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Drug Saf. 2016 May;39(5):433-41. doi: 10.1007/s40264-016-0393-1.

An Integrative Data Science Pipeline to Identify Novel Drug Interactions that Prolong the QT Interval.

Author information

1
Department of Physiology and Cellular Biophysics, Columbia University, New York, NY, USA.
2
Department of Biomedical Informatics, Columbia University, 622 West 168th St. PH20, New York, NY, 10032, USA.
3
Departments of Systems Biology and Medicine, Columbia University, New York, NY, USA.
4
Department of Pharmacology, Columbia University, New York, NY, USA.
5
AZCERT, Inc., Oro Valley, AZ, USA.
6
Department of Biomedical Informatics, Columbia University, 622 West 168th St. PH20, New York, NY, 10032, USA. nick.tatonetti@columbia.edu.
7
Departments of Systems Biology and Medicine, Columbia University, New York, NY, USA. nick.tatonetti@columbia.edu.
8
Observational Health Data Science and Informatics, New York, NY, USA. nick.tatonetti@columbia.edu.

Abstract

INTRODUCTION:

Drug-induced prolongation of the QT interval on the electrocardiogram (long QT syndrome, LQTS) can lead to a potentially fatal ventricular arrhythmia known as torsades de pointes (TdP). Over 40 drugs with both cardiac and non-cardiac indications are associated with increased risk of 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 lack of direct evidence for LQTS.

OBJECTIVE:

We hypothesized that LQTS could be identified latently using an adverse event (AE) fingerprint of more commonly reported AEs. We aimed to generate an integrated data science pipeline that addresses current limitations by identifying latent signals for QT-DDIs in the US FDA's Adverse Event Reporting System (FAERS) and retrospectively validating these predictions using electrocardiogram data in electronic health records (EHRs).

METHODS:

We trained a model to identify an AE fingerprint for risk of TdP for single drugs and applied this model to drug pair data to predict novel DDIs. In the EHR at Columbia University Medical Center, we compared the QTc intervals of patients prescribed the flagged drug pairs with patients prescribed either drug individually.

RESULTS:

We created an AE fingerprint consisting of 13 latently detected side effects. This model significantly outperformed a direct evidence control model in the detection of established interactions (p = 1.62E-3) and significantly enriched for validated QT-DDIs in the EHR (p = 0.01). Of 889 pairs flagged in FAERS, eight novel QT-DDIs were significantly associated with prolonged QTc intervals in the EHR and were not due to co-prescribed medications.

CONCLUSIONS:

Latent signal detection in FAERS validated using the EHR presents an automated and data-driven approach for systematically identifying novel QT-DDIs. The high-confidence hypotheses flagged using this method warrant further investigation.

PMID:
26860921
PMCID:
PMC4835515
DOI:
10.1007/s40264-016-0393-1
[Indexed for MEDLINE]
Free PMC Article
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