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Partnership formed to determine risk and progression of type 2 diabetes


Montreal, QC – The IRCM (Institut de recherches cliniques de Montréal) and Nuclea Biotechnologies Inc. have formed a new strategic partnership to validate quantitative mass spectrometry-based assays for insulin, proinsulin, and c-peptide. The goal of the partnership is to develop new assays to determine risk and progression of type 2 diabetes.

 

Benoit Coulombe, PhD, researcher at the IRCM, uses a translational proteomics platform, based on mass spectrometry, to develop assays ranging from biomarker discovery to validation. Nuclea focuses on diagnostic development and validation, with a recent emphasis placed on mass spectrometry-based assays.

 

The partnership will include measuring clinical samples taken from patient cohorts and analyzed in both locations (the IRCM and Nuclea) to demonstrate the precision, sensitivity, and reproducibility of the assays. Nuclea will incorporate these new assays into its CLIA lab (clinical laboratory improvement amendments) in Cambridge, MA.

 

“We are enthusiastic to partner with Nuclea on this project,” says Dr. Coulombe, director of the translational proteomics research unit and the proteomics discovery platform at the IRCM. “Validation of these assays across the two laboratories will represent an added value in clinical and research environments.”

 

“Mass spectrometry-based assays for key protein targets have reached a tipping point in their development, and can now become routine in clinical laboratories,” says Mary Lopez, COO and VP of proteomic discovery at Nuclea Biotechnologies. “Nuclea’s high quality manufacturing and service capabilities combined with the new high-resolution mass spectrometry will provide urgently-needed tests for type 2 diabetes.”

 

Detecting and quantifying insulin and its therapeutic analogs could have applications in the medical or forensic fields, as well as in sports doping. New mass spectrometry-based assays have demonstrated a high degree of reliability and robustness, which are important factors for treatment decisions and to predict response to a given therapy in type 2 diabetes.