BAYESIAN JOINT MODELING OF CHEMICAL STRUCTURE AND DOSE RESPONSE CURVES.

Journal Article (Journal Article)

Today there are approximately 85,000 chemicals regulated under the Toxic Substances Control Act, with around 2,000 new chemicals introduced each year. It is impossible to screen all of these chemicals for potential toxic effects, either via full organism in vivo studies or in vitro high-throughput screening (HTS) programs. Toxicologists 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. With our Bayesian partially Supervised Sparse and Smooth Factor Analysis (BS3 FA) model, 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 (i.e., without in vivo or in vitro testing) by taking advantage of a large database of chemicals that have already been tested for toxicity in HTS 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. An R package for BS3 FA is available online at https://github.com/kelrenmor/bs3fa.

Full Text

Duke Authors

Cited Authors

  • Moran, KR; Dunson, D; Wheeler, MW; Herring, AH

Published Date

  • September 2021

Published In

Volume / Issue

  • 15 / 3

Start / End Page

  • 1405 - 1430

PubMed ID

  • 35765365

Pubmed Central ID

  • PMC9236276

Electronic International Standard Serial Number (EISSN)

  • 1941-7330

International Standard Serial Number (ISSN)

  • 1932-6157

Digital Object Identifier (DOI)

  • 10.1214/21-aoas1461

Language

  • eng