Project description:Per- and polyfluoroalkyl substances (PFAS) are a very large (thousands of chemicals) category; these substances have important industrial and consumer product applications. PFAS are highly persistent in the environment, and some are known to pose human health hazard. Regulatory agencies worldwide are considering restrictions and outright bans of PFAS; however, little data exists to make informed decisions. Therefore, a prioritization strategy is urgently needed for evaluation of potential hazards of PFAS. Structure-based grouping could expedite selection of PFAS for testing; still, the hypothesis that structure-effect relationships exist requires confirmation. We tested 26 structurally diverse PFAS from 8 groups in two human cell types from organs that are thought to be targets for PFAS. We used human induced pluripotent stem cell-derived hepatocytes and cardiomyocytes and tested concentration-response effects on both cell function and gene expression. Few phenotypic effects were observed in hepatocytes, but negative chronotropy was observed for 8 of 26 PFAS. Substance- and cell-dependent transcriptomic changes were more pronounced; however, little evidence of group-specific effects was observed. In hepatocytes, we found up-regulation of stress-related and extracellular matrix organization pathways, and down-regulation of fat metabolism. In cardiomyocytes, contractility-related pathways were most affected. Using these data, we derived phenotypic and transcriptomic point of departure estimates and compared them to predicted PFAS exposures. The conservative estimates for bioactivity and exposure were used to derive margin-of-exposure (MOE) for each PFAS. We found that most (23 of 26) PFAS had MOE>1. Overall, our data suggests that chemical structure-based grouping of PFAS may not be an appropriate strategy to predict their biological effects. This means that testing of the individual PFAS would be needed for confident decision-making. Our proposed strategy of using two human cell types and considering both phenotypic and transcriptomic effects, combined with dose-response analysis, may be used for prioritization of PFAS.

Project description: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 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.

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A decision analytic approach to exposure-based chemical prioritization.

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A decision analytic approach to exposure-based chemical prioritization.

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