New and Emerging Treatments for Generalized Pustular Psoriasis: Focus on IL-36 Receptor Inhibitors
Bibliographic record
Abstract
Generalized Pustular Psoriasis (GPP) is a rare and severe subtype of psoriasis that significantly impacts patients' quality of life. Until recently, no specific treatment modalities were available, and treatment for GPP followed the guidelines for the treatment of plaque psoriasis, consisting of conventional treatments, such as retinoids, methotrexate, and even biologics, which although effective in some cases, may be associated with significant side effects, necessitating more effective and safe options. The pathophysiology of Generalized Pustular Psoriasis is complex and not fully understood, but there is some overlap with the pathogenesis of Plaque Psoriasis. In GPP, the innate immune system seems to play a more significant role, with the interleukin (IL)-36 pathway being fundamentally involved. Spesolimab and imsidolimab, two recently developed therapeutic agents, target the IL-36 inflammatory pathway by binding to the IL-36 receptor (IL-36R). Both biologics have already been evaluated in phase 1 and 2 clinical trials and have shown promising results in terms of safety and efficacy. IL-36 receptor inhibitors demonstrated great efficacy and good safety profile in the management of patients with GPP, demonstrating their potential to emerge as a leading treatment option. This review aims to explore and summarize the current scientific literature on the most recently developed treatments for GPP.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".