Modeling aerosol bolus inhalations in the human lung with the Multiple Path Particle Deposition model: Comparison...

Area of Science:

• Respiratory physiology
• Aerosol science
• Computational modeling

Background:

• One-dimensional (1D) aerosol dosimetry models often neglect complex aerosol mixing in the lungs.
• Aerosol dispersion arises from various physical mechanisms across different lung regions, posing challenges for 1D models.
• Existing models sometimes approximate dispersion as a diffusive process, lacking accuracy.

Purpose of the Study:

• To improve the multiple path particle dosimetry model (MPPD) by enhancing its alveolar mixing module.
• To incorporate flow irreversibility, particle trapping in alveoli, and tracheobronchial mixing into the MPPD.
• To couple the enhanced MPPD with computational fluid particle dynamics (CFPD) for oral airway dispersion predictions.

Main Methods:

• Developed an improved alveolar mixing module for the MPPD, accounting for flow irreversibility and particle trapping.
• Integrated tracheobronchial region mixing mechanisms into the model.
• Coupled the enhanced MPPD with CFPD-based predictions for aerosol bolus dispersion in the oral airway.
• Validated the model against experimental data for aerosol bolus inhalation at various lung depths.

Main Results:

• The enhanced MPPD accurately predicted aerosol deposition, dispersion, and mode shift for inhaled boluses.
• Model computations showed good agreement with subject-specific and test-specific experimental data.
• The model successfully captured aerosol behavior across different penetration depths and breathing patterns.

Conclusions:

• The improved MPPD provides a more accurate representation of inhaled aerosol behavior in the respiratory tract.
• The combined dispersion-deposition model is valuable for optimizing targeted drug delivery strategies.
• This model serves as a useful tool for assessing health risks associated with aerosol exposure.