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MOAtox: A comprehensive mode of action and acute aquatic toxicity database for predictive model development

大型水蚤 水生毒理学 急性毒性 毒性 黑头呆鱼 小鱼 数量结构-活动关系 数据库 生物 毒理 慢性毒性 环境化学 生态学 渔业 化学 生物信息学 计算机科学 有机化学
作者
Mace G. Barron,Crystal R. Lilavois,Todd M. Martin
出处
期刊:Aquatic Toxicology [Elsevier BV]
卷期号:161: 102-107 被引量:101
标识
DOI:10.1016/j.aquatox.2015.02.001
摘要

The mode of toxic action (MOA) has been recognized as a key determinant of chemical toxicity and as an alternative to chemical class-based predictive toxicity modeling. However, the development of quantitative structure activity relationship (QSAR) and other models has been limited by the availability of comprehensive high quality MOA and toxicity databases. The current study developed a dataset of MOA assignments for 1213 chemicals that included a diversity of metals, pesticides, and other organic compounds that encompassed six broad and 31 specific MOAs. MOA assignments were made using a combination of high confidence approaches that included international consensus classifications, QSAR predictions, and weight of evidence professional judgment based on an assessment of structure and literature information. A toxicity database of 674 acute values linked to chemical MOA was developed for fish and invertebrates. Additionally, species-specific measured or high confidence estimated acute values were developed for the four aquatic species with the most reported toxicity values: rainbow trout (Oncorhynchus mykiss), fathead minnow (Pimephales promelas), bluegill (Lepomis macrochirus), and the cladoceran (Daphnia magna). Measured acute toxicity values met strict standardization and quality assurance requirements. Toxicity values for chemicals with missing species-specific data were estimated using established interspecies correlation models and procedures (Web-ICE; http://epa.gov/ceampubl/fchain/webice/), with the highest confidence values selected. The resulting dataset of MOA assignments and paired toxicity values are provided in spreadsheet format as a comprehensive standardized dataset available for predictive aquatic toxicology model development.
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