Network-based repurposing identifies anti-alarmins as drug candidates to control severe lung inflammation in COVID-19

Abstract

While establishing worldwide collective immunity with anti SARS-CoV-2 vaccines, COVID-19 remains a major health issue with dramatic ensuing economic consequences. In the transition, repurposing existing drugs remains the fastest cost-effective approach to alleviate the burden on health services, most particularly by reducing the incidence of the acute respiratory distress syndrome associated with severe COVID-19. We undertook a computational repurposing approach to identify candidate therapeutic drugs to control progression towards severe airways inflammation during COVID-19. Molecular profiling data were obtained from public sources regarding SARS-CoV-2 infected epithelial or endothelial cells, immune dysregulations associated with severe COVID-19 and lung inflammation induced by other respiratory viruses. From these data, we generated a protein-protein interactome modeling the evolution of lung inflammation during COVID-19 from inception to an established cytokine release syndrome. This predictive model assembling severe COVID-19-related proteins supports a role for known contributors to the cytokine storm such as IL1β, IL6, TNFα, JAK2, but also less prominent actors such as IL17, IL23 and C5a. Importantly our analysis points out to alarmins such as TSLP, IL33, members of the S100 family and their receptors (ST2, RAGE) as targets of major therapeutic interest. By evaluating the network-based distances between severe COVID-19-related proteins and known drug targets, network computing identified drugs which could be repurposed to prevent or slow down progression towards severe airways inflammation. This analysis confirmed the interest of dexamethasone, JAK2 inhibitors, estrogens and further identified various drugs either available or in development interacting with the aforementioned targets. We most particularly recommend considering various inhibitors of alarmins or their receptors, currently receiving little attention in this indication, as candidate treatments for severe COVID-19.

What Happens to the Immune System after Vaccination or Recovery from COVID-19?

“…All of these observations have prompted researchers to develop strategies to modulate proinflammatory cytokine levels, and a possible treatment to reduce the incidence of ARDS in COVID-19 patients has been studied [16,[35][36][37].…”

SARS-CoV-2-Induced TSLP Is Associated with Duration of Hospital Stay in COVID-19 Patients

“…The targeted inhibition of TSLP could help prevent the damaging sequelae of COVID-19. In support of this suggestion, a recent report suggested that alarmin cytokines such as TSLP are targets of major therapeutic interest in COVID-19, although few clinical trials are being conducted at present to target these during COVID-19 infection [ 34 ].…”

A comprehensive review of artificial intelligence and network based approaches to drug repurposing in Covid-19

“… Title Datasets used Repurposed Drugs Evaluation Criteria Tools used Ref. 1 Network medicine framework for identifying drug-repurposing opportunities for COVID-19 13 Datasets, DrugBank, STRING 989 Drugs, 77 Validated in VeroE6 Cells, 76/77 validated in Human Cells Network proximity, network diffusion, Network AI Experimental, Ensembl algorithmic prediction [176] 2 Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence CTDbase, STRING, Hetionet, DrugBank 22 drugs and drug combinations Validation with multiple sources, In-vitro screening GSEA, Plotly, Gephi, GNN [178] 3 COVID-19 Multi-Targeted Drug Repurposing Using Few-Shot Learning Chemical Molecules, ZINC, ChEMBL, – Loss function and metrics Few-shot learning [179] 4 Machine learning and network medicine approaches for drug repositioning for COVID-19 STRING, ChEMBL, DrugBank – In-vitro, In-vivo, clinical trials, and CMAP – [180] 5 Using informative features in machine learning based method for COVID-19 drug repurposing STRING, DrugBank, Uniprot 80 % of the predictions were verified Statistical and clinical evidence Enrichment analysis, [182] 6 An integrative drug repositioning framework discovered a potential therapeutic agent targeting COVID-19 Literature, DrugBank, CTD, Uniprot, CMap 41 Drugs wet-lab, Expression profiles of patients and drug perturbation cells, clinical trials data Molecular docking [183] 7 Network-based repurposing identifies anti-alarmins as drug candidates to control severe lung inflammation in COVID-19 Uniprot, STRING, CMap, LINCS, GEO – Gene expression profiling, CMap ranking LINCS tools [184] …”

“… Title Datasets used Repurposed Drugs Evaluation Criteria Tools used Ref. 1 Designing a Network Proximity-Based Drug Repurposing Strategy for COVID-19 BioGRID – Network proximity/ Network Diffusion Cytoscape, VarElect tool [169] 2 Network medicine framework for identifying drug-repurposing opportunities for COVID-19 13 Datasets, DrugBank, STRING 989 Drugs, 77 Validated in VeroE6 Cells, 76/77 validated in Human Cells Network proximity, network diffusion, Network AI Experimental, Ensembl algorithmic prediction [176] 3 Drug repurposing for coronavirus (SARS-CoV-2) based on gene co-expression network analysis DGIDb, gene co-expression, DrugBank 5 Drugs Gene enrichment analysis for Genes and miRNA Node degree and centralities [195] 4 Network-based repurposing identifies anti-alarmins as drug candidates to control severe lung inflammation in COVID-19 Uniprot, STRING, CMap, LINCS, GEO – Gene expression profiling CMap ranking [184] 5 Integrative In Silico Investigation Reveals the Host-Virus Interactions in Repurposed Drugs Against SARS-CoV-2 STITCH, KEGG, BioGRID, PubChem, IID, – Enrichment analysis, Molecular docking DAVID, GOplot, AutoDock Vina, Cytoscape, Ligplot+ [196] 6 Discovery of Potential Therapeutic Drugs for COVID-19 Through Logistic Matrix Factorization with Kernel Diffusion – 4 Drugs 5-fold cross validations, AUC, AUPRs, recall, similarity diffusion Molecular docking [197] 7 HeTDR: Drug repositioning based on heterogeneous networks and text mining Mesh, DrugBank, PubMed 10 Drugs AUPR, AUROC, F1-measure – [198] 8 ...…”