Pediatric <i>In Vitro</i> and <i>In Silico</i> Models of Deposition via Oral and Nasal Inhalation
Bibliographic record
Abstract
Respiratory tract deposition models provide a useful method for optimizing the design and administration of inhaled pharmaceutical aerosols, and can be useful for estimating exposure risks to inhaled particulate matter. As aerosol must first pass through the extrathoracic region prior to reaching the lungs, deposition in this region plays an important role in both cases. Compared to adults, much less extrathoracic deposition data are available with pediatric subjects. Recently, progress in magnetic resonance imaging and computed tomography scans to develop pediatric extrathoracic airway replicas has facilitated addressing this issue. Indeed, the use of realistic replicas for benchtop inhaler testing is now relatively common during the development and in vitro evaluation of pediatric respiratory drug delivery devices. Recently, in vitro empirical modeling studies using a moderate number of these realistic replicas have related airway geometry, particle size, fluid properties, and flow rate to extrathoracic deposition. Idealized geometries provide a standardized platform for inhaler testing and exposure risk assessment and have been designed to mimic average in vitro deposition in infants and children by replicating representative average geometrical dimensions. In silico mathematical models have used morphometric data and aerosol physics to illustrate the relative importance of different deposition mechanisms on respiratory tract deposition. Computational fluid dynamics simulations allow for the quantification of local deposition patterns and an in-depth examination of aerosol behavior in the respiratory tract. Recent studies have used both in vitro and in silico deposition measurements in realistic pediatric airway geometries to some success. This article reviews the current understanding of pediatric in vitro and in silico deposition modeling via oral and nasal inhalation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".