POS1155 IMPROVEMENT OF FATIGUE IN PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS TREATED WITH DAPIROLIZUMAB PEGOL: 48-WEEK RESULTS FROM A PHASE 3 TRIAL
Bibliographic record
Abstract
Background: Fatigue is a common manifestation of systemic lupus erythematosus (SLE). It is associated with severe impairments to patients' quality of life, diminishes their function, and can be particularly difficult to treat [1-3]. Dapirolizumab pegol (DZP) is a novel, polyethylene glycol (PEG)-conjugated antigen-binding fragment (Fab'), lacking an Fc domain. DZP binds CD40L, blocking CD40-CD40L interactions and CD40 activation, and has broad modulatory effects on SLE immunopathology [4, 5]. In the phase 3 PHOENYCS GO trial (NCT04294667) in patients with SLE, DZP resulted in significant improvement in disease activity at Week 48 versus placebo (PBO) and was generally well tolerated [6]. Objectives: To report the impact of DZP on patient-reported fatigue in patients with SLE participating in the phase 3 PHOENYCS GO trial. Methods: PHOENYCS GO was a 48-week, global, randomised, double-blind, PBO-controlled trial. Patients aged ≥16 years with moderate-to-severe, active SLE characterised by persistently active or frequently flaring/relapsing-remitting disease activity despite stable standard of care (SOC) medication (antimalarials, glucocorticoids and/or immunosuppressants) were included. Patients were randomised 2:1 to intravenous DZP 24 mg/kg plus SOC medication (DZP+SOC) or PBO+SOC every 4 weeks. Fatigue was assessed using Functional Assessment of Chronic Illness Therapy (FACIT)-Fatigue and FATIGUE-PRO, a measure recently developed to capture the patient experience of fatigue in SLE [7]. FACIT-Fatigue assesses levels of fatigue during usual daily activities over the past seven days, based on responses to 13 questions using a five-point Likert scale [8, 9]. The FACIT-Fatigue score ranges from 0 to 52, with lower scores indicating more fatigue and an increase in score over time reflecting improvement. FATIGUE-PRO captures the patient experience of fatigue and consists of 31 items across three scales: Physical Fatigue, Mental Fatigue and Susceptibility to Fatigue [7]. A score ranging from 0 to 100 is calculated for each scale based on patients' responses about how frequently they experienced fatigue in the past seven days, with higher scores indicating more fatigue and a decrease in score over time reflecting improvement. The least squares (LS) mean change from baseline is reported for FACIT-Fatigue at Weeks 12, 24, 36 and 48, and for the three FATIGUE-PRO scales at Weeks 4, 8, 12, 24, 36 and 48. The LS mean, difference between DZP+SOC and PBO+SOC, 95% CIs and p-values were computed using a mixed model for repeated measurements (MMRM). For FACIT-Fatigue, the proportion of patients with an improvement of ≥4 (minimal clinically important difference [MCID]) [10] at Week 48 is also reported. The difference in proportion of responders between DZP+SOC and PBO+SOC, 95% CIs and p-values were estimated and tested using the Cochran-Mantel-Haenszel (CMH) risk difference estimate controlling for stratification factors. All p-values are nominal and were not controlled for multiplicity. Analyses were performed on the full analysis set. Results: Overall, 85.4% of randomised patients receiving DZP+SOC and 79.6% receiving PBO+SOC completed the study to Week 48 on treatment. Baseline fatigue scores were comparable between patients receiving DZP+SOC (n=208) and PBO+SOC (n=107; Table 1). Mean baseline FACIT-Fatigue scores were <30 for both groups (median: DZP+SOC: 28.0; PBO+SOC: 27.0), indicating substantial fatigue. Patients receiving DZP+SOC demonstrated consistently larger LS mean change from baseline in FACIT-Fatigue score, reflecting greater improvement, compared with PBO+SOC at all assessed visits (nominal p<0.05 for all; Figure 1). At Week 48, the LS mean change from baseline in FACIT-Fatigue was 8.9 versus 5.2 for patients receiving DZP+SOC versus PBO+SOC (difference: 3.7; nominal p=0.0024; Figure 1). A greater proportion of patients receiving DZP+SOC (50.5%) achieved an improvement of ≥4 (MCID) in FACIT-Fatigue at Week 48 compared with PBO+SOC (35.5%; difference: 14.5% [95% CI: 3.0, 25.9]; nominal p=0.0131). Similarly, the LS mean change from baseline in scores for all three FATIGUE-PRO scales was greater for patients receiving DZP+SOC compared with PBO+SOC at Week 48 (nominal p<0.05; Figure 1). Greater differences (nominal p<0.05) in patients receiving DZP+SOC compared with PBO+SOC were observed in the Physical Fatigue scale as early as Week 4 and at all visits from Week 12 onwards, in the Mental Fatigue scale at Weeks 36 and 48, and in the Susceptibility to Fatigue scale at all visits from Week 8 onwards. Conclusion: Improvements in FACIT-Fatigue and all FATIGUE-PRO scales were greater in patients treated with DZP+SOC versus PBO+SOC. Alongside the previously reported significant improvements in overall SLE disease activity, [6] these data support the potential of DZP as a valuable treatment option for improving fatigue in SLE. REFERENCES: [1] Tench CM. Rheumatology 2000;39:1249–54. [2] Ahn GE. Int J Clin Rheum 2012;7:217–27. [3] Cleanthous S. Rheumatol Ther 2022;9:95–108. [4] Cutcutache I. Arthritis Rheumatol 2023;75 (suppl 9). [5] Powlesland A. Annals Rheum Dis 2024;83 (suppl 1):261. [6] Clowse M. Arthritis Rheumatol 2024;76 (suppl 9). [7] Morel T. Rheumatology 2022;61:3329–40. [8] Cella D. Semin Hematol 1997;34:13–9. [9] Yellen SB. J Pain Symptom Manage 1997;13:63–74. [10] Lai J. J Rheumatol 2011;38:672–9. Acknowledgements: This study was funded by UCB and Biogen. Medical writing support provided by Costello Medical and funded by UCB and Biogen. Disclosure of Interests: Ioannis Parodis Speaker's bureau for Amgen, AstraZeneca, Gilead, GSK, Janssen, Novartis, Otsuka and Roche, received grant/research support from Amgen, AstraZeneca, Aurinia, BMS, Eli Lilly, GSK, Otsuka and Roche, Caroline Gordon Consultant for Alumis, Amgen, AstraZeneca, Sanofi and UCB, Joan Merrill Consultant for AbbVie, Alexion, Almiral, Alumis, Amgen, AstraZeneca, Aurinia, Biogen, BMS, Eli Lilly, EMD Serono, Equillium, Genentech, Gilead, GSK, Kezar, Merck, Novartis, Ono, Remegen, Sanofi, Takeda, Tenent, UCB, Veloxis and Zenas, received grant/research support from AstraZeneca, BMS and GSK, Matthias Schneider Speaker's bureau for AstraZeneca and GSK, consultant for Abbvie, AstraZeneca, BMS, GSK, Novartis, Otsuka and Roche, received grant/research support from AstraZeneca and GSK, Zahi Touma Consultant for AbbVie, AstraZeneca, BMS, GSK, Roche and UCB/Biogen, received grant/research support from AstraZeneca and GSK, Teri Jimenez Shareholder of UCB, employee of UCB, Thomas Morel Shareholder of UCB, employee of UCB, Mina Nejati Shareholder of Biogen, employee of Biogen, Christian Stach Shareholder of UCB, employee of UCB, Christine de la Loge Consultant for UCB, Laurent Arnaud Speaker's bureau for Alexion, Amgen, AstraZeneca, Abbvie, Biogen, BMS, Boehringer-Ingelheim, Cêmka, GSK, Grifols, Janssen, LFB, Eli Lilly, Menarini France, Medac, Novartis, Otsuka, Pfizer, Roche-Chugaï, Sêmeia and UCB, consultant for Alexion, Amgen, AstraZeneca, Abbvie, Biogen, BMS, Boehringer-Ingelheim, Cêmka, GSK, Grifols, Janssen, LFB, Eli Lilly, Menarini France, Medac, Novartis, Otsuka, Pfizer, Roche-Chugaï, Sêmeia and UCB, received grant/research support from AstraZeneca and GSK. © 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".