Graphs for Good
Developing a new drug can take almost a decade of research and development and cost multiple billions of dollars before being approved by regulators. For patients of rare diseases, the attention to those diseases is difficult to justify from a population and resource perspective, even though the impacts are often severe. What if there was a way to find hypotheses for existing, approved drugs that could be considered as possible treatments for these rare diseases?
Repurposing existing drugs is gaining traction as a way of accelerating therapeutics development. With that challenge in mind, we will talk about how AI enables us to extract knowledge into large knowledge graphs and then mine that knowledge for opportunities to connect rare diseases with potential already on-market therapeutics. We use a real story about a patient with Carney Complex, a disease with no known treatment, to illustrate how applying subgraph queries that relate drugs to diseases using genetic evidence to identify potential drug repurposing candidates revealed a potential candidate treatment for Carney Complex. We’ll talk about the implications for this ability in the future.
Ultimately, we’ll share a real tangible story illustrating how AI and knowledge graphs together have the potential to shine a ray of hope into the world delivering a remarkable impact for patients.