机器学习与机械建模相结合的应用,用于预测小分子的等离子体暴露
Panteleimon D Mavroudis1, Donato Teutonico2, Alexandra Abos3
1Quantitative Pharmacology Research, DMPK, Sanofi, Cambridge, MA, United States.
Frontiers in systems biology
|August 14, 2025
概括
这项研究引入了一个新的框架,将机器学习和机械模型结合起来,用于预测药物暴露. 这种方法通过改善临床前药物动力学预测,使药物开发中的早期决策成为可能.
科学领域:
- 药理学和药物开发领域
- 计算化学计算化学
- 生物技术是生物技术.
背景情况:
- 准确预测新分子的血暴露对于评估疗效和毒性至关重要.
- 传统方法依赖于临床前药理动力学 (PK) 数据,这些数据可能是限制性的.
- 早期预测有助于在药物开发中做出关键决策,包括分子查和剂量选择.
研究的目的:
- 提出和评估一种用于临床前药物暴露预测的新框架.
- 整合机器学习 (ML) 与基于机制的建模,以提高PK预测.
- 通过使用各种机理学和生理学基础的药理动力学 (PBPK) 模型,评估拟议框架的可行性.
主要方法:
- 使用ML开发了一个框架,将分子结构与物理化学 (PC) 和PK特性联系起来.
- 使用 ML 衍生的 PC/PK 参数作为机械模型的输入 (例如 1-compartment, PBPK).
- 评估了多种PBPK分布模型和参数化策略 (体内与体外清除).
主要成果:
- 拟议的框架充分预测了大多数测试场景中的PK概况.
- 仅使用肝脏微体内在清除率 (CLint) 进行预测时发现了限制.
- 强调考虑不同分布模型的可变性的重要性.
结论:
- 集成的ML和机械模型框架显示了早期药物暴露预测的可行性.
- 这种方法可以支持药物开发管道中的早期和更明智的决策.
- 为了进行可靠的预测,需要对清除途径和分布模型变异性的进一步调查.
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