Finding new indications for existing drugs is a
promising venue for pharma companies when it comes
to reducing drug development costs. In recent
years, 30% of regulatory approvals by the FDA have
been for new uses of previously approved drugs and
repurposing approach to drug discovery and
development can streamline the time- and
cost-intensive process of bringing new therapies
to market, which can take the industry up to 20
years and cost in excess of $2.5 billion.
Currently, the discovery of new therapeutic uses
for existing medicines is largely the result of
serendipitous findings or isolated research. Our
collaborations with pharma companies work to
design, build, and deploy a systematic process for
drug repurposing, potentially becoming a blueprint
for use across the industry.
Our systematic approach for drug repurposing
combines human insight with unique
machine-learning and causal inference algorithms.
We apply the algorithms on vast amounts of
observational real-world data accessed through IBM
Watson Health, as well as on drug information from
pharmacological knowledge bases, such as
DrugBank, to test hundreds of candidates for repurposing
in various disease domains.