The role of helper lipids in lipid nanoparticles (LNPs) designed for oligonucleotide delivery

生物物理学 体内 化学 内体 脂质体 聚乙二醇化 寡核苷酸 药物输送 脂质双层 生物化学 阳离子脂质体 转染 细胞 聚乙二醇 生物 DNA 生物技术 有机化学 基因
作者
Xinwei Cheng,Robert J. Lee
出处
期刊:Advanced Drug Delivery Reviews [Elsevier]
卷期号:99: 129-137 被引量:578
标识
DOI:10.1016/j.addr.2016.01.022
摘要

Lipid nanoparticles (LNPs) have shown promise as delivery vehicles for therapeutic oligonucleotides, including antisense oligos (ONs), siRNA, and microRNA mimics and inhibitors. In addition to a cationic lipid, LNPs are typically composed of helper lipids that contribute to their stability and delivery efficiency. Helper lipids with cone-shape geometry favoring the formation hexagonal II phase, such as dioleoylphosphatidylethanolamine (DOPE), can promote endosomal release of ONs. Meanwhile, cylindrical-shaped lipid phosphatidylcholine can provide greater bilayer stability, which is important for in vivo application of LNPs. Cholesterol is often included as a helper that improves intracellular delivery as well as LNP stability in vivo. Inclusion of a PEGylating lipid can enhance LNP colloidal stability in vitro and circulation time in vivo but may reduce uptake and inhibit endosomal release at the cellular level. This problem can be addressed by choosing reversible PEGylation in which the PEG moiety is gradually released in blood circulation. pH-sensitive anionic helper lipids, such as fatty acids and cholesteryl hemisuccinate (CHEMS), can trigger low-pH-induced changes in LNP surface charge and destabilization that can facilitate endosomal release of ONs. Generally speaking, there is no correlation between LNP activity in vitro and in vivo because of differences in factors limiting the efficiency of delivery. Designing LNPs requires the striking of a proper balance between the need for particle stability, long systemic circulation time, and the need for LNP destabilization inside the target cell to release the oligonucleotide cargo, which requires the proper selection of both the cationic and helper lipids. Customized design and empirical optimization is needed for specific applications.
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