走向可概括的数据驱动药理学与可解释的神经ODEs
Yaning Cui1, Xiaohong Ji1, Wentao Guo1,2
1DP Technology, Beijing 100089, China.
Journal of chemical information and modeling
|February 25, 2026
概括
一个新的神经框架Uni-PK通过整合分子数据和个体因素,准确地建模药物度-时间概况. 这种方法提高了个性化药物的药理动力学预测,减少了动物试验.
科学领域:
- 药理动力学和计算生物学
- 药物开发和精准医学 药物开发和精准医学
- 医疗保健中的人工智能
背景情况:
- 准确的药物度-时间 (C-t) 概况建模对于药物开发和个性化剂量至关重要.
- 传统的药理动力学 (PK) 模型面临着由于刚性假设和广泛的参数化而导致的可扩展性和适应性的局限性.
- 需要先进的建模方法,可以有效地处理各种化合物和患者群体.
研究的目的:
- 引入Uni-PK,一个统一的神经框架,用于端到端的药理动力学建模.
- 开发一种可扩展和可解释的解决方案,用于预测药物度动态.
- 通过结合个体间的变异性,实现个性化的临床前和临床应用.
主要方法:
- 通过在 PK 结构中将分子表示与神经普通微分方程 (NODE) 结合起来,开发了 Uni-PK.
- 采用灵活的上下文编码器来整合辅助共变量 (例如,物种,剂量方案) 以进行个性化建模.
- 从分子和个体输入中实现药物度的直接动态轨迹建模,促进在数据稀缺条件下学习.
主要成果:
- Uni-PK在各种管理途径和生理状态的老鼠和人类数据集上表现出强的表现.
- 该框架显示与已确定的药理动力学原理一致,验证了其机械基础.
- 实现端到端的学习能力,即使在数据稀缺和杂的条件下,也优于传统方法.
结论:
- Uni-PK为下一代药理动力学建模提供了一个可扩展,可解释和节省动物的解决方案.
- 化学结构和个体特定信息的整合促进了精密治疗.
- 这种统一的神经框架有可能对药物开发和个性化剂量策略产生重大影响.
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