Application of the PHENotype SIMulator for rapid identification of potential candidates in effective COVID-19 drug repurposing

ABSTRACT

The current, rapidly diversifying pandemic has accelerated the need for efficient and effective identification of potential drug candidates for COVID-19. Knowledge on host-immune response to SARS-CoV-2 infection, however, remains limited with few drugs approved to date. Viable strategies and tools are rapidly arising to address this, especially with repurposing of existing drugs offering significant promise. Here we introduce a systems biology tool, the PHENotype SIMulator, which -by leveraging available transcriptomic and proteomic databases-allows modeling of SARS-CoV-2 infection in host cells in silico to i) determine with high sensitivity and specificity (both>96%) the viral effects on cellular host-immune response, resulting in specific cellular SARS-CoV-2 signatures and ii) utilize these cell-specific signatures to identify promising repurposable therapeutics. Powered by this tool, coupled with domain expertise, we identify several potential COVID-19 drugs including methylprednisolone and metformin, and further discern key cellular SARS-CoV-2-affected pathways as potential druggable targets in COVID-19 pathogenesis. Graphical abstract Application of the PHENotype SIMulator By modeling human host-cell infection with a pathogen in silico - in this case SARS-CoV-2 - we can acquire a cell-specific viral signature and formulate multiple drug repurposing hypotheses;(I) logFold Changes (logFCs) of Differentially Expressed Genes (DEGs) arising from transcriptomic genome wide expression analysis of infected vs. baseline uninfected cells are used to represent a virus in the meta-pathway;(II) we run the PHENSIM simulation by upregulating the viral node and collect all perturbation values computed by PHENSIM for pathway endpoints to define a cell-specific pathogen signature. (III) The same process is applied to expression data arising from whole transcriptome-wide expression analysis of drug treated vs. mock-treated cell lines, yielding a cell-specific drug signature. This process is iterated for each drug we wish to test and collected in a database of drug signatures. (IV) Finally, a Pearson correlation analysis between the pathogen and each drug signature is utilized to score repurposing candidates.Image 1