从化学结构来预测人类的药理动力学:将机械模型与机器学习相结合
Andrea Gruber1, Florian Führer2, Stephan Menz1
1Bayer AG, Pharmaceuticals, R&D, Preclinical Modeling & Simulation, 13353 Berlin, Germany.
Journal of pharmaceutical sciences
|November 4, 2024
概括
本研究引入了一种混合模型,结合了基于生理学的药理动力学和深度学习模型,以从化学结构中预测药物药理动力学 (PK),减少动物试验和改善药物候选物选择.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 药理动力学 (PK) 特性在临床前研究中至关重要,以了解剂量-暴露-效果关系.
- 从化学结构来预测人类PK是非常理想的,以避免昂贵的失败和减少动物试验,但数据限制阻碍了模型开发.
研究的目的:
- 开发一种混合模型,将基于生理学的药理动力学 (PBPK) 模型与深度学习 (DL) 集成在一起,直接从分子结构预测PK参数.
- 在训练预测性PK模型中解决数据稀缺性挑战.
主要方法:
- 结合生理学基础的药理动力学 (PBPK) 模型与深度学习 (DL) 模型进行分子性质预测.
- 开发了一种混合模型,仅使用化学结构数据来预测小分子的PK参数.
主要成果:
- 混合模型准确地预测了健康受试者 (FC错误1.87和1.86) 和患者 (FC错误2.32和2.23) 的口服和静脉接触.
- 证明了该模型能够预测未包含在训练数据中的终点,并验证了其与度-时间配置文件的推断能力.
结论:
- 混合模型可以直接从化学结构中准确预测药物的药理动力学.
- 促进药物候选者的早期选择和优先考虑,可能减少药物发现和开发中的动物试验.
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