Development of FAST-DOSE assay system for the rapid assessment of acute radiation exposure, individual radiosensitivity and injury in victims for a large-scale radiological incident

开发快速剂量测定系统，用于快速评估大规模放射事件受害者的急性辐射暴露、个体放射敏感性和损伤

• 批准号: 10089406
• 负责人: Helen C Turner
• 金额: $ 48.39万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10089406
• 关键词: Academic Medical Centers; Acute; Age; Algorithms; Benchmarking; Biological Assay; Biological Markers; Blood; Blood Proteins; Blood specimen; Bone Marrow; Burn injury; Cell Culture Techniques; Cellular Structures; Computer software; Confidence Intervals; Custom; Data; Data Analyses; Detection; Development; Devices; Dose; Exposure to; FDA approved; Flow Cytometry; Fluorescence; Future; Gold; Hematology; Hematopoietic; Human; Image; Imagery; Immune; Immunoassay; Individual; Industrial Accidents; Inflammation; Inherited; Injury; Ionizing radiation; Ions; Kinetics; Laboratories; Leukocytes; Linear Regressions; Lymphocyte; Machine Learning; Mass Screening; Measures; Medical; Medical center; Modeling; Mus; Noise; Nuclear Accidents; Pathogenicity; Patients; Performance; Physiological; Population; Process; Proteins; Protocols documentation; Radiation; Radiation Accidents; Radiation Dose Unit; Radiation Tolerance; Radiation exposure; Reaction Time; Reproducibility; Research Design; Roentgen Rays; Saliva; Screening procedure; Seeds; Surface Antigens; System; Techniques; Testing; Time; Toxic effect; Trauma; Triage; Uncertainty; Urine; Variant; Vision; White Blood Cell Count procedure; base; biodosimetry; biomarker panel; biomarker performance; biomarker validation; blood damage; brca gene; data exploration; design; dirty bomb; dosimetry; humanized mouse; in vitro testing; in vivo; in-vitro diagnostics; irradiation; machine learning algorithm; mathematical model; medical countermeasure; micronucleus; nonhuman primate; nonlinear regression; performance tests; peripheral blood; point of care; predicting response; predictive modeling; protein biomarkers; response; response biomarker; sex; stem; stem cells

Summary Following a large scale radiological or nuclear event, hundreds of thousands of people may be exposed to ionizing radiation/s and require subsequent dose-dependent medical management. It will be crucial to collect and analyze human biofluids (such as blood, urine, saliva) as soon as possible within the first week for accurate dose prediction and early triage decision. There is a need for FDA-approved in vitro diagnostic high-throughput biodosimetry devices with the ability to determine past radiation exposure with precision and accuracy. At the Center for High Throughput Radiation Biodosimetry, the Columbia University Center for Medical Countermeasures against Radiation (CMCR), we have developed FAST-DOSE (Fluorescent Automated Screening Tool for Dosimetry) assay system, to measure radiation-responsive proteins in human peripheral blood samples for retrospective estimation of radiation dose. The protein panel also includes biomarkers for blood leukocyte subtypes to reflect hematological sensitivity and injury. The FAST- DOSE assay system is intended as an in vitro diagnostic device (IVD) as defined by 21 CFR 809.3. The platform uses a commercial imaging flow cytometry system (ImageStream®X) and associated Image Data Exploration and Analysis Software (IDEAS®) to rapidly quantify changes in biomarker expression levels within specific cellular structures using fluorescent imagery and algorithms for estimation of absorbed dose. The studies planned here are designed to develop and optimize our FAST-DOSE assay system to accurately estimate absorbed dose and assess hematopoietic injury in human lymphocytes after ionizing irradiation. The first objective is to build on our current biomarker validation data for early engagement with the FDA via the pre-submission process. We have used the human ex vivo model and humanized mouse (Hu-NSG) and non- human primate (NHP) models to validate biomarker expression and radiosensitivity in blood leukocytes after acute ionizing radiation exposure. The Specific Aims proposed here are designed to: optimize the assay protocol and identify biomarker dose/time kinetics for accurate dose predictions in vitro and test 1) inter-donor variation, 2) intra-donor variation and 3) inter-laboratory variability (Aim 1); test the effect of specific confounders: age and sex, inheritance with germline BRCA1/2 pathogenic variant, and inflammation and trauma on the biomarker response, before and after irradiation (Aim 2); measure biomarker levels and time kinetics in vivo and correlate with hematopoietic injury, based on peripheral blood leukocyte counts, and stem and progenitor cell levels in the bone marrow of Hu-NSG mice (Aim 3) and, develop mathematical models (using machine learning and regression techniques) to select the best FAST-DOSE biomarkers and their combinations for generating dose predictions based on the ex vivo and in vivo dose response of these biomarkers (Aim 4). Our vision for future development is to develop a more simplified, faster rapid FAST-DOSE assay system whereby the biomarkers could be developed and transitioned for use in a point-of-care (POC) device.

摘要 在大规模的放射性或核事件之后，数十万人可能会接触到 电离辐射/S，需要后续剂量依赖的医疗管理。收集信息将是至关重要的 并尽快在第一周内分析人体体液(如血液、尿液、唾液) 准确的剂量预测和早期的分诊决定。有必要进行FDA批准的体外诊断 能够精确确定过去辐射暴露的高通量生物剂量测定设备 和精确度。在哥伦比亚大学高通量辐射生物剂量测量中心 对于辐射医学对策(CMCR)，我们已经开发出快速剂量(荧光 用于剂量测定的自动筛选工具)分析系统，用于测量辐射响应蛋白质 用于回顾性估算辐射剂量的人体外周血样本。蛋白质小组还 包括血液白细胞亚型的生物标志物，以反映血液学敏感性和损伤。最快的- 剂量测定系统旨在作为21CFR 809.3定义的体外诊断装置。这个 平台使用商用成像流式细胞仪系统(ImageStream®X)和相关的图像数据 探索和分析软件(IDEAS®)可快速量化生物标记物表达水平的变化 使用荧光成像的特定细胞结构和估计吸收剂量的算法。这个 计划在这里进行的研究旨在开发和优化我们的快速剂量检测系统，以准确地 估算电离辐射后人淋巴细胞的吸收剂量并评估其对造血的损伤。这个 第一个目标是在我们目前的生物标记物验证数据的基础上，通过 提交前流程。我们使用了人类体外模型和人源化小鼠(Hu-NSG)和非 人类灵长类动物(NHP)模型验证生物标记物的表达及其对放射敏感性的影响 急性电离辐射暴露。这里提出的具体目标是为了：优化化验 为准确的体外剂量预测和试验1)供体间试验设计和鉴定生物标志物剂量/时间动力学 变异，2)供体内变异和3)实验室间变异(目标1)；测试特定的 混杂因素：年龄和性别、遗传性BRCA1/2致病基因变异、炎症和创伤 照射前后生物标记物的反应(目标2)；测量生物标记物水平和时间动力学 根据外周血白细胞计数，与造血损伤相关，并与干细胞和 HU-NSG小鼠骨髓中祖细胞水平(目标3)，并建立数学模型(使用 机器学习和回归技术)来选择最佳的快速剂量生物标志物及其组合 用于根据这些生物标志物的体外和体内剂量反应生成剂量预测(目标4)。 我们对未来发展的愿景是开发一种更简单、更快、更快速的快速剂量检测系统 由此生物标记物可以被开发和转换以在护理点(POC)设备中使用。