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.

scholarly journals Identification of potential therapeutic targets and mechanisms of COVID-19 through network analysis and screening of chemicals and herbal ingredients

2021 ◽  
Author(s):  
Hong Wang ◽  
Jingqing Zhang ◽  
Zhigang Lu ◽  
Weina Dai ◽  
Chuanjiang Ma ◽  
...  
Keyword(s):  
Drug Targets ◽  
Drug Repurposing ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Therapeutic Effects ◽  
Protein Protein Interaction ◽  
Disease Module ◽  
Approved Drugs ◽  
Data Driven Modeling ◽  
The Relationship

Abstract After experiencing the COVID-19 pandemic, it is widely acknowledged that a rapid drug repurposing method is highly needed. A series of useful drug repurposing tools have been developed based on data-driven modeling and network pharmacology. Based on the disease module, we identified several hub proteins that play important roles in the onset and development of the COVID-19, which are potential targets for repositioning approved drugs. Moreover, different network distance metrics were applied to quantify the relationship between drug targets and COVID-19 disease targets in the protein–protein-interaction (PPI) network and predict COVID-19 therapeutic effects of bioactive herbal ingredients and chemicals. Furthermore, the tentative mechanisms of candidates were illustrated through molecular docking and gene enrichment analysis. We obtained 15 chemical and 15 herbal ingredient candidates and found that different drugs may play different roles in the process of virus invasion and the onset and development of the COVID-19 disease. Given pandemic outbreaks, our method has an undeniable immense advantage in the feasibility analysis of drug repurposing or drug screening, especially in the analysis of herbal ingredients.

scholarly journals Overcoming sparseness of biomedical networks to identify drug repositioning candidates

2020 ◽  
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.

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.

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 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 Mammalian deubiquitinating enzyme inhibitors display in vitro and in vivo activity against malaria parasites and potentiate artemisinin action

2020 ◽  
Author(s):  
Nelson V. Simwela ◽  
Katie R. Hughes ◽  
Michael T. Rennie ◽  
Michael P. Barrett ◽  
Andrew P. Waters
Keyword(s):  
Anticancer Agents ◽  
Drug Targets ◽  
Reverse Genetics ◽  
Enzyme Inhibitors ◽  
Artemisinin Resistance ◽  
Drug Repurposing ◽  
Antimalarial Drugs ◽  
Malaria Parasites

AbstractCurrent malaria control efforts rely significantly on artemisinin combinational therapies which have played massive roles in alleviating the global burden of the disease. Emergence of resistance to artemisinins is therefore, not just alarming but requires immediate intervention points such as development of new antimalarial drugs or improvement of the current drugs through adjuvant or combination therapies. Artemisinin resistance is primarily conferred by Kelch13 propeller mutations which are phenotypically characterised by generalised growth quiescence, altered haemoglobin trafficking and downstream enhanced activity of the parasite stress pathways through the ubiquitin proteasome system (UPS). Previous work on artemisinin resistance selection in a rodent model of malaria, which we and others have recently validated using reverse genetics, has also shown that mutations in deubiquitinating enzymes, DUBs (upstream UPS component) modulates susceptibility of malaria parasites to both artemisinin and chloroquine. The UPS or upstream protein trafficking pathways have, therefore, been proposed to be not just potential drug targets, but also possible intervention points to overcome artemisinin resistance. Here we report the activity of small molecule inhibitors targeting mammalian DUBs in malaria parasites. We show that generic DUB inhibitors can block intraerythrocytic development of malaria parasites in vitro and possess antiparasitic activity in vivo and can be used in combination with additive effect. We also show that inhibition of these upstream components of the UPS can potentiate the activity of artemisinin in vitro as well as in vivo to the extent that ART resistance can be overcome. Combinations of DUB inhibitors anticipated to target different DUB activities and downstream 20s proteasome inhibitors are even more effective at improving the potency of artemisinins than either inhibitors alone providing proof that targeting multiple UPS activities simultaneously could be an attractive approach to overcoming artemisinin resistance. These data further validate the parasite UPS as a target to both enhance artemisinin action and potentially overcome resistance. Lastly, we confirm that DUB inhibitors can be developed into in vivo antimalarial drugs with promise for activity against all of human malaria and could thus further exploit their current pursuit as anticancer agents in rapid drug repurposing programs.Graphical abstract

scholarly journals Translational and Clinical Pharmacology Considerations in Drug Repurposing for X-linked Adrenoleukodystrophy-A Rare Peroxisomal Disorder

2021 ◽  
Author(s):  
Julianne Tieu ◽  
Siddhee Sahasrabudhe ◽  
Paul Orchard ◽  
James Cloyd ◽  
Reena Kartha
Keyword(s):  
Drug Target ◽  
Drug Targets ◽  
Drug Repurposing ◽  
Pharmacokinetics And Pharmacodynamics ◽  
Drug Candidates ◽  
Target Patient Population ◽  
Site Of Action ◽  
Repurposed Drugs ◽  
Gait Disturbances ◽  
Spastic Quadriparesis

X-linked adrenoleukodystrophy (X-ALD) is an inherited, neurodegenerative rare disease that can result in devastating symptoms of blindness, gait disturbances, and spastic quadriparesis due to progressive demyelination. Typically, the disease progresses rapidly, causing death within the first decade of life. With limited treatments available, efforts to determine an effective therapy that can alter disease progression or mitigate symptoms have been undertaken for many years, particularly through drug repurposing. Repurposing has generally been guided through clinical experience and small trials. At this time, none of the drug candidates have been approved for use, which may be due, in part, to the lack of pharmacokinetic/pharmacodynamic (PK/PD) information on the repurposed medications in the target patient population. Greater consideration for the disease pathophysiology, drug pharmacology, and potential drug-target interactions, specifically at the site of action, would improve drug repurposing and facilitate development. Although there is a good understanding of X-ALD pathophysiology, the absence of information on drug targets, pharmacokinetics, and pharmacodynamics hinders the repurposing of drugs for this condition. Incorporating advanced translational and clinical pharmacological approaches in preclinical studies and early stages clinical trials will improve the success of repurposed drugs for X-ALD as well as other rare diseases.

scholarly journals Identifying Nootropic Drug Targets via Large-Scale Cognitive GWAS and Transcriptomics

2020 ◽  
Author(s):  
Max Lam ◽  
Chen Chia-Yen ◽  
Xia Yan ◽  
W. David Hill ◽  
Joey W. Trampush ◽  
...  
Keyword(s):  
Cognitive Ability ◽  
Drug Targets ◽  
Large Scale ◽  
Drug Repurposing ◽  
Gene Identification ◽  
Summary Statistics ◽  
General Cognitive Ability ◽  
Nootropic Drug ◽  
Genome Wide ◽  
Meta Analyses

AbstractBackgroundCognitive traits demonstrate significant genetic correlations with many psychiatric disorders and other health-related traits. Many neuropsychiatric and neurodegenerative disorders are marked by cognitive deficits. Therefore, genome-wide association studies (GWAS) of general cognitive ability might suggest potential targets for nootropic drug repurposing. Our previous effort to identify “druggable genes” (i.e., GWAS-identified genes that produce proteins targeted by known small molecules) was modestly powered due to the small cognitive GWAS sample available at the time. Since then, two large cognitive GWAS meta-analyses have reported 148 and 205 genome-wide significant loci, respectively. Additionally, large-scale gene expression databases, derived from post-mortem human brain, have recently been made available for GWAS annotation. Here, we 1) reconcile results from these two cognitive GWAS meta-analyses to further enhance power for locus discovery; 2) employ several complementary transcriptomic methods to identify genes in these loci with variants that are credibly associated with cognition; and 3) further annotate the resulting genes to identify “druggable” targets.MethodsGWAS summary statistics were harmonized and jointly analysed using Multi-Trait Analysis of GWAS [MTAG], which is optimized for handling sample overlaps. Downstream gene identification was carried out using MAGMA, S-PrediXcan/S-TissueXcan Transcriptomic Wide Analysis, and eQTL mapping, as well as more recently developed methods that integrate GWAS and eQTL data via Summary-statistics Mendelian Randomization [SMR] and linkage methods [HEIDI], Available brain-specific eQTL databases included GTEXv7, BrainEAC, CommonMind, ROSMAP, and PsychENCODE. Intersecting credible genes were then annotated against multiple chemoinformatic databases [DGIdb, KI, and a published review on “druggability”].ResultsUsing our meta-analytic data set (N = 373,617) we identified 241 independent cognition-associated loci (29 novel), and 76 genes were identified by 2 or more methods of gene identification. 26 genes were associated with general cognitive ability via SMR, 16 genes via STissueXcan/S-PrediXcan, 47 genes via eQTL mapping, and 68 genes via MAGMA pathway analysis. The use of the HEIDI test permitted the exclusion of candidate genes that may have been artifactually associated to cognition due to linkage, rather than direct causal or indirect pleiotropic effects. Actin and chromatin binding gene sets were identified as novel pathways that could be targeted via drug repurposing. Leveraging on our various transcriptome and pathway analyses, as well as available chemoinformatic databases, we identified 16 putative genes that may suggest drug targets with nootropic properties.DiscussionResults converged on several categories of significant drug targets, including serotonergic and glutamatergic genes, voltage-gated ion channel genes, carbonic anhydrase genes, and phosphodiesterase genes. The current results represent the first efforts to apply a multi-method approach to integrate gene expression and SNP level data to identify credible actionable genes for general cognitive ability.

Sign in / Sign up

Export Citation Format

Share Document