scholarly journals DMAP: a connectivity map database to enable identification of novel drug repositioning candidates

2015 ◽  
Vol 16 (S13) ◽  
Author(s):  
Hui Huang ◽  
Thanh Nguyen ◽  
Sara Ibrahim ◽  
Sandeep Shantharam ◽  
Zongliang Yue ◽  
...  
Keyword(s):  
Drug Repositioning ◽  
Connectivity Map ◽  
Novel Drug

scholarly journals Prioritizing Small Molecule as Candidates for Drug Repositioning using Machine Learning

2018 ◽  
Author(s):  
Khader Shameer ◽  
Kipp W. Johnson ◽  
Benjamin S. Glicksberg ◽  
Rachel Hodos ◽  
Ben Readhead ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Small Molecule ◽  
Chemical Space ◽  
Drug Repositioning ◽  
Chemical Properties ◽  
Support Vector ◽  
Feature Engineering ◽  
Connectivity Map ◽  
Molecular Features

ABSTRACTDrug repositioning, i.e. identifying new uses for existing drugs and research compounds, is a cost-effective drug discovery strategy that is continuing to grow in popularity. Prioritizing and identifying drugs capable of being repositioned may improve the productivity and success rate of the drug discovery cycle, especially if the drug has already proven to be safe in humans. In previous work, we have shown that drugs that have been successfully repositioned have different chemical properties than those that have not. Hence, there is an opportunity to use machine learning to prioritize drug-like molecules as candidates for future repositioning studies. We have developed a feature engineering and machine learning that leverages data from publicly available drug discovery resources: RepurposeDB and DrugBank. ChemVec is the chemoinformatics-based feature engineering strategy designed to compile molecular features representing the chemical space of all drug molecules in the study. ChemVec was trained through a variety of supervised classification algorithms (Naïve Bayes, Random Forest, Support Vector Machines and an ensemble model combining the three algorithms). Models were created using various combinations of datasets as Connectivity Map based model, DrugBank Approved compounds based model, and DrugBank full set of compounds; of which RandomForest trained using Connectivity Map based data performed the best (AUC=0.674). Briefly, our study represents a novel approach to evaluate a small molecule for drug repositioning opportunity and may further improve discovery of pleiotropic drugs, or those to treat multiple indications.

scholarly journals Using systems medicine to identify a therapeutic agent with potential for repurposing in inflammatory bowel disease

2020 ◽  
Vol 13 (11) ◽  
pp. dmm044040 ◽  
Author(s):  
Katie Lloyd ◽  
Stamatia Papoutsopoulou ◽  
Emily Smith ◽  
Philip Stegmaier ◽  
Francois Bergey ◽  
...  
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Nuclear Translocation ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Peritoneal Macrophages ◽  
Bowel Diseases ◽  
Inflammatory Bowel ◽  
Aseptic Conditions ◽  
Novel Drug

ABSTRACTInflammatory bowel diseases (IBDs) cause significant morbidity and mortality. Aberrant NF-κB signalling is strongly associated with these conditions, and several established drugs influence the NF-κB signalling network to exert their effect. This study aimed to identify drugs that alter NF-κB signalling and could be repositioned for use in IBD. The SysmedIBD Consortium established a novel drug-repurposing pipeline based on a combination of in silico drug discovery and biological assays targeted at demonstrating an impact on NF-κB signalling, and a murine model of IBD. The drug discovery algorithm identified several drugs already established in IBD, including corticosteroids. The highest-ranked drug was the macrolide antibiotic clarithromycin, which has previously been reported to have anti-inflammatory effects in aseptic conditions. The effects of clarithromycin effects were validated in several experiments: it influenced NF-κB-mediated transcription in murine peritoneal macrophages and intestinal enteroids; it suppressed NF-κB protein shuttling in murine reporter enteroids; it suppressed NF-κB (p65) DNA binding in the small intestine of mice exposed to lipopolysaccharide; and it reduced the severity of dextran sulphate sodium-induced colitis in C57BL/6 mice. Clarithromycin also suppressed NF-κB (p65) nuclear translocation in human intestinal enteroids. These findings demonstrate that in silico drug repositioning algorithms can viably be allied to laboratory validation assays in the context of IBD, and that further clinical assessment of clarithromycin in the management of IBD is required.This article has an associated First Person interview with the joint first authors of the paper.

A Novel Drug Repositioning Approach based on Collaborative Metric Learning

2019 ◽  
pp. 1-1 ◽  
Author(s):  
Huimin Luo ◽  
Jianxin Wang ◽  
Cheng Yan ◽  
Min Li ◽  
Fangxiang Wu ◽  
...  
Keyword(s):  
Drug Repositioning ◽  
Metric Learning ◽  
Novel Drug

scholarly journals A review on drug repurposing applicable to COVID-19

2020 ◽  
Author(s):  
Serena Dotolo ◽  
Anna Marabotti ◽  
Angelo Facchiano ◽  
Roberto Tagliaferri
Keyword(s):  
Artificial Intelligence ◽  
Drug Target ◽  
Lower Cost ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Chemical Compounds ◽  
The Moment ◽  
New Applications ◽  
Novel Drug ◽  
Small Chemical

Abstract Drug repurposing involves the identification of new applications for existing drugs at a lower cost and in a shorter time. There are different computational drug-repurposing strategies and some of these approaches have been applied to the coronavirus disease 2019 (COVID-19) pandemic. Computational drug-repositioning approaches applied to COVID-19 can be broadly categorized into (i) network-based models, (ii) structure-based approaches and (iii) artificial intelligence (AI) approaches. Network-based approaches are divided into two categories: network-based clustering approaches and network-based propagation approaches. Both of them allowed to annotate some important patterns, to identify proteins that are functionally associated with COVID-19 and to discover novel drug–disease or drug–target relationships useful for new therapies. Structure-based approaches allowed to identify small chemical compounds able to bind macromolecular targets to evaluate how a chemical compound can interact with the biological counterpart, trying to find new applications for existing drugs. AI-based networks appear, at the moment, less relevant since they need more data for their application.

scholarly journals Drug Repositioning: New Approaches and Future Prospects for Life-Debilitating Diseases and the COVID-19 Pandemic Outbreak

Viruses ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1058
Author(s):  
Zheng Yao Low ◽  
Isra Ahmad Farouk ◽  
Sunil Kumar Lal
Keyword(s):  
Drug Development ◽  
De Novo ◽  
Drug Repositioning ◽  
Personalised Medicine ◽  
Technological Advancement ◽  
The Novel ◽  
Ongoing Research ◽  
Alternative Approach ◽  
Good Potential ◽  
Novel Drug

Traditionally, drug discovery utilises a de novo design approach, which requires high cost and many years of drug development before it reaches the market. Novel drug development does not always account for orphan diseases, which have low demand and hence low-profit margins for drug developers. Recently, drug repositioning has gained recognition as an alternative approach that explores new avenues for pre-existing commercially approved or rejected drugs to treat diseases aside from the intended ones. Drug repositioning results in lower overall developmental expenses and risk assessments, as the efficacy and safety of the original drug have already been well accessed and approved by regulatory authorities. The greatest advantage of drug repositioning is that it breathes new life into the novel, rare, orphan, and resistant diseases, such as Cushing’s syndrome, HIV infection, and pandemic outbreaks such as COVID-19. Repositioning existing drugs such as Hydroxychloroquine, Remdesivir, Ivermectin and Baricitinib shows good potential for COVID-19 treatment. This can crucially aid in resolving outbreaks in urgent times of need. This review discusses the past success in drug repositioning, the current technological advancement in the field, drug repositioning for personalised medicine and the ongoing research on newly emerging drugs under consideration for the COVID-19 treatment.

scholarly journals MeSHDD: Literature-based drug-drug similarity for drug repositioning

2016 ◽  
Vol 24 (3) ◽  
pp. 614-618 ◽  
Author(s):  
Adam S Brown ◽  
Chirag J Patel
Keyword(s):  
Drug Repositioning ◽  
Mesh Term ◽  
Medical Subject Heading ◽  
Mesh Terms ◽  
Interactive Application ◽  
Diabetes Drug ◽  
The Cost ◽  
Novel Drug ◽  
Drug Similarity ◽  
Discovery Pipeline

Objective: Drug repositioning is a promising methodology for reducing the cost and duration of the drug discovery pipeline. We sought to develop a computational repositioning method leveraging annotations in the literature, such as Medical Subject Heading (MeSH) terms. Methods: We developed software to determine significantly co-occurring drug-MeSH term pairs and a method to estimate pair-wise literature-derived distances between drugs. Results We found that literature-based drug-drug similarities predicted the number of shared indications across drug-drug pairs. Clustering drugs based on their similarity revealed both known and novel drug indications. We demonstrate the utility of our approach by generating repositioning hypotheses for the commonly used diabetes drug metformin. Conclusion: Our study demonstrates that literature-derived similarity is useful for identifying potential repositioning opportunities. We provided open-source code and deployed a free-to-use, interactive application to explore our database of similarity-based drug clusters (available at http://apps.chiragjpgroup.org/MeSHDD/).

Literature-based prediction of novel drug indications considering relationships between entities

2017 ◽  
Vol 13 (7) ◽  
pp. 1399-1405 ◽  
Author(s):  
Giup Jang ◽  
Taekeon Lee ◽  
Byung Mun Lee ◽  
Youngmi Yoon
Keyword(s):  
Drug Repositioning ◽  
Novel Drug

There have been many attempts to identify and develop new uses for existing drugs, which is known as drug repositioning.

scholarly journals Primaquine Diphosphate, a Known Antimalarial Drug, Blocks Vascular Leakage Acting Through Junction Stabilization

2021 ◽  
Vol 12 ◽  
Author(s):  
Minyoung Noh ◽  
Haiying Zhang ◽  
Hyejeong Kim ◽  
Songyi Park ◽  
Young-Myeong Kim ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Antimalarial Drug ◽  
Drug Repositioning ◽  
Endothelial Permeability ◽  
Vascular Leakage ◽  
Linear Pattern ◽  
Specific Protease ◽  
Endothelial Integrity ◽  
Endothelial Growth Factors ◽  
Novel Drug

Endothelial barrier integrity is important for vascular homeostasis, and hyperpermeability participates in the progression of many pathological states, such as diabetic retinopathy, ischemic stroke, chronic bowel disease, and inflammatory disease. Here, using drug repositioning, we discovered that primaquine diphosphate (PD), previously known as an antimalarial drug, was a potential blocker of vascular leakage. PD inhibited the linear pattern of vascular endothelial growth factors (VEGF)-induced disruption at the cell boundaries, blocked the formation of VEGF-induced actin stress fibers, and stabilized the cortactin actin rings in endothelial cells. PD significantly reduced leakage in the Miles assay and mouse model of streptozotocin (STZ)-induced diabetic retinopathy. Targeted prediction programs and deubiquitinating enzyme activity assays identified a potential mechanism of action for PD and demonstrated that this operates via ubiquitin specific protease 1 (USP1). USP1 inhibition demonstrated a conserved barrier function by inhibiting VEGF-induced leakage in endothelial permeability assays. Taken together, these findings suggest that PD could be used as a novel drug for vascular leakage by maintaining endothelial integrity.

scholarly journals Evaluation of Connectivity Map shows limited reproducibility in drug repositioning

2019 ◽  
Author(s):  
Nathaniel Lim ◽  
Paul Pavlidis
Keyword(s):  
Success Rate ◽  
Cell Line ◽  
Differential Expression ◽  
Drug Repositioning ◽  
False Positives ◽  
Data Driven ◽  
Connectivity Map ◽  
Expression Levels ◽  
Compound Concentration

SummaryThe Connectivity Map (CMap) is a popular resource designed for data-driven drug repositioning using a large transcriptomic compendium. However, evaluations of its performance are limited. We used two iterations of CMap (CMap 1 and 2) to assess their comparability and reliability. We queried CMap 2 with CMap 1-derived signatures, expecting CMap 2 would highly prioritize the queried compounds; success rate was 17%. Analysis of previously published prioritizations yielded similar results. Low recall is caused by low differential expression (DE) reproducibility both between CMaps and within each CMap. DE strength was predictive of reproducibility, and is influenced by compound concentration and cell-line responsiveness. Reproducibility of CMap 2 sample expression levels was also lower than expected. We attempted to identify the “better” CMap by comparison with a third dataset, but they were mutually discordant. Our findings have implications for CMap usage and we suggest steps for investigators to limit false positives.

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