Infections from seven clinical trials of ixekizumab, an anti-interleukin-17A monoclonal antibody, in patients with moderate-to-severe psoriasis
Bibliographic record
Abstract
BACKGROUND: Infections are associated with biological therapies in psoriasis. OBJECTIVES: To summarize the incidence of infections in patients with moderate-to-severe psoriasis treated with ixekizumab, an anti-interleukin-17A monoclonal antibody. METHODS: Infections are summarized from an integrated database of seven controlled and uncontrolled ixekizumab psoriasis trials. Data are presented from placebo-controlled induction (weeks 0-12; UNCOVER-1, UNCOVER-2 and UNCOVER-3) and maintenance periods (weeks 12-60; UNCOVER-1 and UNCOVER-2), and all patients exposed to ixekizumab pooled from all seven trials. Comparisons with etanercept were made during the induction period of two trials (UNCOVER-2 and UNCOVER-3). Incidence and exposure-adjusted incidence rates (IRs) per 100 patient-years (PYs) are reported. RESULTS: Overall, 4209 patients were treated with ixekizumab (6480 PY). During induction (weeks 0-12), overall infection rates were higher in patients treated with ixekizumab (27%) vs. placebo (23%, P < 0·05); however, specific infection rates were comparable overall across treatment groups. IRs of infections did not increase with longer-term exposure. For all patients treated with ixekizumab (all seven trials), the incidence of serious infections was low (2%, IR 1·3). Candida infections, including eight cases of oesophageal candidiasis, were adequately managed with antifungal therapy, were noninvasive and did not lead to discontinuation. CONCLUSIONS: Overall, infections occurred in a higher percentage of patients treated with ixekizumab vs. placebo during the first 12 weeks of treatment; however, specific infection rates were comparable overall across treatment groups. Incidences of serious infections were low and similar across treatment groups.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".