Computational method predicts new uses for existing medicines
August 18, 2011
The scientists drew 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 across the globe.
The database catalogs changes in gene activity under various conditions, such as in diseased tissues or in response to medications.
The researchers 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 correctly predicted that prednisolone could treat Crohn’s disease, a condition for which it is a standard therapy.
Other matches were surprises. The research team chose to further investigate two such drug-disease combinations: an anti-ulcer medicine (cimetidine) that matched with lung cancer, and an anticonvulsant (topiramate) that matched with inflammatory bowel disease, which includes Crohn’s disease.
To confirm the cimetidine-lung cancer link, the team 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 test 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 work also has more fundamental value, the scientists said. They noticed that diseases with similar molecular processes (for example, those that affect the immune system) clustered together in the analysis. So did drugs with similar effects (for example, those that slow cell division).
The researchers believe that, by studying unexpected members of these clusters, they could learn more about how certain diseases progress and how some drugs work at the molecular level.
Ref.: Sirota M, et al., Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data, Science Translational Medicine, 2011; [DOI: 10.1126/scitranslmed.3001318]
Ref.: Dudley JT, et al., Computational Repositioning of the Anticonvulsant Topiramate for Inflammatory Bowel Disease, Science Translational Medicine, 2011; [DOI: 10.1126/scitranslmed.3002648]