(75d) Lobe-Specific Aerosol Targeting in a 3D Printed Lung Model

Adverse pathology in respiratory diseases such as cancer or pneumonia are localized to regions of the lung, yet current inhalation devices fail to provide controlled regional specificity of delivered therapeutics. Computational fluid dynamics (CFD) simulations have shown feasibility of directing deposition to a lung, lobe, or region,¹ but there have been limited experimental assessments of this targeting approach. Our objective is to develop a lung model and inhalation device to harness local streamlines and direct the aerosol to a lobe of interest, resulting in improved deposition profiles, increased drug concentrations, and minimized off-target effects. We fabricated hollow, patient-derived lung mimics developed from a computed tomography (CT) scan and 3D printed using a Carbon M1 printer and simulated breathing using a vacuum pump. Guided by CFD experiments, a custom “regional targeting device” (RTD) was created to control aerosol inlet location. Sampling ports were placed at the outlet of each lobe to assess distribution of monodisperse polystyrene particles aerosolized in a Collison jet nebulizer at varied flow rates. Airflow profiles that mimic naturally occurring asymmetric inspiratory lung airflow distributions were successfully recreated in our model, in agreement with analogous CFD simulations and in vivo data.² Inlet locations were identified for each lobe that skewed deposition toward the targeted lobe and away from non-targeted lobes. For example, when targeting the lower left lobe, deposition was increased from 23.7% before targeting to 86% with the RTD, a 4x increase with a p-value of 0.002. Corresponding deposition to other lobes decreased significantly, representing decreased off-targeting. Increased targeting efficiency was observed with smaller particles and decreased flow rates. Increasing particle size and flow rate spreads deposition to nearby lobes. This approach can result in dramatic concentration of inhaled therapeutics to a region of interest, future work will continue to evaluate the success of this approach across multiple patient geometries of varied age.

References:

1. Feng, Yu, et al. "An in silico subject-variability study of upper airway morphological influence on the airflow regime in a tracheobronchial tree." Bioengineering4.4 (2017): 90.

2. Sul, Bora, et al. "Assessing airflow sensitivity to healthy and diseased lung conditions in a computational fluid dynamics model validated in vitro." J Biomech Eng-T 140.5 (2018): 051009.