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other·ophthalmology, retina, pediatric ophthalmology, biomedical research, research methods·PMC10365641

Identifying novel candidate compounds for therapeutic strategies in retinopathy of prematurity via computational drug-gene association analysis

Frontiers in Pediatrics · 7 authors, 4 centres

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TL;DR

This computational bioinformatics study used gene enrichment analysis on ROP-associated genes to predict candidate drugs for repurposing.

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Using the ToppGene software, they performed gene enrichment analysis to predict compounds with significant associations to these genes from drug-gene interaction databases. The analysis identified 6,603 chemical compounds with an FDR adjusted P-value cutoff of 0.05, which were filtered to a top 50 list of significant compounds without known severe toxicities. The most common drug classes were antioxidants, anti-diabetics, NSAIDs, and cardiovascular drugs. The top predicted compound was ascorbic acid, associated with 21 of the 47 ROP genes. The study is limited by its in silico nature, as predictions require preclinical and clinical validation. It provides a hypothesis-generating framework for future ROP drug discovery.

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other

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ophthalmology

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AI pipeline

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ORIGINAL PAPER

PMC10365641

Identifying novel candidate compounds for therapeutic strategies in retinopathy of prematurity via computational drug-gene association analysis

Frontiers in Pediatrics

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ABSTRACT

Abstract Purpose Retinopathy of prematurity (ROP) is the leading cause of preventable childhood blindness worldwide. Although interventions such as anti-VEGF and laser have high success rates in treating severe ROP, current treatment and preventative strategies still have their limitations. Thus, we aim to identify drugs and chemicals for ROP with comprehensive safety profiles and tolerability using a computational bioinformatics approach. Methods We generated a list of genes associated with ROP to date by querying PubMed Gene which draws from animal models, human studies, and genomic studies in the NCBI database. Gene enrichment analysis was performed on the ROP gene list with the ToppGene program which draws from multiple drug-gene interaction databases to predict compounds with significant associations to the ROP gene list. Compounds with significant toxicities or without known clinical indications were filtered out from the final drug list. Results The NCBI query identified 47 ROP genes with pharmacologic annotations present in ToppGene. Enrichment analysis revealed multiple drugs and chemical compounds related to the ROP gene list. The top ten most significant compounds associated with ROP include ascorbic acid, simvastatin, acetylcysteine, niacin, castor oil, penicillamine, curcumin, losartan, capsaicin, and metformin. Antioxidants, NSAIDs, antihypertensives, and anti-diabetics are the most common top drug classes derived from this analysis, and many of these compounds have potential to be readily repurposed for ROP as new prevention and treatment strategies. Conclusion This bioinformatics analysis creates an unbiased approach for drug discovery by identifying compounds associated to the known genes and pathways of ROP. While predictions from bioinformatic studies require preclinical/clinical studies to validate their results, this technique could certainly guide future investigations for pathologies like ROP. Keywords: retinopathy of prematurity, bioinformatics & computational biology, bioinformatics, retina, drug discovery, network medicine

CITATION EXPORT

Edward F Xie, Sarah Hilkert Rodriguez, Bingqing Xie et al.. Identifying novel candidate compounds for therapeutic strategies in retinopathy of prematurity via computational drug-gene association analysis. Frontiers in Pediatrics. (2023). https://doi.org/10.3389/fped.2023.1151239

AUTHORS

7 · 4 CENTRES

Edward F Xie, Sarah Hilkert Rodriguez, Bingqing Xie, Mark D’Souza, Gonnah Reem, Dinanath Sulakhe, Dimitra Skondra

1Chicago Medical School, Rosalind Franklin University of Medicine and Science, Chicago, IL, United States
2Department of Ophthalmology and Visual Science, University of Chicago, Chicago, IL, United States
3Department of Medicine, University of Chicago, Chicago, IL, United States
4Center for Research Informatics, The University of Chicago, Chicago, IL, United States