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Accelerating ionizable lipid discovery for mRNA delivery using machine learning and combinatorial chemistry

Nature Materials (2024)Cite this article

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Abstract

To unlock the full promise of messenger (mRNA) therapies, expanding the toolkit of lipid nanoparticles is paramount. However, a pivotal component of lipid nanoparticle development that remains a bottleneck is identifying new ionizable lipids. Here we describe an accelerated approach to discovering effective ionizable lipids for mRNA delivery that combines machine learning with advanced combinatorial chemistry tools. Starting from a simple four-component reaction platform, we create a chemically diverse library of 584 ionizable lipids. We screen the mRNA transfection potencies of lipid nanoparticles containing those lipids and use the data as a foundational dataset for training various machine learning models. We choose the best-performing model to probe an expansive virtual library of 40,000 lipids, synthesizing and experimentally evaluating the top 16 lipids flagged. We identify lipid 119-23, which outperforms established benchmark lipids in transfecting muscle and immune cells in several tissues. This approach facilitates the creation and evaluation of versatile ionizable lipid libraries, advancing the formulation of lipid nanoparticles for precise mRNA delivery.

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Fig. 1: A 4CR for HTS of ionizable lipids.
Fig. 2: HTS and screening of ionizable lipid libraries.
Fig. 3: ML algorithm training using lipid screening data.
Fig. 4: ML-aided screening of an expanded lipid library and validation of the top-performing lipid.

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Data availability

All data supporting the findings of this study are available within the paper and its Supplementary Information files.

Code availability

The source code used for the ML algorithm is available in Supplementary Information.

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Acknowledgements

This work was supported by the National Institutes of Health (grant no. R61AI161805), Translate Bio (Lexington, MA), Canadian Institutes of Health Research (nos PJH-185722 and PJT-190109) and the University of Toronto Data Science Institute Catalyst Grant. B.L. was also supported by the Leslie Dan Faculty of Pharmacy start-up fund, the Connaught Fund (no. 514681), the J. P. Bickell Foundation (grant no. 515159), the Canada Research Chairs Program (no. CRC-2022-00575), Natural Sciences and Engineering Research Council of Canada (no. RGPIN-2023-05124), the Canada Foundation for Innovation, John R. Evans Leaders Fund (no. 43711) and the Cystic Fibrosis Foundation. We acknowledge the use of resources at the Animal Imaging & Preclinical Testing and Flow Cytometry Core Facilities (Swanson Biotechnology Center, David H. Koch Institute for Integrative Cancer Research at the Massachusetts Institute of Technology).

Author information

Author notes

  1. These authors contributed equally: Bowen Li, Idris O. Raji, Akiva G. R. Gordon.

Authors and Affiliations

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

    Bowen Li, Idris O. Raji, Akiva G. R. Gordon, Theresa M. Raimondo, Allen Y. Jiang, Robert S. Langer & Daniel G. Anderson

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

    Bowen Li, Idris O. Raji, Akiva G. R. Gordon, Theresa M. Raimondo, Allen Y. Jiang, Robert S. Langer & Daniel G. Anderson

  3. Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada

    Bowen Li & Andrew Varley

  4. Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada

    Bowen Li

  5. Department of Chemistry, University of Toronto, Toronto, Ontario, Canada

    Bowen Li

  6. Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada

    Bowen Li

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

    Idris O. Raji, Robert S. Langer & Daniel G. Anderson

  8. Department of Statistics, University of Michigan, Ann Arbor, MI, USA

    Lizhuang Sun, Robert S. Langer & Daniel G. Anderson

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

    Favour A. Oladimeji, Robert S. Langer & Daniel G. Anderson

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

    Robert S. Langer & Daniel G. Anderson

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Contributions

B.L. conceived the study and designed the experiments. B.L., I.O.R., A.G.R.G., L.S., T.M.R., F.A.O., A.Y.J. and A.V. performed the experiments and data analysis. B.L. and L.S. conducted the ML training, optimization and analysis. B.L., I.O.R., A.G.R.G., A.V., R.S.L. and D.G.A. wrote and edited the paper. B.L., R.S.L. and D.G.A. acquired funding and supervised the project. All authors provided feedback and helped shape the research, data analysis and paper.

Corresponding authors

Correspondence to Bowen Li or Daniel G. Anderson.

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Competing interests

B.L., I.O.R., A.G.R.G. and D.G.A. (inventors) have filed a patent through MIT (applicant) for the development of the described lipids (Application No.: PCT/US2023/010027). Patent status: pending. D.G.A. receives research funding from Sanofi/Translate and is a founder of ORNA Therapeutics. R.S.L. is a cofounder of Moderna Therapeutics and has been involved with a number of other entities, compensated or uncompensated; a complete list is available at https://www.dropbox.com/s/yc3xqb5s8s94v7x/Rev%20Langer%20COI.pdf?dl=0. The other authors declare no competing interests.

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Nature Materials thanks Roy van der Meel and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Figs. 1–11, Methods and source code.

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Li, B., Raji, I.O., Gordon, A.G.R. et al. Accelerating ionizable lipid discovery for mRNA delivery using machine learning and combinatorial chemistry. Nat. Mater. (2024). https://doi.org/10.1038/s41563-024-01867-3

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