Physiologically based pharmacokinetic modelling andin vivo[I]/Kiaccurately predict P ‐glycoprotein‐mediated drug‐drug interactions with dabigatran etexilate

BACKGROUND AND PURPOSE: In vitro inhibitory potency (Ki )-based predictions of P-glycoprotein (P-gp)-mediated drug-drug interactions (DDIs) are hampered by the substantial variability in inhibitory potency. In this study, in vivo-based [I]/Ki values were used to predict the DDI risks of a P-gp substrate dabigatran etexilate (DABE) using physiologically based pharmacokinetic (PBPK) modelling. EXPERIMENTAL APPROACH: A baseline PBPK model was established with digoxin, a known P-gp substrate. The Km (P-gp transport) of digoxin in the baseline PBPK model was adjusted to Km (i) to fit the change of digoxin pharmacokinetics in the presence of a P-gp inhibitor. Then 'in vivo' [I]/Ki of this P-gp inhibitor was calculated using Km (i) /Km . Baseline PBPK model was developed for DABE, and the 'in vivo' [I]/Ki was incorporated into this model to simulate the static effect of P-gp inhibitor on DABE pharmacokinetics. This approach was verified by comparing the observed and the simulated DABE pharmacokinetics in the presence of five different P-gp inhibitors. KEY RESULTS: This approach accurately predicted the effects of five P-gp inhibitors on DABE pharmacokinetics (98-133% and 89-104% for the ratios of AUC and Cmax respectively). The effects of 16 other P-gp inhibitors on the pharmacokinetics of DABE were also confidently simulated. CONCLUSIONS AND IMPLICATIONS: 'In vivo' [I]/Ki and PBPK modelling, used in combination, can accurately predict P-gp-mediated DDIs. The described framework provides a mechanistic basis for the proper design of clinical DDI studies, as well as avoiding unnecessary clinical DDI studies.