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

Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

279
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...
279
Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

264
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...
264
Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

353
Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
353
Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance01:07

Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance

301
Drug transporters are critical in drug absorption, distribution, and excretion processes. They should be included in physiological-based pharmacokinetic (PBPK) models, which help predict human drug disposition. However, predicting this is challenging during drug development, especially when liver transport is involved. However, with a realistic representation of body transport processes, an accurate model may be possible.
A recent model describes pravastatin's hepatobiliary excretion,...
301
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

2.2K
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...
2.2K
Clearance Models: Physiological Models01:09

Clearance Models: Physiological Models

328
Drug clearance is a critical pharmacokinetic process involving the irreversible removal of drugs from the body through various organs over a specified time period. Physiological models are indispensable in determining organ-specific clearance, defined by the proportion of the drug eliminated per unit of time from the organ's blood volume.
The organ's clearance rate depends on the blood flow to the organ and the extraction ratio (E). The extraction ratio describes the organ's...
328

You might also read

Related Articles

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

Sort by
Same author

The double-edged nature of antibody bivalency: mathematical and experimental analysis of cell surface antigen occupancy and opsonization.

mAbs·2026
Same author

PBPK Modelling of PROTACs: Learnings from ARV-110 as a Case Example.

The AAPS journal·2026
Same author

TMDD Modeling for Antibodies Directed Against Soluble Targets: Parameter Values for De Novo Modeling and Simulation From Experimental Data.

Clinical pharmacology and therapeutics·2026
Same author

A hybrid PKPD agent-based model of the tumour immune interaction: effects of anti-cancer combination therapy.

Journal of pharmacokinetics and pharmacodynamics·2026
Same author

Physiologically-Based Pharmacokinetic Modeling to Support Pediatric Clinical Development: An IQ Working Group Perspective on the Current Status and Challenges.

CPT: pharmacometrics & systems pharmacology·2025
Same author

An Asymptotic Analysis of Bivalent Monoclonal Antibody-Antigen Binding.

Bulletin of mathematical biology·2025

Related Experiment Video

Updated: Feb 10, 2026

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

8.7K

Structural identifiability of physiologically based pharmacokinetic models.

James W T Yates1

  • 1DMPK, AstraZeneca, Alderley Park, Cheshire, UK. james.yates@astrazeneca.com

Journal of Pharmacokinetics and Pharmacodynamics
|March 29, 2006
PubMed
Summary

This study presents a new method for analyzing drug kinetics models. The approach simplifies assessing if experimental data can uniquely determine drug parameters in complex physiologically based pharmacokinetic (PBPK) models.

More Related Videos

Use of Rabbit Eyes in Pharmacokinetic Studies of Intraocular Drugs
10:02

Use of Rabbit Eyes in Pharmacokinetic Studies of Intraocular Drugs

Published on: July 23, 2016

33.4K
A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

69.8K

Related Experiment Videos

Last Updated: Feb 10, 2026

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

8.7K
Use of Rabbit Eyes in Pharmacokinetic Studies of Intraocular Drugs
10:02

Use of Rabbit Eyes in Pharmacokinetic Studies of Intraocular Drugs

Published on: July 23, 2016

33.4K
A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

69.8K

Area of Science:

  • Pharmacokinetics
  • Systems Biology
  • Mathematical Modeling

Background:

  • Accurate characterization of drug pharmacokinetics requires unique estimation of internal rate constants from experimental data.
  • Physiologically Based Pharmacokinetic (PBPK) models are crucial for understanding drug behavior but can become complex.
  • Assessing the identifiability of parameters in PBPK models is essential before experimental design.

Purpose of the Study:

  • To develop and present a novel method for structural identifiability analysis of complex PBPK models.
  • To simplify the analysis of modified PBPK models by considering subsystems individually.
  • To evaluate the identifiability of parameters in multi-subsystem PBPK models.

Main Methods:

  • Structural identifiability analysis applied to modified PBPK models.
  • Decomposition of complex PBPK models into individual subsystems for analysis.
  • Evaluation of global and local identifiability for model parameters.

Main Results:

  • The novel method simplifies the structural identifiability analysis of complex PBPK models.
  • Complex PBPK models with parallel subsystems were found to be globally identifiable in several cases.
  • In some instances, parameter identifiability reduced to local identifiability for additional parameters.

Conclusions:

  • The presented method offers a more manageable approach to analyzing the identifiability of complex PBPK models.
  • Individual subsystem analysis facilitates a clearer understanding of parameter identifiability in intricate pharmacokinetic models.
  • Caution is advised when dealing with PBPK models featuring multiple eliminating peripheral tissues.