In a major step toward predicting adverse drug reactions, systems biologists at Mount Sinai School of Medicine in New York City have integrated genetic, cellular and clinical information to find out why certain medicines can trigger fatal heart arrhythmias. The new framework could be used to study other cardiac disorders and certain neurological diseases, including epilepsy and autism, and could aid the advance of personalized medicine.
Since the completion of the Human Genome Project 10 years ago, scientists have compiled vast amounts of data about our genes and the proteins they make. From this information, they know where genes are located, which mutations lead to disease and how proteins interact with each other.
Researchers also better understand how slight genetic variations, or single-nucleotide polymorphisms (SNPs), in each of our genomes can alter our individual responses to medications.
The Mount Sinai researchers now have taken the next step: They have integrated this genetic information into a framework that can detect and predict a drug's dangerous action. Their results appear in the April 20 issue of Science Signaling.
Led by biochemist Ravi Iyengar, the researchers specifically wanted to understand how drugs can produce a side effect similar to the heart arrhythmias seen in people with a congenital condition called long QT syndrome, which causes a prolonged "QT interval" during the heart's electrical cycle.
"Many drugs, including some that treat nausea or others that treat acid reflux, are known to cause arrhythmias," says Iyengar, who directs one of the National Centers for Systems Biology funded by NIH's National Institute of General Medical Sciences (NIGMS). "By doing a network analysis, one can start to figure out the common mechanism."
Scientists have identified 13 genes associated with long QT, and the Iyengar team hypothesized that the drugs that cause the arrhythmias act upon the genes' proteins, as well as partnering and neighboring proteins.
To prove this, the researchers started with the currently known human interactome--a map of some 11,000 human proteins and their interactions. Using computation, they narrowed it down to the proteins associated with the long QT genes.
They found that these 1,629 proteins formed their own area, or "neighborhood," within the interactome. About one-quarter of the proteins also belonged to 31 other disease neighborhoods, including those for other arrhythmia-related diseases, congestive heart failure, insomnia, autism, schizophrenia and epilepsy.
Iyengar says these results show that complex diseases can have identifiable boundaries yet may share common molecular features. The overlap, he adds, also indicates that people who have one disease may have the propensity to develop the others.
Mapping the long QT neighborhood let the Iyengar group explore the relationship between these proteins and drugs that cause arrhythmias. The researchers used several databases, including the U.S. Food and Drug Administration's Adverse Events Reporting System, to find that drugs known to cause long QT do act on proteins within the local neighborhood. The framework also identified other drugs likely to cause heart arrhythmias.
"One can start to explain why these adverse drug events are occurring," says Iyengar.
Using genome-wide association studies that look at genetic variation across a population, the researchers also identified additional SNPs that may serve as biological markers for QT interval-related diseases and potentially for how an individual might respond to certain drugs.
"The ability to predict these variations and adverse drug events is a true research advance," says Sarah Dunsmore, a physiologist at NIGMS. "The work also demonstrates a practical, real-life application of systems biology, a relatively young field that is still looking for new ways to integrate vast and complex information into insightful biomedical knowledge and applications."
While Iyengar says the framework is just the first step toward predicting a drug's adverse actions, he and others can use it to generate new hypotheses to test in the lab or to help improve drug design and development. The Iyengar group is already developing a similar framework for cancer drugs.
The ultimate goal, explains Iyengar, is for doctors to use the framework as a tool for choosing the safest medications for each patient.