scholarly journals Transcriptomics-based drug repositioning pipeline identifies therapeutic candidates for COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Brian L. Le ◽  
Gaia Andreoletti ◽  
Tomiko Oskotsky ◽  
Albert Vallejo-Gracia ◽  
Romel Rosales ◽  
...  
Keyword(s):  
Drug Repositioning ◽  
The Novel ◽  
Connectivity Map ◽  
Kolmogorov Smirnov ◽  
Differential Gene ◽  
Matching Strategy ◽  
Initial Results ◽  
Smirnov Statistic ◽  
Potential Therapeutics ◽  
Validation Experiments

AbstractThe 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 16 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.

scholarly journals Transcriptomics-based drug repositioning pipeline identifies therapeutic candidates for COVID-19

2021 ◽  
Author(s):  
Brian Le ◽  
Gaia Andreoletti ◽  
Tomiko Oskotsky ◽  
Albert Vallejo-Gracia ◽  
Romel Rosales Ramirez ◽  
...  
Keyword(s):  
Drug Repositioning ◽  
The Novel ◽  
Connectivity Map ◽  
Kolmogorov Smirnov ◽  
Differential Gene ◽  
Matching Strategy ◽  
Initial Results ◽  
Smirnov Statistic ◽  
Potential Therapeutics ◽  
Validation Experiments

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.

scholarly journals Transcriptomics-based drug repositioning pipeline identifies therapeutic candidates for COVID-19

2020 ◽  
Author(s):  
Brian L. Le ◽  
Gaia Andreoletti ◽  
Tomiko Oskotsky ◽  
Albert Vallejo-Gracia ◽  
Romel Rosales ◽  
...  
Keyword(s):  
Drug Repositioning ◽  
The Novel ◽  
Connectivity Map ◽  
Kolmogorov Smirnov ◽  
Differential Gene ◽  
Matching Strategy ◽  
Initial Results ◽  
Smirnov Statistic ◽  
Potential Therapeutics ◽  
Validation Experiments

AbstractThe 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.

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 Differential Interactome Based Drug Repositioning Unraveled Abacavir, Exemestane, Nortriptyline Hydrochloride, and Tolcapone as Potential Therapeutics for Colorectal Cancers

2021 ◽  
Vol 1 ◽  
Author(s):  
Hande Beklen ◽  
Sema Arslan ◽  
Gizem Gulfidan ◽  
Beste Turanli ◽  
Pemra Ozbek ◽  
...  
Keyword(s):  
Drug Repositioning ◽  
Positive Control ◽  
Drug Candidates ◽  
Novel Biomarkers ◽  
Critical Requirement ◽  
Nortriptyline Hydrochloride ◽  
In Silico Molecular Docking ◽  
Prognostic Power ◽  
Potential Therapeutics

There is a critical requirement for alternative strategies to provide the better treatment in colorectal cancer (CRC). Hence, our goal was to propose novel biomarkers as well as drug candidates for its treatment through differential interactome based drug repositioning. Differentially interacting proteins and their modules were identified, and their prognostic power were estimated through survival analyses. Drug repositioning was carried out for significant target proteins, and candidate drugs were analyzed via in silico molecular docking prior to in vitro cell viability assays in CRC cell lines. Six modules (mAPEX1, mCCT7, mHSD17B10, mMYC, mPSMB5, mRAN) were highlighted considering their prognostic performance. Drug repositioning resulted in eight drugs (abacavir, ribociclib, exemestane, voriconazole, nortriptyline hydrochloride, theophylline, bromocriptine mesylate, and tolcapone). Moreover, significant in vitro inhibition profiles were obtained in abacavir, nortriptyline hydrochloride, exemestane, tolcapone, and theophylline (positive control). Our findings may provide new and complementary strategies for the treatment of CRC.

The distinction machine: Physics journals from the perspective of the Kolmogorov–Smirnov statistic

2019 ◽  
Vol 13 (4) ◽  
pp. 100982
Author(s):  
Yurij L. Katchanov ◽  
Yulia V. Markova ◽  
Natalia A. Shmatko
Keyword(s):  
Kolmogorov Smirnov ◽  
Physics Journals ◽  
Smirnov Statistic

scholarly journals 58-04: Initial results of pulmonary vein isolation using the novel third-generation cryoballoon

EP Europace ◽  
2016 ◽  
Vol 18 (suppl_1) ◽  
pp. i169-i169 ◽  
Author(s):  
Alexander Fürnkranz ◽  
Bordignon Stefano ◽  
Daniela Dugo ◽  
Perotta Laura ◽  
Fabrizio Bologna ◽  
...  
Keyword(s):  
Pulmonary Vein ◽  
Pulmonary Vein Isolation ◽  
Third Generation ◽  
The Novel ◽  
Initial Results

Predicting Aircraft Detectability

1979 ◽  
Vol 21 (3) ◽  
pp. 277-291 ◽  
Author(s):  
Akerman Alexander ◽  
Robert E. Kinzly
Keyword(s):  
Field Tests ◽  
Contrast Threshold ◽  
Large Set ◽  
Threshold Data ◽  
Detection Probabilities ◽  
Model Predictions ◽  
Kolmogorov Smirnov ◽  
Aircraft Detection ◽  
Parameter Estimation Techniques ◽  
Smirnov Statistic

A visual search model, VIDEM, has been formulated for predicting the detectability of a single, unknown target in an unstructured surround. The intended application is aircraft detection. The model consists of four components: a liminal contrast threshold, a frequency-of-seeing curve, a soft shell search representation, and discrete cumulation of single glimpse detection probabilities. The formulation was developed by registering five existing models against three controlled search experiments. The five models used represent all appropriate laboratory threshold data, including those of Blackwell, Lamar, Sloan, and Taylor. The search experiments included a large set of aircraft field tests, with precise photometric target measurements correlated to the detection events. The model registrations were done using nonlinear parameter estimation techniques and by comparing model predictions to actual event cumulatives with the Kolmogorov-Smirnov statistic. The resultant VIDEM model is a derivative of Sloan's data, cast into the popular visual lobe equations of Lamar.

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