摘要
抗体药物制剂中聚集体和颗粒的产生是生物医药研发过程的重要质量问题,颗粒包含可见颗粒及亚可见颗粒(sub-visible particle,SbvP),两者的形成息息相关,是注射剂质量控制的重要检测项。近年来,高浓度抗体药物制剂的临床应用呈现增加的趋势,制剂中的高浓度抗体降低了单剂量包装容器中药液的灌装量,因此无法满足常规SbvP检测方法中样品的检测体积,故现有检测方法在检测小容量的单剂量包装样品时具有一定的局限性。此外,高浓度抗体会影响药物制剂的黏度、折光系数等理化性质,同样对现有的检测方法提出了挑战。本文通过归纳分析《美国药典》及近期的文献资料,对高浓度抗体药物制剂中的SbvP的检测方法、面临的挑战和应对策略等进行综述,为高浓度抗体药物制剂的SbvP检测方法的进一步研究提供参考。
Formation of aggregates and particulates in biological drug products remains one of the major quality issues in the development of biotherapeutics.Particulates,including visible particles and sub-visible particles(SbvP)that are highly correlated in their formation,are one of the critical quality attributes in quality control for injectable drug products.In recent years,the application of high-concentration antibody products in clinical treatment has significantly increased.The increased protein concentration leads to the significant reduction of filled product volume in a single container closure system which cannot satisfy the minimum volume requirement of current SbvP analysis and brings its testing challenge.The current SbvP test is limited to the small product package of high concentration drug products.Moreover,high concentration protein will affect the physical and chemical properties of drug preparactions,such as high viscosity and reflection coefficient,also brings the challenges of particulates detection.This article summarizes the‘United States Pharmacopoeia’and recent literatures in analytical methods,challenges and strategies of SbvP analysis,and provides a reference for further study on particulate analysis of high concentration antibody drug products.
作者
丁毅
张雅婷
郭欢欢
熊菲
纪仁智
邱波
DING Yi;ZHANG Ya-ting;GUO Huan-huan;XIONG Fei;CHI Jen-chih;QIU Bo(The Process Development Department of BeiGene(Guangzhou)Biologies Manufacturing Company,Guangzhou 510555,China)
出处
《中国新药杂志》
CAS
CSCD
北大核心
2022年第8期762-766,共5页
Chinese Journal of New Drugs
关键词
高浓度抗体药物
光阻法
亚可见颗粒
微流成像法
方法优化
high concentration antibody drug product
light obscuration
sub-visible particle
microflow imaging
method optimization