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

Enantioselective biomarkers of maize toxicity induced by hexabromocyclododecane based on submicroscopic structure, gene expression and molecular docking

Hexabromocyclododecane (HBCD), as a monitored chemical of the Chemical Weapons Convention, the Stockholm Convention and the Action Plan for New Pollutants Treatment in China, raises significant concerns on its impact of human health and food security. This study investigated enantiomer-specific biomarkers of HBCD in maize (Zea mays L.). Upon exposure to HBCD enantiomers, the maize root tip cell wall exhibited thinning, uneven cell gaps, and increased deposition on the cell outer wall. Elevated malondialdehyde (MDA) indicated lipid peroxidation, with higher mitochondrial membrane potential (MMP) inhibition in (+)-enantiomer treatments (47.2%-57.9%) than (-)-enantiomers (14.4%-37.4%). The cell death rate significantly increased by 37.7%-108.8% in roots and 16.4%-62.4% in shoots, accompanied by the upregulation of superoxide dismutase isoforms genes. Molecular docking presenting interactions between HBCD and target proteins, suggested that HBCD has an affinity for antioxidant enzyme receptors with higher binding energy for (+)-enantiomers, further confirming their stronger toxic effects. All indicators revealed that oxidative damage to maize seedlings was more severe after treatment with (+)-enantiomers compared to (-)-enantiomers. This study elucidates the biomarkers of phytotoxicity evolution induced by HBCD enantiomers, providing valuable insights for the formulation of more effective policies to safeguard environmental safety and human health in the future.

Regulation of PXR in drug metabolism: chemical and structural perspectives

INTRODUCTION

Pregnane X receptor (PXR) is a master xenobiotic sensor that transcriptionally controls drug metabolism and disposition pathways. PXR activation by pharmaceutical drugs, natural products, environmental toxins, etc. may decrease drug efficacy and increase drug-drug interactions and drug toxicity, indicating a therapeutic value for PXR antagonists. However, PXR's functions in physiological events, such as intestinal inflammation, indicate that PXR activators may be useful in certain disease contexts.

AREAS COVERED

We review the reported roles of PXR in various physiological and pathological processes including drug metabolism, cancer, inflammation, energy metabolism, and endobiotic homeostasis. We then highlight specific cellular and chemical routes that modulate PXR activity and discuss the functional consequences. Databases searched and inclusive dates: PubMed, 1 January 1980 to 10 January 2024.

EXPERT OPINION

Knowledge of PXR's drug metabolism function has helped drug developers produce small molecules without PXR-mediated metabolic liabilities, and further understanding of PXR's cellular functions may offer drug development opportunities in multiple disease settings.

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.

Comparative study of the toxicity mechanisms of quinolone antibiotics on soybean seedlings: Insights from molecular docking and transcriptomic analysis

The ecological effects of quinolone antibiotics (QNs) on non-target organisms have received widespread attention. The toxicological mechanisms of three common QNs, that is, enrofloxacin, levofloxacin, and ciprofloxacin, on soybean seedlings were investigated in this study. Enrofloxacin and levofloxacin caused significant growth inhibition, ultrastructural alterations, photosynthetic suppression, and stimulation of the antioxidant system, with levofloxacin exhibiting the strongest toxic effects. Ciprofloxacin (<1 mg·L-1) did not have a significant effect on the soybean seedlings. As the concentrations of enrofloxacin and levofloxacin increased, antioxidant enzyme activities, malondialdehyde content, and hydrogen peroxide levels also increased. Meanwhile, the chlorophyll content and chlorophyll fluorescence parameters decreased, indicating that the plants underwent oxidative stress and photosynthesis was suppressed. The cellular ultrastructure was also disrupted, which was manifested by swollen chloroplasts, increased starch granules, disintegration of plastoglobules, and mitochondrial degradation. The molecular docking results suggested that the QNs have an affinity for soybean target protein receptors (4TOP, 2IUJ, and 1FHF), with levofloxacin having the highest binding energy (-4.97, -3.08, -3.8, respectively). Transcriptomic analysis has shown that genes were upregulated under the enrofloxacin and levofloxacin treatments were mainly involved in ribosome metabolism and processes to synthesize oxidative stress-related proteins. Downregulated genes in the levofloxacin treatment were primarily enriched in photosynthesis-related pathways, indicating that levofloxacin significantly inhibited gene expression for photosynthesis. Genes expression level by quantitative real-time PCR analysis was consistent with the transcriptomic results. This study confirmed the toxic effect of QNs on soybean seedlings, and provided new insights into the environmental risks of antibiotics.

Regulation of Nuclear Receptors PXR and CAR by Small Molecules and Signal Crosstalk: Roles in Drug Metabolism and Beyond

Pregnane X receptor (PXR) and constitutive androstane receptor (CAR) are ligand-activated transcription factors that regulate the expression of drug metabolizing enzymes and drug transporters. Since their discoveries, they have been studied as important factors for regulating processes related to drug efficacy, drug toxicity, and drug-drug interactions. However, their vast ligand-binding profiles extend into additional spaces, such as endogenously produced chemicals, microbiome metabolites, dietary compounds, and environmental pollutants. Therefore, PXR and CAR can respond to an enormous abundance of stimuli, resulting in significant shifts in metabolic programs and physiologic homeostasis. Naturally, PXR and CAR have been implicated in various diseases related to homeostatic perturbations, such as inflammatory bowel disorders, diabetes, and certain cancers. Recent findings have injected the field with new signaling mechanisms and tools to dissect the complex PXR and CAR biology and have strengthened the potential for future PXR and CAR modulators in the clinic. Here, we describe the historical and ongoing importance of PXR and CAR in drug metabolism pathways and how this history has evolved into new mechanisms that regulate and are regulated by these xenobiotic receptors, with a specific focus on small molecule ligands. To effectively convey the impact of newly emerging research, we have arranged five diverse and representative key recent advances, four specific challenges, and four perspectives on future directions. SIGNIFICANCE STATEMENT: PXR and CAR are key transcription factors that regulate homeostatic detoxification of the liver and intestines. Diverse chemicals bind to these nuclear receptors, triggering their transcriptional tuning of the cellular metabolic response. This minireview revisits the importance of PXR and CAR in pharmaceutical drug responses and highlights recent results with implications beyond drug metabolism.

Ensemble Docking Approach to Mitigate Pregnane X Receptor-Mediated CYP3A4 Induction Risk

Three structurally closely related dopamine D1 receptor positive allosteric modulators (D1 PAMs) based on a tetrahydroisoquinoline (THIQ) scaffold were profiled for their CYP3A4 induction potentials. It was found that the length of the linker at the C5 position greatly affected the potentials of these D1 PAMs as CYP3A4 inducers, and the level of induction correlated well with the activation of the pregnane X receptor (PXR). Based on the published PXR X-ray crystal structures, we built a binding model specifically for these THIQ-scaffold-based D1 PAMs in the PXR ligand-binding pocket via an ensemble docking approach and found the model could explain the observed CYP induction disparity. Combined with our previously reported D1 receptor homology model, which identified the C5 position as pointing toward the solvent-exposed space, our PXR-binding model coincidentally suggested that structural modifications at the C5 position could productively modulate the CYP induction potential while maintaining the D1 PAM potency of these THIQ-based PAMs.

Evaluation of the binding performance of flavonoids to estrogen receptor alpha by Autodock, Autodock Vina and Surflex-Dock

Molecular docking is a widely used method to predict the binding modes of small-molecule ligands to the target binding site. However, it remains a challenge to identify the correct binding conformation and the corresponding binding affinity for a series of structurally similar ligands, especially those with weak binding. An understanding of the various relative attributes of popular docking programs is required to ensure a successful docking outcome. In this study, we systematically compared the performance of three popular docking programs, Autodock, Autodock Vina, and Surflex-Dock for a series of structurally similar weekly binding flavonoids (22) binding to the estrogen receptor alpha (ERα). For these flavonoids-ERα interactions, Surflex-Dock showed higher accuracy than Autodock and Autodock Vina. The hydrogen bond overweighting by Autodock and Autodock Vina led to incorrect binding results, while Surflex-Dock effectively balanced both hydrogen bond and hydrophobic interactions. Moreover, the selection of initial receptor structure is critical as it influences the docking conformations of flavonoids-ERα complexes. The flexible docking method failed to further improve the docking accuracy of the semi-flexible docking method for such chemicals. In addition, binding interaction analysis revealed that 8 residues, including Ala350, Glu353, Leu387, Arg394, Phe404, Gly521, His524, and Leu525, are the key residues in ERα-flavonoids complexes. This work provides reference for assessing molecular interactions between ERα and flavonoid-like chemicals and provides instructive information for other environmental chemicals.

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.

Machine Learning: New Ideas and Tools in Environmental Science and Engineering

The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper model interpretation, and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.

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.

Synthesis process optimization and field trials of insecticide candidate NKY-312

NKY-312 is a highly active insecticide candidate with a simple structure. In order to carry out field trials and toxicity tests, its scale preparation is urgently needed, but the final step of the original synthetic route is a low-yielding sulfonylation reaction that generates a high proportion of a bissulfonylated by-product, its foliar contact activities against bean aphid (80% at 100 mg/kg) is significantly lower than that of NKY-312 (100% at 5 mg/kg), and uses pyridine as the solvent. In this work, we developed a highly selective (4-dimethylaminopyridine)-catalyzed monosulfonylation reaction that avoids the use of pyridine as a solvent and shows a much higher yield (98% yield with 98% HPLC purity) than the original reaction (68%). Then, we carried out the field trials and toxicity tests. In field experiments, the activities of NKY-312 against rice planthopper and wheat aphid were equal to pymetrozine and imidacloprid respectively.

Virtual screening of potentially endocrine-disrupting chemicals against nuclear receptors and its application to identify PPARγ-bound fatty acids

Nuclear receptors (NRs) are key regulators of energy homeostasis, body development, and sexual reproduction. Xenobiotics binding to NRs may disrupt natural hormonal systems and induce undesired adverse effects in the body. However, many chemicals of concerns have limited or no experimental data on their potential or lack-of-potential endocrine-disrupting effects. Here, we propose a virtual screening method based on molecular docking for predicting potential endocrine-disrupting chemicals (EDCs) that bind to NRs. For 12 NRs, we systematically analyzed how multiple crystal structures can be used to distinguish actives and inactives found in previous high-throughput experiments. Our method is based on (i) consensus docking scores from multiple structures at a single functional state (agonist-bound or antagonist-bound), (ii) multiple functional states (agonist-bound and antagonist-bound), and (iii) multiple pockets (orthosteric site and alternative sites) of these NRs. We found that the consensus enrichment from multiple structures is better than or comparable to the best enrichment from a single structure. The discriminating power of this consensus strategy was further enhanced by a chemical similarity-weighted scoring scheme, yielding better or comparable enrichment for all studied NRs. Applying this optimized method, we screened 252 fatty acids against peroxisome proliferator-activated receptor gamma (PPARγ) and successfully identified 3 previously unknown fatty acids with Kd = 100-250 μM including two furan fatty acids: furannonanoic acid (FNA) and furanundecanoic acid (FUA), and one cyclopropane fatty acid: phytomonic acid (PTA). These results suggested that the proposed method can be used to rapidly screen and prioritize potential EDCs for further experimental evaluations.

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.

A big data approach with artificial neural network and molecular similarity for chemical data mining and endocrine disruption prediction

CONTEXT:

Chemical toxicity prediction at early stage drug discovery phase has been researched for years, and newest methods are always investigated. Research data comprising chemical physicochemical properties, toxicity, assay, and activity details create massive data which are becoming difficult to manage. Identifying the desired featured chemical with the desired biological activity from millions of chemicals is a challenging task.

AIMS:

In this study, we investigate and explore big data technologies and machine learning approaches to do an efficient chemical data mining for endocrine receptor disruption prediction and virtual compound screening. The power of artificial neural network (ANN) in predicting chemicals' activity toward androgen receptor (AR) and estrogen receptor (ER) and thereby classifying into human endocrine disruptor or nondisruptor is investigated.

SUBJECTS AND METHODS:

Molecules are collected along with their Inhibitory Concentration (IC ₅₀) values toward AR and ER. Training and test datasets are created with active and inactive classes of molecules. Molecular fingerprints of Electro Topological State (E-State) are generated for describing every compound. ANN machine learning model is created using Apache Spark and implemented in Hadoop big data environment. Test chemical's structural similarity toward active class of training compounds is estimated and combined with ANN model for improving prediction accuracy.

RESULTS:

AR and ER predictive models applied on corresponding test datasets gave 86.31% and 89.57% accuracies, respectively, in correctly classifying molecules as disruptor or nondisruptor. Molecular fragments and functional groups are ranked based on their importance in forming ANN model and influence toward the AR and ER disruption behavior. Training molecules that are specific to the test molecules' endocrine disruption prediction are retrieved based on the structural similarity values.

CONCLUSIONS:

The current study demonstrates a new approach of chemical endocrine receptor disruption prediction combining ANN machine learning method and molecular similarity in a big data environment. This method of predictive modeling can be further tested with more receptors and hormones and predictive power can be examined.

Exploring the PXR ligand binding mechanism with advanced Molecular Dynamics methods

The Pregnane X Receptor (PXR) is a ligand-activated transcription factor belonging to the nuclear receptor family. PXR can bind diverse drugs and environmental toxicants with different binding modes, making it an intriguing target for drug discovery. Here we investigated the binding mechanism of the SR12813 ligand to elucidate the significant steps, from the ligand entrance pathway into the binding cavity, to the ligand-induced conformational changes, and to the exploration of its alternative binding geometries. We used the advanced Molecular Dynamics-based methods implemented in the BiKi suite and developed specific methodological approaches to overcome the complexity induced by the buried and flexible binding cavity. The adopted methods provided a full dynamic description of the binding event and allowed rationalization of the observed multiple binding modes. These results suggest that the same approach could be exploited for the study of other binding processes with similar characteristics.

Different Types of Molecular Docking Based on Variations of Interacting Molecules

Molecular docking plays an important role in drug discovery research by facilitating target identification, target validation, virtual screening for lead identification and lead optimization. Depending upon the nature of the disease of interest, targets can be either protein or DNA while drugs are mostly organic small molecules. Different types of molecular docking techniques like protein-protein or protein-DNA or protein-small molecule or DNA-small molecule are employed for achieving the above mentioned objectives. This chapter provides a clear idea of the position of molecular docking in drug discovery with detailed discussion on different types of molecular docking based on the varieties of interacting partners. Subsequently the authors provide a detailed list of tools that can be used for docking in drug discovery and discus some examples of molecular docking in drug discovery before concluding with a remark on future areas of improvement in molecular docking related to drug discovery.

Different Types of Molecular Docking Based on Variations of Interacting Molecules

Molecular docking plays an important role in drug discovery research by facilitating target identification, target validation, virtual screening for lead identification and lead optimization. Depending upon the nature of the disease of interest, targets can be either protein or DNA while drugs are mostly organic small molecules. Different types of molecular docking techniques like protein-protein or protein-DNA or protein-small molecule or DNA-small molecule are employed for achieving the above mentioned objectives. This chapter provides a clear idea of the position of molecular docking in drug discovery with detailed discussion on different types of molecular docking based on the varieties of interacting partners. Subsequently the authors provide a detailed list of tools that can be used for docking in drug discovery and discus some examples of molecular docking in drug discovery before concluding with a remark on future areas of improvement in molecular docking related to drug discovery.

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

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

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

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

Hierarchical dose-response modeling for high-throughput toxicity screening of environmental chemicals

High-throughput screening (HTS) of environmental chemicals is used to identify chemicals with high potential for adverse human health and environmental effects from among the thousands of untested chemicals. Predicting physiologically relevant activity with HTS data requires estimating the response of a large number of chemicals across a battery of screening assays based on sparse dose-response data for each chemical-assay combination. Many standard dose-response methods are inadequate because they treat each curve separately and under-perform when there are as few as 6-10 observations per curve. We propose a semiparametric Bayesian model that borrows strength across chemicals and assays. Our method directly parametrizes the efficacy and potency of the chemicals as well as the probability of response. We use the ToxCast data from the U.S. Environmental Protection Agency (EPA) as motivation. We demonstrate that our hierarchical method provides more accurate estimates of the probability of response, efficacy, and potency than separate curve estimation in a simulation study. We use our semiparametric method to compare the efficacy of chemicals in the ToxCast data to well-characterized reference chemicals on estrogen receptor α (ERα) and peroxisome proliferator-activated receptor γ (PPARγ) assays, then estimate the probability that other chemicals are active at lower concentrations than the reference chemicals.

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.

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.

Applications and limitations of in silico models in drug discovery

Drug discovery in the late twentieth and early twenty-first century has witnessed a myriad of changes that were adopted to predict whether a compound is likely to be successful, or conversely enable identification of molecules with liabilities as early as possible. These changes include integration of in silico strategies for lead design and optimization that perform complementary roles to that of the traditional in vitro and in vivo approaches. The in silico models are facilitated by the availability of large datasets associated with high-throughput screening, bioinformatics algorithms to mine and annotate the data from a target perspective, and chemoinformatics methods to integrate chemistry methods into lead design process. This chapter highlights the applications of some of these methods and their limitations. We hope this serves as an introduction to in silico drug discovery.

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

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

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.

Activation of a tunicate (Ciona intestinalis) xenobiotic receptor orthologue by both natural toxins and synthetic toxicants

Vertebrate xenobiotic receptors are ligand-activated nuclear receptors (NRs) that bind exogenous biologically active chemicals before activating the transcription of genes involved in xenobiotic metabolism and excretion. Typically, xenobiotic receptors have ligand binding domains (LBDs) that can accommodate a structurally diverse array of molecules and in addition display high levels of inter-taxa sequence diversity suggestive of positive selection. Pursuing the idea that xenobiotic receptors may adaptively evolve to bind toxic chemicals commonly present in an organism's environment/diet, we examined ligand binding by a xenobiotic receptor orthologue of a marine filter-feeding organism. The solitary tunicate Ciona intestinalis (Phylum Chordata) genome encodes an orthologue of the vertebrate pregnane X receptor (PXR) and vitamin D receptor (VDR), here denoted CiVDR/PXRα. In a luciferase reporter assay the CiVDR/PXRα was activated, at nanomolar concentrations, by two of four natural marine microalgal biotoxins tested (okadaic acid, EC50 = 18.2 ± 0.9 nM and pectenotoxin-2, EC50 = 37.0 ± 3.5 nM) along with 1 of 11 synthetic toxicants (esfenvalerate: EC50 = 0.59 ± 0.7 μM). Two related C. intestinalis NRs, orthologous to vertebrate farnesoid X receptor and liver X receptors, respectively, along with the PXR of a freshwater fish (zebrafish, Danio rerio), were not activated by any of the 15 chemicals tested. In contrast, human PXR was activated by okadaic acid at similar concentrations to CiVDR/PXRα (EC50 = 7.2 ± 1.1 nM) but not by pectenotoxin-2. A common features pharmacophore developed for the CiVDR/PXRα ligand consisted of an off-center hydrogen bond acceptor flanked by two hydrophobic regions. The results of this study are consistent with the original hypothesis that natural toxins, present in the diet of filter-feeding marine invertebrates, may have acted as selective agents in the molecular evolution of tunicate xenobiotic receptors. Bioassays based on tunicate xenobiotic receptor activation may find application in marine environmental monitoring and bioprospecting.

Pregnane X receptor activation induces FGF19-dependent tumor aggressiveness in humans and mice

The nuclear receptor pregnane X receptor (PXR) is activated by a range of xenochemicals, including chemotherapeutic drugs, and has been suggested to play a role in the development of tumor cell resistance to anticancer drugs. PXR also has been implicated as a regulator of the growth and apoptosis of colon tumors. Here, we have used a xenograft model of colon cancer to define a molecular mechanism that might underlie PXR-driven colon tumor growth and malignancy. Activation of PXR was found to be sufficient to enhance the neoplastic characteristics, including cell growth, invasion, and metastasis, of both human colon tumor cell lines and primary human colon cancer tissue xenografted into immunodeficient mice. Furthermore, we were able to show that this PXR-mediated phenotype required FGF19 signaling. PXR bound to the FGF19 promoter in both human colon tumor cells and "normal" intestinal crypt cells. However, while both cell types proliferated in response to PXR ligands, the FGF19 promoter was activated by PXR only in cancer cells. Taken together, these data indicate that colon cancer growth in the presence of a specific PXR ligand results from tumor-specific induction of FGF19. These observations may lead to improved therapeutic regimens for colon carcinomas.

In vivo and in vitro characterization of a first-in-class novel azole analog that targets pregnane X receptor activation

The pregnane X receptor (PXR) is a master regulator of xenobiotic clearance and is implicated in deleterious drug interactions (e.g., acetaminophen hepatotoxicity) and cancer drug resistance. However, small-molecule targeting of this receptor has been difficult; to date, directed synthesis of a relatively specific PXR inhibitor has remained elusive. Here we report the development and characterization of a first-in-class novel azole analog [1-(4-(4-(((2R,4S)-2-(2,4-difluorophenyl)-2-methyl-1,3-dioxolan-4-yl)methoxy)phenyl)piperazin-1-yl)ethanone (FLB-12)] that antagonizes the activated state of PXR with limited effects on other related nuclear receptors (i.e., liver X receptor, farnesoid X receptor, estrogen receptor α, peroxisome proliferator-activated receptor γ, and mouse constitutive androstane receptor). We investigated the toxicity and PXR antagonist effect of FLB-12 in vivo. Compared with ketoconazole, a prototypical PXR antagonist, FLB-12 is significantly less toxic to hepatocytes. FLB-12 significantly inhibits the PXR-activated loss of righting reflex to 2,2,2-tribromoethanol (Avertin) in vivo, abrogates PXR-mediated resistance to 7-ethyl-10-hydroxycamptothecin (SN-38) in colon cancer cells in vitro, and attenuates PXR-mediated acetaminophen hepatotoxicity in vivo. Thus, relatively selective targeting of PXR by antagonists is feasible and warrants further investigation. This class of agents is suitable for development as chemical probes of PXR function as well as potential PXR-directed therapeutics.