Search

Validating numerical models against experimental models of nasal spray deposition is challenging since many aspects must be considered. That being said, it is a critical step in the product development process of nasal spray devices. This work presents the validation process of a nasal deposition model, which demonstrates a high degree of consistency of the numerical model with experimental data when the nasal cavity is segmented into two regions but not into three. Furthermore, by modelling the flow as stationary, the computational cost is drastically reduced while maintaining quality of particle deposition results. Thanks to this reduction, a sensitivity analysis of the numerical model could be performed, consisting of 96 simulations. The objective was to quantify the impact of four inputs: the spray half cone angle, mean spray exit velocity, breakup length from the nozzle exit and the diameter of the nozzle spray device, on the three quantities of interest: the percentage of the accumulated number of particles deposited on the anterior, middle and posterior sections of the nasal cavity. The results of the sensitivity analysis demonstrated that the deposition on anterior and middle sections are sensitive to injection angle and breakup length, and the deposition on posterior section is only, but highly, sensitive to the injection velocity. [Display omitted] [ABSTRACT FROM AUTHOR]

Validating numerical models against experimental models of nasal spray deposition is challenging since many aspects must be considered. That being said, it is a critical step in the product development process of nasal spray devices. This work presents the validation process of a nasal deposition model, which demonstrates a high degree of consistency of the numerical model with experimental data when the nasal cavity is segmented into two regions but not into three. Furthermore, by modelling the flow as stationary, the computational cost is drastically reduced while maintaining quality of particle deposition results. Thanks to this reduction, a sensitivity analysis of the numerical model could be performed, consisting of 96 simulations. The objective was to quantify the impact of four inputs: the spray half cone angle, mean spray exit velocity, breakup length from the nozzle exit and the diameter of the nozzle spray device, on the three quantities of interest: the percentage of the accumulated number of particles deposited on the anterior, middle and posterior sections of the nasal cavity. The results of the sensitivity analysis demonstrated that the deposition on anterior and middle sections are sensitive to injection angle and breakup length, and the deposition on posterior section is only, but highly, sensitive to the injection velocity. [Display omitted] [ABSTRACT FROM AUTHOR]

Targeted nasal drug delivery can provide improved efficacy for drug formulations to be delivered at high efficacy rates. Some parameters that influence drug delivery have a dependency on the patient's technique of administration and the spray device itself. When the different parameters, each having a specific range of values are combined, the combinatory permutations for studying its effects on particle deposition become large. In this study, we combine six input spray parameters (the spray half-cone angle, the mean spray exit velocity, the breakup length from the nozzle exit, the diameter of the nozzle spray device, the particle size, and the sagittal angle of the spray) with a range of values to produce 384 combinations of spray characteristics. This was repeated for three inhalation flow rates of 20, 40, and 60 L/min. To reduce the computational costs of a full transient Large Eddy Simulation flow field, we create a time-averaged frozen field and perform the time integration of particle trajectories through the flow field to determine the particle deposition in four anatomical regions of the nasal cavity (anterior, middle, olfactory and posterior) for each of the 384 spray field. A sensitivity analysis determined the significance of each input variable on the deposition. It was found the particle size distribution significantly affected deposition in the olfactory and posterior regions, while the spray device insertion angle was significant for deposition in the anterior and middle regions. Five machine learning models were evaluated based on 384 cases and it was found that despite the small sample dataset the simulation data was sufficient to provide accurate machine-learning predictions. [Display omitted] [ABSTRACT FROM AUTHOR]