CMINNs:分区模型信息的神经网络 - 解锁药物动态
Nazanin Ahmadi Daryakenari1, Shupeng Wang2, George Karniadakis2
1Center for Biomedical Engineering, Brown University, Providence, RI, USA.
Computers in biology and medicine
|November 28, 2024
概括
这项研究引入了一种使用分数计算和物理信息神经网络 (PINNs) 来增强药理动力学 (PK) 和药理动力学 (PD) 建模的新方法. 该方法改善了药物吸收,分布和作用的预测,特别是在癌症等复杂的生物系统中.
科学领域:
- 药理动力学和药理动力学 (PKPD)
- 计算生物学 计算生物学
- 数学建模的数学建模
背景情况:
- 传统的PKPD模型与复杂的药物动态 (如异常扩散和药物在异质组织中被捕获) 进行斗争.
- 多部件模型虽然有用,但对于药物开发来说可能过于复杂.
- 需要简化但全面的建模方法来预测药物行为.
研究的目的:
- 开发一种增强的 PK 和集成的 PK-PD 建模方法,使用分数计算和时间变化的参数.
- 为了有效地模拟异质组织中的异常扩散,药物捕获和逃脱率.
- 提供有关癌症药物动态的见解,特别是在多剂量管理方面.
主要方法:
- 微积分计算或时间变化的参数与常数/零位常数参数的集成.
- 物理信息神经网络 (PINNs) 和分数PINNs (fPINNs) 的应用.
- 结合普通微分方程 (ODEs) 与整数/分数导数和神经网络进行参数估计.
主要成果:
- 该方法成功地模拟了异常扩散,并捕捉了毒品陷/逃跑动态.
- 提高药物吸收率和分布式延迟反应的预测.
- 释放对药物耐药性,持久性和药理学耐受性的新见解.
结论:
- 拟议的fPINN框架为PKPD建模提供了一种强大而简单的 (两个分数ODE) 方法.
- 这种方法显著改善了复杂的药物动态和药物效应的描绘.
- 这些发现可以简化药物开发,提高癌症治疗预测,并为治疗策略提供信息.
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