使用机器学习模型和体外验证方法预测细胞染色体P450介导的生物激活
Xin-Man Hu1, Yan-Yao Hou1, Xin-Ru Teng1
1State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources/Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources (Ministry of Education of China), Collaborative Innovation Center for Guangxi Ethnic Medicine, School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, 15 Yucai Road, Guilin, 541004, People's Republic of China.
Archives of toxicology
|March 16, 2024
概括
机器学习模型准确地预测了P450介导的生物激活,这是药物毒性的关键因素. 这种方法有助于设计更安全的药物,并在临床环境中评估药物不良反应.
科学领域:
- 药物代谢和药理动力学
- 计算化学计算化学
- 毒理学 毒理学 毒理学
背景情况:
- 细胞染色体P450 (P450) 介导的生物激活产生反应性代谢物 (RMs),这是药物诱导的肝毒性和药物失效的主要原因.
- 预测P450生物激活对于药物开发至关重要,以减轻不良药物反应 (ADRs).
研究的目的:
- 开发和验证用于预测P450介导生物激活的机器学习模型.
- 确定有助于生物活化预测的关键分子描述因素.
- 评估预测生物激活对药物安全性的临床相关性.
主要方法:
- 使用随机森林,随机子空间,SVM和天真贝叶斯算法开发预测模型.
- 对环,异环和硫异环的文学衍生生物激活数据集进行培训和测试模型.
- 使用2D描述符,如拓索引和负载固有值.
- 通过外部数据集预测和体外IC50转移实验进行验证.
主要成果:
- 随机森林模型表现出高预测性能,AUC值为0.949,0.973和0.958对于各自的测试集.
- 拓指数,2D自相关性和负载固有值被确定为重要的预测特征.
- 这些模型成功地预测了诸如selpercatinib和encorafenib等药物的生物激活,而体外实验证实了encorafenib和tirbanibulin的生物激活潜力.
结论:
- 开发的机器学习策略提供了一种可靠的方法来预测P450介导的生物激活.
- 这种方法可以在早期药物安全性评估和设计具有降低毒性的新药候选药物方面发挥重要作用.
- 这些发现支持将计算毒理学整合到药物发现管道中,以尽量减少ADRs.
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