Abstract
The COVID-19 pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has become the current health concern and threat to the entire world. Thus, the world needs the fast recognition of appropriate drugs to restrict the spread of this disease. The global effort started to identify the best drug compounds to treat COVID-19, but going through a series of clinical trials and our lack of information about the details of the virus’s performance has slowed down the time to reach this goal. In this work, we try to select the subset of human proteins as candidate sets that can bind to approved drugs. Our method is based on the information on human-virus protein interaction and their effect on the biological processes of the host cells. We also define some informative topological and statistical features for proteins in the protein-protein interaction network. We evaluate our selected sets with two groups of drugs. The first group contains the experimental unapproved treatments for COVID-19, and we show that from 17 drugs in this group, 15 drugs are approved by our selected sets. The second group contains the external clinical trials for COVID-19, and we show that 85% of drugs in this group, target at least one protein of our selected sets. We also study COVID-19 associated protein sets and identify proteins that are essential to disease pathology. For this analysis, we use DAVID tools to show and compare disease-associated genes that are contributed between the COVID-19 comorbidities. Our results for shared genes show significant enrichment for cardiovascular-related, hypertension, diabetes type 2, kidney-related and lung-related diseases. In the last part of this work, we recommend 56 potential effective drugs for further research and investigation for COVID-19 treatment. Materials and implementations are available at: https://github.com/MahnazHabibi/Drug-repurposing.
Highlights
Recent studies on coronaviruses as a family of positive-strand RNA viruses tried to find that a newly emerged virus belongs to a new or any existing species of this family of viruses
To identify a set of disease-associated genes related to COVID-19 as drug targets, we study the subset of genes that are associated with the aforementioned diseases in our candidate set
To identify diseaseassociated genes as drug targets related to COVID-19, we study the subset of disease-associated genes correlated with mentioned diseases in the selected set (T1)
Summary
Recent studies on coronaviruses as a family of positive-strand RNA viruses tried to find that a newly emerged virus belongs to a new or any existing species of this family of viruses. Targeting the virus-host interaction network can offer an innovative strategy for forming effective cures for viral infections like SARS-CoV-2 [8]. Since the outbreak of COVID-19, many research groups have been trying to propose network-based methods to find some effective repurposed drugs to perform against SARS-CoV-2 [9, 10]. It is noticeable that the identification of dependency between host proteins and virus infection can provide significant insights to find suitable drug targets for developing antivirals medicine against SARS-CoV-2. We show that from 17 unapproved drugs used in medical centers for COVID-19 treatment, 15 drugs have at least one Topological network based drug repurposing target in our selected set. We recommended 56 drugs for more research and investigation that related to the significant disease pathways as candidate drugs for COVID-19 treatment
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