为PFAS提供可重复的基于结构的化学分类,以通知和评估毒性和毒性动力学测试。

IF 3.1 Q2 TOXICOLOGY Computational Toxicology Pub Date : 2022-11-01 DOI:10.1016/j.comtox.2022.100250
Grace Patlewicz, Ann M. Richard, Antony J. Williams, Richard S. Judson, Russell S. Thomas
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引用次数: 6

摘要

全氟烷基和多氟烷基物质是一类广泛使用的合成化学品,其持久性、生物累积性和毒性令人担忧。虽然少数全氟辛烷磺酸已根据其危害特征进行了表征,但绝大多数全氟辛烷酸尚未进行研究。美国环境保护局(EPA)开展了一项研究项目,通过一系列不同的体外高通量毒性和毒代动力学测试筛选约150种PFAS,以告知化学类别和跨方法。先前的一份出版物描述了选择最初的75种PFAS的基本原理,而在本文中,我们描述了如何应用和扩展各种类别的方法,以告知从我们的约430种商业采购的PFAS库中选择第二组75种PFAS。特别是,我们关注对PFAS进行前瞻性分析的挑战,以及我们如何寻求开发和应用基于客观结构的类别来描述测试库和其他PFAS清单。我们还说明了一旦实验数据可用,如何用其他信息丰富这些类别,以便于跨推断进行阅读。灵活、客观、可重复和化学直观的类别可用于探索全氟辛烷磺酸,这是在优先考虑全氟辛烷酸进行进一步测试和评估方面迈出的重要一步。
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Towards reproducible structure-based chemical categories for PFAS to inform and evaluate toxicity and toxicokinetic testing

Per- and Polyfluoroalkyl substances (PFAS) are a class of synthetic chemicals that are in widespread use and present concerns for persistence, bioaccumulation and toxicity. Whilst a handful of PFAS have been characterised for their hazard profiles, the vast majority of PFAS have not been studied. The US Environmental Protection Agency (EPA) undertook a research project to screen ∼150 PFAS through an array of different in vitro high throughput toxicity and toxicokinetic tests in order to inform chemical category and read-across approaches. A previous publication described the rationale behind the selection of an initial set of 75 PFAS, whereas herein, we describe how various category approaches were applied and extended to inform the selection of a second set of 75 PFAS from our library of approximately 430 commercially procured PFAS. In particular, we focus on the challenges in grouping PFAS for prospective analysis and how we have sought to develop and apply objective structure-based categories to profile the testing library and other PFAS inventories. We additionally illustrate how these categories can be enriched with other information to facilitate read-across inferences once experimental data become available. The availability of flexible, objective, reproducible and chemically intuitive categories to explore PFAS constitutes an important step forward in prioritising PFAS for further testing and 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
期刊最新文献
Evaluation of QSAR models for tissue-specific predictive toxicology and risk assessment of military-relevant chemical exposures: A systematic review From model performance to decision support – The rise of computational toxicology in chemical safety assessments 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 The OECD (Q)SAR Assessment Framework: A tool for increasing regulatory uptake of computational approaches A developmental and reproductive toxicity adverse outcome pathway network to support safety assessments
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