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PFIM 4.0, an extended R program for design evaluation and optimization in nonlinear mixed-effect models.

Cyrielle Dumont1, Giulia Lestini2, Hervé Le Nagard2

  • 1IAME, UMR 1137, INSERM and University Paris Diderot, Sorbonne Paris Cité, Paris, F-75018, France; University of Lille, EA 2694, Public Health: Epidemiology and Healthcare Quality, ILIS, Lille, F-59000, France.

Computer Methods and Programs in Biomedicine
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PubMed
Summary

PFIM 4.0 enhances nonlinear mixed-effect model (NLMEM) analysis for drug development. This R tool optimizes study design using the Fisher information matrix (FIM), offering advanced features for pharmacokinetic/pharmacodynamic modeling and adaptive trial designs.

Keywords:
D-optimalityDesignFisher information matrixNonlinear mixed-effect modelPFIM

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Area of Science:

  • Pharmacometrics and statistical modeling in drug development.
  • Application of nonlinear mixed-effect models (NLMEMs) for longitudinal data analysis.

Background:

  • Nonlinear mixed-effect models (NLMEMs) are crucial for analyzing longitudinal data in drug development.
  • The Fisher information matrix (FIM) aids in designing clinical trials, offering an alternative to simulations.
  • PFIM is an R tool for study design evaluation and optimization.

Purpose of the Study:

  • Introduce PFIM 4.0, an extended version of the PFIM software.
  • Highlight new features for enhanced design evaluation and optimization of longitudinal studies.
  • Demonstrate the utility of PFIM 4.0 in pharmacokinetic/pharmacodynamic (PK/PD) studies.

Main Methods:

  • PFIM 4.0 incorporates an expanded PK/PD model library and accommodates additional random effects and discrete covariates.
  • User-defined models can be specified via R functions, with options for fixed parameters or sampling times.
  • Includes advanced FIM outputs (eigenvalues, condition numbers) and supports adaptive designs by incorporating previous results.
  • Implements Bayesian individual FIM for maximum a posteriori estimation and reports predicted shrinkage.

Main Results:

  • Illustrates adaptive design benefits in population pharmacokinetic studies using previous results.
  • Demonstrates Bayesian individual design optimization for pharmacodynamic studies.
  • Highlights the utility of Bayesian individual FIM for therapeutic drug monitoring and predicting individual parameter estimation precision.

Conclusions:

  • PFIM 4.0 is a valuable and freely available tool for the evaluation and optimization of longitudinal study designs in pharmacometrics.
  • The software simplifies complex analyses and supports advanced modeling techniques.
  • Facilitates efficient study design, particularly for adaptive and Bayesian approaches.