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

Author

Listed:
  • Yue Xu

    (University of Toronto)

  • Shihao Ma

    (University of Toronto
    Vector Institute for Artificial Intelligence
    University Health Network)

  • Haotian Cui

    (University of Toronto
    Vector Institute for Artificial Intelligence
    University Health Network)

  • Jingan Chen

    (University of Toronto
    University of Toronto)

  • Shufen Xu

    (University of Toronto)

  • Fanglin Gong

    (University of Toronto
    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
    Vector Institute for Artificial Intelligence
    University Health Network
    University Health Network)

  • Bowen Li

    (University of Toronto
    University of Toronto
    University of Toronto
    University of Toronto)

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.

Suggested Citation

  • 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 & Bowen Li, 2024. "AGILE platform: a deep learning powered approach to accelerate LNP development for mRNA delivery," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50619-z
    DOI: 10.1038/s41467-024-50619-z
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    1. Andy Hsien-Wei Yeh & Christoffer Norn & Yakov Kipnis & Doug Tischer & Samuel J. Pellock & Declan Evans & Pengchen Ma & Gyu Rie Lee & Jason Z. Zhang & Ivan Anishchenko & Brian Coventry & Longxing Cao &, 2023. "De novo design of luciferases using deep learning," Nature, Nature, vol. 614(7949), pages 774-780, February.
    2. Kiran Musunuru & Alexandra C. Chadwick & Taiji Mizoguchi & Sara P. Garcia & Jamie E. DeNizio & Caroline W. Reiss & Kui Wang & Sowmya Iyer & Chaitali Dutta & Victoria Clendaniel & Michael Amaonye & Aar, 2021. "In vivo CRISPR base editing of PCSK9 durably lowers cholesterol in primates," Nature, Nature, vol. 593(7859), pages 429-434, May.
    3. Xuexiang Han & Hanwen Zhang & Kamila Butowska & Kelsey L. Swingle & Mohamad-Gabriel Alameh & Drew Weissman & Michael J. Mitchell, 2021. "An ionizable lipid toolbox for RNA delivery," Nature Communications, Nature, vol. 12(1), pages 1-6, December.
    4. Kathryn A. Whitehead & J. Robert Dorkin & Arturo J. Vegas & Philip H. Chang & Omid Veiseh & Jonathan Matthews & Owen S. Fenton & Yunlong Zhang & Karsten T. Olejnik & Volkan Yesilyurt & Delai Chen & Sc, 2014. "Degradable lipid nanoparticles with predictable in vivo siRNA delivery activity," Nature Communications, Nature, vol. 5(1), pages 1-10, September.
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