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

Nature Biotechnology (2024)Cite this article

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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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Fig. 1: Workflow for LiON, DL-based LNP design.
Fig. 2: LiON for optimizing intravenous liver delivery of branched-ester-based library.
Fig. 3: Combining LiON with 4CR chemistry.
Fig. 4: Respiratory tract delivery.
Fig. 5: LNP delivery testing in ferret model.

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Data availability

All code and data used for training models can be found at GitHub via https://github.com/jswitten/LNP_ML (ref. 63). Source data are provided with this paper.

Code availability

All code and data used for training models can be found at GitHub via https://github.com/jswitten/LNP_ML (ref. 63).

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Acknowledgements

This work was supported by Sanofi and the National Institutes of Health (grant no. HL162564-02 and R61AI161805 to D.G.A. and grants no. HL152960, DK054759 and 75N92019C00010 to J.F.E.). It was also funded in part by a grant from the Alpha-1 Foundation. J.W. was supported by the Cystic Fibrosis Foundation awards WITTEN19XX0 and 004831F5222 and the Convergence Scholars Program through the Marble Center for Cancer Nanomedicine. This work was supported in part by the Koch Institute support grant 5P30-CA14051 from the National Cancer Institute. We thank the Koch Institute’s Robert A. Swanson (1969) Biotechnology Center for technical support, specifically the Animal Imaging and Preclinical Testing, Flow Cytometry, and Peterson (1957) Nanotechnology Materials core facilities.

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  1. These authors contributed equally: Jacob Witten, Idris Raji, Rajith S. Manan.

Authors and Affiliations

  1. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Jacob Witten, Idris Raji, Rajith S. Manan, Emily Beyer, Sandra Bartlett, Dien Nguyen, Allen Yujie Jiang, Elise MacDonald, Yizong Hu, Haseeb Mughal, Ava Self, Robert Langer & Daniel G. Anderson

  2. David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA

    Jacob Witten, Idris Raji, Rajith S. Manan, Sandra Bartlett, Dien Nguyen, Favour Oladimeji, Allen Yujie Jiang, Elise MacDonald, Yizong Hu, Haseeb Mughal, Ava Self, Evan Collins, Robert Langer & Daniel G. Anderson

  3. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Jacob Witten, Favour Oladimeji, Evan Collins & Robert Langer

  4. Department of Anesthesiology, Boston Children’s Hospital, Boston, MA, USA

    Idris Raji

  5. Department of Anatomy and Cell Biology, University of Iowa Carver College of Medicine, Iowa City, IA, USA

    Yinghua Tang, Mehrnoosh Ebadi, Junying Lei, Ziying Yan & John F. Engelhardt

  6. Harvard and MIT Division of Health Science and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA

    Robert Langer & Daniel G. Anderson

  7. Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA

    Robert Langer & Daniel G. Anderson

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Contributions

J.W., I.R., R.S.M., E.B., S.B., Y.T., M.E., J.L., D.N., F.O., A.Y.J., E.M., Y.H., H.M., A.S., E.C., Z.Y. and J.F.E. performed experiments and analyzed data. J.W., R.L. and D.G.A. discussed the results and wrote the paper with input from all authors. R.L. and D.G.A. acquired funding and supervised the project. J.W., I.R. and R.S.M. contributed equally and all reserve the right to list themselves first on their curricula vitae (CVs).

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Correspondence to Daniel G. Anderson.

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Competing interests

J.W., I.R. and D.G.A. have filed a patent for the 4CR ketone biodegradable lipid library described herein and J.W., R.S.M. and D.G.A. have filed a patent for the branched-ester library described herein. Z.Y. and J.F.E. are consultants for Spirovant Sciences. D.G.A. receives research funding from Sanofi/Translate Bio and is a Founder of oRNA Tx. R.L. is a co-founder and board of director of Moderna. He also serves on the board and has equity in Particles For Humanity. For a list of entities with which R.L. is, or has been recently involved compensated or uncompensated, see https://www.dropbox.com/s/yc3xqb5s8s94v7x/Rev%20Langer%20COI.pdf?dl=0. The other authors declare no competing interests.

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Supplementary Figs. 1–28, Tables 1–7 and lipid synthesis protocols.

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Witten, J., Raji, I., Manan, R.S. et al. Artificial intelligence-guided design of lipid nanoparticles for pulmonary gene therapy. Nat Biotechnol (2024). https://doi.org/10.1038/s41587-024-02490-y

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