Action Potential Recording and Pro-arrhythmia Risk Analysis in Human Ventricular Trabeculae

To assess drug-induced pro-arrhythmic risk, especially Torsades de Pointe (TdP), new models have been proposed, such as in-silico modeling of ventricular action potential (AP) and stem cell-derived cardiomyocytes (SC-CMs). Previously we evaluated the electrophysiological profile of 15 reference drugs in hESC-CMs and hiPSC-CMs for their effects on intracellular AP and extracellular field potential, respectively. Our findings indicated that SC-CMs exhibited immature phenotype and had the propensity to generate false positives in predicting TdP risk. To expand our knowledge with mature human cardiac tissues for drug-induced pro-arrhythmic risk assessment, human ventricular trabeculae (hVT) from ethically consented organ donors were used to evaluate the effects of the same 15 drugs (8 torsadogenic, 5 non-torsadogenic, and 2 discovery molecules) on AP parameters at 1 and 2 Hz. Each drug was tested blindly with 4 concentrations in duplicate trabeculae from 2 hearts. To identify the pro-arrhythmic risk of each drug, a pro-arrhythmic score was calculated as the weighted sum of percent drug-induced changes compared to baseline in various AP parameters, including AP duration and recognized pro-arrhythmia predictors such as triangulation, beat-to-beat variability and incidence of early-afterdepolarizations, at each concentration. In addition, to understand the translation of this preclinical hVT AP-based model to clinical studies, a ratio that relates each testing concentration to the human therapeutic unbound Cmax (Cmax) was calculated. At a ratio of 10, for the 8 torsadogenic drugs, 7 were correctly identified by the pro-arrhythmic score; 1 was mislabeled. For the 5 non-torsadogenic drugs, 4 were correctly identified as safe; 1 was mislabeled. Calculation of sensitivity, specificity, positive predictive value, and negative predictive value indicated excellent performance. For example, at a ratio of 10, scores for sensitivity, specificity, positive predictive value and negative predictive values were 0.88, 0.8, 0.88 and 0.8, respectively. Thus, the hVT AP-based model combined with the integrated analysis of pro-arrhythmic score can differentiate between torsadogenic and non-torsadogenic drugs, and has a greater predictive performance when compared to human SC-CM models.