Ixekizumab—An Effective and Safe Treatment for Moderate-to-Severe Plaque Psoriasis in Patients Previously Treated With Other IL-17 Inhibitors: Results From IXORA-P
Bibliographic record
Abstract
Background: The impact of treatment with interleukin 17 (IL-17) inhibitors on the efficacy of subsequent IL-17 inhibitor therapy is unknown. Objective: To evaluate the impact of previous treatment with IL-17 inhibitors on the 52-week efficacy of ixekizumab in patients with moderate-to-severe psoriasis. Methods: In a phase 3, randomized, double-blinded trial (IXORA-P; NCT02513550), patients with moderate-to-severe plaque psoriasis were randomized (2:1:1) to ixekizumab 80 mg every 2 weeks (IXE Q2W, n = 611), every 4 weeks (IXE Q4W, n = 310), or IXE Q4W/IXE Q2W dose adjustment (per predefined criteria; n = 306). Psoriasis Area and Severity Index 75%, 90%, and 100% response rates (PASI 75, PASI 90, and PASI 100) were assessed. Results: Overall, 288 (23.5%) of 1227 patients were IL-17 inhibitor experienced (brodalumab, 22.6%; secukinumab, 1.1%). The PASI 75, 90, and 100 at week 52 were similar between IL-17 inhibitor-naive and IL-17 inhibitor-experienced patients. The PASI 75 at week 52 for IL-17 inhibitor-naive and -experienced patients was 85% and 89% (IXE Q2W), 79% and 81% (IXE Q4W), and 83% and 85% (IXE Q4W/IXE Q2W), respectively. The PASI 90 at week 52 for IL-17 inhibitor-naive and -experienced patients was 79% and 82% (IXE Q2W), 65% and 67% (IXE Q4W), and 73% and 75% (IXE Q4W/IXE Q2W), respectively. The PASI 100 at week 52 for IL-17 inhibitor-naive and -experienced patients was 60% and 59% (IXE Q2W), 44% and 42% (IXE Q4W), and 49% and 52% (IXE Q4W/IXE Q2W), respectively. Safety findings were generally similar between IL-17 inhibitor-naive and -experienced patients. Conclusion: Ixekizumab was demonstrated to be an effective and safe therapeutic option for patients previously treated with other IL-17 inhibitors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".