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High-Throughput Pharmacokinetics for Environmental Chemicals (SOT)
Citation:
Wambaugh, J. High-Throughput Pharmacokinetics for Environmental Chemicals (SOT). Presented at Society of Toxicology Annual Meeting, Research Triangle Park, NC, March 22 - 26, 2015. https://doi.org/10.23645/epacomptox.5082775
Impact/Purpose:
This presentation is part of a Symposium/Workshop at the Society of Toxicology Annual meeting in San Diego, presented on March 24, 2015. The session is entitled "INCORPORATING IN VITRO PHARMACOKINETIC DATA AND TOOLS INTO TOXICITY TESTING AND RISK ASSESSMENTS: STATE OF THE SCIENCE."
Description:
High throughput screening (HTS) promises to allow prioritization of thousands of environmental chemicals with little or no in vivo information. For bioactivity identified by HTS, toxicokinetic (TK) models are essential to predict exposure thresholds below which no significant bioactivity is expected. Successful in vitro to in vivo extrapolation (IVIVE) methods have been developed for pharmaceutical compounds to determine TK from limited in vitro measurements and chemical structure-derived property predictions. These high throughput (HT) TK methods provide a less resource–intensive alternative to traditional TK model development with in vivo data. Here we evaluated the domain of applicability and assumptions of previous HTTK approaches using in vivo data and simulations. By studying 369 xenobiotics with literature HTTK data, we differentiated those xenobiotics for which HTTK approaches are likely to be sufficient, from those that may require additional data. We used in vivo data for 88, mostly pharmaceutical, chemicals to determine those chemical-specific properties (e.g., in vitro HTTK data, physico-chemical descriptors, chemical structure, and predicted transporter affinities) that correlate with poor HTTK predictive ability. We then developed a HT physiologically-based TK (HTPBTK) model parameterized with HTTK data for 292 and 51 chemicals in human and rat, respectively. We used this HTPBTK model to determine that the assumptions that have been previously used for IVIVE are largely appropriate except for highly bioaccumulative compounds. Guided by statistical analysis comparing in vitro predictions with in vivo data, we propose a framework for chemical TK triage, guided by confidence in HTTK model predictions. We believe that we can rapidly identify the chemicals that are well described by simple approaches and focus additional research on those chemicals where more complicated TK (e.g., active transport) is indicated. This abstract does not necessarily reflect U.S. EPA policy.
