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Record W4411411356 · doi:10.1016/j.ard.2025.05.922

POS0540 Long-Term Safety and Efficacy of Upadacitinib or Adalimumab in Patients With Rheumatoid Arthritis: 7-Year Data From the SELECT-COMPARE Study

2025· article· en· W4411411356 on OpenAlexaff
R. Fleischmann, Jerzy Świerkot, P. Durez, L. Bessette, X. Bu, Irina Fish, A. Gara, Dolores Caballero, Charles Peterfy, Yoshiya Tanaka, Eduardo Mysler

Bibliographic record

VenueAnnals of the Rheumatic Diseases · 2025
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMedicineAdalimumabRheumatoid arthritisTerm (time)Internal medicine

Abstract

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Background: Upadacitinib (UPA), an oral JAK inhibitor, has demonstrated greater clinical responses and a favourable benefit–risk profile compared to adalimumab (ADA) in the long-term extension (LTE) of the SELECT-COMPARE study over 5 years [1-3]. Objectives: To assess the safety and efficacy of UPA versus ADA from the ongoing SELECT-COMPARE study through the 7-year cutoff. Methods: Patients with RA and an inadequate response to MTX were randomized 2:2:1 to UPA 15 mg once daily, placebo (PBO), or ADA 40 mg every other week, with all patients receiving MTX as background treatment. To address inadequate responses, if patients showed less than a 20% improvement in tender or swollen joint counts at weeks 14, 18, or 22, or if they failed to achieve CDAI low disease activity (LDA) by week 26, they were switched to an alternative treatment: patients receiving PBO or ADA were switched to UPA, and those receiving UPA were switched to ADA. All remaining patients receiving PBO were switched to UPA at week 26. Patients who completed the 48-week double-blind phase had the option to continue with open-label UPA or ADA for up to 10 years. Safety was evaluated by calculating exposure-adjusted event rates of treatment-emergent adverse events (TEAEs) per 100 patient-years up to the 7-year cutoff date (September 19, 2024) for all patients receiving ≥ 1 dose of UPA or ADA. Efficacy was evaluated through 372 weeks (~7 years) based on as observed data by treatment sequence, focusing on achievement of low disease activity (CDAI ≤ 10) and remission (CDAI ≤ 2.8), as well as DAS28(CRP) ≤ 3.2 and < 2.6. Additionally, efficacy data for CDAI and DAS28(CRP) responses were analysed by the originally assigned randomized UPA or ADA group applying nonresponder imputation (NRI) for rescue treatments, study drug discontinuation, and missing data. All P values are nominal. Results: A total of 1,629 patients were randomized and treated (PBO, N=651; UPA, N=651; ADA, N=327). At the 7-year cutoff date, 820 patients remained on UPA or ADA across treatment groups (continuous UPA, n=216; PBO to UPA, n=338; continuous ADA, n=82; ADA to UPA, n=80; UPA to ADA, n=104). UPA was generally well tolerated, displaying similar rates of TEAEs, serious TEAEs, TEAEs leading to discontinuation of the study drug, and COVID-related TEAEs when compared to ADA (Figure 1). While most adverse events of special interest, such as MACE, VTE, and malignancies excluding nonmelanoma skin cancer (NMSC), occurred at similar rates for both treatments, UPA showed numerically higher rates of herpes zoster, creatine phosphokinase elevation, NMSC, lymphopenia, and hepatic disorders than ADA. Disease activity targets showed consistent achievement over the LTE period (Figure 2). Of those who remained in the trial, > 88% of patients receiving continuous UPA or continuous ADA achieved CDAI LDA and/or DAS28(CRP) ≤ 3.2 at week 372 based on as observed data. Generally similar proportions of patients receiving continuous UPA vs continuous ADA achieved CDAI remission and DAS28(CRP) < 2.6 over time. Numerically greater proportions of patients who switched from ADA to UPA after inadequate initial response achieved CDAI and DAS28(CRP) targets over 7 years compared to those who switched from UPA to ADA. When evaluating efficacy responses by NRI, recognizing the limitation of applying NRI over a long time period, patients randomized to UPA showed greater efficacy compared to those randomized to ADA at week 372, with higher rates of CDAI LDA (31.2% vs 23.5%), CDAI remission (21.8% vs 13.1%), DAS28(CRP) ≤ 3.2 (28.7% vs 20.8%), and DAS28(CRP) < 2.6 (26.4% vs 16.8%) (all comparisons, nominal P <.05). Conclusion: Up to the 7-year cutoff date, the safety profile of UPA remained consistent with previous study-specific results and the integrated UPA phase 3 safety analysis,[2,3] with no new safety concerns identified. Treatment with both continuous UPA and continuous ADA resulted in stable maintenance of disease activity targets over the 7-year treatment period. In patients with an early insufficient response to their original treatment who switched to the alternate therapy, those who were switched from ADA to UPA showed numerically greater efficacy than those who switched from UPA to ADA. REFERENCES: [1] Conaghan P, et al. Rheumatol Ther . 2022;9:191-206. [2] Fleischmann R, et al. RMD Open . 2024;10:e004007. [3] Burmester GR, et al. RMD Open . 2023;9:e002735. Acknowledgements: AbbVie and the authors thank the participants, study sites, and investigators who participated in this clinical trial (NCT02629159). AbbVie funded this trial and participated in the trial design, research, analysis, data collection, interpretation of data, and the review and approval of the publication. All authors had access to relevant data and participated in the drafting, review, and approval of this publication. No honoraria or payments were made for authorship. Medical writing support was provided by Samira Mawla, PharmD, who is a University of Southern California Pharmaceutical Industry Fellow, supported by AbbVie. Disclosure of Interests: Roy Fleischmann has served as a consultant for AbbVie, Almirall, Artiva Biotherapeutics, Atomwise, Biohaven Pharmaceuticals, BMS, Cyoxone, Deep Cure, Dren Bio, ECOR, Galvani, Gates Bio, Gilead, GSK, Halia, Immunovant, ImmuneMed, InventisBio, Istesso, Janssen, Janux, Eli Lilly, Monte Rosa, Overland, Novartis, Pfizer, Synact, TPG, UCB, Vyne, Xencor, received grant/research support from AbbVie, Amgen, Biosplice, Bristol-Myers Squibb, Flexion, Gilead, Horizon, Eli Lilly, Galvani, Janssen, Novartis, Pfizer, Sanofi-Aventis, Selecta, Teva, UCB, Viela, and Vorso, Jerzy Swierkot has served as a member of a speakers bureau for AbbVie, Accord, BMS, Janssen, MSD, Pfizer, Roche, Sandoz, and UCB, served as a consultant of AbbVie, Accord, BMS, Janssen, MSD, Pfizer, Roche, Sandoz, and UCB, received grant/research support from AbbVie, Accord, BMS, Janssen, MSD, Pfizer, Roche, Sandoz, and UCB, Patrick Durez has received speaker fees from AbbVie, Galapagos, Lilly, Nordimed, and Thermofischer, Louis Bessette has served as a member of a speakers bureau for AbbVie, AstraZeneca, Amgen, Bristol-Meyers Squibb, Celgene, Eli Lilly, Fresenius Kabi, Gilead, Janssen, Merck, Novartis, Pfizer, Roche, Sanofi-Aventis, Teva, and UCB, served as a consultant for AbbVie, AstraZeneca, Amgen, Bristol-Meyers Squibb, Celgene, Eli Lilly, Fresenius Kabi, Gilead, Janssen, Merck, Novartis, Pfizer, Roche, Sanofi-Aventis, Teva, and UCB, received grant/research support from AbbVie, AstraZeneca, Amgen, Bristol-Meyers Squibb, Celgene, Eli Lilly, Fresenius Kabi, Gilead, Janssen, Merck, Novartis, Pfizer, Roche, Sanofi-Aventis, Teva, and UCB, Xianwei Bu is an employee of AbbVie and may hold AbbVie stock or stock options, Irina K Fish is an employee of AbbVie and may hold AbbVie stock or stock options, Andrew Gara is an employee of AbbVie and may hold AbbVie stock or stock options, Diane Caballero is an employee of AbbVie and may hold AbbVie stock or stock options, Charles Peterfy is a shareholder of Spire Sciences, Inc., employee of Spire Sciences, Inc., served as a consultant for Daiichi Sankyo, Eli Lilly, Five Prime, Genentech, Gilead, GlaxoSmithKline, Istesso, Labcorp, Paradigm, SetPoint, Sorrento, and UCB, Yoshiya Tanaka has received speaker fees and/or honoraria from AbbVie, Asahi Kasei, Astellas, BMS, Chugai, Daiichi-Sankyo, Eisai, GSK, Janssen, Lilly, Mitsubishi Tanabe, MSD, Novartis, Ono, Pfizer, Sanofi, Taisho Toyama, Takeda, UCB, and YL Biologics, received research grants from AbbVie, Asahi Kasei, Astellas, BMS, Chugai, Daiichi-Sankyo, Eisai, GSK, Janssen, Lilly, Mitsubishi Tanabe, MSD, Novartis, Ono, Pfizer, Sanofi, Taisho Toyama, Takeda, UCB, and YL Biologics, Eduardo Mysler has served as a member of advisory boards and speaker bureaus for AbbVie, Amgen, AZ, BMS, Janssen, Lilly, Novartis, Pfizer, Roche, Sandoz, SanAlpine Immunology, and HI Bio, received research grants from AbbVie, Amgen, AZ, BMS, Janssen, Lilly, Novartis, Pfizer, Roche, Sandoz, SanAlpine Immunology, and HI Bio. © 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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.035
GPT teacher head0.324
Teacher spread0.290 · 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 source (direct Gemma or distilled Codex), 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 routes1
Has abstractyes

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