Connectivity map-based drug repositioning of bortezomib to reverse the metastatic effect of GALNT14 in lung cancer

Oncogene ◽  
2020 ◽  
Vol 39 (23) ◽  
pp. 4567-4580 ◽  
Author(s):  
Ok-Seon Kwon ◽  
Haeseung Lee ◽  
Hyeon-Joon Kong ◽  
Eun-Ji Kwon ◽  
Ji Eun Park ◽  
...  
Oncogene ◽  
2021 ◽  
Vol 40 (10) ◽  
pp. 1921-1921
Author(s):  
Ok-Seon Kwon ◽  
Haeseung Lee ◽  
Hyeon-Joon Kong ◽  
Eun-Ji Kwon ◽  
Ji Eun Park ◽  
...  

2018 ◽  
Author(s):  
Khader Shameer ◽  
Kipp W. Johnson ◽  
Benjamin S. Glicksberg ◽  
Rachel Hodos ◽  
Ben Readhead ◽  
...  

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.


2015 ◽  
Vol 16 (S13) ◽  
Author(s):  
Hui Huang ◽  
Thanh Nguyen ◽  
Sara Ibrahim ◽  
Sandeep Shantharam ◽  
Zongliang Yue ◽  
...  

2019 ◽  
Author(s):  
Nathaniel Lim ◽  
Paul Pavlidis

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.


2021 ◽  
Author(s):  
Brian Le ◽  
Gaia Andreoletti ◽  
Tomiko Oskotsky ◽  
Albert Vallejo-Gracia ◽  
Romel Rosales Ramirez ◽  
...  

Abstract The novel SARS-CoV-2 virus emerged in December 2019 and has few effective treatments. We applied a computational drug repositioning pipeline to SARS-CoV-2 differential gene expression signatures derived from publicly available data. We utilized three independent published studies to acquire or generate lists of differentially expressed genes between control and SARS-CoV-2-infected samples. Using a rank-based pattern matching strategy based on the Kolmogorov-Smirnov Statistic, the signatures were queried against drug profiles from Connectivity Map (CMap). We validated sixteen of our top predicted hits in live SARS-CoV-2 antiviral assays in either Calu-3 or 293T-ACE2 cells. Validation experiments in human cell lines showed that 11 of the 16 compounds tested to date (including clofazimine, haloperidol and others) had measurable antiviral activity against SARS-CoV-2. These initial results are encouraging as we continue to work towards a further analysis of these predicted drugs as potential therapeutics for the treatment of COVID-19.


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