Use of clearance concepts and modeling techniques in the prediction of metabolic drug–drug interactions

General Framework to Quantitatively Predict Pharmacokinetic Induction Drug-Drug Interactions Using In Vitro Data

INTRODUCTION

Metabolic inducers can expose people with polypharmacy to adverse health outcomes. A limited fraction of potential drug-drug interactions (DDIs) have been or can ethically be studied in clinical trials, leaving the vast majority unexplored. In the present study, an algorithm has been developed to predict the induction DDI magnitude, integrating data related to drug-metabolising enzymes.

METHODS

The area under the curve ratio (AUC_ratio) resulting from the DDI with a victim drug in the presence and absence of an inducer (rifampicin, rifabutin, efavirenz, or carbamazepine) was predicted from various in vitro parameters and then correlated with the clinical AUC_ratio (N = 319). In vitro data including fraction unbound in plasma, substrate specificity and induction potential for cytochrome P450s, phase II enzymes and uptake, and efflux transporters were integrated. To represent the interaction potential, the in vitro metabolic metric (IVMM) was generated by combining the fraction of substrate metabolised by each hepatic enzyme of interest with the corresponding in vitro fold increase in enzyme activity (E) value for the inducer.

RESULTS

Two independent variables were deemed significant and included in the algorithm: IVMM and fraction unbound in plasma. The observed and predicted magnitudes of the DDIs were categorised accordingly: no induction, mild, moderate, and strong induction. DDIs were assumed to be well classified if the predictions were in the same category as the observations, or if the ratio between these two was < 1.5-fold. This algorithm correctly classified 70.5% of the DDIs.

CONCLUSION

This research presents a rapid screening tool to identify the magnitude of potential DDIs utilising in vitro data which can be highly advantageous in early drug development.

Usefulness of Two-Compartment Model-Assisted and Static Overall Inhibitory-Activity Method for Prediction of Drug-Drug Interaction

Our study of drug-drug interaction (DDI) started with the clarification of unusually large DDI observed between ramelteon (RAM) and fluvoxamine (FLV). The main cause of this DDI was shown to be the extremely small hepatic availability of RAM (vF_h). Traditional DDI prediction assuming the well-stirred hepatic extraction kinetic ignores the relative increase of vF_h by DDI, while we could solve this problem by use of the tube model. Ultimately, we completed a simple and useful method for prediction of DDI. Currently, DDI prediction becomes more complex and difficult when examining issues such as dynamic changes in perpetrator level, inhibitory metabolites, etc. The regulatory agents recommend DDI prediction by use of some sophisticated methods. However, they seem problematic in requiring plural in vitro data that reduce the flexibility and accuracy of the simulation. In contrast, our method is based on the static and two-compartment models. The two-compartment model has advantages in that it uses common pharmacokinetics (PK) parameters determined from the actual clinical data, guaranteeing the simulation of the reference standard in DDI. Our studies confirmed that dynamic changes in perpetrator level do not make a difference between static and dynamic methods. DDIs perpetrated by FLV and itraconazole were successfully predicted by use of the present method where two DDI predictors [perpetrator-specific inhibitory activities toward CYP isoforms (pA_i, CYPs) and victim-specific fractional CYP-isoform contributions to the clearance (vf_m, CYPs)] are determined successively as shown in the graphical abstract. Accordingly, this approach will accelerate DDI prediction over the traditional methods.

Novel strategies for microdose studies using non-radiolabeled compounds

Microdose studies using non-radiolabeled compounds enable assessment of the clinical pharmacokinetics of drug candidates in humans without the need to synthesize radiolabeled compounds. We have demonstrated that the quantification limits of many drugs measured by LC-MS/MS are low enough to allow estimation of their pharmacokinetic parameters following administration of a microdose. Our previous microdose studies with LC-MS/MS demonstrated the linear pharmacokinetics of fexofenadine between microdoses and therapeutic doses. We also obtained time profiles of plasma concentrations of nicardipine and its multiple metabolites following administration of a microdose. A significant advantage of using non-radiolabeled compounds is the ability to perform cassette microdose studies. By administering multiple drug candidates to the same subject, we can select compounds with appropriate pharmacokinetic properties simultaneously. We can also clarify major factors dominating the pharmacokinetics of drug candidates by cocktail microdosing of the test compounds and probe substrates with or without specific inhibitors for enzymes/transporters.