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Wednesday, April 6 • 2:00pm - 4:30pm
Identifying Novel Drug Interactions Using Data Science: Drug Interaction Prediction Using Latent Signals and EHRs (DIPULSE) [P6]

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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).

Demo/Poster Presenter
avatar for Tal Lorberbaum

Tal Lorberbaum

PhD Candidate in Physiology and Cellular Biophysics, College of Physicians and Surgeons
Tal Lorberbaum is a 3rd year PhD candidate in the lab of Nicholas Tatonetti at Columbia University Medical Center. Tal's work focuses on developing computational approaches to analyze large clinical and biological datasets for the purpose of predicting and understanding adverse effects... Read More →

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Attendees (1)