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, comorbidity, or for their association to drugs tentatively
repurposed to treat COVID-19. Focusing specifically on SARS subnetwork, we
identified 282 repurposable drugs, including some the most rumored off-label
drugs for COVID-19 treatments, as well as a new combination therapy of 5 drugs,
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,
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.Comment: 42 pages, 9 figure