DrugGenEx-Net: a novel computational platform for systems pharmacology and gene expression-based drug repurposing

Drug repurposing/repositioning, which aims to find novel indications for existing drugs, contributes to reducing the time and cost for drug development. For the recent decade, gene expression profiles of drug stimulating samples have been successfully used in drug repurposing. However, most of the existing methods neglect the gene modules and the interactions among the modules, although the cross-talks among pathways are common in drug response. It is essential to develop a method that utilizes the cross-talks information to predict the reliable candidate associations. In this study, we developed MNBDR (Module Network Based Drug Repositioning), a novel method that based on module network to screen drugs. It integrated protein–protein interactions and gene expression profile of human, to predict drug candidates for diseases. Specifically, the MNBDR mined dense modules through protein–protein interaction (PPI) network and constructed a module network to reveal cross-talks among modules. Then, together with the module network, based on existing gene expression data set of drug stimulation samples and disease samples, we used random walk algorithms to capture essential modules in disease development and proposed a new indicator to screen potential drugs for a given disease. Results showed MNBDR could provide better performance than popular methods. Moreover, functional analysis of the essential modules in the network indicated our method could reveal biological mechanism in drug response.

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Computational platform Way2Drug: from the prediction of biological activity to drug repurposing

Russian Chemical Bulletin ◽

10.1007/s11172-017-1954-x ◽

2017 ◽

Vol 66 (10) ◽

pp. 1832-1841 ◽

Cited By ~ 14

Author(s):

D. S. Druzhilovskiy ◽

A. V. Rudik ◽

D. A. Filimonov ◽

T. A. Gloriozova ◽

A. A. Lagunin ◽

...

Keyword(s):

Biological Activity ◽

Drug Repurposing ◽

Computational Platform

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DeSigN: connecting gene expression with therapeutics for drug repurposing and development

BMC Genomics ◽

10.1186/s12864-016-3260-7 ◽

2017 ◽

Vol 18 (S1) ◽

Cited By ~ 33

Author(s):

Bernard Kok Bang Lee ◽

Kai Hung Tiong ◽

Jit Kang Chang ◽

Chee Sun Liew ◽

Zainal Ariff Abdul Rahman ◽

...

Keyword(s):

Gene Expression ◽

Drug Repurposing

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A microfluidic device and computational platform for high-throughput live imaging of gene expression

Nature Methods ◽

10.1038/nmeth.2185 ◽

2012 ◽

Vol 9 (11) ◽

pp. 1101-1106 ◽

Cited By ~ 59

Author(s):

Wolfgang Busch ◽

Brad T Moore ◽

Bradley Martsberger ◽

Daniel L Mace ◽

Richard W Twigg ◽

...

Keyword(s):

Gene Expression ◽

Microfluidic Device ◽

High Throughput ◽

Live Imaging ◽

Computational Platform

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Integrative transcriptome imputation reveals tissue-specific and shared biological mechanisms mediating susceptibility to complex traits

10.1101/532929 ◽

2019 ◽

Cited By ~ 1

Author(s):

Wen Zhang ◽

Georgios Voloudakis ◽

Veera M. Rajagopal ◽

Ben Reahead ◽

Joel T. Dudley ◽

...

Keyword(s):

Gene Expression ◽

Complex Traits ◽

Large Scale ◽

Association Studies ◽

Drug Repurposing ◽

Joint Analysis ◽

Expression Trait ◽

Significant Expression ◽

Insight Into ◽

Trait Associations

AbstractTranscriptome-wide association studies integrate gene expression data with common risk variation to identify gene-trait associations. By incorporating epigenome data to estimate the functional importance of genetic variation on gene expression, we improve the accuracy of transcriptome prediction and the power to detect significant expression-trait associations. Joint analysis of 14 large-scale transcriptome datasets and 58 traits identify 13,724 significant expression-trait associations that converge to biological processes and relevant phenotypes in human and mouse phenotype databases. We perform drug repurposing analysis and identify known and novel compounds that mimic or reverse trait-specific changes. We identify genes that exhibit agonistic pleiotropy for genetically correlated traits that converge on shared biological pathways and elucidate distinct processes in disease etiopathogenesis. Overall, this comprehensive analysis provides insight into the specificity and convergence of gene expression on susceptibility to complex traits.

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Drug Repurposing and New Therapeutic Strategies for SARS-CoV-2 Disease Using a Novel Molecular Modeling-AI Hybrid Workflow

10.26434/chemrxiv.12449081.v1 ◽

2020 ◽

Author(s):

Scott Bembenek

Keyword(s):

Molecular Modeling ◽

Therapeutic Targets ◽

Drug Repurposing ◽

Preclinical Research ◽

Main Protease ◽

Novel Coronavirus ◽

Approved Drugs ◽

Usual Process ◽

Potential Inhibitors ◽

Computational Platform

The recent outbreak of the novel coronavirus (SARS-CoV-2) poses a significant challenge to the scientific and medical communities to find immediate treatments. The usual process of identifying viable molecules and transforming them into a safe and effective drug takes 10-15 years, with around 5 years of that time spent in preclinical research and development alone. The fastest strategy is to identify existing drugs or late-stage clinical molecules (originally intended for other therapeutic targets) that already have some level of efficacy. To this end, we tasked our novel molecular modeling-AI hybrid computational platform with finding potential inhibitors of the SARS-CoV-2 main protease (Mpro, 3CLpro). Over 13,000 FDA-approved drugs and clinical candidates (represented by just under 30,000 protomers) were examined. This effort resulted in the identification of several promising molecules. Moreover, it provided insight into key chemical motifs surely to be beneficial in the design of future inhibitors. Finally, it facilitated a unique perspective into other potentially therapeutic targets and pathways for SARS-CoV-2.

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Drug Repurposing and New Therapeutic Strategies for SARS-CoV-2 Disease Using a Novel Molecular Modeling-AI Hybrid Workflow

10.26434/chemrxiv.12449081 ◽

2020 ◽

Author(s):

Scott Bembenek

Keyword(s):

Molecular Modeling ◽

Therapeutic Targets ◽

Drug Repurposing ◽

Preclinical Research ◽

Main Protease ◽

Novel Coronavirus ◽

Approved Drugs ◽

Usual Process ◽

Potential Inhibitors ◽

Computational Platform

The recent outbreak of the novel coronavirus (SARS-CoV-2) poses a significant challenge to the scientific and medical communities to find immediate treatments. The usual process of identifying viable molecules and transforming them into a safe and effective drug takes 10-15 years, with around 5 years of that time spent in preclinical research and development alone. The fastest strategy is to identify existing drugs or late-stage clinical molecules (originally intended for other therapeutic targets) that already have some level of efficacy. To this end, we tasked our novel molecular modeling-AI hybrid computational platform with finding potential inhibitors of the SARS-CoV-2 main protease (Mpro, 3CLpro). Over 13,000 FDA-approved drugs and clinical candidates (represented by just under 30,000 protomers) were examined. This effort resulted in the identification of several promising molecules. Moreover, it provided insight into key chemical motifs surely to be beneficial in the design of future inhibitors. Finally, it facilitated a unique perspective into other potentially therapeutic targets and pathways for SARS-CoV-2.

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