RIVM, Centre for Sustainability, Environment and Health, Bilthoven, The Netherlands.
Department of Environmental Sciences, Faculty of Science, Radboud University Nijmegen, Nijmegen, The Netherlands.
Environ Toxicol Chem. 2019 Dec;38(12):2764-2770. doi: 10.1002/etc.4601. Epub 2019 Nov 9.
Ecological risk assessments are hampered by limited availability of ecotoxicity data. The present study aimed to explore the possibility of deriving species sensitivity distribution (SSD) parameters for nontested compounds, based on simple physicochemical characteristics, known SSDs for data-rich compounds, and a quantitative structure-activity relationship (QSAR)-type approach. The median toxicity of a data-poor chemical for species assemblages significantly varies with values of the physicochemical descriptors, especially when based on high-quality SSD data (from either acute median effect concentrations or chronic no-observed-effect concentrations). Beyond exploratory uses, we discuss how the precision of QSAR-based SSDs can be improved to construct models that accurately predict the SSD parameters of data-poor chemicals. The current models show that the concept of QSAR-based SSDs supports screening-level evaluations of the potential ecotoxicity of compounds for which data are lacking. Environ Toxicol Chem 2019;38:2764-2770. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.
生态风险评估受到有限的生态毒性数据的阻碍。本研究旨在探索基于简单理化特性、数据丰富化合物的已知物种敏感度分布（SSD）以及定量构效关系（QSAR）方法，从非测试化合物中推导出 SSD 参数的可能性。数据匮乏的化学物质对物种组合的中等毒性与理化描述符的值显著不同，尤其是基于高质量 SSD 数据（来自急性中值效应浓度或慢性无观察效应浓度）时。除了探索性使用之外，我们还讨论了如何提高基于 QSAR 的 SSD 的精度，以构建能够准确预测数据匮乏化学品 SSD 参数的模型。目前的模型表明，基于 QSAR 的 SSD 的概念支持对缺乏数据的化合物的潜在生态毒性进行筛选水平评估。Environ Toxicol Chem 2019;38:2764-2770。 © 2019 作者。环境毒理化学由 Wiley Periodicals, Inc. 代表 SETAC 出版。
