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Adalimumab and its biosimilar IBI303 show distinct response clusters in ankylosing spondylitisTrial shows two different ways patients respond to adalimumab

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Key Takeaway
Note that distinct response clusters exist in ankylosing spondylitis; however, predictive models require further validation.

This Phase III randomized controlled trial enrolled 438 patients with active ankylosing spondylitis to evaluate outcomes using adalimumab or its biosimilar, IBI303. The study utilized consensus clustering of 10 core response variables over a 24-week follow-up period.

Analysis identified two distinct longitudinal clusters: C1 (favorable-response, 56.2%, n=246) and C2 (less favorable-response, 43.8%, n=192). At week 2, the C1 group had significantly higher rates of ASDAS-Inactive Disease than C2 (Risk Difference [RD]: -0.21; 95% CI 0.16, 0.26 vs. 0.00, 0.02; p < 0.001). At week 24, the difference remained significant with a Risk Difference of -0.49 (95% CI 0.57, 0.69 vs. 0.09, 0.18; p < 0.001).

A multivariable model for C2 identification achieved a C-statistic of 0.880 (95% CI 0.859, 0.915). Safety data and specific adverse event rates were not reported.

While the study provides a potential framework for early patient stratification based on response trajectories, the findings are currently hypothesis-generating. The predictive model for C2 identification requires further independent validation before it can be used for prospective clinical patient triage.

How this fits prior evidence

How this fits prior evidence: This study addresses a gap in identifying patient response trajectories for those treated with adalimumab or its biosimilar. While previous evidence confirmed that switching between adalimumab and its biosimilar adalimumab-atto yields similar pharmacokinetics, this trial focuses on the longitudinal clinical outcomes of patients on these therapies. It provides a potential framework for stratification that is not addressed by existing data on genetic loci or other biologics like vunakizumab.

Living with ankylosing spondylitis means dealing with persistent joint and spine inflammation. While many patients use adalimumab, not everyone experiences the same level of relief. A recent study of 438 patients looked closely at how people responded to this medication over a 24-week period.

The researchers found that patients actually fall into two distinct groups. One group, making up about 56% of those studied, showed a favorable response to the treatment. The other group, about 44%, had a less favorable response. By looking at several different health markers, the team was able to see these clear differences in how the body reacted to the medicine.

While the study found a way to predict which patients might fall into the less-favorable group, it is still early days. The researchers noted that this finding is currently used to generate new ideas for how doctors might sort patients into different care plans. Because these results need more independent testing before they can be used in everyday clinics, talk with your doctor about how these findings apply to your specific treatment plan.

What this means for you:
Patients with ankylosing spondylitis fall into two distinct groups based on how they respond to adalimumab.

Common questions

What did the study find about how patients react to adalimumab?

The study found that patients with active ankylosing spondylitis fall into two groups. One group, making up 56.2% of the participants, showed a favorable response. The other group, making up 43.8%, showed a less favorable response to the treatment over 24 weeks.

How different were the results between the two groups?

The first group had significantly higher rates of inactive disease at both week 2 and week 24 compared to the second group. At week 24, the favorable group showed a much higher rate of inactive disease than the less favorable group.

Can doctors use these results to choose treatments right now?

The findings are currently used to generate new ideas for how to sort patients into different care plans. However, the researchers noted that the predictive model needs more independent testing before it can be used in daily clinical practice.

Study Details

Study typeRct
EvidenceLevel 2
PublishedJun 2026
View Original Abstract ↓
BackgroundPatients with active ankylosing spondylitis (AS) exhibit substantial heterogeneity in their clinical responses to tumor necrosis factor alpha inhibitors (TNFi). Consensus clustering, an unsupervised cluster discovery method, may identify AS subgroups with more homogeneous treatment response patterns to adalimumab (ADA), a widely prescribed TNFi.MethodsWe performed longitudinal consensus clustering based on 8 repeated measurements of 10 core response variables in 438 patients with active AS enrolled in a 24-week phase III randomized controlled trial of ADA or its biosimilar, IBI303. Baseline characteristics and important endpoints—including the Assessment of SpondyloArthritis International Society (ASAS)-based and Ankylosing Spondylitis Disease Activity Score Inactive Disease (ASDAS)-based response criteria—were compared between the identified clusters. Predictive models of cluster membership reconstructed from data beyond week 2 were developed and internally validated to facilitate early, prospective identification of the clusters based on baseline and week-2 data.ResultsTwo longitudinal clusters were characterized: a favorable-response cluster (C1, n = 246, 56.2%) and a less favorable-response cluster (C2, n = 192, 43.8%). Compared with C1, C2 was characterized by older age, longer disease duration, and more frequent prior TNFi exposure, despite comparable baseline C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR). For the majority of clinical endpoints except CRP and BASMI, significant divergence emerged as early as week 2 and was sustained through week 24. For instance, C1 achieved significantly higher rates of ASDAS-Inactive Disease (ASDAS-ID) than C2 at week 2 (0.21 [95% CI 0.16, 0.26] vs. 0.01 [95% CI 0.00, 0.02]; Risk Difference [RD]: -0.21 [95% CI -0.26, -0.15], p < 0.001) and week 24 (0.63 [95% CI 0.57, 0.69] vs. 0.14 [95% CI 0.09, 0.18]; RD: -0.49 [95% CI -0.57, -0.42], p < 0.001). A multivariable model incorporating one baseline and eight week-2 variables had an optimism-corrected C-statistic of 0.880 [95% CI 0.859, 0.915] to correctly identify C2.ConclusionsThese observed response trajectories exhibited distinct baseline characteristics and early response divergence at week 2. While these findings provide a hypothesis-generating framework for early patient stratification, their utility for prospective clinical patient triage requires further independent validation.
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