High-Throughput Models for Exposure-Based Chemical
Prioritization in the ExpoCast Project
- Publication date
- Publisher
Abstract
The
United States Environmental Protection Agency (U.S. EPA) must
characterize potential risks to human health and the environment associated
with manufacture and use of thousands of chemicals. High-throughput
screening (HTS) for biological activity allows the ToxCast research
program to prioritize chemical inventories for potential hazard. Similar
capabilities for estimating exposure potential would support rapid
risk-based prioritization for chemicals with limited information;
here, we propose a framework for high-throughput exposure assessment.
To demonstrate application, an analysis was conducted that predicts
human exposure potential for chemicals and estimates uncertainty in
these predictions by comparison to biomonitoring data. We evaluated
1936 chemicals using far-field mass balance human exposure models
(USEtox and RAIDAR) and an indicator for indoor and/or consumer use.
These predictions were compared to exposures inferred by Bayesian
analysis from urine concentrations for 82 chemicals reported in the
National Health and Nutrition Examination Survey (NHANES). Joint regression
on all factors provided a calibrated consensus prediction, the variance
of which serves as an empirical determination of uncertainty for prioritization
on absolute exposure potential. Information on use was found to be
most predictive; generally, chemicals above the limit of detection
in NHANES had consumer/indoor use. Coupled with hazard HTS, exposure
HTS can place risk earlier in decision processes. High-priority chemicals
become targets for further data collection