| Summary: | Abstract Background The international SARS-CoV-2 pandemic has resulted in an urgent need to identify new anti-viral drugs for treatment of COVID-19. The initial step to identifying potential candidates usually involves in vitro screening that includes standard cytotoxicity controls. Under-appreciated is that viable, but stressed or otherwise compromised cells, can also have a reduced capacity to replicate virus. A refinement proposed herein for in vitro drug screening thus includes a simple growth assay to identify drug concentrations that cause cellular stress or “cytomorbidity”, as distinct from cytotoxicity or loss of viability. Methods A simple rapid bioassay is presented for antiviral drug screening using Vero E6 cells and inhibition of SARS-CoV-2 induced cytopathic effects (CPE) measured using crystal violet staining. We use high cell density for cytotoxicity assays, and low cell density for cytomorbidity assays. Results The assay clearly illustrated the anti-viral activity of remdesivir, a drug known to inhibit SARS-CoV-2 replication. In contrast, nitazoxanide, oleuropein, cyclosporine A and ribavirin all showed no ability to inhibit SARS-CoV-2 CPE. Hydroxychloroquine, cyclohexamide, didemnin B, γ-mangostin and linoleic acid were all able to inhibit viral CPE at concentrations that did not induce cytotoxicity. However, these drugs inhibited CPE at concentrations that induced cytomorbidity, indicating non-specific anti-viral activity. Conclusions We describe the methodology for a simple in vitro drug screening assay that identifies potential anti-viral drugs via their ability to inhibit SARS-CoV-2-induced CPE. The additional growth assay illustrated how several drugs display anti-viral activity at concentrations that induce cytomorbidity. For instance, hydroxychloroquine showed anti-viral activity at concentrations that slow cell growth, arguing that its purported in vitro anti-viral activity arises from non-specific impairment of cellular activities. The cytomorbidity assay can therefore rapidly exclude potential false positives.
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