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Updated: Jun 29, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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提高数据驱动的动态建模与自动区分的预测能力:库普曼和神经ODE方法

C Ricardo Constante-Amores1, Alec J Linot2, Michael D Graham1

  • 1Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA.

Chaos (Woodbury, N.Y.)
|April 4, 2024
PubMed
概括

一种新方法改进了库普曼运算符近似用于复杂系统动态预测. 这种数据驱动的方法优于用词典学习 (EDMD-DL) 进行扩展动态模式分解,并提供与状态空间模型相比具有竞争力的结果.

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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

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