12 azole-containing compounds, were predicted within twofold limits for both CYP3A4 and CYP2B6. The concordance for the prediction of CYP3A4 induction was 87% with another validation set, 23 marketed drugs. However, the prediction of CYP2B6 induction tended to be overestimated for these marketed drugs. The regression models show that lipophilicity mostly contributes to CYP3A4 induction, whereas not only the lipophilicity but also the molecular polarity is important for CYP2B6 induction. Our regression models, especially that for CYP3A4 induction, might provide useful methods to avoid potent CYP3A4 or CYP2B6 inducers during the lead optimization stage without performing induction assays in human hepatocytes.

Screening Ingredients from Herbs against Pregnane X Receptor in the Study of Inductive Herb-Drug Interactions: Combining Pharmacophore and Docking-Based Rank Aggregation

The issue of herb-drug interactions has been widely reported. Herbal ingredients can activate nuclear receptors and further induce the gene expression alteration of drug-metabolizing enzyme and/or transporter. Therefore, the herb-drug interaction will happen when the herbs and drugs are coadministered. This kind of interaction is called inductive herb-drug interactions. Pregnane X Receptor (PXR) and drug-metabolizing target genes are involved in most of inductive herb-drug interactions. To predict this kind of herb-drug interaction, the protocol could be simplified to only screen agonists of PXR from herbs because the relations of drugs with their metabolizing enzymes are well studied. Here, a combinational in silico strategy of pharmacophore modelling and docking-based rank aggregation (DRA) was employed to identify PXR's agonists. Firstly, 305 ingredients were screened out from 820 ingredients as candidate agonists of PXR with our pharmacophore model. Secondly, DRA was used to rerank the result of pharmacophore filtering. To validate our prediction, a curated herb-drug interaction database was built, which recorded 380 herb-drug interactions. Finally, among the top 10 herb ingredients from the ranking list, 6 ingredients were reported to involve in herb-drug interactions. The accuracy of our method is higher than other traditional methods. The strategy could be extended to studies on other inductive herb-drug interactions.

In silico methods for predicting drug-drug interactions with cytochrome P-450s, transporters and beyond

Drug-drug interactions (DDIs) are associated with severe adverse effects that may lead to the patient requiring alternative therapeutics and could ultimately lead to drug withdrawal from the market if they are severe. To prevent the occurrence of DDI in the clinic, experimental systems to evaluate drug interaction have been integrated into the various stages of the drug discovery and development process. A large body of knowledge about DDI has also accumulated through these studies and pharmacovigillence systems. Much of this work to date has focused on the drug metabolizing enzymes such as cytochrome P-450s as well as drug transporters, ion channels and occasionally other proteins. This combined knowledge provides a foundation for a hypothesis-driven in silico approach, using either cheminformatics or physiologically based pharmacokinetics (PK) modeling methods to assess DDI potential. Here we review recent advances in these approaches with emphasis on hypothesis-driven mechanistic models for important protein targets involved in PK-based DDI. Recent efforts with other informatics approaches to detect DDI are highlighted. Besides DDI, we also briefly introduce drug interactions with other substances, such as Traditional Chinese Medicines to illustrate how in silico modeling can be useful in this domain. We also summarize valuable data sources and web-based tools that are available for DDI prediction. We finally explore the challenges we see faced by in silico approaches for predicting DDI and propose future directions to make these computational models more reliable, accurate, and publically accessible.

Toward predicting drug-induced liver injury: parallel computational approaches to identify multidrug resistance protein 4 and bile salt export pump inhibitors

Drug-induced liver injury (DILI) is an important cause of drug toxicity. Inhibition of multidrug resistance protein 4 (MRP4), in addition to bile salt export pump (BSEP), might be a risk factor for the development of cholestatic DILI. Recently, we demonstrated that inhibition of MRP4, in addition to BSEP, may be a risk factor for the development of cholestatic DILI. Here, we aimed to develop computational models to delineate molecular features underlying MRP4 and BSEP inhibition. Models were developed using 257 BSEP and 86 MRP4 inhibitors and noninhibitors in the training set. Models were externally validated and used to predict the affinity of compounds toward BSEP and MRP4 in the DrugBank database. Compounds with a score above the median fingerprint threshold were considered to have significant inhibitory effects on MRP4 and BSEP. Common feature pharmacophore models were developed for MRP4 and BSEP with LigandScout software using a training set of nine well characterized MRP4 inhibitors and nine potent BSEP inhibitors. Bayesian models for BSEP and MRP4 inhibition/noninhibition were developed with cross-validated receiver operator curve values greater than 0.8 for the test sets, indicating robust models with acceptable false positive and false negative prediction rates. Both MRP4 and BSEP inhibitor pharmacophore models were characterized by hydrophobic and hydrogen-bond acceptor features, albeit in distinct spatial arrangements. Similar molecular features between MRP4 and BSEP inhibitors may partially explain why various drugs have affinity for both transporters. The Bayesian (BSEP, MRP4) and pharmacophore (MRP4, BSEP) models demonstrated significant classification accuracy and predictability.

Developing Adnectins that target SRC co-activator binding to PXR: a structural approach toward understanding promiscuity of PXR

The human pregnane X receptor (PXR) is a promiscuous nuclear receptor that functions as a sensor to a wide variety of xenobiotics and regulates expression of several drug metabolizing enzymes and transporters. We have generated "Adnectins", derived from 10th fibronectin type III domain ((10)Fn3), that target the PXR ligand binding domain (LBD) interactions with the steroid receptor co-activator-1 (SRC-1) peptide, displacing SRC-1 binding. Adnectins are structurally homologous to the immunoglobulin superfamily. Three different co-crystal structures of PXR LBD with Adnectin-1 and CCR1 (CC chemokine receptor-1) antagonist Compound-1 were determined. This structural information was used to modulate PXR affinity for a related CCR1 antagonist compound that entered into clinical trials for rheumatoid arthritis. The structures of PXR with Adnectin-1 reveal specificity of Adnectin-1 in not only targeting the interface of the SRC-1 interactions but also engaging the same set of residues that are involved in binding of SRC-1 to PXR. Substituting SRC-1 with Adnectin-1 does not alter the binding conformation of Compound-1 in the ligand binding pocket. The structure also reveals the possibility of using Adnectins as crystallization chaperones to generate structures of PXR with compounds of interest.

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.

An updated review on drug-induced cholestasis: mechanisms and investigation of physicochemical properties and pharmacokinetic parameters

Drug-induced cholestasis is an important form of acquired liver disease and is associated with significant morbidity and mortality. Bile acids are key signaling molecules, but they can exert toxic responses when they accumulate in hepatocytes. This review focuses on the physiological mechanisms of drug-induced cholestasis associated with altered bile acid homeostasis due to direct (e.g., bile acid transporter inhibition) or indirect (e.g., activation of nuclear receptors, altered function/expression of bile acid transporters) processes. Mechanistic information about the effects of a drug on bile acid homeostasis is important when evaluating the cholestatic potential of a compound, but experimental data often are not available. The relationship between physicochemical properties, pharmacokinetic parameters, and inhibition of the bile salt export pump among 77 cholestatic drugs with different pathophysiological mechanisms of cholestasis (i.e., impaired formation of bile vs. physical obstruction of bile flow) was investigated. The utility of in silico models to obtain mechanistic information about the impact of compounds on bile acid homeostasis to aid in predicting the cholestatic potential of drugs is highlighted.

LASSO-ing Potential Nuclear Receptor Agonists and Antagonists: A New Computational Method for Database Screening

Nuclear receptors (NRs) are important biological macromolecular transcription factors that are implicated in multiple biological pathways and may interact with other xenobiotics that are endocrine disruptors present in the environment. Examples of important NRs include the androgen receptor (AR), estrogen receptors (ER), and the pregnane X receptor (PXR). In this study we have utilized the Ligand Activity by Surface Similarity Order (LASSO) method, a ligand-based virtual screening strategy to derive structural (surface/shape) molecular features used to generate predictive models of biomolecular activity for AR, ER, and PXR. For PXR, twenty-five models were built using between 8 to 128 agonists and tested using 3000, 8000, and 24,000 drug-like decoys including PXR inactive compounds (N=228). Preliminary studies with AR and ER using LASSO suggested the utility of this approach with 2-fold enrichment factors at 20%. We found that models with 64–128 PXR actives provided enrichment factors of 10-fold (10% actives in the top 1% of compounds screened). The LASSO models for AR and ER have been deployed and are freely available online, and they represent a ligand-based prediction method for putative NR activity of compounds in this database.

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.

Methods for the quantitative evaluation and prediction of CYP enzyme induction using human in vitro systems

IMPORTANCE OF THE FIELD

For successful drug development, it is important to investigate the potency of candidate drugs causing drug-drug interactions (DDI) during the early stages of development. The most common mechanisms of DDIs are the inhibition and induction of CYP enzymes. Therefore, it is important to develop co.mpounds with lower potencies for CYP enzyme induction.

AREAS COVERED IN THIS REVIEW

The aim of the present paper is to present an overview of the current knowledge of CYP induction mechanisms, particularly focusing on the transcriptional gene activation mediated by pregnane X receptor, aryl hydrocarbon receptor and constitutive androstane receptor. The adoptable options of in vitro assay methods for evaluating CYP induction are also summarized. Finally, we introduce a method for the quantitative prediction of CYP3A4 induction considering the turnover of CYP3A4 mRNA and protein in hepatocytes based on the data obtained from a reporter gene assay.

WHAT THE READER WILL GAIN

In order to predict in vivo CYP enzyme induction quantitatively based on in vitro information, an understanding of the physiological induction mechanisms and the features of each in vitro assay system is essential. We also present the estimation method of in vivo CYP induction potency of each compound based on the in vitro data which are routinely obtained but not necessarily utilized maximally in pharmaceutical companies.

TAKE HOME MESSAGE

It is desirable to select compounds with lower potencies for the inductive effect. For this purpose, an accurate prioritization procedure to evaluate the induction potency of each compound in a quantitative manner considering the pharmacologically effective concentration of each compound is necessary.

PXR ligand classification model with SFED-weighted WHIM and CoMMA descriptors

Understanding which type of endogenous and exogenous compounds serve as agonists for the nuclear pregnane X receptor (PXR) would be valuable for drug discovery and development, because PXR regulates a large number of genes related to xenobiotic metabolism. Although several models have been proposed to classify human PXR activators and non-activators, models with better predictability are necessary for practical purposes in drug discovery. Grid-weighted holistic invariant molecular (G-WHIM) and comparative molecular moment analysis (G-CoMMA) type 3D descriptors that contain information about the solvation free energy of target molecules were developed. With these descriptors, prediction models built using decision tree (DT)-, support vector machine (SVM)-, and Kohonen neural network (KNN)-based models exhibited better predictability than previously proposed models. Solvation free energy density-weighted G-WHIM and G-CoMMA descriptors reveal new insights into PXR ligand classification, and incorporation with machine learning methods (DT, SVM, KNN) exhibits promising results, especially SVM and KNN. SVM- and KNN-based models exhibit accuracy around 0.90, and DT-based models exhibit accuracy around 0.8 for both the training and test sets.

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.

QSAR classification of metabolic activation of chemicals into covalently reactive species

Metabolic activation of chemicals into covalently reactive species might lead to toxicological consequences such as tissue necrosis, carcinogenicity, teratogenicity, or immune-mediated toxicities. Early prediction of this undesirable outcome can help in selecting candidates with increased chance of success, thus, reducing attrition at all stages of drug development. The ensemble modelling of mixed features was used for the development of a model to classify the metabolic activation of chemicals into covalently reactive species. The effects of the quality of base classifiers and performance measure for sorting were examined. An ensemble model of 13 naive Bayes classifiers was built from a diverse set of 1,479 compounds. The ensemble model was validated internally with five-fold cross validation and it has achieved sensitivity of 67.4% and specificity of 93.4% when tested on the training set. The final ensemble model was made available for public use.

Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms

Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure–activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein–ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.

Accessing, using, and creating chemical property databases for computational toxicology modeling

Toxicity data is expensive to generate, is increasingly seen as precompetitive, and is frequently used for the generation of computational models in a discipline known as computational toxicology. Repositories of chemical property data are valuable for supporting computational toxicologists by providing access to data regarding potential toxicity issues with compounds as well as for the purpose of building structure-toxicity relationships and associated prediction models. These relationships use mathematical, statistical, and modeling computational approaches and can be used to understand the mechanisms by which chemicals cause harm and, ultimately, enable prediction of adverse effects of these chemicals to human health and/or the environment. Such approaches are of value as they offer an opportunity to prioritize chemicals for testing. An increasing amount of data used by computational toxicologists is being published into the public domain and, in parallel, there is a greater availability of Open Source software for the generation of computational models. This chapter provides an overview of the types of data and software available and how these may be used to produce predictive toxicology models for the community.

Identifying and characterizing promiscuous targets: implications for virtual screening

INTRODUCTION

Ligand-based shape matching approaches have become established as important and popular virtual screening (VS) techniques. However, despite their relative success, the question of how to best choose the initial query compounds and their conformations remains largely unsolved. This issue gains importance when dealing with promiscuous targets, that is, proteins that bind multiple ligand scaffold families in one or more binding site. Conventional shape matching VS approaches assume that there is only one binding mode for a given protein target. This may be true for some targets, but it is certainly not true in all cases. Several recent studies have shown that some protein targets bind to different ligands in different ways.

AREAS COVERED

The authors discuss the concept of promiscuity in the context of virtual drug screening, and present and analyze several examples of promiscuous targets. The article also reports on the impact of the query conformation on the performance of shape-based VS and the potential to improve VS performance by using consensus shape clustering techniques.

EXPERT OPINION

The notion of polypharmacology is becoming highly relevant in drug discovery. Understanding and exploiting promiscuity present challenges and opportunities for drug discovery endeavors. The examples of promiscuity presented here suggest that promiscuous targets and ligands are much more common than previously assumed, and this should be taken into account in practical VS protocols. Although some progress has been made, there is a need to develop more sophisticated computational techniques and protocols that can identify and characterize promiscuous targets on a genomic scale.

Utility of protein structures in overcoming ADMET-related issues of drug-like compounds

The number of solved X-ray structures of proteins relevant for ADMET processes of drug molecules has increased remarkably over recent years. In principle, this development offers the possibility to complement the quantitative structure-property relationship (QSPR)-dominated repertoire of in silico ADMET methods with protein-structure-based approaches. However, the complex nature and the weak nonspecific ligand-binding properties of ADMET proteins take structural biology methods and current docking programs to the limit. In this review we discuss the utility of protein-structure-based design and docking approaches aimed at overcoming issues related to plasma protein binding, active transport via P-glycoprotein, hERG channel mediated cardiotoxicity and cytochrome P450 inhibition, metabolism and induction.

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

IMPORTANCE OF THE FIELD

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.

AREAS COVERED IN THIS REVIEW

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.

WHAT THE READER WILL GAIN

This review summarizes the state of modeling hERG and CYP binding towards this goal since 2003 using ML algorithms.

TAKE HOME MESSAGE

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.

Regulation of human pregnane X receptor and its target gene cytochrome P450 3A4 by Chinese herbal compounds and a molecular docking study

The pregnane X receptor (PXR) plays a critical role in the regulation of human cytochrome P450 3A4 (CYP3A4) gene. In this study, we investigated the effect of an array of compounds isolated from Chinese herbal medicines on the activity of PXR using a luciferase reporter gene assay in transiently transfected HepG2 and Huh7 cells and on the expression of PXR and CYP3A4 in LS174T cells. Furthermore, molecular docking was performed to investigate the binding modes of herbal compounds with PXR. Praeruptorin A and C, salvianolic acid B, sodium danshensu, protocatechuic aldehyde, cryptotanshinone, emodin, morin, and tanshinone IIA significantly transactivated the CYP3A4 reporter gene construct in either HepG2 or Huh7 cells. The PXR mRNA expression in LS174T cells was significantly induced by physcion, protocatechuic aldehyde, salvianolic acid B, and sodium danshensu. However, epifriedelanol, morin, praeruptorin D, mulberroside A, tanshinone I, and tanshinone IIA significantly down-regulated the expression of PXR mRNA in LS174T cells. All the herbal compounds tested can be readily docked into the ligand-binding cavity of PXR mainly through hydrogen bond and aromatic interactions with Ser247, Gln285, His407, and Arg401. These findings suggest that herbal medicines can significantly regulate PXR and CYP3A4 and this has important implication in herb-drug interactions.

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

Human pregnane X receptor (hPXR) plays a key role in regulating metabolism and clearance of endogenous and exogenous substances. Identification of novel hPXR activators among commercial drugs may aid in avoiding drug-drug interactions during coadministration. We applied ligand-based computational approaches for virtual screening of a commonly prescribed drug database (SCUT). Bayesian classification models were generated with a training set comprising 177 compounds using Fingerprints and 117 structural descriptors. A cell-based luciferase reporter assay was used for evaluation of chemical-mediated hPXR activation in HepG2 cells. All compounds were tested at 10 μM concentration with rifampicin and dimethyl sulfoxide as positive and negative controls, respectively. The Bayesian models showed specificity and overall prediction accuracy up to 0.92 and 0.69 for test set compounds. Screening the SCUT database with this model retrieved 105 hits and 17 compounds from the top 25 hits were chosen for in vitro testing. The reporter assay confirmed that nine drugs, i.e., fluticasone, nimodipine, nisoldipine, beclomethasone, finasteride, flunisolide, megestrol, secobarbital, and aminoglutethimide, were previously unidentified hPXR activators. Thus, the present study demonstrates that novel hPXR activators can be efficiently identified among U.S. Food and Drug Administration-approved and commonly prescribed drugs, which should lead to detection and prevention of potential drug-drug interactions.

Evaluation of computational docking to identify pregnane X receptor agonists in the ToxCast database

BACKGROUND

The pregnane X receptor (PXR) is a key transcriptional regulator of many genes [e.g., cytochrome P450s (CYP2C9, CYP3A4, CYP2B6), MDR1] involved in xenobiotic metabolism and excretion.

OBJECTIVES

As part of an evaluation of different approaches to predict compound affinity for nuclear hormone receptors, we used the molecular docking program GOLD and a hybrid scoring scheme based on similarity weighted GoldScores to predict potential PXR agonists in the ToxCast database of pesticides and other industrial chemicals. We present some of the limitations of different in vitro systems, as well as docking and ligand-based computational models.

METHODS

Each ToxCast compound was docked into the five published crystallographic structures of human PXR (hPXR), and 15 compounds were selected based on their consensus docking scores for testing. In addition, we used a Bayesian model to classify the ToxCast compounds into PXR agonists and nonagonists. hPXR activation was determined by luciferase-based reporter assays in the HepG2 and DPX-2 human liver cell lines.

RESULTS

We tested 11 compounds, of which 6 were strong agonists and 2 had weak agonist activity. Docking results of additional compounds were compared with data reported in the literature. The prediction sensitivity of PXR agonists in our sample ToxCast data set (n = 28) using docking and the GoldScore was higher than with the hybrid score at 66.7%. The prediction sensitivity for PXR agonists using GoldScore for the entire ToxCast data set (n = 308) compared with data from the NIH (National Institutes of Health) Chemical Genomics Center data was 73.8%.

CONCLUSIONS

Docking and the GoldScore may be useful for prioritizing large data sets prior to in vitro testing with good sensitivity across the sample and entire ToxCast data set for hPXR agonists.

Virtual screening of selective multitarget kinase inhibitors by combinatorial support vector machines

Multitarget agents have been increasingly explored for enhancing efficacy and reducing countertarget activities and toxicities. Efficient virtual screening (VS) tools for searching selective multitarget agents are desired. Combinatorial support vector machines (C-SVM) were tested as VS tools for searching dual-inhibitors of 11 combinations of 9 anticancer kinase targets (EGFR, VEGFR, PDGFR, Src, FGFR, Lck, CDK1, CDK2, GSK3). C-SVM trained on 233-1,316 non-dual-inhibitors correctly identified 26.8%-57.3% (majority >36%) of the 56-230 intra-kinase-group dual-inhibitors (equivalent to the 50-70% yields of two independent individual target VS tools), and 12.2% of the 41 inter-kinase-group dual-inhibitors. C-SVM were fairly selective in misidentifying as dual-inhibitors 3.7%-48.1% (majority <20%) of the 233-1,316 non-dual-inhibitors of the same kinase pairs and 0.98%-4.77% of the 3,971-5,180 inhibitors of other kinases. C-SVM produced low false-hit rates in misidentifying as dual-inhibitors 1,746-4,817 (0.013%-0.036%) of the 13.56 M PubChem compounds, 12-175 (0.007%-0.104%) of the 168 K MDDR compounds, and 0-84 (0.0%-2.9%) of the 19,495-38,483 MDDR compounds similar to the known dual-inhibitors. C-SVM was compared to other VS methods Surflex-Dock, DOCK Blaster, kNN and PNN against the same sets of kinase inhibitors and the full set or subset of the 1.02 M Zinc clean-leads data set. C-SVM produced comparable dual-inhibitor yields, slightly better false-hit rates for kinase inhibitors, and significantly lower false-hit rates for the Zinc clean-leads data set. Combinatorial SVM showed promising potential for searching selective multitarget agents against intra-kinase-group kinases without explicit knowledge of multitarget agents.

Identifying Novel Type ZBGs and Nonhydroxamate HDAC Inhibitors Through a SVM Based Virtual Screening Approach

Histone deacetylase inhibitors (HDACi) have been successfully used for the treatment of cancers and other diseases. Search for novel type ZBGs and development of non-hydroxamate HDACi has become a focus in current research. To complement this, it is desirable to explore a virtual screening (VS) tool capable of identifying different types of potential inhibitors from large compound libraries with high yields and low false-hit rates similar to HTS. This work explored the use of support vector machines (SVM) combined with our newly developed putative non-inhibitor generation method as such a tool. SVM trained by 702 pre-2008 hydroxamate HDACi and 64334 putative non-HDACi showed good yields and low false-hit rates in cross-validation test and independent test using 220 diverse types of HDACi reported since 2008. The SVM hit rates in scanning 13.56 M PubChem and 168K MDDR compounds are comparable to HTS rates. Further structural analysis of SVM virtual hits suggests its potential for identification of non-hydroxamate HDACi. From this analysis, a series of novel ZBG and cap groups were proposed for HDACi design.

Identification of small molecule aggregators from large compound libraries by support vector machines

Small molecule aggregators non-specifically inhibit multiple unrelated proteins, rendering them therapeutically useless. They frequently appear as false hits and thus need to be eliminated in high-throughput screening campaigns. Computational methods have been explored for identifying aggregators, which have not been tested in screening large compound libraries. We used 1319 aggregators and 128,325 non-aggregators to develop a support vector machines (SVM) aggregator identification model, which was tested by four methods. The first is five fold cross-validation, which showed comparable aggregator and significantly improved non-aggregator identification rates against earlier studies. The second is the independent test of 17 aggregators discovered independently from the training aggregators, 71% of which were correctly identified. The third is retrospective screening of 13M PUBCHEM and 168K MDDR compounds, which predicted 97.9% and 98.7% of the PUBCHEM and MDDR compounds as non-aggregators. The fourth is retrospective screening of 5527 MDDR compounds similar to the known aggregators, 1.14% of which were predicted as aggregators. SVM showed slightly better overall performance against two other machine learning methods based on five fold cross-validation studies of the same settings. Molecular features of aggregation, extracted by a feature selection method, are consistent with published profiles. SVM showed substantial capability in identifying aggregators from large libraries at low false-hit rates.

Challenges predicting ligand-receptor interactions of promiscuous proteins: the nuclear receptor PXR

Transcriptional regulation of some genes involved in xenobiotic detoxification and apoptosis is performed via the human pregnane X receptor (PXR) which in turn is activated by structurally diverse agonists including steroid hormones. Activation of PXR has the potential to initiate adverse effects, altering drug pharmacokinetics or perturbing physiological processes. Reliable computational prediction of PXR agonists would be valuable for pharmaceutical and toxicological research. There has been limited success with structure-based modeling approaches to predict human PXR activators. Slightly better success has been achieved with ligand-based modeling methods including quantitative structure-activity relationship (QSAR) analysis, pharmacophore modeling and machine learning. In this study, we present a comprehensive analysis focused on prediction of 115 steroids for ligand binding activity towards human PXR. Six crystal structures were used as templates for docking and ligand-based modeling approaches (two-, three-, four- and five-dimensional analyses). The best success at external prediction was achieved with 5D-QSAR. Bayesian models with FCFP_6 descriptors were validated after leaving a large percentage of the dataset out and using an external test set. Docking of ligands to the PXR structure co-crystallized with hyperforin had the best statistics for this method. Sulfated steroids (which are activators) were consistently predicted as non-activators while, poorly predicted steroids were docked in a reverse mode compared to 5α-androstan-3β-ol. Modeling of human PXR represents a complex challenge by virtue of the large, flexible ligand-binding cavity. This study emphasizes this aspect, illustrating modest success using the largest quantitative data set to date and multiple modeling approaches.

Promiscuous proteins generally bind a large array of diverse ligand structures. This may be facilitated by a very large binding site, multiple binding sites, or a flexible binding site that can adjust to the size of the ligand. These aspects also increase the complexity of predicting whether a molecule will bind or not to such proteins which frequently function as exogenous compound sensors to respond to toxic stress. For example, transporters may prevent absorption of some molecules, and enzymes may convert them to more readily excretable compounds (or alternatively activate them prior to further clearance by other detoxification enzymes). Nuclear hormone receptors may respond to ligands and then affect downstream gene expression to upregulate both enzymes and transporters to increase the clearance for the same or different molecules. We have assessed the ability of many different ligand-based and structure-based computational approaches to model and predict the activation of human PXR by steroidal compounds. We find the most effective computational approach to identify potential steroidal PXR agonists which are clinically relevant due to their widespread use in clinical medicine and the presence of mimics in the environment.

Predicting the clinical relevance of drug interactions from pre-approval studies

Drug interactions (DIs) may result in adverse drug events that could be prevented, but in many cases the available information on potential DIs is not easily transferable to clinical practice. The majority of studies date from preclinical or premarketing phases, using animals or human-derived sources that may not accurately reflect the growing clinical complexity of high-risk populations, such as the elderly, women, children, patients with chronic disease, polytherapy and impaired organ functions. Thus, at the time of approval of a new drug the information in the summary of product characteristics refers to potential DIs, but lacks specific management recommendations and is of limited clinical utility. Therefore, we set out to review in vitro and in vivo methods to predict and quantify potential DIs, to see whether these studies could help the physician tackle daily problems of the assessment and choice of combined drug therapies, and to propose, from a clinical point of view, how premarketing studies could be improved so as to help the physician at the patient's bedside. Preclinical and premarketing study design needs to be improved to make information easily accessible and clinically transferable. Studies should also take into account appropriate sample size, duration, co-morbidity, number of coadministered drugs, within- and between-subject variability, specific at-risk populations and/or drugs with a relatively narrow therapeutic window, and clinical endpoints. After premarketing development in situations where there is potential high risk of serious adverse events, specific phase IV studies (and/or active pharmacovigilance studies) should be required to monitor and quantitatively assess their clinical impact.

Predicting inhibitors of acetylcholinesterase by regression and classification machine learning approaches with combinations of molecular descriptors

PURPOSE

Acetylcholinesterase (AChE) is both a therapeutic target for Alzheimer's disease and a target for organophosphorus, carbamates and chemical warfare agents. Prediction of the likelihood of compounds interacting with this enzyme is therefore important from both therapeutic and toxicological perspectives.

MATERIALS AND METHODS

Support vector machine classification and regression models with molecular descriptors derived from Shape Signatures and the Molecular Operating Environment (MOE) application software were built and tested using a set of piperidine AChE inhibitors (N = 110).

RESULTS

The combination of the alignment free Shape Signatures and 2D MOE descriptors with the Support Vector Regression method outperforms the models based solely on 2D and internal 3D (i3D) MOE descriptors, and is comparable with the best previously reported PLS model based on CoMFA molecular descriptors (r(2)(test,SVR) = 0.48 vs. r(2)(test,PLS) = 0.47 from Sutherland et al. J Med Chem 47:5541-5554, 2004). Support Vector Classification algorithms proved superior to a classifier based on scores from the molecular docking program GOLD, with the overall prediction accuracies being Q(SVC(10CV)) = 74% and Q(SVC(LNO)) = 67% vs. Q(GOLD) = 56%.

CONCLUSIONS

These new machine learning models with combined descriptor schemes may find utility for predicting novel AChE inhibitors.

Elucidating the 'Jekyll and Hyde' nature of PXR: the case for discovering antagonists or allosteric antagonists

The pregnane X receptor belongs to the nuclear hormone receptor superfamily and is involved in the transcriptional control of numerous genes. It was originally thought that it was a xenobiotic sensor controlling detoxification pathways. Recent studies have shown an increasingly important role in inflammation and cancer, supporting its function in abrogating tissue damage. PXR orthologs and PXR-like pathways have been identified in several non-mammalian species which corroborate a conserved role for PXR in cellular detoxification. In summary, PXR has a multiplicity of roles in vivo and is being revealed as behaving like a "Jekyll and Hyde" nuclear hormone receptor. The importance of this review is to elucidate the need for discovery of antagonists of PXR to further probe its biology and therapeutic applications. Although several PXR agonists are already reported, virtually nothing is known about PXR antagonists. Here, we propose the development of PXR antagonists through chemical, genetic and molecular modeling approaches. Based on this review it will be clear that antagonists of PXR and PXR-like pathways will have widespread utility in PXR biology and therapeutics.