Big Data Offers Insight to Drug Interactions

Scientists have discovered a novel way to use big data to determine potentially harmful drug interactions.

Many patients must take multiple medications for multiple diseases, especially elderly patients. While these medications can be safe on their own, taking multiple medications drastically increases the risk of drug-drug interactions.

These interactions can cause serious complications that may result in hospitalization, or even death. Many drug interactions are already known, but some are unknown to physicians, patients, and drug manufacturers.

Identifying combinations of medications that increase the risk of adverse events has become a priority as polypharmacy increases, according to the study published by the Journal of the American College of Cardiology. The scientists created an algorithm to search the FDA’s drug-side effect database for drug pairs that resulted in side effects of the condition, with the initial search resulting in hundreds of pairs.

To narrow down the results, they also examined electrocardiograms from a database of patient records to determine if the resulting pairs had an effect on the heart. They discovered 4 pairs of drugs that drastically increased the risk of the heart condition.

Then, the scientists tested a pair of the drugs on in vitro heart cells, and discovered the mechanism behind why the drugs disrupted the heart’s electrical activity. They found that ceftriaxone (Rocephin) and lansoprazole (Prevacid) taken together can increase the risk of long QT syndrome, which causes abnormal heart rhythms and can be fatal.

When taken together, the drugs blocked the hERG pathway, which maintains QT interval. Taking the drugs alone was not shown to increase cardiovascular risks. However, patients who took these medications together were 1.4 times more likely to have a prolonged QT interval compared with patients who were taking these drugs alone, according to the study.

“What’s most surprising is that you can go from a database of billions of data points to making a prediction that 2 molecules together can change the functions of a protein in a single heart cell,” said the study’s senior author, Nicholas Tatonetti, PhD. “It means these algorithms are really useful in medical research.”

These findings suggest that big data may be able to develop better methods for detecting drug interactions, and prevent adverse health events. The scientists have informed the FDA and the manufacturers of their findings, so that warnings can be updated.

They are planning to move forward and test the other 3 pairs of potentially dangerous drugs, and must also conduct clinical trials to further validate their findings.

“Three independent lines of evidence show us that this is a signal worth paying attention to,” said the study’s lead author Tal Lorberbaum. “We hope that a clinical trial will confirm that this is an actionable discovery.”