新聞

Identification of potential treatments for COVID-19 through artificial intelligence-enabled phenomic analysis of human cells infected with SARS-CoV-2

To identify potential therapeutic stop-gaps for SARS-CoV-2, we evaluated a library of 1,670 approved and reference compounds in an unbiased, cellular image-based screen for their ability to suppress the broad impacts of the SARS-CoV-2 virus on phenomic profiles of human renal cortical epithelial cells using deep learning. In our assay, remdesivir is the only antiviral tested with strong efficacy, neither chloroquine nor hydroxychloroquine have any beneficial effect in this human cell model, and a small number of compounds not currently being pursued clinically for SARS-CoV-2 have efficacy. We observed weak but beneficial class effects of β-blockers, mTOR/PI3K inhibitors and Vitamin D analogues and a mild amplification of the viral phenotype with β-agonists.
 
Silmitasertib (CX-4945) is a selective inhibitor of protein kinase casein kinase 2 (CK2) currently in clinical development in oncology11 (Figure 2f). CK2 was recently suggested as a therapeutic target for SARS-CoV-2 based on large scale interactome data and hypothesized to promote an antiviral cellular state by reducing turnover of stress granules12. Alternatively, results from SARS-CoV-1 suggest that CK2 inhibition may prevent nucleocapsid shuttling from the nucleus to the cytoplasm13 .
 

期刊連結 
https://www.biorxiv.org/content/10.1101/2020.04.21.054387v1