2. Academic Validation
  3. Artificial intelligence-guided design of lipid nanoparticles for pulmonary gene therapy

Artificial intelligence-guided design of lipid nanoparticles for pulmonary gene therapy

  • Nat Biotechnol. 2024 Dec 10:10.1038/s41587-024-02490-y. doi: 10.1038/s41587-024-02490-y.
Jacob Witten # 1 2 3,  Idris Raji # 1 2 4,  Rajith S Manan # 1 2,  Emily Beyer 1,  Sandra Bartlett 1 2,  Yinghua Tang 5,  Mehrnoosh Ebadi 5,  Junying Lei 5,  Dien Nguyen 1 2,  Favour Oladimeji 2 3,  Allen Yujie Jiang 1 2,  Elise MacDonald 1 2,  Yizong Hu 1 2,  Haseeb Mughal 1 2,  Ava Self 1 2,  Evan Collins 2 3,  Ziying Yan 5,  John F Engelhardt 5,  Robert Langer 1 2 3 6 7,  Daniel G Anderson 8 9 10 11
Affiliations

Affiliations

  • 1 Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • 2 David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • 3 Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • 4 Department of Anesthesiology, Boston Children's Hospital, Boston, MA, USA.
  • 5 Department of Anatomy and Cell Biology, University of Iowa Carver College of Medicine, Iowa City, IA, USA.
  • 6 Harvard and MIT Division of Health Science and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • 7 Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • 8 Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. dgander@mit.edu.
  • 9 David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. dgander@mit.edu.
  • 10 Harvard and MIT Division of Health Science and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA. dgander@mit.edu.
  • 11 Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA. dgander@mit.edu.
  • # Contributed equally.
PMID: 39658727 DOI: 10.1038/s41587-024-02490-y
Abstract

Ionizable lipids are a key component of lipid nanoparticles, the leading nonviral messenger RNA delivery technology. Here, to advance the identification of ionizable lipids beyond current methods, which rely on experimental screening and/or rational design, we introduce lipid optimization using neural networks, a deep-learning strategy for ionizable lipid design. We created a dataset of >9,000 lipid nanoparticle activity measurements and used it to train a directed message-passing neural network for prediction of nucleic acid delivery with diverse lipid structures. Lipid optimization using neural networks predicted RNA delivery in vitro and in vivo and extrapolated to structures divergent from the training set. We evaluated 1.6 million lipids in silico and identified two structures, FO-32 and FO-35, with local mRNA delivery to the mouse muscle and nasal mucosa. FO-32 matched the state of the art for nebulized mRNA delivery to the mouse lung, and both FO-32 and FO-35 efficiently delivered mRNA to ferret lungs. Overall, this work shows the utility of deep learning for improving nanoparticle delivery.

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