AGILE platform: a deep learning powered approach to accelerate LNP development for mRNA delivery

Yue Xu, Shihao Ma, Haotian Cui, Jingan Chen, Shufen Xu, Fanglin Gong, Alex Golubovic, Muye Zhou, Kevin Chang Wang, Andrew Varley, Rick Xing Ze Lu, Bo Wang () and Bowen Li ()
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Yue Xu: University of Toronto
Shihao Ma: University of Toronto
Haotian Cui: University of Toronto
Jingan Chen: University of Toronto
Shufen Xu: University of Toronto
Fanglin Gong: University of Toronto
Alex Golubovic: University of Toronto
Muye Zhou: University of Toronto
Kevin Chang Wang: University of Toronto
Andrew Varley: University of Toronto
Rick Xing Ze Lu: University of Toronto
Bo Wang: University of Toronto
Bowen Li: University of Toronto

Nature Communications, 2024, vol. 15, issue 1, 1-16

Abstract: Abstract Ionizable lipid nanoparticles (LNPs) are seeing widespread use in mRNA delivery, notably in SARS-CoV-2 mRNA vaccines. However, the expansion of mRNA therapies beyond COVID-19 is impeded by the absence of LNPs tailored for diverse cell types. In this study, we present the AI-Guided Ionizable Lipid Engineering (AGILE) platform, a synergistic combination of deep learning and combinatorial chemistry. AGILE streamlines ionizable lipid development with efficient library design, in silico lipid screening via deep neural networks, and adaptability to diverse cell lines. Using AGILE, we rapidly design, synthesize, and evaluate ionizable lipids for mRNA delivery, selecting from a vast library. Intriguingly, AGILE reveals cell-specific preferences for ionizable lipids, indicating tailoring for optimal delivery to varying cell types. These highlight AGILE’s potential in expediting the development of customized LNPs, addressing the complex needs of mRNA delivery in clinical practice, thereby broadening the scope and efficacy of mRNA therapies.

Date: 2024
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DOI: 10.1038/s41467-024-50619-z

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