High Throughput Exposure Prioritization of Chemicals Using a Screening-Level Probabilistic SHEDS-Lite Exposure Model

Abstract

Background: EPA's National Exposure Research Laboratory, in collaboration with its National Center for Computational Toxicology, is developing a strategy for high-throughput exposure-based prioritization of chemicals under the ExpoCast program. These novel modeling approaches for screening, evaluating and classifying chemicals based on the potential for biologically-relevant human exposures will inform toxicity testing and prioritization for chemical risk assessment. Methods: The new modeling approach is derived from the Stochastic Human Exposure and Dose-Simulation Model (SHEDS) for multimedia pollutants. To accommodate high-throughput chemical assessments, SHEDS has been numerically and operationally reduced to a “Lite” form that reduces user burden and increases run speed. Initially, the SHEDS-Lite model has been applied to chemicals associated with consumer products used in homes. Model input data for these evaluations come from recent NERL/NCCT efforts to characterize usage (prevalence, frequency, and magnitude), chemical composition, and exposure scenarios for a wide range of consumer products. Near-field indirect scenarios uses a fugacity-based source-to-concentration module to estimate indoor concentrations by media (air, dust, and surfaces), while near-field direct scenarios are addressed via appropriate exposure equations. The concentration estimates, relevant exposure factors, exposure predictions, and human activity data are then used by the model to rapidly generate population distributions of potential exposures via dermal, non-dietary ingestion, and inhalation pathways. Results: Population dietary exposures for chemicals expected in foods is being produced by a dietary module linked to the residential module to produce total population exposure estimates. Preliminary exposure results obtained from applying this rapid computational assessment tool for a number of chemicals are compared with NHANES biomonitoring data and other modeling approaches.