Genomic sciences and the growing interest in precision medicine are having a variety of different effects on healthcare delivery. For example, patients are now being triaged into special clinics focusing on the most aggressive tumor types or tumors of unknown primary (see: New Clinic for High-Risk Prostate Cancer Patients at the University of Michigan; Specialized Clinic Opens for Patients with Cancer of Unknown Primary). We are also now seeing the emergence of hospital executives with portfolio emphasizing precision medicine (see: Do We Need Vice Deans/Vice Presidents for Precision Medicine?; A Closer Look at a New Yale Pathology Outreach Venture). Now comes news about the development of a new process for discovering new disease targets for old drugs. It utilizes some of the newly discovered genetic signatures for diseases and then harnesses the power of Big Data to match old drugs for new treatments of these diseases (see: Hiding in Plain Sight: Finding New Targets for Old Drugs), Below is an excerpt from the article with the details:
In late 2011, mice with small-cell lung cancer had their tumors reduced by an antidepressant called imipramine. The basis of the study was the idea that the cancer switches certain genes on, while imipramine turns them off. A neat trick, but no one would have thought to test it if it hadn’t been for analytics developed by Stanford data scientist Atul Butte. Butte thinks of diseases not in terms of symptoms but of the genes they activate (or deactivate). In that light, conditions that seem unrelated, like heart attack and muscular dystrophy, are kindred, because they show similar genetic patterns. So would heart attack medicine work on muscular dystrophy? Possibly. Butte...formed NuMedii to find out. The company combs public data to identify drugs and diseases with contrasting gene-expression profiles...Two years ago these analytics suggested that the class of antidepressants that includes imipramine might work for small-cell lung cancer. That led to the mouse trials ...and tests in humans are now under way. The typical interval between discovery and clinical trials is three to six years, but NuMedii’s approach—repurposing drugs that have already been proven safe in humans—could propel compounds from hypothesis to human much faster.
The slogan of NuMedii is clever -- translating big data into new medicines. Another new term for me, chemoinformatics, crops up in the NuMedii web site. Here's more information about how the company is using Big Data to discover novel drug-disease matches (see: NuMedii taps Thomson Reuters for data to power digital discovery model). The passage below from this article contains yet another phrase that is new to me: computationally found drug-disease match.
Over the past year, the "digital" development model pioneered by NuMedii has begun to deliver, with a Phase IIa trial of a computationally found drug-disease match now underway. The next clinical candidates are in databases awaiting discovery, and this week NuMedii inked a deal to increase its chances of finding them. The Stanford University spinout has partnered with Thomson Reuters to gain access to its MetaCore and Integrity content. By combining its Big Data technology with findings gathered by Thomson Reuters--which include biological, chemical and pharmacological information on more than 365,000 compounds--NuMedii believes it can find new uses for old drugs. The Thomson Reuters databases include information on the developmental status, patent protection and effects of bioactive compounds, each of which is a potential candidate for repurposing by NuMedii.