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

Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

538
Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
538
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

54
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.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
54
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

35
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
35
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

55
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
55
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

208
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
208
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

26
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
26

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

Compact 2D Laser Scattering System for Quantitative Structural Analysis in Colloidal Assemblies.

ACS nano·2025
Same author

Developing Topics.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same author

Developing Topics.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same author

Effect of ART1 on the efficacy of oxaliplatin in colorectal cancer under high-cholesterol conditions.

Histology and histopathology·2025
Same author

Excess <sup>40</sup>Ar retention in metamorphosed amphibole complicates <sup>40</sup>Ar/<sup>39</sup>Ar geochronological interpretation of diabase.

Scientific reports·2025
Same author

Cervical Lymphatic Bypass for Alzheimer Disease: Toward Standardized Monitoring in a New Frontier of Supermicrosurgery.

Plastic and reconstructive surgery. Global open·2025

相关实验视频

Updated: May 23, 2025

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

10.9K

机器学习模型用于药物基因组变异效应预测 - 最近的发展和未来的前沿

Roman Tremmel1,2, Antoine Honore3, Yoomi Park4,5

  • 1Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany.

Pharmacogenomics
|May 22, 2025
PubMed
概括

机器学习模型预测了遗传变异对药物反应的影响. 这些工具有助于克服精准医学中的挑战,通过功能性地表征罕见变体.

关键词:
不知重要性的变体.深度学习是一种深度学习.药物代谢 药物代谢进化保护的进化保护.机器学习是机器学习.蛋白质的功能 蛋白质的功能变体效应预测变体效应预测

更多相关视频

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
00:06

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

13.6K
Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

12.9K

相关实验视频

Last Updated: May 23, 2025

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

10.9K
In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
00:06

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

13.6K
Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

12.9K

科学领域:

  • 基因组学就是基因组学.
  • 药物基因组学 药物基因组学
  • 计算生物学 计算生物学

背景情况:

  • 遗传变异显著影响个体药物反应和毒性.
  • 数以百万计的罕见遗传变异仍然没有功能性特征,阻碍了精准医学.
  • 机器学习 (ML) 提供了先进的方法来预测变量效应.

研究的目的:

  • 审查目前基于ML的药物基因组学变异效应预测指标.
  • 讨论这些工具的方法差异,优势和局限性.
  • 探索用于预测基质特异性和表观病变的新兴方法.

主要方法:

  • 利用DNA和蛋白质序列,进化保存和单元型结构.
  • 采用深度学习模型用于进化保护和生物物理性质.
  • 通过整体方法整合多个预测模型,以提高准确性.

主要成果:

  • ML模型显著提高了对药物反应变异效应的预测.
  • 深度学习和整体方法显示出更高的准确性,稳定性和可解释性.
  • 新兴的方法解决了基质特异性和变异性表观症.

结论:

  • 基于ML的工具对于功能性地表征与药物相关的遗传变异至关重要.
  • 这些预测因素提供了一个可行的策略,将基因组数据转化为药物遗传学建议.
  • 在ML的进步是实现精准医学的全部潜力的关键.