Mount Sinai researchers develop framework to explain and predict adverse drug reactions

Researchers say their framework could be applied to the study of medications that treat many high-need diseases such as epilepsy and autism

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NEW YORK—In the last two decades, some of the pharmaceutical industry's most successful drugs have had to be pulled from the market after they caused adverse events in patients. Although this risk is usually small, these adverse events obviously have serious consequences for both patients and drugmakers—but researchers at the Mount Sinai School of Medicine believe they are forging a path to lower that risk. Using genetic, cellular and clinical information to explore why some drugs cause heart arrhythmias in patients, the researchers say they have provided a new framework to harness this data in a way that can detect and predict a drug's potential adverse effects.

Publishing their work in the April 20 issue of the journal Science Signaling, the Mount Sinai researchers say their framework could be applied to the study of medications that treat many high-need diseases such as epilepsy and autism. The research team, led by Dr. Ravi Iyengar, director of the Experimental Therapeutics Institute and the Systems Biology Center New York, set out to explore why certain drugs caused arrhythmias similar to those seen in people with Long-QT Syndrome (LQTS), a congenital heart defect that causes changes in the electrical activity of the heart.

These arrhythmias, which can be fatal, are caused by mutated genes. To date, scientists have found 13 genes associated with LQTS. Iyengar's team hypothesized that the drugs that cause arrhythmias act upon the genes' proteins, as well as partnering and neighboring proteins.

Using computation, the researchers learned that the proteins formed their own grouping, or a so-called "neighborhood" within the human interactome, Iyengar says. Other diseases form similarly selective neighborhoods, and comparison of the LQTS neighborhood with other disease-centered neighborhoods suggested a molecular basis for associations between seemingly unrelated diseases that have increased risk of cardiac complications.

"We did something very similar to what explorers did when they discovered a new country or continent," Iyengar explains. "When explorers landed at a certain place, they asked themselves, 'how might I now look at this land?' This is essentially what we did. We looked at genes known to cause LQTS and asked whether there was a neighborhood that was selectively associated with LQTS. Using the explorer analogy, we calculated the distance someone had to walk from one door to another. Our work is a statistical measure of that distance."

By combining the LQTS neighborhood with published genome-wide association study data, the researchers identified previously unknown single-nucleotide polymorphisms likely to affect the QT interval. Certain proteins in this neighborhood overlapped with other neighborhoods associated with other diseases like congestive heart failure, insomnia, autism, schizophrenia, and epilepsy. This discovery showed that several diseases share common molecular features, which could mean people with these conditions are susceptible to other diseases that have proteins in overlapping neighborhoods, Iyengar says.

Iyengar's team then cross-referenced their framework with adverse event reporting databases, including that of the U.S. Food and Drug Administration (FDA), to find that drugs known to cause the electrical malfunction leading to arrhythmia do act on proteins within the same local neighborhood. The framework identified drugs from disease categories ranging from cancer to antifungal treatments that may pose risk for arrhythmias. With the LQTS neighborhood as a classifier, the team predicted drugs likely to have risks for QT effects and validated these predictions with the FDA's Adverse Events Reporting System.

The Mount Sinai team's work illustrates how network analysis can enhance the detection of adverse drug effects associated with drugs in clinical use, Iyengar says.

"We don't want to oversell this," he cautions, "because right now, the predictive power is not very high. We want to get it from 80 to 95 percent as more interactions are discovered and this data gets better refined. This will increase the predicting power, and our work is a first shot—we'll keep doing it for other diseases."

Ultimately, Iyengar says he hopes the Mount Sinai team's framework will help predict and prevent harmful side effects, which have had terrible consequences for both patients and drug developers. He cites Johnson & Johnson's problems with Propulsid, an acid-reflux treatment the company pulled in 2000 after a small number of patients developed irregular, potentially fatal heartbeats, as an example of an incident that can perhaps become a thing of the past.

"When drugs go into broad usage, you start to see these cases," Iyengar says. "Even if we can identify the people who are at risk, a drug might work for 99 percent of the people, but then that 1 percent of people who will have adverse events leads to trouble. The drug will have to be pulled, even though for most people, it is a very effective drug. In the future, we may be able to have different variants of a drug, because drug developers may be able to 'tweak' the chemicals to lessen the side effects in patients you know have certain mutations. We hope we have made one small step to move that along."

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