Accelerating ionizable lipid discovery for mRNA delivery using machine learning and combinatorial chemistry
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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- Authors
- Li, B., Raji, I. O., Gordon, A. G. R., Sun, L., Raimondo, T. M., Oladimeji, F. A., Jiang, A. Y., Varley, A., Langer, R. S., & Anderson, D. G.
- Affiliations
- University Health Network (CA)
- Boston Children's Hospital (US)
- Koch Institute for Integrative Cancer Research At MIT (US)
- University of Michigan (US)
- Harvard–MIT Division of Health Sciences and Technology (US)
- University of Toronto (CA)
- Corresponding author
- Li, Bowen
- Anderson, Daniel G.
- ORCID
- Venue
- Nature Materials
- Published
- 2024-05-13
- Volume
- 23
- Issue
- 7
- Pages
- 1002-1008
- ISSN
- 1476-1122 (ISSN-L)
- 1476-4660
- OpenAlex
- W4396862222
- Semantic Scholar
- 2731628dcca41e9574dd9fafd46ad4129b5d193b
- Cited by
- 236
- References
- 32
- Citations by year
- 2026: 104 · 2025: 108 · 2024: 24
- FWCI
- 27.18
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Materials (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (5r61ai161805-03)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Interference and Gene Delivery
- Physical Sciences › Materials Science › Biomaterials › Nanoparticle-Based Drug Delivery
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmaceutical Science › Advancements in Transdermal Drug Delivery
- Keywords
- ionizable lipids, lipid nanoparticles, mRNA delivery, mRNA therapeutics, combinatorial chemistry, machine learning, transfection efficiency, muscle cells, immune cells
- MeSH terms
- Machine Learning, Animals, Humans, Lipids, RNA, Messenger, Combinatorial Chemistry Techniques, Mice, Nanoparticles
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