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

 描述(申请人提供)：慢性阻塞性肺疾病(COPD)以不可逆转的肺功能丧失为特征，是全球主要的死亡原因之一。虽然众所周知，吸入污染物，如烟草烟雾，会导致慢性阻塞性肺疾病，但尚不清楚为什么只有20%的接触者会患上这种疾病。也不清楚为什么一些COPD患者遭受快速而严重的肺功能下降，而另一些人随着时间的推移保持相对稳定。对这种疾病的起源和发展缺乏了解，这是制定有效的预防和治疗战略的主要限制。本研究的目的是探讨呼吸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的人中，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受到呼吸道炎症的影响。为了验证这一假设，我们将回顾18名健康和患病吸烟者的肺结构-功能关系，通过分析他们的呼吸道形态(从CT扫描重建)，以及与五年来肺功能变化的关系。这些数据完全来自大型NIH COPDgene(R)研究，这项研究是其中的一项辅助研究。我们将寻找两种类型的COPD预测因子：结构性和功能性。结构预测因子将通过分析受试者的气道形态测量来确定，包括支气管角、气道腔和气道曲率。功能预报器将与气流模式和相关的颗粒沉积有关。由于肺部流动和沉积的活体成像的准确性和分辨率有限，我们将进行虚拟吸入实验。每个患者的支气管树的物理模型将 用合成材料进行3D打印，并连接到振荡流动回路上。将使用水作为工作介质，并将调整流量和通风频率，以补偿水与空气性质的差异。MRI测量将提供高空间分辨率的流速图，粒子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独特的专业知识组合以及明尼苏达大学磁共振研究中心提供的一流资源实现的。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。这也将是朝着深入了解局部颗粒沉积迈出的根本一步，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.