Assessing drug distribution in tissues expressing P-glycoprotein using physiologically based pharmacokinetic modeling: identification of important model parameters through global sensitivity analysis.

The structural complexity of a PBPK model is usually accompanied with significant uncertainty in estimating its input parameters. In the last decade, the global sensitivity analysis, which accounts for the variability of all model input parameters simultaneously as well as their correlations, has gained a wide attention as a powerful probing technique to identify and control biological model uncertainties. However, the current sensitivity analysis techniques used in PBPK modeling often neglect the correlation between these input parameters. We introduce a new strategy in the PBPK modeling field to investigate how the uncertainty and variability of correlated input parameters influence the outcomes of the drug distribution process based on a model we recently developed to explain and predict drug distribution in tissues expressing P-glycoprotein (P-gp). As direct results, we will also identify the most important input parameters having the largest contribution to the variability and uncertainty of model outcomes. We combined multivariate random sampling with a ranking procedure. Monte-Carlo simulations were performed on the PBPK model with eighteen model input parameters. Log-normal distributions were assumed for these parameters according to literature and their reported correlations were also included. A multivariate sensitivity analysis was then performed to identify the input parameters with the greatest influence on model predictions. The partial rank correlation coefficients (PRCC) were calculated to establish the input-output relationships. A moderate variability of predicted C(last) and C(max) was observed in liver, heart and brain tissues in the presence or absence of P-gp activity. The major statistical difference in model outcomes of the predicted median values has been obtained in brain tissue. PRCC calculation confirmed the importance for a better quantitative characterisation of input parameters related to the passive diffusion and active transport of the unbound drug through the blood-tissue membrane in heart and brain. This approach has also identified as important input parameters those related to the drug metabolism for the prediction of model outcomes in liver and plasma. The proposed Monte-Carlo/PRCC approach was aimed to address the effect of input parameters correlation in a PBPK model. It allowed the identification of important input parameters that require additional attention in research for strengthening the physiological knowledge of drug distribution in mammalian tissues expressing P-gp, thereby reducing the uncertainty of model predictions.