Center for Drug Safety and Effectiveness, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Clin Pharmacol Drug Dev. 2019 Oct;8(7):914-921. doi: 10.1002/cpdd.658. Epub 2019 Feb 1.
Biologics, especially monoclonal antibodies, are increasingly important in the pharmaceutical marketplace. Population pharmacokinetic (PK) analyses could be useful to guide the need for dose adjustments among special populations, yet it is unknown how commonly such analyses are performed during biologics development. We summarized the characteristics of population PK models of biologics and examined their role in informing the drug labels. To do so, we extracted relevant characteristics of 86 biologics approved by the U.S. Food and Drug Administration's Center for Drug Evaluation and Research between 2003 and 2017. Ninety-four percent of monoclonal antibodies (51 of 54 biologics), 75% of fusion proteins with Fc receptor (6 of 8 biologics), and 33% of other proteins (8 of 24 biologics) included population PK analyses. Of these analyses, approximately half (45%) used a 2-compartment model with linear clearance as the base model structure. Body size was the most frequently included covariate in the final models (included in 94% of the 64 biologics in which covariate analysis was performed), although age (11%), sex (35%), race (26%), and renal function (27%) were also included in some models. In 70% to 90% of cases in which the effect of these covariates was examined, information regarding the effect of these on PK was included in the label. These results suggest that population PK analyses provide important information about the impact of intrinsic factors on the PK in the label of biologics by the U.S. Food and Drug Administration.
生物制剂,特别是单克隆抗体,在药物市场中越来越重要。群体药代动力学(PK)分析有助于指导特殊人群的剂量调整,但目前尚不清楚在生物制剂开发过程中此类分析的实施频率如何。我们总结了生物制剂群体 PK 模型的特征,并考察了它们在为药品标签提供信息方面的作用。为此,我们提取了 2003 年至 2017 年间美国食品和药物管理局药物评价和研究中心批准的 86 种生物制剂的相关特征。94%的单克隆抗体(54 种生物制剂中的 51 种)、75%的具有 Fc 受体的融合蛋白(8 种生物制剂中的 6 种)和 33%的其他蛋白(24 种生物制剂中的 8 种)包含群体 PK 分析。在这些分析中,约一半(45%)使用 2 室模型和线性清除率作为基本模型结构。在最终模型中,体型是最常包含的协变量(在进行协变量分析的 64 种生物制剂中的 94%中包含),尽管年龄(11%)、性别(35%)、种族(26%)和肾功能(27%)也包含在一些模型中。在这些协变量影响被检查的 70%至 90%的情况下,标签中包含了这些因素对 PK 的影响信息。这些结果表明,群体 PK 分析为美国食品和药物管理局的生物制剂标签中关于内在因素对 PK 影响的信息提供了重要依据。