Depemokimab reduces exacerbations in severe asthma versus other biologics: A multilevel network meta-regression
Bibliographic record
Abstract
Background: In the SWIFT-1/2 trials, depemokimab, an ultra-long-acting biologic with enhanced IL-5 binding affinity, high potency, extended half-life and twice-yearly dosing, significantly reduced exacerbations with sustained suppression of type 2 inflammation assessed by blood eosinophil count in patients with type 2 asthma. Aims: To estimate the relative efficacy of depemokimab compared with other biologics approved for asthma. Methods: A systematic literature review identified published randomised clinical trials of asthma biologics. Fixed and random effects Bayesian models estimated annualised rate ratios with/without adjustment for patient baseline characteristics. Differences in effect modifiers between trials were adjusted using multilevel network meta-regression. Results: Seventeen trials were included. Depemokimab showed a statistically significant reduction in the risk of clinically significant exacerbations across all models versus placebo, against benralizumab, dupilumab (300 mg) and omalizumab in unadjusted models, and against omalizumab in partially adjusted models (Figure). No significant differences were observed between depemokimab and dupilumab 200 mg/mepolizumab/reslizumab/tezepelumab. Conclusions: Our results show no statistically significant differences in annual exacerbation rates across assessed biologics, suggesting comparable benefits for asthma. Funding: GSK (212680). erj;66/suppl_69/PA4610/F1 F1 F1
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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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.048 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".