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Nature Medicine | Phase 2 Trial of Sonelokimab for Active Psoriatic Arthritis

Nature Medicine | Phase 2 Trial of Sonelokimab for Active Psoriatic Arthritis
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This article reviews a Phase 2 randomized, double-blind, placebo-controlled trial evaluating the efficacy and safety of sonelokimab in patients with active psoriatic arthritis. The study demonstrates that sonelokimab is significantly superior to placebo on multiple key endpoints and is generally well tolerated.

 

Literature Overview
The article, titled 'Sonelokimab, an IL-17A/IL-17F-inhibiting nanobody for active psoriatic arthritis: a randomized, placebo-controlled phase 2 trial,' published in the journal Nature Medicine, reviews and summarizes the treatment advances in psoriatic arthritis (PsA), particularly focusing on dual inhibition strategies targeting IL-17A and IL-17F. The study evaluates the effects of different doses of sonelokimab in treating active PsA through a randomized controlled trial and compares its efficacy to adalimumab and placebo.

Background Knowledge
Psoriatic arthritis (PsA) is a chronic, multisystem inflammatory disease closely related to the IL-17 family of cytokines. Current treatments for PsA include traditional DMARDs and biologics, such as TNF or IL-17A inhibitors. Despite available treatment options, a significant proportion of patients still fail to achieve minimal disease activity (MDA). IL-17A and IL-17F play important roles in the pathogenesis of PsA, and simultaneous inhibition of both may provide better efficacy. Traditional monoclonal antibodies usually have a larger molecular weight (about 150 kDa), which may limit their accumulation in hard-to-reach inflamed tissues, while nanobodies (about 40 kDa) have smaller molecular weights and better tissue penetration, making them potentially advantageous in treating PsA. This study evaluates the efficacy and safety of a nanobody that targets both IL-17A and IL-17F, as well as albumin, in PsA.

 

 

Research Methods and Experiments
The study is a global, multicenter, randomized, double-blind, placebo-controlled Phase 2 clinical trial involving 207 patients with active PsA. Participants were randomly assigned to receive different doses of sonelokimab (SLK), adalimumab (ADA), or placebo. The primary endpoint was the proportion of patients achieving ACR50 at week 12. Key secondary endpoints included ACR20 and PASI90. Additionally, the study assessed multiple disease activity measures, including MDA, PASI100, and patient-reported outcomes (PROs). All analyses were conducted on the intention-to-treat (ITT) population, with missing data handled using non-responder imputation (NRI) and mixed models for repeated measures (MMRM).

Key Conclusions and Perspectives

  • At week 12, the ACR50 response rates in the SLK 60-mg WI and 120-mg WI groups were 46.3% and 46.5%, respectively, significantly higher than the placebo group (20.0%).
  • At week 12, the ACR20 response rates in the SLK 60-mg WI and 120-mg WI groups were 78.0% and 72.1%, respectively, both significantly better than the placebo group (37.5%).
  • The SLK 60-mg WI and 120-mg WI groups achieved PASI90 response rates of 76.9% and 59.3%, respectively, at week 12, significantly higher than the placebo group (15.4%).
  • At week 24, the ACR50 response rates in the SLK treatment groups increased to 58.1–61.0%, while patients who switched from placebo to SLK treatment had an ACR50 response rate of 54.1%.
  • SLK treatment also showed significant improvements in high-threshold composite endpoints such as ACR70 + PASI100 and MDA. The MDA response rates at week 24 were 61% (60-mg WI) and 48% (120-mg WI).
  • SLK was generally well tolerated, with the main treatment-related adverse events including nasopharyngitis, upper respiratory tract infections, injection site erythema, and headache. The incidence of oral candidiasis was low (2.4–2.1%).
  • The study also found that SLK efficacy was consistent regardless of patient sex, weight, or methotrexate use. Higher response rates were observed in patients with baseline PASI ≥ 10 or DAPSA > 28.

Research Significance and Prospects
This study is the first global Phase 2 clinical trial to evaluate the potential of a nanobody that simultaneously targets IL-17A and IL-17F in the treatment of PsA. Due to its smaller molecular weight and albumin-binding properties, SLK may accumulate more effectively in inflamed tissues, potentially providing superior efficacy. The results support the need for future Phase 3 clinical trials to further confirm the efficacy and safety of SLK in a larger patient population.

 

 

Conclusion
This study is the first to evaluate the Phase 2 clinical trial results of sonelokimab (SLK) in patients with psoriatic arthritis (PsA). SLK is a nanobody that targets both IL-17A and IL-17F, with a smaller molecular weight and enhanced tissue penetration. The trial results show that SLK is significantly superior to placebo on multiple disease activity measures, including ACR50, ACR70, PASI90, and PASI100, and also demonstrates improvement in patient-reported outcomes (PROs). Additionally, SLK shows consistent efficacy across different subgroups (e.g., sex, weight, methotrexate use) and is generally well tolerated. Although this is a Phase 2 study with a relatively small sample size in certain disease domains (e.g., dactylitis and enthesitis), which limited statistical significance in those areas, the sustained improvements in key endpoints suggest SLK has potential in the treatment of PsA. Future Phase 3 trials will assess the long-term efficacy and safety of SLK in a broader patient population, further validating its potential as a next-generation treatment for PsA.

 

Reference:
Iain B McInnes, Laura C Coates, Philip J Mease, Christopher T Ritchlin, and Joseph F Merola. Sonelokimab, an IL-17A/IL-17F-inhibiting nanobody for active psoriatic arthritis: a randomized, placebo-controlled phase 2 trial. Nature Medicine.
ΔG Prediction
Using PPB-Affinity, currently the largest protein-protein binding affinity database, as training data, the magnitude of protein complex binding affinity (ΔG) is predicted using invariant point notation based on geometric deep learning techniques through three-dimensional characterisation of protein complexes.
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