Pharmacokinetic-pharmacodynamic modeling in the data analysis and interpretation of drug-induced QT/QTc prolongation

Abstract

In this review, factors affecting the QT interval and the methods that are currently in use in the analysis of drug effects on the QT interval duration are overviewed with the emphasis on (population) pharmacokinetic-pharmacodynamic (PK-PD) modeling. Among which the heart rate (HR) and the circadian rhythm are most important since they may interfere with the drug effect and need to be taken into account in the data analysis. The HR effect or the RR interval (the distance between 2 consecutive R peaks) effect is commonly eliminated before any further analysis, and many formulae have been suggested to correct QT intervals for changes in RR intervals. The most often used are Bazett and Fridericia formulae introduced in 1920. They are both based on the power function and differ in the exponent parameter. However, both assume the same exponent for different individuals. More recent findings do not confirm this assumption, and individualized correction is necessary to avoid under- or overcorrection that may lead to artificial observations of drug-induced QT interval prolongation. Despite the fact that circadian rhythm in QT and QTc intervals is a well-documented phenomenon, it is usually overlooked when drug effects are evaluated. This may result in a false-positive outcome of the analysis as the QTc peak due to the circadian rhythm may coincide with the peak of the drug plasma concentration. In view of these effects interfering with a potential drug effect on the QTc interval and having in mind low precision of QT interval measurements, a preferable way to evaluate the drug effect is to apply a population PK-PD modeling. In the literature, however, there are only a few publications in which population PK-PD modeling is applied to QT interval prolongation data, and they all refer to antiarrhythmic agents. In this review, after the most important sources of variability are outlined, a comprehensive population PK-PD model is presented that incorporates an individualized QT interval correction, a circadian rhythm in the individually corrected QT intervals, and a drug effect. The model application is illustrated using real data obtained with 2 compounds differing in their QT interval prolongation potential. The usefulness of combining data of several studies is stressed. Finally, the standard approach based on the raw observations and formal statistics, as described in the Preliminary Concept paper of the International Conference on Harmonization, is briefly compared with the method based on population PK-PD modeling, and the advantages of the latter are outlined.