Inhalation dosimetry of nasally inhaled respiratory aerosols in the human respiratory tract with locally remodeled conducting lungs

Abstract

Objective: Respiratory diseases are often accompanied by alterations to airway morphology. However, inhalation dosimetry data in remodeled airways are scarce due to the challenges in reconstructing diseased respiratory morphologies. This study aims to study the airway remodeling effects on the inhalation dosimetry of nasally inhaled nanoparticles in a nose–lung geometry that extends to G9 (ninth generation).

Materials and methods: Statistical shape modeling was used to develop four diseased lung models with varying levels of bronchiolar dilation/constriction in the left-lower (LL) lobe (i.e. M1–M4). Respiratory airflow and particle deposition were simulated using a low Reynolds number k–ω turbulence model and a Lagrangian tracking approach.

Results: Significant discrepancies were observed in the flow partitions between the left and right lungs, as well as between the lower and upper lobes of the left lung, which changed by 10-fold between the most dilated and constricted models.Much lower doses were predicted on the surface of the constricted LL bronchioles G4–G9, as well as into the peripheral airways beyond G9 of the LL lung. However, the LL lobar remodeling had little effect on the dosimetry in the nasopharynx, as well as on the total dosimetry in the nose–lung geometry (up to G9).

Conclusion: It is suggested that airway remodeling may pose a higher viral infection risk to the host by redistributing the inhaled viruses to healthy lung lobes. Airway remodeling effects should also be considered in the treatment planning of inhalation therapies, not only because of the dosimetry variation from altered lung morphology but also its evolution as the disease progresses.

Keywords:

• Statistical shape modeling (SSM)
• airway remodeling
• bronchial constriction
• viral infection
• inhalation therapy
• COVID-19

Acknowledgements

William Zouzas and Randy Luong at UMass Lowell Biomedical Engineering were gratefully acknowledged for reviewing this manuscript. We also wish to thank Dr. Kitaoka Hiroko at Tokyo University of Agriculture and Technology, Japan for sharing the morphing module Lung4Cer to generate lung models as the training set.

Disclosure statement

No potential conflict of interest was reported by the author(s).