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High Throughput Pharmacokinetics for Environmental Chemicals (FutureToxII)
Citation:
Wambaugh, J., C. Strope, C. Nicolas, R. Pearce, R. Judson, Rocky Goldsmith, H. El-Masri, B. Wetmore, J. Sluka, A. Tropsha, S. Bosgra, I. Shah, R. Thomas, AND Woodrow Setzer. High Throughput Pharmacokinetics for Environmental Chemicals (FutureToxII). Presented at SOT CCT FutureTox II, Chapel Hill, NC, January 16 - 17, 2014.
Impact/Purpose:
This methodology allows prioritization of PK resources and characterizes the confidence in HTPK model predictions for potentially thousands of environmental chemicals that currently have no PK data.
Description:
Pharmacokinetic (PK) models are critical to determine whether chemical exposures produce potentially hazardous tissue concentrations. For bioactivity identified in vitro (e.g. ToxCast) – hazardous or not – PK models can forecast exposure thresholds, below which no significant bioactivity is expected. Successful methods have been developed for pharmaceutical compounds to determine PK from limited in vitro measurements and chemical structure-derived property predictions. These high throughput (HT) PK methods provide a more rapid and less resource–intensive alternative to traditional PK model development. Unfortunately, predictions from HTPK approaches have demonstrated mixed success for environmental chemicals when compared to predictions made by PK models developed with extensive in vivo data. Here we tested assumptions of previous HTPK approaches using a simple physiologically-based PK (PBPK) model and in vitro data for 232 chemicals in human and 39 chemicals in rat. We then analyzed the discrepancy between the predictions of HTPK and in vivo literature PK data for 44, mostly pharmaceutical, chemicals, using the method of best subsets to identify those properties that correlate with poor predictive ability (e.g., in vitro HTPK data, physico-chemical descriptors, chemical structure, and predicted transporter affinities). We propose a framework for PK triage in stages: First, in vitro measurements and in silico predictions determine whether the simplest HTPK approaches are likely to be sufficient. Then identify and collected any additional, targeted in vitro data that is needed. Finally, identify those chemicals most likely to require traditional, in vivo PK methods. This methodology allows prioritization of PK resources and characterizes the confidence in HTPK model predictions for potentially thousands of environmental chemicals that currently have no PK data. This abstract does not necessarily reflect EPA policy.
URLs/Downloads:
WAMBAUGH-FUTURETOX-ABSTRACT.PDF (PDF, NA pp, 313.867 KB, about PDF)WAMBAUGH-SOTCCTFUTURETOXII-2014-POSTER3.PDF (PDF, NA pp, 793.641 KB, about PDF)