NFFinder: an online bioinformatics tool for searching similar transcriptomics experiments in the context of drug repositioning

Setoaín, Javier; Franch, Mónica; Martínez, Marta; Tabas-Madrid, Daniel; Sorzano, C.O.S.; Bakker, Annette; González-Couto, Eduardo; Elvira, Juan; Pascual-Montano, Alberto

doi:10.1093/nar/gkv445

Cited by 64 publications

(37 citation statements)

References 26 publications

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“…Network based approaches have been extensively exploited in computational drug repositioning for identifying novel drug targets, interactions, and indications [106]. Typically, in these models, the nodes in Peer-reviewed version available at Pharmaceuticals 2018, 11, 57; doi:10.3390/ph11020057 incorporated using phenotype data such as side-effect [108][109][110], transcriptional [90,92,111], drug-disease [112,113] and signaling pathway data [24].…”

Section: Network Modelsmentioning

confidence: 99%

Changing Trends in Computational Drug Repositioning

Yella

Yaddanapudi

Wang

et al. 2018

Preprint

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Maximizing the indications potential and revenue from drugs that are already marketed offers a new take on the famous mantra of the Nobel Prize-winning pharmacologist, Sir James Black, “The most fruitful basis for the discovery of a new drug is to start with an old drug”. However, rational design of drug mixtures poses formidable challenges because of the lack of or limited information about in vivo cell regulation, mechanisms of genetic pathway activation, and in vivo pathway interactions. Most of the repositioned drugs therefore are the result of “serendipity” - based on late phase clinical studies of unexpected findings. One of the reasons that the connection between drug candidates and their potential adverse drug reactions or new applications could not be identified earlier is that the underlying mechanism associating them is either very intricate and unknown or dispersed and buried in a sea of information. Discovery of such multi-domain pharmacomodules - pharmacologically relevant sub-networks of biomolecules and/or pathways - from collection of databases by independent/simultaneous mining of multiple datasets is an active area of research. Here, while presenting some of the promising bioinformatics approaches and pipelines, we summarize and discuss the current and evolving landscape of computational drug repositioning.

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Section: Network Modelsmentioning

confidence: 99%

Changing Trends in Computational Drug Repositioning

Yella

Yaddanapudi

Wang

et al. 2018

Preprint

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“…These data were taken from [54] 9 , which contains 888 drugs and 881 substructures, and the description of the 881 chemical substructures is available at PubChem's website. The pairwise chemical similarity between two drugs d1 and d2 is computed as the Tanimoto coefficient of their fingerprints using the "proxy" R package 10 .…”

Section: Homogeneous Sub-graphs G1 Chemical Substructuresmentioning

confidence: 99%

Heter-LP: A heterogeneous label propagation algorithm and its application in drug repositioning

Shahreza

Ghadiri

Mousavi

et al. 2017

Journal of Biomedical Informatics

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Drug repositioning offers an effective solution to drug discovery, saving both time and resources by finding new indications for existing drugs. Typically, a drug takes effect via its protein targets in the cell. As a result, it is necessary for drug development studies to conduct an investigation into the interrelationships of drugs, protein targets, and diseases. Although previous studies have made a strong case for the effectiveness of integrative network-based methods for predicting these interrelationships, little progress has been achieved in this regard within drug repositioning research. Moreover, the interactions of new drugs and targets (lacking any known targets and drugs, respectively) cannot be accurately predicted by most established methods. In this paper, we propose a novel semi-supervised heterogeneous label propagation algorithm named Heter-LP, which applies both local and global network features for data integration. To predict drug-target, disease-target, and drug-disease associations, we use information about drugs, diseases, and targets as collected from multiple sources at different levels. Our algorithm integrates these various types of data into a heterogeneous network and implements a label propagation algorithm to find new interactions. Statistical analyses of 10-fold cross-validation results and experimental analyses support the effectiveness of the proposed algorithm.

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“…An innovative therapeutic approach to treat infectious diseases produced by intracellular pathogens is to perturb host pathways used by the microorganisms to enter, replicate and/or survive inside host cells through host-directed therapies (HDT). The use of host-directed antimicrobial drugs (HDAD) facilitates overcoming antimicrobial resistance [ 9 ] and permits the testing of drugs that were designed to treat conditions other than infections (drug repositioning) [ 10 , 11 ]. The difficulty of this approach is identifying host metabolic pathways or biological processes relevant for the development of infection; however, key knowledge of host–pathogen interactions between salmonid fish and P. salmonis that denote potential targets for HDT is already available.…”

Section: Introductionmentioning

confidence: 99%

Pharmacological iron-chelation as an assisted nutritional immunity strategy against Piscirickettsia salmonis infection

Caruffo

Mandaković

Mejías

et al. 2020

Vet Res

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Salmonid Rickettsial Septicaemia (SRS), caused by Piscirickettsia salmonis, is a severe bacterial disease in the Chilean salmon farming industry. Vaccines and antibiotics are the current strategies to fight SRS; however, the high frequency of new epizootic events confirms the need to develop new strategies to combat this disease. An innovative opportunity is perturbing the host pathways used by the microorganisms to replicate inside host cells through host-directed antimicrobial drugs (HDAD). Iron is a critical nutrient for P. salmonis infection; hence, the use of iron-chelators becomes an excellent alternative to be used as HDAD. The aim of this work was to use the iron chelator Deferiprone (DFP) as HDAD to treat SRS. Here, we describe the protective effect of the iron chelator DFP over P. salmonis infections at non-antibiotic concentrations, in bacterial challenges both in vitro and in vivo. At the cellular level, our results indicate that DFP reduced the intracellular iron content by 33.1% and P. salmonis relative load during bacterial infections by 78%. These findings were recapitulated in fish, where DFP reduced the mortality of rainbow trout challenged with P. salmonis in 34.9% compared to the non-treated group. This is the first report of the protective capacity of an iron chelator against infection in fish, becoming a potential effective host-directed therapy for SRS and other animals against ferrophilic pathogens.

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NFFinder: an online bioinformatics tool for searching similar transcriptomics experiments in the context of drug repositioning

Cited by 64 publications

References 26 publications

Changing Trends in Computational Drug Repositioning

Changing Trends in Computational Drug Repositioning

Heter-LP: A heterogeneous label propagation algorithm and its application in drug repositioning

Pharmacological iron-chelation as an assisted nutritional immunity strategy against Piscirickettsia salmonis infection

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