1 Biometrics and Data Management, Global Product Development, Groton, CT, USA.
2 Early Clinical Development, Worldwide Research & Development, Cambridge, MA, USA.
Stat Methods Med Res. 2018 Sep;27(9):2694-2721. doi: 10.1177/0962280216684528. Epub 2017 Jan 8.
Characterizing clinical dose-response is a critical step in drug development. Uncertainty in the dose-response model when planning a dose-ranging study can often undermine efficiency in both the design and analysis of the trial. Results of a previous meta-analysis on a portfolio of small molecule compounds from a large pharmaceutical company demonstrated a consistent dose-response relationship that was well described by the maximal effect model. Biologics are different from small molecules due to their large molecular sizes and their potential to induce immunogenicity. A model-based meta-analysis was conducted on the clinical efficacy of 71 distinct biologics evaluated in 91 placebo-controlled dose-response studies published between 1995 and 2014. The maximal effect model, arising from receptor occupancy theory, described the clinical dose-response data for the majority of the biologics (81.7%, n = 58). Five biologics (7%) with data showing non-monotonic trend assuming the maximal effect model were identified and discussed. A Bayesian model-based hierarchical approach using different joint specifications of prior densities for the maximal effect model parameters was used to meta-analyze the whole set of biologics excluding these five biologics ( n = 66). Posterior predictive distributions of the maximal effect model parameters were reported and they could be used to aid the design of future dose-ranging studies. Compared to the meta-analysis of small molecules, the combination of fewer doses, narrower dosing ranges, and small sample sizes further limited the information available to estimate clinical dose-response among biologics.
描述临床剂量反应是药物开发的关键步骤。在规划剂量范围研究时，剂量反应模型的不确定性通常会降低试验设计和分析的效率。先前对大型制药公司小分子化合物组合进行的荟萃分析结果表明，存在一致的剂量反应关系，该关系可以很好地用最大效应模型来描述。由于生物制剂的分子较大，并且具有诱导免疫原性的潜力，因此它们与小分子不同。对 1995 年至 2014 年间发表的 91 项安慰剂对照剂量反应研究中评估的 71 种不同生物制剂的临床疗效进行了基于模型的荟萃分析。源于受体占有率理论的最大效应模型描述了大多数生物制剂（81.7%，n=58）的临床剂量反应数据。确定并讨论了 5 种（7%）数据显示出最大效应模型假设下非单调趋势的生物制剂。使用最大效应模型参数的不同联合先验密度的贝叶斯基于模型的分层方法被用于对排除这 5 种生物制剂的整个生物制剂数据集（n=66）进行荟萃分析。报告了最大效应模型参数的后验预测分布，它们可用于辅助未来剂量范围研究的设计。与小分子的荟萃分析相比，生物制剂中可用的剂量组合较少、剂量范围较窄和样本量较小，这进一步限制了可用信息来估计临床剂量反应。
