Current studies on nontarget analysis and toxicities of PFASs are disconnected, due to the challenges posed by the large numbers (>1,000) and diverse structures of PFASs. The SECC method provides a high-throughput experimental way to tackle the challenge of prioritizing PFASs according to key proteins, especially when their authentic standards are not available. While this study is focused on hL-FABP due to its critical role in regulating the toxicokinetics of PFASs, the protein-centric method could also be adapted to screen PFASs binding to other key proteins, such as PPARs. Computational toxicology is the predominant strategy for high-throughput predictions of toxicities of chemical contaminants. This dataset is not publicly accessible because: Data generated and owned by external academic lab with chemicals provided by the EPA under an MTA. EPA's contribution was assisting in manuscript writing. It can be accessed through the following means: Contact the Corresponding author: Hui Peng, e-mail: hui.peng@utoronto.ca, Department of Chemistry, University of Toronto, Toronto, Ontario, M5S3H6, Canada. Format: Not available. This dataset is associated with the following publication: Yang, D., J. Han, D. Ross Hall, J. Sun, J. Fu, S. Kutarna, K. Houck, C. LaLone, J. Doering, C. Ng, and H. Peng. Nontarget Screening of Per- and Polyfluoroalkyl Substances Binding to Human Liver Fatty Acid Binding Protein. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 54(9): 5676-5686, (2020).

Current studies on nontarget analysis and toxicities of PFASs are disconnected, due to the challenges posed by the large numbers (>1,000) and diverse structures of PFASs. The SECC method provides a high-throughput experimental way to tackle the challenge of prioritizing PFASs according to key proteins, especially when their authentic standards are not available. While this study is focused on hL-FABP due to its critical role in regulating the toxicokinetics of PFASs, the protein-centric method could also be adapted to screen PFASs binding to other key proteins, such as PPARs. Computational toxicology is the predominant strategy for high-throughput predictions of toxicities of chemical contaminants. This dataset is not publicly accessible because: Data generated and owned by external academic lab with chemicals provided by the EPA under an MTA. EPA's contribution was assisting in manuscript writing. It can be accessed through the following means: Contact the Corresponding author: Hui Peng, e-mail: hui.peng@utoronto.ca, Department of Chemistry, University of Toronto, Toronto, Ontario, M5S3H6, Canada. Format: Not available. This dataset is associated with the following publication: Yang, D., J. Han, D. Ross Hall, J. Sun, J. Fu, S. Kutarna, K. Houck, C. LaLone, J. Doering, C. Ng, and H. Peng. Nontarget Screening of Per- and Polyfluoroalkyl Substances Binding to Human Liver Fatty Acid Binding Protein. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 54(9): 5676-5686, (2020).

Data processing was conducted using the Anaconda distribution of Python 3.9 and associated libraries. Jupyter notebooks are available at https://github.com/patlewig/nts_pfas. Datasets supporting the manuscript are accessible at https://doi.org/10.23645/epacomptox.26524327. This dataset is associated with the following publication: Patlewicz, G., R. Judson, A. Williams, T. Butler, S. Barone, K. Carstens, J. Cowden, J. Dawson, S. Degitz, K. Fay, A. Lowit, S. Padilla, K. Friedman, M. Phillips, D. Turk, J. Wambaugh, B. Wetmore, and R. Thomas. Development of chemical categories for per- and polyfluoroalkyl substances (PFAS) and the proof-of-concept approach to the identification of potential candidates for tiered toxicological testing and human health assessment. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 31: 100327, (2024).

Supplementary material for "Kreutz, A.; Clifton, M.S.; Henderson, W.M.; Smeltz, M.G.; Phillips, M.; Wambaugh, J.F.; Wetmore, B.A. Category-Based Toxicokinetic Evaluations of Data-Poor Per- and Polyfluoroalkyl Substances (PFAS) using Gas Chromatography Coupled with Mass Spectrometry. Toxics 2023, 11, 463. https://doi.org/10.3390/toxics11050463". This dataset is associated with the following publication: Kreutz, A., M. Clifton, W. Henderson, M. Smeltz, M. Phillips, J. Wambaugh, and B. Wetmore. Category-Based Toxicokinetic Evaluations of Data-Poor Per- and Polyfluoroalkyl Substances (PFAS) using Gas Chromatography Coupled with Mass Spectrometry. Toxics. MDPI, Basel, SWITZERLAND, 11(5): 463, (2023).

Supplementary material for "Kreutz, A.; Clifton, M.S.; Henderson, W.M.; Smeltz, M.G.; Phillips, M.; Wambaugh, J.F.; Wetmore, B.A. Category-Based Toxicokinetic Evaluations of Data-Poor Per- and Polyfluoroalkyl Substances (PFAS) using Gas Chromatography Coupled with Mass Spectrometry. Toxics 2023, 11, 463. https://doi.org/10.3390/toxics11050463". This dataset is associated with the following publication: Kreutz, A., M. Clifton, W. Henderson, M. Smeltz, M. Phillips, J. Wambaugh, and B. Wetmore. Category-Based Toxicokinetic Evaluations of Data-Poor Per- and Polyfluoroalkyl Substances (PFAS) using Gas Chromatography Coupled with Mass Spectrometry. Toxics. MDPI, Basel, SWITZERLAND, 11(5): 463, (2023).

We used systematic evidence map methods to summarize the available epidemiological and animal bioassay evidence for an expanded set of ~15,000 PFAS that were identified as PFAS by EPA's Center for Computational Toxicology and Exposure (CCTE). This work is a continuation of our previous 2022 and 2024 SEMs that inventoried evidence on a separate set of ~500 PFAS (Carlson et al, 2022, https://doi.org/10.1289/EHP10343; Radke et al, 2022, https://doi.org/10.1289/EHP11185; Carlson et al., 2024; https://doi.org/10.1289/EHP14191) and a 2023 evidence map on an additional 345 PFAS (Shirke et al. 2024, https://doi.org/10.1289/EHP13423 ). The comprehensive PFAS dashboard includes evidence identified from our past SEMs and completed US EPA assessments. This dataset is associated with the following publication: Shirke, A., E. Radke, R. Jones, B. Allen, C. Lin, A. Ross, N. Vetter, C. Lemeris, p. hartman, S. Eftim, A. Varghese, R. Blain, H. Hubbard, A. Williams, K. Thayer, and L. Carlson. Systematic Evidence Map for the Per- and Polyfluoroalkyl Substances (PFAS) Universe. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 0(0): 1-45, (2025).

R-language data files from "Carstens KE, Freudenrich T, Wallace K, Choo S, Carpenter A, Smeltz M, Clifton MS, Henderson WM, Richard AM, Patlewicz G, Wetmore BA, Paul Friedman K, Shafer T. Evaluation of Per- and Polyfluoroalkyl Substances (PFAS) In Vitro Toxicity Testing for Developmental Neurotoxicity. Chem Res Toxicol. 2023 Mar 20;36(3):402-419. doi: 10.1021/acs.chemrestox.2c00344. Epub 2023 Feb 23. PMID: 36821828.". This dataset is associated with the following publication: Carstens, K., T. Freudenrich, K. Wallace, S. Choo, A. Carpenter, M. Smeltz, M. Clifton, M. Henderson, A. Richard, G. Patlewicz, B. Wetmore, K. Friedman, and T. Shafer. Evaluation of Per- and Polyfluoroalkyl Substances (PFAS) In Vitro Toxicity Testing for Developmental Neurotoxicity. CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, USA, 36(3): 402-419, (2023).

Samples were collected for a comparison method development study with the University of Minnesota and the U.S. Geological Survey (USGS) National Water Quality Laboratory (NWQL) in Lakewood, Colorado. Widely used liquid chromatography-tandem mass spectrometry (LC-MS/MS) methods fail to capture large fractions of total organofluorine in environmental samples confounding the assessment and remediation of fluorinated pollution. Fluorine nuclear magnetic resonance (19F-NMR) is an inclusive method for organofluorine analysis that preserves chemical information about chemical compound classes of organofluorine.

We used systematic evidence map methods to summarize the available epidemiological and animal bioassay evidence for an expanded set of ~345 PFAS that were prioritized in 2019 by the EPA’s Center for Computational Toxicology and Exposure (CCTE) for in vitro toxicity and toxicokinetic screening. This work builds upon our previously published evidence map for ~150 PFAS chemicals (Carlson et al. 2022, https://doi.org/10.1289/EHP10343). This dataset is associated with the following publication: Shirke, A., E. Radke-Farabaugh, C. Lin, R. Blain, N. Vetter, c. lemeris, p. hartman, H. Hubbard, M. Angrish, X. Arzuaga Andino, J. Congleton, J. Davis, L. Dishaw, R. Jones, R. Judson, J. Kaiser, A. Kraft, L. Lizarraga, P. Noyes, G. Patlewicz, M. Taylor, A. Williams, K. Chialton, and L. Carlson. Expanded Systematic Evidence Map for Hundreds of Per- and Polyfluoroalkyl Substances (PFAS) and Comprehensive PFAS Human Health Dashboard. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 132(2): CID: 026001, (2024).