Targeted drug delivery to the inferior meatus cavity of the nasal airway using a nasal spray device with angled tip

Optimized gravity-driven intranasal drop administration delivers significant doses to the ostiomeatal complex and maxillary sinus

Chronic and allergic rhinosinusitis impacts approximately 12% of the global population. Challenges in rhinosinusitis treatment include paranasal sinus inaccessibility and variability in delivery efficiency among individuals. This study addresses these challenges of drug delivery by developing a high-efficiency, low-variability protocol for nasal drop delivery to the ostiomeatal complex (OMC) and maxillary sinus. Patient-specific nasal casts were dissected to reveal the configurations of conchae and meatus, providing insights into anatomical features amendable for sinus delivery. Fluorescent dye-enhanced videos visualized the dynamic liquid translocation in transparent nasal casts, allowing real-time assessment and quick adjustment to delivery parameters. Dosimetry to the OMC and maxillary sinus were quantified as drop count and mass using a precision scale. Key delivery factors, including the device type, formulation, and head-chin orientation, were systematically investigated in a cohort of ten nasal casts. Results show that both the squeeze bottle and soft-mist nasal pump yielded notably low doses to the OMC with high variability, and no dose from these two devices was detected within the maxillary sinuses. In contrast, the proposed approach, which included a curved nozzle surpassing the nasal valve and leveraged gravity-driven liquid translocation along the lateral nasal wall, delivered significant doses to the OMC and maxillary sinus. Iterative experimentations identified the optimal head tilt to be 40° and chin tilt to be° from the lateral recumbent position. Statistical analyses established the drop count required for effective OMC/sinus delivery. The proposed delivery protocol holds the potential to enhance chronic rhinosinusitis treatment outcomes with low variability. The dual role of nasal anatomy in posing challenges and offering opportunities highlights the need for future investigations using diverse formulations in a larger cohort of nasal models.

Numerical analysis of airflow and particle deposition in multi-fidelity designs of nasal replicas following nasal administration

BACKGROUND AND OBJECTIVE

An improved understanding of flow behaviour and particle deposition in the human nasal airway is useful for optimising drug delivery and assessing the implications of pollutants and toxin inhalation. The geometry of the human nasal cavity is inherently complex and presents challenges and manufacturing constraints in creating a geometrically realistic replica. Understanding how anatomical structures of the nasal airway affect flow will shed light on the mechanics underpinning flow regulation in the nasal pharynx and provide a means to interpret flow and particle deposition data conducted in a nasal replica or model that has reduced complexity in terms of their geometries. This study aims to elucidate the effects of sinus and reduced turbinate length on nasal flow and particle deposition efficiencies.

METHODS

A complete nasal airway with maxillary sinus was first reconstructed using magnetic resonance imaging (MRI) scans obtained from a healthy human volunteer. The basic model was then modified to produce a model without the sinus, and another with reduced turbinate length. Computational fluid dynamics (CFD) was used to simulate flow in the nasal cavity using transient flow profiles with peak flow rates of 15 L/min, 35 L/min and 55 L/min. Particle deposition was investigated using discrete phase modelling (DPM).

RESULTS

Results from this study show that simplifying the nasal cavity by removing the maxillary sinus and curved sections of the meatus only has a minor effect on airflow. By mapping the spatial distribution of monodisperse particles (10 μm) in the three models using a grid map that consists of 30 grids, this work highlights the specific nasal airway locations where deposition efficiencies are highest, as observed within a single grid. It also shows that lower peak flow rates result in higher deposition differences in terms of location and deposition quantity, among the models. The highest difference in particle deposition among the three nasal models is ∼10%, and this is observed at the beginning of the middle meatus and the end of the pharynx, but is only limited to the 15 L/min peak flow rate case. Further work demonstrating how the outcome may be affected by a wider range of particle sizes, less specific to the pharmaceutical industries, is warranted.

CONCLUSION

A physical replica manufactured without sections of the middle meatus could still be adequate in producing useful data on the deposition efficiencies associated with an intranasal drug formulation and its delivery device.

Machine learning and sensitivity analysis for predicting nasal drug delivery for targeted deposition

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.

Prediction of transport, deposition, and resultant immune response of nasal spray vaccine droplets using a CFPD-HCD model in a 6-year-old upper airway geometry to potentially prevent COVID-19

This study focuses on the transport, deposition, and triggered immune response of intranasal vaccine droplets to the angiotensin-converting-enzyme-2-rich region, i.e., the olfactory region (OR), in the nasal cavity of a 6-year-old female to possibly prevent corona virus disease 19 (COVID-19). To investigate how administration strategy can influence nasal vaccine efficiency, a validated multi-scale model, i.e., computational fluid-particle dynamics (CFPD) and host-cell dynamics (HCD) model, was employed. Droplet deposition fraction, size change, residence time, and the area percentage of OR covered by the vaccine droplets, and triggered immune system response were predicted with different spray cone angles, initial droplet velocities, and compositions. Numerical results indicate that droplet initial velocity and composition have negligible influences on the vaccine delivery efficiency to OR. In contrast, the spray cone angle can significantly impact the vaccine delivery efficiency. The triggered immunity was not significantly influenced by the administration investigated in this study due to the low percentage of OR area covered by the droplets. To enhance the effectiveness of the intranasal vaccine to prevent COVID-19 infection, it is necessary to optimize the vaccine formulation and administration strategy so that the vaccine droplets can cover more epithelial cells in OR to minimize the number of available receptors for SARS-CoV-2.

Prediction of Transport, Deposition, and Resultant Immune Response of Nasal Spray Vaccine Droplets using a CFPD-HCD Model in a 6-Year-Old Upper Airway Geometry to Potentially Prevent COVID-19

This study focuses on the transport, deposition, and triggered immune response of intranasal vaccine droplets to the Angiotensin-converting enzyme 2-rich region (i.e., the olfactory region (OR)) in the nasal cavity of a 6-year-old female to possibly prevent COVID-19. To investigate how administration strategy can influence nasal vaccine efficiency, a validated multiscale model (i.e., computational fluid-particle dynamics (CFPD) and host-cell dynamics (HCD) model) was employed. Droplet deposition fraction, size change, residence time, and the area percentage of OR covered by the vaccine droplets and triggered immune system response were predicted with different spray cone angles, initial droplet velocities, and compositions. Numerical results indicate that droplet initial velocity and composition have negligible influences on the vaccine delivery efficiency to OR. In contrast, the spray cone angle can significantly impact the vaccine delivery efficiency. The triggered immunity was not significantly influenced by the administration investigated in this study, due to the low percentage of OR area covered by the droplets. To enhance the effectiveness of the intranasal vaccine to prevent COVID-19 infection, it is necessary to optimize the vaccine formulation and administration strategy so that the vaccine droplets can cover more epithelial cells in OR to minimize the available receptors for SARS-CoV-2.