Project 452590

Idiopathic Pulmonary Fibrosis (IPF) is a progressive and fatal scarring lung disease of unknown cause. This disease takes away the lives of 5,000 Canadians every year, and the majority of IPF patients will require home oxygen treatment and die within 5 years after diagnosis. Despite some advances in drug therapy over the past few years, this dismal survival rate is a clear indication that we do not yet have adequately effective drug treatments for IPF. New techniques for analyzing lung cells at a very refined level (single-cell technologies) have emerged over the past few years. In addition, there is ever-increasing knowledge about the mechanisms of action of existing drugs used for other diseases. In this proposal, we will use advanced computer algorithms (machine learning) to combine single-cell data from IPF patients at different stages of the disease with comprehensive datasets about existing drugs that could potentially be repurposed for IPF treatment. This approach has tremendous potential to identify new and safe drugs (since they are already approved for other conditions) for IPF, which could, if successful, save thousands of lives in Canada and millions worldwide.