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, 19 (1), 54

Support for Phosphoinositol 3 Kinase and mTOR Inhibitors as Treatment for Lupus Using In-Silico Drug-Repurposing Analysis

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Support for Phosphoinositol 3 Kinase and mTOR Inhibitors as Treatment for Lupus Using In-Silico Drug-Repurposing Analysis

Daniel Toro-Domínguez et al. Arthritis Res Ther.

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Keywords: Autoimmunity; Drug discovery; Drug repurposing; Gene expression; Lincscloud; Systemic lupus erythematosus.

Figures

Fig. 1
Fig. 1
Integrative drug-repurposing analysis. Fourteen signatures of SLE were obtained from 14 different datasets. Each signature was queried on the Lincscloud database and a set of drugs and knock-down and knock-in genes was obtained with similarity scores. The median similarity score and empirical p values were calculated to select significant results across all datasets. Bottom: summary interpretation of the positively and negatively correlated results. NCBI GEO National Center for Biotechnology Information Gene Expression Omnibus, SLE systemic lupus erythematosus
Fig. 2
Fig. 2
Heatmap of significant drugs representing similarity scores for each drug in the results of each dataset. Rows: results of the different datasets used for the analysis. Datasets classified according to the blood cell type (see key). Columns: different drugs sorted decreasingly by the median of similarity scores, from left to right (Color figure online)

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