Recent developments in the computational simulation of dry powder inhalers

Capecelatro, Jesse; Longest, P. Worth; Boerman, Connor; Sulaiman, Mostafa; Sundaresan, Sankaran

doi:10.1016/j.addr.2022.114461

Cited by 17 publications

(5 citation statements)

References 158 publications

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“…In order to simulate the airflow and aerosol transport inside the nasal airway, local dynamic LES equations are coupled with the aerosol motion equations by considering one-way coupling, which ignores the impact of the aerosols on the gas-phase, as is the case for aerosol delivery to the nasal cavity [ 9 , 18 , 29 , 44 ]. In this study, airflow was simulated by solving the Eulerian equations of continuity and momentum.…”

Section: Methodsmentioning

confidence: 99%

Numerical and Machine Learning Analysis of the Parameters Affecting the Regionally Delivered Nasal Dose of Nano- and Micro-Sized Aerosolized Drugs

et al. 2023

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The nasal epithelium is an important target for drug delivery to the nose and secondary organs such as the brain via the olfactory bulb. For both topical and brain delivery, the targeting of specific nasal regions such as the olfactory epithelium (brain) is essential, yet challenging. In this study, a numerical model was developed to predict the regional dose as mass per surface area (for an inhaled mass of 2.5 mg), which is the biologically most relevant dose metric for drug delivery in the respiratory system. The role of aerosol diameter (particle diameter: 1 nm to 30 µm) and inhalation flow rate (4, 15 and 30 L/min) in optimal drug delivery to the vestibule, nasal valve, olfactory and nasopharynx is assessed. To obtain the highest doses in the olfactory region, we suggest aerosols with a diameter of 20 µm and a medium inlet air flow rate of 15 L/min. High deposition on the olfactory epithelium was also observed for nanoparticles below 1 nm, as was high residence time (slow flow rate of 4 L/min), but the very low mass of 1 nm nanoparticles is prohibitive for most therapeutic applications. Moreover, high flow rates (30 L/min) and larger micro-aerosols lead to highest doses in the vestibule and nasal valve regions. On the other hand, the highest drug doses in the nasopharynx are observed for nano-aerosol (1 nm) and fine microparticles (1–20 µm) with a relatively weak dependence on flow rate. Furthermore, using the 45 different inhalation scenarios generated by numerical models, different machine learning models with five-fold cross-validation are trained to predict the delivered dose and avoid partial differential equation solvers for future predictions. Random forest and gradient boosting models resulted in R2 scores of 0.89 and 0.96, respectively. The aerosol diameter and region of interest are the most important features affecting delivered dose, with an approximate importance of 42% and 47%, respectively.

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Section: Methodsmentioning

confidence: 99%

Numerical and Machine Learning Analysis of the Parameters Affecting the Regionally Delivered Nasal Dose of Nano- and Micro-Sized Aerosolized Drugs

et al. 2023

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“…Despite rapid advances in computational drug delivery in recent years, variability and uncertainty in predictive dosimetry remain largely unquantified. A reliable dosimetry prediction requires accurate representations of the respiratory system, breathing conditions, and pharmaceutical properties [ 2 ]. In practice, various sources of uncertainty exist for these inputs, potentially leading to a strong bias in predicted dosages [ 3 , 4 , 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis and Uncertainty Quantification of Nanoparticle Deposition from Tongue Morphological Variations

Yang,

Si,

2024

Life

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The human tongue has highly variable morphology. Its role in regulating respiratory flows and deposition of inhaled aerosols remains unclear. The objective of this study was to quantify the uncertainty of nanoparticle deposition from the variability in tongue shapes and positions and to rank the importance of these morphological factors. Oropharyngeal models with different tongue postures were reconstructed by modifying an existent anatomically accurate upper airway geometry. An LRN k-ω model was applied to solve the multiregime flows, and the Lagrangian tracking approach with near-wall treatment was used to simulate the behavior and fate of inhaled aerosols. Once the database of deposition rates was completed, a surrogate model was trained using Gaussian process regression with polynomial kernels and was validated by comparing its predictions to new CFD simulations. Input sensitivity analysis and output updateability quantification were then performed using the surrogate model. Results show that particle size is the most significant parameter in determining nanoparticle deposition in the upper airway. Among the morphological factors, the shape variations in the central tongue had a higher impact on the total deposition than those in the back tongue and glottal aperture. When considering subregional deposition, mixed sensitivity levels were observed among morphological factors, with the back tongue being the major factor for throat deposition and the central tongue for oral deposition. Interaction effects between flow rate and morphological factors were much higher than the effects from individual parameters and were most significant in the throat (pharyngolaryngeal region). Given input normal variances, the nanoparticle deposition exhibits logarithmical normal distributions, with much lower uncertainty in 100-nm than 2-nm aerosols.

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“…In the field of pharmaceutical aerosol delivery, Computational Fluid Dynamics (CFD) provides an effective approach to capture the transport of particles and droplets within inhaler devices, patient interfaces (PIs), and the respiratory airways ( Bass et al, 2021 ; Capecelatro et al, 2022 ; Chen et al, 2021 ; Longest et al, 2019 ; Longest & Holbrook, 2012 ; Ruzycki et al, 2013 ). An accurate, i.e., well-validated, CFD model can be applied to design and optimize efficient inhalation devices and PIs ( Bass et al, 2021 , 2022 ; Bass & Longest, 2020 ; Longest et al, 2015 ; Longest & Farkas, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Development of an effective two-equation turbulence modeling approach for simulating aerosol deposition across a range of turbulence levels

Jubaer,

Thomas,

Farkas

et al. 2024

Journal of Aerosol Science

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Recent developments in the computational simulation of dry powder inhalers

Cited by 17 publications

References 158 publications

Numerical and Machine Learning Analysis of the Parameters Affecting the Regionally Delivered Nasal Dose of Nano- and Micro-Sized Aerosolized Drugs

Numerical and Machine Learning Analysis of the Parameters Affecting the Regionally Delivered Nasal Dose of Nano- and Micro-Sized Aerosolized Drugs

Sensitivity Analysis and Uncertainty Quantification of Nanoparticle Deposition from Tongue Morphological Variations

Development of an effective two-equation turbulence modeling approach for simulating aerosol deposition across a range of turbulence levels

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