Drug target identification through systems biology

Haanstra, Jurgen R.; Bakker, Barbara M.

doi:10.1016/j.ddtec.2015.06.002

Cited by 23 publications

(10 citation statements)

References 46 publications

(53 reference statements)

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“…Leaving aside that this is not true technically [11], a single drug target may not be what systems biology had to be developed for. Systems may be more likely to lead to the identification of network functions as therapy targets, with multi-drugging or non-specific drugs as the prospective.…”

Section: Resultsmentioning

confidence: 94%

Systems Pharmacology: An opinion on how to turn the impossible into grand challenges

Westerhoff

Nakayama²,

Mondeel

et al. 2015

Drug Discovery Today: Technologies

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Section: Resultsmentioning

confidence: 94%

Systems Pharmacology: An opinion on how to turn the impossible into grand challenges

Westerhoff

Nakayama²,

Mondeel

et al. 2015

Drug Discovery Today: Technologies

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“…Moreover, because of above characteristics, this approach is only suitable to identify targets of one drug once and cannot afford target identification of many compounds simultaneously, such as active components in herbs. With the indirect approach, such as system biology approaches, including proteomics, transcriptomics and metabolomics, are the major tools for target identification and have an unbiased attitude towards all active compounds 5 . A proteomic or transcriptomics approach for identification of binding proteins for a given small molecule or compounds in herbs involves comparison of the protein expression profiles for a given cell or tissue in the presence or absence of the given molecule(s).…”

mentioning

confidence: 99%

Drug target identification using network analysis: Taking active components in Sini decoction as an example

Chen

Jiang

Cao

et al. 2016

Sci Rep

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Identifying the molecular targets for the beneficial effects of active small-molecule compounds simultaneously is an important and currently unmet challenge. In this study, we firstly proposed network analysis by integrating data from network pharmacology and metabolomics to identify targets of active components in sini decoction (SND) simultaneously against heart failure. To begin with, 48 potential active components in SND against heart failure were predicted by serum pharmacochemistry, text mining and similarity match. Then, we employed network pharmacology including text mining and molecular docking to identify the potential targets of these components. The key enriched processes, pathways and related diseases of these target proteins were analyzed by STRING database. At last, network analysis was conducted to identify most possible targets of components in SND. Among the 25 targets predicted by network analysis, tumor necrosis factor α (TNF-α) was firstly experimentally validated in molecular and cellular level. Results indicated that hypaconitine, mesaconitine, higenamine and quercetin in SND can directly bind to TNF-α, reduce the TNF-α-mediated cytotoxicity on L929 cells and exert anti-myocardial cell apoptosis effects. We envisage that network analysis will also be useful in target identification of a bioactive compound.

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“…The assumption of these approaches is that similar drugs show similar patterns of interactions with targets in drug-target interaction network [18,19]. Chen et al [15] presented networkbased random walk with restart method, called NRWRH, to predict relationships between drugs and targets by integrating drug-drug chemical structure similarity network, protein-protein sequence Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/neucom similarity network and known drug-target interaction network into a heterogeneous network.…”

Section: Introductionmentioning

confidence: 99%