Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Rational Dosage Regimen: Maintenance Dose and Loading Dose01:24

Rational Dosage Regimen: Maintenance Dose and Loading Dose

3.7K
A rational dosage regimen considers a drug's pharmacokinetics, including its absorption, distribution, metabolism, and elimination from the body. By understanding these factors, the appropriate dosage can be determined, and the dosing schedule can be designed to achieve and maintain the desired therapeutic effect while minimizing adverse effects.
In most cases, drugs are administered repetitively or infused continuously to maintain a steady-state concentration in the body. At a steady...
3.7K
Drug Dosage Regimen: Overview01:15

Drug Dosage Regimen: Overview

3.4K
A drug dosage regimen describes the specific instructions and schedule for administering a drug to a patient. It considers factors such as drug dosage, frequency, route of administration, and duration of treatment. Designing an appropriate dosage regimen for a patient aims to achieve a target drug concentration at the site of action.
Typically, the starting dose and dosing interval are guided by the manufacturer's recommendations based on clinical trials conducted during and after drug...
3.4K
Dosage Regimen: Fixed Dose01:01

Dosage Regimen: Fixed Dose

1.8K
Fixed-dose regimens are a common approach to administer drugs to achieve and maintain desired levels of the drug in the body. In this dosing strategy, a specific amount of medication is given at regular intervals, often multiple times a day, to ensure a consistent drug concentration in the bloodstream.
Fixed-dose regimens can be used for various routes of administration, including intravenous (IV) injections and oral medications. For IV administration, a predetermined amount of the drug is...
1.8K
Two-Compartment Open Model: IV Infusion01:15

Two-Compartment Open Model: IV Infusion

178
A two-compartment model is a vital tool in pharmacokinetics, providing an essential understanding of drug behavior, especially for those administered via zero-order intravenous infusion. This model outlines two compartments: the central compartment, where elimination occurs, and the peripheral compartment.
The model illustrates the decrease in plasma drug concentration from the central compartment with a specific equation. It shows that under steady-state conditions, the drug's input rate...
178
Nonlinear Pharmacokinetics: Overview01:19

Nonlinear Pharmacokinetics: Overview

214
Nonlinear or dose-dependent pharmacokinetics is a phenomenon that occurs when the pharmacokinetic parameters of certain drugs deviate from linear pharmacokinetics at higher doses. These drugs do not follow the expected first-order kinetics, where the rate of drug elimination is directly proportional to the drug concentration. Instead, they exhibit a nonlinear relationship, which can be attributed to several factors.
Nonlinearity can arise due to the saturation of plasma protein-binding or...
214
One-Compartment Model: IV Infusion01:09

One-Compartment Model: IV Infusion

129
Intravenous (IV) infusion is often utilized when continuous and controlled drug delivery is necessary, such as during surgery or in the treatment of chronic diseases. This method offers numerous advantages, including immediate drug action, precise control over dosage, and bypassing the first-pass metabolism.
The one-compartment model for IV infusion uses mathematical equations to describe the rate of change in drug quantity in the body. At steady-state or infusion equilibrium, the drug input...
129

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Diagnostic Accuracy of Eight Estimated Glomerular Filtration Rate Equations for Assessing Kidney Function in Korean Pediatric Patients.

Annals of laboratory medicine·2026
Same author

Perception of Family Genetic Testing for Hereditary Breast and Ovarian Cancer: A Survey of Patients and General Public.

Journal of Korean medical science·2026
Same author

The Novel HLA-DPB1*1759:01 Allele Was Identified by Next-Generation Sequencing.

HLA·2026
Same author

RNA stability enhancers for durable base-modified mRNA therapeutics.

Nature biotechnology·2025
Same author

Early detection of patients with narcotic use disorder using a modified morphine equivalent daily dose score based on an analysis of real-world prescription patterns: a retrospective cohort study.

Ewha medical journal·2025
Same author

Current practices in peripheral blood stem cell processing and cryopreservation: a nationwide survey of Korean transplant centers.

Blood research·2025

Related Experiment Video

Updated: May 16, 2025

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

8.2K

Optimizing Initial Vancomycin Dosing in Hospitalized Patients Using Machine Learning Approach for Enhanced

Heonyi Lee1, Yi-Jun Kim2,3, Jin-Hong Kim4

  • 1Interdisciplinary Program in Bioinformatics, College of Natural Sciences, Seoul National University, Seoul, Republic of Korea.

Journal of Medical Internet Research
|March 31, 2025
PubMed
Summary

A new machine learning algorithm, OPTIVAN, optimizes initial vancomycin dosing by predicting therapeutic ranges, reducing the need for adjustments. This personalized approach enhances vancomycin treatment effectiveness.

Keywords:
algorithmarea under curvemachine learningpharmacokineticstherapeutic drug monitoringvancomycinvancomycin dosing

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.6K
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

14.1K

Related Experiment Videos

Last Updated: May 16, 2025

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

8.2K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.6K
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

14.1K

Area of Science:

  • Pharmacokinetics and Pharmacodynamics
  • Machine Learning in Medicine
  • Antibiotic Dosing Optimization

Background:

  • Standard vancomycin dosing often leads to suboptimal outcomes due to inter-patient variability.
  • Therapeutic Drug Monitoring (TDM) adjustments are reactive, highlighting the need for predictive, personalized initial dosing.

Purpose of the Study:

  • To develop and evaluate a machine learning (ML)-based algorithm for predicting optimal initial vancomycin doses.
  • To ensure initial vancomycin doses fall within the therapeutic range, defined by the 24-hour area under the curve to minimum inhibitory concentration.

Main Methods:

  • A retrospective cohort of 415 patients receiving intravenous vancomycin was used for training and testing (7:3 ratio).
  • Four ML models (gradient boosting, random forest, SVM, XGB) were evaluated to develop the OPTIVAN algorithm.
  • The OPTIVAN algorithm was validated using an external cohort (n=268) and integrated into a web-based clinical support tool.

Main Results:

  • The Support Vector Machine (SVM) model, incorporated into OPTIVAN, showed the best predictive performance with an AUROC of 0.832 (training) and 0.720 (validation).
  • Key covariates for the SVM model included age, BMI, glucose, BUN, eGFR, hematocrit, and dose/weight.
  • Consistent performance was observed across various patient subgroups, including different renal functions, sexes, and BMIs.

Conclusions:

  • The OPTIVAN algorithm offers a significant advancement in personalized initial vancomycin dosing, overcoming limitations of current TDM.
  • This ML-driven approach can reduce subsequent dosage adjustments and improve vancomycin treatment efficacy.
  • A user-friendly, web-based application of the OPTIVAN algorithm is available for practical clinical use.