Drug target ontology to classify and integrate drug discovery data

Lin, Yuling; Mehta, Saurabh; Küçük-McGinty, Hande; Turner, John Paul; Vidović, Dušica; Forlin, Michele; Koleti, Amar; Nguyen, Doan; Jensen, Lars Juhl; Guha, Rajarshi; Mathias, Stephen L.; Ursu, Oleg; Stathias, Vasileios; Duan, Jianbin; Nabizadeh, Nooshin; Chung, Caty; Mader, Christopher C.; Visser, Ubbo; Yang, Jeremy J.; Bologa, Cristian; Oprea, Tudor I.; Schürer, Stephan C.

doi:10.1186/s13326-017-0161-x

Cited by 67 publications

(33 citation statements)

References 39 publications

(48 reference statements)

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“…In order to explore in more detail the features of different classes of TARGET proteins, we classified them using categories from the drug target ontology ( Lin et al, 2017 ) ( Figure 5 ). We used similar categories to classify the TOXPROT set (for more details see Methods section), and we classified METAB genes into carriers, enzymes, and transporters using the information from DrugBank.…”

Section: Resultsmentioning

confidence: 99%

Network, Transcriptomic and Genomic Features Differentiate Genes Relevant for Drug Response

et al. 2018

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Understanding the mechanisms underlying drug therapeutic action and toxicity is crucial for the prevention and management of drug adverse reactions, and paves the way for a more efficient and rational drug design. The characterization of drug targets, drug metabolism proteins, and proteins associated to side effects according to their expression patterns, their tolerance to genomic variation and their role in cellular networks, is a necessary step in this direction. In this contribution, we hypothesize that different classes of proteins involved in the therapeutic effect of drugs and in their adverse effects have distinctive transcriptomics, genomics and network features. We explored the properties of these proteins within global and organ-specific interactomes, using multi-scale network features, evaluated their gene expression profiles in different organs and tissues, and assessed their tolerance to loss-of-function variants leveraging data from 60K subjects. We found that drug targets that mediate side effects are more central in cellular networks, more intolerant to loss-of-function variation, and show a wider breadth of tissue expression than targets not mediating side effects. In contrast, drug metabolizing enzymes and transporters are less central in the interactome, more tolerant to deleterious variants, and are more constrained in their tissue expression pattern. Our findings highlight distinctive features of proteins related to drug action, which could be applied to prioritize drugs with fewer probabilities of causing side effects.

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Section: Resultsmentioning

confidence: 99%

Network, Transcriptomic and Genomic Features Differentiate Genes Relevant for Drug Response

et al. 2018

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“… 11 Consistent with this is the fact that the current kinase inhibitors target not only a narrow range of targets, but also a narrow range of pathways including angiogenesis, cell adhesion, immune system signaling (cytokine, T cell receptor, B cell receptor), and anti-apoptotic pathways. 17 For example, all kinase inhibitors for renal cell carcinoma target angiogenic pathways. 18 It is likely that the most optimal strategy for treating cancers is targeting multiple orthogonal pathways that work in a synergistic manner, as opposed to targeting kinases with overlapping pathways.…”

Section: Introductionmentioning

confidence: 99%

The Clinical Kinase Index: A Method to Prioritize Understudied Kinases as Drug Targets for the Treatment of Cancer

Essegian

Khurana

Stathias

et al. 2020

Cell Reports Medicine

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Summary The approval of the first kinase inhibitor, Gleevec, ushered in a paradigm shift for oncological treatment—the use of genomic data for targeted, efficacious therapies. Since then, over 48 additional small-molecule kinase inhibitors have been approved, solidifying the case for kinases as a highly druggable and attractive target class. Despite the role deregulated kinase activity plays in cancer, only 8% of the kinome has been effectively “drugged.” Moreover, 24% of the 634 human kinases are understudied. We have developed a comprehensive scoring system that utilizes differential gene expression, pathological parameters, overall survival, and mutational hotspot analysis to rank and prioritize clinically relevant kinases across 17 solid tumor cancers from The Cancer Genome Atlas. We have developed the clinical kinase index (CKI) app ( http://cki.ccs.miami.edu ) to facilitate interactive analysis of all kinases in each cancer. Collectively, we report that understudied kinases have potential clinical value as biomarkers or drug targets that warrant further study.

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“…Graph databases provides data visualization, speed of query execution, flexibility and extensibility while still protecting data integrity. [23] Scientific applications, most often, depend on multiple joins and reciprocal queries, which would be computationally intensive in other storage formats. [28] Further, adding new data (extensibility) adds an extra layer of computational encumbrance.…”

Section: Graph Databasesmentioning

confidence: 99%

“…They provide alternatives to error prone and time consuming exercises to gather results from multiple sources, which may involve one or many of the tasks like cross referencing across web services, manual browsing, performing federated queries across databases, etc. Many such projects exist ( [19,20,15,21,22,23] ) and, like CompoundDB4j, provide a comprehensive data-centered integration to provide methods to collate and analyze data sources of disparate provenance and storage formats. Deriving from the above, we have developed an ordered network of wellconnected data built on a single graph database to discover synergies between the following entities: Compounds (drugs), Targets, Activities, Assays, Proteins, Pathways, Diseases, Patent Records, Drug Interactions, Metabolites, and Structure Alerts.…”

Section: Introductionmentioning

confidence: 99%

CompoundDB4j: Integrated Drug Resource of Heterogeneous Chemical Databases

Murali

Königs

Deekshitula

et al. 2020

Molecular Informatics

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Computational approaches to analyze various drug/ compound centered analysis often present a need to map attributes from multiple drug databases. In this study, we provide a Neo4j repository that integrates two of the most prominent open source drug databases, DrugBank and ChEMBL, with a goal of establishing an integrated data visualization and analysis tool for drug discovery studies. The drugs present in DrugBank are mapped to their counterparts in ChEMBL. The integration of these resources and the harmonization using knowledge graph serialization using Neo4j lead to identification of relationships between drugs and other related features that are otherwise spread across two different resources. A common data format, a prerequisite to populate the Neo4j database, enables users to identify new relationships central to drug discovery research, like Drug Target Interactions (DTI). The resource is freely available at: https://github.com/ambf0632/CompoundDB4j

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Drug target ontology to classify and integrate drug discovery data

Cited by 67 publications

References 39 publications

Network, Transcriptomic and Genomic Features Differentiate Genes Relevant for Drug Response

Network, Transcriptomic and Genomic Features Differentiate Genes Relevant for Drug Response

The Clinical Kinase Index: A Method to Prioritize Understudied Kinases as Drug Targets for the Treatment of Cancer

CompoundDB4j: Integrated Drug Resource of Heterogeneous Chemical Databases

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