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Bayesian estimation in NONMEM.

Curtis K Johnston1, Timothy Waterhouse1, Matthew Wiens1

  • 1Metrum Research Group, Tariffville, Connecticut, USA.

CPT: Pharmacometrics & Systems Pharmacology
|November 29, 2023
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Summary
This summary is machine-generated.

Bayesian estimation offers powerful solutions for drug development. This tutorial details Bayesian model development, assessment, and prior selection using pharmacokinetic modeling software NONMEM.

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Area of Science:

  • Pharmacometrics and computational biology
  • Drug development and regulatory science

Background:

  • Bayesian estimation is a valuable statistical methodology for drug development.
  • Its application in drug development remains underutilized despite its potential.
  • Understanding Bayesian principles is crucial for effective model-based drug design.

Purpose of the Study:

  • To provide a tutorial on Bayesian model development, assessment, and prior selection.
  • To demonstrate the practical implementation of Bayesian modeling in drug development.
  • To highlight the utility of Bayesian methods using a pharmacokinetic (PK) model example.

Main Methods:

  • Outline principles of Bayesian model development and assessment.
  • Explain strategies for prior selection in Bayesian analysis.
  • Utilize nonlinear mixed-effects modeling software NONMEM for demonstration.
  • Apply Bayesian modeling to an example pharmacokinetic (PK) model.

Main Results:

  • The tutorial effectively demonstrates Bayesian model development and assessment.
  • Practical implementation of Bayesian modeling using NONMEM is showcased.
  • The example highlights the application of Bayesian principles to PK modeling.

Conclusions:

  • Bayesian estimation is a powerful, albeit underutilized, tool in drug development.
  • This tutorial provides a foundational understanding for applying Bayesian methods.
  • Effective use of Bayesian modeling can enhance drug development decision-making.