The Effectiveness of Risankizumab Following Guselkumab Failure in Moderate-to-Severe Psoriasis Patients: A Retrospective Study
Bibliographic record
Abstract
BACKGROUND: Psoriasis is a chronic inflammatory skin disease with a strong genetic predisposition and autoimmune component that is often treated with immunomodulators such as biologic therapy. Guselkumab is a biologic treatment option that selectively targets the p19 subunit of interleukin (IL)-23; risankizumab is a more recently developed monoclonal antibody of the same class that targets IL-23p19. There is limited research around effective treatment response with intra class switching within IL-23-targeted therapies for the treatment of moderate-to-severe plaque psoriasis. OBJECTIVES: The purpose is to assess patient response to risankizumab after guselkumab failure for the treatment of plaque psoriasis. METHODS: A retrospective chart review was conducted for 13 patients meeting inclusion criteria. Physical examination findings were converted to a 5-point static physicians' global assessment (sPGA) score. Baseline, 4-month, and 12-month sPGA scores were assigned from visits immediately prior to and during their course of risankizumab treatment. sPGA scores were analyzed to compare changes between baseline and 4 months and 12 months of therapy. RESULTS: Patients treated with risankizumab had lower sPGA scores after both 4 and 12 months compared to their baseline sPGA score. 46% of patients met the primary outcome of an sPGA score of 0 or 1 at 4 months of risankizumab, increasing to 90% of patients at 12 months. CONCLUSIONS: Our findings reflect an improvement in sPGA scores when patients are treated with risankizumab following guselkumab failure. This highlights the benefit of in-class switch to risankizumab when patients with moderate-to-severe plaque psoriasis have failed multiple treatments including guselkumab.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".