A framework for 2-stage global sensitivity analysis of GastroPlus™ compartmental models

Megerle L. Scherholz, James Forder, Ioannis P. Androulakis

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Parameter sensitivity and uncertainty analysis for physiologically based pharmacokinetic (PBPK) models are becoming an important consideration for regulatory submissions, requiring further evaluation to establish the need for global sensitivity analysis. To demonstrate the benefits of an extensive analysis, global sensitivity was implemented for the GastroPlus™ model, a well-known commercially available platform, using four example drugs: acetaminophen, risperidone, atenolol, and furosemide. The capabilities of GastroPlus were expanded by developing an integrated framework to automate the GastroPlus graphical user interface with AutoIt and for execution of the sensitivity analysis in MATLAB®. Global sensitivity analysis was performed in two stages using the Morris method to screen over 50 parameters for significant factors followed by quantitative assessment of variability using Sobol’s sensitivity analysis. The 2-staged approach significantly reduced computational cost for the larger model without sacrificing interpretation of model behavior, showing that the sensitivity results were well aligned with the biopharmaceutical classification system. Both methods detected nonlinearities and parameter interactions that would have otherwise been missed by local approaches. Future work includes further exploration of how the input domain influences the calculated global sensitivity measures as well as extending the framework to consider a whole-body PBPK model.

Original languageEnglish (US)
Pages (from-to)309-327
Number of pages19
JournalJournal of Pharmacokinetics and Pharmacodynamics
Volume45
Issue number2
DOIs
StatePublished - Apr 1 2018

All Science Journal Classification (ASJC) codes

  • Pharmacology

Keywords

  • Global sensitivity analysis
  • Morris method
  • PBPK modeling
  • Sobol sensitivity method

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