Reading some of the FDA blogs review, I was interested to read that "for the second consecutive year, [the FDA] approved more drugs to treat rare diseases than any previous year in our history." This is great news for the patients affected by these rare or orphan diseases, and there is of course potential for applications of such drugs and the knowledge around these diseases across the wider population and in broader healthcare.
Text analytics can play a part in developing better understanding around the biology of these rare diseases. There's a great example of this application of text mining from Madhusudan Natarajan at Shire Pharmaceuticals. Shire develops and provides healthcare in the areas of behavioural health, gastrointestinal conditions, rare diseases, and regenerative medicine, and Madhu has presented his research using text analytics to uncover disease severity and genotype-phenotype associations for Hunter Syndrome (also known as Mucopolysaccharidosis II).
We hosted a webinar with Madhu, and in this webinar, he illustrates some of the challenges for R&D for orphan diseases, particularly around text mining for mutation and variant patterns, which can be reported in so many different ways in the literature.
Webinar: A systematic examination of gene-disease associations through text mining approaches
Text analytics for rare disease genotype-phenotype annotations: Mucopolysaccharidosis II or Hunter syndrome is an X-linked deficiency in iduronate-2-sulfatase. Onset of the severe form usually presents at 2 – 4 years of age, and the disease presents with symptoms including bone deformities, hearing loss, frequent respiratory infections, cardiomyopathy, hepatosplenomegaly, and often some level of neurocognitive impairment.