Clinical Pharmacology & Pharmacometrics, Bristol-Myers Squibb, Princeton, New Jersey, USA.
AAPS J. 2018 Feb 26;20(2):35. doi: 10.1208/s12248-018-0194-9.
The rapidly increasing number of therapeutic biologics in development has led to a growing recognition of the need for improvements in immunogenicity assessment. Published data are often inadequate to assess the impact of an antidrug antibody (ADA) on pharmacokinetics, safety, and efficacy, and enable a fully informed decision about patient management in the event of ADA development. The recent introduction of detailed regulatory guidance for industry should help address many past inadequacies in immunogenicity assessment. Nonetheless, careful analysis of gathered data and clear reporting of results are critical to a full understanding of the clinical relevance of ADAs, but have not been widely considered in published literature to date. Here, we review visualization and modeling of immunogenicity data. We present several relatively simple visualization techniques that can provide preliminary information about the kinetics and magnitude of ADA responses, and their impact on pharmacokinetics and clinical endpoints for a given therapeutic protein. We focus on individual sample- and patient-level data, which can be used to build a picture of any trends, thereby guiding analysis of the overall study population. We also discuss methods for modeling ADA data to investigate the impact of immunogenicity on pharmacokinetics, efficacy, and safety.
开发中的治疗性生物制剂数量迅速增加,人们越来越认识到需要改进免疫原性评估。已发表的数据通常不足以评估抗药物抗体 (ADA) 对药代动力学、安全性和疗效的影响,并且无法在发生 ADA 时就患者管理做出充分知情的决策。最近为行业发布的详细监管指南应该有助于解决免疫原性评估中过去存在的许多不足之处。尽管如此,对收集到的数据进行仔细分析和明确报告结果对于全面了解 ADA 的临床相关性至关重要,但迄今为止在已发表的文献中并未广泛考虑。在这里,我们回顾了免疫原性数据的可视化和建模。我们提出了几种相对简单的可视化技术,这些技术可以提供关于 ADA 反应动力学和幅度及其对药代动力学和临床终点的影响的初步信息,这些信息适用于特定的治疗性蛋白。我们重点介绍个体样本和患者水平的数据,这些数据可用于描绘任何趋势,从而指导对整个研究人群的分析。我们还讨论了用于建模 ADA 数据以研究免疫原性对药代动力学、疗效和安全性的影响的方法。