Characterizing regional drug delivery within the nasal airways
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it