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Scoring and ranking of metabolic trees to computationally prioritize chemicals for testing using fit-for-purpose in vitro estrogen receptor assay
Citation:
Mansouri, K., J. Leonard, C. Tan, M. Yoon, P. McMullen, AND R. Clewell. Scoring and ranking of metabolic trees to computationally prioritize chemicals for testing using fit-for-purpose in vitro estrogen receptor assay. OpenTox USA 2017, Durham, NC, July 12 - 13, 2017.
Impact/Purpose:
Although the traditional toxicity-testing paradigm allows for detailed characterization of pharmacokinetic behaviors such as absorption, distribution, metabolism, and elimination (ADME) for a certain chemical under well-controlled conditions within in vivo animal studies, such testing also holds numerous disadvantages. These include a large investment in resources and difficulty in extrapolating results from animals dosed with high chemical concentrations to relevant human health outcomes. Recognizing such issues, the National Toxicology Program proposed a new “roadmap” for toxicity testing in the 21st century that involved development of rapid profiling strategies meant to reduce or refine the use of animal studies while still remaining scientifically sound and promoting human and animal welfare. High-throughput (HT) in vitro screening assays arose from this initiative to act as a means for rapidly investigating the effects of thousands of chemicals across hundreds of biological endpoints linked to disease outcomes relevant to both human and ecosystem health. The aim of this work is to design a computational and in vitro approach to prioritize compounds and perform a quantitative safety assessment by including the considerations of metabolism.
Description:
Increasing awareness about endocrine disrupting chemicals (EDCs) in the environment has driven concern about their potential impact on human health and wildlife. Tens of thousands of natural and synthetic xenobiotics are presently in commerce with little to no toxicity data and therefore uncertainty about their impact on estrogen receptor (ER) signaling pathways and other toxicity endpoints. As such, there is a need for strategies that make use of available data to prioritize chemicals for testing. One of the major achievements within the EPA’s Endocrine Disruptor Screening Program (EDSP), was the network model combining 18 ER in vitro assays from ToxCast to predict in vivo estrogenic activity. This model overcomes the limitations of single in vitro assays at different steps of the ER pathway. However, it lacks many relevant features required to estimate safe exposure levels and the composite assays do not consider the complex metabolic processes that might produce bioactive entities in a living system. This problem is typically addressed using in vivo assays. The aim of this work is to design a computational and in vitro approach to prioritize compounds and perform a quantitative safety assessment. To this end, we pursue a tiered approach taking into account bioactivity and bioavailability of chemicals and their metabolites using a human uterine epithelial cell (Ishikawa)-based assay. This biologically relevant fit-for-purpose assay was designed to quantitatively recapitulate in vivo human response and establish a margin of exposure. In order to overcome the overwhelming number of metabolites to test, a prioritization workflow was developed based on ToxCast chemicals (1677) and their predicted metabolites (15,406). A scoring function was used to rank the metabolic trees of the considered chemicals combining in vitro data from ToxCast and the literature in addition to in silico data from the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) consensus and five of its single QSAR models. The bioavailability of the parent chemicals as well as the metabolites and their structures were predicted using ChemAxon metabolizer software. The designed workflow categorized the metabolic trees into true positives, true negatives, false positives and false negatives. The final output was a top priority list of 345 ranked chemicals and related metabolites from the ToxCast library as well as an additional list of 593 purchasable chemicals with known CASRNs. We are currently moving forward to test the highest-priority metabolic trees in the Ishikawa assay and are using a liver bioreactor to confirm important metabolites.
