NEW YORK – Precision medicine firm Optithera, ELNA Medical Group, Génome Québec, and Genome Canada said on Monday that they have inked an agreement for the development and distribution of a test that will be used to help predict the risk of complications in patients with type 2 diabetes.
Optithera has created a prognostic test that combines genomic information and artificial intelligence-based analysis to determine before the onset of symptoms a patient's risk of cardiorenal complications such as myocardial infarction, stroke, and diabetic nephropathy. ELNA Medical Group plans to offer the test through its national network of clinics, and Génome Québec's Genomic Applications Partnership Program is providing funding for genomics research that will support further development of the test.
The partners said they are collectively investing C$12.8 million (US$9.5 million) in a public-private partnership, dubbed "Predict to Prevent: A Novel Genomic-Derived Score to Enhance the Prognosis of Type 2 (T2) Diabetes Patients at High Risk of Complications." They did not clarify how much of the funding will be provided by each firm or by Génome Québec as part of Genome Canada's Genomic Applications Partnership Program.
"Having demonstrated the effectiveness of our genomic test in predicting diabetic nephropathy, we are now in the final stage before this test is brought to market," said Optithera founding President Pavel Hamet, who is also a researcher at the University of Montreal Hospital Research Centre. "We need to evaluate the impact of this innovation in Canadian clinical settings directly with diabetic patients and their physicians, as well as assess its potential implications for the healthcare system in terms of reducing the economic burden of treating [type 2] diabetes."
Hamet is co-leading the research with Johanne Tremblay, who is also at the University of Montreal Hospital Research Centre.
The project includes analysis of nearly 600 genomic variants that are associated with cardiovascular and renal disease and a risk prediction model that was developed by analyzing clinical and genetic data of participants from 17 countries during prior studies.