一个集成的AI-PBPK平台,用于预测人体内药物命运和组织分布在人类和物种间的推断
Wei Wang1, Nannan Wang1, Yiyang Wu1
1State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau, China.
Clinical pharmacology and therapeutics
|May 26, 2025
概括
这项研究引入了一个AI-PBPK平台,仅使用分子结构来预测体内的药物行为. 这种方法通过有效估计药物动力学特征并指导临床试验候选人选择来加速药物开发.
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
- 药物开发 药物开发
背景情况:
- 传统的药理动力学 (PK) 估计是昂贵的,耗时的,并且在评估协同药物特性方面有限.
- 早期药物开发需要强大的方法来预测体内药物的命运和组织分布.
- 优化PK资料对于成功的临床试验至关重要.
研究的目的:
- 开发一个集成的人工智能 (AI) 和基于生理学上的药物动力学 (PBPK) 平台,以从分子结构中快速估计PK.
- 预测关键药物特性和预测PK曲线,无需额外的培训.
- 为了验证AI-PBPK模型与广泛的人类PK数据对比.
主要方法:
- 人工智能模型被训练来预测八个关键的物理化学和ADME特性.
- 这些预测被输入到PBPK模型中,以预测PK曲线.
- 从PK-DB数据库中使用71个IV和606个口腔人类PK数据集验证了AI-PBPK方法.
主要成果:
- 人工智能-PBPK模型展示了强大的预测,大多数AUC值在2-3倍的误差范围内.
- 药物器官选择性的准确预测得到了实现.
- 跨物种推断优化了高血清除药物的预测.
结论:
- 开发的AI-PBPK战略有效地解决了药物发现中的PK挑战.
- 这个综合平台通过指导候选人选择来提高药物开发效率.
- 该系统有助于将具有良好的PK配置文件的药物推进到临床试验中.
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