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

基于人工智能的类器官感染 COVID-19 成像平台

• 批准号: 553515-2020
• 负责人: Nabi, Ivan
• 金额: $ 3.64万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=704563
• 关键词: Artificial; intelligence; based; imaging; platform

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.

改变已被批准在人体上使用的药物的用途是识别具有既定药理学和安全性特征的SARS-CoV-2抑制剂的有效方法，这些药物可以迅速转化为临床用于治疗新冠肺炎。在之前基于人工智能(AI)的图像分析能够检测到寨卡病毒感染细胞内质网变化的基础上，我们现在将应用基于AI的图像分析来检测SARS-CoV-2对内质网、自噬小体和其他细胞器的变化，以便从重新定位的化合物库中识别抑制药物候选。最初，肺上皮细胞CALU-3将在不列颠哥伦比亚大学生命科学研究所的遏制级别3(CL3)查找器设施中感染SARS-CoV-2，并使用人工智能来识别与SARS-CoV-2感染相关的生物特征。然后，我们将筛选一个包含约3,000种抑制剂的重用库，以确定那些能够最好地抑制SARS-CoV-2感染的药物。随后，我们将通过我们的行业合作伙伴干细胞技术公司建立肾、脑和肺呼吸道有机化合物的二次筛查，以确定最好和最有希望的SARS-CoV-2抑制剂。我们将评估新的先导化合物与其他候选药物的协同作用。这笔赠款将支持开发最先进的人工智能图像分析，以加强对细胞和器官的SARS-CoV-2感染的分类，并提供改进的量化方法来评估抗SARS-CoV-2药物的疗效。基于人工智能的图像分析方法的开发将支持新疗法的开发，以针对当前的新冠肺炎危机提供即时支持，并开发新的方法来对多种不同的组织器官进行图像处理，以应用于各种疾病模型。