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对小干扰RNA脂质纳米颗粒制剂中的专利化合物进行分析。

Profiling patent compounds in lipid nanoparticle formulations of siRNA.

作者信息

Han Yunfeng, Wang Mengyang, Chen Yu, Ouyang Defang, Zheng Ying, Hu Yuanjia

机构信息

State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao SAR, China.

Centre for Pharmaceutical Regulatory Sciences, University of Macau, Macao SAR, China.

出版信息

Mol Ther Nucleic Acids. 2024 Oct 18;35(4):102362. doi: 10.1016/j.omtn.2024.102362. eCollection 2024 Dec 10.

Abstract

Lipid nanoparticles (LNPs) have emerged as a prominent delivery system for nucleic acid drugs, attracting significant attention, especially through the successful development of several commercial products. As a key component in LNPs, cationic lipids have long served as a key technical barrier to block competitors by building up a complex patent thicket. However, there have been few studies as yet that have comprehensively analyzed the patented compounds in LNP formulations, despite a large number of technical reviews and original articles. In this context, this study focuses on analyzing the macroscopic landscapes and microscopic molecular characteristics of LNP patents, aiming to provide a valuable reference for researchers and developers in making informed technological and commercial decisions. By mining 2,994 patents, 265 formulas, 7,674 compounds, and 28,789 fragments, this work sketches the empirical golden ratio of lipid materials in LNP formulation, discloses the advanced technology in the formulation, characterizes high-frequency fragments of heads, linkers and tails in both novel cationic lipids as well as targeting lipids, and establishes a virtual focus library of LNP materials.

摘要

脂质纳米颗粒(LNPs)已成为核酸药物的一种重要递送系统,备受关注,尤其是通过几款商业产品的成功开发。作为LNPs的关键组成部分,阳离子脂质长期以来一直是构建复杂专利壁垒以阻碍竞争对手的关键技术障碍。然而,尽管有大量的技术综述和原创文章,但目前很少有研究全面分析LNP制剂中的专利化合物。在此背景下,本研究专注于分析LNP专利的宏观格局和微观分子特征,旨在为研究人员和开发者做出明智的技术和商业决策提供有价值的参考。通过挖掘2994项专利、265种配方、7674种化合物和28789个片段,这项工作勾勒出LNP制剂中脂质材料的经验黄金比例,揭示了制剂中的先进技术,表征了新型阳离子脂质和靶向脂质中头部、连接基和尾部的高频片段,并建立了LNP材料的虚拟焦点库。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/acc6/11565460/6e806b9ded9b/fx1.jpg

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