Assessment of interaction potential of AZD2066 using in vitro metabolism tools, physiologically based pharmacokinetic modelling and in vivo cocktail data

Abstract

Static and dynamic (PBPK) prediction models were applied to estimate the drug-drug interaction (DDI) risk of AZD2066. The predictions were compared to the results of an in vivo cocktail study. Various in vivo measures for tolbutamide as a probe agent for cytochrome P450 2C9 (CYP2C9) were also compared. In vitro inhibition data for AZD2066 were obtained using human liver microsomes and CYP-specific probe substrates. DDI prediction was performed using PBPK modelling with the SimCYP simulator™ or static model. The cocktail study was an open label, baseline, controlled interaction study with 15 healthy volunteers receiving multiple doses of AD2066 for 12days. A cocktail of single doses of 100mg caffeine (CYP1A2 probe), 500mg tolbutamide (CYP2C9 probe), 20mg omeprazole (CYP2C19 probe) and 7.5mg midazolam (CYP3A probe) was simultaneously applied at baseline and during the administration of AZD2066. Bupropion as a CYP2B6 probe (150mg) and 100mg metoprolol (CYP2D6 probe) were administered on separate days. The pharmacokinetic parameters for the probe drugs and their metabolites in plasma and urinary recovery were determined. In vitro AZD2066 inhibited CYP1A2, CYP2B6, CYP2C9, CYP2C19 and CYP2D6. The static model predicted in vivo interaction with predicted AUC ratio values of >1.1 for all CYP (except CYP3A4). The PBPK simulations predicted no risk for clinical relevant interactions. The cocktail study showed no interaction for the CYP2B6 and CYP2C19 enzymes, a possible weak inhibition of CYP1A2, CYP2C9 and CYP3A4 activities and a slight inhibition (29%) of CYP2D6 activity. The tolbutamide phenotyping metrics indicated that there were significant correlations between CLform and AUCTOL, CL, Aemet and LnTOL24h. The MRAe in urine showed no correlation to CLform. DDI prediction using the static approach based on total concentration indicated that AZD20066 has a potential risk for inhibition. However, no DDI risk could be predicted when a more in vivo-like dynamic prediction method with the PBPK with SimCYP™ software based on early human PK data was used and more parameters (i.e. free fraction in plasma, no DDI risk) were taken into account. The clinical cocktail study showed no or low risks for clinical relevant DDI interactions. Our findings are in line with the hypothesis that the dynamic prediction method predicts DDI in vivo in humans better than the static model based on total plasma concentrations.