A Computational Approach for Pathway-Based Systemic Drug Influence

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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Using gene networks to drug target identification

Journal of Integrative Bioinformatics ◽

10.1515/jib-2005-14 ◽

2005 ◽

Vol 2 (1) ◽

pp. 48-57 ◽

Cited By ~ 10

Author(s):

Zhenran Jiang ◽

Yanhong Zhou

Keyword(s):

Gene Networks ◽

Drug Targets ◽

Gene Network ◽

Target Identification ◽

Biological Data ◽

Potential Drug ◽

Drug Target Identification ◽

Novel Drug ◽

Potential Drug Targets ◽

Novel Drug Targets

Abstract The complete genome sequences have provided a plethora of potential drug targets. Gene network technique holds the promise of providing a conceptual framework for analysis of the profusion of biological data being generated on potential drug targets and providing insights to understand the biological regulatory mechanisms in diseases, which are playing an increasingly important role in searching for novel drug targets from the information contained in genomics. In this paper, we discuss some of the network-based approaches for identifying drug targets, with the emphasis on the gene network strategy. In addition, some of the relevant data resources and computational tools are given.

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

PLoS ONE ◽

10.1371/journal.pone.0257784 ◽

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.

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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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An Approach for Predicting Essential Genes Using Multiple Homology Mapping and Machine Learning Algorithms

BioMed Research International ◽

10.1155/2016/7639397 ◽

2016 ◽

Vol 2016 ◽

pp. 1-9 ◽

Cited By ~ 5

Author(s):

Hong-Li Hua ◽

Fa-Zhan Zhang ◽

Abraham Alemayehu Labena ◽

Chuan Dong ◽

Yan-Ting Jin ◽

...

Keyword(s):

Machine Learning ◽

Drug Targets ◽

Essential Genes ◽

Machine Learning Algorithms ◽

Biological Knowledge ◽

Independent Dataset ◽

Novel Approach ◽

Gene Sets ◽

Tenfold Cross Validation ◽

Potential Drug Targets

Investigation of essential genes is significant to comprehend the minimal gene sets of cell and discover potential drug targets. In this study, a novel approach based on multiple homology mapping and machine learning method was introduced to predict essential genes. We focused on 25 bacteria which have characterized essential genes. The predictions yielded the highest area under receiver operating characteristic (ROC) curve (AUC) of 0.9716 through tenfold cross-validation test. Proper features were utilized to construct models to make predictions in distantly related bacteria. The accuracy of predictions was evaluated via the consistency of predictions and known essential genes of target species. The highest AUC of 0.9552 and average AUC of 0.8314 were achieved when making predictions across organisms. An independent dataset fromSynechococcus elongatus, which was released recently, was obtained for further assessment of the performance of our model. The AUC score of predictions is 0.7855, which is higher than other methods. This research presents that features obtained by homology mapping uniquely can achieve quite great or even better results than those integrated features. Meanwhile, the work indicates that machine learning-based method can assign more efficient weight coefficients than using empirical formula based on biological knowledge.

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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.

Download Full-text