Breath Hold Facilitates Targeted Deposition of Aerosolized Droplets in a 3D Printed Bifurcating Airway Tree.

The lungs have long been considered a desired route for drug delivery but, there is still a lack of strategies to rationally target delivery sites especially in the presence of heterogeneous airway disease. Furthermore, no standardized system has been proposed to rapidly test different ventilation strategies and how they alter the overall and regional deposition pattern in the airways. In this study, a 3D printed symmetric bifurcating tree model mimicking part of the human airway tree was developed that can be used to quantify the regional deposition patterns of different delivery methodologies. The model is constructed in a novel way that allows for repeated measurements of regional deposition using reusable parts. During ventilation, nebulized ~3-micron-sized fluid droplets were delivered into the model. Regional delivery, quantified by precision weighing individual airways, was highly reproducible. A successful strategy to control regional deposition was achieved by combining an inspiratory wave form with a "breath hold" pause after each inspiration. Specifically, the second generation of the tree was successfully targeted, and deposition was increased by up to four times in generation 2 when compared to a ventilation without the breath hold (p < 0.0001). Breath hold was also demonstrated to facilitate deposition into blocked regions of the model, which mimic airway closure during an asthma that receive no flow during inhalation. Additionally, visualization experiments demonstrated that in the absence of fluid flow, the deposition of 3-micron water droplets is dominated by gravity, which, to our knowledge, has not been confirmed under standard laboratory conditions.

A Markov chain model of particle deposition in the lung.

Particle deposition in the lung during inhalation is of interest to a wide range of biomedical sciences due to the noninvasive therapeutic route to deliver drugs to the lung and other organs via the blood stream. Before reaching the alveoli, particles must transverse the bifurcating network of airways. Computational fluid mechanical studies are often used to estimate high-fidelity flow patterns through the large conducting airways, but there is a need for reduced-dimensional modeling that enables rapid parameter optimization while accommodating the complete airway network. Here, we introduce a Markov chain model with each state corresponding to an airway segment in which a particle may be located. The local flows and transition probabilities of the Markov chain, verified against computational fluid dynamics simulations, indicate that the independent effects of three fundamental forces (gravity, fluid drag, diffusion) provide a sufficient approximation of overall particle behavior. The model enables fast computation of how different inhalation strategies, called flow policies, determine total particle escape rates and local particle deposition. In a 3-dimensional airway tree model, the optimal flow policy minimizing the risk of deposition at each generation, compared to other inlet flow waveforms, predicted significantly higher probability of escape defined as the fraction of particles exiting the tree. The model also predicts a small influence of body orientation with respect to a gravitational field on total escape probability, but a significant effect of airway narrowing on regional deposition. In summary, this model provides insight into inhalation strategies for targeted drug delivery.

CT Imaging-Based Low-Attenuation Super Clusters in Three Dimensions and the Progression of Emphysema.

BACKGROUND: Distributions of low-attenuation areas in two-dimensional (2-D) CT lung slices are used to quantify parenchymal destruction in patients with COPD. However, these segmental approaches are limited and may not reflect the true three-dimensional (3-D) tissue processes that drive emphysematous changes in the lung. The goal of this study was to instead evaluate distributions of 3-D low-attenuation volumes, which we hypothesized would follow a power law distribution and provide a more complete assessment of the mechanisms underlying disease progression. METHODS: CT scans and pulmonary function test results were acquired from an observational database for N = 12 patients with COPD and N = 12 control patients. The data set included baseline and two annual follow-up evaluations in patients with COPD. Three-dimensional representations of the lungs were reconstructed from 2-D axial CT slices, with low-attenuation volumes identified as contiguous voxels < -960 Hounsfield units. RESULTS: Low-attenuation sizes generally followed a power law distribution, with the exception of large, individual outliers termed "super clusters," which deviated from the expected distribution. Super cluster volume was correlated with disease severity (% total low attenuation, ρ = 0.950) and clinical measures of lung function including FEV1 (ρ = -0.849) and diffusing capacity of the lung for carbon monoxide Dlco (ρ = -0.874). To interpret these results, we developed a personalized computational model of super cluster emergence. Simulations indicated disease progression was more likely to occur near existing emphysematous regions, giving rise to a biomechanical, force-induced mechanism of super cluster growth. CONCLUSIONS: Low-attenuation super clusters are defining, quantitative features of parenchymal destruction that dominate disease progression, particularly in advanced COPD.