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

Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

653
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...
653
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

461
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
461
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

223
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...
223
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

502
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...
502
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

293
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
293
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

226
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...
226

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

Advancing quantitative clinical pharmacology competencies in Francophone Africa through an on-line learning framework.

Journal of pharmacokinetics and pharmacodynamics·2026
Same author

UGT1A1 genotype testing for irinotecan: A guideline developed by the UK Centre of Excellence in Regulatory Science and Innovation in Pharmacogenomics (CERSI-PGx).

British journal of clinical pharmacology·2026
Same author

ACKR1/Duffy-null genotype testing for clozapine: A guideline developed by the UK Centre of Excellence in Regulatory Science and Innovation in Pharmacogenomics (CERSI-PGx).

British journal of clinical pharmacology·2026
Same author

Limited Visibility and Perception of the Clinical Relevance of Clopidogrel Pharmacogenetics in Cardiology Literature.

Clinical and translational science·2026
Same author

Development and validation of clinical prediction models for personalized renal function monitoring in people with heart failure in primary care: the RENAL-HF study protocol.

European heart journal. Digital health·2026
Same author

Translational bacterial biomarkers and pharmacodynamic models in the treatment of tuberculosis.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences·2026

相关实验视频

Updated: Jan 8, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.5K

在高维度药物基因组学中使用多重输入改进基因型输入:用机器学习方法进行评估.

Innocent G Asiimwe1, Tao You2, Daniel F Carr2

  • 1Department of Health Data Science, Institute of Population Health Sciences, University of Liverpool, Liverpool, UK.

Clinical pharmacology and therapeutics
|December 17, 2025
PubMed
概括

多重归算通过提高数据准确性来提高高维药基因组学的可靠性. 这种方法优于处理遗传数据缺失的传统方法,从而更好地发现重要的遗传关联.

更多相关视频

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

39.8K
Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

1.3K

相关实验视频

Last Updated: Jan 8, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.5K
Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

39.8K
Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

1.3K

科学领域:

  • 遗传学 遗传学是一种遗传学.
  • 生物信息学是一种生物信息学.
  • 统计遗传学 统计遗传学

背景情况:

  • 在高维基遗传数据集中处理丢失的数据是具有挑战性的.
  • 传统的归算方法在复杂的遗传分析中往往不足.
  • 多重归算 (MI) 已建立,但在此背景下未得到充分利用.

研究的目的:

  • 在高维基遗传数据中比较机器学习 (ML) 和传统的归算和共变量选择方法.
  • 开发和评估一个包含基因型概率和归算不确定性的MI框架.
  • 评估MI在恢复药物基因组学关联和改善发现方面的表现.

主要方法:

  • 开发了一种新的多重归算框架,使用基因型概率,INFO分数和失踪百分比.
  • 采用随机森林和惩罚性回归来减少可扩展的共变量选择的维度.
  • 使用药理动力学结核模拟,1000个基因组项目的SNP数据和临床华法林数据集 (War-PATH,IWPC,英国生物银行) 的验证方法.

主要成果:

  • 在模拟中,多次归算显著改善了信心区间覆盖率 (高达94%) 与单次归算 (0%) 相比.
  • 在临床数据集中,MI成功地恢复了已知的药物基因组学关联 (例如,CYP2C9,VKORC1) 并确定了新信号 (例如,rs4697699).
  • 处罚回归在高效应SNP选择中表现出色 (F1=0.897),而GWAS+随机森林在低效应场景中表现更好 (F1=0.657).

结论:

  • 多重归算在高维的药物基因组学研究中提高了可靠性和发现能力.
  • ML方法显示了SNP选择的潜力,但需要进一步研究以获得一致的好处.
  • 对生物库规模分析的可扩展性和通用性仍然是未来研究的关键领域.