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Anddre Osmar Valdivia, Ye He, Xinjun Ren, Dejia Wen, Lijie Dong, Hossein Nazari, Xiaorong Li; Probable Treatment Targets for Diabetic Retinopathy Based on an Integrated Proteomic and Genomic Analysis. Trans. Vis. Sci. Tech. 2023;12(2):8. https://doi.org/10.1167/tvst.12.2.8.
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Using previously approved medications for new indications can expedite the lengthy and expensive drug development process. We describe a bioinformatics pipeline that integrates genomics and proteomics platforms to identify already-approved drugs that might be useful to treat diabetic retinopathy (DR).
Proteomics analysis of vitreous humor samples from 12 patients undergoing pars plana vitrectomy for DR and a whole genome dataset (UKBiobank TOPMed-imputed) from 1330 individuals with DR and 395,155 controls were analyzed independently to identify biological pathways associated with DR. Common biological pathways shared between both datasets were further analyzed (STRING and REACTOME analyses) to identify target proteins for probable drug modulation. Curated target proteins were subsequently analyzed by the BindingDB database to identify chemical compounds they interact with. Identified chemical compounds were further curated through the Expasy SwissSimilarity database for already-approved drugs that interact with target proteins.
The pathways in each dataset (proteomics and genomics) converged in the upregulation of a previously unknown pathway involved in DR (RUNX2 signaling; constituents MMP-13 and LGALS3), with an emphasis on its role in angiogenesis and blood–retina barrier. Bioinformatics analysis identified U.S. Food and Drug Administration (FDA)-approved medications (raltitrexed, pemetrexed, glyburide, probenecid, clindamycin hydrochloride, and ticagrelor) that, in theory, may modulate this pathway.
The bioinformatics pipeline described here identifies FDA-approved drugs that can be used for new alternative indications. These theoretical candidate drugs should be validated with experimental studies.
Our study suggests possible drugs for DR treatment based on an integrated proteomics and genomics pipeline. This approach can potentially expedite the drug discovery process by identifying already-approved drugs that might be used for new indications.
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