免疫原性
可视化
临床试验
计算机科学
医学
数据科学
数据挖掘
病理
免疫系统
免疫学
作者
Chaitali Passey,Satyendra Suryawanshi,Kinjal Sanghavi,Manish Gupta
出处
期刊:Aaps Journal
[Springer Nature]
日期:2018-02-26
卷期号:20 (2)
被引量:10
标识
DOI:10.1208/s12248-018-0194-9
摘要
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.
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