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高阶结构数据的技术决策:蛋白质治疗候选物筛选过程中的高阶结构表征

Technical decision making with higher order structure data: higher order structure characterization during protein therapeutic candidate screening.

作者信息

Jiang Yijia, Li Cynthia, Li Jenny, Gabrielson John P, Wen Jie

机构信息

Attribute Sciences, Amgen Inc., Thousand Oaks, California, 91320.

出版信息

J Pharm Sci. 2015 Apr;104(4):1533-8. doi: 10.1002/jps.24406. Epub 2015 Feb 25.

DOI:10.1002/jps.24406
PMID:25716705
Abstract

Protein therapeutics differ considerably from small molecule drugs because of the presence of higher order structure (HOS), post-translational modifications, inherent molecular heterogeneity, and unique stability profiles. At early stages of development, multiple molecular candidates are often produced for the same biological target. In order to select the most promising molecule for further development, studies are carried out to compare and rank order the candidates in terms of their manufacturability, purity, and stability profiles. This note reports a case study on the use of selected HOS characterization methods for candidate selection and the role of HOS data in identifying potential challenges that may be avoided by selecting the optimal molecular entity for continued development.

摘要

蛋白质疗法与小分子药物有很大不同,因为存在高级结构(HOS)、翻译后修饰、固有的分子异质性和独特的稳定性特征。在开发的早期阶段,通常会针对同一生物靶点产生多个分子候选物。为了选择最有前景的分子进行进一步开发,需要开展研究以根据候选物的可制造性、纯度和稳定性特征进行比较和排序。本报告介绍了一个案例研究,该研究使用选定的高级结构表征方法进行候选物选择,以及高级结构数据在识别潜在挑战方面的作用,而通过选择最佳分子实体进行持续开发可能避免这些挑战。

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