Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

48
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
48
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

88
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
88
Compartment Models: Single-Compartment Model01:14

Compartment Models: Single-Compartment Model

2.2K
The single-compartment model serves as a simplified representation of the human body. This model assumes that the body functions as a single, well-mixed open compartment. When a drug is administered intravenously, it enters the body and quickly distributes uniformly. The drug then undergoes biotransformation and elimination, ultimately leaving the body. The volume of this compartment is referred to as the apparent volume of distribution into which the drug can uniformly distribute. In this...
2.2K
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

123
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
123
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

36
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
36
Compartment Models: Two-Compartment Model01:20

Compartment Models: Two-Compartment Model

5.4K
The two-compartment model divides the body into central and peripheral compartments to account for varying blood perfusion rates among organs and tissues, affecting drug distribution. The central compartment includes blood and highly perfused tissues with rapid drug distribution, while the peripheral compartment contains tissues with slower drug distribution. After a single IV bolus dose, the drug concentration is high in plasma and low in tissues. The drug distribution between compartments...
5.4K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Undetectable Hydroxyurea Levels in the Majority of Sickle Cell Disease Patients, Especially in Young Children.

American journal of hematology·2026
Same author

Reagent-specific anti-Xa DOAC safety cutoffs can be established using multiple LMWH-calibrated anti-Xa assays.

Clinical chemistry and laboratory medicine·2026
Same author

Pediatric pharmacokinetics and pharmacodynamics of guanabenz for the treatment of vanishing white matter.

Scientific reports·2026
Same author

Psychometric evaluation of Patient-Reported Outcomes Measurement Information System (PROMIS) in pediatric sickle cell disease in Europe.

European journal of pediatrics·2026
Same author

Advancing Precision Dosing of 5-FU: Population PK Model Development, Limited Sampling Strategies, and Fit-for-Use Application.

Clinical pharmacokinetics·2026
Same author

Three-year safety, efficacy, and renal outcomes of mitapivat treatment in sickle cell disease: results from the phase 2 open-label study.

Haematologica·2026

相关实验视频

Updated: Jun 22, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K

在深模型中混合效应估计:变化方法优于一级近似方法.

Alexander Janssen1, Frank C Bennis2, Marjon H Cnossen3

  • 1Department of Clinical Pharmacology, Hospital Pharmacy, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands. a.janssen@amsterdamumc.nl.

Journal of pharmacokinetics and pharmacodynamics
|July 4, 2024
PubMed
概括
此摘要是机器生成的。

这项研究将混合效应估计引入深模型 (DCM) 框架. 变异推理 (VI) 证明了针对个性化药物剂量的准确和稳定的预测,优于传统方法.

关键词:
估计方法估计方法.机器学习 机器学习药理动力学 药理动力学药学指标 (Pharmacometrics) 是一个指标.变量推理 变量推理

更多相关视频

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.8K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.5K

相关实验视频

Last Updated: Jun 22, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.8K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.5K

科学领域:

  • 药学指标 (Pharmacometrics) 是一个指标.
  • 计算生物学 计算生物学
  • 统计建模 统计建模

背景情况:

  • 深模型 (DCM) 是药理动力学/药理动力学 (PK/PD) 分析的强大工具.
  • 估计DCM的混合效应对于个性化医学和优化治疗策略至关重要.
  • 目前用于DCM中混合效应估计的方法在准确性和稳定性方面存在局限性.

研究的目的:

  • 扩展深层隔间模型 (DCM) 框架用于混合效应估计.
  • 为了比较第一阶 (FO,FOCE) 和变异推理 (VI) 算法的性能,用于DCM中的混合效应估计.
  • 通过模拟和现实世界的数据来评估这些方法的准确性和稳定性.

主要方法:

  • 在DCM框架内实施混合效应估计.
  • 一级条件估计 (FOCE),一级 (FO) 和变量推理 (VI) 算法的比较.
  • 使用模拟数据集和来自血友病A患者的真实数据进行验证.

主要成果:

  • 使用路径衍生梯度估计器的变化推理 (VI) 在近似后部分布方面显示出高准确性.
  • 在模拟中,FO和VI方法都产生了准确的种群参数和共变效应,而FOCE则显示不稳定性和不准确的估计.
  • FO和VI方法在真实世界的血友病A数据上产生了类似的结果,其中一些FO模型显示出分歧;FOCE仍然不稳定.

结论:

  • 使用深模型 (DCM) 估计混合效应是可行的和有效的.
  • 变量推理 (VI) 为DCM中混合效应估计提供了一个稳定而准确的替代方案,在复杂模型中可能超过一级 (FO) 方法.
  • 这些发现支持在药量计模型中使用VI进行个性化治疗优化.