A computational model predicts adjunctive pharmacotherapy for cardiac safety via selective inhibition of the late cardiac Na current. (October 2016) - Yang, Pei-Chi; El-Bizri, Nesrine; Romero, Lucia; Giles, Wayne R.; Rajamani, Sridharan; Belardinelli, Luiz; Clancy, Colleen E.
Abstract: Background: The QT interval is a phase of the cardiac cycle that corresponds to action potential duration (APD) including cellular repolarization (T-wave). In both clinical and experimental settings, prolongation of the QT interval of the electrocardiogram (ECG) and related proarrhythmia have been so strongly associated that a prolonged QT interval is largely accepted as surrogate marker for proarrhythmia. Accordingly, drugs that prolong the QT interval are not considered for further preclinical development resulting in removal of many promising drugs from development. While reduction of drug interactions with hERG is an important goal, there are promising means to mitigate hERG block. Here, we examine one possibility and test the hypothesis that selective inhibition of the cardiac late Na current (INaL ) by the novel compound GS-458967 can suppress proarrhythmic markers. Methods and results: New experimental data has been used to calibrate INaL in the Soltis-Saucerman computationally based model of the rabbit ventricular action potential to study effects of GS-458967 on INaL during the rabbit ventricular AP. We have also carried out systematic in silico tests to determine if targeted block of INaL would suppress proarrhythmia markers in ventricular myocytes described byTRIaD : T riangulation, R everse use dependence, beat-to-beat I nstability of action potential duration, and temporal and spatial action potential duration D ispersion . Conclusions: Our computer modeling approach based on experimental data, yields results that suggest that selective inhibition of INaL modifies allTRIaD related parameters arising from acquired Long-QT Syndrome, and thereby reduced arrhythmia risk. This study reveals the potential for adjunctive pharmacotherapy via targeted block of INaL to mitigate proarrhythmia risk for drugs with significant but unintended off-target hERG blocking effects. Highlights: A computational modeling approach was used to demonstrate the potential for adjunctive pharmacotherapy. A multiscale modeling based on experimentally determined drug-channel interactions was applied to predict drug safety. The in silico screen can be expanded with low cost and high efficiency to examine arrhythmias for preclinical drug testing.