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

## TL;DR

This paper introduces a mathematical model to estimate mucus clearance rates in different regions of the lung using imaging data.

## Contribution

A novel computational model that provides detailed regional estimates of mucociliary clearance rates in the airways.

## Key findings

- The model achieved a per-point mean absolute error of 0.085 ± 0.016% when fitted to imaging data from healthy controls.
- Hypertonic saline inhalation increased mucociliary clearance rates in cystic fibrosis patients across multiple lung regions.
- The model reduced fitted clearance rate coefficients from 114 to 5 with minimal loss in accuracy.

## Abstract

The mucociliary clearance (MC) system clears mucus, pathogens, and toxins from the airways. Whole lung MC rate can be measured using gamma camera imaging after the inhalation of radiolabeled particulate. We sought a means to evaluate the therapeutic effect of clearance enhancing therapies in different airway size groups. We developed a mathematical model of mucus transport in the right lung that, when informed by imaging data, estimates MC rate and unclearable activity at points across the airway tree. We fit the model to imaging studies from 11 healthy controls (HC), resulting in a per-point mean absolute error (MAE) of 0.085 ± 0.016% of the total particulate deposition. Using principal component analysis and hierarchical clustering, we reduced the number of fitted clearance rate coefficients from 114 to 5 with only an 8.7% increase in MAE. These 5 cluster groups were closely associated with specific regions of the lung and likely with specific airway size groups. Comparing the HC group to a cystic fibrosis (CF) group we found only one cluster with significantly depressed MC rates in CF corresponding to the lower lobe. The inhalation of 7% hypertonic saline (HS) by the CF group increased MC rate in all clusters and decreased unclearable activity in 4/5 clusters. The computational model described provides detailed regional estimates of MC rate when applied to clearance imaging studies. If further informed, this model may provide a valuable tool for studying small airways obstructive disease and evaluating mucus clearance-enhancing therapies in the lung.