AGILE 平台：一种基于深度学习的方法，可加速用于 mRNA 递送的 LNPs 开发。
AGILE platform: a deep learning powered approach to accelerate LNP development for mRNA delivery.
机构信息
Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada.
Department of Computer Science, University of Toronto, Toronto, ON, Canada.
出版信息
Nat Commun. 2024 Jul 26;15(1):6305. doi: 10.1038/s41467-024-50619-z.
Ionizable lipid nanoparticles (LNPs) are seeing widespread use in mRNA delivery, notably in SARS-CoV-2 mRNA vaccines. However, the expansion of mRNA therapies beyond COVID-19 is impeded by the absence of LNPs tailored for diverse cell types. In this study, we present the AI-Guided Ionizable Lipid Engineering (AGILE) platform, a synergistic combination of deep learning and combinatorial chemistry. AGILE streamlines ionizable lipid development with efficient library design, in silico lipid screening via deep neural networks, and adaptability to diverse cell lines. Using AGILE, we rapidly design, synthesize, and evaluate ionizable lipids for mRNA delivery, selecting from a vast library. Intriguingly, AGILE reveals cell-specific preferences for ionizable lipids, indicating tailoring for optimal delivery to varying cell types. These highlight AGILE's potential in expediting the development of customized LNPs, addressing the complex needs of mRNA delivery in clinical practice, thereby broadening the scope and efficacy of mRNA therapies.
可离子化脂质纳米颗粒（LNPs）在 mRNA 递送上的应用越来越广泛，特别是在 SARS-CoV-2 mRNA 疫苗中。然而，除了 COVID-19 之外，mRNA 疗法的扩展受到缺乏针对不同细胞类型的 LNPs 的限制。在这项研究中，我们提出了 AI 指导的可离子化脂质工程（AGILE）平台，这是深度学习和组合化学的协同组合。AGILE 通过高效的文库设计、通过深度神经网络进行的虚拟脂质筛选以及对不同细胞系的适应性，简化了可离子化脂质的开发。使用 AGILE，我们可以快速设计、合成和评估用于 mRNA 递送的可离子化脂质，从庞大的文库中进行选择。有趣的是，AGILE 揭示了可离子化脂质对细胞的特异性偏好，表明针对不同细胞类型的最佳递送进行了定制。这些突出了 AGILE 在加快定制 LNPs 开发方面的潜力，解决了临床实践中 mRNA 递送的复杂需求，从而拓宽了 mRNA 疗法的范围和效果。
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