Henry M. Jackson Foundation for the Advancement of Military Medicine, Wright-Patterson Air Force Base, Dayton, Ohio 45433, United States.
Warfighter Medical Optimization Division, 711 Human Performance Wing, Air Force Research Laboratory, Wright-Patterson Air Force Base, Dayton, Ohio 45433, United States.
Chem Res Toxicol. 2021 Feb 15;34(2):345-354. doi: 10.1021/acs.chemrestox.0c00256. Epub 2020 Nov 18.
While exposure of humans to environmental hazards often occurs with complex chemical mixtures, the majority of existing toxicity data are for single compounds. The Globally Harmonized System of chemical classification (GHS) developed by the Organization for Economic Cooperation and Development uses the additivity formula for acute oral toxicity classification of mixtures, which is based on the acute toxicity estimate of individual ingredients. We evaluated the prediction of GHS category classifications for mixtures using toxicological data collected in the Integrated Chemical Environment (ICE) developed by the National Toxicology Program (United States Department of Health and Human Services). The ICE database contains acute oral toxicity data for ∼10,000 chemicals and for 582 mixtures with one or multiple active ingredients. By using the available experimental data for individual ingredients, we were able to calculate a GHS category for only half of the mixtures. To expand a set of components with acute oral toxicity data, we used the Collaborative Acute Toxicity Modeling Suite (CATMoS) implemented in the Open Structure-Activity/Property Relationship App to make predictions for active ingredients without available experimental data. As a result, we were able to make predictions for 503 mixtures/formulations with 72% accuracy for the GHS classification. For 186 mixtures with two or more active ingredients, the accuracy rate was 76%. The structure-based analysis of the misclassified mixtures did not reveal any specific structural features associated with the mispredictions. Our results demonstrate that CATMoS together with an additivity formula can be used to predict the GHS category for chemical mixtures.
虽然人类接触环境危害通常涉及复杂的化学混合物,但大多数现有的毒性数据都是针对单一化合物的。经济合作与发展组织(OECD)制定的全球化学品统一分类和标签制度(GHS)使用混合物急性口服毒性分类的加和公式,该公式基于各成分的急性毒性估计。我们使用美国卫生与公众服务部国家毒理学计划(NTP)开发的综合化学环境(ICE)中收集的毒理学数据来评估混合物 GHS 类别的预测。ICE 数据库包含约 10000 种化学物质和 582 种具有一种或多种活性成分的混合物的急性口服毒性数据。通过使用各成分的现有实验数据,我们仅能计算一半混合物的 GHS 类别。为了扩展具有急性口服毒性数据的一组成分,我们使用 Open Structure-Activity/Property Relationship App 中的协同急性毒性建模套件(CATMoS)对没有可用实验数据的活性成分进行预测。结果,我们能够对 503 种混合物/配方进行预测,GHS 分类的准确率为 72%。对于 186 种含有两种或更多活性成分的混合物,准确率为 76%。对分类错误的混合物进行基于结构的分析并未揭示出与错误预测相关的任何特定结构特征。我们的结果表明,CATMoS 与加和公式一起可用于预测化学混合物的 GHS 类别。