Drug repurposing for COVID-19 based on an integrative meta-analysis of SARS-CoV-2 induced gene signature in human airway epithelium

Drug repurposing has the potential to bring existing de-risked drugs for effective intervention in an ongoing pandemic—COVID-19 that has infected over 131 million, with 2.8 million people succumbing to the illness globally (as of April 04, 2021). We have used a novel `gene signature’-based drug repositioning strategy by applying widely accepted gene ranking algorithms to prioritize the FDA approved or under trial drugs. We mined publically available RNA sequencing (RNA-Seq) data using CLC Genomics Workbench 20 (QIAGEN) and identified 283 differentially expressed genes (FDR<0.05, log2FC>1) after a meta-analysis of three independent studies which were based on severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) infection in primary human airway epithelial cells. Ingenuity Pathway Analysis (IPA) revealed that SARS-CoV-2 activated key canonical pathways and gene networks that intricately regulate general anti-viral as well as specific inflammatory pathways. Drug database, extracted from the Metacore and IPA, identified 15 drug targets (with information on COVID-19 pathogenesis) with 46 existing drugs as potential-novel candidates for repurposing for COVID-19 treatment. We found 35 novel drugs that inhibit targets (ALPL, CXCL8, and IL6) already in clinical trials for COVID-19. Also, we found 6 existing drugs against 4 potential anti-COVID-19 targets (CCL20, CSF3, CXCL1, CXCL10) that might have novel anti-COVID-19 indications. Finally, these drug targets were computationally prioritized based on gene ranking algorithms, which revealed CXCL10 as the common and strongest candidate with 2 existing drugs. Furthermore, the list of 283 SARS-CoV-2-associated proteins could be valuable not only as anti-COVID-19 targets but also useful for COVID-19 biomarker development.

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Drug Repurposing of potential drug targets for treatment of COVID-19

10.1101/2021.06.02.21258223 ◽

2021 ◽

Author(s):

Rajaneesh Gupta ◽

Enyinna Nwachuku ◽

Benjamin Zusman ◽

Ruchira Jha ◽

Ava Puccio

Keyword(s):

Gene Networks ◽

Drug Targets ◽

Drug Repositioning ◽

Meta Analysis ◽

Drug Repurposing ◽

Gene Signature ◽

Airway Epithelial Cells ◽

Airway Epithelial ◽

Gene Ranking ◽

Ranking Algorithms

Drug repurposing has the potential to bring existing de-risked drugs for effective intervention in an ongoing pandemic-COVID-19 that has infected over 131 million, with 2.8 million people succumbing to the illness globally (as of April 04, 2021). We have used a novel `gene signature'-based drug repositioning strategy by applying widely accepted gene ranking algorithms to prioritize the FDA approved or under trial drugs. We mined publically available RNA sequencing (RNA-Seq) data using CLC Genomics Workbench 20 (QIAGEN) and identified 283 differentially expressed genes (FDR<0.05, log2FC>1) after a meta-analysis of three independent studies which were based on severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) infection in primary human airway epithelial cells. Ingenuity Pathway Analysis (IPA) revealed that SARS-CoV-2 activated key canonical pathways and gene networks that intricately regulate general anti-viral as well as specific inflammatory pathways. Drug database, extracted from the Metacore and IPA, identified 15 drug targets (with information on COVID-19 pathogenesis) with 46 existing drugs as potential-novel candidates for repurposing for COVID-19 treatment. We found 35 novel drugs that inhibit targets (ALPL, CXCL8, and IL6) already in clinical trials for COVID-19. Also, we found 6 existing drugs against 4 potential anti-COVID-19 targets (CCL20, CSF3, CXCL1, CXCL10) that might have novel anti-COVID-19 indications. Finally, these drug targets were computationally prioritized based on gene ranking algorithms, which revealed CXCL10 as the common and strongest candidate with 2 existing drugs. Furthermore, the list of 283 SARS-CoV-2-associated proteins could be valuable not only as anti-COVID-19 targets but also useful for COVID-19 biomarker development.

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A Computational Approach for Pathway-Based Systemic Drug Influence

Processes ◽

10.3390/pr9061063 ◽

2021 ◽

Vol 9 (6) ◽

pp. 1063

Author(s):

Shinuk Kim

Keyword(s):

Gene Networks ◽

Drug Targets ◽

Drug Repositioning ◽

Drug Efficacy ◽

Rapid Expansion ◽

Gene Sets ◽

Development Risks ◽

Drug Influence ◽

Potential Drug Targets ◽

Systemic Drug

Drug repositioning is a well-known method used to reduce the time, cost, and development risks involved in bringing a new drug to the market. The rapid expansion of high-throughput datasets has enabled computational research that can suggest new potential uses for existing drugs. Some computational methods allow the prediction of potential drug targets of a given disease from a systematic network. Despite numerous efforts, the path of many drugs’ efficacy in the human body remains unclear. Therefore, the present study attempted to understand drug efficacy by systematically focusing on functional gene sets. The purpose of this study was to carry out modeling to identify systemic gene networks (called drug paths) in drug-specific pathways. In our results, we found five different paths for five different drugs.

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Computational guided approach for drug repurposing against SARS-CoV-2

Future Virology ◽

10.2217/fvl-2020-0403 ◽

2021 ◽

Author(s):

Jigisha Anand ◽

Tanmay Ghildiyal ◽

Aakanksha Madhwal ◽

Rishabh Bhatt ◽

Devvret Verma ◽

...

Keyword(s):

Drug Targets ◽

De Novo ◽

Drug Repositioning ◽

Drug Repurposing ◽

Computational Docking ◽

Drug Candidates ◽

Docking Tool ◽

Inhibitory Potential ◽

Approved Drugs ◽

Tract Infections

Background: In the current SARS-CoV-2 outbreak, drug repositioning emerges as a promising approach to develop efficient therapeutics in comparison to de novo drug development. The present investigation screened 130 US FDA-approved drugs including hypertension, cardiovascular diseases, respiratory tract infections (RTI), antibiotics and antiviral drugs for their inhibitory potential against SARS-CoV-2. Materials & methods: The molecular drug targets against SARS-CoV-2 proteins were determined by the iGEMDOCK computational docking tool. The protein homology models were generated through SWISS Model workspace. The pharmacokinetics of all the ligands was determined by ADMET analysis. Results: The study identified 15 potent drugs exhibiting significant inhibitory potential against SARS-CoV-2. Conclusion: Our investigation has identified possible repurposed drug candidates to improve the current modus operandi of the treatment given to COVID-19 patients.

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Proteome-wide Mendelian randomization in global biobank meta-analysis reveals multi-ancestry drug targets for common diseases

10.1101/2022.01.09.21268473 ◽

2022 ◽

Author(s):

Huiling Zhao ◽

humaira Rasheed ◽

Therese Haugdahl Nost ◽

Yoonsu Cho ◽

Yi Liu ◽

...

Keyword(s):

Drug Targets ◽

Mendelian Randomization ◽

Meta Analysis ◽

Drug Repurposing ◽

Population Data ◽

Limited Data ◽

Specific Effects ◽

Clinical Trial Evidence ◽

Males And Females ◽

Trial Evidence

Proteome-wide Mendelian randomization (MR) shows value in prioritizing drug targets in Europeans, but limited data has made identification of causal proteins in other ancestries challenging. Here we present a multi-ancestry proteome-wide MR analysis pipeline based on cross-population data from the Global Biobank Meta-analysis Initiative (GBMI). We estimated the causal effects of 1,545 proteins on eight complex diseases in up to 32,658 individuals of African ancestries and 1.22 million individuals of European ancestries. We identified 45 and seven protein-disease pairs with MR and genetic colocalization evidence in the two ancestries respectively. 15 protein-disease pairs showed evidence of differential effects between males and females. A multi-ancestry MR comparison identified two protein-disease pairs with MR evidence of an effect in both ancestries, seven pairs with European-specific effects and seven with African-specific effects. Integrating these MR signals with observational and clinical trial evidence, we were able to evaluate the efficacy of one existing drug, identify seven drug repurposing opportunities and predict seven novel effects of proteins on diseases. Our results highlight the value of proteome-wide MR in informing the generalisability of drug targets across ancestries and illustrate the value of multi-cohort and biobank meta-analysis of genetic data for drug development.

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

PLoS Computational Biology ◽

10.1371/journal.pcbi.1008686 ◽

2021 ◽

Vol 17 (2) ◽

pp. e1008686

Author(s):

Giulia Fiscon ◽

Federica Conte ◽

Lorenzo Farina ◽

Paola Paci

Keyword(s):

Drug Targets ◽

De Novo ◽

Drug Repositioning ◽

Drug Repurposing ◽

Gene Set Enrichment Analysis ◽

Thrombin Inhibitors ◽

New Combination ◽

Human Coronavirus ◽

Human Interactome ◽

Off Label

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.

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Drug Repurposing Approaches: Existing Leads For Novel Threats And Drug Targets

Current Protein and Peptide Science ◽

10.2174/1389203721666200921152853 ◽

2020 ◽

Vol 21 ◽

Author(s):

Talambedu Usha ◽

Sushil K. Middha ◽

Anusha A. Kukanur ◽

Rachamadugu V. Shravani ◽

Mahantesh N. Anupama ◽

...

Keyword(s):

Drug Targets ◽

Drug Repositioning ◽

Drug Repurposing ◽

Risk Process ◽

Clinical Analysis ◽

Literature Mining ◽

Semantic Inference ◽

Inference Network ◽

Academic Publications ◽

Old Drugs

: Drug Repurposing (DR) is an alternative to the traditional drug discovery process. It is cost and time effective, with high returns and low risk process that can tackle the increasing need for interventions for varied diseases and new outbreaks. Repurposing of old drugs for other diseases has gained a wider attention, as there have been several old drugs approved by FDA for new diseases. In the global emergency of COVID19 pandemic, this is one of the strategies implemented in repurposing of old anti-infective, anti-rheumatic and anti-thrombotic drugs. The goal of the current review is to elaborate the process of DR, its advantages, repurposed drugs for a plethora of disorders, and the evolution of related academic publications. Further, detailed are the computational approaches: literature mining and semantic inference, network-based drug repositioning, signature matching, retrospective clinical analysis, molecular docking and experimental phenotypic screening. We discuss the legal and economical potential barriers in DR, existent collaborative models and recommendations for overcoming these hurdles and leveraging the complete potential of DR in finding new indications.

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Genome-wide meta-analysis of pneumonia suggests a role for mucin biology and provides novel drug repurposing opportunities

10.1101/2021.01.24.21250424 ◽

2021 ◽

Author(s):

William R. Reay ◽

Michael P. Geaghan ◽

Murray J. Cairns ◽

Keyword(s):

Drug Targets ◽

Meta Analysis ◽

Low Frequency ◽

Drug Repurposing ◽

The Elderly ◽

Risk Scores ◽

Polygenic Effect ◽

Genome Wide ◽

A Genome ◽

Mucin Genes

ABSTRACTPneumonia remains one of the leading causes of death worldwide, particularly amongst the elderly and young children. We performed a genome-wide meta-analysis of lifetime pneumonia diagnosis (N=266,277), that encompassed the largest collection of cases published to date. Genome-wide significant associations with pneumonia were uncovered for the first time beyond the major histocompatibility complex region, with three novel loci, including a signal fine-mapped to a cluster of mucin genes. Moreover, we demonstrated evidence of a polygenic effect of common and low frequency pneumonia associated variation impacting several other mucin genes and O-glycosylation, further suggesting a role for these processes in pneumonia pathophysiology. The pneumonia GWAS was then leveraged to identify drug repurposing opportunities, including evidence that supports the use of lipid modifying agents in the prevention and treatment of the disorder. We also propose how polygenic risk could be utilised for precision drug repurposing through pneumonia risk scores constructed using variants mapped to pathways with known drug targets. In summary, we provide novel insights into the genetic architecture of pneumonia susceptibility, with future study warranted to functionally interrogate novel association signals and evaluate the suitability of the compounds prioritised by this study as repositioning candidates.

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Overcoming sparseness of biomedical networks to identify drug repositioning candidates

10.1101/2020.06.07.138966 ◽

2020 ◽

Cited By ~ 1

Author(s):

Aleksandar Poleksic

Keyword(s):

Biological Networks ◽

Drug Targets ◽

Drug Repositioning ◽

Drug Repurposing ◽

Drug Efficacy ◽

Computational Procedure ◽

Computational Method ◽

Biomedical Data ◽

Disease Associations ◽

Disease Associated Genes

AbstractModeling complex biological systems is necessary to understand biochemical interactions behind pharmacological effects of drugs. Successful in silico drug repurposing requires a thorough exploration of diverse biochemical concepts and their relationships, including drug’s adverse reactions, drug targets, disease symptoms, as well as disease associated genes and their pathways, to name a few. We present a computational method for inferring drug-disease associations from complex but incomplete and biased biological networks. Our method employs the compressed sensing technique to overcome the sparseness of biomedical data and, in turn, to enrich the set of verified relationships between different biomedical entities. We present a strategy for identifying network paths supportive of drug efficacy as well as a computational procedure capable of combining different network patterns to better distinguish treatments from non-treatments. The data and programs are freely available at http://bioinfo.cs.uni.edu/AEONET.html.

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TMPRSS2 and furin are both essential for proteolytic activation of SARS-CoV-2 in human airway cells

Life Science Alliance ◽

10.26508/lsa.202000786 ◽

2020 ◽

Vol 3 (9) ◽

pp. e202000786 ◽

Cited By ~ 51

Author(s):

Dorothea Bestle ◽

Miriam Ruth Heindl ◽

Hannah Limburg ◽

Thuy Van Lam van ◽

Oliver Pilgram ◽

...

Keyword(s):

Serine Protease ◽

Drug Targets ◽

Therapeutic Potential ◽

Severe Disease ◽

Acute Infection ◽

Airway Epithelial Cells ◽

Surface Glycoprotein ◽

Proteolytic Activation ◽

Human Airway ◽

Airway Cells

The novel emerged SARS-CoV-2 has rapidly spread around the world causing acute infection of the respiratory tract (COVID-19) that can result in severe disease and lethality. For SARS-CoV-2 to enter cells, its surface glycoprotein spike (S) must be cleaved at two different sites by host cell proteases, which therefore represent potential drug targets. In the present study, we show that S can be cleaved by the proprotein convertase furin at the S1/S2 site and the transmembrane serine protease 2 (TMPRSS2) at the S2′ site. We demonstrate that TMPRSS2 is essential for activation of SARS-CoV-2 S in Calu-3 human airway epithelial cells through antisense-mediated knockdown of TMPRSS2 expression. Furthermore, SARS-CoV-2 replication was also strongly inhibited by the synthetic furin inhibitor MI-1851 in human airway cells. In contrast, inhibition of endosomal cathepsins by E64d did not affect virus replication. Combining various TMPRSS2 inhibitors with furin inhibitor MI-1851 produced more potent antiviral activity against SARS-CoV-2 than an equimolar amount of any single serine protease inhibitor. Therefore, this approach has considerable therapeutic potential for treatment of COVID-19.

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Gene Signature-Based Drug Repositioning

10.5772/intechopen.101377 ◽

2021 ◽

Author(s):

Zhilong Jia ◽

Xinyu Song ◽

Jinlong Shi ◽

Weidong Wang ◽

Kunlun He

Keyword(s):

Drug Repositioning ◽

Drug Repurposing ◽

Gene Signature ◽

Learning Technology ◽

Omics Data ◽

Future Trends ◽

Omics Technology ◽

Global Projects ◽

Approved Drugs

With the advent of dynamical omics technology, especially the transcriptome and proteome, a huge amount of data related to various diseases and approved drugs are available under multi global projects or researches with their interests. These omics data and new machine learning technology largely promote the translation of drug research into clinical trials. We will cover the following topics in this chapter. 1) An introduction to the basic discipline of gene signature-based drug repurposing; 2) databases of genes, drugs and diseases; 3) gene signature databases of the approved drugs; 4) gene signature databases of various diseases; 5) gene signature-based methods and tools for drug repositioning; 6) new omics technology for drug repositioning; 7) drug repositioning examples with reproducible code. And finally, discuss the future trends and conclude.

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