POS0773 LOW UVEITIS RATES IN PATIENTS WITH AXIAL SPONDYLOARTHRITIS OR PSORIATIC ARTHRITIS TREATED WITH BIMEKIZUMAB: LONG-TERM RESULTS FROM PHASE 2B/3 TRIALS
Bibliographic record
Abstract
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.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".