Literature DB >> 20930556

Identification of small molecules enhancing autophagic function from drug network analysis.

Francesco Iorio1, Antonella Isacchi, Diego di Bernardo, Nicola Brunetti-Pierri.   

Abstract

Enhancing autophagy is a potentially effective strategy for the treatment of several human disorders. Therefore, there is a great effort in developing drugs modulating autophagy, and various approaches have been taken towards this goal. Gene expression has been considered an important biomarker for drug activity for prediction of drug mode of action. However, the lack of efficient method of analysis has hampered recognition of drug mode of action based on the analysis of gene expression profiles. A novel and robust tool for prediction of drug mode of action and drug repositioning overcomes the limitations of previously available methods. This novel tool is based on a data set of expression profiles derived from a large number of drugs integrated into a "drug network" constructed by comparing the transcriptional responses induced in human cell lines. Automatic analysis of the topology of the drug network makes it possible to classify compounds and to predict unreported effects of well-known drugs. Using this tool, it was possible to identify fasudil as a new enhancer of autophagy.

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Year:  2010        PMID: 20930556     DOI: 10.1073/pnas.1000138107

Source DB:  PubMed          Journal:  Autophagy        ISSN: 1554-8627            Impact factor:   16.016


  23 in total

1.  Combination treatment with fasudil and clioquinol produces synergistic anti-tumor effects in U87 glioblastoma cells by activating apoptosis and autophagy.

Authors:  Mingliang He; Ming Luo; Qingyu Liu; Jingkao Chen; Kaishu Li; Meiguang Zheng; Yinlun Weng; Leping Ouyang; Anmin Liu
Journal:  J Neurooncol       Date:  2016-01-02       Impact factor: 4.130

Review 2.  Synthetic molecules: helping to unravel plant signal transduction.

Authors:  Wei Xuan; Evan Murphy; Tom Beeckman; Dominique Audenaert; Ive De Smet
Journal:  J Chem Biol       Date:  2013-03-03

Review 3.  Interplay of oxidative, nitrosative/nitrative stress, inflammation, cell death and autophagy in diabetic cardiomyopathy.

Authors:  Zoltán V Varga; Zoltán Giricz; Lucas Liaudet; György Haskó; Peter Ferdinandy; Pál Pacher
Journal:  Biochim Biophys Acta       Date:  2014-07-02

Review 4.  Drug repurposing: progress, challenges and recommendations.

Authors:  Sudeep Pushpakom; Francesco Iorio; Patrick A Eyers; K Jane Escott; Shirley Hopper; Andrew Wells; Andrew Doig; Tim Guilliams; Joanna Latimer; Christine McNamee; Alan Norris; Philippe Sanseau; David Cavalla; Munir Pirmohamed
Journal:  Nat Rev Drug Discov       Date:  2018-10-12       Impact factor: 84.694

Review 5.  Network based elucidation of drug response: from modulators to targets.

Authors:  Francesco Iorio; Julio Saez-Rodriguez; Diego di Bernardo
Journal:  BMC Syst Biol       Date:  2013-12-13

6.  In silico repositioning-chemogenomics strategy identifies new drugs with potential activity against multiple life stages of Schistosoma mansoni.

Authors:  Bruno J Neves; Rodolpho C Braga; José C B Bezerra; Pedro V L Cravo; Carolina H Andrade
Journal:  PLoS Negl Trop Dis       Date:  2015-01-08

7.  A gene-signature progression approach to identifying candidate small-molecule cancer therapeutics with connectivity mapping.

Authors:  Qing Wen; Chang-Sik Kim; Peter W Hamilton; Shu-Dong Zhang
Journal:  BMC Bioinformatics       Date:  2016-05-11       Impact factor: 3.169

8.  Transcriptional data: a new gateway to drug repositioning?

Authors:  Francesco Iorio; Timothy Rittman; Hong Ge; Michael Menden; Julio Saez-Rodriguez
Journal:  Drug Discov Today       Date:  2012-08-07       Impact factor: 7.851

9.  A Semi-Supervised Approach for Refining Transcriptional Signatures of Drug Response and Repositioning Predictions.

Authors:  Francesco Iorio; Roshan L Shrestha; Nicolas Levin; Viviane Boilot; Mathew J Garnett; Julio Saez-Rodriguez; Viji M Draviam
Journal:  PLoS One       Date:  2015-10-09       Impact factor: 3.240

10.  Drug repositioning for orphan genetic diseases through Conserved Anticoexpressed Gene Clusters (CAGCs).

Authors:  Ivan Molineris; Ugo Ala; Paolo Provero; Ferdinando Di Cunto
Journal:  BMC Bioinformatics       Date:  2013-10-02       Impact factor: 3.169

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