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AI-guided optimization for thermostable mRNA vaccines

An AI-driven framework called AGENT, which combines high-throughput experimentation with Bayesian optimization, produced solid-state mRNA vaccine formulations that retained full bioactivity after 2 months of storage at 37 °C, according to a Nature Biotechnology summary of research by Tian et al. The formulations elicited immune responses non-inferior to fresh vaccines and enabled cold-chain-free delivery via microneedle patches in rodents and nonhuman primates. The work, published in Nature Biotechnology (doi:10.1038/s41587-026-03331-w), points to thermostable mRNA vaccines that do not require cold-chain storage.

read2 min views2 publishedSep 28, 2026
AI-guided optimization for thermostable mRNA vaccines
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AGENT, an AI-driven framework integrating high-throughput experimentation with Bayesian optimization, enables the rapid engineering of thermostable, solid-state mRNA vaccines. These formulations retained full bioactivity after storage at 37 °C for 2 months, elicited immune responses non-inferior to fresh vaccines, and enabled cold-chain-free delivery via microneedle patches in rodents and nonhuman primates.

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References #

  1. Scott, G. Y. et al. Enhancing vaccine quality and accessibility: strategies for efficient storage and distribution in resource-constrained environment. Outbreak Manag. Response1 , 2443439 (2025).A review article that presents vaccine accessibility.
  2. Vander Straeten, A. et al. A microneedle vaccine printer for thermostable COVID-19 mRNA vaccines. *Nat. Biotechnol.*42 , 510–517 (2024).This paper reports COVID-19 mRNA delivery using microneedle patches.
  3. Sakers, S. H. et al. Development of a microneedle patch for delivery of mRNA-lipid nanoparticles. *Drug Deliv. Transl. Res.*16 , 2256–2271 (2026).This paper reports mRNA-loaded microneedle patches with a clinically relevant LNP composition .
  4. Shahriari, B., Swersky, K., Wang, Z., Adams, R. P. & De Freitas, N. Taking the human out of the loop: a review of Bayesian optimization. Proc. IEEE104 , 148–175 (2015).A review article that presents Bayesian optimization.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This is a summary of: Tian, J. et al. Accelerated discovery of thermostable mRNA–lipid nanoparticle vaccines using data-efficient AI. Nat. Biotechnol. https://doi.org/10.1038/s41587-026-03331-w (2026).

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AI-guided optimization for thermostable mRNA vaccines.

                    *Nat Biotechnol*  (2026). https://doi.org/10.1038/s41587-026-03330-x

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- DOI: https://doi.org/10.1038/s41587-026-03330-x
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