Data Science as a Tool to Uncover Dangerous Drug Interactions

Data science could become a new tool to help researchers discover dangerous drug-drug interactions that have yet to come to light.

With an estimated 20% of Americans taking two or more prescription drugs, drugs that may be dangerous when taken together is a growing issue. While prescription drugs on their own have side effects, with hundreds of different drugs on the market, the safety of many drug combinations are unknown.

Researchers at Columbia University Medical Center in New York decided to try and find what drug combinations might lead to abnormal heart rhythms that are potential fatal, a condition known as QT prolongation. Starting with a list of drugs known to potentially cause this problem, the team created an algorithm to sift through an FDA side effects database. Hundreds of potential drug combinations came up.

They then combined this information with 1.6 million electrocardiogram results from a Columbia database to see which drug combinations might have lead to QT prolongation. That narrowed the list down to just 8 combinations.

But one combination in particular caught their eye, they reported in the Journal of The American College of Cardiology. People who were taking the antibiotic Rocephin (ceftriaxone) along with the stomach acid reducing medicine Prevacid (lansoprazole) were 1.4 times more likely to have QT prolongation than people who took only one of the drugs. (A long QT interval can upset the careful timing of the heartbeat and trigger dangerous heart rhythms.)

The results may indicate that data science techniques can be used to find safety signals in drug combinations by mining through vast databases.

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