Center to Find Drug Combinations that Reduce Side Effects

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Newswise (New York, NY Sept. 11, 2014) A research team from the Icahn School of Medicine at Mount Sinai today received a $12 million grant from the National Institutes of Health to create a center that will screen massive data sets for new uses of existing drugs, and confirm them in human cell tests. The centers first mission will be to find FDA-approved drugs that reduce side effects when paired with hundreds of leading drugs against common, deadly diseases.

With advances in inexpensive computing power, and stored data collections becoming truly massive in the era of big data, researchers are just now able to design algorithms and models that pull previously unrecognized disease and drug treatment patterns from databases. These computational patterns are predictive, and researchers can validate them with experiments.

The goal of our new center is to detect changes made in human heart, liver and nerve cells as otherwise useful drugs cause side effects, and to find the combinations of existing drugs that reduce these side effects, said Ravi Iyengar, PhD, the Dorothy H. and Lewis Rosenstiel Professor in the Department of Pharmacology and Systems Therapeutics within the Icahn School of Medicine at Mount Sinai, and the lead investigator for the center grant.

Our center embodies a third way to reduce the side effects that limit the use of so many treatments, along with two traditional approaches: fine-tuning a drugs chemical structure or tailoring its use for each individuals genetics, he added.

Hidden Signatures The new grant will fund a Drug Toxicity Signature Generation Center at Mount Sinai as part the NIH Common Funds LINCS program, the Library of Integrated Network-based Cellular Signatures program. Each signature is a confirmed set of genetic and protein responses within a type of cell to a drug or drug combination.

The team will find such signatures by combining high-throughput experiments on cell responses to drugs with statistical analyses of side effect, gene and protein interaction databases. Interestingly, the team starts with stem cells and then converts them into the heart, liver and nerve cells used in the experiments. The new centers goal is to generate 2,000 signatures per year for further testing.

To anchor the signatures to human diseases and treatments, the team will search the U.S. Food and Drug Administrations Adverse Event Reporting System (FAERS) database to find cases where adding a second drug reduced the side effects associated with a commonly used primary treatment. FAERS has for decades collected such data from individuals, health professionals, drug companies and hospitals, and the millions of records on patients taking multiple drugs now in this public database are free and open to all researchers for analysis.

To translate FAERS-generated drug combinations that reduce toxicity into networks of mechanism-based cell response signatures, the team will then run the experimental results through other databases, including NIH databases of human DNA sequences and interactions between proteins. These networks will be filtered using sophisticated modeling techniques to increase the reliability of the signatures. The most promising signatures can then form the basis for targeted animal and human clinical studies on drug repurposing.

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Center to Find Drug Combinations that Reduce Side Effects