Method validation and new peak detection for the liquid chromatography-mass spectrometry multi-attribute method.

Mass Spectrometry Method validation Multi-attribute method New peak detection Quality Control

Journal

Journal of pharmaceutical and biomedical analysis
ISSN: 1873-264X
Titre abrégé: J Pharm Biomed Anal
Pays: England
ID NLM: 8309336

Informations de publication

Date de publication:
20 Sep 2023
Historique:
received: 15 03 2023
revised: 27 06 2023
accepted: 04 07 2023
medline: 28 8 2023
pubmed: 15 7 2023
entrez: 14 7 2023
Statut: ppublish

Résumé

The multi-attribute method (MAM) is a liquid chromatography-mass spectrometry (LC-MS) peptide mapping technique that has been proposed as a replacement for several conventional quality control (QC) methods for therapeutic proteins. In addition to quantification of multiple product quality attributes (PQAs), MAM can also monitor impurities using a new peak detection (NPD) feature. Here, results are provided from method validation and NPD studies of an MAM approach applied to rituximab as a model monoclonal antibody (mAb). Twenty-one rituximab PQAs were monitored, including oxidation, pyroglutamination, deamidation, lysine clipping, and glycosylation. The PQA monitoring aspect of the method was validated according to ICH Guidance. Accuracy, precision, specificity, detection and quantitation limits, linearity, range, and robustness were demonstrated for this MAM approach with minimal issues. All PQAs were successfully validated except for several oxidation sites, which did not pass intermediate precision criteria. The variability found in oxidation measurements was attributed to artificial oxidation during sample preparation and could likely be alleviated through several approaches. The NPD aspect of the method was also evaluated. A spike-in approach was used to assess the limits of detection and quantitation (LOD/LOQ) of the NPD feature of MAM. For NPD, the peak intensity threshold was found to be the most critical parameter for accurate detection of impurities since a low threshold can result in false positives while a high threshold can obscure the detection of true peaks. Overall, the MAM approach presented and validated here has been demonstrated to be suitable for both targeted monitoring of rituximab PQAs and non-targeted detection of new peaks that represent impurities.

Identifiants

pubmed: 37451094
pii: S0731-7085(23)00333-3
doi: 10.1016/j.jpba.2023.115564
pii:
doi:

Substances chimiques

Rituximab 4F4X42SYQ6
Antibodies, Monoclonal 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

115564

Informations de copyright

Published by Elsevier B.V.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Mercy Oyugi reports financial support was provided by Oak Ridge Institute for Science and Education. Xiaoshi Wang reports financial support was provided by Oak Ridge Institute for Science and Education. Xiangkun Yang reports financial support was provided by Oak Ridge Institute for Science and Education. Di Wu reports financial support was provided by Oak Ridge Institute for Science and Education.

Auteurs

Mercy Oyugi (M)

Office of Biotechnology Products, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD 20903, USA; Office of Testing and Research, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD 20903, USA.

Xiaoshi Wang (X)

Office of Biotechnology Products, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD 20903, USA; Office of Testing and Research, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD 20903, USA.

Xiangkun Yang (X)

Office of Testing and Research, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD 20903, USA; Prime Medicine, Cambridge, MA 02139, USA.

Di Wu (D)

Office of Testing and Research, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD 20903, USA; AbbVie, South San Francisco, CA 94080, USA.

Sarah Rogstad (S)

Office of Testing and Research, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD 20903, USA. Electronic address: sarah.rogstad@fda.hhs.gov.

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Classifications MeSH