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Identifying Metabolically Active Chemicals Using a Consensus Quantitative Structure Activity Relationship Model for Estrogen Receptor Binding
Citation:
Leonard, J., C. Stevens, K. Mansouri, D. Chang, AND C. Tan. Identifying Metabolically Active Chemicals Using a Consensus Quantitative Structure Activity Relationship Model for Estrogen Receptor Binding. Opentox USA 2017, Durham, NC, July 12 - 13, 2017.
Impact/Purpose:
Traditional toxicity testing provides insight into the mechanisms underlying toxicological responses but requires a high investment in a large number of resources. The new paradigm of testing approaches involves rapid screening studies able to evaluate thousands of chemicals across hundreds of biological targets through use of in vitro assays. However, in vitro assays can also lead to false negatives when the complex metabolic processes that render a chemical bioactive in a living system might be unable to be replicated in an in vitro environment. This study provides a workflow to consider this issue.
Description:
Traditional toxicity testing provides insight into the mechanisms underlying toxicological responses but requires a high investment in a large number of resources. The new paradigm of testing approaches involves rapid screening studies able to evaluate thousands of chemicals across hundreds of biological targets through use of in vitro assays. Endocrine disrupting chemicals (EDCs) are of concern due to their ability to alter neurodevelopment, behavior, and reproductive success of humans and other species. A recent integrated computational model examined results across 18 ER-related assays in the ToxCast in vitro screening program to eliminate chemicals that produce a false signal by possibly interfering with the technological attributes of an individual assay. However, in vitro assays can also lead to false negatives when the complex metabolic processes that render a chemical bioactive in a living system might be unable to be replicated in an in vitro environment. In the current study, the influence of metabolism was examined for over 1,400 chemicals considered inactive using the integrated computational model. Over 2,000 first-generation and over 4,000 second-generation metabolites were generated for the inactive chemicals using in silico techniques. Next, a consensus model comprised of individual structure activity relationship (SAR) models was used to predict ER-binding activity for each of the metabolites. Binding activity was predicted for 8-10% of the metabolites within each generation. Additionally, it was found that approximately 20% of the inactive parents have at least one potentially active metabolite. The approaches presented here can be used to identify potential parents that are inactive under in vitro conditions but that might become metabolically active in a living organism.
