Aims Given the similarities in QTc response between dogs and humans, dogs are used in pre‐clinical cardiovascular safety studies. The objective of our investigation was to characterize the PKPD relationships and identify translational gaps across species following the administration of three compounds known to cause QTc interval prolongation, namely cisapride, d, l‐sotalol and moxifloxacin. Methods Pharmacokinetic and pharmacodynamic data from experiments in conscious dogs and clinical trials were included in this analysis. First, pharmacokinetic modelling and deconvolution methods were applied to derive drug concentrations at the time of each QT measurement. A Bayesian PKPD model was then used to describe QT prolongation, allowing discrimination of drug‐specific effects from other physiological factors known to alter QT interval duration. A threshold of ≥10 ms was used to explore the probability of prolongation after drug administration. Results A linear relationship was found to best describe the pro‐arrhythmic effects of cisapride, d,l‐sotalol and moxifloxacin both in dogs and in humans. The drug‐specific parameter (slope) in dogs was statistically significantly different from humans. Despite such differences, our results show that the probability of QTc prolongation ≥10 ms in dogs nears 100% for all three compounds at the therapeutic exposure range in humans. Conclusions Our findings indicate that the slope of PKPD relationship in conscious dogs may be used as the basis for the prediction of drug‐induced QTc prolongation in humans. Furthermore, the risk of QTc prolongation can be expressed in terms of the probability associated with an increase ≥10 ms, allowing direct inferences about the clinical relevance of the pro‐arrhythmic potential of a molecule.

Early in the course of clinical development of new non-antiarrhythmic drugs, it is important to assess the propensity of these 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) groups over the sampling interval, thereby neglecting any potential exposure-effect relationship and nonlinearity in the underlying physiological fluctuation in QT values. Thus far, most of the attempted models for characterizing drug-induced QTc-interval prolongation have disregarded the possibility of model parameterization in terms of drug-specific and system-specific properties. Using a database consisting of three compounds with known dromotropic activity, we built a bayesian hierarchical pharmacodynamic (PD) model to describe QT interval, encompassing an individual correction factor for heart rate, an oscillatory component describing the circadian variation, and a truncated maximum-effect model to account for drug effect. The explicit description of the exposure-effect relationship, incorporating various sources of variability, offers advantages over the standard regulatory approach.

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