用AI/ML模型预测药物诱导的小分子肝损伤的严重程度
Mohan Rao1, Vahid Nassiri2, Cristóbal Alhambra1
1Discovery, Product Development and Supply (DPDS), Preclinical Sciences and Translational Safety (PSTS), Predictive Investigative and Translational Toxicology (PITT), Janssen Pharmaceutical Companies of Johnson and Johnson, La Jolla, California 92121, United States.
Chemical research in toxicology
|June 9, 2023
概括
预测药物诱导性肝损伤 (DILI) 对药物开发至关重要. 一个整合的AI/ML模型结合了物理化学特性和非目标相互作用,改善了对小分子的DILI风险预测.
科学领域:
- 药理学和毒理学 药理学和毒理学
- 计算化学的计算化学
- 人工智能在药物发现中的作用
背景情况:
- 药物诱导性肝损伤 (DILI) 是药物开发中的一个主要挑战,导致昂贵的消耗.
- 现有的预测模型往往忽略了肝脏表达的蛋白质和药物相互作用.
- 早期识别DILI风险对于降低开发成本和时间表至关重要.
研究的目的:
- 开发一个集成的人工智能/机器学习 (AI/ML) 模型,用于预测小分子中的DILI严重程度.
- 通过结合物理化学性质和in silico预测的目标外相互作用来改进DILI风险评估.
- 通过包括肝脏表达的蛋白质标来弥补当前预测模型中的差距.
主要方法:
- 从FDA数据中编制了603种化合物的数据集,按DILI严重程度分类 (大多数DILI,较少DILI,没有DILI).
- 使用六种机器学习方法 (SVM,RF,LR,WA,PLR,k-NN,NB,ANN) 开发了一个共识AI/ML模型.
- 综合的物理化学特性 (例如,fsp3,log S) 和预测的目标外相互作用 (例如,PTGS1,CYP2C9) 进行预测.
主要成果:
- 共识模型在0.88的曲线下实现了接收器运行特征面积,在识别大多数DILI和无DILI化合物时具有0.73的灵敏度和0.9的特异性.
- 物理化学性质和大约43个已识别的目标之外的物质是DILI严重性的重要预测因素.
- 关键的确定目标之外的目标包括PTGS1,PTGS2,PPARγ,RXRA和CYP2C9.9.
结论:
- 综合的AI/ML方法显著提高了DILI预测能力,而不是仅使用化学性质的模型.
- 将物理化学数据与预测的生物相互作用相结合,为DILI风险评估提供了更强大的方法.
- 这种计算策略可以通过识别潜在的DILI负债来帮助早期药物发现和开发.
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