Karel Van Ammel

Drug-induced long QT in isolated rabbit Purkinje fibers: importance of action potential duration, triangulation and early afterdepolarizations

In the present study, we investigated three drug-induced long-QT syndromes in isolated rabbit Purkinje fibers in order to identify the relationship of action potential duration (APD), triangulation of action potentials (APD(90)-APD(40)) and early afterdepolarizations. Isolated rabbit Purkinje fibers were superperfused in Tyrode solution with solvent, indapamide (1 x 10 (-4) M, an I(ks) blocker mimicking long QT1), dofetilide (1 x 10 (-9), 1 x 10 (-8) or 1 x 10 (-7) M, an I(kr) blocker mimicking long QT2) or anthopleurin (1 x 10 (-8) M, an inhibitor of the inactivation of the I(Na(+)) current mimicking long QT3) (n=8 per group) for 25 min, and stimulated at 1 Hz for 20 min and at 0.2 Hz for another 5 min. Indapamide did not change APD and triangulation or elicit early afterdepolarizations even in the presence of beta-adrenergic stimulation with isoproterenol. Dofetilide concentration-dependently prolonged APD(90), increased triangulation and elicited early afterdepolarizations. Anthopleurin markedly increased APD(90) as well as triangulation and elicited early afterdepolarizations. The induction of early afterdepolarizations by dofetilide and anthopleurin was associated with a prolongation of APD(90) or an increase in triangulation, but not with a change in APD(40). Moreover, the degree of the increase in the triangulation was larger than that of APD(90) in long QT2 (dofetilide-induced) and long QT3 (anthopleurin-induced) models in isolated rabbit Purkinje fibers. Our present study indicates that rabbit Purkinje fibers can be used as long QT2 (dofetilide-mimicking) and LQT3 (anthopleurin-mimicking) syndrome models, and confirms that drug-induced long QT1 (indapamide-mimicking) is absent. Our present study also shows the relationship between a prolongation of APD(90) or increase in triangulation and the induction of early afterdepolarizations with dofetilide (I(kr) blocker) and anthopleurin (I(Na) modulator) in isolated rabbit Purkinje fibers.

Predicting drug‐induced slowing of conduction and pro‐arrhythmia: identifying the ‘bad’ sodium current blockers

Background and purpose:u2002 The regulatory guidelines (ICHS7B) for the identification of only drug‐induced long QT and pro‐arrhythmias have certain limitations.

The fentanyl/etomidate-anaesthetised beagle (FEAB) dog: A versatile in vivo model in Cardiovascular Safety Research

The purpose of conducting cardiovascular safety pharmacology studies is to investigate the pharmacological profiles of new molecular entities (NMEs) and provide data that can be used for optimization of a possible new drug, and help make a selection of NMEs for clinical development. An anaesthetised dog preparation has been used for more than two decades by our department to measure multiple cardiovascular and respiratory parameters and to evaluate different scientific models, leading to more in-depth evaluation of drug-induced cardiovascular effects. An anaesthetic regime developed in house (induction with lofentanil, scopolamine and succinylcholine, and maintenance with fentanyl and etomidate) gives us a preparation free of pain and stress, with minimal effects on the cardiovascular system. This anaesthetic regime had minimal influences on circulating catecholamine levels, on the baroreflex sensitivity, and on all measured basal parameters compared to conscious dogs. All parameters were stable for at least 3 h, with acceptable tolerance intervals, evaluated over 99 safety studies with 3 vehicle treatments (saline, 10% and 20% hydroxypropyl-beta-cyclodextrin). This translates into a highly sensitive model for detecting possible drug-induced effects of NMEs with different mechanisms of action such as: Ca-, Na-, I(Kr)-, I(Ks)-channel blockers, K- and Ca-channel activators, alpha1- and beta-agonists, and muscarinic antagonists. Fentanyl in combination with etomidate is a successful anaesthetic regime in humans [Stockham, R.J., Stanley, T.H., Pace, N.L., King, K., Groen, F. & Gillmor, S.T. (1987). Induction of anaesthesia with fentanyl or fentanyl plus etomidate in high-risk patients. Journal of Cardiothoracic Anesthesia. 1(1), 19-23.]. In the anaesthetised dog, QT correction factors (Van de Water correction and body temperature correction) and risk factors (total, short-term and long-term instability) have been evaluated, using this regime [Van de Water, A., Verheyen, J., Xhonneux, R. & Reneman, R. (1989). An improved method to correct the QT interval of the electrocardiogram for changes in heart rate. Journal of Pharmacological Methods, 22, 207-217.; van der Linde, H.J., Van Deuren, B., Teisman, A., Towart, R. & Gallacher, D.J. (2008). The effect of changes in core body temperature on the QT interval in beagle dogs: A previously ignored phenomenon, with a method for correction. British Journal of Pharmacology, 154, 1474-1481.; van der Linde, H.J., Van de Water, A., Loots, W., Van Deuren, B., Lu, H.R., Van Ammel, K., et al. (2005) A new method to calculate the beat-to-beat instability of QT duration in drug-induced long QT in anaesthetised dogs. Journal of Pharmacological and Toxicological Methods, 52, 168-177.]. Furthermore, this anaesthetic protocol has been used to create different scientific models (long QT, short QT) with different specific end-points (ventricular fibrillation, adrenergic- or pause-dependent TdP) and also their specific precursors: e.g. aftercontractions, phase 2 EADs, phase 3 EADs, DADs, T-wave morphology changes, T-wave alternans, R-on-T, transmural and interventricular dispersion [Gallacher, D.J., Van de Water, A., van der Linde, H.J., Hermans, A.N., Lu, H.R., Towart, R., et al. (2007). In vivo mechanisms precipitating torsade de pointes in canine model of drug-induced long QT1 syndrome. Cardiovascular Research, 76-2, 247-256.]. This paper gives a brief overview of the stability, reproducibility, sensitivity and utility of a well-validated anaesthetised dog model.

Analysis of cross-over designs with serial correlation within periods using semi-parametric mixed models

The use of semi-parametric mixed models has proven useful in a wide variety of settings. Here, we focus on the application of the methodology in the particular case of a cross-over design with relatively long sequences of repeated measurements within each treatment period and for each subject. Other than an overall measure of the difference between each one of the experimental groups and the control group, specific time point comparisons may also be of interest. To that effect, we propose the use of flexible semi-parametric mixed models, enabling the construction of simulation-based simultaneous confidence bands. The bands take into account both between- and within-subject variabilities, while simultaneously correcting for multiple time point comparisons. Owing to the relatively long sequences of measurements per subject, the presence of serially correlated errors is anticipated and investigated. We illustrate how several formulations of semi-parametric mixed models can be fitted and the construction of simulation-based simultaneous confidence bands using SAS PROC MIXED.

Application of Semiparametric Mixed Models and Simultaneous Confidence Bands in a Cardiovascular Safety Experiment with Longitudinal Data

Several pharmacological studies involve experiments aimed at testing for a difference between experimental groups wherein the data are longitudinal in nature, frequently with long sequences per subject. Oftentimes, treatment effect, if present, is not constant over time. In such situations, imposing a parametric mean structure can be too complicated and/or restrictive. A more flexible approach is to model the mean using a semiparametric smooth function, estimated using, for example, penalized smoothing splines. We formulate a series of models exhibiting how the group-specific mean profiles could possibly differ. Once an appropriate model is chosen, interest lies in identifying specific time points where the groups differ. For this purpose, we propose the use of simultaneous confidence bands around the fitted models wherein the bands take into account within and between-subject variability, as well as variability arising from smoothing.