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Evaluation of High-Throughput Chemical Exposure Models: Case Study of Matched Environmental and Biological Measurements
Citation:
Isaacs, K., Woodrow Setzer, R. Dodson, R. Rudel, D. Biryol, AND J. Wambaugh. Evaluation of High-Throughput Chemical Exposure Models: Case Study of Matched Environmental and Biological Measurements. ISES-ISEE 2018 Annual Meeting, Ottawa, CANADA, August 26 - 29, 2018.
Impact/Purpose:
U.S. EPA, under its ExpoCast program, is developing high-throughput near-field modeling methods to estimate chemical exposure and to provide real-world context to high-throughput screening (HTS) data. These novel modeling methods include reverse methods, which infer parent chemical exposures from biomonitoring measurements, and forward models to predict multi-pathway exposures from chemical use information and/or residential media concentrations. This work evaluates these methods by examining their ability to interpret monitoring data.
Description:
U.S. EPA, under its ExpoCast program, is developing high-throughput near-field modeling methods to estimate chemical exposure and to provide real-world context to high-throughput screening (HTS) data. These novel modeling methods include reverse methods, which infer parent chemical exposures from biomonitoring measurements, and forward models to predict multi-pathway exposures from chemical use information and/or residential media concentrations. Both modeling methods were used to characterize the relationship between near-field environmental and biomarker measurements. Indoor air, house dust, and urine samples of 120 females (aged 60 to 80 years) were analyzed. In the measured data, 78% of the residential media measurements (across 80 chemicals) and 54% of the urine measurements (across 21 chemicals) were below the limit of quantification. Due to the degree of censoring in both air and dust data, we applied a latent variable estimation approach based on an expected relationship between air and dust concentrations. Using the partitioning model of Weschler and Nazaroff (Atmos. Env., 2010) and assumptions about the variance in concentrations within homes, we jointly estimated chemical-specific geometric mean air and dust concentrations from the observed measurements. This resulted in 14 chemicals with matched air, dust, and urine metabolite data. The indoor air and dust concentrations were compared to population median exposures inferred from urine metabolite concentrations using a reverse-dosimetry approach. Median air and dust concentrations were found to be correlated with inferred exposures; forward model predictions were used to characterize pathway contributions to aggregate exposures. These results demonstrate that the forward and reverse methods being developed in ExpoCast can aid in the interpretation of measured urinary biomarkers via inference of corresponding intake of parent chemicals and elucidation of contributing exposure pathways.
