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

基本信息

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
项目类别：
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

项目摘要

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，会导致慢性盆腔衰弱 1.76 亿人遭受疼痛，包括严重痛经和性交困难、不孕以及生活质量下降全世界女性和青少年子宫内膜异位症相关疼痛的治疗主要是手术和/或药物治疗。手术切除疾病会导致 50% 的疼痛在 2-5 年内复发，主要是药物治疗。几十年来没有变化，包括非甾体抗炎药 (NSAID) 和降低激素水平的药物雌激素水平或对抗其作用，并导致不同的症状缓解。大规模基因组、转录组和其他分子分析技术，结合药物靶点的网络概念和表型筛选的力量的部署，提供了推进合理药物再利用和数据驱动药物开发的前所未有的机遇项目 3 的目标是利用子宫内膜异位症转录组学数据与公开数据相结合。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和基于表达逆转扰乱分子网络的现有药物的联合疗法消除与疾病相关的细胞功能障碍，并在体外和体外验证人类子宫内膜细胞中的选定药物在目标 1 中，我们将使用基于转录组学的计算药物-子宫内膜异位症疼痛的动物模型。基于表达逆转重新利用以确定潜在的新单药和联合疗法利用公共转录组学数据，我们的假设是药物之间的表达谱相反。重新定位候选者和疾病特征将导致治疗预测。确定感兴趣的化合物 (COIs) 抑制原发性炎症信号反应的能力通过使用离体高通量质量标签条形码检测来检测人类免疫细胞和子宫内膜细胞。我们寻求在计算机中确定的最有希望的 COIs（目标 1）将改善子宫内膜异位症症状最后，在目标 3 中，我们将通过抑制子宫内膜和/或免疫细胞中的促炎症信号反应。确定感兴趣的化合物在临床前子宫内膜异位症模型中减轻疼痛的功效。发现用于治疗子宫内膜异位症的 COIs 将改变子宫内膜异位症我们预计这项研究将作为新研究的基础。发现了子宫内膜组织中的新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，则进行针对女性子宫内膜异位症相关疼痛的临床试验。希望这种新颖的方法能够改变“一刀切”的激素治疗模式子宫内膜异位症相关的盆腔疼痛，并将治疗选择扩展到新的和既定的疗法重新调整目标以改善数百万受影响妇女和青少年的生活，并扩大以下领域的研究渠道这个空间。