The Great Lakes PrecISE Partnership

摘要： 严重哮喘患者缺乏为治疗指南提供信息的数据仍然是一个主要的未得到满足的需求。 我们的愿景是开发一种针对严重哮喘的个性化药物治疗方法，针对其 发病机制并最终可翻译到社区卫生保健环境中实施。因为 传统的临床试验设计对于具有多种表型的疾病是无效的，我们的中心假设是 适应性试验设计的应用将确定严重哮喘的亚型 对精确的干预措施作出反应。这一提议的科学依据包括最近的数据表明 血嗜酸性粒细胞升高、FEV1最大逆转和血浆IL-6浓度是 频繁加重，这是严重哮喘的一个主要组成部分。这些表型中的每一种都有精确的 FDA批准的用于哮喘或其他疾病的干预措施，即抗IL-5抗体，长效 M受体拮抗剂和抗IL-6受体抗体。我们提出了以下具体目标来测试这一点 假设：1)确定以表型为导向的干预措施对哮喘发生率的影响 采用序贯贝叶斯适应性临床试验设计，按血中嗜酸性粒细胞分层， 最大FEV1逆转和血浆IL-6浓度；2)验证监测的预测价值 在基线和治疗三个月后评估生物标志物。这些生物标志物包括痰 嗜酸性粒细胞、心率变异性、C反应蛋白水平、空气滞留和其他成像指标 低剂量胸部CT扫描和呼吸道氧化应激检测。我们的多学科调查团队 五大湖精准伙伴关系中的转化科学临床医生可以接触到不同的人群 来自威斯康星州和伊利诺伊州北部的严重哮喘患者，这将使我们能够非常积极地 这一协作网络中的合作伙伴。我们希望迅速确定从治疗中受益的患者亚群 目前没有被认为是哮喘药典的一部分。对这些生物标志物的有效评估 如果不偏离典型的治疗发展，精确的治疗是不可能的 进程。

Abstract: The lack of data to inform treatment guidelines in patients with severe asthma remains a major unmet need. Our vision is to develop a personalized medicine approach for the treatment of severe asthma, directed at its pathogenesis and ultimately translatable to the community health care setting for implementation. Because traditional clinical trial designs are inefficient for diseases with multiple phenotypes, our central hypothesis is that the application of an adaptive trial design will identify subtypes of severe asthma that will be most responsive to precise interventions. The scientific rationale for this proposal includes recent data showing that elevated blood eosinophils, maximum FEV1 reversal, and plasma IL-6 concentrations are risk factors for frequent exacerbations which are a major component of severe asthma. Each of these phenotypes has precise interventions that are FDA approved for asthma or other diseases, namely anti-IL-5 antibodies, long acting muscarinic antagonists, and anti-IL6 receptor antibodies. We propose the following Specific Aims to test this hypothesis: 1) To determine the impact of phenotype-directed interventions on the frequency of asthma exacerbations using a sequential Bayesian adaptive clinical trial design, stratified by blood eosinophils, maximum FEV1 reversal, and plasma IL-6 concentrations; and 2) To validate the predictive value of monitoring biomarkers assessed at baseline and after three months of treatment. These biomarkers include sputum eosinophils, heart rate variability, C-reactive protein levels, air trapping and other imaging metrics assessed by low dose chest CT scan, and measures of airway oxidant stress. Our team of multi-disciplinary investigators and translational science clinicians in the Great Lakes PrecISE Partnership has access to a diverse population of patients with severe asthma from Wisconsin and Northern Illinois which will enable us to be a very active partner in this collaborative network. We expect to rapidly identify patient subsets that benefit from therapies not currently considered as part of the asthma pharmacopoeia. The efficient assessment of these biomarkers and precise treatments would not be possible without this departure from the typical therapeutic development process.