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

Drug Accumulation During Multiple Dosing: Repetitive IV Injections01:21

Drug Accumulation During Multiple Dosing: Repetitive IV Injections

229
Calculating drug dosage and accumulation in multiple-dose regimens is crucial for achieving therapeutic efficacy while avoiding toxicity. This involves determining the plasma drug concentrations over time to optimize dosing schedules. The principle of superposition is fundamental in this process, allowing for the prediction of drug concentration in plasma following multiple doses based on single-dose data.The principle of superposition asserts that the plasma concentration-time curves from...
229
Drug Accumulation During Multiple Dosing: Intermittent IV Infusions01:24

Drug Accumulation During Multiple Dosing: Intermittent IV Infusions

217
Intermittent intravenous (IV) infusion is a method of drug administration where medications are delivered over short infusion periods followed by intervals of no drug delivery. This approach helps to prevent sustained high drug concentrations in the bloodstream, reducing the risk of adverse effects associated with prolonged exposure. Unlike continuous infusion, steady-state concentrations may not be achieved during a single dosing cycle but can be reached through repeated...
217
Determination of Multiple Dosing Parameters: Steady-State, Minimum and Maximum Concentrations01:15

Determination of Multiple Dosing Parameters: Steady-State, Minimum and Maximum Concentrations

215
Gentamicin, an aminoglycoside antibiotic, is commonly administered via intermittent intravenous infusion to treat severe infections. An intermittent one-hour infusion of gentamicin, administered at eight-hour intervals, allows for precise control of plasma drug concentrations, minimizing toxicity while ensuring therapeutic efficacy. Pharmacokinetic principles govern the dynamics of plasma concentrations and can be mathematically described using specific equations.The plasma drug concentration...
215
Determination of Multiple Dosing Parameters: Loading and Maintenance Doses01:25

Determination of Multiple Dosing Parameters: Loading and Maintenance Doses

198
A loading dose is an essential pharmacological strategy to rapidly achieve the target plasma drug concentration necessary for an immediate therapeutic effect. This approach is especially critical for drugs characterized by slow absorption or extended half-lives, where delaying therapeutic plasma levels could compromise treatment outcomes. By administering a loading dose, clinicians ensure a prompt onset of drug action, even for agents with complex pharmacokinetic profiles.Achieving steady-state...
198
Dosage Regimen: Individualization01:24

Dosage Regimen: Individualization

151
Individualization in dosing regimens is the customization of medication doses for individual patients. Its necessity arises from the goal of maximizing therapeutic benefits while minimizing risks. This approach is pivotal because human responses to drugs can vary widely; what is effective for one person may be inadequate or excessive for another. Interpatient (intersubject) variability refers to differences in drug responses between individuals, while intrapatient (intrasubject) variability...
151
Estimation of k and VD of Aminoglycosides01:20

Estimation of k and VD of Aminoglycosides

193
Aminoglycosides are a class of antibiotics used to treat various bacterial infections. Clinicians must determine the elimination rate constant (k) and volume of distribution (VD) to optimize therapeutic efficacy and minimize toxicity. The k value represents the rate at which the drug is removed from the body, and the VD reflects the degree to which the drug distributes into body tissues. Accurately estimating these parameters allows healthcare professionals to tailor drug dosing to individual...
193

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

Model Ensembling and Machine Learning Approaches to Predict the First Dose of Amoxicillin in Intensive Care.

CPT: pharmacometrics & systems pharmacology·2026
Same author

Refractory hypokalemia associated with levetiracetam: a case report.

The Pan African medical journal·2026
Same author

Population pharmacokinetics of dalbavancin: external validation, model averaging, and implications for precision dosing in prolonged therapy.

Antimicrobial agents and chemotherapy·2026
Same author

Efficacy of doxycycline in treatment of Staphylococcus spp. prosthetic joint infections: a CRIOGO multicentre case-control study.

The Journal of antimicrobial chemotherapy·2026
Same author

Preemptive Pharmacogenetics in Renal Transplantation: A Real-World Assessment of Pharmacogenetic Actionability.

Therapeutic drug monitoring·2026
Same author

Risk factors and outcome of extended-spectrum β-lactamase-producing Enterobacterales bacteraemia in high-risk neutropenic patients in haematology: A multicentre retrospective study.

International journal of antimicrobial agents·2026

相关实验视频

Updated: Jan 9, 2026

A Reference Broth Microdilution Method for Dalbavancin In Vitro Susceptibility Testing of Bacteria that Grow Aerobically
11:28

A Reference Broth Microdilution Method for Dalbavancin In Vitro Susceptibility Testing of Bacteria that Grow Aerobically

Published on: September 9, 2015

29.7K

使用机器学习预测长时间的达尔巴万辛暴露:个性化再剂量的验证策略.

Hamza Sayadi1,2, Matthieu Gregoire3,4, Yeleen Fromage1

  • 1Department of Pharmacology, Toxicology and Pharmacovigilance, Dupuytren University Hospital (CHU Dupuytren), Limoges, France.

Antimicrobial agents and chemotherapy
|December 10, 2025
PubMed
概括

机器学习模型可以预测复杂的格拉姆阳性感染中的dalbavancin度. 这种方法支持个性化剂量决策,提高治疗疗效,减少不必要的再剂量.

关键词:
蒙特卡罗模拟的蒙特卡罗模拟达尔巴万西尼 (dalbavancine) 是一种机器学习是机器学习.基于模型的精确剂量定量.人口的药理动力学治疗药物监测 治疗药物监测

更多相关视频

Nanomechanics of Drug-target Interactions and Antibacterial Resistance Detection
11:56

Nanomechanics of Drug-target Interactions and Antibacterial Resistance Detection

Published on: October 25, 2013

14.6K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K

相关实验视频

Last Updated: Jan 9, 2026

A Reference Broth Microdilution Method for Dalbavancin In Vitro Susceptibility Testing of Bacteria that Grow Aerobically
11:28

A Reference Broth Microdilution Method for Dalbavancin In Vitro Susceptibility Testing of Bacteria that Grow Aerobically

Published on: September 9, 2015

29.7K
Nanomechanics of Drug-target Interactions and Antibacterial Resistance Detection
11:56

Nanomechanics of Drug-target Interactions and Antibacterial Resistance Detection

Published on: October 25, 2013

14.6K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K

科学领域:

  • 药理学 药理学是指药理学的学科.
  • 传染性疾病 传染性疾病
  • 机器学习 机器学习

背景情况:

  • 达尔巴万辛是一种脂糖,用于复杂的格拉姆阳性感染,但由于药理动力学变异性和MIC异质性而面临挑战.
  • 优化达尔巴万辛的剂量对于有效的治疗至关重要,特别是其长效的特征.

研究的目的:

  • 开发和验证机器学习 (ML) 模型,以预测与药理动力学/药理动力学目标相对应的dalbavancin血度.
  • 为了使达尔巴万辛治疗的早期,个性化的再剂量决定.

主要方法:

  • 在模拟的药物动力学概况上训练ML模型 (支持矢量机器).
  • 使用独立的模拟数据集和真实世界的队列 (利莫日和南特大学医院) 验证的模型.
  • 输入特征包括患者人口统计学,肌素清除率,MIC,以及剂前单个血度.

主要成果:

  • 在验证设置中,ML模型实现了高精度 (>88%) 和灵敏度 (>90%).
  • 临床验证表明准确度接近95%,没有观察到假阴性.
  • ML模型在达尔巴万辛剂量准确度和灵敏度方面表现优于传统的贝叶斯估计.

结论:

  • 机器学习方法提供了一个实用策略,用于对dalbavancin的模型信息精确剂量.
  • 这种方法支持早期,个性化的再定量决策,补充贝叶斯预测,减少序列采样.