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Coupling Data Mining and Laboratory Experiments to Discover Drug Interactions Causing QT Prolongation.
Tal Lorberbaum1, Kevin J Sampson2, Jeremy B Chang3
1Department of Physiology and Cellular Biophysics, Columbia University, New York, New York; Department of Biomedical Informatics, Columbia University, New York, New York.
Journal of the American College of Cardiology
|October 15, 2016
Summary
A new data-driven pipeline effectively identifies QT interval-prolonging drug-drug interactions (QT-DDIs). The combination of ceftriaxone and lansoprazole significantly increases the risk of acquired long QT syndrome.
Area of Science:
- Pharmacology
- Cardiology
- Data Science
Background:
- QT interval-prolonging drug-drug interactions (QT-DDIs) pose a risk for life-threatening arrhythmias.
- These interactions are often discovered post-market, leading to prolonged periods of risk.
Purpose of the Study:
- To develop a data-driven pipeline for discovering QT-DDIs.
- To identify novel QT-DDIs using a combination of real-world data and laboratory validation.
Main Methods:
- Mining 1.8 million adverse event reports for QT-DDI signals.
- Corroborating signals using 1.6 million electrocardiogram results from EHR data.
- Validating top candidates using in vitro hERG channel block assays.
Main Results:
- Identified ceftriaxone and lansoprazole as a QT-DDI pair.
- EHR data showed significantly longer QTc intervals and increased likelihood of QTc > 500 ms in patients taking both drugs.
- In vitro experiments confirmed hERG channel blockade by the drug combination at clinical concentrations.
Conclusions:
- Combining data mining with laboratory experiments efficiently identifies QT-DDIs.
- Ceftriaxone and lansoprazole combination therapy is associated with an elevated risk of acquired long QT syndrome.
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