Commercial Translation of Biomarker-based Platform for Personalized Forecasting of Rapid Lung Function Decline

摘要 一种预测慢性肺病患者肺功能下降的算法， 令人向往我们开发了一个基于血液生物标志物的平台，可以预测CF患者的肺功能下降。 患者有可能告知治疗干预措施，以防止/减缓衰退，缩短病程 （降低成本）的临床试验，并加快检查潜在的治疗，允许更快 交付给患者。此外，分子生物标志物的行为可以为治疗提供信息， 增加缓解疾病进展的成功率。我们开发了一种血液生物标记物， 预测功能衰退的算法，并在此提出通过实现 3个里程碑; 1）（R61阶段，第1年）：完成蛋白质组学信息的设计和测试 GUI，将常规收集的临床/人口统计学数据与标记数据相结合，以生成实时 使用CF学习网络的肺功能下降的风险预测和现场测试GUI效用，2）（R33阶段， 第2年）：在一个大型队列中完成对我们标记物的临床试验的验证， CCHMC和Battelle Memorial Institute外部的样本，以及3）（R33阶段，第3年）：完成 最佳算法参数的选择提供了最佳的特异性和灵敏度， 我们的标记单独和/或组合。开发、验证和传播我们的 蛋白质组学知情的风险预测平台将为与FDA的提交前会议奠定基础 用于该技术的商业翻译及其向CF护理和患者社区的交付。

ABSTRACT An algorithm that predicts lung function decline in patients with chronic lung disease is highly desirable. We have developed a blood biomarker-based platform that predicts lung function decline in CF patients with the potential to inform therapeutic interventions to prevent/slow decline, shorten the length (reducing cost) of clinical trials, and accelerate the examination of potential therapies allowing faster delivery to the patients. Furthermore, the behavior of molecular biomarkers can inform therapy and increase success of alleviating disease progression. We have developed a blood biomarker-informed algorithm that predicts functions decline and propose here to deliver it to the CF care community by achieving the 3 milestone; 1) (R61 Phase, Year 1): Complete the design and testing of proteomics-informed GUI that integrates routinely collected clinical/demographic data with marker data to generate real-time risk prediction of lung function decline and field test GUI utility using the CF learning network, 2) (R33 Phase, Year 2): Complete a validation of a clinical test of our markers in a large cohort of banked longitudinal samples at CCHMC and externally at Battelle Memorial Institute, and 3) (R33 Phase, Year 3): Complete the selection of the optimal algorithm parameters that provide the best specificity and sensitivity for our markers individually and/or in combination. Development, validation, and dissemination of our proteomics-informed risk prediction platform will set the stage for a pre-submission meeting with the FDA for commercial translation of the technology and its delivery to the CF care and patient community.