National Center for Computational Toxicology

Key Contact: William Jack Jones, Ecosystems Research Division, National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Athens, GA, jones.jack@epa.gov, (706) 355-8228

EPA is faced with long lists of chemicals that need to be assessed for hazard. A major gap in evaluating chemical risk is accounting for metabolic activation of chemicals resulting in increased toxicity. The proposed research will develop a capability for forecasting the metabolism of xenobiotic chemicals of EPA interest, to predict the most likely formed chemical metabolites, and to interface that information with toxic effect models allowing prediction of parent chemical toxic potential and the identity of chemical metabolites of equal or greater toxicity than the parent chemical. An existing metabolism simulator will be refined by focusing on reaction types leading to enhanced toxicity. Criteria for selection of transformation reactions will focus on: a) metabolic transformations most likely to increase toxicity for the effects endpoint of concern; and b) transformations which are currently simulated with low reliability. The toxic effect endpoint considered is endocrine disruption mediated by direct chemical binding to the estrogen receptor (ER). The enzymatic reaction types initially proposed for study are those that result in hydroxylated metabolites predicted to bind the ER with greater affinity than the parent chemical, e.g., ring hydroxylation and O-dealkylation reactions resulting in hydroxylated products. Chemicals for study will be selected from lists of EPA concern provided by the Office of Prevention, Pesticides, and Toxic Substances (OPPTS) and will include chemicals not predicted to be estrogenic as parent chemical but for which forecasted metabolites are predicted to bind ER. Efforts are already underway, in collaboration with the Office of Pesticides Program (OPP), to expand and test a quantitative structure-activity relationship (QSAR) for prediction of chemical binding to the ER; that effort is not a direct part of this proposal. However, the existing QSAR will be used to predict ER binding affinity of chemicals on the OPP inert ingredient list, as well as to predict ER binding of metabolites forecasted to be produced from parent chemicals based upon prediction by an existing metabolism simulator. Predictions of bioactivated metabolites will be used to prioritize chemicals for further study along with transformation reliability estimates provided with simulator outputs. The least reliable of the reactions found most crucial to forecasting bioactivation will be studied first. Chemical metabolism maps will be determined in vitro using rat microsomes; additional data will be collected from the published literature. Newly generated metabolic maps and transformation rate data will be used to re-train the metabolism simulator, increasing representation of reaction types in the database to improve reliability. Analytical methods used to verify bioactivated metabolites formed in rat microsomes will be optimized to detect metabolites formed in fish liver tissue slices. Metabolically-competent liver slices from male fish that produce vitellogenin when exposed to xenobiotics interacting with the ER will be used to study chemical bioactivation to ER-active forms. Data from these studies are used to improve the metabolic simulator and used to prioritize chemicals for testing that have the potential to be bioactivated to more toxic species. Finally, prioritized chemical lists (based upon predicted toxic effects of parent chemical and metabolites) with reliability estimates will be provided to OPPTS for chemical evaluations and ranking for toxicity testing. This research will expand the knowledge-base of metabolic pathways and transformation products for important groups of toxic chemicals. To accomplish this goal, an existing metabolism database will be enhanced with pertinent literature information and newly measured metabolism data with demonstrated significance to a toxicity endpoint of concern. Thus, an approach that integrates metabolism simulation with toxic effects modeling will be demonstrated and will provide prioritized lists of chemicals and their metabolites most likely to present the greatest risk for ER mediated endocrine disruption.

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