A Framework for Simplification of Quantitative Systems Pharmacology
Models in Clinical Pharmacology
Abstract
Quantitative systems pharmacology (QSP) is a relatively new discipline
within modelling and simulation that has gained wide attention over the
past few years. The application of QSP models spans drug-target
identification and validation, through all drug development phases as
well as clinical applications. Due to their detailed mechanistic nature,
QSP models are capable of extrapolating knowledge to predict outcomes in
scenarios that have not been tested experimentally making them an
important resource in experimental and clinical pharmacology. However,
these models are complicated to work with due to their size and inherent
complexity. This makes many applications of QSP models for simulation,
parameter estimation and trial design computationally intractable. A
number of techniques have been developed to simplify QSP models into
smaller models that are more amenable to further analyses while
retaining their accurate predictive capabilities. Different
simplification techniques have different strengths and weaknesses and
hence different utilities. Understanding the utilities of different
methods is essential for selection of the best method for a particular
situation. In this paper, we have created an overall framework for model
simplification techniques that allows a natural categorisation of
methods based on their utility. We provide a brief description of the
concept underpinning the different methods and example applications. A
summary of the utilities of methods is intended provide a guide to
modellers in their model endeavours to simplify these complicated
models.