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Chem Res Toxicol. 2022 Jun 20;35(6):992-1000. doi: 10.1021/acs.chemrestox.1c00443. Epub 2022 May 13.
Computational modeling grounded in reliable experimental data can help design effective non-animal approaches to predict the eye irritation and corrosion potential of chemicals. The National Toxicology Program (NTP) Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM) has compiled and curated a database of eye irritation studies from the scientific literature and from stakeholder-provided data. The database contains 810 annotated records of 593 unique substances, including mixtures, categorized according to UN GHS and US EPA hazard classifications. This study reports a set of models to predict EPA and GHS hazard classifications for chemicals and mixtures, accounting for purity by setting thresholds of 100% and 10% concentration. We used two approaches to predict classification of mixtures: conventional and mixture-based. Conventional models evaluated substances based on the chemical structure of its major component. These models achieved balanced accuracy in the range of 68-80% and 87-96% for the 100% and 10% test concentration thresholds, respectively. Mixture-based models, which accounted for all known components in the substance by weighted feature averaging, showed similar or slightly higher accuracy of 72-79% and 89-94% for the respective thresholds. We also noted a strong trend between the pH feature metric calculated for each substance and its activity. Across all the models, the calculated pH of inactive substances was within one log10 unit of neutral pH, on average, while for active substances, pH varied from neutral by at least 2 log10 units. This pH dependency is especially important for complex mixtures. Additional evaluation on an external test set of 673 substances obtained from ECHA dossiers achieved balanced accuracies of 64-71%, which suggests that these models can be useful in screening compounds for ocular irritation potential. Negative predictive value was particularly high and indicates the potential application of these models in a bottom-up approach to identify nonirritant substances.
基于可靠实验数据的计算模型有助于设计有效的非动物方法，以预测化学品的眼睛刺激性和腐蚀性。美国国家毒理学计划（NTP）机构间替代毒理学方法评估中心（NICEATM）已经从科学文献和利益相关者提供的数据中汇编和整理了一个眼睛刺激性研究数据库。该数据库包含了 810 个注释记录的 593 种独特物质，包括混合物，根据联合国 GHS 和美国 EPA 危害分类进行分类。本研究报告了一组用于预测化学品和混合物的 EPA 和 GHS 危害分类的模型，通过设定 100%和 10%浓度的阈值来考虑纯度。我们使用两种方法来预测混合物的分类：常规和基于混合物的方法。常规模型根据其主要成分的化学结构来评估物质。这些模型在 100%和 10%测试浓度阈值下的平衡准确率分别在 68-80%和 87-96%之间。基于混合物的模型，通过加权特征平均考虑物质中的所有已知成分，在各自的阈值下显示出相似或略高的准确率 72-79%和 89-94%。我们还注意到，为每个物质计算的 pH 特征度量值与其活性之间存在很强的趋势。在所有模型中，平均而言，不活跃物质的计算 pH 值在一个对数单位内接近中性 pH 值，而对于活跃物质，pH 值从中性变化至少 2 个对数单位。这种 pH 依赖性对于复杂混合物尤其重要。在从 ECHA 档案中获得的 673 种物质的外部测试集上进行的额外评估，实现了 64-71%的平衡准确率，这表明这些模型可用于筛选具有眼睛刺激性潜力的化合物。负预测值特别高，表明这些模型在识别非刺激性物质的自下而上方法中有潜在的应用。
