Revolutionary Method Unveiled to Detect Fake Vaccines in Global Supply Chains

Research published this week and led by University of Oxford researchers describes a first-of-its-kind method capable of distinguishing authentic and falsified vaccines by applying machine learning to mass spectral data. The method proved effective in differentiating between a range of authentic and ‘faked’ vaccines previously found to have entered supply chains.

This latest research will bring the world community one step closer to being able to tell apart falsified, ineffective vaccines from the real thing, making us all safer. It has been a tremendous collaborative effort, with everyone having this same important goal in mind.

Co-author Professor Nicole Zitzmann (Department of Biochemistry, University of Oxford)

The results of the study provide a proof-of-concept method that could be scaled to address the urgent need for more effective global vaccine supply chain screening. A key benefit is that it uses clinical mass spectrometers already distributed globally for medical diagnostics.

The global population is increasingly reliant on vaccines to maintain population health with billions of doses used annually in immunisation programs worldwide. The vast majority of vaccines are of excellent quality. However, a rise in substandard and falsified vaccines threaten global public health. Besides failing to treat the disease for which they were intended, these can have serious health consequences, including death, and reduce confidence in vaccines. Unfortunately, there is currently no global infrastructure in place to monitor supply chains using screening methods developed to identify ineffective vaccines.

In this new study, researchers developed and validated a method that is able to distinguish authentic and falsified vaccines using instruments developed for identifying bacteria in hospital microbiology laboratories. The method is based on matrix-assisted laser desorption/ionisation-mass spectrometry (MALDI-MS), a technique used to identify the components of a sample by giving the constituent molecules a charge and then separating them. The MALDI-MS analysis is then combined with open-source machine learning. This provides a reliable multi-component model which can differentiate authentic and falsified vaccines, and is not reliant on a single marker or chemical constituent.

This innovative research provides compelling evidence that MALDI mass spectrometry techniques could be used in accessible systems for screening for vaccine falsification globally, especially in centres with hospital microbiology laboratories, enhancing public health and confidence in vaccines.

Co-author Professor Paul Newton (Centre for Tropical Medicine and Global Health, University of Oxford)

The method successfully distinguished between a range of genuine vaccines – including for influenza (flu), hepatitis B virus, and meningococcal disease – and solutions commonly used in falsified vaccines, such as sodium chloride.

Professor James McCullagh, study co-leader and Professor of Biological Chemistry in the Department of Chemistry, University of Oxford said: ‘We are thrilled to see the method’s effectiveness and its potential for deployment into real-world vaccine authenticity screening. This is an important milestone for the Vaccine Identity Evaluation (VIE) consortium which focusses on the development and evaluation of innovative devices for detecting falsified and substandard vaccines, supported by multiple research partners including the World Health Organization (WHO), medicine regulatory authorities and vaccine manufacturers.’

The study ‘Using matrix assisted laser desorption ionisation mass spectrometry combined with machine learning for vaccine authenticity screening’ has been published in npj Vaccines.

This research was funded by two anonymous philanthropic families, the Oak Foundation, the Wellcome Trust and the World Health Organization (WHO).

The study was led by a team at the Mass Spectrometry Research Facility in the Department of Chemistry and the Department of Biochemistry, University of Oxford and was part of a research consortium involving teams from the Rutherford Appleton Laboratory of STFC at Harwell and the Departments of Chemistry, Biochemistry and Nuffield Department of Medicine Centre for Global Health Research at the University of Oxford.