聚合物药用辅料相对分子质量及其分布的测定方法研究进展
Research Progress on Analytical Methods for Relative Molecular Weight and Its Distribution of Polymeric Pharmaceutical Excipients
刘佳 1沈淑炜 1姚涵 1陈国广 2陈蕾3
作者信息
- 1. 南京工业大学药学院,江苏南京 211816
- 2. 南京工业大学药学院,江苏南京 211816;江苏省药物研究所,江苏南京 211816
- 3. 国家药典委员会,北京 100061
- 折叠
摘要
相对分子质量及其分布是聚合物药用辅料的重要功能性指标,与聚合物辅料的溶解性能、机械性能、流变性能等多种性质密切相关.聚合物药用辅料的相对分子质量及其分布的测定方法主要包括端基分析法、黏度法、场流分离法和分子排阻色谱(SEC)法等.其中,传统的端基分析法、黏度法、场流分离法均受相对分子质量测试范围和相对分子质量类型的限制,在应用中存在一定的局限性.SEC是一种基于体积排阻分离的新型液相色谱技术,由于其分离效率高、准确度高、操作简单且易于自动化,己逐渐成为测定聚合物药用辅料相对分子质量及其分布的有效手段.该文就SEC的特点、在测定聚合物药用辅料相对分子质量及其分布中的应用进展,以及存在的问题进行归纳分析,旨在更好地推动SEC技术在聚合物药用辅料中的应用.
Abstract
Relative molecular weight and its distribution are important functionality-related characteristics for polymeric pharmaceutical excipients.They are closely related to many properties of polymeric excipients,such as the solubility,mechanical and rheological properties.Many methods have been developed to determine the relative molecular weight and its distribution,including end-group analysis,viscosity method,field flow fractionation and size exclusion chromatography(SEC).The former three traditional methods have certain limitations in applications due to the range and the type of relative molecular weight.SEC is a new liquid chromatographic technique based on volume exclusion separation,and it has gradually become an effective technique for determination of relative molecular weight and molecular weight distribution of polymeric pharmaceutical excipients due to its advantages of high separation efficiency,high accuracy,simple operation and easy automation.In this study,the characteristics and applications of the SEC technique to the determination of relative molecular weight and its distribution of polymeric pharmaceutical excipients are summarized.
关键词
分子排阻色谱/端基分析/黏度法/场流分离/聚合物药用辅料/相对分子质量/分子量分布Key words
size exclusion chromatography/end-group analysis/viscosity method/field flow fractionation/polymeric pharmaceutical excipient/relative molecular weight/molecular weight distribution引用本文复制引用
出版年
2024
NETL
NSTL
万方数据