2022
DOI: 10.1021/acs.analchem.2c03820
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Optimized Multi-Attribute Method Workflow Addressing Missed Cleavages and Chromatographic Tailing/Carry-Over of Hydrophobic Peptides

Abstract: Peptide mapping by liquid chromatography mass spectrometry (LC-MS) and the related multi-attribute method (MAM) are well-established analytical tools for verification of the primary structure and mapping/quantitation of co-and posttranslational modifications (PTMs) or product quality attributes in biopharmaceutical development. Proteolytic digestion is a key step in peptide mapping workflows, which traditionally is laborintensive, involving multiple manual steps. Recently, simple hightemperature workflows with… Show more

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Cited by 12 publications
(29 citation statements)
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“…In the present study, the different samples were prepared in one single laboratory, and aliquots of the digests were shipped to the other participants. As a result, any observed variability could not be attributed to the tryptic digestion procedure, which is known to be one of the potential sources of variability in peptide MAM workflows [ 17 ]. In addition, the data treatment was also performed in one unique laboratory having a high degree of expertise, and various software solutions were considered to evaluate its impact, including two 21 CFR Part 11-compliant software solutions, UNIFI and waters_connect, and an open-source solution, Skyline.…”
Section: Resultsmentioning
confidence: 99%
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“…In the present study, the different samples were prepared in one single laboratory, and aliquots of the digests were shipped to the other participants. As a result, any observed variability could not be attributed to the tryptic digestion procedure, which is known to be one of the potential sources of variability in peptide MAM workflows [ 17 ]. In addition, the data treatment was also performed in one unique laboratory having a high degree of expertise, and various software solutions were considered to evaluate its impact, including two 21 CFR Part 11-compliant software solutions, UNIFI and waters_connect, and an open-source solution, Skyline.…”
Section: Resultsmentioning
confidence: 99%
“…It should be noted that all the samples employed in this study were prepared in the same laboratory to avoid variability due to sample preparation. To apply the MAM methodology in a QC environment, sample preparation should be carefully controlled, for instance by using automation [ 17 ]. New peak detection was not considered in this study, as it is strongly dependent on the software solution used and cannot be readily validated, nor is it easily amenable to QC testing.…”
Section: Discussionmentioning
confidence: 99%
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“…The authors demonstrated the global applicability and robustness of this workflow at four independent sites in Europe. Kristensen et al 33 described an optimized MAM workflow addressing missed cleavages and chromatographic tailing and carry-over of hydrophobic peptides. A two-step digestion at high and low temperatures was used to reduce the number of missed cleavages and obtain a more complete digestion profile; a C4 column was used to reduce the chromatographic peak tailing and carry-over for hydrophobic peptides.…”
Section: Part I Technical and Regulatory Considerations Of Implementi...mentioning
confidence: 99%
“…A two-step digestion at high and low temperatures was used to reduce the number of missed cleavages and obtain a more complete digestion profile; a C4 column was used to reduce the chromatographic peak tailing and carry-over for hydrophobic peptides. 33 Guan et al 35 showed better stability of digests quenched by 8 M guanidine-HCl, 250 mM acetate, pH 4.7 at a volume ratio of 1:3 compared to those quenched by 2% formic acid at a volume ratio of 1:10 (digestion quenching solution: digests). Finally, Millan-Martin et al recently published comprehensive MAM workflows for biotherapeutic characterization and cGMP testing 36 that included detailed protocols of sample preparations used for MAM from previous publications.…”
Section: Part I Technical and Regulatory Considerations Of Implementi...mentioning
confidence: 99%