What is the optimal sequential therapy after secondary IL-17A inhibitor failure in psoriasis: switching to an IL-23 inhibitor or to another IL-17A inhibitor?
Bibliographic record
Abstract
Background An increasing number of psoriatic patients are experiencing secondary failure with interleukin (IL)-17A inhibitors, highlighting the urgency to identify effective switching strategies. We evaluated the effectiveness and treatment patterns of intraclass versus interclass switching therapies in psoriasis patients experiencing secondary failure to IL-17A inhibitors over a 28-week period.Methods This single-center, retrospective analysis included psoriatic patients who experienced secondary failure to either ixekizumab or secukinumab and subsequently switched to another IL-17A inhibitor (ixekizumab or secukinumab; intraclass switching group) or to an IL-23 inhibitor (guselkumab; interclass switching group).Results 80 patients were enrolled, including 47 in the intraclass switching group and 33 in the interclass switching group. The mean psoriasis area and severity index and dermatology life quality index were lower in the intraclass switching group compared to the interclass switching group at each time point over 28 weeks (p < .05). The discontinuation rate was higher in the interclass switching group (24.1%) compared to the intraclass switching group (2.7%; p < .05). Previous exposure to ≥2 biologics working on different pathways was identified as a risk factor for treatment failure in the interclass switching group.Conclusion An intraclass switch following IL-17A inhibitors failure yielded better treatment outcomes than a switch to guselkumab.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".