Identifying FDA-approved drugs with multimodal properties against COVID-19 using a data-driven approach and a lung organoid model of SARS-CoV-2 entry

Abstract

Background: Vaccination programs have been launched worldwide to halt the spread of COVID-19. However, the identification of existing, safe compounds with combined treatment and prophylactic properties would be beneficial to individuals who are waiting to be vaccinated, particularly in less economically developed countries, where vaccine availability may be initially limited.

Methods: We used a data-driven approach, combining results from the screening of a large transcriptomic database (L1000) and molecular docking analyses, with in vitro tests using a lung organoid model of SARS-CoV-2 entry, to identify drugs with putative multimodal properties against COVID-19.

Results: Out of thousands of FDA-approved drugs considered, we observed that atorvastatin was the most promising candidate, as its effects negatively correlated with the transcriptional changes associated with infection. Atorvastatin was further predicted to bind to SARS-CoV-2's main protease and RNA-dependent RNA polymerase, and was shown to inhibit viral entry in our lung organoid model.

Conclusions: Small clinical studies reported that general statin use, and specifically, atorvastatin use, are associated with protective effects against COVID-19. Our study corroborrates these findings and supports the investigation of atorvastatin in larger clinical studies. Ultimately, our framework demonstrates one promising way to fast-track the identification of compounds for COVID-19, which could similarly be applied when tackling future pandemics.

Keywords: Atorvastatin; COVID-19; Chemoinformatics; Connectivity mapping; Drug repositioning; Drug screening; Drug testing; Lung organoids; Molecular docking; Pandemic.

Fig. 1

Our analysis strategy. We used a connectivity mapping approach in combination with chemoinformatic analyses and in vitro tests using a lung organoid model, to investigate drugs with putative multimodal effects against SARS-CoV-2/COVID-19. Created with Biorender.com

Fig. 2

Visualization of the predicted interactions between atorvastatin, raloxifene and trifluoperazine, and the SARS-CoV-2 Mpro (left panel) and RdRp (right panel)

Fig. 3

The dose-dependent drug screening of three drugs whose transcriptional profiles correlate negatively with the host transcriptional signature associated with SARS-CoV-2 infection in the connectivity mapping analysis, which were further selected based on their predicted ability to bind to SARS-CoV-2 Mpro and RdRp. A Atorvastatin was the only drug that showed a dose-dependent reduction in viral entry in the SARS-CoV-2 lung organoid model, alongside our positive control (doxycycline). The bar plot shows the relative luciferase activity of multiple candidate drugs at 100 μM, 33 μM and 10 μM. The data was normalized to DMSO treated control. Data is presented as mean ± SEM. Two-way ANOVA, **P < 0.01, ***P < 0.001. B The dose-dependent efficacy curves of atorvastatin, and C doxycycline, suggest that these drugs inhibit viral entry in the SARS-CoV-2 lung organoid model. Their maximum half cytotoxicity concentrations are above the tested range. The data was normalized to DMSO-treated control. Data is presented as mean ± SEM

Fig. 4

Gene ontology analysis of the genes differentially regulated in A549 cells upon treatment with atorvastatin, based on transcriptomic data from the L1000 repository. Volcano plots show the GO terms enriched within the A downregulated and B upregulated genes after treatment with this drug. The Y axis shows significance in the − log10 scale, and the X axis, the enrichment ratio for each GO term (represented as dots in the graph)