Selection of topically applied non-steroidal anti-inflammatory drugs for oral cancer chemoprevention
Bibliographic record
Abstract
746 Topical delivery of non-steroidal anti-inflammatory drugs through the oral mucosa has been used for oral cancer chemoprevention. Local permeability of these agents is one of the major concerns. Here we propose an approach to predict the permeability of topically applied agents for oral cancer chemoprevention. There are two routes of drug transport into the oral mucosa, paracellular and transcellular, with the paracellular route being the major one. Absorption of the topically applied agents into the oral mucosa is a process of passive diffusion. In theory, the total flux through the oral mucosa (J max ) can be estimated by adding the transcellular flux (J TC ) and the paracellular flux (J PC ). To target the Cox-2 enzyme in oral epithelial cells, it is desirable to maximize the theoretical activity index, the ratio of J TC to IC 50 of a Cox-2 inhibitor (J TC /IC 50-Cox-2 ) . Since we are unable to calculate J TC of a drug in oral mucosa, skin J max was calculated with aqueous solubility and log P , which are predicted by the ACD Suite (Version 8.0, Advanced Chemical Development Inc., Toronto, Canada). Theoretical activity indices were then calculated based on the IC 50 values in the literature. Among the 12 commonly used drugs, celecoxib, nimesulide and ibuprofen had the highest theoretical activity indices, and may be the agents of choice to target Cox-2 in oral epithelial cells through topical application. Based on these calculations, a long-term chemopreventive experiment using celecoxib (3% or 6%) through topical application was performed in a DMBA-induced hamster oral cancer model. Both 3% and 6% reduced the incidence of squamous cell carcinoma at the post-initiation stage. Such a cancer preventive effect was correlated with inhibition of prostaglandin E 2 biosynthesis. (Supported by NIH grant CA101235)
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".