Leveraging Omics-Based Computational Approaches to Identify and Validate Novel Therapeutic Candidates for Endometriosis

利用基于组学的计算方法来识别和验证子宫内膜异位症的新治疗候选药物

• 批准号: 10699970
• 负责人: Marina Sirota
• 金额: $ 41.2万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10699970
• 关键词: Affect; Animal Model; Bar Codes; Behavioral; Bioinformatics; Biological Assay; COVID-19; Cells; Clinical; Clinical Trials; Collaborations; Combination Drug Therapy; Combined Modality Therapy; Cytometry; Data; Databases; Dermatomyositis; Development; Disease; Drug Combinations; Drug Screening; Drug Targeting; Dysmenorrhea; Dyspareunia; Endometrial; Equipment; Estrogens; Excision; Functional disorder; Gene Abnormality; Gene Expression; Gene Expression Profile; Genomics; Goals; Hormonal; Hormones; Human; Immune; In Vitro; Infertility; Inflammatory; Inflammatory Bowel Diseases; Interleukin-1 beta; Interleukin-6; Libraries; Malignant neoplasm of lung; Maps; Mediating; Medical; Menstrual cycle; Modeling; Molecular; Molecular Profiling; Network-based; Non-Steroidal Anti-Inflammatory Agents; Operative Surgical Procedures; Pain; Pathogenesis; Pelvic Pain; Pelvis; Pharmaceutical Preparations; Phase; Phenotype; Positioning Attribute; Pre-Clinical Model; Premature Birth; Process; Property; Prostaglandins; Public Domains; Quality of life; Relapse; Research; Rodent Model; Signal Pathway; Signal Transduction; Symptoms; TNF gene; Techniques; Technology; Teenagers; Testing; Therapeutic; Tissues; Training; Translational Research; Validation; Woman; cell type; chronic pelvic pain; community engagement; cytokine; drug development; drug repurposing; efficacy evaluation; endometriosis; immunoregulation; improved; in silico; in vivo; innovation; interest; multidisciplinary; novel; novel strategies; novel therapeutics; pre-clinical; preclinical development; reduce symptoms; response; screening; skills; therapeutic candidate; transcriptomics

ABSTRACT – PROJECT 3 Endometriosis is a common, estrogen-dependent, inflammatory disorder that causes debilitating chronic pelvic pain including severe dysmenorrhea and dyspareunia, infertility, and a reduced quality of life for 176 million women and teens worldwide. Treatment of endometriosis-associated pain is mainly surgical and/or medical. Surgical removal of disease results in 50% relapse of pain within 2-5 years. Medical treatments, largely unchanged over decades, comprise nonsteroidal anti-inflammatory drugs (NSAIDs) and hormones that lower estrogen levels or oppose its actions, and result in variable symptom relief. The development and availability of large-scale genomic, transcriptomic, and other molecular profiling technologies, in combination with the deployment of the network concept of drug targets and the power of phenotypic screening, provide an unprecedented opportunity to advance rational drug repurposing and data-driven development of drug combinations. The goal of Project 3 is to leverage endometriosis transcriptomics data combined with publicly available drug screening data and apply a computational drug-repurposing pipeline to identify single agent and combination therapies from existing drugs based on expression reversal perturbing molecular networks away from disease-associated cellular dysfunction, and validate select drugs in human endometrial cells in vitro and an animal model of endometriosis pain. In Aim 1, we will use transcriptomic-based computational drug- repurposing to identify potential new single agent and combination therapeutics based on expression reversal leveraging public transcriptomics data. Our hypothesis is that the inverse expression profiles between the drug repositioning candidates and the disease signatures will result in therapeutic predictions. In Aim 2, we will determine the capacity of compounds of interest (COIs) to inhibit inflammatory signaling responses in primary human immune and endometrial cells through the use of an ex vivo high-throughput mass-tag barcoding assay. We hypothesize that the most promising COIs identified in silico (Aim 1) will improve endometriosis symptoms by inhibiting pro-inflammatory signaling responses in endometrial and/or immune cells. Finally, in Aim 3 we will determine the efficacy of compounds of interest to alleviate pain in a preclinical endometriosis model. We hypothesize that the COIs identified for the treatment of endometriosis will alter the endometriotic microenvironment to alleviate pain. We anticipate this study will serve as the basis for studies on newly discovered novel targets and drug-repurposing as well as functional validation in endometrial tissue as well as testing in preclinical models, and if successful, clinical trials for endometriosis-associated pain in women. We hope that this novel approach will change the paradigm of “one size fits all” hormonal treatment for endometriosis-associated pelvic pain and expand therapeutic options to new therapies and established therapies repurposed to improve the lives of millions of affected women and teens and expand the research pipeline in this space.

摘要-项目3 子宫内膜异位症是一种常见的雌激素依赖性炎症性疾病， 疼痛，包括严重痛经和性交困难，不孕症，以及1.76亿人的生活质量下降 全世界的女性和青少年牙周炎相关疼痛的治疗主要是手术和/或药物。 手术切除导致50%的疼痛在2 - 5年内复发。医学治疗，主要是 几十年不变，包括非甾体抗炎药（NSAID）和激素，降低 雌激素水平或对抗其作用，并导致不同的症状缓解。开发和提供 大规模基因组、转录组和其他分子分析技术，结合 部署药物靶点的网络概念和表型筛选的力量，提供了一个 推动合理药物再利用和数据驱动的药物开发的前所未有的机会 组合。项目3的目标是利用子宫内膜异位症转录组学数据， 可用的药物筛选数据，并应用计算药物再利用管道来识别单一药物， 基于表达逆转扰乱分子网络的现有药物的联合疗法 从疾病相关的细胞功能障碍，并验证选择药物在人类子宫内膜细胞在体外， 子宫内膜异位症疼痛的动物模型。在目标1中，我们将使用基于转录组学的计算药物- 基于表达逆转重新利用以鉴定潜在的新的单一药剂和组合疗法 利用公共转录组学数据。我们的假设是，药物之间的反向表达谱 重新定位候选者和疾病特征将导致治疗预测。在目标2中，我们将 确定感兴趣的化合物（COI）抑制原发性结肠癌中炎症信号传导应答的能力， 人免疫细胞和子宫内膜细胞，通过使用离体高通量质量标签条形码分析。 我们假设，最有希望的COI确定在硅片（目的1）将改善子宫内膜异位症的症状 通过抑制子宫内膜和/或免疫细胞中的促炎信号应答。最后，在目标3中， 确定感兴趣的化合物在临床前子宫内膜异位症模型中缓解疼痛的功效。我们 假设确定用于治疗子宫内膜异位症的COI将改变子宫内膜异位症的发病率， 微环境来缓解疼痛。我们预计这项研究将作为研究的基础， 在子宫内膜组织中发现了新的靶点和药物再利用以及功能验证， 在临床前模型中进行测试，如果成功的话，还将进行女性乳腺癌相关疼痛的临床试验。我们 我希望这种新的方法将改变"一刀切"的激素治疗模式， 盆腔炎相关的盆腔疼痛，并将治疗选择扩展到新疗法和既定疗法 重新用于改善数百万受影响的妇女和青少年的生活，并扩大研究渠道， 这个空间