Accelerating ionizable lipid discovery for mRNA delivery using machine learning and combinatorial chemistry. - National Genomics Data Center (CNCB-NGDC)

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

Bowen Li, Idris O Raji, Akiva G R Gordon, Lizhuang Sun, Theresa M Raimondo, Favour A Oladimeji, Allen Y Jiang, Andrew Varley, Robert S Langer, Daniel G Anderson

Author Information

Bowen Li: David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. bw.li@utoronto.ca. ORCID
Idris O Raji: David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. ORCID
Akiva G R Gordon: David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. ORCID
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. ORCID
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. ORCID
Andrew Varley: Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada. ORCID
Robert S Langer: David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. ORCID
Daniel G Anderson: David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. dgander@mit.edu. ORCID

PMID: 38740955 DOI: 10.1038/s41563-024-01867-3

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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Machine Learning

Lipids

RNA, Messenger

Combinatorial Chemistry Techniques

Nanoparticles

Animals

Humans

Mice

Lipids

RNA, Messenger

Journal Article