A Novel Approach to Predicting Particle Deposition in a Human Lung Using a Lumped, Oscillatory Respiratory Model

Inhalation has become widely accepted as the optimal drug delivery mechanism for respiratory diseases, which often requires targeting a particular region of the lung. Mathematical models are key to under- standing the factors that influence drug transport and deposition in the lung. This study proposes a simple zero-dimensional typical path model that couples oscillatory breathing mechanics and particle deposition over multiple breathing cycles. Lung regions are lumped into compartments, and respiration is modeled using a circuit analog framework to capture airflows, lung pressures, and volumes in each lumped region. Particle transport and deposition are modeled by a mass-balance equation in which particles dynamically move between compartments with the flow and deposit based on dynamic compartment concentrations. Initially, lung compliances are assumed constant over a breathing cycle. The model underpredicts deposition of both fine and coarse particles compared to experimental data. However, particle retention (deposited plus suspended) increases in the compliant airways under oscillatory respiration, suggesting that lung compliance is an important factor in the transport and distribution of particles in the lung. Global sensitivity analysis is performed on the model using Morris screening, which confirms that chest wall and lower lung compliances have a high influence on particle dynamics. The initial model is then modified to include dynamic lung and chest wall compliances, which improves the deposition of coarse particles, but not of fine particles. To address this, the original lumping of the lung regions is modified, which essentially changes the way that particles are advected within compartments. This improves the deposition of fine particles. In this dissertation, we demonstrate that simple zero-dimensional models are capable of incorporating dynamic flows and physiological effects, and can, therefore, be adapted to individual patients of various demographics in future research.