Identifying imaging-based biomarkers of COPD development through virtual inhalation experiments

 描述（由申请人提供）：慢性阻塞性肺疾病（COPD）的特征是肺功能的不可逆丧失，是全球主要死亡原因之一。虽然吸入的污染物（如烟草烟雾）可导致COPD，但人们不理解为什么只有20%的暴露者会患上这种疾病。目前还不清楚为什么一些患有COPD的人会遭受肺功能的快速和严重下降，而其他人则随着时间的推移保持相对稳定。对这种疾病的起源和发展的这种不了解是制定有效预防和治疗战略的主要限制。本研究的目的是调查气道的先天解剖特征是否以及如何参与疾病的发展。特别是，我们假设，在患有COPD的个体中，支气管树的解剖特征导致空气流动模式增强颗粒沉积，这一特征使他们更容易发生气道炎症。为了验证这一假设，我们将回顾性地探讨18名健康和患病吸烟者的肺结构-功能关系，通过分析他们的气道形态（从CT扫描重建），与五年内肺功能的变化有关。这些数据完全来自NIH的大型COPDGene（r）研究，本研究是该研究的辅助研究。我们将寻找两种类型的COPD预测因子：结构和功能。将通过分析受试者的气道形态学（包括分支角度、气道管腔和气道曲率）来确定结构预测因子。功能预测因素将与气流模式和相关的颗粒沉积有关。由于在肺部的流动和沉积的体内成像具有有限的精度和分辨率，我们将进行虚拟吸入实验。每个患者支气管树的物理模型将 可以用合成材料3D打印并连接到振荡流回路。水将被用作工作流体，并且将调整流速和通风频率以补偿水相对于空气的性质差异。MRI测量将以高空间分辨率提供整个模型的流速图，颗粒轨迹的数值积分将提供沉积模式。我们的创新方法是由我们的研究团队的专业知识的独特组合，并在明尼苏达大学磁共振研究中心提供一流的资源成为可能。如果我们的假设得到证实，它将为COPD的早期症状前检测铺平道路。这也将代表着深入了解局部颗粒沉积的基本步骤，这对于实现更有效和更有针对性的吸入给药是必要的。

 DESCRIPTION (provided by applicant): Chronic Obstructive Pulmonary Disease (COPD) is characterized by an irreversible loss of lung function, and is among the leading causes of death worldwide. While it is well established that inhaled pollutants, such as tobacco smoke, can lead to COPD, it is not understood why only 20% of the exposed individuals develop the disease. It is also unclear why some of those who develop the COPD suffer rapid and sever decline of lung function, while others remain relatively stable over time. Such poor understanding of the origin and progression of the disease is a major limitation for the development of effective strategies for both prevention and treatment. The goal of this study is to investigate whether and how innate anatomical features of the airways participate to the disease development. In particular we hypothesize that, in individuals that develop COPD, the anatomical characteristics of the bronchial tree result in air flow patterns that enhance particle deposition, a feature that makes them more susceptible to airway inflammation. To test this hypothesis we will retrospectively explore the lung structure-function relationship in 18 healthy and diseased smokers, by analyzing their airway morphology (reconstructed from CT scans), in relation to the change in lung function over five years. The data is entirely available from the large NIH COPDGene(r) study, from which this study is an Ancillary Study. We will seek two types of predictors of COPD: structural and functional. The structural predictors will be identified by analyzing the subjects' airway morphometry, including branching angle, airway lumen, and airway curvature. The functional predictors will be related to the airflow pattern and associated particle deposition. Because in vivo imaging of flow and deposition in the lungs has limited accuracy and resolution, we will perform virtual inhalation experiments. Physical models of each patient bronchial tree will be 3D printed in synthetic materials and attached to an oscillatory flow circuit. Water will be used as a working fluid, and flow rate and ventilation frequency will be adjusted to compensate for the difference in properties of water with respect to air. MRI measurements will provide the flow velocity map throughout the models at high spatial resolution, and numerical integration of the particle trajectory will provide the deposition pattern. Our innovative approach is made possible by our research team's unique combination of expertise, and by the top-notch resources available at the University of Minnesota Center for Magnetic Resonance Research. If our hypothesis is confirmed, it will pave the way towards early, pre-symptomatic detection of COPD. It will also represent a fundamental step towards an in-depth understanding of local particle deposition, which is necessary to achieve more effective and targeted inhalation drug delivery.