Data Science Day @ Columbia University

Drug-induced prolongation of the QT interval on the electrocardiogram (long QT syndrome, LQTS) can lead to a potentially fatal ventricular arrhythmia called Torsades de Pointes (TdP). Over 40 drugs with both cardiac and non-cardiac indications have been found to increase risk for TdP, but drug-drug interactions contributing to LQTS (QT-DDIs) remain poorly characterized. Traditional methods for mining observational healthcare data are poorly equipped to detect QT-DDI signals due to low reporting numbers and a lack of direct evidence for LQTS. We developed an integrative data science pipeline that effectively circumvents these limitations by identifying latent signals for QT-DDIs in the FDA’s Adverse Event Reporting System and retrospectively validating these predictions using electrocardiogram data in electronic health records. The method generated 8 novel QT-DDIs. We experimentally validated one of these pairs – lansoprazole (proton-pump inhibitor) and ceftriaxone (cephalosporin antibiotic) – using patch-clamp electrophysiology to assess block of the hERG channel (IKr current).