Exploring the impact of sample flowrate on in vitro measurements of metered dose inhaler performance
Bibliographic record
Abstract
Pharmacopoeial methods for measurement of the aerodynamic particle size distribution (APSD) of metered dose inhalers (MDIs) by cascade impaction specify a sampling flow rate of 28.3L/min. However, there is little data within the literature to rationalize this figure, or to support its clinical relevance. In addition, the standard United States Pharmacopoeia Induction Port (USP IP) used for testing is known to inaccurately reflect deposition behavior in the upper airway, further compromising the relevance of testing, for product development. This article describes experimental studies of the effect of sampling flow rate on APSD data gathered using an Andersen Cascade Impactor (ACI). Tests were carried out using two different formulations to assess the influence of formulation composition. In addition, comparative testing with an Alberta Idealised Throat, in place of the USP IP, to ensure more realistic representation of the upper airway. The results show how measured APSD and fine particle dose, the dose than on the basis of size would be expected to deposit in the lung, vary as a function of test methodology, providing insight as to how the testing can be modified towards greater clinical relevance.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".