Drug-Gene Association Analysis to Identify Novel AMD Therapeutics

Abstract

Purpose : By 2040, patients with age-related macular degeneration (AMD) are expected to reach 288 million; however, no cure or definite prevention exists to date. The purpose of this study is to use advanced bioinformatics tools to study drug-gene associations in order to identify drugs/compounds interacting with genes involved in AMD.

Methods : We queried PubMed Gene to compile a comprehensive list of genes described in AMD, wet AMD, dry AMD, intermediate AMD, and geographic atrophy (GA) to date. We also combined some of the sub-types to reflect the genes that play a role at distinct stages of the disease. Gene enrichment analysis was performed using ToppGene on AMD-related gene lists against the drug databases. This analysis estimates the statistical significance of overrepresented compounds from multiple drug-gene interaction databases to construct a Pharmacome. We selected compounds with adjusted p-value <0.05. Compounds with no clinical indications (e.g., paraquat and ethanol) were filtered out from the resulting drug lists.

Results : Among 77,146 candidate drugs/compounds in the database, our analysis revealed multiple drugs/compounds with significant interactions to genes involved in AMD. Amongst the top 20 most significant, drug classes such as anti-diabetics, antioxidants, statins, and flavonoids were identified. Metformin, the most common anti-diabetic medication, was identified as the drug with the strongest association to wet-AMD genes and was also in the top 20 for dry AMD subtypes. Curcumin, a flavonoid polyphenol, was identified as the drug with the strongest associations to all AMD-affiliated genes as well as to genes in the dry AMD subtype (both intermediate and GA). Other top compounds identified include statins and various antioxidants – glutathione, N-acetylcysteine, vitamins E & C – which were seen in the top 10 results of our AMD conditions.

Conclusions : This bioinformatics drug-gene interactions approach predicts drugs which can affect multiple genes involved in AMD. Multiple drugs with well-known pharmacodynamics and safety profiles like metformin and statin as well as antioxidants/nutrients like glutathione and curcumin are revealed by this analysis. Predicted bioinformatics studies require further validation from preclinical and clinical studies; however, this unbiased approach could identify potential candidates among existing drugs/compounds that could be repurposed for AMD.

This is a 2021 ARVO Annual Meeting abstract.