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Related Concept Videos

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.
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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...
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Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
Pharmacokinetic Models: Comparison and Selection Criterion01:26

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.
Pharmacodynamic Models: Linear Concentration–Effect Model01:15

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The linear concentration–effect model, underpinned by the principle that pharmacological effect (E) is directly proportional to plasma drug concentration (C), emerges as a pivotal simplification of the Emax model for conditions where C is significantly less than EC50. This model portrays a linear trajectory of the concentration–effect relationship when drug levels are markedly below the EC50 threshold.Despite its inherent assumption of continuous effect augmentation with increasing drug...

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Updated: May 22, 2026

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

OpenPMX Software for Nonlinear Mixed-Effect Models in Pharmacometrics: Precision Compared With NONMEM First-Order

Douglas J Eleveld1, Jeroen V Koomen1,2, Jasper Stevens3,4

  • 1University of Groningen, University Medical Center Groningen, Department of Anesthesiology, Groningen, the Netherlands.

CPT: Pharmacometrics & Systems Pharmacology
|May 21, 2026
PubMed
Summary
This summary is machine-generated.

OpenPMX offers a transparent, open-source alternative for nonlinear mixed-effects modeling. Its performance in estimating model parameters is comparable, and sometimes superior, to the industry standard NONMEM software.

Keywords:
estimationmodelingpharmacometricssoftware

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Area of Science:

  • Pharmacometrics
  • Computational Statistics
  • Software Development

Background:

  • Nonlinear mixed-effects models are crucial in pharmacometrics.
  • Current industry standards like NONMEM lack detailed documentation for technical implementation.
  • A need exists for transparent and accessible modeling tools.

Purpose of the Study:

  • To introduce OpenPMX, an open-source software for nonlinear mixed-effects modeling.
  • To compare the performance of OpenPMX against NONMEM regarding parameter estimation bias and accuracy.
  • To promote transparency and collaboration in mixed-effects modeling.

Main Methods:

  • Developed OpenPMX with open-source licensing and minimal dependencies.
  • Compared OpenPMX and NONMEM using five population models with varying complexity.
  • Performed repeated simulations and estimations, calculating bias, RMSE, and precision differences.

Main Results:

  • OpenPMX demonstrated comparable bias and RMSE to NONMEM.
  • In several cases, OpenPMX exhibited slightly better performance than NONMEM.
  • The software is characterized by low complexity and open technical details.

Conclusions:

  • OpenPMX provides a viable, transparent, and efficient alternative for nonlinear mixed-effects modeling.
  • Its open-source nature facilitates inspection, auditing, and scientific collaboration.
  • The software's performance validates its potential as an industry tool.