Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Pharmacokinetic Models: Comparison and Selection Criterion

Pharmacokinetic Models: Comparison and Selection Criterion

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.

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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...

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This relationship...

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

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

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...

Pharmacokinetic Models: Overview

Pharmacokinetic Models: Overview

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 assumptions,...

Analysis of Population Pharmacokinetic Data

Analysis of Population Pharmacokinetic Data

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...

You might also read

Related Articles

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

Sort by

Same author

Integrating Population Approaches With Physiologically Based Pharmacokinetic Models: A Novel Framework for Parameter Estimation.

CPT: pharmacometrics & systems pharmacology·2026

Same author

Population Pharmacokinetic and Pharmacodynamic Modeling for the Prediction of the Extended Amlitelimab Phase 3 Dosing Regimen in Atopic Dermatitis.

CPT: pharmacometrics & systems pharmacology·2025

Same author

Automated identification of honey bee pollen loads for field-applied palynological studies.

The New phytologist·2025

Same author

Covariate Model Selection Approaches for Population Pharmacokinetics: A Systematic Review of Existing Methods, From SCM to AI.

CPT: pharmacometrics & systems pharmacology·2025

Same author

Wake dynamics in buoyancy-driven flows: Steady-state-Hopf-mode interaction with O(2) symmetry revisited.

Physical review. E·2024

Same author

Population Pharmacokinetic Modeling and Determination of Individual Exposure to Avalglucosidase Alfa in Adolescent and Adult Patients With Late-Onset Pompe Disease: Analysis of Pooled Data From Phase I to III Clinical Trials.

Therapeutic drug monitoring·2023

Search research articles

Related Experiment Video

Updated: Jun 19, 2026

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

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

Published on: July 22, 2025

PMX-CovEval: A Framework Including a Simulated Pharmacokinetic Database for Covariate Model Building Methods

Mélanie Karlsen^1,2, Jérôme Azé¹, Sandra Bringay^1,3

¹LIRMM, Laboratory of Computer Science, Robotics and Microelectronics in Montpellier, CNRS, Montpellier University, Montpellier, France.

CPT: Pharmacometrics & Systems Pharmacology

|June 17, 2026

Summary

A new framework, PMX-CovEval, offers standardized datasets and models for evaluating covariate model building (CMB) methods in population pharmacokinetics (popPK). This resource aids reproducible comparison of CMB strategies for better drug development.

Keywords:

benchmarking covariate model building covariate modeling covariate screening pharmacometrics population pharmacokinetic simulated data

More Related Videos

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Published on: September 26, 2025

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Related Experiment Videos

Last Updated: Jun 19, 2026

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

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

Published on: July 22, 2025

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Published on: September 26, 2025

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

Pharmacometrics
Pharmacokinetics
Computational Biology

Background:

Covariate model building (CMB) is crucial in population pharmacokinetics (popPK) for understanding drug variability.
Existing methods for CMB lack a standardized framework for evaluation and benchmarking.
This necessitates the development of a unified resource to compare different CMB strategies.

Purpose of the Study:

Introduce PMX-CovEval, a comprehensive framework for evaluating CMB methods in popPK.
Provide a standardized and reproducible resource for benchmarking various CMB techniques.
Facilitate systematic comparison of CMB strategies in real-world pharmacokinetic modeling.

Main Methods:

Developed PMX-CovEval, a framework comprising 127 diverse scenarios.
Included pharmacokinetic (PK) datasets, model files for NONMEM and Monolix, and empirical Bayes estimates (EBEs).
Designed scenarios to reflect real-world complexity while ensuring practical usability.

Main Results:

PMX-CovEval offers a unified resource with PK datasets, model files, and EBEs.
The framework supports evaluation of standard CMB techniques available on PsN and Monolix.
Initial benchmarking results are provided to demonstrate the framework's utility.

Conclusions:

PMX-CovEval establishes a standardized, reproducible framework for CMB method evaluation in popPK.
This resource will aid researchers in systematically comparing and selecting optimal CMB strategies.
The framework promotes advancements in covariate model building for improved pharmacokinetic analyses.