Bimekizumab safety in patients with moderate-to-severe plaque psoriasis: pooled data from up to 3 years of treatment in randomized phase III trials
Bibliographic record
Abstract
BACKGROUND: Patients with psoriasis require long-term management; therefore, understanding the long-term safety of new treatments, such as bimekizumab (BKZ), is crucial. OBJECTIVES: To evaluate BKZ's 3-year safety profile in patients with moderate-to-severe plaque psoriasis. METHODS: Three years of safety data were pooled from three phase III trials (BE VIVID, BE READY and BE SURE) and their ongoing open-label extension (BE BRIGHT). Treatment-emergent adverse events (TEAEs) are reported using exposure-adjusted incidence rates (EAIRs) per 100 patient-years (PY). RESULTS: In total, 1495 patients received at least one BKZ dose; total BKZ exposure was 3876.4 PY. The overall EAIR of TEAEs was 175.5/100 PY and decreased with longer exposure to BKZ. The most commonly reported TEAEs were nasopharyngitis, oral candidiasis and upper respiratory tract infection (EAIRs of 15.0/100 PY, 10.1/100 PY and 6.5/100 PY, respectively); 99.3% of oral candidiasis events were mild or moderate in severity, none were serious and few led to discontinuation. EAIRs of other TEAEs of interest were low, including serious infections (1.2/100 PY), adjudicated inflammatory bowel disease (0.2/100 PY) and laboratory elevations in aspartate aminotransferase or alanine aminotransferase (> 5 × upper limit of normal: 0.6/100 PY). CONCLUSIONS: In these analyses pooled across 3 years, no new safety signals were observed with longer exposure to BKZ. The vast majority of oral candidiasis events were mild or moderate in severity, as reported previously.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.047 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.020 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".