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Title: Support for phosphoinositol 3 kinase and mTOR inhibitors as treatment for lupus using in-silico drug-repurposing analysis.
Authors: Toro-Domínguez, Daniel
Carmona-Sáez, Pedro
Alarcón-Riquelme, Marta E
Keywords: Autoimmunity;Drug discovery;Drug repurposing;Gene expression;Lincscloud;Systemic lupus erythematosus
metadata.dc.subject.mesh: Data Mining
Databases, Genetic
Databases, Pharmaceutical
Drug Repositioning
Lupus Erythematosus, Systemic
Phosphoinositide-3 Kinase Inhibitors
TOR Serine-Threonine Kinases
Issue Date: 11-Mar-2017
Abstract: Systemic lupus erythematosus (SLE) is an autoimmune disease with few treatment options. Current therapies are not fully effective and show highly variable responses. In this regard, large efforts have focused on developing more effective therapeutic strategies. Drug repurposing based on the comparison of gene expression signatures is an effective technique for the identification of new therapeutic approaches. Here we present a drug-repurposing exploratory analysis using gene expression signatures from SLE patients to discover potential new drug candidates and target genes. We collected a compendium of gene expression signatures comprising peripheral blood cells and different separate blood cell types from SLE patients. The Lincscloud database was mined to link SLE signatures with drugs, gene knock-down, and knock-in expression signatures. The derived dataset was analyzed in order to identify compounds, genes, and pathways that were significantly correlated with SLE gene expression signatures. We obtained a list of drugs that showed an inverse correlation with SLE gene expression signatures as well as a set of potential target genes and their associated biological pathways. The list includes drugs never or little studied in the context of SLE treatment, as well as recently studied compounds. Our exploratory analysis provides evidence that phosphoinositol 3 kinase and mammalian target of rapamycin (mTOR) inhibitors could be potential therapeutic options in SLE worth further future testing.
metadata.dc.identifier.doi: 10.1186/s13075-017-1263-7
Appears in Collections:Producción 2020

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