通过人工智能-生理学基础的药物动力学 (AI-PBPK) 建模,通过早期药物发现预测药学动力学效应
Keheng Wu1, Xue Li1, Zhou Zhou1
1Yinghan Pharmaceutical Technology (Shanghai) Co., Ltd., Shanghai, China.
Frontiers in pharmacology
|March 4, 2024
概括
这项研究引入了一个AI-PBPK平台,用于预测药物药理动力学 (PK) 和药理动力学 (PD) 在药物发现早期的结果,改善化合物选择以获得更好的临床结果.
科学领域:
- 药理动力学和药理动力学
- 药物发现 药物发现 药物发现
- 人工智能在医学中的应用
背景情况:
- 药理动力学/药理动力学 (PK/PD) 模型将药物度与作用联系起来,但并不总是预测临床结果.
- 早期药物发现面临不确定性,因为人类的临床结果不能直接观察到.
- 早期优化药物特性对于减少开发时间和成本至关重要.
研究的目的:
- 引入一个AI-PBPK平台,用于预测早期药物发现中的PK和PD结果.
- 为了使研究人员能够将药物PK配置文件与所需的PD效应结合起来.
- 为了加快确定有前途的候选药物.
主要方法:
- 开发了一个集成的AI-PBPK平台,结合机器学习和基于机制的PD建模.
- 利用机器学习来预测模型参数.
- 采用基于机制的PD模型,根据PK预测预测PD结果.
主要成果:
- AI-PBPK平台成功地预测了目标化合物的PK和PD结果.
- 通过对五种竞争性酸阻断剂 (P-CAB) 化合物的案例研究证明了平台的实用性.
- 使用已知的P-CABs,诺普拉赞和雷瓦普拉赞验证了模型.
结论:
- AI-PBPK平台有助于早期预测药物的疗效和安全性.
- 这种方法有助于在最初的药物发现阶段将PK配置文件与所需的PD效应对齐.
- 这种方法有可能简化药物开发并提高成功率.
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