College of Life Science, Northwest Normal University, Lanzhou, Gansu 730070, PR China; State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan 610041, PR China.
College of Life Science, Northwest Normal University, Lanzhou, Gansu 730070, PR China.
Toxicol In Vitro. 2020 Jun;65:104812. doi: 10.1016/j.tiv.2020.104812. Epub 2020 Feb 25.
Some drugs have the potential to cause cellular degeneration of cochlear and/or vestibular system, leading to temporary or permanent hearing loss, innitus, ataxia, dizziness, ear infections, hyperacusis, vertigo, nystagmus and other ear problems. Thus, precise assessment of ototoxicity has become a strong urge task for the toxicologist. In this research, the in silico prediction model of ototoxicity was developed based on 2612 diverse chemicals by using naïve Bayes classifier approach. A set of 7 molecular descriptors considered as important for ototoxicity was selected by genetic algorithm method, and some structural alerts for ototoxicity were identified. The established naïve Bayes prediction model produced 90.2% overall prediction accuracy for the training set and 88.7% for the external test set. We hope the established naïve Bayes prediction model should be employed as precise and convenient computational tool for assessing and screening the chemical-induced ototoxicity in drug development, and these important information of ototoxic chemical structures could provide theoretical guidance for hit and lead optimization in drug design.
一些药物具有引起耳蜗和/或前庭系统细胞退化的潜力，导致暂时性或永久性听力损失、耳鸣、共济失调、头晕、耳部感染、听觉过敏、眩晕、眼球震颤和其他耳部问题。因此，对耳毒性进行精确评估已成为毒理学家的强烈要求。在这项研究中，我们通过朴素贝叶斯分类器方法，基于 2612 种不同的化学物质，开发了一种用于预测耳毒性的计算模型。通过遗传算法方法选择了一组被认为对耳毒性重要的 7 个分子描述符，并确定了一些结构警示物。建立的朴素贝叶斯预测模型对训练集的总体预测准确率为 90.2%，对外部测试集的预测准确率为 88.7%。我们希望建立的朴素贝叶斯预测模型能够成为药物开发中评估和筛选化学物质诱导的耳毒性的精确、便捷的计算工具，这些有关耳毒性化学结构的重要信息可以为药物设计中的命中和先导优化提供理论指导。
