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

Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...
Response Surface Methodology01:16

Response Surface Methodology

Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
Pharmacodynamic Models: Direct Effect Model and Indirect Response Model01:29

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model

Pharmacodynamic models are essential tools in understanding the relationship between drug concentrations and their effects on biological systems. By characterizing the dynamics of drug action, these models guide dose selection, optimize therapeutic efficacy, and inform the development of new drugs. Two major classes of pharmacodynamic models include direct effect and indirect response models.Direct Effect ModelsDirect effect models describe the immediate relationship between drug concentration...
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
Experimental Designs01:16

Experimental Designs

An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...

You might also read

Related Articles

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

Sort by
Same author

Plasma CXCL13 and fibrosis biomarkers in COVID-19 compared with idiopathic pulmonary fibrosis.

Scientific reports·2026
Same author

High Nasopharyngeal SARS-CoV-2 Load and Delayed Clearance in Hospitalized Patients With Blood Autoantibodies Neutralizing Type I Interferons.

The Journal of infectious diseases·2026
Same author

Objective First, Method Second: Why the Estimand Definition Comes First in Pharmacometric Covariate Modeling.

CPT: pharmacometrics & systems pharmacology·2026
Same author

Assessing Covariate Clinical Relevance in High-Dimensional PK Analysis: A Comparison of SCM+, FFEM, and FREM Approaches.

CPT: pharmacometrics & systems pharmacology·2026
Same author

Advances and Further Comparison of Software Tools for Fisher Information Matrix-Based Design Evaluation in Pharmacometrics.

Pharmaceutical research·2026
Same author

Levels of circulating kidney injury markers and IL-10 identify non-critically ill patients with COVID-19 at risk of death.

JCI insight·2026
Same journal

SynTME: A tumor microenvironment-aware, pharmacology-inspired multi-stage framework for drug synergy prediction.

Computer methods and programs in biomedicine·2026
Same journal

MMFVS-Net: A triple-symmetric cross-attention network for multimodal optical image fusion and high-accuracy virtual staining of breast cancer tissues.

Computer methods and programs in biomedicine·2026
Same journal

A novel Milstein-stochastic epidemiologically-informed neural network for approaching epidemic dynamics: Application to Mpox disease.

Computer methods and programs in biomedicine·2026
Same journal

Accounting for approximation errors using surrogate-based parameter estimation of cardiac mechanics digital twins.

Computer methods and programs in biomedicine·2026
Same journal

Facial iPPG heatmap patterns based on period-aware autoencoder show association with carotid atherosclerosis towards non-contact hemodynamic assessment.

Computer methods and programs in biomedicine·2026
Same journal

Explainable machine learning models predict liver fibrosis risk and outcome in the general population: Development and multi-cohort external validation.

Computer methods and programs in biomedicine·2026
See all related articles

Related Experiment Video

Updated: Jun 19, 2026

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)
13:54

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)

Published on: August 18, 2023

Design evaluation and optimisation in multiple response nonlinear mixed effect models: PFIM 3.0.

Caroline Bazzoli1, Sylvie Retout, France Mentré

  • 1INSERM, U738, Paris, France. caroline.bazzoli@inserm.fr

Computer Methods and Programs in Biomedicine
|November 7, 2009
PubMed
Summary
This summary is machine-generated.

New R function PFIM 3.0 enables design evaluation and optimization for multiple response nonlinear mixed effect models (NLMEM), crucial for pharmacometrics. This tool enhances joint pharmacokinetic-pharmacodynamic analyses.

More Related Videos

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Related Experiment Videos

Last Updated: Jun 19, 2026

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)
13:54

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)

Published on: August 18, 2023

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

  • Pharmacometrics
  • Computational Biology
  • Statistical Modeling

Background:

  • Nonlinear mixed effect models (NLMEM) with multiple responses are vital in pharmacometrics, particularly for joint pharmacokinetic-pharmacodynamic (PKPD) analyses.
  • Existing R functions (PFIM 1.2, PFIMOPT 1.0) are limited to single-response models, necessitating improved tools for complex NLMEM.
  • Efficient design evaluation and optimization are critical for robust NLMEM studies.

Purpose of the Study:

  • To introduce PFIM 3.0, an extended R function for design evaluation and optimization of multiple response NLMEM.
  • To provide a user-friendly tool that accommodates complex models, including those defined by differential equations.
  • To enhance the capabilities for joint PKPD analysis in pharmacometrics.

Main Methods:

  • PFIM 3.0 utilizes the Fisher information matrix based on model linearization, extended for multiple response models.
  • The function incorporates a library of analytical pharmacokinetic models and supports user-defined models via differential equations.
  • An alternative Fedorov-Wynn algorithm is implemented for practical D-optimal design optimization among user-specified sampling times.

Main Results:

  • PFIM 3.0 effectively handles multiple response NLMEM, offering enhanced model specification and optimization features.
  • The Fedorov-Wynn algorithm provides practical D-optimal designs by optimizing within user-defined sampling times.
  • The R function is freely available, promoting wider adoption and application in pharmacometrics.

Conclusions:

  • PFIM 3.0 is an efficient and user-friendly tool for design evaluation and optimization in multiple response NLMEM.
  • The enhanced capabilities facilitate complex PKPD analyses, illustrated by a warfarin case study.
  • This advancement supports the development of more robust and optimized drug development studies.