Molecular modeling for screening environmental chemicals for estrogenicity: use of the toxicant-target approach.

There is a paucity of relevant experimental information available for the evaluation of the potential health and environmental effects of many man made chemicals. Knowledge of the potential pathways for activity provides a rational basis for the extrapolations inherent in the preliminary evaluation of risk and the establishment of priorities for obtaining missing data for environmental chemicals. The differential step in many mechanisms of toxicity may be generalized as the interaction between a small molecule (a potential toxicant) and one or more macromolecular targets. An approach based on computation of the interaction between a potential molecular toxicant and a library of macromolecular targets of toxicity has been proposed for preliminary chemical screening. In the current study, the interaction between a series of environmentally relevant chemicals and models of the rat estrogen receptors (ER) was computed and the results compared to an experimental data set of their relative binding affinities. The experimental data set consists of 281 chemicals, selected from the U.S. EPA's Toxic Substances Control Act (TSCA) inventory, that were initially screened using the rat uterine cytosolic ER-competitive binding assay. Secondary analysis, using Lineweaver-Burk plots and slope replots, was applied to confirm that only 15 of these test chemicals were true competitive inhibitors of ER binding with experimental inhibition constants (K(i)) less than 100 microM. Two different rapid computational docking methods have been applied. Each provides a score that is a surrogate for the strength of the interaction between each ligand-receptor pair. Using the score that indicates the strongest interaction for each pair, without consideration of the geometry of binding between the toxicant and the target, all of the active molecules were discovered in the first 16% of the chemicals. When a filter is applied on the basis of the geometry of a simplified pharmacophore for binding to the ER, the results are improved, and all of the active molecules were discovered in the first 8% of the chemicals. In order to obtain no false negatives in the model that includes the pharmacophore filter, only 8 molecules are false positives. These results indicate that molecular docking algorithms that were designed to find the chemicals that act most strongly at a receptor (and therefore are potential pharmaceuticals) can efficiently separate weakly active chemicals from a library of primarily inactive chemicals. The advantage of using a pharmacophore filter suggests that the development of filters of this type for other receptors will prove valuable.

Computational molecular modeling for evaluating the toxicity of environmental chemicals: prioritizing bioassay requirements.

BACKGROUND: The human health risk from exposure to environmental chemicals often must be evaluated when relevant elements of the preferred data are unavailable. Therefore, strategies are needed that can predict this information and prioritize the outstanding data requirements for the risk evaluation. Many modes of molecular toxicity require the chemical or one of its biotransformation products to interact with specific biologic macromolecules (i.e., proteins and DNA). Molecular modeling approaches may be adapted to study the interactions of environmental chemicals with biomolecular targets. OBJECTIVE: In this commentary we provide an overview of the challenges that arise from applying molecular modeling tools developed and commonly used for pharmaceutical discovery to the problem of predicting the potential toxicities of environmental chemicals. DISCUSSION: The use of molecular modeling tools to predict the unintended health and environmental consequences of environmental chemicals differs strategically from the use of the same tools in the pharmaceutical discovery process in terms of the goals and potential applications. It also requires consideration of the greater diversity of chemical space and binding affinity domains than is covered by pharmaceuticals. CONCLUSION: Molecular modeling methods offer one of several complementary approaches to evaluate the risk to human health and the environment as a result of exposure to environmental chemicals. These tools can streamline the hazard assessment process by simulating possible modes of action and providing virtual screening tools that can help prioritize bioassay requirements. Tailoring these strategies to the particular challenges presented by environmental chemical interactions make them even more effective.

High-throughput in-silico prediction of ionization equilibria for pharmacokinetic modeling.

Chemical ionization plays an important role in many aspects of pharmacokinetic (PK) processes such as protein binding, tissue partitioning, and apparent volume of distribution at steady state (Vdss). Here, estimates of ionization equilibrium constants (i.e., pKa) were analyzed for 8132 pharmaceuticals and 24,281 other compounds to which humans might be exposed in the environment. Results revealed broad differences in the ionization of pharmaceutical chemicals and chemicals with either near-field (in the home) or far-field sources. The utility of these high-throughput ionization predictions was evaluated via a case-study of predicted PK Vdss for 22 compounds monitored in the blood and serum of the U.S. population by the U.S. Centers for Disease Control and Prevention National Health and Nutrition Examination Survey (NHANES). The chemical distribution ratio between water and tissue was estimated using predicted ionization states characterized by pKa. Probability distributions corresponding to ionizable atom types (IATs) were then used to analyze the sensitivity of predicted Vdss on predicted pKa using Monte Carlo methods. 8 of the 22 compounds were predicted to be ionizable. For 5 of the 8 the predictions based upon ionization are significantly different from what would be predicted for a neutral compound. For all but one (foramsulfuron), the probability distribution of predicted Vdss generated by IAT sensitivity analysis spans both the neutral prediction and the prediction using ionization. As new data sets of chemical-specific information on metabolism and excretion for hundreds of chemicals are being made available (e.g., Wetmore et al., 2015), high-throughput methods for calculating Vdss and tissue-specific PK distribution coefficients will allow the rapid construction of PK models to provide context for both biomonitoring data and high-throughput toxicity screening studies such as Tox21 and ToxCast.

Activity profiles of 309 ToxCast™ chemicals evaluated across 292 biochemical targets.

Understanding the potential health risks posed by environmental chemicals is a significant challenge elevated by the large number of diverse chemicals with generally uncharacterized exposures, mechanisms, and toxicities. The present study is a performance evaluation and critical analysis of assay results for an array of 292 high-throughput cell-free assays aimed at preliminary toxicity evaluation of 320 environmental chemicals in EPA's ToxCast™ project (Phase I). The chemicals (309 unique, 11 replicates) were mainly precursors or the active agent of commercial pesticides, for which a wealth of in vivo toxicity data is available. Biochemical HTS (high-throughput screening) profiled cell and tissue extracts using semi-automated biochemical and pharmacological methodologies to evaluate a subset of G-protein coupled receptors (GPCRs), CYP450 enzymes (CYPs), kinases, phosphatases, proteases, HDACs, nuclear receptors, ion channels, and transporters. The primary screen tested all chemicals at a relatively high concentration 25 µM concentration (or 10 µM for CYP assays), and a secondary screen re-tested 9132 chemical-assay pairs in 8-point concentration series from 0.023 to 50 µM (or 0.009-20 µM for CYPs). Mapping relationships across 93,440 chemical-assay pairs based on half-maximal activity concentration (AC50) revealed both known and novel targets in signaling and metabolic pathways. The primary dataset, summary data and details on quality control checks are available for download at http://www.epa.gov/ncct/toxcast/.

Benzo[a]pyrene and benz[c]phenanthrene: the effect of structure on the binding of water molecules to the diol epoxides.

The interactions with water of the diol epoxides (DEs) of both a planar and a nonplanar PAH have been examined using molecular dynamics. To determine probable water locations around the DE for later use in the study of DE protonation, molecular dynamics simulations using the OPLS force field were carried out on diol epoxides surrounded by a 22 A box of explicit water molecules. Results for 30 ps simulations indicate that 10-60% of the time, depending strongly on the conformation and type of the DE, there is a water molecule forming a hydrogen bond with the epoxide oxygen. The patterns seen in the frequency at which a DE binds a water molecule reflect patterns seen in the relationship between the type of PAH DE and amount of DNA adduct formation. Examination of the orientations and arrangements of the water and DEs during the simulations showed that the bound waters existed in several preferred configurations which are also dependent upon the PAH DE geometry.