1.2.2 Sensitivity Analysis
Sensitivity analysis is a technique that determines how changes in the values of parameters affect a given response variable of interest. Originally, sensitivity analysis was employed to determine the robustness of a system to variability or uncertainty in parameter values. This has been extended to guide model-order reduction by determining the system’s state that has the least influence on system outputs. In addition to reducing the number of nodes (compartments), sensitivity analysis can also be used as an edge (reactions) reduction method to determine which reactions contribute least to the output of the system and eliminate those reactions by setting their associated rate parameters to zero [44].
Several algorithms for global sensitivity analysis have been developed with Fourier amplitude and Sobol’s sensitivity analysis being common [45,46]. Jayachandran et al. employed Sobol’s method and achieved a 50% reduction in model parameters for a model of 6-mercaptopurine effects in children with acute lymphoblastic leukaemia. The technique enabled the estimation of model parameters for individual patients and subsequent use in treatment individualisation [21]. Gueorguieva et al. applied an extended Fourier amplitude sensitivity analysis test to reduce a 14-compartment whole body PBPK model of diazepam while preserving the dynamical behaviour of the arterial concentration compartment [47].