PharmOmics: A species- and tissue-specific drug signature database and gene-network-based drug repositioning tool

Chen, Yen‐Wei; Diamante, Graciel; Ding, Jessica; Nghiem, Thien; Yang, Jessica; Ha, Seung Min; Cohn, Peter; Arneson, Douglas; Blencowe, Montgomery; García, Jennifer; Zaghari, Nima; Patel, Paul; Yang, Xia

doi:10.1016/j.isci.2022.104052

Cited by 19 publications

(22 citation statements)

References 74 publications

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“…Jaccard scores and z-scores for the drugs were calculated using the online tool PharmOmics ( http://mergeomics.research.idre.ucla.edu/ ) ( Chen et al, 2022 ) and were included in the final summary model. Drugs with a Jaccard score more than the mean value of all the drugs, and a z-score less than −2 were output as potential therapeutics for LUAD (one side p -value < 0.05).…”

Section: Methodsmentioning

confidence: 99%

Lung adenocarcinoma-related target gene prediction and drug repositioning

Huang

Siriwanna²,

Cho³

et al. 2022

Front. Pharmacol.

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Lung cancer is the leading cause of cancer deaths globally, and lung adenocarcinoma (LUAD) is the most common type of lung cancer. Gene dysregulation plays an essential role in the development of LUAD. Drug repositioning based on associations between drug target genes and LUAD target genes are useful to discover potential new drugs for the treatment of LUAD, while also reducing the monetary and time costs of new drug discovery and development. Here, we developed a pipeline based on machine learning to predict potential LUAD-related target genes through established graph attention networks (GATs). We then predicted potential drugs for the treatment of LUAD through gene coincidence-based and gene network distance-based methods. Using data from 535 LUAD tissue samples and 59 precancerous tissue samples from The Cancer Genome Atlas, 48,597 genes were identified and used for the prediction model building of the GAT. The GAT model achieved good predictive performance, with an area under the receiver operating characteristic curve of 0.90. 1,597 potential LUAD-related genes were identified from the GAT model. These LUAD-related genes were then used for drug repositioning. The gene overlap and network distance with the target genes were calculated for 3,070 drugs and 672 preclinical compounds approved by the US Food and Drug Administration. At which, bromoethylamine was predicted as a novel potential preclinical compound for the treatment of LUAD, and cimetidine and benzbromarone were predicted as potential therapeutic drugs for LUAD. The pipeline established in this study presents new approach for developing targeted therapies for LUAD.

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Section: Methodsmentioning

confidence: 99%

Lung adenocarcinoma-related target gene prediction and drug repositioning

Huang

Siriwanna²,

Cho³

et al. 2022

Front. Pharmacol.

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“…Using similarity scores based on properties of inferred regulatory networks, the CLUEreg tool of GRAND allows users to query a list of “high-targeted” and “low-targeted” genes or transcriptional factors of the disease to identify single or combinations of compounds that might “reverse” aberrant regulatory patterns [ 119 ]. Other examples of open-sourced platforms include PharmOmics and NeDRex; the former is a knowledgebase supporting gene-network-based drug repurposing and the latter allows heterogeneous network construction to mine disease modules for drug prioritization [ 100 , 120 ]. While these platforms would be easy to use with curated networks, users are limited by the scope of the current platforms, and how regularly they are updated.…”

Section: Network-based Drug Repurposing In Psychiatrymentioning

confidence: 99%

“…The Human Transcription Factors [81] RegulomeDB [82] Catalog of inferred sequence binding preferences [83] JASPAR [84] UniPROBE [85] TRANSFAC [86] Multiple collections: OmniPath [87] Schizophrenia [88,89] Bipolar disorder [90,91] Phenome Side effects: SIDER [92] Drug targets: DrugBank [55] PharmGKB [93] Drug-Gene Interaction Database (DGIdb) [94] DrugCentral [95] canSARblack [96] KEGG DRUG [97] IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb) [98] Search Tool for Interacting Chemicals (STITCH) [99,100] Therapeutic Target Database (TTD) [101] Drug Signatures Database (DSigDB) [102] Pharos [103] Binding assay profiles: Psychoactive Drug Screening Program (PDSP) [104] BindingDB [105] Disease-associated targets: Online Mendelian Inheritance in Man (OMIM) [106] ClinVar [107] MalaCards [108] DisGeNET [109] Human Phenotype Ontology (HPO) [110] Monarch [111] GPCards [112] Disease symptoms: Human symptoms-disease network [113] Human Phenotype Ontology (HPO) [110] DMPatternUMLS [114] Clinical trials: ClinicalTrials.gov [115] Opioid use disorders [116] Schizophrenia [117] Schizophrenia, bipolar disorder, autism spectrum disorder [118] Network-based drug d...…”

Section: Chemical Structuresmentioning

confidence: 99%

Repurposing Drugs via Network Analysis: Opportunities for Psychiatric Disorders

et al. 2022

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Despite advances in pharmacology and neuroscience, the path to new medications for psychiatric disorders largely remains stagnated. Drug repurposing offers a more efficient pathway compared with de novo drug discovery with lower cost and less risk. Various computational approaches have been applied to mine the vast amount of biomedical data generated over recent decades. Among these methods, network-based drug repurposing stands out as a potent tool for the comprehension of multiple domains of knowledge considering the interactions or associations of various factors. Aligned well with the poly-pharmacology paradigm shift in drug discovery, network-based approaches offer great opportunities to discover repurposing candidates for complex psychiatric disorders. In this review, we present the potential of network-based drug repurposing in psychiatry focusing on the incentives for using network-centric repurposing, major network-based repurposing strategies and data resources, applications in psychiatry and challenges of network-based drug repurposing. This review aims to provide readers with an update on network-based drug repurposing in psychiatry. We expect the repurposing approach to become a pivotal tool in the coming years to battle debilitating psychiatric disorders.

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“…This gene signature-based approach has been adopted by previous research to identify drugs that can be repositioned to treat a variety of diseases including, but not limited to, cancer, Alzheimer’s, hyperlipidaemia, hypertension, and inflammatory disease ( Corbett et al, 2012 ; Hall et al, 2014 ; Guney et al, 2016 ; Subramanian et al, 2017 ; Cheng et al, 2018 ; Carvalho et al, 2021 ; Wu et al, 2022 ). To date, drug repositioning to target gene signatures has primarily involved identifying directly overlapping drug genes and disease genes (herein referred to as overlap analysis) ( Subramanian et al, 2017 ; Wang et al, 2018 ; Chen et al, 2022 ). More recently, network analysis has been greatly employed in this area as it offers distinct advantages over more traditional statistical methods.…”

Section: Introductionmentioning

confidence: 99%

“…Herein, we adopt both a network-based and overlap-based drug repositioning methodology, to identify existing drugs that can specifically target the aberrant expression profile of ESCC and impede oncogenesis. To do this, we used previously published data for in-silico drug repositioning analysis utilising the PharmOmics webserver ( Chen et al, 2022 ). The repositioning analysis consisted of two arms, the ‘overlap analysis’ arm and the ‘network analysis’ arm ( Figure 1 ), which utilise two methods of drug repositioning.…”

Section: Introductionmentioning

confidence: 99%

Drug repositioning for esophageal squamous cell carcinoma

Bennett

Huang

et al. 2022

Front. Genet.

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Esophageal cancer (EC) remains a significant challenge globally, having the 8th highest incidence and 6th highest mortality worldwide. Esophageal squamous cell carcinoma (ESCC) is the most common form of EC in Asia. Crucially, more than 90% of EC cases in China are ESCC. The high mortality rate of EC is likely due to the limited number of effective therapeutic options. To increase patient survival, novel therapeutic strategies for EC patients must be devised. Unfortunately, the development of novel drugs also presents its own significant challenges as most novel drugs do not make it to market due to lack of efficacy or safety concerns. A more time and cost-effective strategy is to identify existing drugs, that have already been approved for treatment of other diseases, which can be repurposed to treat EC patients, with drug repositioning. This can be achieved by comparing the gene expression profiles of disease-states with the effect on gene-expression by a given drug. In our analysis, we used previously published microarray data and identified 167 differentially expressed genes (DEGs). Using weighted key driver analysis, 39 key driver genes were then identified. These driver genes were then used in Overlap Analysis and Network Analysis in Pharmomics. By extracting drugs common to both analyses, 24 drugs are predicted to demonstrate therapeutic effect in EC patients. Several of which have already been shown to demonstrate a therapeutic effect in EC, most notably Doxorubicin, which is commonly used to treat EC patients, and Ixazomib, which was recently shown to induce apoptosis and supress growth of EC cell lines. Additionally, our analysis predicts multiple psychiatric drugs, including Venlafaxine, as repositioned drugs. This is in line with recent research which suggests that psychiatric drugs should be investigated for use in gastrointestinal cancers such as EC. Our study shows that a drug repositioning approach is a feasible strategy for identifying novel ESCC therapies and can also improve the understanding of the mechanisms underlying the drug targets.

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PharmOmics: A species- and tissue-specific drug signature database and gene-network-based drug repositioning tool

Cited by 19 publications

References 74 publications

Lung adenocarcinoma-related target gene prediction and drug repositioning

Lung adenocarcinoma-related target gene prediction and drug repositioning

Repurposing Drugs via Network Analysis: Opportunities for Psychiatric Disorders

Drug repositioning for esophageal squamous cell carcinoma

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