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Enregistrement W4411394543 · doi:10.1016/j.ard.2025.06.020

POS0659 EFFICACY, SAFETY AND IMMUNOGENICITY OF THE PROPOSED TOCILIZUMAB BIOSIMILAR RGB-19, INTRAVENOUSLY ADMINISTERED TO PARTICIPANTS WITH ACTIVE RHEUMATOID ARTHRITIS: WEEK 24 DATA FROM A PHASE 3 STUDY

2025· article· en· W4411394543 sur OpenAlexaff
Ernest Choy, P. Emery, Hiromasa Matsuno, Masato Okada, Z. Polgari, K. Horvát-Karajz, G. Dancer, Yukie Karibe, Shinobu Masuda, Jeffrey Kiefer, G. R. Burmester

Notice bibliographique

RevueAnnals of the Rheumatic Diseases · 2025
Typearticle
Langueen
DomaineImmunology and Microbiology
ThématiqueBiosimilars and Bioanalytical Methods
Établissements canadiensInstitute of Infection and Immunity
Organismes subventionnairesnon disponible
Mots-clésMedicineBiosimilarTocilizumabImmunogenicityRheumatoid arthritisPharmacologyImmunologyInternal medicineAntibody

Résumé

récupéré en direct d'OpenAlex

Background: RGB-19, a proposed biosimilar to tocilizumab, is a monoclonal antibody that competitively inhibits the binding of interleukin-6 (IL-6) to its receptor. Objectives: To demonstrate the clinical equivalence in efficacy, and compare serum concentration, pharmacodynamics (PD), safety and immunogenicity, of RGB-19 and reference tocilizumab (hereafter tocilizumab) in participants with rheumatoid arthritis (RA). Methods: This was a Phase 3, randomised, double-blind study in Japanese adults with active RA and an inadequate response to methotrexate, a baseline Disease Activity Score based on 28 joints with erythrocyte sedimentation rate (DAS28-ESR) of ≥3.2, and swollen/tender joints (≥6 from 66/68). Participants were assigned 1:1 to intravenous (IV) infusions of RGB-19 8 mg/kg or IV tocilizumab 8 mg/kg every 4 weeks to W (week) 52 and followed to W54. Primary endpoint was DAS28-ESR mean change from baseline (CfB) at W12 (equivalence margin: ±0.6 for the 2-sided 95% confidence interval of the pooled difference between groups), complemented by secondary efficacy, serum concentration, PD, immunogenicity and safety endpoints to W24. Results: Overall, 368 participants were randomised. A total of 182 (RGB-19) and 185 (tocilizumab) participants were included in the primary analysis. Median age (range) was 57 (20–75) years; most were female (76%) with Class I/II RA (86%) and median time (range) since first RA diagnosis of 2.95 (0.2–46.3) years. Baseline characteristics were balanced between groups. The analysis of covariance (ANCOVA) of the primary endpoint results showed equivalence in efficacy between RGB-19 and tocilizumab at W12 (adjusted mean CfB in DAS28-ESR: RGB-19 −3.62, tocilizumab −3.41; difference point estimate: −0.21 [95% confidence interval −0.43, 0.02]; Table 1). This was supported by the results of a sensitivity (tipping point) analysis and secondary endpoint analyses, including similar ACR, EULAR, CDAI, and SDAI achievement/response rates at W24 (Table 1). Median serum drug concentration, absolute neutrophil count, C-reactive protein level, and soluble IL-6 receptor values were similar for both treatments up to W24. Safety profiles were similar between treatments up to W24 (Table 2) with no new safety signals identified. Both treatments had similarly low immunogenicity (anti-drug antibody positive participants: RGB-19 5 [2.7%]; tocilizumab 8 [4.3%]); there were no treatment-related adverse events leading to death in either arm. Conclusion: Equivalence in efficacy was shown between RGB-19 and tocilizumab. Serum drug concentration, PD, safety, and immunogenicity profiles were similar between treatments. Additional, longer-term efficacy, safety and immunogenicity outcomes up to W52 are anticipated. REFERENCES: NIL . Acknowledgements: Study funded by Gedeon Richter and Mochida Pharmaceutical. Medical writing support provided by Timothy Davies, PhD, of Avalere Health, funded by Gedeon Richter. Disclosure of Interests: Ernest Choy has received research grants, speaking fees, consultancies or honoraria from Abbvie, Bio-Cancer, Biocon, Biogen, Chugai Pharma, Eli Lilly, Fresenius Kai, Galapagos, Gedeon Richter, Gilead, Janssen, Pfizer, Sanofi, UCB and Viatris, Paul Emery has provided expert advice to Abbvie, Activa, Astra-Zeneca, BMS, Boehringer Ingelheim, Galapagos, Gilead, Immunovant, Janssen, Lilly, and Novartis, and contributed to clinical trials of Abbvie, BMS, Lilly, Novartis, and Samsung, Hiroaki Matsuno has received honoraria and/or lecture fees from Chugai Pharmaceutical, Daiichi Sankyo, and Eli Lilly, and has received consulting fees from Mochida Pharmaceutical, and Nichi-Iko Pharmaceutical, Masato Okada has received speaking fees and/or honoraria from Astellas, Eli Lilly and Company, GSK, and Janssen, Zsofia Polgari is a Gedeon Richter employee, Karoly Horvát-Karajz is a Gedeon Richter employee, Gordana Dancer is a Gedeon Richter employee, Yusuke Karibe is a Mochida Pharmaceutical employee, Suguru Masuda is a Mochida Pharmaceutical employee, Joachim Kiefer is a Gedeon Richter employee, Gerd R. Burmester has received honoraria for lectures and consulting from Celltrion, Chugai, Fresenius, Gedeon Richter, and Sanofi. © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Essai randomisé · Signal consensuel: Essai randomisé
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,021

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,002
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0020,002
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0010,001
Science ouverte0,0010,000
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0060,002

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,093
Tête enseignante GPT0,368
Écart entre enseignants0,275 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeEssai randomisé
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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