Application of an In Vitro Psoriatic Skin Model to Study Cutaneous Metabolization of Tazarotene
Bibliographic record
Abstract
Psoriasis is an inflammatory skin disease characterized by the presence of whitish and scaly plaques, which can cover up to 90% of the body surface. These plaques result from the hyperproliferation and abnormal differentiation of keratinocytes. Dermopharmaceutical testing of new therapies is limited by healthy and pathological skin models, which are not closely enough mimicking their in vivo counterparts. In this study, we exploited percutaneous absorption and Ultra Performance Liquid Chromatography (UPLC) analyses in order to determine the metabolic capacity of our psoriatic skin model. Skin substitutes were reconstructed according to the self-assembly method and tested regarding their percutaneous absorption of a topical formulation of tazarotene, followed by UPLC analyses. Histological and immunofluorescence analyses confirmed both the healthy and psoriatic phenotypes. Results from percutaneous absorption showed a significant level of tazarotene metabolite (tazarotenic acid) when the formulation was applied over 24 h on the skin substitutes. The presence of tazarotenic acid in the dermis and the epidermis of healthy and psoriatic skin substitutes confirms the metabolic capacity of both skin models, and thereby their ability to screen new molecules with antipsoriatic potential. In conclusion, the present data suggest that our psoriatic skin model could possibly be used in clinic to screen in vitro responses of patient to a panel of drugs without having them experiencing the drawback of each drug.
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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 | 0.000 |
| 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 | 0.001 |
| 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".