Program Predicts New Uses for Existing Medicines Using Gene Expression Patterns

For the first time, scientists are utilizing computers and genomic data to predict new uses for existing medicines.

A US National Institutes of Health (NIH; Bethesda, MD,USA)-funded computational study analyzed genomic and drug data to predict new uses for medicines that are already on the market. A team led by Atul J. Butte, MD, PhD, of Stanford University (Palo Alto, CA, USA) reported its findings in two articles in the August 17, 2011, online issue of the journal Science Translational Medicine.

“Bringing a new drug to market typically takes about US$1 billion, and many years of research and development,” said Rochelle M. Long, PhD, who directs the NIH Pharmacogenomics Research Network. “If we can find ways to repurpose drugs that are already approved, we could improve treatments and save both time and money.”

The scientists collected their data from the NIH National Center for Biotechnology Information Gene Expression Omnibus, a publicly available database that contains the results of thousands of genomic studies on a wide range of topics, submitted by researchers worldwide. The resource catalogs changes in gene activity under various conditions, such as in diseased tissues or in response to medications.

Dr. Butte’s group focused on 100 diseases and 164 drugs. They created a computer program to search through the thousands of possible drug-disease combinations to find drugs and diseases whose gene expression patterns essentially cancelled each other out. For example, if a disease increased the activity of certain genes, the program tried to match it with one or more drugs that decreased the activity of those genes.

Many of the drug-disease matches were known, and are already in clinical use, supporting the validity of the approach. For example, the analysis accurately predicted that prednisolone could treat Crohn’s disease, a condition for which it is a standard therapy. Other matches were surprises. Dr. Butte’s team chose to investigate additionally two such drug-disease combinations: an antiulcer medicine (cimetidine) that matched with lung cancer, and an anticonvulsant (topiramate) that matched with inflammatory bowel disease, which includes Crohn’s disease.

To validate the cimetidine-lung cancer link, the investigators tested cimetidine on human lung cancer cells in lab dishes and implanted in mice. In both cases, the drug slowed the growth of the cancer cells compared to the control group (cells or mice) that had not received cimetidine.

To evaluate whether the anticonvulsant topiramate had an effect on inflammatory bowel diseases, the researchers administered the drug to rats that had bowel disease symptoms--diarrhea and inflammation, ulcers, and microscopic damage in the colon. The drug decreased all of these symptoms, sometimes even better than prednisolone.

The research also has a more basic value: the scientists noticed that diseases with similar molecular processes (for example, those that affect the immune system) clustered together in the analysis. So did agents with similar effects (for example, those that slow cell division). The researchers believe that, by examining unexpected members of these clusters, they could learn more about how specific diseases progress and how some drugs work at the molecular level.

“This work is still at an early stage, but it is a promising proof of principle for a creative, fast, and affordable approach to discovering new uses for drugs we already have in our therapeutic arsenal,” concluded Dr. Long said.

The project’s findings were published August 17, 2011, in the journal Science Translational Medicine, and August 17, 2011, in the NIH/National Institute of General Medical Sciences' Inside Life Science.

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National Institutes of Health
Stanford University