Artificial intelligence-based imaging platform for COVID-19 infection of organoids

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Repurposing drugs already approved for use in humans is a valid approach to identify SARS-CoV-2 inhibitors with established pharmacological and safety profiles that can be rapidly translated to the clinic for treatment of COVID-19. Based on prior work in which artificial intelligence (AI)-based image analysis was able to detect changes to the endoplasmic reticulum of Zika virus infected cells, we will now apply AI-based image analysis to detect alterations to the endoplasmic reticulum, autophagosomes and other cellular organelles due to SARS-CoV-2 in order to identify inhibitory drug candidates from repurposing compound libraries. Initially, lung epithelial Calu-3 cells will be infected with SARS-CoV-2 in the containment level 3 (CL3) FINDER facility in the Life Sciences Institute at UBC and use AI to identify biosignatures associated with SARS-CoV-2 infection. We will then screen a repurposing library of ~3,000 inhibitors to identify those best able to inhibit SARS-CoV-2 infection. We will subsequently establish a secondary screen of kidney, brain and lung airway organoids, available through our industry partner STEMCELL Technologies Inc. to identify the best and most promising SARS-CoV-2 inhibitors. We will evaluate the new lead compounds for synergistic effect with other drug candidates. This grant will support the development of a state-of-the-art AI image analysis to enhance classification of SARS-CoV-2 infection of cells and organoids and provide improved, quantitative approaches to assess anti-SARS-CoV-2 drug efficacy. Development of AI-based image analysis approaches will support development of novel therapeutics for immediate support against the current COVID-19 crisis and also develop novel approaches to image multiple and varied tissue organoids for application to various disease models.