Targeting inhaled aerosol delivery to upper airways in children: Insight from computational fluid dynamics (CFD)
Bibliographic record
Abstract
Despite the prevalence of inhalation therapy in the treatment of pediatric respiratory disorders, most prominently asthma, the fraction of inhaled drugs reaching the lungs for maximal efficacy remains adversely low. By and large drug delivery devices and their inhalation guidelines are typically derived from adult studies with child dosages adapted according to body weight. While it has long been recognized that physiological (e.g. airway sizes, breathing maneuvers) and physical transport (e.g. aerosol dynamics) characteristics are critical in governing deposition outcomes, such knowledge has yet to be extensively adapted to younger populations. Motivated by such shortcomings, the present work leverages in a first step in silico computational fluid dynamics (CFD) to explore opportunities for augmenting aerosol deposition in children based on respiratory physiological and physical transport determinants. Using an idealized, anatomically-faithful upper airway geometry, airflow and aerosol motion are simulated as a function of age, spanning a five year old to an adult. Breathing conditions mimic realistic age-specific inhalation maneuvers representative of Dry Powder Inhalers (DPI) and nebulizer inhalation. Our findings point to the existence of a single dimensionless curve governing deposition in the conductive airways via the dimensionless Stokes number (Stk). Most significantly, we uncover the existence of a distinct deposition peak irrespective of age. For the DPI simulations, this peak (∼ 80%) occurs at Stk ≈ 0.06 whereas for nebulizer simulations, the corresponding peak (∼ 45%) occurs in the range of Stk between 0.03-0.04. Such dimensionless findings hence translate to an optimal window of micron-sized aerosols that evolves with age and varies with inhalation device. The existence of such deposition optima advocates revisiting design guidelines for optimizing deposition outcomes in pediatric inhalation therapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".