David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Nat Mater. 2024 Jul;23(7):1002-1008. doi: 10.1038/s41563-024-01867-3. Epub 2024 May 13.
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.
为了充分发挥信使（mRNA）疗法的潜力，扩展脂质纳米颗粒的工具包至关重要。然而，脂质纳米颗粒开发中的一个关键组成部分仍然是确定新的可离子化脂质。在这里，我们描述了一种加速发现用于 mRNA 递送的有效可离子化脂质的方法，该方法结合了机器学习和先进的组合化学工具。从一个简单的四组分反应平台开始，我们创建了一个包含 584 种可离子化脂质的化学多样性文库。我们筛选含有这些脂质的脂质纳米颗粒的 mRNA 转染效力，并将数据用作训练各种机器学习模型的基础数据集。我们选择表现最好的模型来探测一个包含 40,000 种脂质的广阔虚拟文库，合成并实验评估标记的前 16 种脂质。我们确定了脂质 119-23，它在转染几种组织中的肌肉和免疫细胞方面优于已建立的基准脂质。这种方法促进了多功能可离子化脂质库的创建和评估，推进了用于精确 mRNA 递送的脂质纳米颗粒的配方。
