OP0096 Impact of sonelokimab, a novel IL-17A- and IL-17F-inhibiting Nanobody, on multidomain clinical outcomes in active psoriatic arthritis: results from the randomized, double-blind, placebo-controlled Phase 2 ARGO trial
Notice bibliographique
Résumé
Background: Sonelokimab is a novel IL-17A- and IL-17F-inhibiting Nanobody that binds to both IL-17A and IL-17F with similarly high affinity and is designed to target difficult-to-reach sites of inflammation due to its small size and albumin-binding domain. The randomized, placebo-controlled Phase 2 ARGO trial in patients with active psoriatic arthritis (PsA) met its primary endpoint at Week 12 with sonelokimab 60mg and 120mg (with induction dosing) achieving significantly higher rates of ACR50 vs. placebo [1], with response rates further increasing through Week 24 [2]. The involvement of multiple domains, including skin and joints, in the majority of patients with PsA leads to impaired function and drastically impacts patients' quality of life [3]. Multidomain outcomes that capture disease impact across different tissues and assessment types are therefore important to evaluate. Objectives: Describe the multidomain and high-hurdle outcomes with sonelokimab in the Phase 2 ARGO trial of patients with active PsA. Methods: ARGO (NCT05640245) was a global, randomized, prospective, double-blind, placebo-controlled trial, conducted across 42 sites in Europe and the United States. Eligible patients were ≥18 years old with a confirmed diagnosis of active PsA (Swollen Joint Count [SJC] 66 ≥3; Tender Joint Count [TJC] 68 ≥3) and active psoriasis or a dermatologist-confirmed history of psoriasis. Patients were randomized 1:1:1:1:1 to sonelokimab 60mg with no induction (NI), sonelokimab 60mg or sonelokimab 120mg (every 4 weeks from Week 8, with induction doses at Weeks 0, 2, 4, and 6), placebo, or adalimumab 40mg every 2 weeks (reference arm; not powered for statistical comparison). Randomization was stratified by sex (male/female) and prior biologic use (yes/no). Patients receiving placebo switched to sonelokimab 120mg NI at Week 12. Additionally, patients with SJC and TJC improvement <20% were reassigned treatment at Week 12. Multidomain endpoints included minimal disease activity (MDA) rates and concomitant achievement of ACR70 + PASI 100 (PASI assessed in patients with BSA >3%). Missing data were imputed as non-response. Results: Overall, 207 patients were randomized. At Week 24, up to 61% of patients achieved MDA with sonelokimab (Table 1). When considering pooled data across all sonelokimab arms, ≥50% of patients achieved MDA across key subgroups, including female patients (52%) and patients with a weight ≥100kg (53%), hsCRP levels >ULN (56%), or ≥3% BSA involvement at baseline (55%). Notably, 40% of patients across sonelokimab arms who had active moderate-to-severe psoriasis at baseline (PASI ≥10) also achieved MDA at Week 24 (20% in the adalimumab reference arm). The composite outcome of ACR70 + PASI 100 was achieved by up to 48% of patients receiving sonelokimab at Week 24, demonstrating simultaneous achievement of high-hurdle endpoints across disease domains (Table 1). Robust achievement of ACR70 responses at Week 24 was observed across all sonelokimab dose groups (Table 1), with high rates (>37%) also observed across key subgroups of interest for pooled sonelokimab doses (sex [male vs. female], weight [<100kg vs. ≥100kg], concomitant DMARDs, hsCRP levels [>ULN vs. ≤ULN]). ACR70 was also achieved by up to 39% of patients with more severe joint disease at baseline (DAPSA >28; 120mg dose arm). Additionally, up to 63% of patients achieved complete skin clearance (PASI 100; Table 1), with robust responses also reported across subgroups of interest, including patients with moderate-to-severe psoriasis at baseline (45% achievement across pooled sonelokimab doses). Sonelokimab was well tolerated and there were no unexpected safety findings. There were no cases of IBD, MACE, depression, or SIB. Four cases (2%) of mild or moderate oral candidiasis were recorded. Conclusion: Sonelokimab demonstrated strong efficacy on multidomain clinical outcomes, as well as on individual high-hurdle composite joint and skin response outcomes, with consistent and robust benefits seen across key patient subgroups. There were no unexpected safety findings. Phase 3 studies (IZAR-1: NCT06641076 and IZAR-2: NCT06641089) are ongoing to evaluate sonelokimab 60mg and 120mg in active PsA. REFERENCES: [1] McInnes et al . Efficacy and safety of sonelokimab, a novel IL-17A- and IL-17F-inhibiting Nanobody®, in patients with active psoriatic arthritis (PsA): Results from the global, randomized, double-blind, placebo-controlled Phase 2 ARGO trial. EULAR 2024 (Presentation OP0195). [2] McInnes et al . Efficacy and safety of sonelokimab, a novel IL-17A- and IL-17F-inhibiting Nanobody, in patients with active PsA: 24-week results from a global, randomized, double-blind, placebo-controlled Phase 2 trial. ACR 2024 (Presentation 2582). [3] Ogdie et al. Experiences and treatment preferences in patients with psoriatic arthritis: A cross-sectional study in the ArthritisPower registry. Rheumatol Ther. 2022;9:735–751. Table 1Composite and high-hurdle outcomes at Week 24n, (%)SLK60mg NIn=41SLK60mgn=41SLK120mgn=43ADA refn=42MDA19 (46.3)25 (61.0)22 (51.2)19 (45.2)ACR70 + PASI 100*10 (31.3)9 (34.6)13 (48.1)6 (18.8)ACR7016 (39.0)16 (39.0)18 (41.9)15 (35.7)PASI 100*19 (59.4)16 (61.5)17 (63.0)16 (50.0)NRI, except for patients who switched treatment due to lack of joint response or who completed the study at Week 12 where the last observation during Part A was carried forward.*% based on patients with ≥3% BSA at baseline in the ITT population (SLK 60mg NI, n=32; SLK 60mg, n=26; SLK 120mg, n=27; ADA ref, n=32).ACR, American College of Rheumatology; ADA ref, adalimumab reference; BSA, body surface area; ITT, intention-to-treat; MDA, minimal disease activity; NI, no induction; NRI, non-responder imputation; PASI, Psoriasis Area and Severity Index; SLK, sonelokimab. Acknowledgements: NIL . Disclosure of Interests: Iain B. McInnes has been paid as a speaker for AbbVie, Janssen, UCB, and Novartis, has worked as a paid consultant for AbbVie, Lilly, UCB, MoonLake Immunotherapeutics, Dextera, Novartis, Astra Zeneca, BMS, GSK, Montai, Absci, Compugen, and Cabaletta, has received grants/research support from Lilly, UCB, Dextera, Novartis, Astra Zeneca, and GSK, Lihi Eder has been paid as a speaker for AbbVie, Pfizer, Novartis, Fresenius Kabi, UCB, has worked as a paid consultant for Novartis, Eli Lilly, BMS, Johnson & Johnson, AbbVie, has received grants/research support from Novartis, Eli Lilly, BMS, Johnson & Johnson, AbbVie, Fresenius Kabi, UCB, Christopher T. Ritchlin has worked as a paid consultant for AbbVie, UCB, MoonLake Immunotherapeutics, BMS, Novartis, Janssen, Solarea, Lilly, Alexis Ogdie has worked as a paid consultant for AbbVie, Amgen, BMS, Celgene, Corrona, Gilead, GSK, Janssen, Lilly, Novartis, Spyre, Takeda, TREG, Pfizer, UCB, has received grants/research support from AbbVie (to Penn), Amgen (to Forward), BMS (to Forward), Janssen (to Penn), Novartis (to Penn), Pfizer (to Penn), Forward/National Databank for Rheumatic Diseases, NIH/NIAMS, Rheum Research Foundation, Arthur Kavanaugh has worked as a paid consultant for Amgen, BMS, Lilly, Janssen, Pfizer, Novartis, MoonLake Immunotherapeutics, UCB, Laura C. Coates has been paid as a speaker for AbbVie, Amgen, Lilly, Janssen, Novartis, Pfizer and UCB, has worked as a paid consultant for AbbVie, Amgen, BMS, Lilly, Enlivex, Janssen, MoonLake Immunotherapeutics, Novartis, Pfizer, Takeda and UCB, has received grants/research support from AbbVie, Amgen, Janssen and UCB, Georg Schett: None declared, Alan Kivitz has been paid as a speaker for AbbVie, CME speaker for Excel Continuing Education, GSK, Lilly, Pfizer, Sanofi - Regeneron, UCB, has shares/share options in Pfizer, GSK, Gilead, Novartis, Amgen, has worked as a paid consultant for AbbVie, Coval, Ecor1, Fresenius Kabi, Genzyme, Gilead, Grunenthal, GSK, Halia, Horizon, Innovaderm, Janssen, MoonLake Immunotherapeutics, Pacira, Prime (educational), Prometheus, Santa Ana Bio Inc, Synact, Takeda - Nimbus, UCB, VYNE, XBiotech, Xencor, Nuala Brennan is a shareholder in MoonLake Immunotherapeutics, is an employee of MoonLake Immunotherapeutics, Alex Godwood is a shareholder of MoonLake Immunotherapeutics, is an employee of MoonLake Immunotherapeutics, Matthew R. Thomas has stock options in MoonLake Immunotherapeutics, is an employee of MoonLake Immunotherapeutics, Eva Cullen has presented at congresses as part of current employment with MoonLake Immunotherapeutics, is a shareholder of MoonLake Immunotherapeutics, is an employee of MoonLake Immunotherapeutics, Kristian Reich has been paid as a speaker for LEO Pharma, Lilly Pharma, Almirall S.A. AbbVie, and Janssen-Cilag, is a shareholder of MoonLake Immunotherapeutics, is an employee of MoonLake Immunotherapeutics, has worked as a paid consultant for LEO Pharma, Lilly, Almirall S.A., AbbVie, and Janssen-Cilag, has received grants/research support from LEO Pharma, Lilly, Almirall S.A, and AbbVie, Joseph F. Merola has been a consultant and/or investigator for Amgen, Astra-Zeneca, Boehringer Ingelheim, BMS, AbbVie, Dermavant, Lilly, Incyte, MoonLake Immunotherapeutics, Novartis, Janssen, UCB, Sanofi-Regeneron, Sun Pharma, Biogen, Pfizer and Leo Pharma, has been a consultant and/or investigator for Amgen, Astra-Zeneca, Boehringer Ingelheim, BMS, AbbVie, Dermavant, Lilly, Incyte, MoonLake Immunotherapeutics, Novartis, Janssen, UCB, Sanofi-Regeneron, Sun Pharma, Biogen, Pfizer and Leo Pharma, Philip J. Mease has been paid as a speaker for AbbVie, Amgen, Eli Lilly, Johnson & Johnson, Novartis, Pfizer, UCB, has worked as a paid consultant for AbbVie, Acelyrin, Amgen, BMS, Century, Cullinan Biotech, Lilly, Inmagene, Johnson & Johnson, MoonLake Immunotherapeutics, Novartis, Pfizer, Takeda, UCB, and Genascence (Data Safety Board only), has received grants/research support from AbbVie, Acelyrin, Amgen, BMS, Lilly, Johnson & Johnson, Novartis, Pfizer, 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 | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».