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

Journal: Nature materials
PMID: 38740955

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.

Authors

  • Bowen Li
    Department of Pediatric Cardiology, West China Second University Hospital, Sichuan University, Chengdu, China.
  • Idris O Raji
    David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Akiva G R Gordon
    David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Lizhuang Sun
    Department of Statistics, University of Michigan, Ann Arbor, MI, USA.
  • Theresa M Raimondo
    David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Favour A Oladimeji
    Harvard and MIT Division of Health Science and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Allen Y Jiang
    David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Andrew Varley
    Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada.
  • Robert S Langer
    David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Daniel G Anderson
    David H Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139; dgander@mit.edu.

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