Effects of benralizumab on serum biomarkers and biomarker response after shifting from mepolizumab to benralizumab in eosinophilic granulomatosis with polyangiitis (EGPA)
Bibliographic record
Abstract
Introduction: Asthma, eosinophilia, and vasculitis are key features of EGPA. This study explored the effects of treatment on the serum proteome in EGPA, utilising samples from the 52-week double-blind (DB) period and Year 1 of the open-label extension (OLE) from the MANDARA study ( NCT04157348 ). Methods: Upon completion of the DB period, patients could enter the OLE, where they continued benralizumab (benra/benra; n=66) or switched from mepolizumab to benralizumab (mepo/benra; n=62). Serum samples at Week 0 (before treatment), 4, 48, 52, 76 and 100 were analysed for 3072 proteins using Olink® Explore. Differential protein expression was assessed by linear regression and mixed models. Gene set enrichment analysis was performed at the protein level using pathway annotations from Metabase v22.3.7. Results: We previously showed during the DB period, 6 eosinophil-related proteins (IL-5Rα, eotaxin-1, eotaxin-2, galectin-10, MBP, and MBP-2) were differentially expressed over time between treatment arms, (false discovery rate 5%). During the OLE, the effect on these 6 proteins was similar in both groups, with levels in the mepo/benra group changing to align with the effect seen with benralizumab during the DB period. A hypothesis-free analysis of proteins changing in the mepo/benra group compared with the benra/benra group during the OLE, re-identified 4/6 proteins and identified 1 more, RNASE3, a marker of eosinophil activation. Conclusion: Benralizumab-specific effects on the differentially expressed proteins were replicated during the OLE in the group switching from mepolizumab to benralizumab treatment.
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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 | 0.000 |
| 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) | 0.001 | 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".