Department of Clinical pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, 250014, China.
Department of Clinical pharmacy, The First Affiliated Hospital of Shandong First Medical University, Shandong First Medical University, Jinan, 250014, China.
Mol Divers. 2021 May;25(2):847-859. doi: 10.1007/s11030-020-10069-3. Epub 2020 Mar 12.
Respiratory sensitization has been considered an important toxicological endpoint, because of the severe risk to human health. A great part of sensitization events were caused by low molecular weight (< 1000) respiratory sensitizers in the past decades. However, there is currently no widely accepted test method that can identify prospective low molecular weight respiratory sensitisers. Herein, we performed the study of modeling and insights into molecular basis of low molecular weight respiratory sensitizers with a high-quality data set containing 136 respiratory sensitizers and 518 nonsensitizers. We built a number of classification models by using OCHEM tools, and a consensus model was developed based on the ten best individual models. The consensus model showed good predictive ability with a balanced accuracy of 0.78 and 0.85 on fivefold cross-validation and external validation, respectively. The readers can predict the respiratory sensitization of organic compounds via https://ochem.eu/article/114857 . The effect of several molecular properties on respiratory sensitization was also evaluated. The results indicated that these properties differ significantly between respiratory sensitizers and nonsensitizers. Furthermore, 14 privileged substructures responsible for respiratory sensitization were identified. We hope the models and the findings could provide useful help for environmental risk assessment.
呼吸道致敏已被认为是一个重要的毒理学终点，因为它对人类健康存在严重风险。在过去几十年中，很大一部分致敏事件是由低分子量（<1000）呼吸道致敏剂引起的。然而，目前还没有被广泛接受的测试方法可以识别潜在的低分子量呼吸道致敏剂。在此，我们使用包含 136 种呼吸道致敏剂和 518 种非致敏剂的高质量数据集，对低分子量呼吸道致敏剂的建模和分子基础进行了研究。我们使用 OCHEM 工具构建了多个分类模型，并基于十个最佳的个体模型开发了一个共识模型。共识模型在五重交叉验证和外部验证中的平衡准确性分别为 0.78 和 0.85，具有良好的预测能力。读者可以通过 https://ochem.eu/article/114857 来预测有机化合物的呼吸道致敏性。还评估了几种分子性质对呼吸道致敏性的影响。结果表明，这些性质在呼吸道致敏剂和非致敏剂之间存在显著差异。此外，确定了 14 个负责呼吸道致敏的特权子结构。我们希望这些模型和研究结果能够为环境风险评估提供有用的帮助。
