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Comparative Safety and Effectiveness of Biosimilar and Originator Rituximab for Induction or Maintenance in ANCA-Associated Vasculitis: 6-Month Results of a Longitudinal Cohort Study

2025· article· en· W4412108571 on OpenAlexaffvenue
Arielle Mendel, Lillian Barra, Sasha Bernatsky, Alison Clifford, Mojtaba Dabaghjamanesh, Natasha Dehghan, Aurore Fifi‐Mah, Jean‐Paul Makhzoum, Rosalie Meunier, Nataliya Milman, Medha Soowamber, Jan Cohen Tervaert, Elaine Yacyshyn, Nader Khalidi, Christian Pagnoux

Bibliographic record

VenueThe Journal of Rheumatology · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBiosimilars and Bioanalytical Methods
Canadian institutionsUniversity of British ColumbiaAlberta Hospital EdmontonWestern UniversityUniversity of TorontoHôpital du Sacré-Cœur de MontréalMcGill University Health CentreUniversity of OttawaMount Sinai HospitalUniversity of CalgarySt. Joseph’s Healthcare HamiltonLawson Health Research InstituteUniversity of Alberta
Fundersnot available
KeywordsMedicineBiosimilarRituximabInternal medicineGranulomatosis with polyangiitisCohortVasculitisAdverse effectMaintenance therapyMicroscopic polyangiitisTolerabilitySurgeryChemotherapyDiseaseLymphoma

Abstract

fetched live from OpenAlex

Objectives To evaluate the effectiveness and safety of rituximab biosimilars compared to the originator in Canadians with granulomatosis with polyangiitis (GPA) and microscopic polyangiitis (MPA), and outcomes following originator to biosimilar switching. Methods We recruited adults with GPA or MPA who started rituximab originator or biosimilar for induction or maintenance or switched from originator to biosimilar maintenance between 01/2018-09/2023. Eligible participants either started the index treatment in the prior 6 months or were followed within an existing vasculitis cohort. Six-month outcomes include remission (Birmingham Vasculitis Activity Score [BVAS] v3 of 0), relapse (rise in BVAS after achieving remission, requiring treatment), change in Vasculitis Damage Index (VDI), and serious adverse events (SAEs). Results We enrolled 200 participants from 9 centers: 126 who started induction (52 originator, 74 biosimilar), 58 who started maintenance (22 originator, 36 biosimilar), and 16 who switched from originator to biosimilar maintenance (median 2 years [IQR 1.4-2.2] of originator maintenance prior to switching). Mean age was 57.1 (SD 17.4), 53% were female, 79% White, and 69% had GPA. Baseline characteristics across subgroups are reported in Table 1. 190 (95%) participants had follow-up visits at Month 6 or died prior to this visit. Over a mean follow-up 189 days [SD 56], 2 minor relapses occurred in PR3-ANCA+ individuals, one in the biosimilar induction subgroup (10 weeks), and one in the originator maintenance group (at 4 months). Among induction recipients, 48/49 (98%) in the originator group and 66/71 (93%) in the biosimilar group were in remission at Month 6. All in the originator and biosimilar maintenance subgroups were in remission at Month 6, and all 16 who switched from originator to biosimilar maintenance remained in remission during follow-up. Mean change in VDI was similar between biosimilar and originator subgroups. One or more SAEs occurred in 4/49 (8%) of the originator induction subgroup, 11/71 (15%) of the biosimilar induction subgroup, 2/21 (10%) originator maintenance subgroup, 2/33 (6%) of the biosimilar maintenance group, and 3 (19%) of the ‘switch’ group. Two deaths occurred in the biosimilar induction subgroup (1 alveolar hemorrhage, 1 COVID-19) and 1 death occurred in the switch group (infection, 5.5 months after switching). Table 1. Baseline cohort characteristics at time of starting rituximab originator, biosimilar, or switching from originator to biosimilar (N=200) 1 Conclusion In this cohort, we did not observe differences in remission or relapses at 6 months between RTX originator or biosimilar induction or maintenance. Disease remained stable in those who switched from originator to biosimilar maintenance. Supported by a CIORA grant

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.310

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.322
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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