There are currently 25 vaccines to fight COVID-19 in clinical evaluation, another 139 vaccines in a pre-clinical stage, and many more being researched.

But many of those vaccines, if they are at all successful, might not produce an immune response in portions of the population. That's because some people's bodies will react differently to the materials in the vaccine that are supposed to stimulate virus-fighting T cells.

And so just figuring out how much coverage a vaccine has, meaning, how many people it will stimulate to mount an immune response, is a big part of the vaccine puzzle.

With that challenge in mind, scientists at Massachusetts Institute of Technology on Monday unveiled a machine learning approach that can predict the probability that a particular vaccine design will reach a certain proportion of the population. That doesn't mean they can guarantee its effectiveness, but the scientists' work can aid in knowing up-front whether a given vaccine will have large gaps in who it can help.

The good news is, the MIT scholars have used their approach to design a novel COVID-19 vaccine on the computer that has far better coverage than many of the designs that have been published in the literature this year. They're now testing the design in animals.

The bad news is, there could very well be large gaps in coverage of some of the existing vaccines already being explored by companies and labs, according to one of the authors of the report, David K. Gifford, who is with MIT's Computer Science and Artificial Intelligence Laboratory.

"While they may protect more than 50% of the population, certain individuals and older individuals may not be protected," Gifford told ZDNet in an email, when asked about vaccines currently under trial and in development.

The long path to a vaccine

Vaccines in development were not the direct subject of the work. Most of those vaccines are closed designs; no one knows exactly how they are composed. Instead, Gifford and colleagues designed vaccines from scratch, and then analyzed how effective they are, and extrapolated the findings to a group of vaccines whose composition is known.

Based on that, one can infer there might be problems with vaccines whose exact composition is not known.

It must be borne in mind that any in silico vaccine design such as the kind discussed here is only the beginning of a process that can take years to go through in vivo testing, in animals and then in humans, to establish both safety (non-toxicity), and efficacy, meaning that it actually confers a significant immune response.

But the work shows the ability of large computer models to dramatically speed up the initial work of searching through many, many possible combinations within a universe of possible ingredients, a search that can itself take years at the front end of a drug development pipeline.

This is the latest in large-scale, in-silico efforts against pathogens seen this year from MIT. Back in March, ZDNet reported on how MIT scientists used large-scale machine learning to search many combinations of compounds to come up with a novel antibiotic for a germ nothing else could kill.

Image Credit:  Amanda Scott/Envato

Thanks to Heinz V. Hoenen.  Follow him on twitter: @HeinzVHoenen