Topical treatment with CYP26 inhibitor talarozole (R115866) dose dependently alters the expression of retinoid-regulated genes in normal human epidermis
Bibliographic record
Abstract
BACKGROUND: An alternative approach to retinoid therapy is to inhibit the cytochrome P450 (CYP)-mediated catabolism of endogenous all-trans retinoic acid in the skin by applying retinoic acid metabolism blocking agents such as talarozole (R115866). OBJECTIVES: To study the effects of topical talarozole on retinoid biomarkers in normal skin in a randomized phase I trial. METHODS: Gels containing talarozole (0.35% or 0.07%) and vehicle were applied once daily for 9 days on either buttock of 16 healthy volunteers. Epidermal shave biopsies (for mRNA analysis) and punch biopsies (for histology and immunofluorescence analysis) were collected from the treatment areas. Genes encoding the following were studied by quantitative real-time polymerase chain reaction: cellular retinoic acid binding protein 2 (CRABP2), cytokeratins (KRT2 and KRT4), CYP26A1, CYP26B1, CYP26C1 and CYP2S1, two enzymes in the retinol metabolism (retinal dehydrogenase-2 and retinol acyltransferase) and two proinflammatory cytokines [interleukin (IL)-1alpha and tumour necrosis factor-alpha]. RESULTS: Talarozole treatment increased the mRNA expression of CRABP2, KRT4, CYP26A1 and CYP26B1 dose dependently, and decreased the expression of KRT2 and IL-1alpha compared with vehicle-treated skin. No mRNA change in retinol-metabolizing enzymes was obtained. There was no induction of epidermal thickness or overt skin inflammation in talarozole-treated skin. Immunofluorescence analysis confirmed an upregulation of KRT4 protein, but no upregulation of CYP26A1 and CYP26B1 expression was detected. CONCLUSIONS: Talarozole influences the biomarker pattern consistently with increased retinoic acid stimulation. The low irritancy of talarozole at the two examined dosages is a possible advantage over topical retinoids.
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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".