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植物药Crofelemer 的化学稳定性:用于复杂混合物药物比较特征分析的模型体系。

Chemical Stability of the Botanical Drug Substance Crofelemer: A Model System for Comparative Characterization of Complex Mixture Drugs.

机构信息

Department of Pharmaceutical Chemistry, University of Kansas, Lawrence, Kansas 66047.

Department of Physics and Astronomy, University of Kansas, Lawrence, Kansas 66047.

出版信息

J Pharm Sci. 2017 Nov;106(11):3257-3269. doi: 10.1016/j.xphs.2017.06.022. Epub 2017 Jul 5.

Abstract

As the second of a 3-part series of articles in this issue concerning the development of a mathematical model for comparative characterization of complex mixture drugs using crofelemer (CF) as a model compound, this work focuses on the evaluation of the chemical stability profile of CF. CF is a biopolymer containing a mixture of proanthocyanidin oligomers which are primarily composed of gallocatechin with a small contribution from catechin. CF extracted from drug product was subjected to molecular weight-based fractionation and thiolysis. Temperature stress and metal-catalyzed oxidation were selected for accelerated and forced degradation studies. Stressed CF samples were size fractionated, thiolyzed, and analyzed with a combination of negative-ion electrospray ionization mass spectrometry (ESI-MS) and reversed-phase-HPLC with UV absorption and fluorescence detection. We further analyzed the chemical stability data sets for various CF samples generated from reversed-phase-HPLC-UV and ESI-MS using data-mining and machine learning approaches. In particular, calculations based on mutual information of over 800,000 data points in the ESI-MS analytical data set revealed specific CF cleavage and degradation products that were differentially generated under specific storage/degradation conditions, which were not initially identified using traditional analysis of the ESI-MS results.

摘要

作为本期关于使用 Crofelemer(CF)作为模型化合物开发用于复杂混合物药物比较特征化的数学模型的 3 部分系列文章中的第 2 部分,本工作重点评估 CF 的化学稳定性概况。CF 是一种生物聚合物,含有混合物的原花青素低聚物,主要由没食子酸与少量儿茶素组成。从药物产品中提取的 CF 进行基于分子量的分级和硫解。选择温度应激和金属催化氧化进行加速和强制降解研究。对受应力的 CF 样品进行大小分级,硫解,并结合负离子电喷雾电离质谱(ESI-MS)和反相-HPLC 与紫外吸收和荧光检测进行分析。我们进一步使用数据挖掘和机器学习方法分析了来自反相-HPLC-UV 和 ESI-MS 的各种 CF 样品的化学稳定性数据集。特别是,基于 ESI-MS 分析数据集超过 800,000 个数据点的互信息计算揭示了在特定存储/降解条件下差异生成的特定 CF 断裂和降解产物,这些产物最初未使用 ESI-MS 结果的传统分析方法识别。

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