Can Bayes Prevent QTC-interval prolongation? A challenge beyond random effects.
Kevin M. Krudys, Oscar Della-Pasqua
Clinical Pharmacology & Discovery Medicine, GlaxoSmithKline
Objectives: Early in the course of clinical development, it is important to be able to assess the propensity of non-antiarrhythmic drugs to prolong the QT/QTc interval. The current regulatory guidelines suggest using the largest time-matched mean difference between drug and placebo (baseline-adjusted) over the sampling interval[1], thereby neglecting any exposure-effect relationship and underlying nonlinearity in physiological fluctuation of QT interval. Thus far, most modelling attempts used to characterise drug-induced QT interval prolongation do not account for the limitations in clinical data or disregard model parameterisation in terms of drug-specific and system-specific properties. The aim of this study is to use a Bayesian approach to characterise the exposure-effect relationship of three compounds known to prolong the QT/QTc interval. We show the advantage of this approach to avoid false positives/negatives and optimise the design of QT/QTc specific studies.
Methods: The database consisted of 4 studies of moxifloxacin (400 mg), one study of grepafloxacin (600 mg) and one study of d,l-sotalol (160mg) with a total of 453 subjects. Population PK models were built for each compound to simulate individual concentration values at the times of QT measurements. The pharmacodynamic (PD) model describing QT interval comprises three components: an individual correction factor for RR interval (heart rate), an oscillatory component describing the circadian variation and a truncated Emax model to capture drug effect[2]. Model building was performed in WinBUGS version 1.4. The posterior distributions provided by WinBUGS allowed for the simulation of scenarios to investigate the impact of different study designs.
Results: The PD model provided estimates of a heart rate correction factor of mean (95% credible interval) 0.30(0.28 - 0.32) and a 24 hour circadian component with amplitude 3.34(2.32 - 4.39) ms and phase 3.23(3.01 - 3.62) hrs. The estimated QT prolongation due to moxifloxacin, grepafloxacin and d,l-sotalol at the 75th percentile of the observed concentration range was 7.19(6.06 - 8.35), 19.43(15.83 - 23.13) and 13.68(11.52 - 15.94) ms, respectively. Simulations suggest that the use of a PK/PD model can establish the QT liability of a compound using considerably fewer subject and/or samples than the standard approach.
Conclusions: A population model of QT interval was developed for three compounds known to cause QT prolongation. The explicit description of the exposure-effect relationship, incorporating various sources of variability offers advantages compared to the standard regulatory approach in that it yields estimates of the exposure required to reach clinically relevant increase in QT interval without the requirement for parameterisation of maximum effect (Emax). In addition, the posterior (predictive) distribution can be used directly to translate liability to QT interval prolongation across varying dose ranges.
References:
[1] Guidance for Industry: E14 Clinical evaluation of QT/QTc interval prolongation and proarrhythmic potential for non-antiarrhythmic drugs. http://www.fda.gov/cder/guidance/6922fnl.pdf
[2] Bachman WJ, Gillespie WR. Truncated sigmoid Emax models: a reparameterization of the sigmoid Emax model for use with truncated PK/PD data. In American Society for Clinical Pharmacology and Therapeutics (ASCEPT) Meeting 1998.
