美国麻省理工学院Daniel G. Anderson团队，实现了人工智能引导的肺部基因治疗脂质纳米颗粒设计。相关论文于2024年12月10日发表在《自然—生物技术》杂志上。

据悉，脂质纳米颗粒是领先的非病毒信使RNA递送技术，可离子化脂质是脂质纳米颗粒的关键组成成分。

为了推动离子化脂质的识别，超越当前依赖于实验筛选和/或合理设计的方法，研究人员引入了使用神经网络的脂质优化，这是一种用于离子化脂质设计的深度学习策略。

研究人员创建了一个包含超过9000个脂质纳米颗粒活性测量的数据集，并利用该数据集训练了一个定向消息传递神经网络，用于预测具有多样脂质结构的核酸递送。使用神经网络进行的脂质优化预测了体外和体内的RNA递送，并能推断出与训练集不同的结构。

研究人员在计算机模拟中评估了160万种脂质，并确定了两种结构，FO-32和FO-35，它们能够局部递送mRNA至小鼠肌肉和鼻粘膜。FO-32与目前小鼠肺部雾化mRNA递送的技术水平相当，而FO-32和FO-35都能高效地将mRNA递送到雪貂的肺部。总体而言，这项工作展示了深度学习在改善纳米颗粒递送中的应用价值。

附：英文原文

Title: Artificial intelligence-guided design of lipid nanoparticles for pulmonary gene therapy

Author: Witten, Jacob, Raji, Idris, Manan, Rajith S., Beyer, Emily, Bartlett, Sandra, Tang, Yinghua, Ebadi, Mehrnoosh, Lei, Junying, Nguyen, Dien, Oladimeji, Favour, Jiang, Allen Yujie, MacDonald, Elise, Hu, Yizong, Mughal, Haseeb, Self, Ava, Collins, Evan, Yan, Ziying, Engelhardt, John F., Langer, Robert, Anderson, Daniel G.

Issue&Volume: 2024-12-10

Abstract: Ionizable lipids are a key component of lipid nanoparticles, the leading nonviral messenger RNA delivery technology. Here, to advance the identification of ionizable lipids beyond current methods, which rely on experimental screening and/or rational design, we introduce lipid optimization using neural networks, a deep-learning strategy for ionizable lipid design. We created a dataset of >9,000 lipid nanoparticle activity measurements and used it to train a directed message-passing neural network for prediction of nucleic acid delivery with diverse lipid structures. Lipid optimization using neural networks predicted RNA delivery in vitro and in vivo and extrapolated to structures divergent from the training set. We evaluated 1.6 million lipids in silico and identified two structures, FO-32 and FO-35, with local mRNA delivery to the mouse muscle and nasal mucosa. FO-32 matched the state of the art for nebulized mRNA delivery to the mouse lung, and both FO-32 and FO-35 efficiently delivered mRNA to ferret lungs. Overall, this work shows the utility of deep learning for improving nanoparticle delivery.

DOI: 10.1038/s41587-024-02490-y

Source: https://www.nature.com/articles/s41587-024-02490-y