Pediatric <i>In Vitro</i> and <i>In Silico</i> Models of Deposition via Oral and Nasal Inhalation
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it