基于机器学习的药物诱导肝毒性预测:一种OVA-QSTR方法
Feyza Kelleci Çeli K1, Gül Karaduman1,2
1Vocational School of Health Services, Karamanoğlu Mehmetbey University, 70200 Karaman, Turkey.
Journal of chemical information and modeling
|July 26, 2023
概括
这项研究开发了一个计算模型来预测药物诱导性肝损伤 (DILI),而无需对动物进行测试. 该模型根据药物可能导致肝损伤的可能性准确分类药物,有助于更安全的药物开发.
科学领域:
- 计算毒理学计算毒理学
- 药物监督 药物监督 药物监督
- 药物安全性评估 药物安全性评估
背景情况:
- 药物诱导的肝毒性或药物诱导的肝损伤 (DILI) 是患者发病率和死亡率的重要原因.
- 肝毒数据库将药物分类为风险类别 (A-E),以减轻肝毒性.
- 评估药物诱导的肝损伤的现有方法通常涉及动物试验.
研究的目的:
- 开发一种用于评估新药分子对肝脏损伤潜力的预测模型.
- 在不依赖实验动物的情况下将药物分类到LiverTox风险类别.
- 为制药诱导的肝损伤提供预营销风险评估的计算工具.
主要方法:
- 利用来自LiverTox数据库的678种有机药物分子的数据集.
- 实施了一种一对所有定量结构-毒性关系 (OvA-QSTR) 模型.
- 使用贝叶斯网络 (BayesNet) 来实现和分析模型.
主要成果:
- OvA-QSTR模型表现出强的性能,精度回忆曲线 (PRC) 的面积从0.718到0.869.
- 这些模型成功地预测了 LiverTox 风险类别的药物,其肝脏毒性潜力尚不清楚.
- 开发的模型为药物诱导的肝损伤提供了可靠的预测.
结论:
- OvA-QSTR模型提供了一个强大的,非基于动物的方法来评估药物诱导的肝损伤潜力.
- 这种方法有助于对药物安全进行可靠的销售前风险评估.
- 该研究提供了与药物诱导性肝损伤 (DILI) 相关的各种风险水平的预测.
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