Off-Label High-Dose Secukinumab for the Treatment of Moderate-to-Severe Psoriasis
Bibliographic record
Abstract
BACKGROUND: Secukinumab is an anti-IL-17A monoclonal antibody approved for the treatment of moderate-to-severe psoriasis in adult patients. Despite its favourable safety and efficacy profile in clinical trials, some patients in clinical practice fail to respond adequately to the approved maintenance regimen of 300 mg subcutaneous monthly. Some clinicians manage these patients by using off-label high-dose secukinumab regimens, which include shortening the dosing interval to 300 mg every 2 or 3 weeks instead of monthly, or increasing the monthly dose to 450 mg. OBJECTIVE: This study aims to investigate the safety and efficacy of high-dose secukinumab regimens for the treatment of psoriasis to inform real-world clinical practice. METHODS: We performed a retrospective chart review at 5 dermatology clinics for adult patients diagnosed with moderate-to-severe psoriasis treated with an off-label high-dose secukinumab regimen. Efficacy was measured using the Psoriasis Area and Severity Index or a Physician Global Assessment score of 0 or 1 after dose escalation. Adverse events were recorded to assess safety outcomes. RESULTS: Twenty-five patients were included in this case series, and 14 of them achieved efficacy from dose escalation with secukinumab based on our study endpoints. There was 1 case of the common cold and 1 upper respiratory tract infection reported after dose escalation. CONCLUSION: Our study provides evidence that dose escalation with secukinumab results in clinical benefit and is well tolerated among patients with moderate-to-severe psoriasis who failed to respond adequately to the approved regimen. This work necessitates larger studies to fully characterize the efficacy and long-term safety profile of secukinumab dose escalation.
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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 | 0.001 | 0.002 |
| 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".