Randomized Controlled Trial of the Topical Jak Inhibitor Delgocitinib Cream in Patients with Frontal Fibrosing Alopecia
Bibliographic record
Abstract
BACKGROUND: Frontal fibrosing alopecia (FFA) is a cicatricial alopecia with generally poor prognosis if untreated and no approved treatment options. OBJECTIVE: The aim of this study was to evaluate changes in the molecular signature of FFA lesions after application of delgocitinib cream. Safety, tolerability, and efficacy were also investigated. METHODS: This was a phase 2a, randomized, double-blind, exploratory trial of 20 mg/g delgocitinib cream (2%) versus cream vehicle in patients with FFA. RESULTS: A total of 30 adult females with FFA were randomized to delgocitinib cream (n = 15) or cream vehicle (n = 15). After 12 weeks, expression of the T helper 1-related biomarker CXCL9 was significantly downregulated (-3.10; P < .05), whereas there were nonsignificant reductions in CXCL10 (-2.60; P < .1) and IFN-γ (-1.49; P = .22) in lesions treated with delgocitinib cream but not cream vehicle. Delgocitinib-treated lesions had a small but significant mean improvement in transcriptomic profile (4%; P < .001), whereas lesions treated with cream vehicle worsened (33%). Delgocitinib cream was well-tolerated and associated with improvements in exploratory clinical severity endpoints. LIMITATIONS: Limitations of the trial include small sample size, biomarker analyses only being conducted to week 12, and the exploratory nature of efficacy endpoints. CONCLUSION: Delgocitinib cream resulted in an improvement in the transcriptomic profile of lesions and may have potential as a topical treatment for FFA. This study is registered with NCT05332366.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".