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