Numerical Simulation and Experimental Testing to Improve Olfactory Drug Delivery with Electric Field Guidance of Charged Particles

Even though the direct nose-to-brain drug delivery has many clinical benefits, there are limited successes in delivering medication aerosols to the olfactory mucosa with standard inhalation devices. In this study, different delivery techniques were assessed in terms of their capacities to deliver drug aerosols to the olfactory epithelium. Specifically, the feasibility of electric field guidance of charged aerosols to the olfactory mucosa was evaluated in an image-based nose model both numerically and experimentally. Multi-sectional nasal cast replicas were fabricated using a 3-D printer to measure the olfactory deposition rates and visualize the deposition distributions. An intranasal deposition test platform was developed that comprised an electric field guidance system, a dry powder charging device, and a point-release nozzle. Numerical simulations were conducted using both ANSYS Fluent and COMSOL. We demonstrated that it is feasible to control charged particles inside the human nose use an external electric field. Both the point-release technique and electric field guidance of drug particles are essential in attaining optimal olfactory doses. Consistent deposition patterns were achieved between in vitro experiments and computational simulations. Future investigations are warrnated for further improvements of olfactory delivery through refining the particle generation, charging, and releasing, and navigation systems

Pulmonary Oxygen Exchange in a Rhythmically Expanding–Contracting Alveolus–Capillary Model

Pulmonary gas exchanges are vital to human health, and disruptions to this process have been associated with many respiratory diseases. Previous gas exchange studies have predominately relied on whole-body testing and theoretical analysis with 1D or static models. However, pulmonary gas exchanges are inherently a dynamic process in 3D spaces with instantaneous interactions between air, blood, and tissue. This study aimed to develop a computational model for oxygen exchange that considered all factors mentioned above. Therefore, an integrated alveolus–membrane–capillary geometry was developed with prescribed rhythmic expansion/contraction. Airflow ventilation, blood perfusion, and oxygen diffusion were simulated using COMSOL. The temporal and spatial distribution of blood flow and oxygen within the capillaries were simulated under varying breathing depths and cardiac outputs. The results showed highly nonuniform blood flow distributions in the capillary network, while the rhythmic oscillation further increased this nonuniformity, leading to stagnant blood flow in the distal vessels. A static alveolus–capillary geometry underestimated perfusion by 11% for normal respirations, and the deviation grew with breathing depth. The rhythmic motion caused a phase lag in the blood flow. The blood PO2 reached equilibrium with the alveolar air after traveling 1/5–1/3 of the capillary network. The time to reach this equilibrium was significantly influenced by the air–blood barrier diffusivity, while it was only slightly affected by the perfusion rate. The computational platform in this study could be instrumental in obtaining refined knowledge of pulmonary O2 exchanges

Pulmonary Oxygen Exchange in a Rhythmically Expanding–Contracting Alveolus–Capillary Model

Pulmonary gas exchanges are vital to human health, and disruptions to this process have been associated with many respiratory diseases. Previous gas exchange studies have predominately relied on whole-body testing and theoretical analysis with 1D or static models. However, pulmonary gas exchanges are inherently a dynamic process in 3D spaces with instantaneous interactions between air, blood, and tissue. This study aimed to develop a computational model for oxygen exchange that considered all factors mentioned above. Therefore, an integrated alveolus–membrane–capillary geometry was developed with prescribed rhythmic expansion/contraction. Airflow ventilation, blood perfusion, and oxygen diffusion were simulated using COMSOL. The temporal and spatial distribution of blood flow and oxygen within the capillaries were simulated under varying breathing depths and cardiac outputs. The results showed highly nonuniform blood flow distributions in the capillary network, while the rhythmic oscillation further increased this nonuniformity, leading to stagnant blood flow in the distal vessels. A static alveolus–capillary geometry underestimated perfusion by 11% for normal respirations, and the deviation grew with breathing depth. The rhythmic motion caused a phase lag in the blood flow. The blood PO2 reached equilibrium with the alveolar air after traveling 1/5–1/3 of the capillary network. The time to reach this equilibrium was significantly influenced by the air–blood barrier diffusivity, while it was only slightly affected by the perfusion rate. The computational platform in this study could be instrumental in obtaining refined knowledge of pulmonary O2 exchanges

Deciphering Exhaled Aerosol Fingerprints for Early Diagnosis and Personalized Therapeutics of Obstructive Respiratory Diseases in Small Airways

Respiratory diseases often show no apparent symptoms at their early stages and are usually diagnosed when permanent damages have been made to the lungs. A major site of lung pathogenesis is the small airways, which make it highly challenging to detect using current techniques due to the diseases’ location (inaccessibility to biopsy) and size (below normal CT/MRI resolution). In this review, we present a new method for lung disease detection and treatment in small airways based on exhaled aerosols, whose patterns are uniquely related to the health of the lungs. Proof-of-concept studies are first presented in idealized lung geometries. We subsequently describe the recent developments in feature extraction and classification of the exhaled aerosol images to establish the relationship between the images and the underlying airway remodeling. Different feature extraction algorithms (aerosol density, fractal dimension, principal mode analysis, and dynamic mode decomposition) and machine learning approaches (support vector machine, random forest, and convolutional neural network) are elaborated upon. Finally, future studies and frequent questions related to clinical applications of the proposed aerosol breath testing are discussed from the authors’ perspective. The proposed breath testing has clinical advantages over conventional approaches, such as easy-to-perform, non-invasive, providing real-time feedback, and is promising in detecting symptomless lung diseases at early stages

Lower Inspiratory Breathing Depth Enhances Pulmonary Delivery Efficiency of ProAir Sprays

Effective pulmonary drug delivery using a metered-dose inhaler (MDI) requires a match between the MDI sprays, the patient’s breathing, and respiratory physiology. Different inhalers generate aerosols with distinct aerosol sizes and speeds, which require specific breathing coordination to achieve optimized delivery efficiency. Inability to perform the instructed breathing maneuver is one of the frequently reported issues during MDI applications; however, their effects on MDI dosimetry are unclear. The objective of this study is to systemically evaluate the effects of breathing depths on regional deposition in the respiratory tract using a ProAir-HFA inhaler. An integrated inhaler mouth-throat-lung geometry model was developed that extends to the ninth bifurcation (G9). Large-eddy simulation (LES) was used to compute the airflow dynamics due to concurrent inhalation and orifice flows. The discrete-phase Lagrangian model was used to track droplet motions. Experimental measurements of ProAir spray droplet sizes and speeds were used as initial and boundary conditions to develop the computational model for ProAir-pulmonary drug delivery. The time-varying spray plume from a ProAir-HFA inhaler into the open air was visualized using a high-speed imaging system and was further used to validate the computational model. The inhalation dosimetry of ProAir spray droplets in the respiratory tract was compared among five breathing depths on a regional, sub-regional, and local basis. The results show remarkable differences in airflow dynamics within the MDI mouthpiece and the droplet deposition distribution in the oral cavity. The inhalation depth had a positive relationship with the deposition in the mouth and a negative relationship with the deposition in the five lobes beyond G9 (small airways). The highest delivery efficiency to small airways was highest at 15 L/min and declined with an increasing inhalation depth. The drug loss inside the MDI was maximal at 45–60 L/min. Comparisons to previous experimental and numerical studies revealed a high dosimetry sensitivity to the inhaler type and patient breathing condition. Considering the appropriate inhalation waveform, spray actuation time, and spray properties (size and velocity) is essential to accurately predict inhalation dosimetry from MDIs. The results highlight the importance of personalized inhalation therapy to match the patient’s breathing patterns for optimal delivery efficiencies. Further complimentary in vitro or in vivo experiments are needed to validate the enhanced pulmonary delivery at 15 L/min

Liquid Film Translocation Significantly Enhances Nasal Spray Delivery to Olfactory Region: A Numerical Simulation Study

Previous in vivo and ex vivo studies have tested nasal sprays with varying head positions to enhance the olfactory delivery; however, such studies often suffered from a lack of quantitative dosimetry in the target region, which relied on the observer’s subjective perception of color changes in the endoscopy images. The objective of this study is to test the feasibility of gravitationally driven droplet translocation numerically to enhance the nasal spray dosages in the olfactory region and quantify the intranasal dose distribution in the regions of interest. A computational nasal spray testing platform was developed that included a nasal spray releasing model, an airflow-droplet transport model, and an Eulerian wall film formation/translocation model. The effects of both device-related and administration-related variables on the initial olfactory deposition were studied, including droplet size, velocity, plume angle, spray release position, and orientation. The liquid film formation and translocation after nasal spray applications were simulated for both a standard and a newly proposed delivery system. Results show that the initial droplet deposition in the olfactory region is highly sensitive to the spray plume angle. For the given nasal cavity with a vertex-to-floor head position, a plume angle of 10° with a device orientation of 45° to the nostril delivered the optimal dose to the olfactory region. Liquid wall film translocation enhanced the olfactory dosage by ninefold, compared to the initial olfactory dose, for both the baseline and optimized delivery systems. The optimized delivery system delivered 6.2% of applied sprays to the olfactory region and significantly reduced drug losses in the vestibule. Rheological properties of spray formulations can be explored to harness further the benefits of liquid film translocation in targeted intranasal deliveries

The application of statistical shape modeling for lung morphology in aerosol inhalation dosimetry

Even with the advance in medical imaging techniques such as CT/MRI, it is still challenging and time-consuming to reconstruct anatomically accurate lung geometries. It is even more challenging to study variability in inhalation dosimetry or pulmonary drug delivery, which requires a large cohort of lung models to ensure statistically significant results. This study used the statistical shape modeling (SSM) that bases on a limited number of lung models (40) to generate infinitely large numbers of parameterized models, which can span all major features inherent in the database of lung geometries. We demonstrated this model in lung models with more than 400 outlets (G9), which first identified the principal components (PCs) of base models, and then regenerated new models by systematically varying the mode (eigenvector) and its eigenvalues. The new models included airway remodeling at varying locations (left upper lobe and right lower lobe) and with varying levels of airway distensibility (compliance) and constriction (resistance). Airflow and aerosol dynamics within these lung geometries were numerically computed and compared. Results showed that even though the airway remodeling can be local, its influences on flow partition and deposition distribution can be global. Asthma-induced bronchiolar constriction, when severe, can strikingly alter the airflow and particle deposition mapping throughout the lungs. The highest deposition variability due to airway remodeling was found to come from particles of 4–10 μm in the upper lobes, and of 10–20 μm in the lower lobe. Statistical shape modeling is an imaging processing method that has often been used in computer sciences. This is the first study, to the author's knowledge, that SSM was applied in lung models with high complexity to quantify the resultant variances from these geometry remodeling. This method was also applied to lung models with 3000 outlets (G11) to generate diseased lung models at varying locations

Molecular Binding Contributes to Concentration Dependent Acrolein Deposition in Rat Upper Airways: CFD and Molecular Dynamics Analyses

Existing in vivo experiments show significantly decreased acrolein uptake in rats with increasing inhaled acrolein concentrations. Considering that high-polarity chemicals are prone to bond with each other, it is hypothesized that molecular binding between acrolein and water will contribute to the experimentally observed deposition decrease by decreasing the effective diffusivity. The objective of this study is to quantify the probability of molecular binding for acrolein, as well as its effects on acrolein deposition, using multiscale simulations. An image-based rat airway geometry was used to predict the transport and deposition of acrolein using the chemical species model. The low Reynolds number turbulence model was used to simulate the airflows. Molecular dynamic (MD) simulations were used to study the molecular binding of acrolein in different media and at different acrolein concentrations. MD results show that significant molecular binding can happen between acrolein and water molecules in human and rat airways. With 72 acrolein embedded in 800 water molecules, about 48% of acrolein compounds contain one hydrogen bond and 10% contain two hydrogen bonds, which agreed favorably with previous MD results. The percentage of hydrogen-bonded acrolein compounds is higher at higher acrolein concentrations or in a medium with higher polarity. Computational dosimetry results show that the size increase caused by the molecular binding reduces the effective diffusivity of acrolein and lowers the chemical deposition onto the airway surfaces. This result is consistent with the experimentally observed deposition decrease at higher concentrations. However, this size increase can only explain part of the concentration-dependent variation of the acrolein uptake and acts as a concurrent mechanism with the uptake-limiting tissue ration rate. Intermolecular interactions and associated variation in diffusivity should be considered in future dosimetry modeling of high-polarity chemicals such as acrolein

Molecular Binding Contributes to Concentration Dependent Acrolein Deposition in Rat Upper Airways: CFD and Molecular Dynamics Analyses

Existing in vivo experiments show significantly decreased acrolein uptake in rats with increasing inhaled acrolein concentrations. Considering that high-polarity chemicals are prone to bond with each other, it is hypothesized that molecular binding between acrolein and water will contribute to the experimentally observed deposition decrease by decreasing the effective diffusivity. The objective of this study is to quantify the probability of molecular binding for acrolein, as well as its effects on acrolein deposition, using multiscale simulations. An image-based rat airway geometry was used to predict the transport and deposition of acrolein using the chemical species model. The low Reynolds number turbulence model was used to simulate the airflows. Molecular dynamic (MD) simulations were used to study the molecular binding of acrolein in different media and at different acrolein concentrations. MD results show that significant molecular binding can happen between acrolein and water molecules in human and rat airways. With 72 acrolein embedded in 800 water molecules, about 48% of acrolein compounds contain one hydrogen bond and 10% contain two hydrogen bonds, which agreed favorably with previous MD results. The percentage of hydrogen-bonded acrolein compounds is higher at higher acrolein concentrations or in a medium with higher polarity. Computational dosimetry results show that the size increase caused by the molecular binding reduces the effective diffusivity of acrolein and lowers the chemical deposition onto the airway surfaces. This result is consistent with the experimentally observed deposition decrease at higher concentrations. However, this size increase can only explain part of the concentration-dependent variation of the acrolein uptake and acts as a concurrent mechanism with the uptake-limiting tissue ration rate. Intermolecular interactions and associated variation in diffusivity should be considered in future dosimetry modeling of high-polarity chemicals such as acrolein