Chemical Stability of the Botanical Drug Substance Crofelemer: A Model System for Comparative Characterization of Complex Mixture Drugs
| Autor: | Jian Xiong, Peter A. Kleindl, C. Russell Middaugh, M. Laird Forrest, David B. Volkin, Christian Schöneich, Eric J. Deeds, Maulik K. Nariya, Asha Hewarathna, Sangeeta B. Joshi, Olivier Mozziconacci, Adam C. Fisher |
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| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Spectrometry Mass Electrospray Ionization Drug Storage Electrospray ionization Pharmaceutical Science Fractionation Mass spectrometry 01 natural sciences High-performance liquid chromatography Article Machine Learning 03 medical and health sciences Drug Stability Gallocatechin Proanthocyanidins Sulfhydryl Compounds Antidiarrheals Chromatography High Pressure Liquid Chromatography Chemistry 010401 analytical chemistry Temperature 0104 chemical sciences 030104 developmental biology Thiolysis Forced degradation Chemical stability Oxidation-Reduction |
| Zdroj: | Journal of Pharmaceutical Sciences. 106:3257-3269 |
| ISSN: | 0022-3549 |
| DOI: | 10.1016/j.xphs.2017.06.022 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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