Leveraging statistical shape modeling in computational respiratory dynamics: Nanomedicine delivery in remodeled airways

Abstract

Background and ObjectiveAccurate knowledge of the delivered doses to the diseased site in the respiratory tract is crucial to elicit desired therapeutic outcomes. However, such information is still difficult to obtain due to inaccessibility for measurement or visualization, complex network structure, and challenges in reconstructing lung geometries with disease-invoked airway remodeling. This study presents a novel method to simulate the airway remodeling in a mouth-lung geometry extending to G9. MethodsStatistical shape modeling was used to extract morphological features from a lung geometry database and four new models (i.e., M1-M4) were generated with parameter-controlled dilated/constricted bronchioles in the left-lower (LL) lung. The variations in airflow and particle deposition due to the airway remodeling were simulated using a well-tested k-ω turbulence model and a Lagrangian tracking approach. ResultsSignificant variations in flow partitions between the lower and upper lobes of the left lung, as well as between the left and right lungs. The flow partition into the LL lobe varied by 10-fold between the most dilated and constricted models in this study. Significantly lower doses were also predicted on the surface of the constricted LL bronchioles G4-G9, as well as into the peripheral airways beyond G9. However, the total dosimetry in the mouth-lung geometry (up to G9) exhibited low sensitivity to the LL lobar remodeling. Results in this study suggest that the optimal nanomedicine should be 2–10 nm in diameter if targeted at the constricted bronchioles G4-G9 as in topical inhalation therapy but should be larger than 20 nm if targeted at the alveolar region as in systemic therapy. ConclusionsThis study highlights the large dose variability from local airway remodeling and the need to consider these variations in the treatment planning for pneumonia and other obstructive respiratory diseases.