Optimizing Aerosolization Using Computational Fluid Dynamics in a Pediatric Air-Jet Dry Powder Inhaler

Abstract

The objective of this study was to optimize the performance of a high efficiency pediatric inhaler, referred to as the pediatric air-jet DPI, using computational fluid dynamics (CFD) simulations with supporting experimental analysis of aerosol formation. The pediatric air-jet DPI forms an internal flow pathway consisting of an inlet jet of high-speed air, capsule chamber containing a powder formulation, and outlet orifice. Instead of simulating full breakup of the powder bed to an aerosol in this complex flow system, which is computationally expensive, flow-field-based dispersion parameters were sought that correlated with experimentally determined aerosolization metrics. For the pediatric air-jet DPI configuration that was considered, mass median aerodynamic diameter (MMAD) directly correlated with input turbulent kinetic energy normalized by actuation pressure and flow kinetic energy. Emitted dose (ED) correlated best with input flow rate multiplied by the ratio of capillary diameters. Based on these dispersion parameters, an automated CFD process was used over multiple iterations of over 100 designs to identify optimal inlet and outlet capillary diameters, which affected system performance in complex and unexpected ways. Experimental verification of the optimized designs indicated an MMAD <1.6 μm and an ED >90% of loaded dose. While extrathoracic depositional loss will be determined in future studies, at an operating flow rate of 15 LPM it is expected that pediatric mouth-throat or even nose-throat aerosol deposition fractions will be below 10% and potentially less than 5% representing a significant improvement in the delivery efficiency of dry powder pharmaceutical aerosols to children.

Flow and Particle Modelling of Dry Powder Inhalers: Methodologies, Recent Development and Emerging Applications

“…The air-jet DPI consists of a small diameter inlet passage, aerosolisation chamber and small diameter exit passage and gas at positive pressure passes from it and forms a turbulent jet within the aerosolisation chamber. Using CFD, they optimised the performance of the air-jet DPI [ 77 , 89 ].…”

“…This could potentially affect the overall dispersion performance of the DPI. Several recent studies [ 77 , 89 , 97 ] have suggested that the particle dispersion in the capsule was strongly correlated with the parameters which were used to describe the turbulence of the flow. This can be further explored for the development of a low-computational-cost modelling approach considering the effect of particle discharging from the capsule.…”

Comparative Assessment of In Vitro and In Silico Methods for Aerodynamic Characterization of Powders for Inhalation

“…However, intrinsic differences between various in vitro, and in vitro and in silico methods complicate direct comparison between experimental and simulated data. A number of studies in parallel assessed the results of CFD simulations and in vitro experimental results (usually including a single in vitro method such as MSLI, NGI or ACI) [35][36][37][38][39]41,[44][45][46][47][48][49][50][51][52][53]. Overall, some correlation between in vitro and in silico CFD results has been detected [48][49][50][51][52][53], but there is a need for additional comparative assessment of CFD tools in relation to the in vitro methods to evaluate the applicability of CFD in the research and development of various DPI formulations.…”

“…A number of studies in parallel assessed the results of CFD simulations and in vitro experimental results (usually including a single in vitro method such as MSLI, NGI or ACI) [35][36][37][38][39]41,[44][45][46][47][48][49][50][51][52][53]. Overall, some correlation between in vitro and in silico CFD results has been detected [48][49][50][51][52][53], but there is a need for additional comparative assessment of CFD tools in relation to the in vitro methods to evaluate the applicability of CFD in the research and development of various DPI formulations. A recent review study of Zheng et al (2021) also concluded that more direct comparison between the simulated and experimental results is necessary to improve the prediction accuracy of in silico tools [54].…”