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High-Throughput Dietary Exposure Predictions for Chemical Migrants from Food Packaging Materials
Citation:
Biryol, D., K. Phillips, C. Nicolas, J. Wambaugh, AND K. Isaacs. High-Throughput Dietary Exposure Predictions for Chemical Migrants from Food Packaging Materials. ISES Annual Meeting, Henderson, NV, Henderson, NV, October 18 - 23, 2015.
Impact/Purpose:
The National Exposure Research Laboratory (NERL) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA mission to protect human health and the environment. HEASD research program supports Goal 1 (Clean Air) and Goal 4 (Healthy People) of EPA strategic plan. More specifically, our division conducts research to characterize the movement of pollutants from the source to contact with humans. Our multidisciplinary research program produces Methods, Measurements, and Models to identify relationships between and characterize processes that link source emissions, environmental concentrations, human exposures, and target-tissue dose. The impact of these tools is improved regulatory programs and policies for EPA.
Description:
United States Environmental Protection Agency researchers have developed a Stochastic Human Exposure and Dose Simulation High -Throughput (SHEDS-HT) model for use in prioritization of chemicals under the ExpoCast program. In this research, new methods were implemented in SHEDS-HT to predict dietary exposures to chemicals found in food packaging materials due to migration of chemical into foods. These methods consisted of combining an empirical model of chemical migration rates into foods with estimates of daily population food intakes derived from food diaries from the National Health and Nutrition Examination Survey (NHANES). The linear regression model for migration rate was developed by fitting available Food and Drug Administration migration data as a function of storage conditions (time, temperature), food properties (i.e., fatty, aqueous, acidic, alcoholic, dry), packaging properties, and chemical properties (p<2.2 10-16, R2=0.7). More than 7000 codes indicating food type within the NHANES diaries were then categorized as belonging to one of 33 corresponding food packaging groups based on food properties and likely storage conditions. By merging migration rate-based food concentrations with a U.S.-representative sample of NHANES food diaries, population chemical exposures could be estimated. By assuming chemical presence in all packaging within each group, conservative exposures were predicted for 3048 chemicals identified by a variety of government and industry data sources as being used in food packaging. These exposure estimates ranged approximately from 10-6-10-1 mg/kg-BW/day. Predicted food concentrations were favorably compared with an independent dataset of measurements for 10 plasticizers from the literature, (R2 =0.8 and p<9.1×10-22), while exposure predictions were compared to values inferred from NHANES biomarker data for 21 chemicals. Such promising initial evaluations indicate these methods will be useful in the prediction of aggregate chemical exposures for existing or new commercial chemicals for use in prioritization of chemical safety assessments.
