机器学习和PBPK建模方法的组合用于药理动力学预测人类中小分子的药理动力学
Yuelin Li1, Zonghu Wang1, Yuru Li1
1XtalPi Innovation Center, XtalPi Inc., Beijing, 100080, China.
这项研究引入了一种基于机器学习的生理学上的药理动力学 (ML-PBPK) 模型,可以在没有实验数据的情况下预测药物药理动力学 (PK),从而提高药物发现的准确性和效率.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算化学的计算化学
- 药物开发 药物开发
背景情况:
- 基于模型的药物开发加速了发现,减少了动物试验.
- 基于生理学的药理动力学 (PBPK) 和机器学习 (ML) 模型是早期药物发现的关键工具.
- 传统的PBPK模型需要大量的体外数据,这限制了效率和准确性.
研究的目的:
- 开发一个集成ML和PBPK模型的新型计算平台.
- 以高准确度预测分子药理动力学 (PK) 概况.
- 消除对预测PK属性的实验数据的需求.
主要方法:
- 开发了一种全身PBPK模型,与ML模型集成,用于未结合的分数,Caco-2透性和清除.
- 采用了"自下而上"的PBPK建模方法,利用ML衍生的输入.
- 使用40种不同的化合物验证了平台的准确性.
主要成果:
- ML-PBPK模型在预测2倍范围内的度-时间曲线 (AUC) 下的面积时达到65.0%的准确性.
- 这种预测准确度超过了依赖于体外数据的传统PBPK模型 (47.5%准确度).
结论:
- ML-PBPK平台提供了对人类PK参数的高度准确的预测.
- 与传统的PBPK方法相比,这种方法显著减少了实验要求和开发时间.
- 该平台具有指导早期药物发现和开发决策的潜力.
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