Capsule-Based dry powder inhaler evaluation using CFD-DEM simulations and next generation impactor data

Capsule-based, single-dose dry powder inhalers (DPIs) are commonly-used devices to deliver medications to the lungs. This work evaluates the effect of the drug/excipient adhesive bonding and the DPI resistances on the aerosol performance using a combination of empirical multi-stage impactor data and a fully-coupled computational fluid dynamics (CFD) and discrete element method (DEM) model. Model-predicted quantities show that the primary modes of powder dispersion are a function of the device resistance. Lowering the device resistance increases its capacity to transport a wider range of particle size classes toward the outlet and generate more intense turbulence upstream therein.

Highlights

• Coupled CFD-DEM model built to predict the performance of common capsule DPIs.

• Model predicts particle dynamics that affect powder aerosol characteristics.

• Formulation and DPI design interplay affecting fine aerosol generation identified.

• Model results correlate with experimental cascade impactor data.

• Design modifications to optimize capsule DPI performance are proposed.

On the other hand, a higher device resistance increases the velocity of the tangential airflow along the device walls, which in turn increases the intensity of particle/device impaction. Correlating model data and experimental results shows that these differing powder dispersion mechanisms affect different formulations differently, with finer aerosols tending to result when pairing a lower resistance device with formulations that exhibit low API/excipient adhesion, or when pairing a high resistance device with more cohesive formulations.

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Excipients used in the research: lactose, magnesium stearate, HPMC

Lucilla C. Almeida, Rahul Bharadwaj, Avi Eliahu, Carl R. Wassgren, Karthik Nagapudi, Ariel R. Muliadi,
Capsule-Based dry powder inhaler evaluation using CFD-DEM simulations and next generation impactor data,
European Journal of Pharmaceutical Sciences,
Volume 175, 2022, 106226, ISSN 0928-0987,
https://doi.org/10.1016/j.ejps.2022.106226.