Characteristics of rheumatoid arthritis clinical trials over past decade 2013-2023: current landscape and opportunities for improvement

Abstract

Background There is a disconnection between the continued pressing clinical demand for rheumatoid arthritis (RA) treatments and the saturation of the current therapeutic markets. The design of rheumatoid arthritis trials might represent one of significant barrier to advancing therapeutic progress. A comprehensive review was performed to evaluate the characteristics of RA trials registered in ClinicalTrials.gov from 2013 to 2023. Methods The ClinicalTrials.gov database was searched for trials focused on the RA interventional trials from 2013 to 2023. Interventional drug or biological trials were included. Key characteristics of RA trials were summarized and target population, control groups selection, and clinical endpoints were evaluated. Results Between January 2013 and December 2023, 425 RA trials were included. Decreased trial numbers, excessive industry sponsorship, and delayed published results were found. For target population, 28% clinical trials didn’t define distinct RA patients, and 38% of the trials included population with no upper age limit. For control groups, only 36% trials had head-to-head comparisons, 50% were placebo-controlled, where half of placebo-controlled trials were with special design (add-on, early escape, double dummy), and half without any design. For clinical endpoints, ACR20 (24%) and DAS28 (21%) were the most commonly used outcomes, with declining ACR20 and ascending DAS28. Only 7% trials adherence to “treat-to-target” strategy, but the most commonly used outcome measures not aligned with guideline-recommended. Conclusions Our study contributes to a nuanced comprehension of the current landscape of RA trials and offers valuable insights for future improvement. This included the necessity of stratifying the target population based on disease activity or treatment history to achieve precision in treatment; considerations of more stringent or sensitive clinical endpoints to provide better discriminatory power; addressing discrepancies between the endpoints selected for treat-to-target and those recommended by guidelines to choose optimal treatment strategy.