Inter-laboratory study of an optimised peptide mapping workflow using automated trypsin digestion for monitoring monoclonal antibody product quality attributes.

Inter-laboratory study Method transferability Monoclonal antibody Peptide mapping Post-translational modifications (PTMs) Trypsin digestion

Journal

Analytical and bioanalytical chemistry
ISSN: 1618-2650
Titre abrégé: Anal Bioanal Chem
Pays: Germany
ID NLM: 101134327

Informations de publication

Date de publication:
Oct 2020
Historique:
received: 05 05 2020
accepted: 07 07 2020
revised: 09 06 2020
pubmed: 28 7 2020
medline: 4 5 2021
entrez: 26 7 2020
Statut: ppublish

Résumé

Peptide mapping analysis is a regulatory expectation to verify the primary structure of a recombinant product sequence and to monitor post-translational modifications (PTMs). Although proteolytic digestion has been used for decades, it remains a labour-intensive procedure that can be challenging to accurately reproduce. Here, we describe a fast and reproducible protocol for protease digestion that is automated using immobilised trypsin on magnetic beads, which has been incorporated into an optimised peptide mapping workflow to show method transferability across laboratories. The complete workflow has the potential for use within a multi-attribute method (MAM) approach in drug development, production and QC laboratories. The sample preparation workflow is simple, ideally suited to inexperienced operators and has been extensively studied to show global applicability and robustness for mAbs by performing sample digestion and LC-MS analysis at four independent sites in Europe. LC-MS/MS along with database searching was used to characterise the protein and determine relevant product quality attributes (PQAs) for further testing. A list of relevant critical quality attributes (CQAs) was then established by creating a peptide workbook containing the specific mass-to-charge (m/z) ratios of the modified and unmodified peptides of the selected CQAs, to be monitored in a subsequent test using LC-MS analysis. Data is provided that shows robust digestion efficiency and low levels of protocol induced PTMs. Graphical abstract.

Identifiants

pubmed: 32710279
doi: 10.1007/s00216-020-02809-z
pii: 10.1007/s00216-020-02809-z
pmc: PMC7496030
doi:

Substances chimiques

Antibodies, Monoclonal 0
Trypsin EC 3.4.21.4

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

6833-6848

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Auteurs

Silvia Millán-Martín (S)

Characterisation and Comparability Laboratory, National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Blackrock, Co., Dublin, A94 X099, Ireland.

Craig Jakes (C)

Characterisation and Comparability Laboratory, National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Blackrock, Co., Dublin, A94 X099, Ireland.
School of Chemical and Bioprocess Engineering, University College Dublin, Belfield, Dublin 4, D04 V1W8, Ireland.

Sara Carillo (S)

Characterisation and Comparability Laboratory, National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Blackrock, Co., Dublin, A94 X099, Ireland.

Tom Buchanan (T)

Thermo Fisher Scientific, Tudor Rd, Runcorn, WA7 1TA, UK.

Marc Guender (M)

Thermo Fisher Scientific, Reinach TechCenter, Neuhofstrasse 11, 4153, Basel, Switzerland.

Dan Bach Kristensen (DB)

Symphogen, Pederstrupvej 93, 2750, Ballerup, Denmark.

Trine Meiborg Sloth (TM)

Symphogen, Pederstrupvej 93, 2750, Ballerup, Denmark.

Martin Ørgaard (M)

Symphogen, Pederstrupvej 93, 2750, Ballerup, Denmark.

Ken Cook (K)

Thermo Fisher Scientific, Stafford House, 1 Boundary Park, Hemel Hempstead, HP2 7GE, UK.

Jonathan Bones (J)

Characterisation and Comparability Laboratory, National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Blackrock, Co., Dublin, A94 X099, Ireland. jonathan.bones@nibrt.ie.
School of Chemical and Bioprocess Engineering, University College Dublin, Belfield, Dublin 4, D04 V1W8, Ireland. jonathan.bones@nibrt.ie.

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Classifications MeSH