scholarly journals Drug repurposing improves disease targeting 11-fold and can be augmented by network module targeting, applied to COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Inés Rivero-García ◽  
Miguel Castresana-Aguirre ◽  
Luca Guglielmo ◽  
Dimitri Guala ◽  
Erik L. L. Sonnhammer
Keyword(s):  
Network Analysis ◽  
Drug Targets ◽  
Drug Repurposing ◽  
Fold Increase ◽  
Systematic Evaluation ◽  
Reference Level ◽  
Disease Genes ◽  
Network Module ◽  
High Confidence ◽  
Disease Modules

AbstractThis analysis presents a systematic evaluation of the extent of therapeutic opportunities that can be obtained from drug repurposing by connecting drug targets with disease genes. When using FDA-approved indications as a reference level we found that drug repurposing can offer an average of an 11-fold increase in disease coverage, with the maximum number of diseases covered per drug being increased from 134 to 167 after extending the drug targets with their high confidence first neighbors. Additionally, by network analysis to connect drugs to disease modules we found that drugs on average target 4 disease modules, yet the similarity between disease modules targeted by the same drug is generally low and the maximum number of disease modules targeted per drug increases from 158 to 229 when drug targets are neighbor-extended. Moreover, our results highlight that drug repurposing is more dependent on target proteins being shared between diseases than on polypharmacological properties of drugs. We apply our drug repurposing and network module analysis to COVID-19 and show that Fostamatinib is the drug with the highest module coverage.

scholarly journals Drug repurposing improves disease targeting 11-fold on average. New insights from network module targeting and its application to COVID-19.

2020 ◽  
Author(s):  
Inés Rivero ◽  
Miguel Castresana ◽  
Dimitri.Guala ◽  
Erik Sonnhammer
Keyword(s):  
Network Analysis ◽  
Drug Targets ◽  
Drug Repurposing ◽  
Fold Increase ◽  
Systematic Evaluation ◽  
Reference Level ◽  
Disease Genes ◽  
Network Module ◽  
High Confidence ◽  
Disease Modules

This analysis presents a systematic evaluation of the extent of therapeutic opportunities that can be obtained from drug repurposing by connecting drug targets with disease genes. When using FDA-approved indications as a reference level we found that drug repurposing can offer an average of an 11-fold increase in disease coverage, with the maximum number of diseases covered per drug being increased from 134 to 167 after extending the drug targets with their high confidence first neighbors. Additionally, by network analysis to connect drugs to disease modules we found that, on average, drugs target 4 disease modules, yet the similarity between disease modules targeted by the same drug is generally low and the total number of disease modules targeted per drug increases from 134 to 230 when drug targets are extended Moreover, our results highlight that drug repurposing is more dependent on target proteins shared between diseases than on polypharmacological properties of drugs. We apply our drug repurposing and network module analysis to COVID-19 and show that Fostamatinib is the drug with the highest module coverage.

scholarly journals Targeting comorbid diseases via network endopharmacology

2018 ◽  
Author(s):  
Juaquim Aguirre-Plans ◽  
Janet Piñero ◽  
Jörg Menche ◽  
Ferran Sanz ◽  
Laura I Furlong ◽  
...  
Keyword(s):  
Drug Targets ◽  
Natural Extension ◽  
Drug Repurposing ◽  
Enrichment Analysis ◽  
Biological Pathways ◽  
Pathway Enrichment Analysis ◽  
Disease Genes ◽  
Multiple Targets ◽  
Clear Cut ◽  
Traditional Drug

AbstractThe traditional drug discovery paradigm has shaped around the idea of “one target, one disease”. Recently, it has become clear that not only it is hard to achieve single target specificity but also it is often more desirable to tinker the complex cellular network by targeting multiple proteins, causing a paradigm shift towards polypharmacology (multiple targets, one disease). Given the lack of clear-cut boundaries across disease (endo)phenotypes and genetic heterogeneity across patients, a natural extension to the current polypharmacology paradigm is targeting common biological pathways involved in diseases, giving rise to “endopharmacology” (multiple targets, multiple diseases). In this study, leveraging powerful network medicine tools, we describe a recipe for first, identifying common pathways pertaining to diseases and then, prioritizing drugs that target these pathways towards endopharmacology. We present proximal pathway enrichment analysis (PxEA) that uses the topology information of the network of interactions between disease genes, pathway genes, drug targets and other proteins to rank drugs for their interactome-based proximity to pathways shared across multiple diseases, providing unprecedented drug repurposing opportunities. As a proof of principle, we focus on nine autoimmune disorders and using PxEA, we show that many drugs indicated for these conditions are not necessarily specific to the condition of interest, but rather target the common biological pathways across these diseases. Finally, we provide the high scoring drug repurposing candidates that can target common mechanisms involved in type 2 diabetes and Alzheimer’s disease, two phenotypes that have recently gained attention due to the increased comorbidity among patients.

scholarly journals Molecular targets and system biology approaches for drug repurposing against SARS-CoV-2

2020 ◽  
Vol 44 (1) ◽  
Author(s):  
Rahul Kunwar Singh ◽  
Brijesh Singh Yadav ◽  
Tribhuvan Mohan Mohapatra
Keyword(s):  
Network Analysis ◽  
Drug Targets ◽  
Drug Repurposing ◽  
Main Body ◽  
Surface Receptors ◽  
Drug Candidates ◽  
Analysis Tools ◽  
Host Cell Surface ◽  
Almost All ◽  
Novel Drug

Abstract Background COVID-19, a pandemic declared by WHO, has infected about 39.5 million and killed about 1.1 million people throughout the world. There is the urgent need of more studies to identify the novel drug targets and the drug candidates against it to handle the situation. Main body To virtually screen various drugs against SARS-CoV-2, the scientists need the detail information about the various drug targets identified till date. The present review provides the information about almost all the drug targets, including structural and non-structural proteins of virus as well as host cell surface receptors, that can be used for virtual screening of drugs. Moreover, this review also focuses on the different network analysis tools that have been used for the identification of new drug targets and candidate repurposable drugs against SARS-CoV-2. Conclusion This review provides important insights of various drug targets and the network analysis tools to young bioinformaticians and will help in creating pace to the drug repurposing strategy for COVID-19 disease.

scholarly journals Network medicine for disease module identification and drug repurposing with the NeDRex platform

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Sepideh Sadegh ◽  
James Skelton ◽  
Elisa Anastasi ◽  
Judith Bernett ◽  
David B. Blumenthal ◽  
...  
Keyword(s):  
Biological Networks ◽  
Drug Targets ◽  
Unmet Need ◽  
Drug Repurposing ◽  
Use Cases ◽  
Statistical Validation ◽  
Disease Module ◽  
Individual Use ◽  
Algorithmic Approaches ◽  
Disease Modules

AbstractTraditional drug discovery faces a severe efficacy crisis. Repurposing of registered drugs provides an alternative with lower costs and faster drug development timelines. However, the data necessary for the identification of disease modules, i.e. pathways and sub-networks describing the mechanisms of complex diseases which contain potential drug targets, are scattered across independent databases. Moreover, existing studies are limited to predictions for specific diseases or non-translational algorithmic approaches. There is an unmet need for adaptable tools allowing biomedical researchers to employ network-based drug repurposing approaches for their individual use cases. We close this gap with NeDRex, an integrative and interactive platform for network-based drug repurposing and disease module discovery. NeDRex integrates ten different data sources covering genes, drugs, drug targets, disease annotations, and their relationships. NeDRex allows for constructing heterogeneous biological networks, mining them for disease modules, prioritizing drugs targeting disease mechanisms, and statistical validation. We demonstrate the utility of NeDRex in five specific use-cases.

A Survey on Computational Methods for Essential Proteins and Genes Prediction

2019 ◽  
Vol 14 (3) ◽  
pp. 211-225 ◽  
Author(s):  
Ming Fang ◽  
Xiujuan Lei ◽  
Ling Guo
Keyword(s):  
Computational Methods ◽  
Drug Targets ◽  
Disease Genes ◽  
Essential Proteins ◽  
Experimental Identification ◽  
Cellular Life ◽  
Different Types ◽  
The Stability ◽  
Human Disease Genes ◽  
Biology Research

Background: Essential proteins play important roles in the survival or reproduction of an organism and support the stability of the system. Essential proteins are the minimum set of proteins absolutely required to maintain a living cell. The identification of essential proteins is a very important topic not only for a better comprehension of the minimal requirements for cellular life, but also for a more efficient discovery of the human disease genes and drug targets. Traditionally, as the experimental identification of essential proteins is complex, it usually requires great time and expense. With the cumulation of high-throughput experimental data, many computational methods that make useful complements to experimental methods have been proposed to identify essential proteins. In addition, the ability to rapidly and precisely identify essential proteins is of great significance for discovering disease genes and drug design, and has great potential for applications in basic and synthetic biology research. Objective: The aim of this paper is to provide a review on the identification of essential proteins and genes focusing on the current developments of different types of computational methods, point out some progress and limitations of existing methods, and the challenges and directions for further research are discussed.

Systematic Design and Evaluation of Drug Repurposing-Oriented Alzheimer’s Disease Ontology (Preprint)

2020 ◽  
Author(s):  
Fang Li ◽  
Muhammad "Tuan" Amith ◽  
Grace Xiong ◽  
Jingcheng Du ◽  
Yang Xiang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Drug Targets ◽  
Drug Repurposing ◽  
Essential Elements ◽  
Genomic Research ◽  
Core Knowledge ◽  
Disease Ontology ◽  
Hybrid Strategy ◽  
Complementary Strategy

BACKGROUND Alzheimer’s Disease (AD) is a devastating neurodegenerative disease, of which the pathophysiology is insufficiently understood, and the curative drugs are long-awaited to be developed. Computational drug repurposing introduces a promising complementary strategy of drug discovery, which benefits from an accelerated development process and decreased failure rate. However, generating new hypotheses in AD drug repurposing requires multi-dimensional and multi-disciplinary data integration and connection, posing a great challenge in the era of big data. By integrating data with computable semantics, ontologies could infer unknown relationships through automated reasoning and fulfill an essential role in supporting computational drug repurposing. OBJECTIVE The study aimed to systematically design a robust Drug Repurposing-Oriented Alzheimer’s Disease Ontology (DROADO), which could model fundamental elements and their relationships involved in AD drug repurposing and integrate their up-to-date research advance comprehensively. METHODS We devised a core knowledge model of computational AD drug repurposing, based on both pre-genomic and post-genomic research paradigms. The model centered on the possible AD pathophysiology and abstracted the essential elements and their relationships. We adopted a hybrid strategy to populate the ontology (classes and properties), including importing from well-curated databases, extracting from high-quality papers and reusing the existing ontologies. We also leveraged n-ary relations and nanopublication graphs to enrich the object relations, making the knowledge stored in the ontology more powerful in supporting computational processing. The initially built ontology was evaluated by a semiotic-driven and web-based tool Ontokeeper. RESULTS The current version of DROADO was composed of 1,021 classes, 23 object properties and 3,207 axioms, depicting a fundamental network related to computational neuroscience concepts and relationships. Assessment using semiotic evaluation metrics by OntoKeeper indicated sufficient preliminary quality (semantics, usefulness and community-consensus) of the ontology. CONCLUSIONS As an in-depth knowledge base, DROADO would be promising in enabling computational algorithms to realize supervised mining from multi-source data, and ultimately, facilitating the discovery of novel AD drug targets and the realization of AD drug repurposing.

scholarly journals Development of a Novel High-Throughput Screen and Identification of Small-Molecule Inhibitors of the Gα–RGS17 Protein–Protein Interaction Using AlphaScreen

2011 ◽  
Vol 16 (8) ◽  
pp. 869-877 ◽  
Author(s):  
Duncan I. Mackie ◽  
David L. Roman
Keyword(s):  
Small Molecule ◽  
High Throughput ◽  
Protein Interaction ◽  
High Throughput Screening ◽  
Drug Targets ◽  
Screening Method ◽  
Fold Increase ◽  
Rgs Proteins ◽  
High Throughput Screen ◽  
Protein Protein Interaction

In this study, the authors used AlphaScreen technology to develop a high-throughput screening method for interrogating small-molecule libraries for inhibitors of the Gαo–RGS17 interaction. RGS17 is implicated in the growth, proliferation, metastasis, and the migration of prostate and lung cancers. RGS17 is upregulated in lung and prostate tumors up to a 13-fold increase over patient-matched normal tissues. Studies show RGS17 knockdown inhibits colony formation and decreases tumorigenesis in nude mice. The screen in this study uses a measurement of the Gαo–RGS17 protein–protein interaction, with an excellent Z score exceeding 0.73, a signal-to-noise ratio >70, and a screening time of 1100 compounds per hour. The authors screened the NCI Diversity Set II and determined 35 initial hits, of which 16 were confirmed after screening against controls. The 16 compounds exhibited IC50 <10 µM in dose–response experiments. Four exhibited IC50 values <6 µM while inhibiting the Gαo–RGS17 interaction >50% when compared to a biotinylated glutathione-S-transferase control. This report describes the first high-throughput screen for RGS17 inhibitors, as well as a novel paradigm adaptable to many other RGS proteins, which are emerging as attractive drug targets for modulating G-protein-coupled receptor signaling.

scholarly journals Genomics-driven drug discovery based on disease-susceptibility genes

2021 ◽  
Vol 41 (1) ◽  
Author(s):  
Kyuto Sonehara ◽  
Yukinori Okada
Keyword(s):  
Drug Discovery ◽  
Drug Targets ◽  
Disease Susceptibility ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Drug Repurposing ◽  
Susceptibility Genes ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Disease Associations

AbstractGenome-wide association studies have identified numerous disease-susceptibility genes. As knowledge of gene–disease associations accumulates, it is becoming increasingly important to translate this knowledge into clinical practice. This challenge involves finding effective drug targets and estimating their potential side effects, which often results in failure of promising clinical trials. Here, we review recent advances and future perspectives in genetics-led drug discovery, with a focus on drug repurposing, Mendelian randomization, and the use of multifaceted omics data.

scholarly journals Degree Adjusted Large-Scale Network Analysis Reveals Novel Putative Metabolic Disease Genes

Biology ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 107
Author(s):  
Apurva Badkas ◽  
Thanh-Phuong Nguyen ◽  
Laura Caberlotto ◽  
Jochen G. Schneider ◽  
Sébastien De Landtsheer ◽  
...  
Keyword(s):  
Network Analysis ◽  
Network Topology ◽  
Large Scale ◽  
Metabolic Diseases ◽  
Critical Factor ◽  
Disease Genes ◽  
Alcoholic Fatty Liver ◽  
Large Scale Network ◽  
Depth Study ◽  
Public Datasets

A large percentage of the global population is currently afflicted by metabolic diseases (MD), and the incidence is likely to double in the next decades. MD associated co-morbidities such as non-alcoholic fatty liver disease (NAFLD) and cardiomyopathy contribute significantly to impaired health. MD are complex, polygenic, with many genes involved in its aetiology. A popular approach to investigate genetic contributions to disease aetiology is biological network analysis. However, data dependence introduces a bias (noise, false positives, over-publication) in the outcome. While several approaches have been proposed to overcome these biases, many of them have constraints, including data integration issues, dependence on arbitrary parameters, database dependent outcomes, and computational complexity. Network topology is also a critical factor affecting the outcomes. Here, we propose a simple, parameter-free method, that takes into account database dependence and network topology, to identify central genes in the MD network. Among them, we infer novel candidates that have not yet been annotated as MD genes and show their relevance by highlighting their differential expression in public datasets and carefully examining the literature. The method contributes to uncovering connections in the MD mechanisms and highlights several candidates for in-depth study of their contribution to MD and its co-morbidities.

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