Plate Nº 93 · recorded October 10, 2026
Health & Medicine ResearchReported finding
AI-Designed RNA Vaccines Stay Stable for a Year at Room Temperature
MIT researchers used an AI algorithm to redesign RNA vaccine delivery particles, creating vaccines that stayed stable for one year at room temperature and matched Moderna-style shots in mice.
By Nathan Brooks4 min read778 words
In brief
- MIT-designed RNA vaccines remained stable for up to one year at room temperature or two months at 37°C (98°F).
- An AI algorithm found the stable formulation in a few weeks by screening nearly 50 FDA-approved excipients.
- Mice vaccinated after long-term storage showed immune responses equivalent to a Moderna-like RNA vaccine.
- The findings appeared in Nature Biotechnology; the research was partly funded by the Gates Foundation.
- The same algorithm also stabilized a Pfizer-like lipid nanoparticle formulation.
RNA vaccines reformulated with the help of an AI algorithm can survive a full year at room temperature — or two months at 37 degrees Celsius (98 degrees Fahrenheit) — without losing their potency, MIT researchers report in Nature Biotechnology. In mice, Covid-19 vaccines built on the new formulation triggered immune responses just as strong as those from a conventional RNA vaccine similar to Moderna's.
The result matters because one of RNA medicine's biggest practical weaknesses is cold. Messenger RNA, the molecule that instructs cells to build proteins such as viral antigens, is extremely fragile. Today's RNA vaccines protect it inside lipid nanoparticles (LNPs) — tiny fatty shells that shield the RNA and help it enter cells — but those particles still require storage at -20 to -80 degrees Celsius. That requirement makes shipping vaccines to regions without freezer infrastructure difficult and expensive.
How did the researchers do it?
Ana Jaklenec and Robert Langer, senior authors of the study at MIT's Koch Institute for Integrative Cancer Research, set out to make the exact FDA-approved LNP formulations used in the Moderna and Pfizer Covid-19 vaccines more heat-tolerant, rather than inventing entirely new ones.
Their tool was additives called excipients — sugars, salts, or polymers that can be mixed into the nanoparticles to stiffen them against heat. The team first measured how well nearly 50 FDA-approved excipients protected RNA, using a clever readout: they packaged mRNA encoding firefly luciferase, a light-producing protein, and measured how much glow cells emitted. More light meant the RNA had survived intact.
Then came the AI step. Working with MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), the researchers built a machine-learning algorithm that could predict the best excipient mixtures from very small datasets. The team picked the five most promising excipients, let the algorithm suggest ratios, tested two formulations at a time in cells, and fed the results back in. After several rounds, one winner emerged — a process that took only weeks.
"The real beauty of this algorithm is that we can use it with small data sets," Jaklenec said. "It's really hard to run thousands of experiments, so this algorithm allows us to more easily achieve formulations with features that we want — in this case, stability."
The contrast with the pre-AI effort was stark. "Before we implemented the AI algorithm, we spent several months testing different combinations and also doing the prescreening of all the excipients that we could find, but nothing would get us to 100 percent stability," said Khanh Tran, a postdoc and co-lead author of the paper with graduate student Jinbi Tian.
Mina Konaković Luković, a CSAIL assistant professor and co-author, noted that her group had applied such algorithms to automated experimental design before, "but never on a biological problem like vaccine stability." She added: "It was surprising to see how quickly the algorithm converged on a stable formulation — getting there in just a handful of iterations, rather than the exhaustive search that would normally be required."
What did the stability tests show?
The researchers packaged Covid-19 mRNA antigens into the new formulation and dried the particles through vacuum drying, a process that removes water to further protect the RNA. They then stored the vaccines under two punishing conditions:
- 37 degrees Celsius (98 degrees Fahrenheit) for two months
- Room temperature for one year
Mice vaccinated with these long-stored particles mounted immune responses equivalent to those of mice given vaccines carried by LNPs resembling the original Moderna formulation.
The team also used the heat-resistant formulation to build microneedle patches — bandage-like devices covered in hundreds of vaccine-filled microneedles that dissolve into the skin. Those patches produced immune responses similar to injectable RNA vaccines.
What are the limitations?
These are mouse results, and the study did not test the formulation in humans. The researchers also have not yet demonstrated long-term stability across every possible mRNA payload, although they argue that once a heat-resistant version of a given LNP exists, it could be adapted to carry any mRNA cargo. And the algorithm's reach extends beyond one product: the team showed it could also stabilize a Pfizer-like LNP formulation, which uses the same excipients in a different ratio.
The approach could unlock delivery methods that cold chains cannot support. As Tian put it, the work broadens the reach of "not only mRNA vaccines, but also therapeutics or advanced drug-delivery platforms like controlled-release particles or microneedle patches, which requires the formulation to either be in solid state or to be stable at higher temperature."
The research was funded in part by the Gates Foundation.
via nature.com (Original)
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