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

识别治疗慢性术后眼痛的自体血清泪液的多模式生物标志物

基本信息

批准号：
10794761
负责人：
Daniel Rotroff
金额：
$ 155.71万
依托单位：
CLEVELAND CLINIC LERNER COM-CWRU
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-20 至 2025-09-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10794761
关键词：
Address Affect Age Age Years Area Artificial Tears Autologous Bioinformatics Biological Markers Blinded Blood Burning Pain Cataract Extraction Chronic Clinical Clinical Management Clinical Trials Design Collection Cytology DNA Data Science Development Diagnosis Double-Blind Method Economic Burden Enrollment Environmental Wind Epithelial Cells Equipment and supply inventories Eye Face Facial Pain Future Genetic Goals HLA Antigens Heterogeneity Immunology Incidence Individual Inflammation Light Machine Learning Messenger RNA Methodology MicroRNAs Modeling Molecular Molecular Biology Monitor Morbidity - disease rate Numeric Rating Scale Operative Surgical Procedures Ophthalmology Pain Pain management Patients Phase Placebo Effect Placebos Population Postoperative Pain Postoperative Period Prediction of Response to Therapy Procedures Prognosis Quality of life Questionnaires ROC Curve Randomized, Controlled Trials Recording of previous events Reporting Research Design Sampling Secondary pain Serum Severities Signaling Molecule Site Societies Source Surgical Specialties Testing Therapeutic Effect Tissues Trigeminal nerve structure Validation Weight Work aged biomarker identification biomarker signature chemokine clinical biomarkers clinical decision-making clinical predictors clinical translation cytokine drug testing effective therapy efficacious treatment experience eye dryness impression individual patient metabolomics molecular marker multimodality neurotransmission novel novel therapeutics ocular pain ocular surface pain reduction pain symptom painful neuropathy patient response persistent symptom personalized approach predicting response predictive marker prospective responders and non-responders response response biomarker serotonin receptor therapy outcome transcriptomics treatment response treatment stratification

项目摘要

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.

项目概要/摘要白内障手术是 55 岁及以上患者最常见的眼科手术。约10 全世界每年进行数百万台白内障手术。慢性术后眼痛（CPOP）是估计其中约 18% 的患者会出现这种情况。随着社会老龄化，白内障手术数量和因此，CPOP 的发病率预计在未来 20 年内将翻一番。 CPOP 显着影响生活质量，并伴随着巨大的经济负担。 CPOP 的有效治疗方法仍缺乏。开发此类治疗方法的一个主要限制是无法客观地衡量其疗效方式。自体血清泪液（AST）疗法已成为最有效的治疗方法之一科普。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（有反应者与无反应者）并监测对治疗的反应。我们的目标是发现并验证治疗反应和反应监测白内障手术后 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）发现患者对 AST 治疗反应的多模式生物标志物 CPOP 使用最先进的机器学习技术，集成了临床病史、问卷调查和来自疼痛部位（眼睛）和治疗来源（血清）的分子生物标志物。我们将评估遗传学，转录组学、代谢组学等。机器学习将用于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 AST 治疗反应的多模式生物标志物 (UG3 Discovery 阶段）; (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志物评估白内障手术后 CPOP 中 AST 与安慰剂的比较（UH3 验证阶段）。该提案将产生客观而稳健的生物标志物特征有助于 CPOP 患者的 AST 治疗分层。在除了这项研究还将对创建精确方法来指导临床管理产生影响之外 CPOP 患者，该提案的方法和结果将提供一个框架来识别和验证许多其他外科专业中慢性术后疼痛的客观生物标志物。