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Integrating Toxicity, Toxicokinetic, and Exposure Data for Risk-based Chemical Alternatives Assessment
Citation:
Wambaugh, J., B. Wetmore, K. Mansouri, B. Ingle, R. Tornero-Velez, R. Judson, K. Isaacs, K. Phillips, C. Nicolas, Woodrow Setzer, AND R. Thomas. Integrating Toxicity, Toxicokinetic, and Exposure Data for Risk-based Chemical Alternatives Assessment. International Society of Exposure Science annual meeting, Morrisville,NC, October 15 - 19, 2017.
Impact/Purpose:
This is a presentation on new high throughput tools for chemical risk assessment to the International Society of Exposure Science annual meeting session on "Quantitative High-Throughput Exposure Methods for Chemical Alternatives and Comparative Risk Assessment”.
Description:
In order to predict the margin between the dose needed for adverse chemical effects and actual human exposure rates, data on hazard, exposure, and toxicokinetics are needed. In vitro methods, biomonitoring, and mathematical modeling have provided initial estimates for many extant chemicals. Providing predictions for novel compounds, however, will need to rely on screening massive chemical libraries and drawing inference from chemical structure (e.g., quantitative structure activity relationships). This presentation will review the challenges and opportunities for alternatives assessment based upon high throughput tools for exposure and hazard. In vitro high throughput screening (HTS) assays, such as the U.S. Federal Tox21 consortium and the U.S. EPA Toxicity Forecaster (ToxCast) program, have generated bioactivity data for thousands of chemicals. For some endpoints (e.g., estrogen receptor and androgen receptor), models trained on these HTS data allow predictions for novel compounds. In tandem with ToxCast, the Exposure Forecaster (ExpoCast) program provides toxicokinetic (TK) data and exposure estimates to provide context for HTS data. Libraries of TK data, largely obtained from in vitro assays, have served as training sets for machine learning models capable of estimating TK for novel compounds. Biomonitoring data obtained by the US CDC National Health and Nutrition Examination Survey and consumer product formulation data have similarly been used to develop models for exposure prediction (mg/kg/day). Integration of these methods provides a timely, risk-based prioritization strategy that characterizes the dose relationships between in vitro bioactivities and predicted human exposure. National Academy of Sciences in January, 2017 found that "Translation of high-throughput data into risk-based rankings is an important application of exposure data for chemical priority-setting. Recent advances in high-throughput toxicity assessment, notably the ToxCast and Tox21 programs… and in high-throughput computational exposure assessment… have enabled first-tier risk-based rankings of chemicals on the basis of margins of exposure. High throughput risk prioritization needs requires 1) high throughput hazard characterization, 2) high throughput exposure forecasts, and 3) high throughput toxicokinetics (i.e., dosimetry). Providing predictions for novel compounds will need to rely on screening massive chemical libraries and drawing inference from chemical structure (e.g., quantitative structure activity relationships, QSAR).
URLs/Downloads:
ISES-RISKBASEDALTERNATIVES-ABSTRACT.PDF (PDF, NA pp, 80.586 KB, about PDF)ISES2017-RISKBASEDALTERNATIVES-FINAL.PDF (PDF, NA pp, 1925.646 KB, about PDF)