Background: Per- and polyfluoroalkyl substances (PFAS) are a large class of synthetic (man-made) chemicals widely used in consumer products and industrial processes. Thousands of distinct PFAS exist in commerce. The 2019 U.S. Environmental Protection Agency (U.S. EPA) Per- and Polyfluoroalkyl Substances (PFAS) Action Plan outlines a multiprogram national research plan to address the challenge of PFAS. One component of this strategy involves the use of systematic evidence map (SEM) approaches to characterize the evidence base for hundreds of PFAS. Objective: SEM methods were used to summarize available epidemiological and animal bioassay evidence for a set of PFAS that were prioritized in 2019 by the U.S. EPA’s Center for Computational Toxicology and Exposure (CCTE) for in vitro toxicity and toxicokinetic assay testing. Methods: Systematic review methods were used to identify and screen literature using manual review and machine-learning software. The Populations, Exposures, Comparators, and Outcomes (PECO) criteria were kept broad to identify mammalian animal bioassay and epidemiological studies that could inform human hazard identification. A variety of supplemental content was also tracked, including information on in vitro model systems; exposure measurement–only studies in humans; and absorption, distribution, metabolism, and excretion (ADME). Animal bioassay and epidemiology studies meeting PECO criteria were summarized with respect to study design, and health system(s) were assessed. Because animal bioassay studies with exposure duration (or reproductive/developmental study design) were most useful to CCTE analyses, these studies underwent study evaluation and detailed data extraction. All data extraction is publicly available online as interactive visuals with downloadable metadata. Results: More than 40,000 studies were identified from scientific databases. Screening processes identified 44 animal and 148 epidemiology studies from the peer-reviewed literature and 95 animal and 50 epidemiology studies from gray literature that met PECO criteria. Epidemiological evidence (available for 15 PFAS) mostly assessed the reproductive, endocrine, developmental, metabolic, cardiovascular, and immune systems. Animal evidence (available for 40 PFAS) commonly assessed effects in the reproductive, developmental, urinary, immunological, and hepatic systems. Overall, 45 PFAS had evidence across animal and epidemiology data streams. Discussion: Many of the PFAS were data poor. Epidemiological and animal evidence were lacking for most of the PFAS included in our search. By disseminating this information, we hope to facilitate additional assessment work by providing the initial scoping literature survey and identifying key research needs. Future research on data-poor PFAS will help support a mo...

The application of toxic equivalency factors (TEFs) or toxic units to estimate toxic potencies for mixtures of chemicals which contribute to a biological effect through a common mechanism is one approach for filling data gaps. Toxic Equivalents (TEQ) have been used to express the toxicity of dioxin-like compounds (i.e., dioxins, furans, and dioxin-like polychlorinated biphenyls (PCBs)) in terms of the most toxic form of dioxin: 2,3,7,8-tetrachlorodibenzo-p-dioxin (2,3,7,8-TCDD). This study sought to integrate two data gap filling techniques, quantitative structure-activity relationships (QSARs) and TEFs, to predict neurotoxicity TEQs for PCBs. Simon et al. (2007) previously derived neurotoxic equivalent (NEQ) values for a dataset of 87 PCB congeners, of which 83 congeners had experimental data. These data were taken from a set of four different studies measuring different effects related to neurotoxicity, each of which tested overlapping subsets of the 83 PCB congeners. The goals of the current study were to: (i) evaluate an alternative neurotoxic equivalent factor (NEF) derivations from an expanded dataset, relative to those derived by Simon et al., and (ii) develop QSAR models to provide NEF estimates for the large number of untested PCB congeners. The models used multiple linear regression, support vector regression, knearest neighbor and random forest algorithms within a 5-fold cross validation scheme. and position-specific chlorine substitution patterns on the biphenyl scaffold as descriptors. Alternative NEF values were derived but the resulting QSAR models had relatively low predictivity (RMSE ~0.24). This was mostly driven by the large uncertainties in the underlying data and NEF values. The derived NEFs and the QSAR predicted NEFs to fill data gaps should be applied with caution.

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