Characterization of Spray-Dried Powders Using a Coated Alberta Idealized Nasal Inlet
Bibliographic record
Abstract
Background: Dry powders offer the potential to increase stability and reduce cold-chain requirements associated with the distribution of vaccines and other thermally sensitive products. The Alberta Idealized Nasal Inlet (AINI) is a representative geometry for in vitro characterization of nasal products that may prove useful in examining intranasal delivery of powders. Methods: Spray-dried trehalose powders were loaded at 10, 20, and 40 mg doses into active single-dose devices. Primary particle sizes (∼ D v 50 = 10 µm for powder A and 25 µm for powder B), and sizes dispersed by devices, were evaluated using laser diffraction. The interior of the AINI was coated with a glycerol-surfactant mixture to mitigate particle bounce, and flow rates of 7.5 or 15 L/min were drawn through the AINI. Deposition of trehalose powder was determined in the four regions of the AINI (vestibule, turbinates, olfactory, and nasopharynx), a downstream preseparator, and an absolute filter (representing in vitro lung deposition) using liquid chromatography coupled with mass spectrometry. Results: Coating the AINI was effective in mitigating particle bounce for both trehalose powders. No difference in regional nasal deposition was observed when testing at a flow rate of 7.5 versus 15 L/min. A high fraction of both powders penetrated past the vestibule and deposited in the turbinates and nasopharynx for all loaded doses. For powder A, a non-negligible fraction of the recovered dose (up to 7%) is deposited on the filter, representing potential lung exposure. Conversely, a negligible fraction of the total recovered dose was deposited on the filter for powder B. Conclusion: Powders with a larger primary particle size showed reduced penetration through the nasal airways while maintaining high turbinate deposition. Optimized spray-dried powders offer the potential to target delivery to the peripheral nasal airways based on powder particle size while reducing lung exposure.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".