Table1_Identifying novel candidate compounds for therapeutic strategies in retinopathy of prematurity via computational drug-gene association analysis.xlsx

PurposeRetinopathy 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.
MethodsWe 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.
ResultsThe 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.
ConclusionThis 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.

早产儿视网膜病变（Retinopathy of prematurity, ROP）是全球范围内可预防性儿童失明的首要病因。尽管抗血管内皮生长因子（anti-VEGF）、激光治疗等干预手段在治疗重度ROP时可取得较高成功率，但当前的治疗与预防策略仍存在一定局限。为此，本研究拟通过计算生物信息学方法，筛选出具备全面安全性与良好耐受性的ROP治疗用药物及化学物质。
研究方法：本研究通过检索PubMed Gene数据库（该数据库整合了NCBI数据库中的动物模型研究、人体研究及基因组学研究数据），构建了迄今已报道的ROP相关基因列表。随后，依托ToppGene程序（该程序整合了多组药物-基因相互作用数据库）对ROP基因列表开展基因富集分析，以预测与该基因列表存在显著关联的化合物。最终从候选药物列表中剔除了具有显著毒性或无明确临床适应症的化合物。
研究结果：通过NCBI数据库检索，共筛选出47个带有ToppGene药理学注释的ROP相关基因。富集分析结果显示，存在多种与ROP基因列表相关的药物及化学物质。与ROP关联度最高的前十种化合物依次为抗坏血酸、辛伐他汀、乙酰半胱氨酸、烟酸、蓖麻油、青霉胺、姜黄素、氯沙坦、辣椒素及二甲双胍。本分析得到的热门药物类别以抗氧化剂、非甾体抗炎药（NSAIDs）、抗高血压药及降糖药为主，其中多数化合物具备被快速重新定位用于ROP预防与治疗的潜力。
研究结论：本项生物信息学分析通过识别与ROP已知基因及通路相关的化合物，为药物研发提供了一种无偏倚的研究思路。尽管生物信息学研究的预测结果需经临床前/临床研究验证，但该技术无疑可为ROP等疾病的未来研究提供指导。