低维神经ODEs及其在药理动力学中的应用
Dominic Stefan Bräm1, Uri Nahum2, Johannes Schropp3
1Pediatric Pharmacology and Pharmacometrics, University Children's Hospital Basel (UKBB), University of Basel, Basel, Switzerland. dominic.braem@ukbb.ch.
神经普通微分方程 (NODE) 提供了一种新的机器学习方法,用于在药理学和药量学中建模复杂的药物行为. 本研究介绍了用于药理动力学分析的实际NODE应用,证明了它们在描述和模拟药物处置数据方面的有效性.
科学领域:
- 药理学和药量计学是药理学和药量计学.
- 计算生物学 计算生物学
- 机器学习应用 机器学习应用
背景情况:
- 机器学习 (ML) 越来越多地融入科学研究.
- 神经常规微分方程 (NODE) 是动态系统的新型ML工具.
- NODE为药理动力学 (PK) 和药理动力学 (PD) 建模提供了一种新的方法.
研究的目的:
- 介绍用于PK/PD建模的NODE的功能.
- 开发基于 PK 原则的低维 NODE 结构.
- 解决NODE应用中的过拟合和外推等挑战.
主要方法:
- 根据PK原则开发了特定的低维NODE结构.
- 将NODE应用于各种PK建模场景 (多分区,TDD,延迟吸收).
- 对NODE挑战进行了研究并提出了解决方案:过和推断.
主要成果:
- 在各种场景中,NODE有效地描述了PK数据.
- 拟议的NODE结构证明了模拟新对象数据的能力.
- 在 PK 分析和与机械模型的集成中成功说明了 NODE 的应用.
结论:
- 低维NODE为PK分析提供了可行的和有效的工具.
- NODE显示了促进药理学和药量学发展的巨大潜力.
- 这项工作增强了对NODE在药物建模中的理解和实际应用.
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