制定全氟烷基和多氟烷基物质(PFAS)的化学类别,并采用概念验证方法确定可能的候选物质,以便进行分级毒理学测试和人类健康评估

IF 3.1 Q2 TOXICOLOGY
G. Patlewicz , R.S. Judson , A.J. Williams , T. Butler , S. Barone Jr. , K.E. Carstens , J. Cowden , J.L. Dawson , S.J. Degitz , K. Fay , T.R. Henry , A. Lowit , S. Padilla , K. Paul Friedman , M.B. Phillips , D. Turk , J.F. Wambaugh , B.A. Wetmore , R.S. Thomas
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引用次数: 0

摘要

全氟烷基和多氟烷基物质(PFAS)是一类广泛使用的人造化学品,其中许多物质的持久性、生物累积性和毒性令人担忧。虽然已经对少数全氟辛烷磺酸的危害特征进行了描述,但绝大多数全氟辛烷磺酸尚未得到广泛研究。在此,我们开发了一种化学分类方法,并将其应用于可通过化学结构轻易确定特征的全氟辛烷磺酸。将《有毒物质控制法案》(TSCA)第 8(a)(7)条规定中的全氟辛烷磺酸定义应用于分布式结构可搜索毒性(DSSTox)数据库,检索出一份包含 13,054 种全氟辛烷磺酸的初始清单。使用 Catalogic 专家系统模拟了非机密 TSCA 清单中 563 种 PFAS 的可信降解产物,并将符合相同 PFAS 定义的唯一预测 PFAS 降解物(2484 种)添加到清单中,最终得出 15,538 种 PFAS。然后,利用经济合作与发展组织 (OECD) 基于结构的分类方法,将每种 PFAS 划入一个主要类别。根据链长阈值(>=7 vs <7),将一级类别细分为二级类别。利用化学指纹对二级类别进行细分,以便在结构类别总数与基于 Jaccard 指数的类别内结构相似性水平之间取得平衡。考虑到每个类别中相关毒性数据的稀缺性、环境监测清单中的存在情况以及确定可信制造商/进口商的能力,最终得出了 128 个终端结构类别,并从中提出了具有代表性的候选类别子集,以便进行潜在的数据收集。此外,还介绍了根据机理数据和物理化学特性信息更新类别的方法。这种分类方法可作为确定候选数据收集的基础,并可应用于 QSAR 开发、交叉阅读和危害评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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

Per- and Polyfluoroalkyl substances (PFAS) are a class of manufactured chemicals that are in widespread use and many present concerns for persistence, bioaccumulation and toxicity. Whilst a handful of PFAS have been characterized for their hazard profiles, the vast majority have not been extensively studied. Herein, a chemical category approach was developed and applied to PFAS that could be readily characterized by a chemical structure. The PFAS definition as described in the Toxic Substances Control Act (TSCA) section 8(a)(7) rule was applied to the Distributed Structure-Searchable Toxicity (DSSTox) database to retrieve an initial list of 13,054 PFAS. Plausible degradation products from the 563 PFAS on the non-confidential TSCA Inventory were simulated using the Catalogic expert system, and the unique predicted PFAS degradants (2484) that conformed to the same PFAS definition were added to the list resulting in a set of 15,538 PFAS. Each PFAS was then assigned into a primary category using Organisation for Economic Co-operation and Development (OECD) structure-based classifications. The primary categories were subdivided into secondary categories based on a chain length threshold (>=7 vs < 7). Secondary 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. A set of 128 terminal structural categories were derived 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. Refinements to the approach taking into consideration ways in which the categories could be updated by mechanistic data and physicochemical property information are also described. This categorization approach may be used to form the basis of identifying candidates for data collection with related applications in QSAR development, read-across and hazard assessment.

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来源期刊
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
期刊介绍: Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs
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