I attended the Findacure “Drug Repurposing for Rare Diseases” event last week; a small symposium with an interesting mix of attendees – academics, pharma, patient groups, vendors. The main focus was networking, inspired by a series of short talks (see Findacure blog for more information).
- 6,000 to 8,000 identified rare diseases (prevalence less than 5 in 10,000)
- Only approximately 200 have licenced treatments – large unmet need
- 1 in 17 people (6-8% of population) will develop a rare disease
- 30-40 million people in US, 30-40 million in Europe
- 75% of all rare diseases affect children
With the changing landscape from “blockbuster” to more personalised “nichebuster” therapeutics, and the incentives provided by regulatory bodies (such as FDA’s Orphan Drug Designation), rare diseases are an increasing focus of many of Linguamatics’ pharma and biotech customers.
So, I hear you ask – how does text analytics fit into rare diseases drug discovery? It’s simple: Information associated with rare diseases is essential at many stages of drug discovery and development. And, this essential information is often buried in unstructured text - in different data sources, with differing formats, vocabs, etc.