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

doi:10.1038/s41563-024-01867-3

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

Nat Mater. 2024 May 13. doi: 10.1038/s41563-024-01867-3. Online ahead of print.

Authors

Bowen Li^#^{1

2

3

4

5

6}, Idris O Raji^#^{7

8

9}, Akiva G R Gordon^#^{7

8}, Lizhuang Sun¹⁰, Theresa M Raimondo^{7

8}, Favour A Oladimeji¹¹, Allen Y Jiang^{7

8}, Andrew Varley¹², Robert S Langer^{7

8

9

10

11

13}, Daniel G Anderson^{14

15

16

17

18

19}

Affiliations

¹ David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. bw.li@utoronto.ca.
² Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. bw.li@utoronto.ca.
³ Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada. bw.li@utoronto.ca.
⁴ Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada. bw.li@utoronto.ca.
⁵ Department of Chemistry, University of Toronto, Toronto, Ontario, Canada. bw.li@utoronto.ca.
⁶ Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada. bw.li@utoronto.ca.
⁷ David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
⁸ Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
⁹ Department of Anesthesiology, Boston Children's Hospital, Boston, MA, USA.
¹⁰ Department of Statistics, University of Michigan, Ann Arbor, MI, USA.
¹¹ Harvard and MIT Division of Health Science and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA.
¹² Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada.
¹³ Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
¹⁴ David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. dgander@mit.edu.
¹⁵ Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. dgander@mit.edu.
¹⁶ Department of Anesthesiology, Boston Children's Hospital, Boston, MA, USA. dgander@mit.edu.
¹⁷ Department of Statistics, University of Michigan, Ann Arbor, MI, USA. dgander@mit.edu.
¹⁸ Harvard and MIT Division of Health Science and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA. dgander@mit.edu.
¹⁹ Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA. dgander@mit.edu.

^# Contributed equally.

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

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.