Application of Global Sensitivity Analysis Methods to Determine the most Influential Parameters of a Minimal PBPK Model of Quinidine
Dan Liu, Linzhong Li, Masoud Jamei
Simcyp Limited (a Certara Company), Blades Enterprise Centre, John Street, Sheffield, S2 4SU, UK
Objectives: Sensitivity analysis is used to evaluate the effect of model parameters on its outputs in various areas including systems biology and systems pharmacology [1-2]. Global sensitivity analysis (GSA) evaluates the relative contributions of each parameter as well as their interactions to the model outputs by simultaneously varying all parameters. We present an application of GSA methods to a minimal-Physiologically-Based PK (mPBPK) model of Quinidine, a model drug, to identify the most influential model parameters affecting the PK properties of interest.
Methods: Elementary effect GSA method (Morris screening) and variance-based GSA methods (extended Fourier Amplitude Sensitivity Test (eFAST) and Sobol sensitivity analysis) [2] were used to study the model parameters influence on the simulated PK properties, i.e. on Cmax, Tmax, and AUC, of a mPBPK model [3] of Quinidine given orally. For eFAST and Sobol methods, two sensitivity indices were calculated, i.e. first-order sensitivity index evaluating the effect of each parameter without considering its interaction with others, and total sensitivity index assessing the impact of parameters considering their potential interactions. The performance of GSA methods was also evaluated on non-linear and non-monotonic Ishigami-Homma function and g-function [4] by comparing the estimated sensitivity indices/importance with analytical solutions.
Results: In the mPBPK model of Quinidine, GSA sensitivity indices suggest that 1) Dose, Vss, BW, BP, fa, and Fg are the key parameters to influence Cmax; 2) ka and fu are the influential parameters for Tmax; 3) Dose, BP, Vss, BW, fa, Fg, fu, and CLuint, have high impact on AUC24h. Qualitative Morris screening can be as sufficient as quantitative Sobol and eFAST methods to identify the importance of model parameters when comparing with analytical solutions for both Ishigami-Homma function and g-function.
Conclusions: GSA methods were applied to identify the most influential parameters of a mPBPK model of Quinidine. Knowing the physicochemical and plasma/blood binding properties of Quinidine the determined ranking is as expected. Further, in this case, the qualitative Morris screening was as informative of the quantitative methods. Some of the model parameters can be inter-dependent; hence in the next step GSA methods capable of handling such cases (e.g. exSobol) will be used.
References:
[1] Zi, 2011. IET systems biology, 5(6), 336-346.
[2] McNally et al., 2011. Frontiers in pharmacology, 2, 31.
[3] Yeo et al., 2010. Eur. J. Pharm. Sci, 39(5), 298-309.
[4] Saltelli et al. 2008. The Primer, Wiley