In a new study, researchers at Mount Sinai School of Medicine have taken a major step toward the ability to predict adverse drug reactions, using genetic, cellular, and clinical information to learn why some medicines cause heart arrhythmias in patients. Published in the April 20 issue of the journal Science Signaling, the new framework described in the study could potentially be applied to the study of medications that treat other diseases and disorders such as epilepsy and autism.
Researchers have learned over the last decade that human genetic make-up contains slight variations that can alter individual responses to medications. Led by Ravi Iyengar, PhD, Dorothy H and Lewis Rosenstiel Professor and Chair, Department of Pharmacology and Systems Therapeutics, and Director, Systems Biology Center, Mount Sinai School of Medicine, the research team was able to harness genetic information in a way that can detect and predict a drug's adverse effect, such as arrhythmias.
"Arrhythmias are side effects in so many different classes of drugs, for diseases ranging from insomnia to epilepsy," said Dr. Iyengar. "By identifying the mechanism causing these adverse events, we can hopefully predict them in other drugs, and help physicians tailor treatment for patients."
Dr. Iyengar's team wanted to find out 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 are caused by mutated genes, and can be dangerous and potentially fatal. Scientists have identified 13 genes associated with LQTS, and the team hypothesized that the drugs that cause arrhythmias act upon the genes' proteins, as well as partnering and neighboring proteins.
Using computation, researchers learned that the proteins formed their own grouping, or a so-called "neighborhood." 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.
Dr. Iyengar's team then cross-referenced their framework with adverse event reporting databases, including that of the U.S. Food and Drug Administration, 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.
"Now that we know our framework may apply across many disease categories, we hope that physicians will eventually be able to use systems biology to help find the best treatment for their patients," said Dr. Iyengar. "These data will also help us improve drug design and development. We look forward to further pursuing this exciting advance."
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