Abstract

Globally, biomonitoring data demonstrate virtually all humans carry residues of multiple per- and polyfluoroalkyl substances (PFAS). Despite pervasive co-exposure, limited mixtures-based in vivo PFAS toxicity research has been conducted. Perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS) are commonly detected PFAS in human and environmental samples and both produce adverse effects in laboratory animal studies, including maternal and offspring effects when orally administered during pregnancy and lactation. To evaluate the effects of combined exposure to PFOA and PFOS, we orally exposed pregnant Sprague-Dawley rats from gestation day 8 (GD8) to postnatal day 2 (PND2) to PFOA (10–250 mg/kg/d) or PFOS (0.1–5 mg/kg/d) individually to characterize effects and dose response curve parameters, followed by a variable-ratio mixture experiment with a constant dose of PFOS (2 mg/kg/d) mixed with increasing doses of PFOA (3–80 mg/kg/d). The mixture study design was intended to: 1) shift the PFOA dose response curves for endpoints shared with PFOS, 2) allow comparison of dose addition (DA) and response addition (RA) model predictions, 3) conduct relative potency factor (RPF) analysis for multiple endpoints, and 4) avoid overt maternal toxicity. Maternal serum and liver concentrations of PFOA and PFOS were consistent between the individual chemical and mixture experiments. Combined exposure with PFOS significantly shifted the PFOA dose response curves towards effects at lower doses compared to PFOA-only exposure for multiple endpoints and these effects were well predicted by dose addition. For endpoints amenable to mixture model analyses, DA produced equivalent or better estimates of observed data than RA. All endpoints evaluated were accurately predicted by RPF and DA approaches except for maternal gestational weight gain, which produced less-than-additive results in the mixture. Data support the hypothesis of cumulative effects on shared endpoints from PFOA and PFOS co-exposure and dose additive approaches for predictive estimates of mixture effects.

Full Text
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