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

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.

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

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.

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

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.

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

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257784
Author(s):  
Rajaneesh K. Gupta ◽  
Enyinna L. Nwachuku ◽  
Benjamin E. Zusman ◽  
Ruchira M. Jha ◽  
Ava M. Puccio
Keyword(s):  
Gene Networks ◽  
Drug Targets ◽  
Drug Repositioning ◽  
Meta Analysis ◽  
Drug Repurposing ◽  
Gene Signature ◽  
Airway Epithelial Cells ◽  
Gene Ranking ◽  
Human Airway ◽  
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.

scholarly journals A Computational Framework of Host-Based Drug Repositioning for Broad-Spectrum Antivirals against RNA Viruses

2020 ◽  
Author(s):  
Zexu Li ◽  
Yingjia Yao ◽  
Xiaolong Cheng ◽  
Qing Chen ◽  
Wenchang Zhao ◽  
...  
Keyword(s):  
Drug Targets ◽  
De Novo ◽  
Rna Viruses ◽  
Drug Repositioning ◽  
Antiviral Drug ◽  
Natural Compounds ◽  
Influenza Viruses ◽  
Literature Mining ◽  
Computational Framework ◽  
Drug Candidates

RNA viruses are responsible for many types of zoonotic diseases that post great challenges for public health system. Effective therapeutics against these viral infections remains limited. Here we deployed a computational framework for host-based drug repositioning to predict potential antiviral drug candidates from 2352 approved drugs and 1062 natural compounds embedded in Traditional Chinese Medicine herbs. By systematically interrogating public genetic screening data, we comprehensively catalogued human-specific host dependency genes that are indispensable for the successful viral infection corresponding to 10 families and 29 species of RNA viruses. In addition, we utilized these host dependency genes as potential drug targets, and interrogated extensive drug-target interactions through multiple ways such as database retrieval, literature mining and de novo prediction using artificial intelligence-based algorithms. Repurposed drugs or natural compounds were proposed for combating many viral pathogens such as coronaviruses (e.g., SARS-CoV-2), flaviviruses (e.g., Zika virus) and influenza viruses. This study helps to prioritize promising drug candidates for further therapeutic evaluation against these viral-related diseases.

scholarly journals Overcoming sparseness of biomedical networks to identify drug repositioning candidates

2020 ◽  
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.

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

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.

scholarly journals A Computational Framework of Host-Based Drug Repositioning for Broad-Spectrum Antivirals against RNA Viruses

2020 ◽  
Author(s):  
Zexu Li ◽  
Yingjia Yao ◽  
Xiaolong Cheng ◽  
Qing Chen ◽  
Wenchang Zhao ◽  
...  
Keyword(s):  
Drug Targets ◽  
De Novo ◽  
Rna Viruses ◽  
Drug Repositioning ◽  
Antiviral Drug ◽  
Natural Compounds ◽  
Influenza Viruses ◽  
Literature Mining ◽  
Computational Framework ◽  
Drug Candidates

RNA viruses are responsible for many types of zoonotic diseases that post great challenges for public health system. Effective therapeutics against these viral infections remains limited. Here we deployed a computational framework for host-based drug repositioning to predict potential antiviral drug candidates from 2352 approved drugs and 1062 natural compounds embedded in Traditional Chinese Medicine herbs. By systematically interrogating public genetic screening data, we comprehensively catalogued human-specific host dependency genes that are indispensable for the successful viral infection corresponding to 10 families and 29 species of RNA viruses. In addition, we utilized these host dependency genes as potential drug targets, and interrogated extensive drug-target interactions through multiple ways such as database retrieval, literature mining and de novo prediction using artificial intelligence-based algorithms. Repurposed drugs or natural compounds were proposed for combating many viral pathogens such as coronaviruses (e.g., SARS-CoV-2), flaviviruses (e.g., Zika virus) and influenza viruses. This study helps to prioritize promising drug candidates for further therapeutic evaluation against these viral-related diseases.

Recent Advances in Drug Repurposing for Parkinson’s Disease

2019 ◽  
Vol 26 (28) ◽  
pp. 5340-5362 ◽  
Author(s):  
Xin Chen ◽  
Giuseppe Gumina ◽  
Kristopher G. Virga
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Drug Discovery ◽  
De Novo ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Cost Effective ◽  
New Drugs ◽  
Recent Advances ◽  
Clinical Drugs

:As a long-term degenerative disorder of the central nervous system that mostly affects older people, Parkinson’s disease is a growing health threat to our ever-aging population. Despite remarkable advances in our understanding of this disease, all therapeutics currently available only act to improve symptoms but cannot stop the disease progression. Therefore, it is essential that more effective drug discovery methods and approaches are developed, validated, and used for the discovery of disease-modifying treatments for Parkinson’s disease. Drug repurposing, also known as drug repositioning, or the process of finding new uses for existing or abandoned pharmaceuticals, has been recognized as a cost-effective and timeefficient way to develop new drugs, being equally promising as de novo drug discovery in the field of neurodegeneration and, more specifically for Parkinson’s disease. The availability of several established libraries of clinical drugs and fast evolvement in disease biology, genomics and bioinformatics has stimulated the momentums of both in silico and activity-based drug repurposing. With the successful clinical introduction of several repurposed drugs for Parkinson’s disease, drug repurposing has now become a robust alternative approach to the discovery and development of novel drugs for this disease. In this review, recent advances in drug repurposing for Parkinson’s disease will be discussed.

Molecular Topology and Other Promiscuity Determinants as Predictors of Therapeutic Class - A Theoretical Framework to Guide Drug Repositioning?

2018 ◽  
Vol 18 (13) ◽  
pp. 1110-1122 ◽  
Author(s):  
Juan F. Morales ◽  
Lucas N. Alberca ◽  
Sara Chuguransky ◽  
Mauricio E. Di Ianni ◽  
Alan Talevi ◽  
...  
Keyword(s):  
Molecular Descriptors ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Topological Descriptors ◽  
Log P ◽  
Acidity Constant ◽  
Molecular Topology ◽  
Clustering Methods ◽  
Mean Values ◽  
Qsar Models

Much interest has been paid in the last decade on molecular predictors of promiscuity, including molecular weight, log P, molecular complexity, acidity constant and molecular topology, with correlations between promiscuity and those descriptors seemingly being context-dependent. It has been observed that certain therapeutic categories (e.g. mood disorders therapies) display a tendency to include multi-target agents (i.e. selective non-selectivity). Numerous QSAR models based on topological descriptors suggest that the topology of a given drug could be used to infer its therapeutic applications. Here, we have used descriptive statistics to explore the distribution of molecular topology descriptors and other promiscuity predictors across different therapeutic categories. Working with the publicly available ChEMBL database and 14 molecular descriptors, both hierarchical and non-hierchical clustering methods were applied to the descriptors mean values of the therapeutic categories after the refinement of the database (770 drugs grouped into 34 therapeutic categories). On the other hand, another publicly available database (repoDB) was used to retrieve cases of clinically-approved drug repositioning examples that could be classified into the therapeutic categories considered by the aforementioned clusters (111 cases), and the correspondence between the two studies was evaluated. Interestingly, a 3- cluster hierarchical clustering scheme based on only 14 molecular descriptors linked to promiscuity seem to explain up to 82.9% of approved cases of drug repurposing retrieved of repoDB. Therapeutic categories seem to display distinctive molecular patterns, which could be used as a basis for drug screening and drug design campaigns, and to unveil drug repurposing opportunities between particular therapeutic categories.

Old Drugs with New Tricks; Paradigm in Drug Development Pipeline for Alzheimer’s Disease

2020 ◽  
Vol 20 ◽  
Author(s):  
Tanay Dalvi ◽  
Bhaskar Dewangan ◽  
Rudradip Das ◽  
Jyoti Rani ◽  
Suchita Dattatray Shinde ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Drug Development ◽  
Molecular Targets ◽  
Drug Repurposing ◽  
Phase Iii ◽  
Complex Nature ◽  
New Drug ◽  
Development Pipeline ◽  
Old Drugs

: The most common reason behind dementia is Alzheimer’s disease (AD) and it is predicted to be the third lifethreatening disease apart from stroke and cancer for the geriatric population. Till now only four drugs are available in the market for symptomatic relief. The complex nature of disease pathophysiology and lack of concrete evidences of molecular targets are the major hurdles for developing new drug to treat AD. The the rate of attrition of many advanced drugs at clinical stages, makes the de novo discovery process very expensive. Alternatively, Drug Repurposing (DR) is an attractive tool to develop drugs for AD in a less tedious and economic way. Therefore, continuous efforts are being made to develop a new drug for AD by repursing old drugs through screening and data mining. For example, the survey in the drug pipeline for Phase III clinical trials (till February 2019) which has 27 candidates, and around half of the number are drugs which have already been approved for other indications. Although in the past the drug repurposing process for AD has been reviewed in the context of disease areas, molecular targets, there is no systematic review of repurposed drugs for AD from the recent drug development pipeline (2019-2020). In this manuscript, we are reviewing the clinical candidates for AD with emphasis on their development history including molecular targets and the relevance of the target for AD.

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