Accelerated discovery of thermostable mRNA-lipid nanoparticle vaccines using data-efficient AI.

Abstract

The instability of mRNA-lipid nanoparticles (LNPs) necessitates ultra-cold storage, limiting global distribution and their broader application in advanced delivery systems. Solid-state, water-free formulations enhance thermostability and enable integration into emerging delivery modalities such as microneedle patches. Previous efforts to stabilize mRNA-LNPs have been constrained by narrow formulation scope and low-throughput screening methods. Here we introduce Algorithm-Guided Experimental design for lipid Nanoparticle Thermostabilization (AGENT), an artificial intelligence (AI)-driven framework that couples high-throughput experimentation with Bayesian optimization to identify thermostable mRNA-LNP formulations. AGENT extracts maximal information from sparse experimental datasets, enabling efficient formulation optimization in six iterations completed within 1 month. We stabilized mRNA vaccines with two clinically relevant LNPs representative of the Moderna (SM-102-based) and Pfizer-BioNTech (ALC-0315-based) compositions into solid-state formulations that retained 100% bioactivity after storage at 37 °C for more than 2 months. In rodents and non-human primates, thermostable, solid-state vaccine formulations induced antigen-specific immune responses non-inferior to those elicited by intramuscular delivery of freshly prepared soluble vaccines.