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Record W7042212368

Pharmaceutical Aerosols and Dry Powder Formulations: Characterization of Nasal deposition using the Alberta Idealized Nasal Inlet

2025· other· en· W7042212368 on OpenAlexaboutno aff

Bibliographic record

VenueLund University Publications Student Papers (Lund University) · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsDeposition (geology)InhalationNasal sprayNasal administrationDepotParticle depositionAerosolNasal passages
DOInot available

Abstract

fetched live from OpenAlex

Respiratory diseases are a major global health challenge where current inhalation medication is primarily by oral inhalation. An alternative administration route is by nasal inhalation allowing for rapid drug absorption, high patient compliance and avoidance of the first-pass effect. Despite its potential for local and systemic drug delivery there is limited research on active inhalation of dry powders and its regional deposition in the nasal cavity. Highlighting the need to investigate its potential as an administration option. This thesis aims to develop an understanding of nasal deposition of dry powder formulations with the goal of optimizing current formulations to be deposited in the nasal cavity. This was done by developing a method to generate and measure mono- and polydisperse aerosols which was used to characterize the Alberta Idealized Nasal Inlet (AINI) model. The effects of bounce have been evaluated by coating the AINI, and the regional deposition of three formulations have been analyzed in the AINI by active inhalation. Lastly, the possibility of delivering most of a pulmonary formulation to the lungs by nasal inhalation has been evaluated. A method was developed to generate and measure mono- and polydisperse aerosols. Increasing particle sizes and flow rates showed larger deposition in the uncoated and coated AINI. However, coating displayed lower deposition for particles below 3 μm compared to uncoated. This could possibly be due to crevices, when uncoated, causing increased turbulence. Nasal deposition was characterized using the AINI where 5-10 μm aerosols showed a 98-100% deposition at 45 L/min when coated. For a nasal formulation with a D50 > 20 μm and D50 < 30 μm most of the formulation deposited in the nasal cavity independent of flow rate whilst reducing deposition in areas with poor absorption. During the experimental part it was discovered that the adapter’s 90° angle was not ideal since it directed the inhalers outlet into the vestibule walls of the AINI. Initial trials showed (data not published in this report) that adjusting the adapter improved regional deposition, in line with published results for nasal sprays. For a pulmonary formulation with a D50 < 5 μm it deposited both in the nasal cavity and on the filter (representing the lungs in this study). Suggesting a possibility of delivering most of a pulmonary formulation to the lungs by nasal inhalation, but further research is required. Lastly, the prospect of having regional deposition independent of flow rate is a desired property and passive DPIs may hold the answer of achieving this.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.017
GPT teacher head0.269
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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