There seems to be a certain buzz around rare and orphan diseases. Following the Findacure meeting I attended last month, there are two recent events I’d like to mention.
Firstly, I attended the first Cambridge Rare Disease Network summit, held in Cambridge UK, with a fantastic line-up of speakers from a range of professions to discuss current and new initiatives in rare disease. The debates ranged from the use of next generation sequencing for diagnostics, to crowd-sourcing both for science and funding, to drug repurposing, to the views of payers and the issues around pricing.
For me it was also a reminder, particularly from some of the parent speakers, of the impact that rare disease has on individuals and families. All too often we are so busy with the day-to-day of research and business that it's easy to lose sight of the ideal end-goal - treatments for all adults, all children, affected by these disparate and often devastating diseases.
Secondly, this month the FDA released new draft guidance “to navigate the difficult road to approval of drugs for rare diseases”.
Reading through the document, a couple of aspects leapt out as areas where text analytics can bring potential value. The guidance proposes that drug development programs should have a “firm scientific foundation” – with particular mention to the understanding of the natural history and pathophysiology of the disease, and the mechanism of action of the proposed drug. While clinical data is crucial to characterize the disease phenotype, comprehensive searches of genetic, genomic, and scientific literature databases provides the essential underlying substrate for any analysis and understanding. Many of our customers are involved in rare disease research (great visual here) and I hope that the value gained from using text analytics can in some small but significant way accelerate the research towards treatments for these diseases.
Network map showing text-mined associations for tissues with rare diseases: Researchers at a top 10 pharma were able to prioritise diseases for topically applied therapies. I2E was used to find and extract distinct relationships (e.g. affects, affecting, causes, results in, disease of the) between nearly 2000 diseases and organs or tissues such as skin, throat, eyes, etc. The resulting network graph linked 81 diseases – 52 from Orphanet – with three key tissues.