Feasibility Investigation of Cellulose Polymers for Mucoadhesive Nasal Drug Delivery Applications
Bibliographic record
Abstract
The feasibility of various cellulose polymer derivatives, including methylcellulose (MC), hydroxypropyl methylcellulose (HPMC), sodium-carboxymethylcellulose (sodium-CMC), and cationic-hydroxyethylcellulose (cationic-HEC), for use as an excipient to enhance drug delivery in nasal spray formulations was investigated. Three main parameters for evaluating the polymers in nasal drug delivery applications include rheology, ciliary beat frequency (CBF), and permeation across nasal tissue. Reversible thermally induced viscosity enhancement was observed at near nasal physiological temperature when cellulose derivatives were combined with an additional excipient, poly(vinyl caprolactam)-poly(vinyl acetate)-poly(ethylene glycol) graft copolymer (PVCL-PVA-PEG). Cationic-HEC was shown to enhance acyclovir permeation across the nasal mucosa. None of the tested cellulosic polymers caused any adverse effects on porcine nasal tissues and cells, as assessed by alterations in CBF. Upon an increase in polymer concentration, a reduction in CBF was observed when ciliated cells were immersed in the polymer solution, and this decrease returned to baseline when the polymer was removed. While each cellulose derivative exhibited unique advantages for nasal drug delivery applications, none stood out on their own to improve more than one of the performance characteristics examined. Hence, these data may be useful for the development of new cellulose derivatives in nasal drug formulations.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".