New AI-designed ‘universal vaccine’ could protect against future virus outbreaks

A new AI-designed ‘universal’ coronavirus vaccine trialled at University Hospital Southampton (UHS) could protect against viruses that have yet to emerge – potentially saving millions of lives and avoiding costly lockdowns.

A team at the University of Cambridge and spin-out company DIOSynVax used all the available genetic sequence data logged by surveillance programmes around the world relating a large group of viruses including SARS-CoV-2, which caused the COVID pandemic.

This led to the creation of a ‘super antigen’ containing all features common to these viruses, known as Sarbeco coronaviruses, with the aim of providing lasting protection against multiple viruses even as they mutate.

This was subject to a trial involving 39 healthy volunteers at UHS’s National Institute for Health and Care Research (NIHR) Clinical Research Facility and a partnered NIHR facility at Addenbrookes Hospital, Cambridge.

The vaccine triggered immune responses in the volunteers not only to SARS-CoV-2 and SARS, but to related bat viruses that could potentially jump from animals to humans and cause future pandemics.

It is the first time that a vaccine whose active component was designed entirely by computer simulations has been tested in humans. The results, published in the Journal of Infection, show the vaccine is safe and has no significant side-effects.

Professor Saul Faust, chief investigator of the trial and director of the NIHR Southampton Clinical Research Facility at UHS, which sponsored the study, said: “Viruses like Influenza, Coronaviruses and the Ebola group are evolving continuously and by the time vaccines are rolled out, they may be poorly matched - the current ‘reactive’ vaccine system struggles to keep pace.

“This new class of universal vaccines are future-proofed. They not only protect against many variants simultaneously, but potentially against related viruses that haven’t yet emerged and spilt over to humans.

“If we can develop and clinically advance this new class of vaccines before a virus outbreak begins, millions of lives could be saved, lockdowns avoided and the economy preserved.”

The super antigen is compatible with most vaccine delivery systems and, in this trial, it was administered as DNA vaccine through a micro fluid jet – and was needle-free.

Further development of the vaccine is needed before it is ready for public use and a larger Phase 2 trial will next assess the vaccine’s ability to induce immune responses in a wider and more diverse population, and confirm that it generates strong, broadly protective immune responses.

Professor Jonathan Heeney from the Lab of Viral Zoonotics, University of Cambridge’s Department of Veterinary Medicine, is the scientific lead for the research programme.

He said: “We’ve converted vaccine development from being reactive to being future proof. Our vaccines will continue to provide protection against viruses even as they mutate into new strains.

“We’ve overcome the problem of traditional vaccines, which have limited protection. It means we can escape the constant cycle of chasing the virus variants circulating in humans and updating the vaccines to try to catch up, like a dog chasing its tail.”

Professor Marian Knight, Scientific Director for NIHR Infrastructure, said: "The remarkable success of this AI-designed ‘super-antigen’ trial marks a pivotal leap forward in our ability to deliver broad, lasting viral protection.”

She added: “This milestone was only made possible through partnerships between the life sciences sector and our world-class NIHR infrastructure in Cambridge and Southampton, whose Clinical Research Facilities provided the vital expertise and environment needed to safely fast-track this innovation, and bring it one big step closer to patients.”

The research was primarily funded by Innovate UK. The DIOSynVax pipeline includes vaccine candidates for human seasonal Flu and the pandemic influenza threats, haemorrhagic fever viruses, and coronaviruses including SARS-CoV-2.