Using matrix assisted laser desorption ionisation mass spectrometry combined with machine learning for vaccine authenticity screening.
Journal
NPJ vaccines
ISSN: 2059-0105
Titre abrégé: NPJ Vaccines
Pays: England
ID NLM: 101699863
Informations de publication
Date de publication:
28 Aug 2024
28 Aug 2024
Historique:
received:
07
03
2024
accepted:
07
08
2024
medline:
31
8
2024
pubmed:
31
8
2024
entrez:
28
8
2024
Statut:
epublish
Résumé
The global population is increasingly reliant on vaccines to maintain population health with billions of doses used annually in immunisation programmes. Substandard and falsified vaccines are becoming more prevalent, caused by both the degradation of authentic vaccines but also deliberately falsified vaccine products. These threaten public health, and the increase in vaccine falsification is now a major concern. There is currently no coordinated global infrastructure or screening methods to monitor vaccine supply chains. In this study, we developed and validated a matrix-assisted laser desorption/ionisation-mass spectrometry (MALDI-MS) workflow that used open-source machine learning and statistical analysis to distinguish authentic and falsified vaccines. We validated the method on two different MALDI-MS instruments used worldwide for clinical applications. Our results show that multivariate data modelling and diagnostic mass spectra can be used to distinguish authentic and falsified vaccines providing proof-of-concept that MALDI-MS can be used as a screening tool to monitor vaccine supply chains.
Identifiants
pubmed: 39198486
doi: 10.1038/s41541-024-00946-5
pii: 10.1038/s41541-024-00946-5
doi:
Types de publication
Journal Article
Langues
eng
Pagination
155Subventions
Organisme : Oxford University | John Fell Fund, University of Oxford (John Fell OUP Research Fund)
ID : 0011807
Organisme : World Health Organization (WHO)
ID : 2021/1170671-0
Organisme : Wellcome Trust (Wellcome)
ID : 222506/Z/21/Z
Informations de copyright
© 2024. The Author(s).
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