POS1155 IMPROVEMENT OF FATIGUE IN PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS TREATED WITH DAPIROLIZUMAB PEGOL: 48-WEEK RESULTS FROM A PHASE 3 TRIAL
Notice bibliographique
Résumé
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.
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 | 0,003 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 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) | 0,006 | 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 ».