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Activity Number: 166 - Understanding Mixtures in Environmental Epidemiology
Type: Topic Contributed
Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 11:50 AM
Sponsor: Section on Statistics in Epidemiology
Abstract #311123
Title: Bayesian Joint Modeling of Chemical Structure and Dose Response Curves
Author(s): Kelly R. Moran* and Amy Herring and David Dunson
Companies: Duke University and Duke University and Duke University
Keywords: Dimension reduction; Distance learning; Functional prediction; High-throughput screening; Toxicity; QSAR

Today there are approximately 85,000 chemicals regulated under the Toxic Substances Control Act, with around 2,000 new chemicals introduced yearly. Toxicologists, who cannot test all chemicals for potential toxic effects, face the challenge of choosing which chemicals to screen, and predicting the toxicity of as-yet-unscreened chemicals. Our goal is to describe how variation in chemical structure relates to variation in toxicological response to enable in silico toxicity characterization designed to meet both of these challenges. We learn a distance between chemicals targeted to toxicity, rather than one based on molecular structure alone. Our model also enables the prediction of chemical dose-response profiles based on chemical structure by taking advantage of a large database of chemicals that have already been tested for toxicity in high throughput screening programs. We show superior simulation performance in distance learning and modest to large gains in predictive ability compared to existing methods. Results from the high-throughput screening data application elucidate the relationship between chemical structure and a toxicity-relevant high-throughput assay.

Authors who are presenting talks have a * after their name.

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