Identifying multimodal biomarkers for autologous serum tears in the treatment of chronic postoperative ocular pain

项目摘要/摘要 白内障手术是55岁及以上患者最常见的眼科手术。大约10 全世界每年有一百万例白内障手术。慢性术后眼痛(CPOP)是 据估计，大约18%的患者会发生这种情况。随着社会老龄化，白内障手术和 随后，预计CPOP的发病率在未来20年内将翻一番。CPOP实质上影响 这不仅影响生活质量，而且与巨大的经济负担有关。慢性阻塞性肺疾病的有效治疗方法仍然是 缺乏。开发这种疗法的一个主要限制是无法客观地评估它们的疗效。 举止。自体血清泪液(AST)疗法已成为治疗卵巢癌最有效的方法之一。 CPOP。然而，不同患者对AST的反应差异很大。因此，迫切需要 建立强大的生物标志物来预测治疗结果(应答者与无应答者)并监测 对治疗的反应。我们的目标是发现和验证治疗反应和反应监测 白内障术后CPOP患者AST治疗的生物标志物。为了实现这些目标，我们有 组建了一个跨学科团队(眼科、疼痛管理、生物信息学、数据科学、 免疫学和分子生物学)，以发现患者对AST治疗的多模式生物标志物 CPOP使用最先进的机器学习，将临床病历、问卷和 来自疼痛部位(眼睛)和治疗来源(血清)的分子生物标记物。我们将评估遗传学， 转录学、新陈代谢组学等。机器学习将被用来确定一组简约的 用于表征AST反应的非侵入性生物标志物。我们将通过两个具体目标来实现我们的目标： (1)开发白内障术后CPOP中AST治疗反应的多模式生物标志物(UG3发现 阶段)；以及(2)在第一个随机对照试验中验证目标1中确定的多模式生物标记物 评价AST与安慰剂在白内障术后CPOP中的作用(UH3验证阶段)。这项提议将产生 客观和可靠的生物标志物信号有助于CPOP患者的AST治疗分层。在……里面 此外，这项研究还将对创建精确的方法来指导临床管理产生影响 这项提案的方法和结果将提供一个框架，以识别和 在许多其他外科专科中验证慢性术后疼痛的客观生物标志物。

PROJECT SUMMARY/ABSTRACT Cataract surgery is the most common eye procedure in patients aged 55 years and above. Approximately 10 million cataract surgeries are performed annually in the world. Chronic postoperative ocular pain (CPOP) is estimated to develop in about 18% of these patients. As society ages, the number of cataract surgeries and subsequently the incidence of CPOP are expected to double in the next 20 years. CPOP substantially affects the quality of life and is associated with enormous economic burden. Effective treatments for CPOP are still lacking. One major limitation in developing such treatments is the inability to gauge their efficacy in an objective manner. Autologous serum tear (AST) therapy has emerged as one of the most highly efficacious treatments for CPOP. However, responses to AST vary vastly among individual patients. Thus, there is an urgent need to establish robust biomarkers to predict the therapeutic outcomes (responder vs non-responder) and to monitor response to treatment. Our goals are to discover and validate treatment response and response monitoring biomarkers for AST treatment in patients with CPOP after cataract surgery. To meet these goals, we have assembled an interdisciplinary team (ophthalmology, pain management, bioinformatics, data science, immunology, and molecular biology) to discover multimodal biomarkers of response to AST therapy in patients with CPOP using state-of-the art machine-learning that integrates clinical histories, questionnaires, and molecular biomarkers from the site of pain (eye) and the source of treatment (serum). We will assess genetics, transcriptomics, metabolomics, among others. Machine learning will be used to determine a parsimonious set of non-invasive biomarkers for characterizing AST response. We will achieve our goal through two specific aims: (1) develop multimodal biomarkers of AST treatment response in CPOP after cataract surgery (UG3 Discovery Phase); and (2) validate the multimodal biomarkers identified in Aim 1 in the first randomized controlled trial evaluating AST versus placebo in CPOP after cataract surgery(UH3 Validation Phase). This proposal will yield objective and robust biomarker signatures to facilitate AST treatment stratification in patients with CPOP. In addition to the impact this study will have on creating precision approach to guide the clinical management of patients with CPOP, the approaches and findings from this proposal will provide a framework to identify and validate objective biomarkers for chronic postoperative pain in many other surgical specialties.