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

Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

436
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...
436
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

1.3K
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...
1.3K
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

201
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...
201
Nonlinear Pharmacokinetics: Overview01:19

Nonlinear Pharmacokinetics: Overview

662
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...
662
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

174
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.
174
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

148
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...
148

You might also read

Related Articles

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

Sort by
Same author

Air pollution and cause-specific mortality in EU: a review integrating meta-analysis and meta-regression.

Scientific reports·2026
Same author

Obesity, Air Pollution, and Epigenetic Modifications as Risk Factors for Asthma Phenotypes.

International journal of molecular sciences·2026
Same author

The evolution of scientific knowledge in childhood asthma over time.

European respiratory review : an official journal of the European Respiratory Society·2026
Same author

Crystalline Insights into Nasal Mucosa Inflammation and Remodeling: Unveiling Role of Galectin-10.

Biomolecules·2026
Same author

ISOMED - A Stable ISOtope database of MEDiterranean marine food web components.

Scientific data·2025
Same author

The Future of Allergy Management: How Artificial Intelligence Is Changing the Game.

The journal of allergy and clinical immunology. In practice·2025

Related Experiment Video

Updated: Oct 16, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

1.1K

Machine Learning: An Overview and Applications in Pharmacogenetics.

Giovanna Cilluffo1, Salvatore Fasola1, Giuliana Ferrante2

  • 1Institute for Biomedical Research and Innovation, National Research Council, 90146 Palermo, Italy.

Genes
|October 23, 2021
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) techniques are increasingly used in pharmacogenetics for drug development. This review covers ML applications in areas like antidepressants, anti-cancer, and warfarin therapies over the last decade.

Keywords:
pharmacogeneticssupervised machine learningunsupervised machine learning

More Related Videos

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

8.4K
Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons
09:21

Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons

Published on: July 7, 2023

1.7K

Related Experiment Videos

Last Updated: Oct 16, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

1.1K
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

8.4K
Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons
09:21

Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons

Published on: July 7, 2023

1.7K

Area of Science:

  • Biomedicine
  • Pharmacogenetics
  • Artificial Intelligence

Background:

  • Machine learning (ML) is a subfield of artificial intelligence enabling computers to learn without explicit programming.
  • ML has shown significant performance across various biomedical tasks.
  • Pharmacogenetics research increasingly utilizes sophisticated ML algorithms.

Purpose of the Study:

  • To review key Machine Learning techniques and their applications in pharmacogenetics over the past 10 years.
  • To highlight ML's role in understanding drug responses and optimizing therapies.

Main Methods:

  • Narrative review of Machine Learning techniques.
  • Categorization of ML into Supervised (SML) and Unsupervised (UML) based on research goals.
  • Focus on applications in pharmacogenetics, including antidepressant, anti-cancer, and warfarin drugs.

Main Results:

  • ML techniques have demonstrated satisfactory performance in biomedical applications.
  • Supervised ML is used for prediction-focused research.
  • Unsupervised ML is employed for data structure discovery when outcomes are unknown.

Conclusions:

  • The application of advanced ML algorithms is poised to significantly enhance pharmacogenetics knowledge.
  • ML offers powerful tools for personalized medicine and drug development.
  • Continued integration of ML will drive innovation in understanding drug-gene interactions.