机器学习预测启动/关闭目标驱动的临床不良事件
Albert Cao1,2, Luchen Zhang1, Yingzi Bu1,3
1Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI, 48109, United States.
Pharmaceutical research
|August 2, 2024
概括
这项研究开发了一个机器学习模型来预测临床不良事件 (AEs),通过更早地识别毒性来提高药物开发的成功. 该模型达到75%以上的准确性,为预测药物毒性提供了一种新的方法.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算生物学 计算生物学
- 药物开发 药物开发
背景情况:
- 药物开发失败率很高,临床毒性导致30%的失败.
- 现有的动物毒性研究和计算模型在预测临床不良事件 (AE) 方面取得了有限的成功.
研究的目的:
- 开发一种机器学习 (ML) 模型,用于直接预测临床AE.
- 在药物开发中解决当前毒性预测方法的局限性.
主要方法:
- 调整了ConPLex ML模型以预测759种FDA批准的药物与477种蛋白质标之间的药物标相互作用 (IC50值).
- 构建了一个新的ML模型,将药物向相互作用与组织特异性蛋白质表达结合起来,以预测临床AE.
主要成果:
- ConPLex模型成功预测了药物标相互作用,包括非标效应.
- 结合的ML模型表现出对临床AE的强大预测能力,达到75%以上的准确性.
结论:
- 该研究为药物毒性提供了机理性的见解,与目标和目标以外的相互作用有关.
- 经过验证的ML模型可以通过从临床前数据预测临床副作用来减少药物开发失败.
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