In-silico clinical trial using high performance computational modeling of a virtual human cardiac population to assess drug-induced arrhythmic risk

Drug-induced arrhythmia continues to be a major health issue worldwide. The need for reliable pro-arrhythmic predictors became relevant during early phases of the SarsCoV2 pandemic, when it was uncertain whether the use of hydroxychloroquine (HCQ) and azithromycin (AZM) could be more harmful than beneficial due to their reported pro-arrhythmic effects. In this work we describe a computational framework that employs a gender-specific, in-silico cardiac population to assess cardiac drug-induced QT-prolongation after the administration of a single or a combination of potentially cardiotoxic drugs as HCQ and AZM. This novel computational methodology is capable of reproducing the complex behavior of the clinical electrocardiographic response to drug-induced arrhythmic risk, in-silico. Using high performance computing, the computational framework allows the estimation of the arrhythmic risk in a population, given a variety of doses of one or more drugs in a timely manner and providing markers that can be directly related to the clinical scenario. The pro-arrhythmic behavior observed in subjects within the in-silico trial, was also compared to supplemental in-vitro experiments on a reanimated swine hearts. Evidence of transmurally heterogeneous action potential prolongation after the administration of a large dose of HCQ was an observed mechanism of arrhythmia, both in the in-vitro and the in-silico model. The virtual clinical trial also provided remarkably similar results to recent published clinical data. In conclusion, the in-silico clinical trial on the cardiac population is capable of reproducing and providing evidence of the normal phenotype variants that produce distinct arrhythmogenic outcomes after the administration of one or various drugs.