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

Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

4.9K
Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
4.9K
Dosage Regimen: Fixed Dose01:01

Dosage Regimen: Fixed Dose

1.9K
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...
1.9K
Rational Dosage Regimen: Maintenance Dose and Loading Dose01:24

Rational Dosage Regimen: Maintenance Dose and Loading Dose

4.0K
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...
4.0K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

64
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...
64
Drug Dosage Regimen: Overview01:15

Drug Dosage Regimen: Overview

3.5K
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...
3.5K
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

50
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.
50

You might also read

Related Articles

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

Sort by
Same author

Assessment of simulation-based inference methods for stochastic compartmental models in epidemiological research.

PloS one·2026
Same author

Dose Recommendations for Drugs in Patients With Liver Cirrhosis (The ALIVe Study): Protocol for a Multiphase Validation and Consensus Study.

JMIR research protocols·2026
Same author

Data-driven model reveals increased stability of CAG-expanded huntingtin RNA due to MID1 binding.

PLoS computational biology·2026
Same author

PEtab-GUI: a graphical user interface to create, edit, and inspect PEtab parameter estimation problems.

Bioinformatics (Oxford, England)·2026
Same author

Latent transition analysis for longitudinal studies of post-acute infection syndromes.

Nature communications·2026
Same author

Shiny-Calorie: a context-aware application for indirect calorimetry data analysis and visualization using R.

Bioinformatics advances·2026

Related Experiment Video

Updated: Jun 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 Methods for Precision Dosing in Anticancer Drug Therapy: A Scoping Review.

Olga Teplytska1, Moritz Ernst2, Luca Marie Koltermann1

  • 1Department of Clinical Pharmacy, Institute of Pharmacy, University of Bonn, An der Immenburg 4, 53121, Bonn, Germany.

Clinical Pharmacokinetics
|August 17, 2024
PubMed
Summary

Machine Learning (ML) shows promise for optimizing anticancer drug doses. This review found Reinforcement Learning methods frequently used, highlighting ML

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

6.8K
Sample Extraction and Simultaneous Chromatographic Quantitation of Doxorubicin and Mitomycin C Following Drug Combination Delivery in Nanoparticles to Tumor-bearing Mice
08:57

Sample Extraction and Simultaneous Chromatographic Quantitation of Doxorubicin and Mitomycin C Following Drug Combination Delivery in Nanoparticles to Tumor-bearing Mice

Published on: October 5, 2017

10.9K

Related Experiment Videos

Last Updated: Jun 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

6.8K
Sample Extraction and Simultaneous Chromatographic Quantitation of Doxorubicin and Mitomycin C Following Drug Combination Delivery in Nanoparticles to Tumor-bearing Mice
08:57

Sample Extraction and Simultaneous Chromatographic Quantitation of Doxorubicin and Mitomycin C Following Drug Combination Delivery in Nanoparticles to Tumor-bearing Mice

Published on: October 5, 2017

10.9K

Area of Science:

  • Oncology
  • Artificial Intelligence
  • Pharmacology

Background:

  • Machine Learning (ML) techniques have emerged for individualizing anticancer drug doses.
  • Approaches often rely on presumed drug effects or measured biomarkers.
  • Precision dosing in cancer therapy is a key area of research.

Purpose of the Study:

  • To comprehensively summarize the research status of ML in precision dosing for anticancer drugs.
  • To review studies published after 2016.

Main Methods:

  • A scoping review was conducted following Cochrane and Joanna Briggs Institute guidelines.
  • Systematic searches of Medline, Embase, and Cochrane Library databases were performed.
  • Reporting adhered to the PRISMA-ScR checklist.

Main Results:

  • 17 relevant studies were identified.
  • Reinforcement Learning (RL) methods were used in 12 studies, including various Q-Learning algorithms.
  • Classical ML methods were compared, and an AI platform guided dosing in limited patients.

Conclusions:

  • ML methods are clinically relevant for optimizing anticancer drug doses.
  • Many ML algorithms demonstrate potential for model-free predictions.
  • ML approaches may maximize efficacy and minimize toxicity compared to standard protocols.