A physiologically-based model of localized mucociliary clearance in the airways

Area of Science:

• Pulmonary Medicine
• Biomedical Engineering
• Mathematical Modeling

Background:

• The mucociliary clearance (MC) system is crucial for airway health, removing mucus, pathogens, and toxins.
• Whole lung MC rate assessment typically uses gamma camera imaging of radiolabeled particles.
• Evaluating localized clearance and the efficacy of clearance-enhancing therapies requires advanced methods.

Purpose of the Study:

• To develop and validate a mathematical model for estimating regional MC rates and unclearable activity across the airway tree.
• To assess the model's ability to differentiate MC rates in healthy controls (HC) versus cystic fibrosis (CF) patients.
• To evaluate the impact of hypertonic saline (HS) therapy on MC in CF patients using the developed model.

Main Methods:

• A mathematical model of mucus transport was created for the right lung, integrating gamma camera imaging data.
• The model was fitted to imaging studies from 11 HC, achieving a mean absolute error (MAE) of 0.085 ± 0.016%.
• Principal component analysis and hierarchical clustering reduced 114 clearance rate coefficients to 5 clusters, with a minor MAE increase of 8.7%.

Main Results:

• The 5 identified clusters correlated with specific lung regions and airway sizes.
• CF patients exhibited significantly depressed MC rates in one cluster, corresponding to the lower lobe, compared to HC.
• In CF patients, 7% hypertonic saline (HS) treatment increased MC rates across all clusters and reduced unclearable activity in 4/5 clusters.

Conclusions:

• The computational model provides detailed regional MC rate estimates from clearance imaging studies.
• The model effectively differentiates MC deficits in CF patients and quantifies therapeutic responses.
• This modeling approach may become a valuable tool for studying obstructive lung diseases and evaluating mucus clearance therapies.