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Selection and Qualification of Simplified QSP Models When Using Model Order Reduction Techniques.

Chihiro Hasegawa1,2, Stephen B Duffull3

  • 1School of Pharmacy, University of Otago, Dunedin, New Zealand. chihiro.hasegawa@otago.ac.nz.

The AAPS Journal
|November 29, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a weighted criterion and visual predictive check (VPC) to simplify complex quantitative systems pharmacology (QSP) models. This automated approach balances model performance and complexity for better drug development insights.

Keywords:
composite criterionproper lumpingscale reductionsystems modelsvisual predictive check

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Area of Science:

  • Pharmacology and Systems Biology
  • Computational Modeling and Simulation

Background:

  • Quantitative systems pharmacology (QSP) models are vital in drug development for understanding drug mechanisms and identifying targets.
  • High dimensionality of QSP models hinders parameter estimation and model simplification.

Purpose of the Study:

  • To develop a novel weighted composite criterion for reducing QSP model complexity.
  • To establish a method for balancing model performance and dimensionality during simplification.

Main Methods:

  • A weighted composite criterion integrating performance and dimensionality indices was developed.
  • Model qualification involved visual predictive checks (VPC) assessing parameter precision.
  • The method was tested on pharmacokinetic, physiologically based pharmacokinetic (PBPK), and bone mineral density models.

Main Results:

  • The automated search identified known reduced models and novel simpler models for PBPK and bone mineral density.
  • A simplified bone mineral density model adequately described responses up to one year.
  • The approach successfully reduced model order in tested examples.

Conclusions:

  • The proposed weighted criterion and VPC offer an automated, natural approach for QSP model order reduction.
  • This method is applicable to multiscale models and aids in drug development by simplifying complex systems.
  • The technique balances model complexity and performance, facilitating better estimation and understanding.