通过机器学习和PBPK建模,研究Centella asiatica的药物动力学特征
Siriwan Pumkathin1, Yuranan Hanlumyuang2, Worawat Wattanathana2
1Department of Sustainable Energy and Resources Engineering, Faculty of Engineering, Kasetsart University, Bangkok, Thailand.
Journal of biopharmaceutical statistics
|June 11, 2024
概括
一个人工智能框架预测肠道有效透性 (P<0xE2><0x82><0x91>) 用于生理学基础的药理动力学 (PBPK) 建模. 这种方法有助于评估药物和草药物质的药理动力学和生物分布.
科学领域:
- 药理动力学和药物新陈代谢
- 计算生物学和生物信息学
- 药理学和毒理学 药理学和毒理学
背景情况:
- 基于生理学的药理动力学 (PBPK) 建模对于理解物质的分布和处置至关重要.
- 准确的药理动力学参数值对于构建可靠的PBPK模型至关重要.
- 在获得关键的药理学参数方面存在挑战,例如肠道有效透性 (P<0xE2><0x82><0x91>).
研究的目的:
- 开发一种人工智能 (AI) 框架,用于评估肠道有效透性 (P<0xE2><0x82><0x91>).
- 应用AI框架来预测特定化合物的P<0xE2><0x82><0x91>,并将其整合到PBPK建模中.
- 用人工智能驱动的PBPK模拟来评估草本物质的生物分布.
主要方法:
- 利用公开可用的肠道有效透性的数据集 (P<0xE2><0x82><0x91>) 来训练回归机器学习模型.
- 开发并优化了一个XGBoost模型,实现0.68.8的R平方值.
- 应用训练模型来预测P<0xE2><0x82><0x91>的亚化和madecassoside,随后进行PBPK建模.
主要成果:
- XGBoost模型实现了最高的测试准确性,其R平方值为0.68.
- 人工智能框架成功预测了亚西化物和madecassoside的肠道有效透性 (P<0xE2><0x82><0x91>).
- 这些化合物在老鼠中的生物分布的PBPK建模模拟与现有的体内数据保持一致.
结论:
- 拟议的AI框架提供了一种可行的方法来估计关键的药物动力学参数,例如肠道有效透性 (P<0xE2><0x82><0x91>).
- 这一in silico管道促进了对药物和草药物质的药理学概况的调查.
- 该方法可以独立使用或与其他建模系统集成,用于全面的药理动力学分析.
相关概念视频
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