Tackling High Dimensionality in QSP: Guiding Model Order Reduction With Index Analysis

Area of Science:

• Pharmacology
• Computational Biology
• Systems Biology

Background:

• Quantitative systems pharmacology (QSP) models integrate drug pharmacology with biological mechanisms for drug development.
• QSP models are complex and high-dimensional, limiting their use in population analysis.
• Existing model order reduction (MOR) techniques may not be sufficient or guarantee retention of mechanistic features.

Purpose of the Study:

• To present a workflow for selecting and combining MOR techniques for QSP models.
• To introduce index analysis for guiding MOR technique selection.
• To provide a method for checking if reduced models preserve essential mechanistic features.

Main Methods:

• Workflow employing index analysis to guide MOR technique selection and combination.
• Application of the workflow to a small-scale example model.
• Demonstration on a large-scale QSP model of blood coagulation.

Main Results:

• The proposed workflow successfully guides the selection and combination of MOR techniques.
• Index analysis aids in choosing appropriate MOR methods.
• The workflow verifies the preservation of critical mechanistic features in reduced QSP models.

Conclusions:

• The presented workflow enhances the applicability of QSP models in population analysis.
• Index analysis is a valuable tool for managing QSP model complexity.
• This approach facilitates the use of reduced QSP models in drug research and development.