以人工智能为基础的模型为基础的药物开发的机会:对NONMEM和基于人工智能的模型进行比较分析,以预测人口的药理动力学
Bingyu Mao1,2, Yue Gao1, Christine Xu1
1Sanofi, Morristown, NJ, USA.
The AAPS journal
|November 18, 2025
概括
人工智能 (AI) 和机器学习 (ML) 模型在优化药物开发方面表现有前途,在人口药物动力学分析中往往优于非MEM等传统方法. 这些人工智能方法为基于模型的药物开发提供了增强的预测性能.
科学领域:
- 制药指标 (Pharmacometrics) 是一个指标.
- 计算生物学 计算生物学
- 药物开发 药物开发
背景情况:
- 基于模型的药物开发 (MIDD) 使用数学模型来优化药物剂量.
- 非线性混合效应建模 (NONMEM) 是种群药动力学 (PPK) 分析的传统标准.
- 人工智能 (AI) 为PPK建模的预测性能和效率提供了潜在的进步.
研究的目的:
- 评估基于AI的MIDD方法对PPK分析的有效性.
- 将AI/ML模型的性能与传统非线性混合效应 (NLME) 方法 (如NONMEM) 的性能进行比较.
- 评估AI在增强药量计工作流程中的适用性.
主要方法:
- 测试了五个机器学习 (ML) 模型,三个深度学习 (DL) 模型和一个神经普通微分方程 (ODE) 模型.
- 利用基于两部分模型的模拟数据集和来自1770名患者的真实临床数据集.
- 使用诸如根平均平方误差 (RMSE),平均绝对误差 (MAE) 和R平方 (R2) 等指标评估预测性能.
主要成果:
- 在PPK分析中,AI/ML模型经常表现优于NONMEM.
- 性能根据AI模型类型和数据特征而异.
- 神经ODE模型表现出强大的性能和可解释性,特别是在大型数据集.
结论:
- 人工智能/ML方法显示出在MIDD中补充或增强传统PPK建模的潜力.
- 人工智能方法提供了更好的预测性能和计算效率.
- 这些发现支持将AI/ML整合到未来的药量计工作流程中.
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