Characterizing regional drug delivery within the nasal airways
Bibliographic record
Abstract
INTRODUCTION: The nose has been receiving increased attention as a route for drug delivery. As the site of deposition constitutes the first point of contact of the body with the drug, characterization of the regional deposition of intranasally delivered droplets or particles is paramount to formulation and device design of new products. AREAS COVERED: This review article summarizes the recent literature on intranasal regional drug deposition evaluated in vivo, in vitro and in silico, with the aim of correlating parameters measured in vitro with formulation and device performance. We also highlight the relevance of regional deposition to two emerging applications: nose-to-brain drug delivery and intranasal vaccines. EXPERT OPINION: As in vivo studies of deposition can be costly and time-consuming, researchers have often turned to predictive in vitro and in silico models. Variability in deposition is high due in part to individual differences in nasal geometry, and a complete predictive model of deposition based on spray characteristics remains elusive. Carefully selected or idealized geometries capturing population average deposition can be useful surrogates to in vivo measurements. Continued development of in vitro and in silico models may pave the way for development of less variable and more effective intranasal drug products.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".