<i>In Silico</i> Assessment of Acute Oral Toxicity for Mixtures

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 in vivo 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.尽管人类接触环境有害物时往往伴随复杂化学混合物的暴露，但现有绝大多数毒性数据均针对单一化合物。经济合作与发展组织制定的全球化学品统一分类制度（Globally Harmonized System of Chemical Classification, GHS），针对混合物的急性经口毒性分级采用加和公式，该方法基于单一组分的急性毒性估算值。本研究利用美国卫生与公众服务部国家毒理学计划开发的整合化学环境数据库（Integrated Chemical Environment, ICE）中的毒理学数据，评估了混合物GHS类别分级的预测效果。该ICE数据库收录了约10000种单一化学品以及582种含一种或多种有效成分的混合物的体内急性经口毒性数据。仅依靠各组分的现有实验数据，我们仅能为半数混合物推算出其GHS类别。为扩充具备急性经口毒性数据的组分集合，我们借助开放结构-活性/性质关系应用程序中集成的协同急性毒性建模套件（Collaborative Acute Toxicity Modeling Suite, CATMoS），对暂无实验数据的有效成分进行毒性预测。最终，我们可为503种混合物/制剂完成GHS分级预测，预测准确率达72%。针对其中186种含两种及以上有效成分的混合物，预测准确率达到76%。对分类错误的混合物开展基于结构的分析后，未发现与预测错误相关的特定结构特征。本研究结果表明，协同急性毒性建模套件（CATMoS）与加和公式联用，可用于化学混合物的GHS类别预测。