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Related Concept Videos

Proteomics01:33

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...

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

Bowen Li1,2,3,4,5,6, Idris O Raji7,8,9, Akiva G R Gordon7,8

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Researchers developed a faster method using machine learning and chemistry to find new ionizable lipids for messenger RNA (mRNA) therapies. They discovered a novel lipid that effectively delivers mRNA to muscle and immune cells.

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Area of Science:

  • Biotechnology and Pharmaceutical Sciences
  • Drug Delivery Systems
  • Computational Chemistry and Machine Learning

Background:

  • Messenger RNA (mRNA) therapies hold significant promise but require effective delivery vehicles.
  • Lipid nanoparticles (LNPs) are key delivery systems for mRNA, with ionizable lipids being a critical but challenging component to discover.
  • Current methods for identifying novel ionizable lipids are often slow and inefficient, hindering LNP development.

Purpose of the Study:

  • To develop an accelerated approach for discovering novel ionizable lipids for mRNA delivery.
  • To combine machine learning (ML) with combinatorial chemistry for rapid LNP component identification.
  • To identify superior ionizable lipids for enhanced mRNA transfection in various cell types and tissues.

Main Methods:

  • A four-component reaction platform was used to synthesize a diverse library of 584 ionizable lipids.
  • Lipid nanoparticles formulated with these lipids were screened for mRNA transfection efficiency.
  • Machine learning models were trained on screening data to predict the performance of a virtual library of 40,000 lipids, followed by experimental validation of top candidates.

Main Results:

  • A library of 584 chemically diverse ionizable lipids was created and screened.
  • Machine learning models successfully predicted LNP performance, guiding the synthesis of 16 promising candidates.
  • Lipid 119-23 demonstrated superior mRNA transfection efficiency in muscle and immune cells compared to benchmark lipids across multiple tissues.

Conclusions:

  • The integrated approach of ML and combinatorial chemistry significantly accelerates the discovery of effective ionizable lipids.
  • This strategy enables the creation and evaluation of versatile LNP formulations for precise mRNA delivery.
  • The identified lipid 119-23 represents a promising advancement for next-generation mRNA therapeutics.