Assessing the Efficiency of Mixed Effects Modeling in Quantifying Metabolism-based Drug-drug Interactions: Using In Vitro Data as an Aid to Assess Study Power

The clinical assessment of metabolic drug-drug interactions (mDDI) may involve population-based pharmacokinetic (POPPK) assessment as part of Phase 3 clinical trials. The elements of such POPPK study design have not been linked to prior information from in vitro experiments. Using in vitro-in vivo extrapolation techniques, implemented within Simcyp algorithms, the influence of POPPK study design (sample size, concentration-time data points, proportion of the population receiving a concomitant medication (COMED)) was studied in relation to the inhibitory potency of the each COMED. Steady-state concentrations of a candidate compound (compound X; mainly metabolized by cytochrome P450 enzymes, CYP3A4 and CYP2D6) in the presence and absence of COMEDs (including ketoconazole, fluconazole, quinidine and paroxetine as inhibitors) were analyzed using non-linear mixed effect modelling (NONMEM). The NONMEM operator was blind to the nature of the COMEDs and the inhibitory effects on model parameters were classified as either statistically (p>0.01 for a change in objective function) or kinetically (COMED effect>2 fold) significant. Using a population study size of 2000, no false-positive cases were identified and, except in one case, no false-negative interaction was observed when >2.5% of patients had received an interacting COMED. The findings increase the confidence in the ability of the mixed effects modelling approach to identify ‘true’ interactions. However, they also emphasize the importance of study design and the potential value of using pre-clinical information from in vitro studies. Recent US Food and Drug Administration guidance on mDDI has put more emphasis on the use of in vitro systems for detecting and anticipating such effects. Combining these data with the framework of non-linear mixed effect modelling seems a natural progression in the field of assessing mDDI.