Longitudinal model-based meta-analysis in rheumatoid arthritis: an application toward model-based drug development.

Summary-level longitudinal data on the clinical efficacy of drugs for rheumatoid arthritis (RA) are available in the literature. This information can be used to optimize the clinical development of new drugs for RA. The aim of this study was twofold: first, to quantify the time course of the ACR20 score across approved drugs and patient populations, and second, to apply this knowledge in the decision-making process for a specific compound, canakinumab. The integrated analysis included data from 37 phase II-III studies describing 13,474 patients. It showed that, with the tested doses/regimens of canakinumab, there was only a low probability that this drug would be better than the most effective current treatments. This finding supported the decision not to continue with clinical development of canakinumab in RA. This paper presents the first longitudinal model-based meta-analysis of ACR20. The framework can be applied to any other compound targeting RA, thereby supporting internal and external decision making at all clinical development stages.