High-throughput dietary exposure predictions for chemical migrants from food contact substances for use in chemical prioritization

Abstract

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 K_ow, 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 chemicals 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.

Keywords: Chemical prioritization; ExpoCast; Exposure; Food contact substances; Food packaging; High throughput; SHEDS.

Fig. 1

Results of the HT model for migration, and regression coefficients for 436 measurements of 24 chemicals. A. Initial concentration C₀, log K_ow, water solubility (S), and temperature (T) were predictive of higher migration, whereas molecular weight (MW) was predictive of reduced migration. B. Aqueous, acidic, and alcoholic substrates resulted in lower migration (i.e., fatty substrates had highest predicted migration).

Fig. 2

Comparison of 258 measured food concentrations with equilibrium concentration ranges predicted with the empirical migration model. Predicted values (in blue) are means across all foods of the median, 5th, and 95th percentile concentrations. Green bars indicate concentrations predicted using limited literature C0 measurements (Cao et al., 2014; Shen, 2005). LOD = limit of detection.

Fig. 3

A: SHEDS-HT estimated exposures (mg/kg-BW/day) for 34 chemicals with consumer product and/or food contact pathways. B. Predicted aggregate exposures estimated with the calibrated linear model developed from fitting pathway exposures to the NHANES chemicals.