Computer Analysis Could Find New Uses for Existing Drugs

New uses for already existing drugs could be found via a computer program which analyzes drug components and genetic data.

According to two recent studies, new uses for already existing drugs could be found via a computer program which analyzes drug components and genetic data.

Stanford University researchers gathered data from the NIH's Gene Expression Omnibus, a public database which houses the findings of countless genomic studies performed all over the globe.

The purpose of the study, which was funded by the NIH, was to find out whether certain medications and medical conditions had gene expression patterns that could cancel each other out, and if such pairs did exist, then to identify them.

The researchers focused on 100 diseases and 164 drugs, out of which they identified possible matches for 53 of the diseases.

One such match made was of an epilepsy treatment which could possibly treat inflammatory bowel disease. Another was of an ulcer drug which could prove to be an efficient lung cancer treatment.

The researchers also pointed out other benefits of using existing medications to treat ailments other than their intended purposes, such as time and cost reduction involved in the drug development process. Generally, it takes an average of roughly $1 billion and 15 years and to introduce a single new drug to the market.

Though this is definitely a breakthrough, experts are already cautioning physicians against prescribing unconventional drugs to patients based on the study findings. Yves Lussier, a professor of medicine and engineering at the University of Illinois in Chicago, is one such expert, though he did add that the results are "impressive enough to be improved upon and studied further."

Lussier continued to state that should the computer program prove to be effective at detecting potential off-label uses for existing drugs, this device "opens the door to very low-cost, individualized personal therapies".

These studies were published in the journal Science Translational Medicine.