In silico tools to aid risk assessment of endocrine disrupting chemicals

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.

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.

In silico study of molecular mechanisms of action: Estrogenic disruptors among phthalate esters

Phthalate esters (PAEs), as widely used plasticizers, have been concerned for their possible disruption of estrogen functions via binding to and activating the transcription of estrogen receptors (ERs). Nevertheless, the computational interpretation of the mechanism of ERs activities modulated by PAEs at the molecular level is still insufficient, which hinders the reliable screening of the ERs-active PAEs with high speed and high throughput. To bridge the gap, the in silico simulations considering the effects of coactivators were accomplished to explore the molecular mechanism of action for the purpose of predicting the estrogenic potencies of PAEs. The transcriptional activation functions of human ERα (hERα) modulated by PAEs is predicted via the simulations including binding interaction of PAEs and hERα, conformational changes of PAEs-hERα complexes and recruitment of coactivators. Molecular insight into the diverse estrogen mechanism of action among PAEs with regard to hERα agonists and selective estrogen receptor modulators (SERMs) is provided. Agonist-modulated conformational change of hERα leads to the optimal exposure of its Activation Function 2 (AF-2) surface which, in turn, facilitates the recruitment of coactivators, therefore promoting the transcriptional activation functions of hERα. Conversely, binding interaction of hERα with SERMs among PAEs leads to the conformational change with blocked AF-2 surface, thus preventing the recruitment of coactivators and consequently inhibiting the AF-2 activity. The two-hybrid recombinant yeast is experimentally used for verification. The established in silico evaluation methodology exhibits great promise to speed up the prediction of chemicals which work as hERα agonist or SERMs.

Drug discovery technologies to identify and characterize modulators of the pregnane X receptor and the constitutive androstane receptor

The pregnane X receptor (PXR) and the constitutive androstane receptor (CAR) are ligand-activated nuclear receptors (NRs) that are notorious for their role in drug metabolism, causing unintended drug-drug interactions and decreasing drug efficacy. They control the xenobiotic detoxification system by regulating the expression of an array of drug-metabolizing enzymes and transporters that excrete exogenous chemicals and maintain homeostasis of endogenous metabolites. Much effort has been invested in recognizing potential drugs for clinical use that can activate PXR and CAR to enhance the expression of their target genes, and in identifying PXR and CAR inhibitors that can be used as co-therapeutics to prevent adverse effects. Here, we present current technologies and assays used in the quest to characterize PXR and CAR modulators, which range from biochemical to cell-based and animal models.

A combination of ternary classification models and reporter gene assays for the comprehensive thyroid hormone disruption profiles of 209 polychlorinated biphenyls

Computational toxicology is widely applied to screen tens and thousands of potential environmental endocrine disruptors (EDCs) for its great efficiency and rapid evaluation in recent years. Polychlorinated biphenyls (PCBs) with 209 congeners have not been comprehensively tested for their ability to interact with the thyroid receptor (TR), which is one of the most extensively studied targets related to the effects of EDCs. In this study, we aimed to determine the thyroid-disrupting mechanisms of 209 PCBs through the combination of a novel computational ternary classification model and luciferase reporter gene assay. The reporter gene assay was performed to examine the hormone activities of 22 PCBs via TR to classify their profiles as agonistic, antagonistic or inactive. Thus, six agonists, eleven antagonists and seven inactive chemicals against TR were identified in in vitro assays. Then, six relevant variables, including molecular structural descriptors and molecular docking scores, were selected for describing compounds. Machine learning methods (i.e., linear discriminant analysis (LDA) and support vector machines (SVM)) were used to build classification models. The optimal model was obtained by using SVM, with an accuracy of 88.24% in the training set, 80.0% in the test set and 75.0% in 10-fold cross-validation. The remaining 187 PCB congeners' hormone activities were predicted using the obtained models. Out of these PCBs, the SVM model predicted 38 agonists and 81 antagonists. The findings revealed that higher chlorinated PCBs tended to have TR-antagonistic activities, whereas lower chlorinated PCBs were agonists. This study provided a reference for further exploring PCBs' thyroid effect.

A Round Trip from Medicinal Chemistry to Predictive Toxicology

Predictive toxicology is a new emerging multifaceted research field aimed at protecting human health and environment from risks posed by chemicals. Such issue is of extreme public relevance and requires a multidisciplinary approach where the experience in medicinal chemistry is of utmost importance. Herein, we will survey some basic recommendations to gather good data and then will review three recent case studies to show how strategies of ligand- and structure-based molecular design, widely applied in medicinal chemistry, can be adapted to meet the more restrictive scientific and regulatory goals of predictive toxicology. In particular, we will report: Docking-based classification models to predict the estrogenic potentials of chemicals. Predicting the bioconcentration factor using biokinetics descriptors. Modeling oral sub-chronic toxicity using a customized k-nearest neighbors (k-NN) approach.

Predictive Structure-Based Toxicology Approaches To Assess the Androgenic Potential of Chemicals

We present a practical and easy-to-run in silico workflow exploiting a structure-based strategy making use of docking simulations to derive highly predictive classification models of the androgenic potential of chemicals. Models were trained on a high-quality chemical collection comprising 1689 curated compounds made available within the CoMPARA consortium from the US Environmental Protection Agency and were integrated with a two-step applicability domain whose implementation had the effect of improving both the confidence in prediction and statistics by reducing the number of false negatives. Among the nine androgen receptor X-ray solved structures, the crystal 2PNU (entry code from the Protein Data Bank) was associated with the best performing structure-based classification model. Three validation sets comprising each 2590 compounds extracted by the DUD-E collection were used to challenge model performance and the effectiveness of Applicability Domain implementation. Next, the 2PNU model was applied to screen and prioritize two collections of chemicals. The first is a small pool of 12 representative androgenic compounds that were accurately classified based on outstanding rationale at the molecular level. The second is a large external blind set of 55450 chemicals with potential for human exposure. We show how the use of molecular docking provides highly interpretable models and can represent a real-life option as an alternative nontesting method for predictive toxicology.

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.

Computational study involving identification of endocrine disrupting potential of herbicides: Its implication in TDS and cancer progression in CRPC patients

Several environmental pollutants, including herbicides, act as endocrine disrupting chemicals (EDCs). They can cause cancer, diabetes, obesity, metabolic diseases and developmental problems. Present study was conducted to screen 608 herbicides for evaluating their endocrine disrupting potential. The screening was carried out with the help of endocrine disruptome docking program, http://endocrinedisruptome.ki.si (Kolsek et al., 2013). This program screens the binding affinity of test ligands to 12 major nuclear receptors. As high as 252 compounds were capable of binding to at least three receptors wherein 10 of them showed affinity with at-least six receptors based on this approach. The latter were ranked as potent EDCs. Majority of the screened herbicides were acting as antagonists of human androgen receptor (hAR). A homology modeling approach was used to construct the three dimensional structure of hAR to understand their binding mechanism. Docking results reveal that the most potent antiandrogenic herbicides would bind to hydrophobic cavity of modeled hAR and may lead to testicular dysgenesis syndrome (TDS) on fetal exposure. However, on binding to T877 mutant AR they seem to act as agonists in castration-resistant prostate cancer (CRPC) patients.

Artonin E and Structural Analogs from Artocarpus Species Abrogates Estrogen Receptor Signaling in Breast Cancer

The increasing rate of mortality ensued from breast cancer has encouraged research into safer and efficient therapy. The human Estrogen receptor α has been implicated in the majority of reported breast cancer cases. Molecular docking employing Glide, Schrodinger suite 2015, was used to study the binding affinities of small molecules from the Artocarpus species after their drug-like properties were ascertained. The structure of the ligand-binding domain of human Estrogen receptor α was retrieved from Protein Data Bank while the structures of compounds were collected from PubChem database. The binding interactions of the studied compounds were reported as well as their glide scores. The best glide scored ligand, was Artonin E with a score of -12.72 Kcal when compared to other studied phytomolecules and it evoked growth inhibition of an estrogen receptor positive breast cancer cells in submicromolar concentration (3.8-6.9 µM) in comparison to a reference standard Tamoxifen (18.9-24.1 µM) within the tested time point (24-72 h). The studied ligands, which had good interactions with the target receptor, were also drug-like when compared with 95% of orally available drugs with the exception of Artoelastin, whose predicted physicochemical properties rendered it less drug-like. The in silico physicochemical properties, docking interactions and growth inhibition of the best glide scorer are indications of the anti-breast cancer relevance of the studied molecules.

A monograph on the remediation of hazardous phthalates

Phthalates or phthalic acid esters are a group of xenobiotic and hazardous compounds blended in plastics to enhance their plasticity and versatility. Enormous quantities of phthalates are produced globally for the production of plastic goods, whose disposal and leaching out into the surroundings cause serious concerns to the environment, biota and human health. Though in silico computational, in vitro mechanistic, pre-clinical animal and clinical human studies showed endocrine disruption, hepatotoxic, teratogenic and carcinogenic properties, usage of phthalates continues due to their cuteness, attractive chemical properties, low production cost and lack of suitable alternatives. Studies revealed that microbes isolated from phthalate-contaminated environmental niches efficiently bioremediate various phthalates. Based upon this background, this review addresses the enumeration of major phthalates used in industry, routes of environmental contamination, evidences for health hazards, routes for in situ and ex situ microbial degradation, bacterial pathways involved in the degradation, major enzymes involved in the degradation process, half-lives of phthalates in environments, etc. Briefly, this handy module would enable the readers, environmentalists and policy makers to understand the impact of phthalates on the environment and the biota, coupled with the concerted microbial efforts to alleviate the burden of ever increasing load posed by phthalates.

Docking-based classification models for exploratory toxicology studies on high-quality estrogenic experimental data

BACKGROUND

The ethical and practical limitation of animal testing has recently promoted computational methods for the fast screening of huge collections of chemicals.

RESULTS

The authors derived 24 reliable docking-based classification models able to predict the estrogenic potential of a large collection of chemicals provided by the US Environmental Protection Agency. Model performances were challenged by considering AUC, EF1% (EFmax = 7.1), -LR (at sensitivity = 0.75); +LR (at sensitivity = 0.25) and 37 reference compounds comprised within the training set. Moreover, external predictions were made successfully on ten representative known estrogenic chemicals and on a set consisting of >32,000 chemicals.

CONCLUSION

The authors demonstrate that structure-based methods, widely applied to drug discovery programs, can be fairly adapted to exploratory toxicology studies.

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.

Endocrine disruptome--an open source prediction tool for assessing endocrine disruption potential through nuclear receptor binding

Predicting the endocrine disruption potential of compounds is a daunting but essential task. Here we report a new tool for this purpose that we have termed Endocrine Disruptome. It is a free and simple-to-use Web service that runs on an open source platform called Docking interface for Target Systems (DoTS). The molecular docking is handled via AutoDock Vina. Compounds are docked to 18 integrated and well-validated crystal structures of 14 different human nuclear receptors: androgen receptor; estrogen receptors α and β; glucocorticoid receptor; liver X receptors α and β; mineralocorticoid receptor; peroxisome proliferator activated receptors α, β/δ, and γ; progesterone receptor; retinoid X receptor α; and thyroid receptors α and β. Endocrine Disruptome is free of charge and available at http://endocrinedisruptome.ki.si.

Phthalates efficiently bind to human peroxisome proliferator activated receptor and retinoid X receptor α, β, γ subtypes: an in silico approach

This exhaustive in silico study looks into the molecular interactions of phthalates and their metabolites with human peroxisome proliferator-activated receptor (hPPAR) and retinoid X receptor (hRXR) α, β and γ subtypes--the nuclear receptor proteins function as transcription factors by regulating the expression of downstream genes. Apart from the much discussed plasticizer bisphenol A, we examined the binding affinities of 15 common diphthalates and their monophthalates, natural (linoleic acid, conjugated linoleic acid) and synthetic (bezafibrate, pioglitazone, GW 50156) ligands with hPPARs. In addition to these phthalates, specific natural (retinoic and phytanic acids) and synthetic (bexarotene, rosiglitazone) ligands were examined with hRXRs. The Maestro, Schrödinger Suite 2012 was used for the molecular docking study. In general, natural ligands of hPPAR showed less binding efficiencies than phthalic acid esters and drugs. The diphthalate di-iso-decyl phthalate showed the highest G score (-9.99) with hPPAR (γ), while its monophthalate (mono-iso-decyl phthalate) showed a comparatively less G score (-9.56). Though the PPAR modulator GW 50156 showed strong affinity with all hPPAR subtypes, its highest G score (-12.43) was with hPPARβ. Hazardous di(2-ethylhexyl)phthalate generally showed a greater preference to hRXRs than hPPARs, but its highest G score (-10.87) was with hRXRα; while its monophthalate (Mono(2-ethylhexyl)phthalate) showed a lesser G score (-8.59). The drug bexarotene showed the highest G score (-13.32) with hRXRβ. Moreover, bisphenol A showed more affinity towards hRXR. Briefly, this study gives an overview on the preference of phthalic acid esters, natural and synthetic ligands on to hPPAR and hRXR subtypes, which would lead to further in vitro mechanistic as well as in vivo preclinical and clinical studies.

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.

Development and experimental test of support vector machines virtual screening method for searching Src inhibitors from large compound libraries

BACKGROUND

Src plays various roles in tumour progression, invasion, metastasis, angiogenesis and survival. It is one of the multiple targets of multi-target kinase inhibitors in clinical uses and trials for the treatment of leukemia and other cancers. These successes and appearances of drug resistance in some patients have raised significant interest and efforts in discovering new Src inhibitors. Various in-silico methods have been used in some of these efforts. It is desirable to explore additional in-silico methods, particularly those capable of searching large compound libraries at high yields and reduced false-hit rates.

RESULTS

We evaluated support vector machines (SVM) as virtual screening tools for searching Src inhibitors from large compound libraries. SVM trained and tested by 1,703 inhibitors and 63,318 putative non-inhibitors correctly identified 93.53%~ 95.01% inhibitors and 99.81%~ 99.90% non-inhibitors in 5-fold cross validation studies. SVM trained by 1,703 inhibitors reported before 2011 and 63,318 putative non-inhibitors correctly identified 70.45% of the 44 inhibitors reported since 2011, and predicted as inhibitors 44,843 (0.33%) of 13.56M PubChem, 1,496 (0.89%) of 168 K MDDR, and 719 (7.73%) of 9,305 MDDR compounds similar to the known inhibitors.

CONCLUSIONS

SVM showed comparable yield and reduced false hit rates in searching large compound libraries compared to the similarity-based and other machine-learning VS methods developed from the same set of training compounds and molecular descriptors. We tested three virtual hits of the same novel scaffold from in-house chemical libraries not reported as Src inhibitor, one of which showed moderate activity. SVM may be potentially explored for searching Src inhibitors from large compound libraries at low false-hit rates.

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.

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.

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.

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.

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.

A structural view of nuclear hormone receptor: endocrine disruptor interactions

Endocrine-disrupting chemicals (EDCs) represent a broad class of exogenous substances that cause adverse effects in the endocrine system by interfering with hormone biosynthesis, metabolism, or action. The molecular mechanisms of EDCs involve different pathways including interactions with nuclear hormone receptors (NHRs) which are primary targets of a large variety of environmental contaminants. Here, based on the crystal structures currently available in the Protein Data Bank, we review recent studies showing the many ways in which EDCs interact with NHRs and impact their signaling pathways. Like the estrogenic chemical diethylstilbestrol, some EDCs mimic the natural hormones through conserved protein-ligand contacts, while others, such as organotins, employ radically different binding mechanisms. Such structure-based knowledge, in addition to providing a better understanding of EDC activities, can be used to predict the endocrine-disrupting potential of environmental pollutants and may have applications in drug discovery.

Effect of pharmaceutical potential endocrine disruptor compounds on protein disulfide isomerase reductase activity using di-eosin-oxidized-glutathione

Background

Protein Disulfide Isomerase (PDI) in the endoplasmic reticulum of all cells catalyzes the rearrangement of disulfide bridges during folding of membrane and secreted proteins. As PDI is also known to bind various molecules including hormones such as estradiol and thyroxin, we considered the hypothesis that adverse effects of endocrine-disrupter compounds (EDC) could be mediated through their interaction with PDI leading to defects in membrane or secreted proteins.

Methodology/Principal Findings

Taking advantage of the recent description of the fluorescence self quenched substrate di-eosin-oxidized-glutathion (DiE-GSSG), we determined kinetically the effects of various potential pharmaceutical EDCs on the in-vitro reductase activity of bovine liver PDI by measuring the fluorescence of the reaction product (E-GSH). Our data show that estrogens (ethynylestradiol and bisphenol-A) as well as indomethacin exert an inhibition whereas medroxyprogesteroneacetate and nortestosterone exert a potentiation of bovine PDI reductase activity.

Conclusions

The present data indicate that the tested EDCs could not only affect endocrine target cells through nuclear receptors as previously shown, but could also affect these and all other cells by positively or negatively affecting PDI activity. The substrate DiE-GSSG has been demonstrated to be a convenient substrate to measure PDI reductase activity in the presence of various potential EDCs. It will certainely be usefull for the screening of potential effect of all kinds of chemicals on PDI reductase activity.

Isolation and characterization of three estrogen receptor transcripts in Oreochromis mossambicus (Peters)

Exposure of aquatic organisms to 17beta-estradiol (E(2)) induces a variety of estrogen-responsive genes, including vitellogenin (vtg)-the precursor protein of egg yolk in oviparous animals and to date the single most used gene product in screening for estrogenic endocrine disruption. Transcription regulation of vtg by E(2) is dependent on binding of the ligand (E(2)) to a specific nuclear receptor (estrogen receptor, ESR) which in turn binds to an estrogen responsive element (ERE) in the promoter of vtg. Since a local tilapiine, Oreochromis mossambicus (Peters), is targeted as a model for estrogenic endocrine disruption in Southern Africa, a platform of knowledge is necessary for the ontogenic and tissue specific behavior of ESR in this species before vtg levels can be interpreted in relation to such endocrine disruption. Therefore, three ESR cDNA sequences (ESR1, ESR2a and ESR2b) in O. mossambicus were isolated and QPCR protocols were developed to ascertain their quantitative transcript levels in adult brain, gonadal and hepatic tissues. ESR1 transcript levels were highest in female liver tissue compared to males and other tissues, whereas the levels for ESR2a and b were not statistically significantly different between male and female tissues. Quantitative gene levels during development demonstrated a sharp increase in ESR1 during the stage of gonad differentiation (50-60 days post-fertilization) in this species. Finally, an induction experiment in adult male liver tissue confirms the upregulation of ESR1 by E(2).

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.

Virtual screening of Abl inhibitors from large compound libraries by support vector machines

Abl promotes cancers by regulating cell morphogenesis, motility, growth, and survival. Successes of several marketed and clinical trial Abl inhibitors against leukemia and other cancers and appearances of reduced efficacies and drug resistances have led to significant interest in and efforts for developing new Abl inhibitors. In silico methods of pharmacophore, fragment, and molecular docking have been used in some of these efforts. It is desirable to explore other in silico methods capable of searching large compound libraries at high yields and reduced false-hit rates. We evaluated support vector machines (SVM) as a virtual screening tool for searching Abl inhibitors from large compound libraries. SVM trained and tested by 708 inhibitors and 65,494 putative noninhibitors correctly identified 84.4 to 92.3% inhibitors and 99.96 to 99.99% noninhibitors in 5-fold cross validation studies. SVM trained by 708 pre-2008 inhibitors and 65 494 putative noninhibitors correctly identified 50.5% of the 91 inhibitors reported since 2008 and predicted as inhibitors 29,072 (0.21%) of 13.56M PubChem, 659 (0.39%) of 168K MDDR, and 330 (5.0%) of 6638 MDDR compounds similar to the known inhibitors. SVM showed comparable yields and substantially reduced false-hit rates against two similarity based and another machine learning VS methods based on the same training and testing data sets and molecular descriptors. These suggest that SVM is capable of searching Abl inhibitors from large compound libraries at low false-hit rates.

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.

Understanding nuclear receptors using computational methods

Nuclear receptors (NRs) are important targets for therapeutic drugs. NRs regulate transcriptional activities through binding to ligands and interacting with several regulating proteins. Computational methods can provide insights into essential ligand-receptor and protein-protein interactions. These in turn have facilitated the discovery of novel agonists and antagonists with high affinity and specificity as well as have aided in the prediction of toxic side effects of drugs by identifying possible off-target interactions. Here, we review the application of computational methods toward several clinically important NRs (with special emphasis on PXR) and discuss their use for screening and predicting the toxic side effects of xenobiotics.

The steroid and xenobiotic receptor (SXR), beyond xenobiotic metabolism

The steroid and xenobiotic receptor (SXR) (also known as pregnane X receptor or PXR) is a nuclear hormone receptor activated by a diverse array of endogenous hormones, dietary steroids, pharmaceutical agents, and xenobiotic compounds. SXR has an enlarged, flexible, hydrophobic ligand binding domain (LBD) which is remarkably divergent across mammalian species and SXR exhibits considerable differences in its pharmacology among mammals. The broad response profile of SXR has led to the development of "the steroid and xenobiotic sensor hypothesis". SXR has been established as a xenobiotic sensor that coordinately regulates xenobiotic clearance in the liver and intestine via induction of genes involved in drug and xenobiotic metabolism. In the past few years, research has revealed new and mostly unsuspected roles for SXR in modulating inflammation, bone homeostasis, vitamin D metabolism, lipid homeostasis, energy homeostasis and cancer. The identification of SXR as a xenobiotic sensor has provided an important tool for studying new mechanisms through which diet, chemical exposure, and environment ultimately impact health and disease. The discovery and pharmacological development of new PXR modulators might represent an interesting and innovative therapeutic approach to combat various diseases.

Hybrid scoring and classification approaches to predict human pregnane X receptor activators

PURPOSE

The human pregnane X receptor (PXR) is a transcriptional regulator of many genes involved in xenobiotic metabolism and excretion. Reliable prediction of high affinity binders with this receptor would be valuable for pharmaceutical drug discovery to predict potential toxicological responses

MATERIALS AND METHODS

Computational models were developed and validated for a dataset consisting of human PXR (PXR) activators and non-activators. We used support vector machine (SVM) algorithms with molecular descriptors derived from two sources, Shape Signatures and the Molecular Operating Environment (MOE) application software. We also employed the molecular docking program GOLD in which the GoldScore method was supplemented with other scoring functions to improve docking results.

RESULTS

The overall test set prediction accuracy for PXR activators with SVM was 72% to 81%. This indicates that molecular shape descriptors are useful in classification of compounds binding to this receptor. The best docking prediction accuracy (61%) was obtained using 1D Shape Signature descriptors as a weighting factor to the GoldScore. By pooling the available human PXR data sets we revealed those molecular features that are associated with human PXR activators.

CONCLUSIONS

These combined computational approaches using molecular shape information may assist scientists to more confidently identify PXR activators.

Inhibition and induction of human cytochrome P450 enzymes: current status

Variability of drug metabolism, especially that of the most important phase I enzymes or cytochrome P450 (CYP) enzymes, is an important complicating factor in many areas of pharmacology and toxicology, in drug development, preclinical toxicity studies, clinical trials, drug therapy, environmental exposures and risk assessment. These frequently enormous consequences in mind, predictive and pre-emptying measures have been a top priority in both pharmacology and toxicology. This means the development of predictive in vitro approaches. The sound prediction is always based on the firm background of basic research on the phenomena of inhibition and induction and their underlying mechanisms; consequently the description of these aspects is the purpose of this review. We cover both inhibition and induction of CYP enzymes, always keeping in mind the basic mechanisms on which to build predictive and preventive in vitro approaches. Just because validation is an essential part of any in vitro-in vivo extrapolation scenario, we cover also necessary in vivo research and findings in order to provide a proper view to justify in vitro approaches and observations.

Machine learning methods and docking for predicting human pregnane X receptor activation

The pregnane X receptor (PXR) regulates the expression of genes involved in xenobiotic metabolism and transport. In vitro methods to screen for PXR agonists are used widely. In the current study, computational models for human PXR activators and PXR nonactivators were developed using recursive partitioning (RP), random forest (RF), and support vector machine (SVM) algorithms with VolSurf descriptors. Following 10-fold randomization, the models correctly predicted 82.6-98.9% of activators and 62.0-88.6% of nonactivators. The models were validated using separate test sets. The overall ( n = 15) test set prediction accuracy for PXR activators with RP, RF, and SVM PXR models is 80-93.3%, representing an improvement over models previously reported. All models were tested with a second test set ( n = 145), and the prediction accuracy ranged from 63 to 67% overall. These test set molecules were found to cover the same area in a principal component analysis plot as the training set, suggesting that the predictions were within the applicability domain. The FlexX docking method combined with logistic regression performed poorly in classifying this PXR test set as compared with RP, RF, and SVM but may be useful for qualitative interpretion of interactions within the LBD. From this analysis, VolSurf descriptors and machine learning methods had good classification accuracy and made reliable predictions within the model applicability domain. These methods could be used for high throughput virtual screening to assess for PXR activation, prior to in vitro testing to predict potential drug-drug interactions.

Evaluation of virtual screening performance of support vector machines trained by sparsely distributed active compounds

Virtual screening performance of support vector machines (SVM) depends on the diversity of training active and inactive compounds. While diverse inactive compounds can be routinely generated, the number and diversity of known actives are typically low. We evaluated the performance of SVM trained by sparsely distributed actives in six MDDR biological target classes composed of a high number of known actives (983-1645) of high, intermediate, and low structural diversity (muscarinic M1 receptor agonists, NMDA receptor antagonists, thrombin inhibitors, HIV protease inhibitors, cephalosporins, and renin inhibitors). SVM trained by regularly sparse data sets of 100 actives show improved yields at substantially reduced false-hit rates compared to those of published studies and those of Tanimoto-based similarity searching method based on the same data sets and molecular descriptors. SVM trained by very sparse data sets of 40 actives (2.4%-4.1% of the known actives) predicted 17.5-39.5%, 23.0-48.1%, and 70.2-92.4% of the remaining 943-1605 actives in the high, intermediate, and low diversity classes, respectively, 13.8-68.7% of which are outside the training compound families. SVM predicted 99.97% and 97.1% of the 9.997 M PUBCHEM and 167K remaining MDDR compounds as inactive and 2.6%-8.3% of the 19,495-38,483 MDDR compounds similar to the known actives as active. These suggest that SVM has substantial capability in identifying novel active compounds from sparse active data sets at low false-hit rates.

Multivariate data analyses of persistent organic pollutants in maternal adipose tissue in Singapore

Persistent organic pollutants (POPs) were detected in 88 maternal adipose tissue samples collected during year 2004 to 2006, in Singapore. Organochlorine pesticides (OCPs) were the most dominant followed by polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs). Multivariate data analyses (MVA) including principal component analysis (PCA), partial least-squares regression (PLSR), and partial least-squares discriminant analysis (PLS-DA) were applied to elucidate the relationship between concentrations of POPs in adipose tissues and donors' characteristics. Food consumption played the most significant role in accounting for levels of POPs in adipose tissue. Fish and poultry consumption was the route of PCBs and PBDEs in mothers in Singapore, while beta-HCH came mainly from vegetables. An age-dependent accumulation of POPs was found for beta-HCH and PCB congeners, and lactation and gestation functioned as a decontamination processes for PCBs in adipose tissue. Gestational diabetes mellitus (GDM) may change the profile of POPs in adipose tissue, probably due to an alteration in lipid metabolism. POPs investigated here may not be the cause of antenatal complication in pregnant women, and baby gender was not related to the pattern of contaminants in maternal adipose tissue.

3D-MEDNEs: an alternative "in silico" technique for chemical research in toxicology. 2. quantitative proteome-toxicity relationships (QPTR) based on mass spectrum spiral entropy

Low range mass spectra (MS) characterization of serum proteome offers the best chance of discovering proteome-(early drug-induced cardiac toxicity) relationships, called here Pro-EDICToRs. However, due to the thousands of proteins involved, finding the single disease-related protein could be a hard task. The search for a model based on general MS patterns becomes a more realistic choice. In our previous work ( González-Díaz, H. , et al. Chem. Res. Toxicol. 2003, 16, 1318- 1327 ), we introduced the molecular structure information indices called 3D-Markovian electronic delocalization entropies (3D-MEDNEs). In this previous work, quantitative structure-toxicity relationship (QSTR) techniques allowed us to link 3D-MEDNEs with blood toxicological properties of drugs. In this second part, we extend 3D-MEDNEs to numerically encode biologically relevant information present in MS of the serum proteome for the first time. Using the same idea behind QSTR techniques, we can seek now by analogy a quantitative proteome-toxicity relationship (QPTR). The new QPTR models link MS 3D-MEDNEs with drug-induced toxicological properties from blood proteome information. We first generalized Randic's spiral graph and lattice networks of protein sequences to represent the MS of 62 serum proteome samples with more than 370 100 intensity ( I i ) signals with m/ z bandwidth above 700-12000 each. Next, we calculated the 3D-MEDNEs for each MS using the software MARCH-INSIDE. After that, we developed several QPTR models using different machine learning and MS representation algorithms to classify samples as control or positive Pro-EDICToRs samples. The best QPTR proposed showed accuracy values ranging from 83.8% to 87.1% and leave-one-out (LOO) predictive ability of 77.4-85.5%. This work demonstrated that the idea behind classic drug QSTR models may be extended to construct QPTRs with proteome MS data.

A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor

Support vector machines (SVM) and other machine-learning (ML) methods have been explored as ligand-based virtual screening (VS) tools for facilitating lead discovery. While exhibiting good hit selection performance, in screening large compound libraries, these methods tend to produce lower hit-rate than those of the best performing VS tools, partly because their training-sets contain limited spectrum of inactive compounds. We tested whether the performance of SVM can be improved by using training-sets of diverse inactive compounds. In retrospective database screening of active compounds of single mechanism (HIV protease inhibitors, DHFR inhibitors, dopamine antagonists) and multiple mechanisms (CNS active agents) from large libraries of 2.986 million compounds, the yields, hit-rates, and enrichment factors of our SVM models are 52.4-78.0%, 4.7-73.8%, and 214-10,543, respectively, compared to those of 62-95%, 0.65-35%, and 20-1200 by structure-based VS and 55-81%, 0.2-0.7%, and 110-795 by other ligand-based VS tools in screening libraries of >or=1 million compounds. The hit-rates are comparable and the enrichment factors are substantially better than the best results of other VS tools. 24.3-87.6% of the predicted hits are outside the known hit families. SVM appears to be potentially useful for facilitating lead discovery in VS of large compound libraries.

Predictive toxinology: an initial foray using calculated molecular descriptors to describe toxicity using saxitoxins as a model

Molecular descriptors and their mathematical combination have been used for predictive toxicology and risk assessments of environmental pollutants and pharmaceutical leads. However, this approach has not yet been used for natural toxins and may contribute to health and environmental risk assessments of newly discovered toxins without having to undertake whole animal toxicology. To explore this approach, over 3000 descriptors were calculated for each of the 30 saxitoxins for which mouse toxicities have been reported. This dataset was reduced to only 87 descriptors by firstly eliminating descriptors that were the same for all toxins or could not be calculated for all 30 toxins, and then removing those descriptors that did not have a statistically significant linear relationship with toxicity values. From the remaining 87 descriptors, a subset of seven descriptors was identified upon which various mathematical approaches were assessed for their ability to fit the dataset both with and without leave-one-out cross-validation. K-nearest neighbours and support vector machine regression along with various combinations of these seven descriptors fit the toxicity data almost perfectly and also achieved high predictability as measured by leave-one-out cross-validation. Of these seven descriptors, five incorporated weighting by estimates of atomic polarizability and electronic states. Predicted toxicities of several saxitoxins of unknown toxicity bore similarities to the pattern of known potencies of these toxins on various isoforms of the voltage-gated sodium channel. Some of these predicted toxicity values however would not be expected if there was a direct relationship between mammalian sodium channel affinity of the saxitoxins and whole animal toxicity.

Human pregnane X receptor antagonists and agonists define molecular requirements for different binding sites

The pregnane X receptor (PXR) is an important transcriptional regulator of the expression of xenobiotic metabolism and transporter genes. The receptor is promiscuous, binding many structural classes of molecules that act as agonists at the ligand-binding domain, triggering up-regulation of genes, increasing the metabolism and excretion of therapeutic agents, and causing drug-drug interactions. It has been suggested that human PXR antagonists represent a means to counteract such interactions. Several azoles have been hypothesized to bind the activation function-2 (AF-2) surface on the exterior of PXR when agonists are concurrently bound in the ligand-binding domain. In the present study, we have derived novel computational models for PXR agonists using different series of imidazoles, steroids, and a set of diverse molecules with experimental PXR agonist binding data. We have additionally defined a novel pharmacophore for the steroidal agonist site. All agonist pharmacophores showed that hydrophobic features are predominant. In contrast, a qualitative comparison with the corresponding PXR antagonist pharmacophore models using azoles and biphenyls showed that they are smaller and hydrophobic with increased emphasis on hydrogen bonding features. Azole antagonists were docked into a proposed hydrophobic binding pocket on the outer surface at the AF-2 site and fitted comfortably, making interactions with key amino acids involved in charge clamping. Combining computational and experimental data for different classes of molecules provided strong evidence for agonists and antagonists binding distinct regions on PXR. These observations bear significant implications for future discovery of molecules that are more selective and potent antagonists.

Conformational dynamics of the estrogen receptor alpha: molecular dynamics simulations of the influence of binding site structure on protein dynamics

We present 158 ns of unrestrained all-atom molecular dynamics (MD) simulations of the human estrogen receptor alpha ligand binding domain (ERalpha LBD) sampling the conformational changes upon binding of estradiol. The pivotal role of His524 in maintaining the protein structure in the biologically active agonist conformation is elucidated. With His524 modeled as the epsilon-tautomer, a conserved hydrogen bond to the ligand is found in the active complex. Helices 3 and 11 are held together by a hydrogen-bonding network from His524 to Glu339 via Glu419 and Lys531, arresting the ligand in the binding pocket and creating the "mouse trap" binding site for helix 12 (H12). The simulations reveal how His524 serves as a communication point between the two. When estradiol is bound, His524 is positioned correctly for the hydrogen bond network to be established. H12 is then positioned for interaction with the co-activator protein, leading to the biologically active complex. The conformational dynamics of ERalpha LBD is further investigated from simulations of antagonist and apo conformations of the protein. These simulations suggest a likely sequence of events for the transition from the inactive apo structure to the transcriptionally active conformation of ERalpha LBD. Stable conformations are identified where H12 is placed neither in the "mouse trap" nor in the co-activator binding groove, as is the case for antagonist structures of ERalpha LBD. Finally, the influence of such conformations on the biological function of ERalpha is discussed in relationship to the interaction with selective estrogen receptor modulators and endocrine-disrupting compounds.

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

Pregnane X receptor (PXR) regulates drug metabolism and is involved in drug-drug interactions. Prediction of PXR activators is important for evaluating drug metabolism and toxicity. Computational pharmacophore and quantitative structure-activity relationship models have been developed for predicting PXR activators. Because of the structural diversity of PXR activators, more efforts are needed for exploring methods applicable to a broader spectrum of compounds. We explored three machine learning methods (MLMs) for predicting PXR activators, which were trained and tested by using significantly higher number of compounds, 128 PXR activators (98 human) and 77 PXR non-activators, than those of previous studies. The recursive feature-selection method was used to select molecular descriptors relevant to PXR activator prediction, which are consistent with conclusions from other computational and structural studies. In a 10-fold cross-validation test, our MLM systems correctly predicted 81.2 to 84.0% of PXR activators, 80.8 to 85.0% of hPXR activators, 61.2 to 70.3% of PXR nonactivators, and 67.7 to 73.6% of hPXR nonactivators. Our systems also correctly predicted 73.3 to 86.7% of 15 newly published hPXR activators. MLMs seem to be useful for predicting PXR activators and for providing clues to physicochemical features of PXR activation.

The Challenge of Predicting Drug Toxicity in silico

Poor pharmacokinetics, side effects and compound toxicity are frequent causes of late-stage failures in drug development. A safe in silico identification of adverse effects triggered by drugs and chemicals would be highly desirable as it not only bears economical potential but also spawns a variety of ecological benefits: sustainable resource management, reduction of animal models and possibly less risky clinical trials. In computer-aided drug discovery, both existing and hypothetical compounds may be studied; the methods are fast, reproducible, and typically based on human bioregulators, making the question of transferability obsolete. In the recent past, our laboratory contributed towards the development of in silico concepts (--> multi-dimensional QSAR) and validated a series of "virtual test kits" based on the oestrogen, androgen, thyroid, and aryl hydrocarbon receptor (endocrine disruption, receptor-mediated toxicity) as well as on the enzyme cytochrome P450 3A4 (metabolic transformations, drug-drug interactions). The test kits are based on the three-dimensional structure of their target protein (i.e. ER(alphabeta), AR, TR(alphabeta), CYP450) or a surrogate thereof (AhR) and were trained using a representative selection of 362 substances. Subsequent evaluation of 107 compounds different therefrom showed that binding affinities are predicted close to experimental uncertainty. These results suggest that our approach is suited for the in silico identification of adverse effects triggered by drugs and chemicals and encouraged us to compile an Internet Database for the virtual screening of drugs and chemicals for toxic effects.