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Physiologically based pharmacokinetic modelling and in vivo [I]/ K i accurately predict P ‐glycoprotein‐mediated drug‐drug interactions with dabigatran etexilate
Author(s) -
Zhao Yuansheng,
Hu ZheYi
Publication year - 2014
Publication title -
british journal of pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.432
H-Index - 211
eISSN - 1476-5381
pISSN - 0007-1188
DOI - 10.1111/bph.12533
Subject(s) - physiologically based pharmacokinetic modelling , pharmacokinetics , in vivo , pharmacology , p glycoprotein , chemistry , digoxin , potency , drug , medicine , in vitro , biochemistry , biology , multiple drug resistance , heart failure , microbiology and biotechnology , antibiotics
Background and purpose In vitro inhibitory potency ( K i )‐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 ]/ K i 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 K m ( P ‐gp transport) of digoxin in the baseline PBPK model was adjusted to K m i to fit the change of digoxin pharmacokinetics in the presence of a P ‐gp inhibitor. Then ‘ in vivo ’ [ I ]/ K i of this P ‐gp inhibitor was calculated using K m i / K m . Baseline PBPK model was developed for DABE , and the ‘ in vivo ’ [ I ]/ K i 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 C max respectively). The effects of 16 other P ‐gp inhibitors on the pharmacokinetics of DABE were also confidently simulated. Conclusions and implications ‘ In vivo ’ [ I ]/ K i 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.