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Updated: May 10, 2026
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A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans
Published on: March 14, 2019
High-throughput models for exposure-based chemical prioritization in the ExpoCast project
John F Wambaugh1, R Woodrow Setzer, David M Reif
1National Center for Computational Toxicology, United States Environmental Protection Agency , Research Triangle Park, North Carolina 27711, United States. wambaugh.john@epa.gov
Environmental Science & Technology
|June 14, 2013
Summary
This study introduces a high-throughput exposure assessment framework to prioritize chemicals for risk evaluation. By comparing model predictions with biomonitoring data, it estimates uncertainty for better chemical safety decisions.
Area of Science:
- Environmental Science
- Toxicology
- Computational Chemistry
Background:
- The U.S. EPA needs to assess risks of numerous chemicals.
- High-throughput screening (HTS) aids hazard identification (ToxCast).
- Estimating chemical exposure potential is crucial for risk-based prioritization.
Purpose of the Study:
- Propose a framework for high-throughput exposure assessment.
- Predict human exposure potential for chemicals.
- Estimate uncertainty in exposure predictions using biomonitoring data.
Main Methods:
- Evaluated 1936 chemicals using mass balance models (USEtox, RAIDAR) and use indicators.
- Compared model predictions with biomonitoring data from NHANES (urine concentrations).
- Used Bayesian analysis and joint regression for calibrated predictions and uncertainty estimation.
Main Results:
- Consumer/indoor use was the most predictive factor for chemical detection in NHANES.
- A calibrated consensus prediction model was developed, with variance indicating uncertainty.
- The framework successfully integrated exposure and hazard HTS for earlier risk assessment.
Conclusions:
- High-throughput exposure assessment is feasible and valuable for chemical prioritization.
- Integrating exposure HTS with hazard HTS accelerates risk-based decision-making.
- This approach identifies high-priority chemicals for further investigation and data collection.
