SAveRUNNER: A network-based algorithm for drug repurposing and its application to COVID-19

Abstract

The novelty of new human coronavirus COVID-19/SARS-CoV-2 and the lack of effective drugs and vaccines gave rise to a wide variety of strategies employed to fight this worldwide pandemic. Many of these strategies rely on the repositioning of existing drugs that could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we presented a new network-based algorithm for drug repositioning, called SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk), which predicts drug–disease associations by quantifying the interplay between the drug targets and the disease-specific proteins in the human interactome via a novel network-based similarity measure that prioritizes associations between drugs and diseases locating in the same network neighborhoods. Specifically, we applied SAveRUNNER on a panel of 14 selected diseases with a consolidated knowledge about their disease-causing genes and that have been found to be related to COVID-19 for genetic similarity (i.e., SARS), comorbidity (e.g., cardiovascular diseases), or for their association to drugs tentatively repurposed to treat COVID-19 (e.g., malaria, HIV, rheumatoid arthritis). Focusing specifically on SARS subnetwork, we identified 282 repurposable drugs, including some the most rumored off-label drugs for COVID-19 treatments (e.g., chloroquine, hydroxychloroquine, tocilizumab, heparin), as well as a new combination therapy of 5 drugs (hydroxychloroquine, chloroquine, lopinavir, ritonavir, remdesivir), actually used in clinical practice. Furthermore, to maximize the efficiency of putative downstream validation experiments, we prioritized 24 potential anti-SARS-CoV repurposable drugs based on their network-based similarity values. These top-ranked drugs include ACE-inhibitors, monoclonal antibodies (e.g., anti-IFNγ, anti-TNFα, anti-IL12, anti-IL1β, anti-IL6), and thrombin inhibitors. Finally, our findings were in-silico validated by performing a gene set enrichment analysis, which confirmed that most of the network-predicted repurposable drugs may have a potential treatment effect against human coronavirus infections.

In silico drug repurposing in COVID-19: A network-based analysis

“…To discover novel repurposable drugs and evaluate the magnitude to which a given drug can be repositioned for COVID-19, we exploited the recently developed SAveRUNNER algorithm [40] . The rationale behind SAveRUNNER builds on the hypothesis that for a drug to be effective against a specific disease, its associated targets (drug module) and the disease-specific associated genes (disease module) should be located nearby in the human interactome [20] ( Fig.…”

“…1 SAveRUNNER. A) The network-based algorithm used to identify off-label drug indications against COVID-19 [40] . B) The SAveRUNNER outcome network showing the high-confidence predicted drug-disease associations (p-value ≤ 0.05) connecting COVID-19 with 399 FDA-approved non-COVID-19 drugs.…”

“…To predict and prioritize off-label drug indications for COVID-19, we used a novel network-based algorithm for drug repurposing called SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk) [40] , [41] . Specifically, SAveRUNNER predicts drug-disease associations by quantifying the interplay between the drug targets and disease-associated proteins in the human interactome via a novel network-based similarity measure that rewards associations between drugs and diseases located in the same network neighborhoods.…”

“…As disease genes, we used the genes falling in the COVID-19 module; as drug targets, we assembled target information of the 1,873 FDA-approved drugs obtained from DrugBank [35] ; and as a reference interactome we used the version provided by Cheng et al [20] . A comprehensive description of SAveRUNNER algorithm can be found in [40] , [41] .…”

Microarrays and NGS for Drug Discovery

“…As infections with the SARS-CoV-2 virus have become more aggressive, there is an urgent need for evaluating different drugs that may contribute to a better and effective treatment of this infection. The majority of drugs used for SARS-CoV-2 treatment are drugs currently in use for treatment of other diseases [243][244][245], and these have been evaluated for their efficacy using computational drug discovery analysis [246][247][248]. Although NGS has been used primarily for genome identification of SARS-CoV-2 [249][250][251][252], as well as for evaluation of mutations developed during viral spread in different countries [253][254][255], there are some studies wherein RNA sequencing is used for identifying new drug treatments.…”