title: Physiologically based pharmacokinetic modelling and in vivo [I]/K(i) accurately predict P-glycoprotein-mediated drug-drug interactions with dabigatran etexilate.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  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
  '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.
authors.affiliation: The Hamner Institutes for Health Sciences, Research Triangle Park, NC, USA.
authors.forename: Yuansheng
authors.initials: Y
authors.lastname: Zhao
authors.forename: Zhe-Yi
authors.initials: ZY
authors.lastname: Hu
mesh_terms: D020168:ATP Binding Cassette Transporter, Subfamily B, Member 1 / Q000037:antagonists & inhibitors / Q000378:metabolism*; D000638:Amiodarone / Q000031:analogs & derivatives / Q000494:pharmacology; D001562:Benzimidazoles / Q000097:blood / Q000493:pharmacokinetics*; D017291:Clarithromycin / Q000494:pharmacology; D000069604:Dabigatran; D004077:Digoxin / Q000097:blood / Q000493:pharmacokinetics; D000077764:Dronedarone; D004347:Drug Interactions; D006801:Humans; D008954:Models, Biological; D011725:Pyridines / Q000097:blood / Q000493:pharmacokinetics*; D011802:Quinidine / Q000494:pharmacology; D014700:Verapamil / Q000494:pharmacology; D015091:beta-Alanine / Q000031:analogs & derivatives / Q000097:blood
chemical_list: D020168:ATP Binding Cassette Transporter, Subfamily B, Member 1; D001562:Benzimidazoles; D011725:Pyridines; D015091:beta-Alanine; D004077:Digoxin; D014700:Verapamil; D017291:Clarithromycin; D000069604:Dabigatran; D011802:Quinidine; D000077764:Dronedarone; D000638:Amiodarone
keywords: P-glycoprotein; dabigatran etexilate; digoxin; physiologically based pharmacokinetic modelling; ‘in vivo’ Ki