High-Throughput Dietary Exposure Predictions for Chemical Migrants from Food Contact Substances for Use in Chemical Prioritization

Metadata Updated: February 13, 2019

Under the ExpoCast program, United States Environmental Protection Agency (EPA) researchers have developed a high-throughput (HT) framework for estimating aggregate exposures to chemicals from multiple pathways to support rapid prioritization of chemicals. Here, we present methods to estimate HT exposures to chemicals migrating into food from food contact substances (FCS). These methods consisted of combining an empirical model of chemical migration with estimates of daily population food intakes derived from food diaries from the National Health and Nutrition Examination Survey (NHANES). A linear regression model for migration at equilibrium was developed by fitting available migration measurements as a function of temperature, food type (i.e., fatty, aqueous, acidic, alcoholic), initial chemical concentration in the FCS (C0) and chemical properties. The most predictive variables in the resulting model were C0, molecular weight, log Kow, and food type (R2=0.71, p<0.0001). Migration-based concentrations for 1009 chemicals identified via publicly-available data sources as being present in polymer FCSs were predicted for 12 food groups (combinations of 3 storage temperatures and food type). The model was parameterized with screening-level estimates of C0 based on the functional role of chemical in FCS. By combining these concentrations with daily intakes for food groups derived from NHANES, population ingestion exposures of chemical in mg/kg-bodyweight/day (mg/kg-BW/day) were estimated. Calibrated aggregate exposures were estimated for 1931 chemicals by fitting HT FCS and consumer product exposures to exposures inferred from NHANES biomonitoring (R2=0.61, p<0.001); both FCS and consumer product pathway exposures were significantly predictive of inferred exposures. Including the FCS pathway significantly impacted the ratio of predicted exposures to those estimated to produce steady-state blood concentrations equal to in-vitro bioactive concentrations. While these HT methods have large uncertainties (and thus may not be appropriate for assessments of single chemicals), they can provide critical refinement to aggregate exposure predictions used in risk-based chemical priority–setting.

This dataset is associated with the following publication: Biryol, D., C. Nicolas, J. Wambaugh, K. Phillips, and K. Isaacs. High-throughput dietary exposure predictions for chemical migrants from food contact substances for use in chemical prioritization. ENVIRONMENT INTERNATIONAL. Elsevier Science Ltd, New York, NY, USA, 108: 185-194, (2017).

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Metadata Created Date September 29, 2017
Metadata Updated Date February 13, 2019

Metadata Source

Harvested from EPA ScienceHub

Additional Metadata

Resource Type Dataset
Metadata Created Date September 29, 2017
Metadata Updated Date February 13, 2019
Publisher U.S. EPA Office of Research and Development (ORD)
Unique Identifier https://doi.org/10.23719/1374398
Kristin Isaacs
Maintainer Email
Public Access Level public
Bureau Code 020:00
Schema Version https://project-open-data.cio.gov/v1.1/schema
Data Dictionary https://pasteur.epa.gov/uploads/10.23719/1374398/documents/DataDictionary.docx
Data Dictionary Type application/vnd.openxmlformats-officedocument.wordprocessingml.document
Harvest Object Id fce30e2a-6d30-411f-8fe0-f15bbd19dbd7
Harvest Source Id cf9b0004-f9fd-420e-bade-a86839e82acf
Harvest Source Title EPA ScienceHub
License https://pasteur.epa.gov/license/sciencehub-license.html
Data Last Modified 2017-08-08
Program Code 020:095
Publisher Hierarchy U.S. Government > U.S. Environmental Protection Agency > U.S. EPA Office of Research and Development (ORD)
Related Documents https://dx.doi.org/10.1016/j.envint.2017.08.004
Source Datajson Identifier True
Source Hash 59305d14c561bf59ec43a137297b8e86e220eeff
Source Schema Version 1.1

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