You are here:
Uncertainty and Variability in High-Throughput Toxicokinetics for Risk Prioritization
Citation:
Wambaugh, J., B. Wetmore, D. Angus, C. Strock, M. Bacolod, C. Nicolas, C. Ring, R. Pearce, Woodrow Setzer, AND R. Thomas. Uncertainty and Variability in High-Throughput Toxicokinetics for Risk Prioritization. Society of Toxicology Annual Meeting, San Antonio,TX, March 11 - 15, 2018.
Impact/Purpose:
Abstract for presentation at SOT annual meeting. This research quantifies and reduces uncertainty associated with using high throughput toxicokinetics to prioritize the risk posed by hundreds of chemicals in the environment.,
Description:
Streamlined approaches that use in vitro experimental data to predict chemical toxicokinetics (TK) are increasingly being used to perform risk-based prioritization based upon dosimetric adjustment of high-throughput screening (HTS) data across thousands of chemicals. However, assessments of the impact of uncertainty and variability on these TK values and subsequent predictions are needed to guide data interpretation and provide overall confidence in high-throughput TK (HTTK) approaches. In this study, Bayesian methods were developed to provide chemical-specific uncertainty estimates for two in vitro TK parameters: plasma protein binding (fup) and intrinsic hepatic clearance (Clint), using chemical-specific experimental measurements derived. Inclusion of experimental measures across three physiologic plasma protein concentrations reduced the uncertainty in the fup estimates. Uncertainty estimation was additionally conducted for predictions of volume of distribution (Vd) and steady-state serum concentration (Css). Monte Carlo simulation to propagate both measurement uncertainty and biologic variability into the predicted Css values revealed that for most chemicals, variability contributed more than uncertainty to Css estimations of the 95th percentile. Risk-based prioritization of chemicals based upon high throughput exposure estimates and dosimetric adjustment of ToxCast HTS data using Bayesian-derived Css estimates incorporating uncertainty and/or variability demonstrated that prioritization would change for a few chemicals when uncertainty is included. Incorporation of these methods provides a timely risk-based prioritization strategy that considers the relationship between in vitro bioactivities and exposures, overlaid with a metric for TK prediction certainties. This abstract does not necessarily reflect U.S. EPA policy.
URLs/Downloads:
WAMBAUGH-SOT-DRAFT2.PDF (PDF, NA pp, 136.521 KB, about PDF)WAMBAUGH-SOT2018-WEDMARCH14-UVHTTK-2.PDF (PDF, NA pp, 815.241 KB, about PDF)