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Hierarchical dose–response modeling for high‐throughput toxicity screening of environmental chemicals
Author(s) -
Wilson Ander,
Reif David M.,
Reich Brian J.
Publication year - 2014
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12114
Subject(s) - oestrogen receptor , bayesian probability , bayesian hierarchical modeling , computer science , computational biology , bayes' theorem , biology , artificial intelligence , cancer , breast cancer , genetics
Summary High‐throughput screening (HTS) of environmental chemicals is used to identify chemicals with high potential for adverse human health and environmental effects from among the thousands of untested chemicals. Predicting physiologically relevant activity with HTS data requires estimating the response of a large number of chemicals across a battery of screening assays based on sparse dose–response data for each chemical‐assay combination. Many standard dose–response methods are inadequate because they treat each curve separately and under‐perform when there are as few as 6–10 observations per curve. We propose a semiparametric Bayesian model that borrows strength across chemicals and assays. Our method directly parametrizes the efficacy and potency of the chemicals as well as the probability of response. We use the ToxCast data from the U.S. Environmental Protection Agency (EPA) as motivation. We demonstrate that our hierarchical method provides more accurate estimates of the probability of response, efficacy, and potency than separate curve estimation in a simulation study. We use our semiparametric method to compare the efficacy of chemicals in the ToxCast data to well‐characterized reference chemicals on estrogen receptor α (ER α ) and peroxisome proliferator‐activated receptor γ (PPAR γ ) assays, then estimate the probability that other chemicals are active at lower concentrations than the reference chemicals.