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Activity Number: 461 - Bayesian Statistical Methods for High-Throughput Toxicity Testing and Risk Assessment
Type: Topic Contributed
Date/Time: Wednesday, July 31, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Risk Analysis
Abstract #304524 Presentation
Title: Hierarchical Bayesian Methods for High-Throughput in Vitro Population-Based Chemical Screening
Author(s): Weihsueh Chiu* and Fred A Wright and Ivan Rusyn
Companies: Texas A&M University and North Carolina State University and Texas A&M University
Keywords: Bayesian; Hierarchical; Population; In vitro; Chemical screening

Characterizing human variability in susceptibility to chemical toxicity is a critical issue in regulatory decision-making, but is usually addressed by a default 10-fold safety/uncertainty factor. Feasibility of population-based in vitro experimental approaches to more accurately estimate human variability was demonstrated recently using a panel of lymphoblastoid cell lines derived from >1000 different individuals, tested in concentration-response with 170 chemicals. However, routine use of such a large population-based model poses cost and logistical challenges. We have developed a Bayesian approach to make most efficient use of smaller sample sizes (n< 100). We first demonstrate the feasibility of this approach by resampling from previously reported data. It is then applied to a new population-based induced pluripotent stem-cell-derived cardiomyocyte model for cardiotoxiciy. Our results show good concordance between in vitro and in vivo concentration-response for QT prolongation in positive and negative controls. Initial results for over 100 additional chemicals demonstrate its utility for estimating both central tendencies and population variability in chemical screening.

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

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