Bridging the Wet-Dry Lab Divide in Canadian Life Sciences
The growth of artificial intelligence (AI) tools and machine learning (ML) models in drug screening has dramatically increased the number of viable drug targets while slashing the time to market. This shift has fueled a boom in dry labs—computational environments where simulations and mathematical analyses replace the pipettes, reagents, and spectrometers in traditional wet labs.
In Canada, this evolution has created a unique geographical challenge. Although Toronto has solidified its status as a global dry lab and AI powerhouse (anchored by the Vector Institute and Roche’s Data Science Coalition), a bottleneck persists. Once a promising candidate is identified, there may be no nearby wet lab space to continue its research and development. This is why every new wet lab space that opens in the region becomes newsworthy. Another bottleneck is that the in silico models generated with AI aren’t always in a format that meshes easily with wet lab analytical results.
For Canadian biopharmaceutical organizations forecasting growth in the next five years, this gulf between dry lab screening and wet lab research and development is driving commercialization out of Ontario to other regions with more appropriate lab space. Additionally, organizations must consider the cost of not addressing the gap. Manual data entry adds risk and can lead to rework, regulatory delays or failed tech transfers.
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