Computational systems biology approach to identify novel pharmacological targets for diabetic retinopathy.

Diabetic retinopathy was included by the World Health Organization in the eye disease priority list. Up to now, only proliferative diabetic retinopathy can be treated with approved drugs, such as intravitreal anti-vascular endothelial growth factor (VEGF) agents or steroids. In this perspective, there is the urgent need to explore novel pharmacological targets for treatment of diabetic retinopathy. Drug discovery todays exploits the noticeable ability of computational systems biology methods to identify novel drug targets in complex pathologies bearing multifactorial etiology and wide and varying symptomatology. This is especially true for diseases, where the identification of specific molecular mechanisms, and thus drug targets, is a challenging, when not impossible, task. Within this framework, we applied a systems biology approach to identify novel drug targets for diabetic retinopathy. The complexity of diabetic retinopathy was investigated through the analysis of transcriptomics data, retrieved from Gene Expression Omnibus Dataset repository (GEO) datasets. Analysis of GEO datasets was carried out with an enrichment-information approach, which gave as output a series of complex gene-pathway and drug-gene networks. Analysis of these networks identified genes and biological pathways related with inflammation, fibrosis and G protein-coupled receptors that are potentially involved in development of the disease. This analysis provided new clues on novel pharmacological targets, useful to treat diabetic retinopathy.