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

Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

133
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.
133
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

1.0K
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...
1.0K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

119
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...
119
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

100
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
100
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

148
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...
148
Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

87
Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
87

You might also read

Related Articles

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

Sort by
Same author

Physiologically Based Pharmacokinetic Modeling and Pharmacodynamic Target Attainment Analysis for Ceftriaxone Dosing in Pregnant Women: Model Development and Clinical Applications.

Journal of clinical pharmacology·2026
Same author

Machine learning-based unified models for predicting drug clearance from pharmacokinetic animal and study design variables.

PloS one·2026
Same author

Rebuttal to Correspondence on "Effects of Serum Insulin and Insulin-Like Growth Factor 1 Levels on the Association between Fetal Growth and Per- and Polyfluoroalkyl Substance Exposure Based on a Nested Case-Control Study".

Environmental science & technology·2026
Same author

Integration of Machine Learning With PBPK and QSAR Modeling Approaches to Facilitate Drug Discovery and Development.

CPT: pharmacometrics & systems pharmacology·2026
Same author

Development and Human Extrapolation of Physiologically Based Toxicokinetic Models for Chlorinated Polyfluoroalkyl Ether Sulfonates from Rats.

Environmental science & technology·2026
Same author

Building trust in the integration of artificial intelligence into chemical risk assessment: findings from the 2024 ECETOC workshop.

Archives of toxicology·2026
Same journal

Exposure timing is a determinant of fine particulate matter (PM2.5) pulmonary, vascular and metabolic toxicity in male mice.

Toxicological sciences : an official journal of the Society of Toxicology·2026
Same journal

A fond farewell to Jeffrey Peters, editor-in-chief of ToxSci.

Toxicological sciences : an official journal of the Society of Toxicology·2026
Same journal

Aryl hydrocarbon Receptor Nuclear Translocator 2: A Forgotten Per-ARNT-Sim Transcription Factor.

Toxicological sciences : an official journal of the Society of Toxicology·2026
Same journal

ToxMet: a web tool for toxicogenomic data analysis using genome-scale metabolic modeling.

Toxicological sciences : an official journal of the Society of Toxicology·2026
Same journal

T-cell chromatin states reflect individual differences in ex vivo cytokine release and cytotoxicity induced by T-cell engager ERY22 in cynomolgus monkeys.

Toxicological sciences : an official journal of the Society of Toxicology·2026
Same journal

DILI-Context: A Dose- and Exposure-Enriched Knowledge Base for Translational Liver Safety Assessment.

Toxicological sciences : an official journal of the Society of Toxicology·2026
See all related articles

Related Experiment Video

Updated: Aug 27, 2025

Author Spotlight: Developing a Simple and Robust Hepatic Model for Pharmacological and Toxicological Applications
07:23

Author Spotlight: Developing a Simple and Robust Hepatic Model for Pharmacological and Toxicological Applications

Published on: October 20, 2023

1.5K

Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling.

Wei-Chun Chou1,2, Zhoumeng Lin1,2

  • 1Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32610, USA.

Toxicological Sciences : an Official Journal of the Society of Toxicology
|September 26, 2022
PubMed
Summary
This summary is machine-generated.

Integrating machine learning (ML) and artificial intelligence (AI) with physiologically based pharmacokinetic (PBPK) models accelerates drug development. This approach predicts essential ADME parameters, enhancing PBPK model efficiency for new compounds.

Keywords:
in vitro to in vivo extrapolation (IVIVE)artificial intelligencemachine learningpharmacometricsphysiologically based pharmacokinetic (PBPK) modelingrisk assessment

More Related Videos

An Intestine/Liver Microphysiological System for Drug Pharmacokinetic and Toxicological Assessment
08:59

An Intestine/Liver Microphysiological System for Drug Pharmacokinetic and Toxicological Assessment

Published on: December 3, 2020

8.0K
Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.3K

Related Experiment Videos

Last Updated: Aug 27, 2025

Author Spotlight: Developing a Simple and Robust Hepatic Model for Pharmacological and Toxicological Applications
07:23

Author Spotlight: Developing a Simple and Robust Hepatic Model for Pharmacological and Toxicological Applications

Published on: October 20, 2023

1.5K
An Intestine/Liver Microphysiological System for Drug Pharmacokinetic and Toxicological Assessment
08:59

An Intestine/Liver Microphysiological System for Drug Pharmacokinetic and Toxicological Assessment

Published on: December 3, 2020

8.0K
Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.3K

Area of Science:

  • Pharmacokinetics and Computational Toxicology

Background:

  • Physiologically based pharmacokinetic (PBPK) models are crucial for drug development and environmental chemical risk assessment.
  • Developing PBPK models requires extensive species-specific physiological and chemical-specific ADME data, which is often time-consuming and costly.
  • There is a growing need for computational methods to predict PBPK model input parameters, particularly for novel chemical entities.

Purpose of the Study:

  • To review the integration of PBPK modeling with machine learning (ML) and artificial intelligence (AI) computational methods.
  • To outline a paradigm for using ML/AI to predict ADME parameters and enhance PBPK model development.
  • To discuss the potential and challenges of advanced ML/AI techniques like Neural Ordinary Differential Equations (Neural-ODE) in pharmacokinetic modeling.

Main Methods:

  • Data acquisition from public databases for time-concentration pharmacokinetic (PK) data and/or ADME parameters.
  • Development of ML/AI algorithms to predict ADME parameters for PBPK models.
  • Integration of ML/AI predictions into PBPK models to estimate PK summary statistics (e.g., AUC, Cmax).
  • Exploration of Neural-ODE for direct prediction of time-series PK profiles.

Main Results:

  • A three-step paradigm for integrating ML/AI with PBPK modeling has been established.
  • ML/AI approaches can predict ADME parameters, streamlining PBPK model creation.
  • Neural-ODE shows promise for generating time-series PK profiles, potentially outperforming other ML methods.

Conclusions:

  • ML/AI integration offers an efficient pathway for developing robust PBPK models for a wide range of chemicals.
  • Addressing challenges such as data diversity, model interpretability, and Neural-ODE application is crucial for advancing this field.
  • Continued development in ML/AI will significantly facilitate PBPK modeling in drug development and risk assessment.