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

基于生物标记的平台的商业化翻译，用于肺功能快速下降的个性化预测

基本信息

批准号：
10240328
负责人：
Rhonda Szczesniak
金额：
$ 39.75万
依托单位：
CINCINNATI CHILDRENS HOSP MED CTR
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2023-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10240328
关键词：
Address Adolescence Algorithms Asthma Behavior Biological Assay Biological Markers Biometry Blood Caring Cause of Death Chronic Obstructive Airway Disease Chronic lung disease Clinical Clinical Trials Communities Cystic Fibrosis Data Data Analyses Development Disease Disease Progression Doctor of Philosophy Europe Fibrosis Foundations Individual Institutes Intervention Legal patent Length Light Lung Lung diseases Measurement Measures Outcome Patient Care Patient risk Patients Pharmacologic Substance Phase Prognosis Proteomics Publishing Pulmonary Cystic Fibrosis Pulmonary Function Test/Forced Expiratory Volume 1 Research Personnel Retrospective cohort Sampling Sensitivity and Specificity Serum Proteins Severity of illness Stable Disease Statistical Models Technology Testing Therapeutic Therapeutic Intervention Time Translations Validation World Health Organization airway obstruction base blood-based biomarker cohort commercialization cost cystic fibrosis patients design empowered epidemiologic data experience field study functional decline global health graphical user interface interest invention learning network lung injury meetings molecular marker novel novel marker prediction algorithm predictive marker prevent protein biomarkers pulmonary function pulmonary function decline research clinical testing response risk prediction success

项目摘要

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.

抽象的一种预测慢性肺病患者肺功能下降的算法高度理想。我们已经开发了一个基于血液生物标志物的平台，该平台可预测CF的肺功能下降有可能告知治疗干预措施以防止/缓慢下降的患者，缩短了长度（减少成本）临床试验，并加速对潜在疗法的检查，从而更快地交付给患者。此外，分子生物标志物的行为可以为治疗和增加减轻疾病进展的成功。我们已经开发了一个血液中的生物标志物预测功能下降并在此处提议将其传递给CF护理社区的算法 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将常规收集的临床/人口统计数据与标记数据集成以生成实时的GUI 使用CF学习网络的肺功能下降和现场测试GUI实用程序的风险预测，2）（R33阶段， 2年）：在大量的银行纵向中完成对我们标记的临床测试的验证在CCHMC和Battelle Memorial Institute的外部样品，第3阶段）（R33阶段，第3年）：完成选择最佳特异性和灵敏度的最佳算法参数的选择我们的标记单独和/或组合。我们的发展，验证和传播我们蛋白质组学知识的风险预测平台将为与FDA的预选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。