A Network-Biology Led Computational Drug Repurposing Strategy to Prioritize Therapeutic Options for COVID-19 (preprint)

The alarming pandemic situation of novel Severe Acute Respiratory Syndrome Coronavirus 2 (nSARS-CoV-2) infection, high drug development cost and slow process of drug discovery have made repositioning of existing drugs for therapeutics a popular alternative. It involves the repurposing of existing safe compounds which results in low overall development costs and shorter development timeline. In the present study, a computational network-biology approach has been used for comparing three candidate drugs i.e. quercetin, N-acetyl cysteine (NAC), and 2-deoxy-glucose (2-DG) to be effectively repurposed against COVID-19. For this, the associations between these drugs and genes of Severe Acute Respiratory Syndrome (SARS) and the Middle East Respiratory Syndrome (MERS) diseases were retrieved and a directed drug-gene-gene-disease interaction network was constructed. Further, to quantify the associations between a target gene and a disease gene, the shortest paths from the target gene to the disease genes were identified. A vector DV was calculated to represent the extent to which a disease gene was influenced by these drugs. Quercetin was quantified as the best among the three drugs, suited for repurposing with DV of -70.19, followed by NAC with DV of -39.99 and 2-DG with DV of -13.71. The drugs were also assessed for their safety and efficacy balance (in terms of therapeutic index) using network properties. It was found that quercetin was a forerunner than other two drugs.Funding Information: None to declare. Declaration of Interests: None to declare.

A Network-Biology led Computational Drug repurposing Strategy to prioritize therapeutic options for COVID-19 (preprint)

The alarming pandemic situation of novel Severe Acute Respiratory Syndrome Coronavirus 2 (nSARS-CoV-2) infection, high drug development cost and slow process of drug discovery have made repositioning of existing drugs for therapeutics a popular alternative. It involves the repurposing of existing safe compounds which results in low overall development costs and shorter development timeline. In the present study, a computational network-biology approach has been used for comparing three candidate drugs i.e. quercetin, N-acetyl cysteine (NAC), and 2-deoxy-glucose (2-DG) to be effectively repurposed against COVID-19. For this, the associations between these drugs and genes of Severe Acute Respiratory Syndrome (SARS) and the Middle East Respiratory Syndrome (MERS) diseases were retrieved and a directed drug-gene-gene-disease interaction network was constructed. Further, to quantify the associations between a target gene and a disease gene, the shortest paths from the target gene to the disease genes were identified. A vector DV was calculated to represent the extent to which a disease gene was influenced by these drugs. Quercetin was quantified as the best among the three drugs,  suited for repurposing with DV of -70.19, followed by NAC with DV of -39.99 and 2-DG with DV of -13.71. The drugs were also assessed for their safety and efficacy balance (in terms of therapeutic index) using network properties. It was found that quercetin was a forerunner than other two drugs. 

The miRNA-gene regulatory circuits in nSARS-CoV-2 and their potential correlations with pulmonary and thrombotic disorders (preprint)

In view of the worldwide spread of the novel Severe Acute Respiratory Syndrome Coronavirus 2 (nSARS-CoV-2) infection pandemic situation, research to repurpose drugs, identify novel drug targets, vaccine candidates, diagnostic markers etc have created a new race to curb the disease. To uncover nSARS-CoV-2-related important biological features and understanding the molecular basis of this disease, network biology and miRNA-gene regulatory motif-based approach is used. 11 antiviral human-microRNAs (miRNAs) which can potentially target SARS-CoV-2 genes were collated; their direct miRNA interactors were identified and a comprehensive nSARS-CoV-2 responsive miRNA:Transcription Factor (TF):gene coregulatory network was built. 1385 miRNA:TF:gene tripartite, Feed-Forward Loops (FFLs) were identified from the network. The network topology was mapped into the biological space and the overrepresented pathways were identified. Four regulatory circuits: hsa-mir-9-5p-EP300-PLCB4, hsa-mir-324-3p-MYC-HLA-F, hsa-mir-1827-E2F1-CTSV and hsa-mir-1277-5p-SP1-CANX are identified. These miRNA-gene regulatory circuits are found to regulate signalling pathways like virus endocytosis, viral replication, inflammatory response, pulmonary vascularization, cell cycle control, virus spike protein stabilization, antigen presentation, etc. Some novel computational evidences for understanding nSARS-CoV-2 molecular mechanisms controlled by these regulatory circuits is put forth. The novel associations of miRNAs and genes identified with this infection are open for experimental validation. Further, these regulatory circuits also suggest potential correlations/similarity in the molecular mechanisms during nSARS-CoV-2 infection and pulmonary diseases and thromboembolic disorders. A detailed molecular snapshot of TGF-β signalling pathway as the common mechanism that could play an important role in controlling common pathophysiology i.e. systemic inflammation, increased pulmonary pressure, ground glass opacities, D-dimer overexpression is also put forth.

Identification of pulmonary comorbid diseases network based repurposing effective drugs for COVID-19 (preprint)

The number of hospitalization of COVID-19 patients with one or more comorbid diseases is highly alarming. Despite the lack of large clinical data and incomplete understanding of virus pathology, identification of the COVID-19 associated diseases with clinical precision are highly limited. In this regard, our text mining of 6238 PubMed abstracts (as on 23 April 2020) successfully identified broad spectrum of COVID-19 comorbid diseases/disorders (54), and their prevalence on the basis of the number of occurrence of disease terms in the abstracts. The disease ontology based semantic similarity network analysis revealed the six highly comorbid diseases of COVID-19 namely Viral Pneumonia, Pulmonary Fibrosis, Pulmonary Edema, Acute Respiratory Distress Syndrome (ARDS), Chronic Obstructive Pulmonary Disease (COPD) and Asthma. The disease gene bipartite network revealed 15 genes that were strongly associated with several viral pathways including the corona viruses may involve in the manifestation (mild to critical) of COVID-19. Our tripartite network- based repurposing of the approved drugs in the world market revealed six promising drugs namely resveratrol, dexamethasone, acetyl cysteine, Tretinoin, simvastatin and aspirin to treat comorbid symptoms of COVID-19 patients. Our animal studies in rats and literatures strongly supported that resveratrol is the most promising drug to possibly reduce several comorbid symptoms associated with COVID-19 including the severe hypoxemia induced vascular leakage. Overall, the anti-viral properties of resveratrol against corona virus could be readily exploited to effectively control the viral load at early stage of COVID-19 infection through nasal administration.