On August 18, 2024, Computational Toxicology published an article entitled “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.” Co-authored by members of the U.S. Environmental Protection Agency’s (EPA) Center for Computational Toxicology & Exposure (CCTE) and Office of Chemical Safety and Pollution Prevention (OCSPP), the study “describes the approach taken to further refine a relevant PFAS landscape to EPA from which an initial set of structural categories were derived.” The authors applied the structural definition of PFAS used in the Toxic Substances Control Act (TSCA) Section 8(a)(7) rule to the Distributed Structure-Searchable Toxicity (DSSTox) database to retrieve an initial list of 13,054 PFAS. The abstract states that “[p]lausible degradation products from the 563 PFAS on the non-confidential TSCA Inventory were simulated using the Catalogic expert system,” and the authors added the unique predicted PFAS degradants (2,484) that conformed to the same PFAS definition to the list, resulting in a set of 15,538 PFAS. The authors then assigned each PFAS a primary category using Organisation for Economic Co-operation and Development (OECD) structure-based classifications and subdivided the primary categories into secondary categories based on a chain length threshold (>=7 vs < 7). According to the abstract, “s[e]condary categories were subcategorized using chemical fingerprints to achieve a balance between total number of structural categories vs. level of structural similarity within a category based on the Jaccard index.” The authors derived a set of 128 terminal structural categories from which a subset of representative candidates could be proposed for potential data collection, “considering the sparsity of relevant toxicity data within each category, presence on environmental monitoring lists, and the ability to identify plausible manufacturers/importers.” The abstract notes that the article also describes refinements to the approach, “taking into consideration ways in which the categories could be updated by mechanistic data and physicochemical property information.” According to the authors, “[t]his categorization approach may be used to form the basis of identifying candidates for data collection with related applications in [quantitative structure-activity relationship (QSAR)] development, read-across and hazard assessment.”

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