在Monolix和NONMEMEM中实现的低维神经普通微分方程,解释了个体间的可变性
Dominic Stefan Bräm1, Bernhard Steiert2, Marc Pfister1
1Pediatric Pharmacology and Pharmacometrics, University Children's Hospital Basel (UKBB), University of Basel, Basel, Switzerland.
CPT: pharmacometrics & systems pharmacology
|November 18, 2024
概括
神经常规微分方程 (NODE),一种机器学习方法,在药量计软件中实现,用于建模复杂药物处置. 这种方法将已知的生物机制与神经网络相结合,为药理动力学和药理动力学分析提供了强大的工具.
科学领域:
- 药量测量和机器学习
- 计算生物学和药理学 计算生物学和药理学
背景情况:
- 药量计 (PMX) 建模传统上依赖于机械模型.
- 新兴的机器学习 (ML) 方法,如神经普通微分方程 (NODE),为PMX数据分析提供了新的方法.
- 将已知的生物机制与数据驱动的神经网络集成,是一个有前途的混合建模策略.
研究的目的:
- 在已建立的PMX软件包 (Monolix和NONMEM) 中详细说明低维NODE的实现.
- 引入并提出将个人间变异性纳入PMX的NODE模型的方法.
- 为了证明NODE实现在各种PMX数据集中的实用性和可比性.
主要方法:
- 在Monolix和NONMEM中实现低维神经普通微分方程 (NODE).
- 将个人间的变化纳入NODE框架.
- 将NODE模型应用于各种PMX数据集,包括PK,PD,TMDD和生存分析.
主要成果:
- 在Monolix和NONMEM软件中成功实现NODE.
- 在各种PMX应用中,NODE模型实现了与传统建模方法相似的结果.
- 在多个基准数据集上证明了可行性和性能.
结论:
- NODE提供了一个可行的和有效的替代品,用于药量测量中的经典建模.
- 提出的实施方案有助于在PMX软件中实现NODE的可重复性和简单应用.
- 这种混合方法增强了复杂的药理动力学和药理动力学过程的建模.
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