Department of Pharmaceutical Chemistry, University of Kansas, Lawrence, Kansas 66047.
Department of Physics and Astronomy, University of Kansas, Lawrence, Kansas 66047.
J Pharm Sci. 2017 Nov;106(11):3257-3269. doi: 10.1016/j.xphs.2017.06.022. Epub 2017 Jul 5.
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.
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。