基于LSTM对使用ADME和物理化学性能的静脉注射小分子药物的人类PK配置文件和参数的预测
Pingyao Luo1,2, Rong Chen1,2, Zhisong Wu2
1Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Sciences, Peking University, Beijing, China.
CPT: pharmacometrics & systems pharmacology
|October 28, 2025
概括
本研究引入了一种使用长短期记忆 (LSTM) 预测人类药理动力学 (PK) 度-时间概况的机器学习模型. 该模型有效地预测了静脉注射剂量的PK行为,有助于药物开发中的化合物选择.
科学领域:
- 药理动力学和药物开发
- 计算化学和化学信息学
- 机器学习在药理学中的应用
背景情况:
- 准确预测人类药理动力学 (PK) 对于成功开发药物至关重要,但传统方法数据密集且耗时.
- 虽然机器学习 (ML) 在预测ADME和物理化学性质 (ADMEP) 中表现出色,但其应用于度-时间 (C-t) 概况预测的应用尚未得到充分探索.
研究的目的:
- 开发和验证基于长期短期记忆 (LSTM) 的机器学习框架,用于预测静脉注射 (IV) 玻璃剂后的人类PK C-t概况.
- 用ADMEP描述符和剂量信息评估模型的预测性能,并将其与现有方法进行比较.
主要方法:
- 开发了一个基于LSTM的ML框架,使用ADMETlab 3.0的ADMEP描述符和药物剂量信息作为输入.
- 在40种药物的PK数据上训练模型,并在18种药物上进行测试,并从已建立的PK模型中模拟度数据.
主要成果:
- 在所有C-t配置文件中,LSTM模型实现了0.75的R平方.
- 在Cmax的77.8%,清除的55.6%,分布体积的61.1%的2倍误差内实现了预测准确度.
- 模型性能与输入剂量水平和ADMEP描述符相关联,为预测信心提供了洞察力.
结论:
- 基于LSTM的框架提供了一个高效和实用的替代方案,用于预测人体PK配置文件与IV剂量,使用有限数量的化合物.
- 这种方法有望提高早期化合物优先级,并可能减少药物开发中的动物试验.
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