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: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

155
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...
155
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

301
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
301
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

872
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
872
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

1.5K
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...
1.5K
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

152
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...
152
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

158
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
158

You might also read

Related Articles

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

Sort by
Same author

HPV Infection Drives CXCL8<sup>+</sup> Neutrophil-Mediated Immune Suppression in Cervical Cancer.

BioFactors (Oxford, England)·2026
Same author

Comparison of Resistive Index and Volume Flow in Ultrasound of Arteriovenous Fistula for Dialysis Access.

Hemodialysis international. International Symposium on Home Hemodialysis·2026
Same author

Case Report: Incarcerated femoral hernia of the appendix with incidental discovery of goblet cell carcinoma.

Frontiers in medicine·2026
Same author

Landmark developments in nuclear medicine physics and engineering over the last 70 years.

Physics in medicine and biology·2026
Same author

Generative Consistency Models for Estimation of Kinetic Parametric Image Posteriors in Total-Body PET.

IEEE transactions on medical imaging·2026
Same author

Identifying Polymers that Bind or Reject Proteins with Machine Learning: Handling Categorical Features within a GPR Model.

ACS polymers Au·2026

Related Experiment Video

Updated: Nov 8, 2025

Author Spotlight: Evaluation of Protein-Condensate Dynamics in Live Human Cells
06:48

Author Spotlight: Evaluation of Protein-Condensate Dynamics in Live Human Cells

Published on: January 5, 2024

4.6K

PET-ABC: fully Bayesian likelihood-free inference for kinetic models.

Yanan Fan1,2, Gaelle Emvalomenos3,4, Clara Grazian1,2

  • 1School of Mathematics and Statistics, University of New South Wales, Sydney, 2052, Australia.

Physics in Medicine and Biology
|April 21, 2021
PubMed
Summary
This summary is machine-generated.

PET-ABC offers a simple Bayesian method for analyzing dynamic PET scans, providing reliable parameter estimates and superior model selection power compared to traditional methods. This tool enhances the analysis of Positron Emission Tomography data.

Keywords:
ABCBayesian statisticskinetic modelpositron emission tomography

More Related Videos

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
11:22

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Published on: January 30, 2018

10.3K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.7K

Related Experiment Videos

Last Updated: Nov 8, 2025

Author Spotlight: Evaluation of Protein-Condensate Dynamics in Live Human Cells
06:48

Author Spotlight: Evaluation of Protein-Condensate Dynamics in Live Human Cells

Published on: January 5, 2024

4.6K
Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
11:22

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Published on: January 30, 2018

10.3K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.7K

Area of Science:

  • Nuclear Medicine
  • Biophysics
  • Computational Biology

Background:

  • Dynamic Positron Emission Tomography (PET) imaging generates complex data requiring robust statistical inference.
  • Accurate kinetic parameter estimation and model selection are crucial for interpreting PET studies.

Purpose of the Study:

  • To introduce PET-ABC, an intuitive Bayesian method for analyzing single-subject dynamic PET data.
  • To compare the performance of PET-ABC against Weighted Non-linear Least Squares (WNLS) in parameter estimation and model selection.

Main Methods:

  • Simulated dynamic PET data using 1- and 2-tissue compartmental models under various noise conditions.
  • Evaluated parameter estimation reliability and performed model selection using PET-ABC and WNLS.
  • Analyzed a real [11C]raclopride drug challenge study in rats.

Main Results:

  • PET-ABC provided parameter estimates with lower variance and indicated confidence intervals.
  • PET-ABC successfully identified the correct compartmental model for simulated data.
  • The method showed higher statistical power for model selection and detected amphetamine-induced dopamine release in rats.

Conclusions:

  • PET-ABC is a user-friendly Bayesian approach for comprehensive analysis of dynamic PET data.
  • It offers reliable parameter estimates and robust model selection, enhancing PET data interpretation.
  • Freely available software (PETabc package) facilitates its application.