Early divergence of treatment response trajectories to adalimumab or its biosimilar in active ankylosing spondylitis: consensus clustering analysis of a randomized controlled trial - Scorecard - MDSpire

Early divergence of treatment response trajectories to adalimumab or its biosimilar in active ankylosing spondylitis: consensus clustering analysis of a randomized controlled trial

  • By

  • Zhiqiang Zhong

  • Hongjuan Lu

  • Ting Li

  • Lingying Ye

  • Ling Zhou

  • Xiaobing Wang

  • Xin Wu

  • Huji Xu

  • June 23, 2026

  • 0 min

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Clinical Scorecard: Differential Patterns of Treatment Response to Adalimumab and Its Biosimilar in Active Ankylosing Spondylitis: A Consensus Clustering Analysis from a Randomized Controlled Trial

At a Glance

CategoryDetail
ConditionActive Ankylosing Spondylitis
Key MechanismsTumor necrosis factor alpha inhibitors (TNFi) response variability
Target PopulationPatients with active ankylosing spondylitis
Care SettingPhase III randomized controlled trial

Key Highlights

  • Two clusters identified: favorable-response (C1) and less favorable-response (C2)
  • C1 showed significantly higher ASDAS-Inactive Disease rates at week 2 and week 24 compared to C2
  • Older age, longer disease duration, and more prior TNFi exposure characterized C2
  • Consensus clustering utilized 8 repeated measurements of 10 core response variables
  • Multivariable model achieved a C-statistic of 0.880 for predicting cluster membership

Guideline-Based Recommendations

Diagnosis

  • Utilize the 1984 Modified New York criteria for AS diagnosis

Management

  • Consider TNFi therapy for patients with active disease despite NSAID therapy

Monitoring & Follow-up

  • Assess treatment response using ASDAS and ASAS criteria at multiple time points

Risks

  • Monitor for inadequate response to TNFi therapy in a significant proportion of patients

Patient & Prescribing Data

438 patients with active AS enrolled in a clinical trial

Response to adalimumab and its biosimilar varies significantly among patients

Clinical Best Practices

  • Implement early stratification of patients based on predicted treatment response
  • Utilize longitudinal data for assessing treatment efficacy and adjusting therapy

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