In anticipation of #RareDiseaseDay, this past Monday, the FDA held a public meeting to assess current challenges, solutions, commonalities in product development in rare disease research. Rare disease, by definition, affects fewer than 200,000 Americans however, a total estimated 30 million people in the United States are impacted by these rare illnesses. Unfortunately, many of some 7,000 illnesses have no treatment. Here lies the R&D conundrum; small market size of each disease versus the exuberant costs to develop a new drug. What about repurposing already FDA-approved drugs?
Researchers in the Biomedical Informatics Program at Stanford University are beta testing a proactive knowledge graphing approach to tackle that challenge. Sosa et al. recently presented in the 2020 Pacific Symposium on Biocomputing their approach using a Global Network of Biomedical Relationships (GNBR), a large, heterogeneous knowledge graphic database comprised of drug, disease, and gene (or protein) entities linked by a small set of semantic themes derived from the abstracts of biomedical literature. Their work is focused on capturing the knowledge from the literature on existing FDA-approved drugs that might be repurposed to treat rare diseases that differ from their original indications. The team applies this method to explicitly model the uncertainty associated with literature-derived relationships and uses link prediction to generate drug repurposing hypotheses. This work, albeit young, may generate actionable hypotheses and accelerate the discovery of promising new opportunities to treat rare diseases.
Mathematicians, chemists and physicists have powerful languages all their own to share knowledge. Biologists, on the other hand, are challenged with exchanging knowledge in a systematic, comprehensive and computable fashion. BioDati customers can integrate biological knowledge across the literature of pharmacology, genetics, and pathology to generate hypotheses, like the Stanford team for building drug-repurposing hypotheses. At BioDati, we take it to the next level.
With the world’s largest computable biological knowledge base and the most powerful BEL editor — there is no massive data processing exercise required to curate knowledge to drive data mining. Whatever you create with BioDati Studio is completely reusable. Our goal is to elevate biological knowledge in a way that is easy and efficient, to integrate customized scientific knowledge around your research program. Five percent of the world’s population is affected by rare diseases. Together with BioDati, let’s accelerate drug discovery by providing quality scientific knowledge so promising cures can alleviate the suffering of thousands of families with rare diseases.