QSAR model for human pregnane X receptor (PXR) binding: Screening of environmental chemicals and correlations with genotoxicity, endocrine disruption and teratogenicity

Machine learning and traditional QSAR modeling methods: a case study of known PXR activators

Pregnane X receptor (PXR), extensively expressed in human tissues related to digestion and metabolism, is responsible for recognizing and detoxifying diverse xenobiotics encountered by humans. To comprehend the promiscuous nature of PXR and its ability to bind a variety of ligands, computational approaches, viz., quantitative structure-activity relationship (QSAR) models, aid in the rapid dereplication of potential toxicological agents and mitigate the number of animals used to establish a meaningful regulatory decision. Recent advancements in machine learning techniques accommodating larger datasets are expected to aid in developing effective predictive models for complex mixtures (viz., dietary supplements) before undertaking in-depth experiments. Five hundred structurally diverse PXR ligands were used to develop traditional two-dimensional (2D) QSAR, machine-learning-based 2D-QSAR, field-based three-dimensional (3D) QSAR, and machine-learning-based 3D-QSAR models to establish the utility of predictive machine learning methods. Additionally, the applicability domain of the agonists was established to ensure the generation of robust QSAR models. A prediction set of dietary PXR agonists was used to externally-validate generated QSAR models. QSAR data analysis revealed that machine-learning 3D-QSAR techniques were more accurate in predicting the activity of external terpenes with an external validation squared correlation coefficient (R2) of 0.70 versus an R2 of 0.52 in machine-learning 2D-QSAR. Additionally, a visual summary of the binding pocket of PXR was assembled from the field 3D-QSAR models. By developing multiple QSAR models in this study, a robust groundwork for assessing PXR agonism from various chemical backbones has been established in anticipation of the identification of potential causative agents in complex mixtures.

Identifying chemicals based on receptor binding/bioactivation/mechanistic explanation associated with potential to elicit hepatotoxicity and to support structure activity relationship-based read-across

The liver is the most common target organ in toxicology studies. The development of chemical structural alerts for identifying hepatotoxicity will play an important role in in silico model prediction and help strengthen the identification of analogs used in structure activity relationship (SAR)- based read-across. The aim of the current study is development of an SAR-based expert-system decision tree for screening of hepatotoxicants across a wide range of chemistry space and proposed modes of action for clustering of chemicals using defined core chemical categories based on receptor-binding or bioactivation. The decision tree is based on ∼ 1180 different chemicals that were reviewed for hepatotoxicity information. Knowledge of chemical receptor binding, metabolism and mechanistic information were used to group these chemicals into 16 different categories and 102 subcategories: four categories describe binders to 9 different receptors, 11 categories are associated with possible reactive metabolites (RMs) and there is one miscellaneous category. Each chemical subcategory has been associated with possible modes of action (MOAs) or similar key structural features. This decision tree can help to screen potential liver toxicants associated with core structural alerts of receptor binding and/or RMs and be used as a component of weight of evidence decisions based on SAR read-across, and to fill data gaps.

Predicting Endocrine Disruption Using Conformal Prediction - A Prioritization Strategy to Identify Hazardous Chemicals with Confidence

Receptor-mediated molecular initiating events (MIEs) and their relevance in endocrine activity (EA) have been highlighted in literature. More than 15 receptors have been associated with neurodevelopmental adversity and metabolic disruption. MIEs describe chemical interactions with defined biological outcomes, a relationship that could be described with quantitative structure-activity relationship (QSAR) models. QSAR uncertainty can be assessed using the conformal prediction (CP) framework, which provides similarity (i.e., nonconformity) scores relative to the defined classes per prediction. CP calibration can indirectly mitigate data imbalance during model development, and the nonconformity scores serve as intrinsic measures of chemical applicability domain assessment during screening. The focus of this work was to propose an in silico predictive strategy for EA. First, 23 QSAR models for MIEs associated with EA were developed using high-throughput data for 14 receptors. To handle the data imbalance, five protocols were compared, and CP provided the most balanced class definition. Second, the developed QSAR models were applied to a large data set (∼55,000 chemicals), comprising chemicals representative of potential risk for human exposure. Using CP, it was possible to assess the uncertainty of the screening results and identify model strengths and out of domain chemicals. Last, two clustering methods, t-distributed stochastic neighbor embedding and Tanimoto similarity, were used to identify compounds with potential EA using known endocrine disruptors as reference. The cluster overlap between methods produced 23 chemicals with suspected or demonstrated EA potential. The presented models could be utilized for first-tier screening and identification of compounds with potential biological activity across the studied MIEs.

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.

Pregnane X Receptor and the Gut-Liver Axis: A Recent Update

It is well-known that the pregnane X receptor (PXR)/Nr1i2 is a critical xenobiotic-sensing nuclear receptor enriched in liver and intestine and is responsible for drug-drug interactions, due to its versatile ligand binding domain (LBD) and target genes involved in xenobiotic biotransformation. PXR can be modulated by various xenobiotics including pharmaceuticals, nutraceuticals, dietary factors, and environmental chemicals. Microbial metabolites such as certain secondary bile acids (BAs) and the tryptophan metabolite indole-3-propionic acid (IPA) are endogenous PXR activators. Gut microbiome is increasingly recognized as an important regulator for host xenobiotic biotransformation and intermediary metabolism. PXR regulates and is regulated by the gut-liver axis. This review summarizes recent research advancements leveraging pharmaco- and toxico-metagenomic approaches that have redefined the previous understanding of PXR. Key topics covered in this review include: (1) genome-wide investigations on novel PXR-target genes, novel PXR-DNA interaction patterns, and novel PXR-targeted intestinal bacteria; (2) key PXR-modulating activators and suppressors of exogenous and endogenous sources; (3) novel bidirectional interactions between PXR and gut microbiome under physiologic, pathophysiological, pharmacological, and toxicological conditions; and (4) modifying factors of PXR-signaling including species and sex differences and time (age, critical windows of exposure, and circadian rhythm). The review also discusses critical knowledge gaps and important future research topics centering around PXR. SIGNIFICANCE STATEMENT: This review summarizes recent research advancements leveraging O'mics approaches that have redefined the previous understanding of the xenobiotic-sensing nuclear receptor pregnane X receptor (PXR). Key topics include: (1) genome-wide investigations on novel PXR-targeted host genes and intestinal bacteria as well as novel PXR-DNA interaction patterns; (2) key PXR modulators including microbial metabolites under physiological, pathophysiological, pharmacological, and toxicological conditions; and (3) modifying factors including species, sex, and time.

Review of in silico studies dedicated to the nuclear receptor family: Therapeutic prospects and toxicological concerns

Being in the center of both therapeutic and toxicological concerns, NRs are widely studied for drug discovery application but also to unravel the potential toxicity of environmental compounds such as pesticides, cosmetics or additives. High throughput screening campaigns (HTS) are largely used to detect compounds able to interact with this protein family for both therapeutic and toxicological purposes. These methods lead to a large amount of data requiring the use of computational approaches for a robust and correct analysis and interpretation. The output data can be used to build predictive models to forecast the behavior of new chemicals based on their in vitro activities. This atrticle is a review of the studies published in the last decade and dedicated to NR ligands in silico prediction for both therapeutic and toxicological purposes. Over 100 articles concerning 14 NR subfamilies were carefully read and analyzed in order to retrieve the most commonly used computational methods to develop predictive models, to retrieve the databases deployed in the model building process and to pinpoint some of the limitations they faced.

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.

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.

Environmentally relevant levels of λ-cyhalothrin, fenvalerate, and permethrin cause developmental toxicity and disrupt endocrine system in zebrafish (Danio rerio) embryo

Synthetic pyrethroids (SPs) are one of the most widely used pesticides and frequently detected in the aquatic environment. Previous studies have shown that SPs posed high aquatic toxicity, but information on the developmental toxicity and endocrine disruption on zebrafish (Danio rerio) at environmentally relevant concentrations is limited. In this study, zebrafish embryos were employed to examine the adverse effects of λ-cyhalothrin (LCT), fenvalerate (FEN), and permethrin (PM) at 2.5, 10, 25, 125, 500 nM for 96 h. The results showed these 3 SPs caused dose-dependent mortality, malformation rate, and hatching rate. Thyroid hormone triiodothyronine (T₃) levels were significantly decreased after exposure to LCT and FEN. Quantitative real-time PCR analysis was then performed on a series of nuclear receptors (NRs) genes involved in the hypothalamic-pituitary-gonadal (HPG), hypothalamic-pituitary-thyroid (HPT), hypothalamic-pituitary-adrenocortical (HPA) axes, and oxidative-stress-related system. Our results showed that LCT, FEN, and PM downregulated AR expression while upregulated ER1 expression, and caused alteration to ER2a and ER2b expression. As for the expression of TRα and TRβ, they were both decreased following exposure to the 3 SPs. LCT and PM downregulated the MR expression and FEN induced MR expression. In addition, the expression of GR was increased after treating with LCT, while it was suppressed after exposure to FEN and PM. The 3 SPs also caused various alterations to the expression of genes including AhRs, PPARα, and PXR. These findings suggest that these 3 SPs may cause developmental toxicity to zebrafish larvae by disrupting endocrine signaling at environmentally relevant concentrations.

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.

Development of pharmacophore-based classification model for activators of constitutive androstane receptor

Constitutive androstane receptor (CAR) is predominantly expressed in the liver and is important for regulating drug metabolism and transport. Despite its biological importance, there have been few attempts to develop in silico models to predict the activity of CAR modulated by chemical compounds. The number of in silico studies of CAR may be limited because of CAR's constitutive activity under normal conditions, which makes it difficult to elucidate the key structural features of the interaction between CAR and its ligands. In this study, to address these limitations, we introduced 3D pharmacophore-based descriptors with an integrated ligand and structure-based pharmacophore features, which represent the receptor-ligand interaction. Machine learning methods (support vector machine and artificial neural network) were applied to develop an in silico model with the descriptors containing significant information regarding the ligand binding positions. The best classification model built with a solvent accessibility volume-based filter and the support vector machine showed good predictabilities of 87%, and 85.4% for the training set and validation set, respectively. This demonstrates that our model can be used to accurately predict CAR activators and offers structural information regarding ligand/protein interactions.

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.

Molecular insights into the promiscuous interaction of human pregnane X receptor (hPXR) with diverse environmental chemicals and drug compounds

The nuclear receptor member human pregnane X receptor (hPXR) regulates enzymes and transporters involved in xenobiotic detoxification as well as maintains homeostatic balance of bile acids, thyroid and steroid hormones. hPXR can be recognized and activated by a structurally diverse array of environmental chemicals and drug compounds to initiate adverse biological effects, such as perturbing normal physiological functions and causing dangerous drug-drug interactions and exhibiting a high promiscuity in its ligand spectrum. Understanding of the molecular mechanism and biological implication underlying the promiscuous interaction of hPXR with its diverse ligands is fundamentally important for toxicological and pharmaceutical researches. In the current study, molecular docking and hybrid quantum mechanics/molecular mechanics (QM/MM) were employed to investigate the binding mode, structural basis and energetic property of hPXR interactions with various activators and non-activators. It was found that, as compared to non-activators, the activators adopt few dominant modes to tightly interact with hPXR, which are specified by few polar spots located on the hydrophobic surface of hPXR active pocket. Based on the findings, a novel method called multiple binding mode-based quantitative structure-activity relationship (MBMB-QSAR) that characterizes the nonbonded interaction profile of hPXR with its ligand in multiple binding modes was proposed to model and predict the activating potency of small-molecule compounds on hPXR. Several partial least square (PLS) predictors derived from the MBMB-QSAR modeling were demonstrated to be effective for quantitative characterization of the biological behavior of experimentally confirmed activators, and for qualitatively differentiating the activators from a large number of non-activators. From the predictor models it is suggested that the hydrophobic force and electrostatic interaction play an important role in hPXR-ligand binding, while steric factor contributes moderately to the binding.

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.

Triclosan exposure reduces thyroxine levels in pregnant and lactating rat dams and in directly exposed offspring

Thyroid disrupting chemicals can potentially disrupt brain development. Two studies investigating the effect of the antibacterial compound triclosan on thyroxine (T₄) levels in rats are reported. In the first, Wistar rat dams were gavaged with 75, 150 or 300 mg triclosan/kg bw/day throughout gestation and lactation. Total T₄ serum levels were measured in dams and offspring, and all doses of triclosan significantly lowered T₄ in dams, but no significant effects on T₄ levels were seen in the offspring at the end of the lactation period. Since this lack of effect could be due to minimal exposure through maternal milk, a second study using direct per oral pup exposure from postnatal day 3-16 to 50 or 150 mg triclosan/kg bw/day was performed. This exposure pointed to significant T₄ reductions in 16 day old offspring in both dose groups. These results corroborate previous studies showing that in rats lactational transfer of triclosan seems limited. Since an optimal study design for testing potential developmental neurotoxicants in rats, should include exposure during both the pre- and postnatal periods of brain development, we suggest that in the case of triclosan, direct dosing of pups may be the best way to obtain that goal.