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基于脂质的RNA递送系统发现的新兴方法

Emerging Approaches for the Discovery of Lipid-Based RNA Delivery Systems.

作者信息

Meers Paul

机构信息

School of Environmental and Biological Sciences, 272 Foran Hall, Rutgers, The State University of New Jersey, 59 Dudley Rd., New Brunswick, NJ 08901-8250, USA.

出版信息

Pharmaceutics. 2025 Sep 22;17(9):1231. doi: 10.3390/pharmaceutics17091231.

DOI:10.3390/pharmaceutics17091231
PMID:41012567
Abstract

This brief review is a non-comprehensive look at some of the important aspects of lipidic nucleic acid delivery systems with a focus on RNA. In the context of this review on lipid-based formulation, nucleic acids are one of the key cargoes. Here, a brief historical background is given, highlighting a few of the key newly developing approaches to aid formulation design. These new techniques are discussed within a framework of "bottom-up" (rational) versus "top-down" (combinatorial) design. Evolving areas of interest that are discussed include multiplexed formulation and efficacy testing, new principles established in the role of the protein corona, details of the biophysical mechanism of delivery and machine learning approaches to design.

摘要

本简要综述并非对脂质核酸递送系统的所有重要方面进行全面审视,而是聚焦于RNA。在本次关于基于脂质的制剂的综述背景下,核酸是关键的运载物之一。在此,给出简要的历史背景,重点介绍一些有助于制剂设计的关键新发展方法。这些新技术在“自下而上”(理性)与“自上而下”(组合)设计的框架内进行讨论。所讨论的不断发展的感兴趣领域包括多重制剂和功效测试、蛋白质冠层作用中确立的新原理、递送生物物理机制的细节以及用于设计的机器学习方法。

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本文引用的文献

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Pharmaceutics. 2025 Sep 1;17(9):1148. doi: 10.3390/pharmaceutics17091148.
2
Rational design of lipid nanoparticles for enabling gene therapies.用于实现基因治疗的脂质纳米颗粒的合理设计。
Mol Ther Methods Clin Dev. 2025 Jun 18;33(3):101518. doi: 10.1016/j.omtm.2025.101518. eCollection 2025 Sep 11.
3
Enhancing nucleic acid delivery by the integration of artificial intelligence into lipid nanoparticle formulation.通过将人工智能整合到脂质纳米颗粒制剂中来增强核酸递送。
Front Med Technol. 2025 Jun 16;7:1591119. doi: 10.3389/fmedt.2025.1591119. eCollection 2025.
4
TransMA: an explainable multi-modal deep learning model for predicting properties of ionizable lipid nanoparticles in mRNA delivery.TransMA:一种用于预测可电离脂质纳米颗粒在mRNA递送中性质的可解释多模态深度学习模型。
Brief Bioinform. 2025 May 1;26(3). doi: 10.1093/bib/bbaf307.
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Automated and parallelized microfluidic generation of large and precisely-defined lipid nanoparticle libraries.用于大规模且精确定义的脂质纳米颗粒文库的自动化并行微流体制备
bioRxiv. 2025 May 29:2025.05.26.656157. doi: 10.1101/2025.05.26.656157.
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ACS Nano. 2025 Jun 17;19(23):21206-21242. doi: 10.1021/acsnano.4c18274. Epub 2025 Jun 3.
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