Department of Modeling and Simulation, Novartis Pharma AG, Basel, Switzerland.
Clin Pharmacol Ther. 2012 Sep;92(3):352-9. doi: 10.1038/clpt.2012.69. Epub 2012 Jul 4.
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.
关于类风湿关节炎（RA）药物临床疗效的汇总水平纵向数据在文献中是可用的。这些信息可用于优化 RA 新药的临床开发。本研究的目的有两个：第一，量化批准药物和患者人群中 ACR20 评分的时间进程；第二，将这方面的知识应用于特定化合物（卡那单抗）的决策过程中。综合分析包括来自 37 项描述 13474 名患者的 II-III 期研究的数据。结果表明，对于所测试的卡那单抗剂量/方案，该药比目前最有效的治疗方法更有效可能性较低。这一发现支持了停止卡那单抗在 RA 中的临床开发的决定。本文介绍了 ACR20 的第一个基于纵向模型的荟萃分析。该框架可应用于任何其他针对 RA 的化合物，从而支持各个临床开发阶段的内部和外部决策。
