Improving Ex Vivo Nasal Mucosa Experimental Design for Drug Permeability Assessments: Correcting Mucosal Thickness Interference and Reevaluating Fluorescein Sodium as an Integrity Marker for Chemically Induced Mucosal Injury
Bibliographic record
Abstract
Objectives: Ex vivo nasal mucosa models provide physiologically relevant platforms for evaluating nasal drug permeability; however, their application is often limited by high experimental variability and the absence of standardized methodologies. This study aimed to improve experimental design by addressing two major limitations: the confounding effects of mucosal thickness and the questionable reliability of fluorescein sodium (Flu-Na) as an integrity marker for chemically induced mucosal injury. Methods: Permeability experiments were conducted using porcine nasal tissues mounted in Franz diffusion cells, with melatonin and Flu-Na as model compounds. Tissues of varying thickness were collected from both intra- and inter-individual sources, and a numerical simulation-based method was employed to normalize apparent permeability coefficients (Papp) to a standardized mucosal thickness of 0.80 mm. The effects of thickness normalization and chemically induced damage were systematically evaluated. Results: Thickness normalization substantially reduced variability in melatonin Papp, particularly within same-animal comparisons, thereby improving statistical power and data reliability. In contrast, Flu-Na exhibited inconsistent correlations across different pigs and failed to reflect the expected increase in permeability following isopropyl alcohol (IPA)-induced epithelial damage. These results suggest that the relationship between epithelial injury and paracellular transport may be non-linear and not universally applicable under ex vivo conditions, limiting the suitability of Flu-Na as a standalone marker of mucosal integrity. Conclusions: The findings highlight the importance of integrating mucosal thickness correction into standardized experimental protocols and call for a critical reassessment of Flu-Na in nasal drug delivery research.
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How this classification was reachedexpand
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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".