神经控制的微分方程及其在药理动力学和药理动力学中的应用
Zhisong Wu1, Pingyao Luo1, Rong Chen1
1Department of Pharmaceutics, School of Pharmaceutical Sciences, Peking University, Beijing, China.
CPT: pharmacometrics & systems pharmacology
|November 16, 2025
概括
神经控制微分方程 (NCDEs) 为药理动力学和药理动力学提供可解释的机器学习. 这种新的方法准确地模拟复杂的药物行为,特别是多次剂量,增强预测能力.
科学领域:
- 药理学和计算科学 药理学和计算科学
- 机器学习在药物开发中的应用
背景情况:
- 机器学习 (ML) 和人工智能 (AI) 越来越多地用于药理动力学 (PK) 和药理动力学 (PD) 建模.
- 现有的ML方法往往缺乏解释性,无法捕捉潜在的生物动态.
研究的目的:
- 研究神经控制微分方程 (NCDEs) 对于数据驱动的PK/PD建模的适用性.
- 评估NCDE结合机械和数据驱动方法以提高可解释性的能力.
主要方法:
- 应用NCDE,一种新的ML技术,以建模PK/PD配置文件,特别是在多剂量场景中.
- 系统地调查超参数,包括L1规范化和AdaMax优化器,用于模型稳定和概括.
主要成果:
- NCDE成功地将微分方程动态与数据驱动的特征集成,容纳各种输入和不连续动态.
- 学习的NCDE动态与潜在的生物过程之间建立了直接对应,证实了可解释性.
- L1规范化和AdaMax优化器改善了训练稳定性和模型通用性.
结论:
- NCDE证明了PK/PD建模的准确性,概括性和内在解释性.
- NCDE为复杂的药物行为分析提供了可靠和灵活的方法.
- 这种方法有望在制药研究中推进PK/PD预测.
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