一个机器学习框架,以改善老鼠清除预测,并告知基于生理学的药理动力学建模
Andrea Andrews-Morger1, Michael Reutlinger1, Neil Parrott1
1Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland.
Molecular pharmaceutics
|September 15, 2023
概括
机器学习模型准确地预测了生理学基础的药理动力学 (PBPK) 模型的未结合的内在清除 (CLint,u). 与传统方法相比,这种方法可以改善体内清除率的预测,减少对大量体外数据的需求.
科学领域:
- 药理动力学和药物新陈代谢
- 计算化学和分子建模计算化学和分子建模
- 机器学习在药物发现中的作用
背景情况:
- 基于生理学的药理动力学 (PBPK) 模型对于预测药物的疗效和安全至关重要.
- 准确估计未结合的内在清除 (CLint,u) 是药物发现PBPK建模的一个关键挑战.
- 当前的体外到体外抽取方法可能很复杂,需要大量的实验数据.
研究的目的:
- 开发和比较机器学习 (ML) 策略,用于预测PBPK应用中的rat CLint,u.
- 为了评估基于ML的预测的性能与标准的体外自下而上的方法相比.
- 评估ML模型在改善体内药理动力学参数预测方面的有用性.
主要方法:
- 收集了2639种专有化合物的体内和体外数据.
- 开发了三种基于ML的策略来预测CLint,u,包括从体内数据和偏差预测的逆向计算.
- 将ML方法与使用时间交叉验证的标准体外自下而上的方法进行比较.
主要成果:
- 在背后计算的CLint上训练的ML模型,u实现了3.1的绝对平均折叠误差 (AAFE),超过了自下而上的方法 (AAFE 3.6-16).
- 结合偏差预测的ML模型改善了AAFE从16到2.9和logPearson r^2从0.1到0.29,而不是自下而上.
- 机器学习方法提供了诸如减少对实验体内数据的需求和规避某些缩放校正等优势.
结论:
- 机器学习策略为预测PBPK建模中的CLint,u提供了强大而准确的替代方案.
- 基于ML的CLint预测,可以增强现有的药物发现工作流程,并改善体内药物动力学预测.
- 这些计算方法通过提供可靠的清除估计,并可能减少实验负担,从而简化药物开发过程.
关键词:
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