Non-impactor-Based Methods for Sizing of Aerosols Emitted from Orally Inhaled and Nasal Drug Products (OINDPs)
Bibliographic record
Abstract
The purpose of this article is to review non-impactor-based methods for measuring particle size distributions of orally inhaled and nasal pharmaceutical aerosols. The assessment of the size distributions of sprays and aerosols from orally inhaled and nasal drug products by methods not involving multi-stage cascade impaction may offer significant potential advantages in terms of labor savings and reducing the risk for operator-related errors associated with complex-to-undertake impactor-based methods. Indeed, in the case of nasal spray products, cascade impaction is inappropriate and alternative, and preferably non-invasive methods must be sought that minimize size-related bias associated with the measurement process for these relatively large droplets. This review highlights the options that are available to those involved with product quality assessments, providing guidance on relative strengths and weaknesses, as well as highlighting precautions that should be observed to minimize bias. The advent of Raman chemical imaging, which enables an estimate to be made of the proportion of each particle comprising active pharmaceutical ingredient(s) (APIs), necessitates a re-think about the value of classical microscopy image analysis as now being capable of providing API-relevant information from collected aerosols and sprays.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 0.002 |
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".