Inhibition of tumour necrosis factor-? secretion from EpiDerm<sup>TM</sup>tissues by a novel small molecule, UTL-5d
Bibliographic record
Abstract
BACKGROUND: UTL-5d [N-(4-chlorophenyl)-3-carboxyamidyl-5-methylisoxazole] is a small-molecule tumour necrosis factor (TNF)-alpha modulator being investigated for its potential in several immune-mediated diseases, including psoriasis. OBJECTIVES: We aimed to determine whether UTL-5d represents a potential antipsoriasis agent. METHODS: Firstly, a keratinocyte cell-based study was used to study the inhibition of TNF-alpha and gene suppression by UTL-5d in vitro. Secondly, a multilayered human epidermis tissue model, consisting of normal human-derived epidermal keratinocytes, was used to study the dose-dependent reduction of TNF-alpha by UTL-5d as well as the feasibility of using UTL-5d in a lotion formulation. RESULTS: The cell-based study showed that UTL-5d significantly reduced TNF-alpha secretion from keratinocytes (68% reduction at 17 mug mL(-1)) and suppressed JAK3 and MAP3K2 genes by 70% and 40%, respectively. In the human epidermis tissue model, reduction of TNF-alpha by UTL-5d appeared to be dose dependent (8.35-33.4 microg mL(-1)); UTL-5d also reduced cell death induced by ultraviolet (UV) B. Tissues treated by UTL-5d in a preliminary lotion formulation showed significant reduction of TNF-alpha induced by UVB. CONCLUSIONS: Our results indicate that UTL-5d may be worthy of further investigation for its potential as a topical agent for psoriasis.
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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 | 0.000 |
| 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".