Drugs repurposed for COVID-19 by virtual screening of 6,218 drugs and cell-based assay

Abstract

The COVID-19 pandemic caused by SARS-CoV-2 is an unprecedentedly significant health threat, prompting the need for rapidly developing antiviral drugs for the treatment. Drug repurposing is currently one of the most tangible options for rapidly developing drugs for emerging and reemerging viruses. In general, drug repurposing starts with virtual screening of approved drugs employing various computational methods. However, the actual hit rate of virtual screening is very low, and most of the predicted compounds are false positives. Here, we developed a strategy for virtual screening with much reduced false positives through incorporating predocking filtering based on shape similarity and postdocking filtering based on interaction similarity. We applied this advanced virtual screening approach to repurpose 6,218 approved and clinical trial drugs for COVID-19. All 6,218 compounds were screened against main protease and RNA-dependent RNA polymerase of SARS-CoV-2, resulting in 15 and 23 potential repurposed drugs, respectively. Among them, seven compounds can inhibit SARS-CoV-2 replication in Vero cells. Three of these drugs, emodin, omipalisib, and tipifarnib, show anti-SARS-CoV-2 activities in human lung cells, Calu-3. Notably, the activity of omipalisib is 200-fold higher than that of remdesivir in Calu-3. Furthermore, three drug combinations, omipalisib/remdesivir, tipifarnib/omipalisib, and tipifarnib/remdesivir, show strong synergistic effects in inhibiting SARS-CoV-2. Such drug combination therapy improves antiviral efficacy in SARS-CoV-2 infection and reduces the risk of each drug's toxicity. The drug repurposing strategy reported here will be useful for rapidly developing drugs for treating COVID-19 and other viruses.

Keywords: SARS-CoV-2; cell-based assay; docking-based virtual screening; drug combinations; drug repurposing.

Fig. 1.

Drug targets against SARS-CoV-2 and computational drug repurposing strategy. (A) Potential drug targets in SARS-CoV-2 replication cycle. Targets for viral attachment and entry include the viral spike glycoproteins, host receptors (ACE2), and proteases (TMPRSS2). Polyprotein processing can be targeted by inhibiting viral proteases such as main protease M^pro and papain-like proteases. Viral replicase-related enzymes are also attractive drug targets for antiviral activity. RdRp and helicase are important enzymes involved in the transcription and replication of SARS-CoV-2. Among these, the most important and less variable M^pro and RdRp were selected as drug targets in this study. (B) Docking-based virtual screening can identify novel compounds against targets of SARS-CoV-2 among the collection of approved and clinical trial drugs. Computational drug repurposing is an effective approach to identify novel drug-target interactions using the drugs already known to be safe, which provides the advantages of significantly reducing time for drug development and reduced failure rate.

Fig. 2.

Molecular docking of drug candidates on M^pro and RdRp. The binding poses of six drugs (including remdesivir) with RdRp-derived structure (PDB 6M71) using AutoDock Vina: (A) omipalisib, (B) remdesivir, (C) tipifarnib, (D) hypericin, (E) LGH-447, and (F) NS-3728. The binding poses of two drugs with M^pro (PDB 6Y2F) using AutoDock Vina: (G) blonanserin and (H) emodin.

Fig. 3.

Dose–response analysis for the compounds having anti–SARS-CoV-2 activity. (A) A schematic of the immunofluorescence-based assay to examine anti–SARS-CoV-2 activity in Vero cells using the compounds selected from virtual screening. (B) A heatmap representing the percentages of normalized infection of the eight compounds in dose–response, on a scale from 0 to 100, depicting the average of duplicate independent experiments. Dose–response curves of the potent compounds in Vero cells (C) and Calu-3 cells (D). Pink line indicates relative viral inhibition and the blue line indicates relative cell viability. Data are normalized to the average of DMSO-treated wells and shown as the mean ± SD of duplicate independent experiments.

Fig. 4.

Analyses of drug combinations on anti–SARS-CoV-2 activity, cell viability, and their synergistic effects. Two-dimensional matrix of dose–response for relative viral inhibition: (A) omipalisib/remdesivir, (D) tipifarnib/omipalisib, and (G) tipifarnib/remdesivir. The heatmap depicts relative viral inhibition scaled to the range of 0 to 100%. Two-dimensional matrix of dose–response for relative cell viability: (B) omipalisib/remdesivir, (E) tipifarnib/omipalisib, and (H) tipifarnib/remdesivir. The heatmap depicts relative cell viability scaled to the range of 0 to 100%. Topographic two-dimensional map of synergy scores determined by synergyfinder using the data in A, D, and G, respectively: (C) omipalisib/remdesivir, (F) tipifarnib/omipalisib, and (I) tipifarnib/remdesivir. The synergy map highlights synergistic and antagonistic dose regions in red and green colors, respectively. A yellow box represents the area with the highest synergy score obtained by synergyfinder.