The ongoing SARS-CoV-2 pandemic requires efficient and safe antiviral treatment strategies. Drug repurposing represents a fast and low-cost approach to the development of new medical treatment options. The direct antiviral agent remdesivir has been reported to exert antiviral activity against SARS-CoV-2. Whereas remdesivir only has a very short half-life time and a bioactivation, which relies on pro-drug activating enzymes, its plasma metabolite GS-441524 can be activated through various kinases including the adenosine kinase (ADK) that is moderately expressed in all tissues. The pharmacokinetics of GS-441524 argue for a suitable antiviral drug that can be given to patients with COVID-19. Here, we analyzed the antiviral property of a combined treatment with the remdesivir metabolite GS-441524 and the antidepressant fluoxetine in a polarized Calu-3 cell culture model against SARS-CoV-2. The combined treatment with GS-441524 and fluoxetine were well-tolerated and displayed synergistic antiviral effects against three circulating SARS-CoV-2 variants in vitro in the commonly used reference models for drug interaction. Thus, combinatory treatment with the virus-targeting GS-441524 and the host-directed drug fluoxetine might offer a suitable therapeutic treatment option for SARS-CoV-2 infections.

Drug combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of drug resistance. However, discovery of synergistic and effective drug combinations has been a laborious and often serendipitous process. In recent years, identification of combination therapies has been accelerated due to the advances in high-throughput drug screening, but informatics approaches for systems-level data management and analysis are needed. To contribute toward this goal, we created an open-access data portal ( https://drugcomb.fimm.fi ) where the results of drug combination screening studies are accumulated, standardized and harmonized. Through the data portal, we provided the web server to analyze and visualize users’ own drug combination screening data. The users have an option to upload their data to DrugComb, as part of a crowdsourcing data curation effort. To initiate the data repository, we collected 437,932 drug combinations tested on a variety of cancer cell lines. We showed that linear regression approaches, when considering chemical fingerprints as predictors, have the potential to achieve high accuracy of predicting the sensitivity and synergy of drug combinations. All the data and informatics tools are freely available in DrugComb to enable a more efficient utilization of data resources for future drug combination discovery.