POS0773 LOW UVEITIS RATES IN PATIENTS WITH AXIAL SPONDYLOARTHRITIS OR PSORIATIC ARTHRITIS TREATED WITH BIMEKIZUMAB: LONG-TERM RESULTS FROM PHASE 2B/3 TRIALS
Notice bibliographique
Résumé
Background: Acute anterior uveitis (‘uveitis') is a common extra-musculoskeletal manifestation in patients with spondyloarthritis (SpA), and incidence varies with SpA type and disease duration [1-3]. Interleukin (IL)-17 has been implicated in the pathogenesis of uveitis; however, previous data have not demonstrated the efficacy for IL-17A inhibition alone in managing the condition [4, 5]. The exposure-adjusted incidence rate (EAIR) per 100 patient-years (PY) of uveitis was significantly lower in patients with axial SpA (axSpA) randomised to bimekizumab (BKZ; 1.8/100 PY), a dual IL-17A/F inhibitor, versus placebo (15.4/100 PY) after 16 weeks [6]. Objectives: To report long-term incidence of uveitis following BKZ treatment in patients with axSpA or psoriatic arthritis (PsA). Methods: Safety data are reported for two pools, each comprising three phase 2b/3 studies and their open-label extensions, in patients with active axSpA (non-radiographic and radiographic axSpA) and active PsA, respectively (Figure 1). Uveitis events were identified using the preferred terms "autoimmune uveitis", "iridocyclitis", "iritis", and "uveitis", coded according to MedDRA v19.0; note that "acute anterior uveitis" was not a specific preferred term available in MedDRA v19.0. Uveitis rates and EAIRs/100 PY for patients who received ≥1 subcutaneous BKZ 160 mg dose are reported (data cut-off: July 2023). Results: Patients with axSpA (N=848) had a mean age (standard deviation [SD]) of 40.3 (11.9) years, and patients with PsA (N=1,409) had a mean age (SD) of 49.3 (12.4) years, with a mean time since diagnosis (SD) of 6.1 (7.8) and 7.0 (8.0) years, respectively. Of patients with axSpA, 130 (15.3%) had a history of uveitis; 21 (1.5%) patients with PsA had a history of uveitis. The majority of patients with axSpA were HLA-B27 positive (717/848 [84.6%]). In patients with axSpA across the pooled phase 2b/3 axSpA trial data, BKZ exposure was 2,514 PY. Uveitis occurred in 31/848 (3.7%; EAIR [95% CI]: 1.3/100 PY [0.9, 1.8]) patients overall and in 18/130 (13.8%; 4.8/100 PY [2.8, 7.6]) patients with history of uveitis. In patients without a history of uveitis, 13/718 (1.8%; 0.6/100 PY [0.3, 1.1]) patients had uveitis events (Figure 2). All events were mild/moderate, one led to treatment discontinuation. Incidence of uveitis in patients with PsA was low across the pooled phase 2b/3 PsA trial data (total BKZ exposure: 3,656 PY); uveitis occurred in three (0.2%; 0.1/100 PY [0.0, 0.2]) patients overall; one had a history of uveitis. No uveitis events led to treatment discontinuation. Conclusion: Across 2,514 PY in patients with axSpA and 3,656 PY in patients with PsA, the long-term data suggest that the incidence of anterior uveitis in patients treated with BKZ remains low. REFERENCES: [1] Robinson PC. Arthritis Rheumatol. 2015;67:140–51. [2] López-Medina C. RMD Open 2019;5:e001108. [3] Delmás A. RMD Open 2023;9:e002781. [4] Dick AD. J. Ophthalmol. 2013;120:777–87. [5] Kwon OC. Rheumatol. 2024;keae003. [6] Rudwaleit M. Ann Rheum Dis. 2023;82:614–5. [7] Baraliakos X. Ann Rheum Dis. 2024;83:199–213. [8] Mease PJ. Arthritis Rheumatol. 2023;75(suppl 9). Abstract 0511. Acknowledgements: Funded by UCB. Editorial support provided by Costello Medical and funded by UCB. Disclosure of Interests: Irene van der Horst-Bruinsma Fees received for Lectures from BMS, AbbVie, Pfizer, MSD, Consultant for Abbvie, UCB, MSD, Novartis, Lilly, Unrestricted Grants received for investigator-initiated studies from: MSD, Pfizer, AbbVie, UCB, Matthew Brown Speaker for Novartis and Pfizer, Consultant for Clementia, Grey Wolf Therapeutics, Incyte, Ipsen, Pfizer, Regeneron and Xinthera, Grant/research support from UCB, Floris van Gaalen Grants from Jacobus Stichting, Novartis, Stichting ASAS, Stichting Vrienden van Sole Mio and UCB; fees from Novartis; personal fees from AbbVie, BMS, Eli Lilly and MSD, Nigil Haroon Consultant for AbbVie, Eli Lilly, Janssen, Novartis and UCB, Lianne S Gensler Consultant for Acelyrin, Eli Lilly, Janssen, Novartis, Pfizer and UCB, Grants from UCB paid to institution, Alexander Marten Employee of UCB, Myriam Manente Shareholder of UCB, Employee of UCB, George Stojan Employee of UCB, Tom Vaux Shareholder of UCB, Employee of UCB, Katy White Shareholder of UCB, Employee of UCB, Atul Deodhar Speaker for Eli Lilly, J&J, Novartis, Pfizer and UCB, Consulting fees from BMS, Eli Lilly, J&J, MoonLake, Novartis, Pfizer and UCB, Grant/research support from BMS, Eli Lilly, J&J, Novartis, Pfizer and UCB, Martin Rudwaleit Speakers bureau from AbbVie, Boehringer Ingelheim, Chugai, Eli Lilly, Janssen, Novartis, Pfizer and UCB, Consultant of AbbVie, Eli Lilly, Novartis and UCB. © 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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».