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

Quantitative systems pharmacology (QSP) models offer a useful platform to integrate drug pharmacology with knowledge about biological mechanisms across multiple scales and data sources into a unified quantitative framework. This makes them invaluable to address many relevant questions in drug research and development. Despite their potential, however, QSP models are seldom employed in the population analysis context due to their complexity and dimensionality. Model order reduction (MOR) techniques can be used to tackle this challenge. However, a single MOR technique might not be sufficient to achieve an applicable reduced model. Furthermore, to date there is no tool to judge whether the reduced model retains important mechanistic features of the original model. In this tutorial, we present a workflow employing index analysis that guides the selection and combination of MOR techniques and includes a check of the preservation of important mechanistic features by the reduced model. To demonstrate the value of the proposed approach, we first explain the concepts in the context of a small-scale example model and then expand to a well-known large-scale QSP model-the blood coagulation model.