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High-throughput and Comprehensive Drug Surveillance Using Multisegment Injection-Capillary Electrophoresis-Mass Spectrometry
Published on: April 23, 2019
Multi-laboratory development of bioanalytical methods for two cyclic peptides, cyclorasin9A5 and 3003pep
Rika Ishikawa1, Mikio Shirasaki2, Koichi Shiga2
1Division of Medicinal Safety Science, National Institute of Health Sciences, Kawasaki, Kanagawa 210-9501, Japan.
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Cyclic peptides exhibit highly desirable characteristics for therapeutic applications, including selectivity toward specific target molecules and extended half-lives, compared to linear peptides. Due to the intricate nature of cyclic peptide structures, liquid chromatography coupled with mass spectrometry (LC-MS) is preferred for their quantitative bioanalysis. However, a suitable bioanalytical method based on LC-MS faces several challenges, such as the resistance of the precursor ions to fragmentation and the presence of multiple charge states in the target ions. In practice, variations in LC-MS systems are common among different laboratories; however, the effects of such variations on bioanalytical methods for cyclic peptides remain unclear. Therefore, in the present study, LC-MS-based bioanalytical methods were simultaneously developed in different laboratories for the analysis of two cyclic peptides, namely cyclorasin9A5 and 3003pep. Notably, upon application of the methods developed in seven laboratories under different sample preparation conditions and LC-MS parameters, all validation parameters, except the carry-over for cyclorasin9A5, met the acceptance criteria outlined in the ICH M10 guidelines. These findings imply that in the analyses of cyclic peptides, the optimal conditions for the developed LC-MS bioanalytical methods vary depending on the system employed; however, following optimization, the methods exhibit comparable performances regardless of the laboratory setup. Additionally, a putative systemic bias was observed in the surrogate sample analysis of one analyte peptide, highlighting a challenge in comparing data between different laboratories.

