Does Data Mining Help Identify Harmful Drug Interactions?
Coupling data mining of adverse event reports and electronic health records with targeted laboratory experiments, researchers may have found a way to identify and confirm previously unknown drug interactions, according to a study published Oct. 10 in the Journal of the American College of Cardiology. Specifically, the researchers discovered that when taken together, ceftriaxone and lansoprazole may be associated with an increased risk of acquired long QT syndrome.
Using an algorithm called Latent Signal Detection, Nicholas Tatonetti, PhD, and colleagues scanned data from two independent databases to investigate possible QT interval-prolonging drug-drug interactions: 1.8 million adverse event reports from the U.S. Food and Drug Administration’s (FDA) Adverse Event Reporting System and 1.6 million electrocardiograms from 382,221 patients treated at New York-Presbyterian/CUMC between 1996 and 2014. They used a computer to evaluate millions of data points all at once, which flagged the most likely drug-drug interactions. The researchers then applied more traditional analyses and laboratory experiments to validate the predictions.
Results showed that patients taking ceftriaxone and lansoprazole were 40 percent more likely to have a QT interval above 500 ms, which is the current FDA-stated threshold of clinical concern. Among men taking both of these drugs, QT intervals were 12ms longer than men who took either drug alone. This trend was then validated by cellular data from the electrophysiology experiment, which found that together these drugs block one of the cardiac ion channel responsible for controlling heart rhythm. The researchers found that white women and men appear to be more sensitive to this interaction. They explain that the interaction identified in the data analysis was specific to lansoprazole and ceftriaxone, and not other cephalosporin antibiotics.
In an accompanying editorial, Dan M. Roden, MD, and colleagues write that the findings of this study are not robust enough to advise clinicians to avoid this combination in all patients, but it shows that it is important to examine the effects of these drugs individually and in combination in patients. They explain that with an aging population, it is becoming more common for patients to be on multiple medications, making it more important than ever to find a faster data-driven approach to identify potential interactions among a vast number of possible drug pairs patients could be taking.
“Solving the methodological challenges of developing approaches to systematically leverage these data sources will be a next frontier in identifying and preventing adverse drug reactions,” they conclude.
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