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Optimization of capillary zone electrophoresis for charge heterogeneity testing of biopharmaceuticals using enhanced method development principles

Author:
Moritz, Bernd, Locatelli, Valentina, Niess, Michele, Bathke, Andrea, Kiessig, Steffen, Entler, Barbara, Finkler, Christof, Wegele, Harald, Stracke, Jan
Source:
Electrophoresis 2017 v.38 no.24 pp. 3136-3146
ISSN:
0173-0835
Subject:
acetonitrile, additives, antibodies, biopharmaceuticals, capillary zone electrophoresis, experimental design, hydrodynamics, models, pH, polymers, proteins
Abstract:
CZE is a well‐established technique for charge heterogeneity testing of biopharmaceuticals. It is based on the differences between the ratios of net charge and hydrodynamic radius. In an extensive intercompany study, it was recently shown that CZE is very robust and can be easily implemented in labs that did not perform it before. However, individual characteristics of some examined proteins resulted in suboptimal resolution. Therefore, enhanced method development principles were applied here to investigate possibilities for further method optimization. For this purpose, a high number of different method parameters was evaluated with the aim to improve CZE separation. For the relevant parameters, design of experiments (DoE) models were generated and optimized in several ways for different sets of responses like resolution, peak width and number of peaks. In spite of product specific DoE optimization it was found that the resulting combination of optimized parameters did result in significant improvement of separation for 13 out of 16 different antibodies and other molecule formats. These results clearly demonstrate generic applicability of the optimized CZE method. Adaptation to individual molecular properties may sometimes still be required in order to achieve optimal separation but the set screws discussed in this study [mainly pH, identity of the polymer additive (HPC versus HPMC) and the concentrations of additives like acetonitrile, butanolamine and TETA] are expected to significantly reduce the effort for specific optimization.
Agid:
5872342