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