Cheminformatic/bioinformatic analysis of large corporate databases: Application to drug repurposing

Loging, William; Rodriguez‐Esteban, Raul; Hill, Jon; Freeman, Tom C.; Miglietta, John

doi:10.1016/j.ddstr.2011.06.004

Cited by 39 publications

(25 citation statements)

References 33 publications

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“…However, there are ongoing efforts to conduct drug repurposing systematically. To this end, at least three general strategies have been envisioned, namely; chemical, biological and text data mining [16]. One of the several notable examples of drug repurposing is illustrated by the ‘high throughput’ in vivo pharmacology platform theraTRACE® [16].…”

Section: Drug Repurposingmentioning

confidence: 99%

“…To this end, at least three general strategies have been envisioned, namely; chemical, biological and text data mining [16]. One of the several notable examples of drug repurposing is illustrated by the ‘high throughput’ in vivo pharmacology platform theraTRACE® [16]. Of note, Oprea and Mestres recently emphasized that a successful drug repurposing campaign involves not only identifying new targets for old drugs, but also factors such as adverse effect tolerance and analysis of the intellectual property landscape [17].…”

Section: Drug Repurposingmentioning

confidence: 99%

See 1 more Smart Citation

Shifting from the single to the multitarget paradigm in drug discovery

Medina-Franco

Giulianotti

Welmaker

et al. 2013

Drug Discovery Today

404

246

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Increasing evidence that several drug compounds exert their effects through interactions with multiple targets is boosting the development of research fields that challenge the data reductionism approach. In this article, we review and discuss the concepts of drug repurposing, polypharmacology, chemogenomics, phenotypic screening and highthroughput in vivo testing of mixture-based libraries in an integrated manner. These research fields offer alternatives to the current paradigm of drug discovery, from a one target–one drug model to a multiple-target approach. Furthermore, the goals of lead identification are being expanded accordingly to identify not only ‘key’ compounds that fit with a single-target ‘lock’, but also ‘master key’ compounds that favorably interact with multiple targets (i.e. operate a set of desired locks to gain access to the expected clinical effects).

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Section: Drug Repurposingmentioning

confidence: 99%

Section: Drug Repurposingmentioning

confidence: 99%

Shifting from the single to the multitarget paradigm in drug discovery

Medina-Franco

Giulianotti

Welmaker

et al. 2013

Drug Discovery Today

404

246

View full text Add to dashboard Cite

show abstract

“…Beberapa basis data baru untuk reposisi obat juga telah bermunculan seperti Pubmed, Online Mendelian Inheritance, in Man (OMIM), Gene Expression Omnibus, Mouse Genome, Informatics (MGI), Kyoto Encyclopedia of Genes and Genomes (KEGG), BioCarta, IUPHAR Database, ChEMBL, and PubChem (Loging et al, 2011). Sebagai tambahan, pendekatan eksperimental non komputasional seperti skrining fenotip juga digunakan sebagai salah satu usaha reposisi obat.…”

Section: Metode Komputasional Pada Polifarmakologiunclassified

Pemanfaatan bioinformatika dalam bidang pertanian dan kesehatan (The utilization of bioinformatics in the field of agriculture and health)

Parikesit

Anurogo

Putranto

2017

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“…has been published. [15][16][17][18] Although applying wild type of data gives more room for more possibilities to identify drug repositioning candidates, but it may not be the best way to seek repositioning candidates driven by specific interests, especially for finding drugs that can be repositioned for certain types of interested disease, such as cancer, depression. As we know underneath mechanism of drug repositioning is to manage associations between one drug and two types of disease, for example, determining whether one drug used for disease A can also be used for disease B.…”

Section: Introductionmentioning

confidence: 99%

Evidence based computational drug repositioning candidate screening pipeline design: Case Study

Zhu

Liu

Zhang

et al. 2014

2014 8th International Conference on Systems Biology (ISB)

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Traditional drug development is time and cost consuming process, conversely, drug repositioning is an emerging approach to discover novel usages of existing drugs with a better risk-versus-reward trade-off. Computational technology is playing a key role in drug repositioning to screening the best drug repositioning candidates from a large candidate library. Recent efforts made for computer aided drug repositioning are mostly focusing on applying/developing data mining algorithms against wild type of large scale of biomedical data. In this paper, we introduce a novel computational pipeline designed for drug repositioning candidate screening based on existing phenotypical association (disease-disease association) discovery and pathway enrichment analysis by exploring systems biology data relevant to the interested phenotypical association specifically. To demonstrate usability and evaluate efficacy of this novel pipeline, we successfully conducted a case study by identifying potential drug repositioning candidates for Alzheimer's disease (AD) based on the studied phenotypical association between cancer and AD.

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Cheminformatic/bioinformatic analysis of large corporate databases: Application to drug repurposing

Cited by 39 publications

References 33 publications

Shifting from the single to the multitarget paradigm in drug discovery

Shifting from the single to the multitarget paradigm in drug discovery

Pemanfaatan bioinformatika dalam bidang pertanian dan kesehatan (The utilization of bioinformatics in the field of agriculture and health)

Evidence based computational drug repositioning candidate screening pipeline design: Case Study

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