Differences in Particle Deposition Between Members of Imaging-Based Asthma Clusters

Area of Science:

• Pulmonary medicine and imaging analysis
• Computational fluid dynamics (CFD) and aerosol science

Background:

• Four distinct CT imaging-based clusters identified in the Severe Asthma Research Program (SARP) cohort correlate with clinical and demographic metrics.
• Understanding airflow and aerosol deposition patterns within these asthma subtypes is crucial for targeted therapies.

Purpose of the Study:

• To investigate airflow and aerosol deposition in CT-derived airway models representing four asthma imaging clusters using CFD.
• To correlate imaging-based metrics like airway constriction with particle deposition patterns in different asthma severities.

Main Methods:

• CFD simulations of airflow and 1-8 μm particle transport were conducted on CT-based airway models from healthy and asthma subjects.
• Subject selection was based on discriminant imaging variables: J(Total) for regional ventilation and Dh*(sLLL) for airway constriction.
• Comparisons were made between asthma clusters and healthy subjects, and between specific nonsevere and severe asthma clusters.

Main Results:

• Airway constriction in nonsevere (cluster 2) and severe (cluster 4) asthma led to increased aerosol deposition fraction (DF) in the left lower lobe.
• Constricted airflow caused impingement on distal bifurcations, resulting in significant particle deposition.
• Dh*(sLLL) was a more critical factor in particle deposition than J(Total); regional flow fraction correlated with DF for smaller particles.

Conclusions:

• Particle deposition characteristics are linked to cluster-specific imaging metrics, notably airway constriction.
• These findings highlight the potential for optimizing inhaled drug delivery strategies based on individual asthma imaging phenotypes.
• Airway constriction significantly influences aerosol deposition patterns, offering a target for personalized asthma management.