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是高度有效的 令人向往。我们已经开发了一个基于血液生物标记物的平台，可以预测CF患者的肺功能下降 有潜力的患者通知治疗干预措施，以防止/减缓下降，缩短长度 (降低成本)临床试验，并加快对潜在疗法的审查，从而更快地 送到病人手中。此外，分子生物标记物的行为可以为治疗和 提高减缓疾病进展的成功率。我们已经开发出一种血液生物标记物 预测功能下降的算法，并在此建议通过实现以下功能将其交付给CF关怀社区 3个里程碑；1)(R61阶段，第1年)：完成蛋白质组学的设计和测试 将常规收集的临床/人口数据与标记数据集成以生成实时数据的图形用户界面 使用CF学习网络预测肺功能下降的风险和现场测试图形用户界面实用程序，2)(R33阶段， 第2年)：完成对我们的标记物在一大批纵向银行队列中的临床测试的验证 CCHMC和外部的巴特尔纪念研究所的样本，和3)(R33阶段，第3年)：完成 最佳算法参数的选择，以提供最佳的特异性和敏感度 我们的标记单独和/或组合在一起。开发、验证和传播我们的 蛋白质组学信息风险预测平台将为与FDA举行提交前会议奠定基础 用于该技术的商业翻译，并将其交付给CF护理和患者社区。

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.