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基于 AOP 网络的甲状腺激素干扰评估分层测试策略

Toward an AOP Network-Based Tiered Testing Strategy for the Assessment of Thyroid Hormone Disruption.

机构信息

Zebrafishlab, Veterinary Physiology and Biochemistry, Department of Veterinary Sciences, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Belgium.

Badger Technical Services, United States Environmental Protection Agency, Great Lakes Toxicology and Ecology Division, 6201 Congdon Blvd, Duluth, Minnesota 55804, United States.

出版信息

Environ Sci Technol. 2020 Jul 21;54(14):8491-8499. doi: 10.1021/acs.est.9b07205. Epub 2020 Jul 9.

Abstract

A growing number of environmental pollutants are known to adversely affect the thyroid hormone system, and major gaps have been identified in the tools available for the identification, and the hazard and risk assessment of these thyroid hormone disrupting chemicals. We provide an example of how the adverse outcome pathway (AOP) framework and associated data generation can address current testing challenges in the context of fish early life stage tests, and fish tests in general. We demonstrate how a suite of assays covering biological processes involved in the underlying toxicological pathways can be implemented in a tiered screening and testing approach for thyroid hormone disruption, using the levels of assessment of the OECD's Conceptual Framework for the Testing and Assessment of Endocrine Disrupting Chemicals as a guide.

摘要

越来越多的环境污染物已知会对甲状腺激素系统产生不利影响,并且在用于识别这些甲状腺激素干扰化学物质的危害和风险评估的工具方面存在重大差距。我们提供了一个如何利用不良结局途径(AOP)框架和相关数据生成来应对鱼类早期生活阶段测试和一般鱼类测试中当前测试挑战的示例。我们展示了如何使用 OECD 内分泌干扰物测试和评估概念框架的评估水平作为指南,在甲状腺激素干扰的分层筛选和测试方法中实施涵盖潜在毒理学途径中涉及的生物过程的一系列测定。

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