| Literature DB >> 31532498 |
John F Wambaugh1, Barbara A Wetmore2, Caroline L Ring1,3,4, Chantel I Nicolas1,3,5, Robert G Pearce1,3, Gregory S Honda1,3, Roger Dinallo6, Derek Angus6, Jon Gilbert6, Teresa Sierra6, Akshay Badrinarayanan6, Bradley Snodgrass6, Adam Brockman6, Chris Strock6, R Woodrow Setzer1, Russell S Thomas1.
Abstract
High(er) throughput toxicokinetics (HTTK) encompasses in vitro measures of key determinants of chemical toxicokinetics and reverse dosimetry approaches for in vitro-in vivo extrapolation (IVIVE). With HTTK, the bioactivity identified by any in vitro assay can be converted to human equivalent doses and compared with chemical intake estimates. Biological variability in HTTK has been previously considered, but the relative impact of measurement uncertainty has not. Bayesian methods were developed to provide chemical-specific uncertainty estimates for 2 in vitro toxicokinetic parameters: unbound fraction in plasma (fup) and intrinsic hepatic clearance (Clint). New experimental measurements of fup and Clint are reported for 418 and 467 chemicals, respectively. These data raise the HTTK chemical coverage of the ToxCast Phase I and II libraries to 57%. Although the standard protocol for Clint was followed, a revised protocol for fup measured unbound chemical at 10%, 30%, and 100% of physiologic plasma protein concentrations, allowing estimation of protein binding affinity. This protocol reduced the occurrence of chemicals with fup too low to measure from 44% to 9.1%. Uncertainty in fup was also reduced, with the median coefficient of variation dropping from 0.4 to 0.1. Monte Carlo simulation was used to propagate both measurement uncertainty and biological variability into IVIVE. The uncertainty propagation techniques used here also allow incorporation of other sources of uncertainty such as in silico predictors of HTTK parameters. These methods have the potential to inform risk-based prioritization based on the relationship between in vitro bioactivities and exposures. Published by Oxford University Press on behalf of the Society of Toxicology 2019.Entities:
Keywords: Bayesian modeling; IVIVE; high throughput; toxicokinetics; uncertainty; variability
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Year: 2019 PMID: 31532498 PMCID: PMC8136471 DOI: 10.1093/toxsci/kfz205
Source DB: PubMed Journal: Toxicol Sci ISSN: 1096-0929 Impact factor: 4.849