Evaluation of methods for predicting drug-drug interactions by Monte Carlo simulation.

The ratio of the inhibitor concentration to the inhibition constant (K(i)) is used as the index for predicting drug-drug interactions involving metabolic inhibition. The maximum unbound concentration in the circulation (I(p, max, u)) and the maximum unbound concentration at the inlet to the liver (I(u, max)) have been used for the inhibitor concentration. In the present study, the methods for predicting drug-drug interactions using these concentrations were evaluated by Monte Carlo simulation. Information on the pharmacokinetic parameters of drugs and the K(i) values for cytochrome P450(CYP) were obtained from the literature. It was assumed that the pharmacokinetic parameters (intrinsic metabolic clearance, renal clearance and distribution volume for unbound fraction), serum protein binding and K(i) value for substrate and inhibitor are all log-normally distributed. Correlations among the parameters were assessed and were used for further simulations. A change in AUC of the substrate following co-administration of the inhibitor was simulated 1000 times using the physiologically based pharmacokinetic (PBPK) model. The percent of the drug combinations which exhibited a significant increase in the AUC (>125%) was 16.2% of the total combinations. The cases where the I/K(i) using I(u, max) and I(p, max, u) overestimated compared with the actual increased ratio of AUC (false positive prediction) were 41.2% and 16.7%, respectively. The cases where the predicted ratios of AUC from I/K(i) using I(u, max) and I(p, max, u) were comparable with the actual ratio were 3.2% and 8.7%, respectively. The prediction using I(p, max, u) was, thus, more reliable than that using I(u, max). However, in the case of I(u, max), there was no case where the actual increased ratio of AUC was greater than that predicted from I/K(i) (false negative prediction). On the other hand, for I(p, max, u), the rate of false negative prediction was 1.4%. The present study indicates that I(u, max) is better than I(p, max, u) for avoiding false negative predictions and I(p, max, u) is better than I(u, max) for increasing the probability of true positive and true negative predictions and avoiding false positive predictions.In conclusion, it is necessary to use both predictions involving I(u, max) and I(p, max, u) and to use them early on during the development stage of drug candidates. In order to finally choose which compound(s) to take forward to clinical trials, when predicting an interaction, the more quantitative and reliable method based on the PBPK model needs to be used.