Interleukin-17 Inhibitors in the Treatment of Hidradenitis Suppurativa
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Hidradenitis suppurativa (HS) is a chronic, debilitating, inflammatory dermatosis that significantly impacts patients' quality of life, primarily manifesting as inflammatory nodules, abscesses, and tunnels. The pathogenesis of HS is not fully understood and appears to be multifactorial, involving genetic, immunological, and endocrinological factors, as well as dysbiosis of skin microbiota. Increasing evidence highlights the role of the interleukin (IL)-17 pathway in the inflammatory process and pathogenesis of HS. Consequently, IL-17 inhibitors have emerged as a promising alternative to current therapies. Recently, secukinumab received approval from both the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA), while bimekizumab received approval from the EMA, for the treatment of moderate-to-severe HS in adults, with ongoing clinical trials aiming to further clarify the efficacy and safety of other drugs within this class. IL-17 inhibitors have shown effectiveness in treating moderate-to-severe HS, with safety profiles of drugs such as secukinumab and bimekizumab being comparable to their use in other dermatological conditions. On the other hand, innovative drugs such as sonelokimab and izokibep show promising results and are currently in phase III clinical trials. This review provides a comprehensive overview of current knowledge and scientific advances in HS, focusing on the IL-17 pathway's role and its inhibition as a treatment strategy, alongside examining the most recent and significant clinical studies on various IL-17 inhibitors in the treatment of HS.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it