Using matrix assisted laser desorption ionisation mass spectrometry combined with machine learning for vaccine authenticity screening
Issued Date
2024-12-01
Resource Type
eISSN
20590105
Scopus ID
2-s2.0-85202631469
Journal Title
npj Vaccines
Volume
9
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
npj Vaccines Vol.9 No.1 (2024)
Suggested Citation
Clarke R., Bharucha T., Arman B.Y., Gangadharan B., Gomez Fernandez L., Mosca S., Lin Q., Van Assche K., Stokes R., Dunachie S., Deats M., Merchant H.A., Caillet C., Walsby-Tickle J., Probert F., Matousek P., Newton P.N., Zitzmann N., McCullagh J.S.O. Using matrix assisted laser desorption ionisation mass spectrometry combined with machine learning for vaccine authenticity screening. npj Vaccines Vol.9 No.1 (2024). doi:10.1038/s41541-024-00946-5 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/100950
Title
Using matrix assisted laser desorption ionisation mass spectrometry combined with machine learning for vaccine authenticity screening
Author's Affiliation
Central Laser Facility
Mahidol Oxford Tropical Medicine Research Unit
NIHR Oxford Biomedical Research Centre
MESA+ Instituut
University of Huddersfield
University of Oxford
University of East London
Nuffield Department of Medicine
University of Oxford Medical Sciences Division
Agilent Technologies LDA UK Ltd
Mahidol Oxford Tropical Medicine Research Unit
NIHR Oxford Biomedical Research Centre
MESA+ Instituut
University of Huddersfield
University of Oxford
University of East London
Nuffield Department of Medicine
University of Oxford Medical Sciences Division
Agilent Technologies LDA UK Ltd
Corresponding Author(s)
Other Contributor(s)
Abstract
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.