[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127140-en":3,"doc-seo-127140-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127140,687207022233,"Riley","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Using matrix assisted laser desorption ionisation mass spectrometry combined with machine learning for vaccine authenticity screening","Substandard and falsified vaccines threaten global public health and undermine trust in immunisation programmes. With no coordinated infrastructure for monitoring vaccine supply chains, risk-based screening methods are urgently needed. This study develops and validates an open-source machine-learning workflow using MALDI-MS to distinguish authentic from falsified vaccines, using diagnostic mass spectra and multivariate modelling.","npj | vaccines Article  \nPublished in partnership with the Sealy Institute for Vaccine Sciences  \n[https://doi.org/10.1038/s41541-024-00946-5](https://doi.org/10.1038/s41541-024-00946-5)  \nUsing matrix assisted laser desorption ionisation mass spectrometry combined with machine learning for vaccine authenticity screening  \n Check for updates  \nRebecca Clarke 1, Tehmina Bharucha 2,3, Benediktus Yohan Arman 2,3, Bevin Gangadharan 2,3, Laura Gomez Fernandez2,3, Sara Mosca 4, Qianqi Lin 4,12, Kerlijn Van Assche 5,6,7, Robert Stokes8, Susanna Dunachie 6,9,10, Michael Deats5,6,7, Hamid A. Merchant  11,13, Céline Caillet 5,6,7,  \nJohn Walsby-Tickle 1, Fay Probert 1, Pavel Matousek 4,5, Paul N. Newton 5,6,7,  \nNicole Zitzmann 2,3 & James S. O. McCullagh 1   \nThe 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.  \nSafe and effective medicines are crucial to people’s health worldwide but an increase in substandard and falsified pharmaceutical products threatens public health on a global scale. The World Health Organisation estimated that over 10% of pharmaceutical products in lower and middle-income countries were substandard or falsified (SF) in 2017 and has identified SF medicines as one of the urgent health challenges for the next decade1,2.  \nReports of SF vaccine products have been increasing in recent years, including rabies, cholera, meningitis, yellow fever, hepatitis Band coronavirus disease 2019 (COVID-19). For example, in the first 15 months of the global COVID-19 vaccination programme, there were over 184 reports, across 48 countries, of diverted and SF COVID-19 vaccines, involving millions of doses3. A range ofadulteration and falsification incidents have been identified,  \n1Department of Chemistry, University of Oxford, Oxford, OX1 3TA, UK. 2Department of Biochemistry, University of Oxford, Oxford, OX1 3QU, UK. 3Kavli Institute for Nanoscience Discovery, University of Oxford, Oxford, OX1 3QU, UK. 4Central Laser Facility, Research Complex at Harwell, STFC Rutherford Appleton Laboratory, UK Research and Innovation(UKRI), Harwell Campus, Didcot, OX110QX, UK. 5Medicine Quality Research Group, NDM Centre for Global Health Research, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7LG, UK. 6Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, 10400, Thailand. 7Infectious Diseases Data Observatory, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7LG, UK. 8Agilent Technologies LDA UK, Didcot, OX11 0RA, UK. 9NDM Centre for Global Health Research, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7LG, UK. 10NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK. 11Department of Pharmacy, School of Applied Sciences, University of Huddersfield, Huddersfield, HD1 3DH, UK. 12Present address: Hybrid Materials for Opto-Electronics Group, Department of Mo","cbCaioN1Qya1qRXL","https://ap.wps.com/l/cbCaioN1Qya1qRXL","pdf",3138053,1,14,"English","en",105,"# Background\n## Substandard and falsified vaccine risks\n# Method\n## MALDI-MS workflow with open-source machine learning\n# Validation and Results\n## Performance across multiple instruments\n# Implications\n## Proof-of-concept for supply chain monitoring","[{\"question\":\"为什么需要区分真实与假冒疫苗？\",\"answer\":\"假冒与次标准疫苗会危害公共健康并增加发病和死亡风险，同时损害疫苗作为安全药物的信誉。文中强调两类问题的成因与解决方案不同。\"},{\"question\":\"本研究使用了什么技术与建模方法？\",\"answer\":\"研究建立了基于矩阵辅助激光解吸/电离质谱（MALDI-MS）的工作流程，并结合开放源代码的机器学习与统计分析。通过多变量数据建模和诊断性质谱图来区分真实与假冒疫苗。\"},{\"question\":\"该方法如何验证其可行性？\",\"answer\":\"方法在两台在临床应用中广泛使用的不同MALDI-MS仪器上进行了验证。结果显示多变量建模与诊断质谱能够实现真实与假冒疫苗的区分。\"}]","Using matrix assisted laser desorption ionisation mass spectrometry combined with machine learning for vaccine authenticity screening | 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