Pustular psoriasis: Molecular pathways and effects of spesolimab in generalized pustular psoriasis
Bibliographic record
Abstract
Background The IL-36 pathway plays a key role in the pathogenesis of generalized pustular psoriasis (GPP). In a proof-of-concept clinical trial, treatment with spesolimab, an anti–IL-36 receptor antibody, resulted in rapid skin and pustular clearance in patients presenting with GPP flares. Objective We sought to compare the molecular profiles of lesional and nonlesional skin from patients with GPP or palmoplantar pustulosis (PPP) with skin from healthy volunteers, and to investigate the molecular changes after spesolimab treatment in the skin and blood of patients with GPP flares. Methods Pre- and post-treatment skin and blood samples were collected from patients with GPP who participated in a single-arm, phase I study (n = 7). Skin biopsies from patients with PPP (n = 8) and healthy volunteers (n = 16) were obtained for comparison at baseline. Biomarkers were assessed by RNA-sequencing, histopathology, and immunohistochemistry. Results In GPP and PPP lesions, 1287 transcripts were commonly upregulated or downregulated. Selected transcripts from the IL-36 signaling pathway were upregulated in untreated GPP and PPP lesions. In patients with GPP, IL-36 pathway–related signatures, T H 1/T H 17 and innate inflammation signaling, neutrophilic mediators, and keratinocyte-driven inflammation pathways were downregulated by spesolimab as early as week 1. Spesolimab also decreased related serum biomarkers and cell populations in the skin lesions from patients with GPP, including CD3 + T, CD11c + , and IL-36γ + cells and lipocalin-2–expressing cells. Conclusions In patients with GPP, spesolimab showed rapid modulation of commonly dysregulated molecular pathways in GPP and PPP, which may be associated with improved clinical outcomes.
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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) | 0.001 | 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) | 0.002 | 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".