Bimekizumab versus secukinumab efficacy across subgroups of patients with moderate to severe plaque psoriasis: Results from the multicenter, randomized, double-blinded phase 3b BE RADIANT trial
Bibliographic record
Abstract
ObjectiveTo assess the efficacy of bimekizumab (BKZ), compared with secukinumab (SEC), across different subgroups of patients with moderate to severe plaque psoriasis.• In BE RADIANT (NCT03536884), an ongoing phase 3b, randomized, double-blinded, active comparator-controlled trial, superior levels of complete skin clearance (PASI 100 [100% improvement from baseline in Psoriasis Area and Severity Index]) for patients with plaque psoriasis were observed with BKZ compared with SEC, a biologic targeting IL-17A only. 2• Response to treatment with biologics can vary depending on patient characteristics.3• Here, we assess the efficacy of BKZ vs SEC across subgroups of patients enrolled in BE RADIANT over 48 weeks. Methods• Patients received treatment as shown in Figure 1.• Proportions of patients achieving PASI 100 and PASI 90 (≥90% improvement from baseline in Psoriasis Area and Severity Index) at Week 48 are reported for relevant patient subgroups including baseline weight, prior biologic exposure, age, psoriasis disease duration prior to baseline, baseline PASI, and baseline Investigator's Global Assessment (IGA).• Analyses are based on the intention-to-treat (ITT) population, with data for BKZ every 4 weeks (Q4W) and every 8 weeks (Q8W) maintenance dosing regimens pooled.• Missing data were imputed using non-responder imputation (NRI). Results• In BE RADIANT, 373 patients were randomized to BKZ and 370 were randomized to SEC.• Baseline characteristics were similar between the BKZ and SEC treatment arms (Table 1).• At Week 48, more BKZ-vs SEC-treated patients achieved PASI 100 (Figure 2).• This trend was reflected across patient subgroups, with PASI 100 responder rates ranging from 60.5-75.0%for BKZ compared with 33.3-50.7%for SEC-treated patients (Figure 2).• Similar trends across subgroups were seen for PASI 90 responses at Week 48 for BKZ-vs SEC-treated patients (Figure 3).
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".