scholarly journals Drug repurposing through joint learning on knowledge graphs and literature

2018 ◽  
Author(s):  
Mona Alshahrani ◽  
Robert Hoehndorf
Keyword(s):  
Research Group ◽  
Drug Targets ◽  
Feature Learning ◽  
Drug Repurposing ◽  
Sources Of Information ◽  
Heterogeneous Information ◽  
Novel Method ◽  
Generate Vector ◽  
Knowledge Graphs ◽  
Distinguishing Features

AbstractMotivationDrug repurposing is the problem of finding new uses for known drugs, and may either involve finding a new protein target or a new indication for a known mechanism. Several computational methods for drug repurposing exist, and many of these methods rely on combinations of different sources of information, extract hand-crafted features and use a computational model to predict targets or indications for a drug. One of the distinguishing features between different drug repurposing systems is the selection of features. Recently, a set of novel machine learning methods have become available that can efficiently learn features from datasets, and these methods can be applied, among others, to text and structured data in knowledge graphs.ResultsWe developed a novel method that combines information in literature and structured databases, and applies feature learning to generate vector space embeddings. We apply our method to the identification of drug targets and indications for known drugs based on heterogeneous information about drugs, target proteins, and diseases. We demonstrate that our method is able to combine complementary information from both structured databases and from literature, and we show that our method can compete with well-established methods for drug repurposing. Our approach is generic and can be applied to other areas in which multi-modal information is used to build predictive models.Availabilityhttps://github.com/bio-ontology-research-group/[email protected]

scholarly journals Semantic Disease Gene Embeddings (SmuDGE): phenotype-based disease gene prioritization without phenotypes

2018 ◽  
Author(s):  
Mona Alshahrani ◽  
Robert Hoehndorf
Keyword(s):  
Semantic Similarity ◽  
Research Group ◽  
Disease Gene ◽  
Feature Learning ◽  
Interaction Network ◽  
Gene Prioritization ◽  
Model Organisms ◽  
Semantic Similarity Measure ◽  
Disease Gene Prioritization ◽  
Generate Vector

AbstractMotivationIn the past years, several methods have been developed to incorporate information about phenotypes into computational disease gene prioritization methods. These methods commonly compute the similarity between a disease’s (or patient’s) phenotypes and a database of gene-to-phenotype associations to find the phenotypically most similar match. A key limitation of these methods is their reliance on knowledge about phenotypes associated with particular genes which is highly incomplete in humans as well as in many model organisms such as the mouse.ResultsWe developed SmuDGE, a method that uses feature learning to generate vector-based representations of phenotypes associated with an entity. SmuDGE can be used as a trainable semantic similarity measure to compare two sets of phenotypes (such as between a disease and gene, or a disease and patient). More importantly, SmuDGE can generate phenotype representations for entities that are only indirectly associated with phenotypes through an interaction network; for this purpose, SmuDGE exploits background knowledge in interaction networks comprising of multiple types of interactions. We demonstrate that SmuDGE can match or outperform semantic similarity in phenotype-based disease gene prioritization, and furthermore significantly extends the coverage of phenotype-based methods to all genes in a connected interaction network.Availabilityhttps://github.com/bio-ontology-research-group/[email protected]

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 Prediction of Peptide Detectability Based on CapsNet and Convolutional Block Attention Module

2021 ◽  
Vol 22 (21) ◽  
pp. 12080
Author(s):  
Minzhe Yu ◽  
Yushuai Duan ◽  
Zhong Li ◽  
Yang Zhang
Keyword(s):  
Feature Learning ◽  
Peptide Identification ◽  
Performance Assessments ◽  
Network Training ◽  
The Neural Network ◽  
Proteomics Technology ◽  
Experimental Process ◽  
Quantitative Results ◽  
Novel Method ◽  
Peptide Detectability

According to proteomics technology, as impacted by the complexity of sampling in the experimental process, several problems remain with the reproducibility of mass spectrometry experiments, and the peptide identification and quantitative results continue to be random. Predicting the detectability exhibited by peptides can optimize the mentioned results to be more accurate, so such a prediction is of high research significance. This study builds a novel method to predict the detectability of peptides by complying with the capsule network (CapsNet) and the convolutional block attention module (CBAM). First, the residue conical coordinate (RCC), the amino acid composition (AAC), the dipeptide composition (DPC), and the sequence embedding code (SEC) are extracted as the peptide chain features. Subsequently, these features are divided into the biological feature and sequence feature, and separately inputted into the neural network of CapsNet. Moreover, the attention module CBAM is added to the network to assign weights to channels and spaces, as an attempt to enhance the feature learning and improve the network training effect. To verify the effectiveness of the proposed method, it is compared with some other popular methods. As revealed from the experimentally achieved results, the proposed method outperforms those methods in most performance assessments.

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.

scholarly journals Genetic association and causal inference converge on hyperglycaemia as a modifiable factor to improve lung function

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
William R Reay ◽  
Sahar I El Shair ◽  
Michael P Geaghan ◽  
Carlos Riveros ◽  
Elizabeth G Holliday ◽  
...  
Keyword(s):  
Lung Function ◽  
Drug Targets ◽  
Fasting Glucose ◽  
Drug Repurposing ◽  
Gene Interactions ◽  
Improve Lung Function ◽  
Polygenic Scores ◽  
Modifiable Factor ◽  
Respiratory Outcomes ◽  
Novel Drug

Measures of lung function are heritable, and thus, we sought to utilise genetics to propose drug-repurposing candidates that could improve respiratory outcomes. Lung function measures were found to be genetically correlated with seven druggable biochemical traits, with further evidence of a causal relationship between increased fasting glucose and diminished lung function. Moreover, we developed polygenic scores for lung function specifically within pathways with known drug targets and investigated their relationship with pulmonary phenotypes and gene expression in independent cohorts to prioritise individuals who may benefit from particular drug-repurposing opportunities. A transcriptome-wide association study (TWAS) of lung function was then performed which identified several drug–gene interactions with predicted lung function increasing modes of action. Drugs that regulate blood glucose were uncovered through both polygenic scoring and TWAS methodologies. In summary, we provided genetic justification for a number of novel drug-repurposing opportunities that could improve lung function.

scholarly journals Novel method to identify group-specific non-catalytic pockets of human kinome for drug design

RSC Advances ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 2004-2015 ◽  
Author(s):  
Huiwen Wang ◽  
Zeyu Guan ◽  
Jiadi Qiu ◽  
Ya Jia ◽  
Chen Zeng ◽  
...  
Keyword(s):  
Drug Design ◽  
Drug Targets ◽  
Hybrid Approach ◽  
Biological Pathways ◽  
Novel Method ◽  
Human Kinome

Kinase proteins have been intensively investigated as drug targets for decades because of their crucial involvement in many biological pathways. We developed hybrid approach to identify non-catalytic pockets and will benefit the kinome drug design.

scholarly journals Genetically informed precision drug repurposing for lung function and implications for respiratory infection

2020 ◽  
Author(s):  
William R. Reay ◽  
Sahar I. El Shair ◽  
Michael P. Geaghan ◽  
Carlos Riveros ◽  
Elizabeth G. Holliday ◽  
...  
Keyword(s):  
Lung Function ◽  
Drug Targets ◽  
Causal Effect ◽  
Association Studies ◽  
Drug Repurposing ◽  
Human Adenovirus ◽  
Mendelian Randomisation ◽  
Polygenic Scores ◽  
Impaired Lung Function ◽  
Human Proteins

ABSTRACTImpaired lung function is associated with significant morbidity and mortality. Restrictive and obstructive lung disorders are a large contributor to decreased lung function, as well as the acute impact of infection. Measures of pulmonary function are heritable, and thus, we sought to utilise genomics to propose novel drug repurposing candidates which could improve respiratory outcomes. Lung function measures were found to be genetically correlated with metabolic and hormone traits which could be pharmacologically modulated, with a causal effect of increased fasting glucose on diminished lung function supported by latent causal variable models and Mendelian randomisation. We developed polygenic scores for lung function specifically within pathways with known drug targets to prioritise individuals who may benefit from particular drug repurposing opportunities, accompanied by transcriptome-wide association studies to identify drug-gene interactions with potential lung function increasing modes of action. These drug repurposing candidates were further considered relative to the host-viral interactome of three viruses with associated respiratory pathology (SARS-CoV2, influenza, and human adenovirus). We uncovered an enrichment amongst glycaemic pathways of human proteins which putatively interact with virally expressed SARS-CoV2 proteins, suggesting that antihyperglycaemic agents may have a positive effect both on lung function and SARS-CoV2 progression.

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