Evaluation of drug permeability across Ex vivo nasal mucosa: A simulation-based approach to minimize thickness-related variability
Bibliographic record
Abstract
Ex vivo nasal mucosa is commonly used for the study on drug permeability for nasal drug delivery. Thickness variations in porcine nasal mucosa (0.26–1.47 mm) were found to significantly impact permeation curve results in Franz diffusion cell experiments, introducing variability and complicating data interpretation. To mitigate these effects, a numerical simulation method was developed using COMSOL Multiphysics® to normalize permeation curve to a standardized mucosal thickness. Using this method, the permeability of five compounds with diverse solubility and lipophilicity profiles was evaluated. Melatonin, triamcinolone acetonide, and mitragynine exhibited high permeability, while cannflavin A and cannflavin B showed negligible permeability. The apparent permeability coefficients (Papp) of mitragynine, melatonin, and triamcinolone acetonide were initially obscured by differences in mucosal thickness, masking their statistical differences. However, after normalization, statistically significant differences became evident. These findings highlight the critical role of mucosal thickness correction in ex vivo permeability studies to ensure accurate and comparable data across different experimental setups and drug candidates, supporting the development of reliable nasal drug delivery systems. Furthermore, this method can be extended to permeability studies involving other species or even other types of tissues, broadening its applicability and potential in drug delivery research. • Quantified mucosal thickness variability across nasal regions and its impact on drug permeation. • Developed a simulation-based method to correct mucosal thickness for permeability curve normalization. • Compared permeability of five compounds to gain insight into nasal drug delivery performance.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".