A suction blister model to characterize epidermal wound healing and evaluate the efficacy of the topical wound healing agent INM-755 in healthy volunteers
Bibliographic record
Abstract
Non-healing wounds represent a substantial medical burden with few effective treatments available. To address this challenge, we developed a novel epidermal wound healing model using suction blisters in healthy volunteers. This model allowed for the comprehensive assessment of wound healing dynamics and the evaluation of INM-755, a topical cream containing cannabinol, as a potential therapeutic agent. Two clinical studies were conducted: an observational study and an interventional study. In both studies, healthy volunteers underwent a suction blister procedure on their lower back, creating open epidermal wounds. Wound healing parameters were assessed using advanced imaging systems. Skin barrier function and perfusion were evaluated through trans epidermal water loss (TEWL) and dynamic optical coherence tomography (D-OCT), respectively. The observational study demonstrated the successful and reproducible induction of blisters and the removal of epidermal sheet, enabling quantifiable measurements of wound healing parameters over time. Re-epithelialization was observed, revealing recovery of skin barrier function and perfusion. In the interventional study, differences of treatments over time were quantified using the above-described techniques. Despite differences from disease-specific blistering, our developed model provides a valuable platform for studying wound healing mechanisms and assessing novel therapeutic interventions. The sensitivity to treatment effects demonstrated in our study underscores the potential utility of this model in early-phase clinical drug development programs targeting wound healing disorders.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".