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A Workflow for Identifying Metabolically Active Chemicals to Complement in vitro Toxicity Screening
Citation:
Leonard, J., C. Stevens, K. Mansouri, D. Chang, H. Pudukodu, S. Smith, AND C. Tan. A Workflow for Identifying Metabolically Active Chemicals to Complement in vitro Toxicity Screening. Computational Toxicology. Elsevier B.V., Amsterdam, Netherlands, 6:71-83, (2018).
Impact/Purpose:
High throughput toxicity testing holds the promise of providing data for tens of thousands of chemicals that currently have no data due to the cost and time required for animal testing. Interpretation of these results require information linking the perturbations seen in vitro with adverse outcomes in vivo and requires knowledge of how estimated exposure to the chemicals compare to the in vitro concentrations that show an effect. This manuscript presents a workflow for identifying parent chemicals that may be biotransformed in an in vivo system in to metabolites capable of perturbing a molecular target, and we have demonstrated how this workflow may be applied in a case study involving the estrogen receptor pathway. As the number of environmental chemicals in commerce continues to expand, rapid determination of their potential for harming human or ecosystem health becomes critical. Our proposed workflow facilitates detection of those chemicals that may exhibit only weak activity on their own but that may produce adverse outcomes upon metabolic activation. Used in conjunction with approaches for limiting the number of false positives in in vitro assays, our workflow, which provides the ability to also screen for false negatives, can aid in more reliable high throughput risk screening.
Description:
The new paradigm of toxicity testing approaches involves rapid screening of thousands of chemicals across hundreds of biological targets through use of in vitro assays. Such assays may lead to false negatives when the complex metabolic processes that render a chemical bioactive in a living system are unable to be replicated in an in vitro environment. In the current study, a workflow is presented for complementing in vitro testing results with in silico and in vitro techniques to identify inactive parents that may produce active metabolites. A case study applying this workflow involved investigating the influence of metabolism for over 1,400 chemicals considered inactive across18 in vitro assays related to the estrogen receptor (ER) pathway. Over 7,500 first-generation and second-generation metabolites were generated for these in vitro inactive chemicals using an in silico software program. Next, a consensus model comprised of four individual quantitative structure activity relationship (QSAR) models was used to predict ER-binding activity for each of the metabolites. Binding activity was predicted for ∼8-10% of metabolites in each generation, with these metabolites linked to 259 in vitro inactive parent chemicals. Metabolites were enriched in substructures consisting of alcohol, aromatic, and phenol bonds relative to their inactive parent chemicals, suggesting these features are potentially favorable for ER-binding. The workflow presented here can be used to identify parent chemicals that can be potentially bioactive, to aid confidence in high throughput risk screening.
