Molecular Modeling for Screening Environmental Chemicals for Estrogenicity: Use of the Toxicant-Target Approach

Toward a Computational Ecotoxicity Assay

Thousands of anthropogenic chemicals are released into the environment each year, posing potential hazards to human and environmental health. Toxic chemicals may cause a variety of adverse health effects, triggering immediate symptoms or delayed effects over longer periods of time. It is thus crucial to develop methods that can rapidly screen and predict the toxicity of chemicals to limit the potential harmful impacts of chemical pollutants. Computational methods are being increasingly used in toxicity predictions. Here, the method of molecular docking is assessed for screening potential toxicity of a variety of xenobiotic compounds, including pesticides, pharmaceuticals, pollutants, and toxins derived from the chemical industry. The method predicts the binding energy of pollutants to a set of carefully selected receptors under the assumption that toxicity in many cases is related to interference with biochemical pathways. The strength of the applied method lies in its rapid generation of interaction maps between potential toxins and the targeted enzymes, which could quickly yield molecular-level information and insight into potential perturbation pathways, aiding in the prioritization of chemicals for further tests. Two scoring functions are compared: Autodock Vina and the machine-learning scoring function RF-Score-VS. The results are promising, although hampered by the accuracy of the scoring functions. The strengths and weaknesses of the docking protocol are discussed, as well as future directions for improving the accuracy for the purpose of toxicity predictions.

Structure-Based Virtual Screening Protocol for in Silico Identification of Potential Thyroid Disrupting Chemicals Targeting Transthyretin

Thyroid disruption by xenobiotics is associated with a broad spectrum of severe adverse outcomes. One possible molecular target of thyroid hormone disrupting chemicals (THDCs) is transthyretin (TTR), a thyroid hormone transporter in vertebrates. To better understand the interactions between TTR and THDCs, we determined the crystallographic structures of human TTR in complex with perfluorooctanesulfonic acid (PFOS), perfluorooctanoic acid (PFOA), and 2,2',4,4'-tetrahydroxybenzophenone (BP2). The molecular interactions between the ligands and TTR were further characterized using molecular dynamics simulations. A structure-based virtual screening (VS) protocol was developed with the intention of providing an efficient tool for the discovery of novel TTR-binders from the Tox21 inventory. Among the 192 predicted binders, 12 representatives were selected, and their TTR binding affinities were studied with isothermal titration calorimetry, of which seven compounds had binding affinities between 0.26 and 100 μM. To elucidate structural details in their binding to TTR, crystal structures were determined of TTR in complex with four of the identified compounds including 2,6-dinitro-p-cresol, bisphenol S, clonixin, and triclopyr. The compounds were found to bind in the TTR hormone binding sites as predicted. Our results show that the developed VS protocol is able to successfully identify potential THDCs, and we suggest that it can be used to propose THDCs for future toxicological evaluations.

Rational Selection of the 3D Structure of Biomacromolecules for Molecular Docking Studies on the Mechanism of Endocrine Disruptor Action

Molecular modeling has become an essential tool in predicting and simulating endocrine disrupting effects of chemicals. A key prerequisite for successful application of molecular modeling lies in the correctness of 3D structure for biomacromolecules to be simulated. To date, there are several databases that can provide the experimentally-determined 3D structures. However, commonly, there are many challenges or disadvantageous factors, e.g., (a) lots of 3D structures for a given biomacromolecular target in the protein database; (b) the quality variability for those structures; (c) belonging to different species; (d) mutant amino acid residue in key positions, and so on. Once an inappropriate 3D structure of a target biomacromolecule was selected in molecular modeling, the accuracy and scientific nature of the modeling results could be inevitably affected. In this article, based on literature survey and an analysis of the 3D structure characterization of biomacromolecular targets belonging to the endocrine system in protein databases, six principles were proposed to guide the selection of the appropriate 3D structure of biomacromolecules. The principles include considering the species diversity, the mechanism of action, whether there are mutant amino acid residues, whether the number of protein chains is correct, the degree of structural similarity between the ligand in 3D structure and the target compounds, and other factors, e.g., the experimental pH conditions of the structure determined process and resolution.

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.

Informing mechanistic toxicology with computational molecular models

Computational molecular models of chemicals interacting with biomolecular targets provides toxicologists a valuable, affordable, and sustainable source of in silico molecular level information that augments, enriches, and complements in vitro and in vivo efforts. From a molecular biophysical ansatz, we describe how 3D molecular modeling methods used to numerically evaluate the classical pair-wise potential at the chemical/biological interface can inform mechanism of action and the dose-response paradigm of modern toxicology. With an emphasis on molecular docking, 3D-QSAR and pharmacophore/toxicophore approaches, we demonstrate how these methods can be integrated with chemoinformatic and toxicogenomic efforts into a tiered computational toxicology workflow. We describe generalized protocols in which 3D computational molecular modeling is used to enhance our ability to predict and model the most relevant toxicokinetic, metabolic, and molecular toxicological endpoints, thereby accelerating the computational toxicology-driven basis of modern risk assessment while providing a starting point for rational sustainable molecular design.

Computational toxicology: application in environmental chemicals

This chapter provides an overview of computational models that describe various aspects of the source-to-health effect continuum. Fate and transport models describe the release, transportation, and transformation of chemicals from sources of emission throughout the general environment. Exposure models integrate the microenvironmental concentrations with the amount of time an individual spends in these microenvironments to estimate the intensity, frequency, and duration of contact with environmental chemicals. Physiologically based pharmacokinetic (PBPK) models incorporate mechanistic biological information to predict chemical-specific absorption, distribution, metabolism, and excretion. Values of parameters in PBPK models can be measured in vitro, in vivo, or estimated using computational molecular modeling. Computational modeling is also used to predict the respiratory tract dosimetry of inhaled gases and particulates [computational fluid dynamics (CFD) models], to describe the normal and xenobiotic-perturbed behaviors of signaling pathways, and to analyze the growth kinetics of preneoplastic lesions and predict tumor incidence (clonal growth models).

Noncovalent interactions between hydroxylated polycyclic aromatic hydrocarbon and DNA: molecular docking and QSAR study

Polycyclic aromatic hydrocarbons (PAHs) can be hydroxylated by CYP450-oxidases (1A1 and 1B1 mainly) and may cause DNA damage and cancer. However, the mechanism of such interactions has not been fully understood. In this study, an integrated molecular docking and QSAR approach was employed to further investigate the binding interactions between hydroxylated PAHs (HO-PAHs) and calf thymus DNA (CT-DNA). Molecular docking, hydrogen-bonding, hydrophobic and π-π interactions were observed to be characteristic interactions between HO-PAHs and DNA. An optimum QSAR model with good robustness and predictability was developed based on the molecular structural parameters calculated by the density function theory and partial least squares. Additionally, the developed QSAR model indicated that the molecular size, polarizability and electrostatic potential of HO-PAHs were related to the binding affinities to DNA.

Advancing exposure characterization for chemical evaluation and risk assessment

A new generation of scientific tools has emerged to rapidly measure signals from cells, tissues, and organisms following exposure to chemicals. High-visibility efforts to apply these tools for efficient toxicity testing raise important research questions in exposure science. As vast quantities of data from high-throughput screening (HTS) in vitro toxicity assays become available, this new toxicity information must be translated to assess potential risks to human health from environmental exposures. Exposure information is required to link information on potential toxicity of environmental contaminants to real-world health outcomes. In the immediate term, tools are required to characterize and classify thousands of environmental chemicals in a rapid and efficient manner to prioritize testing and assess potential for risk to human health. Rapid risk assessment requires prioritization based on both hazard and exposure dimensions of the problem. To address these immediate needs within the context of longer term objectives for chemical evaluation and risk management, a translation framework is presented for incorporating toxicity and exposure information to inform public health decisions at both the individual and population levels. Examples of required exposure science contributions are presented with a focus on early advances in tools for modeling important links across the source-to-outcome paradigm. ExpoCast, a new U.S. Environmental Protection Agency (EPA) program aimed at developing novel approaches and metrics to screen and evaluate chemicals based on the potential for biologically relevant human exposures is introduced. The goal of ExpoCast is to advance characterization of exposure required to translate findings in computational toxicology to information that can be directly used to support exposure and risk assessment for decision making and improved public health.