自消解脂质纳米粒在mRNA递送中的设计策略与研究进展

Design Strategies and Recent Advances in Self-Immolative Lipid Nanoparticles for mRNA Delivery

摘要: 脂质纳米粒(Lipid nanoparticles, LNPs)是目前最成熟且应用最广泛的非病毒mRNA递送载体，但其仍存在体内代谢缓慢、长期蓄积及潜在慢性毒性等问题，限制了进一步应用。自消解LNPs通过在脂质骨架中引入可降解化学键及级联断裂结构，使载体可在胞内还原及酶促等微环境条件下发生程序化自发降解，体现出“以降解行为反向调控递送性能”的设计理念，从而优化其递送效率与生物相容性。本文从脂质消解化学机制出发，结合脂质类型差异，对自消解LNPs体系进行分类综述，并总结其在mRNA递送中的研究进展。尽管该策略已展现出显著优势，但目前仍面临若干制约临床转化的关键问题，包括：缺乏兼具高特异性与时序可控性的降解键设计原则，降解动力学与递送效率之间尚未建立明确的定量关联模型，以及大动物模型中系统性安全性与长期代谢数据仍相对不足。未来有望结合多重响应设计、高通量筛选与人工智能方法，构建可预测、可编程的递送体系，推动其在精准基因治疗及个体化医学中的应用。

Abstract: Lipid nanoparticles (LNPs) are currently the most mature and widely used non-viral carriers for mRNA delivery. However, their slow in vivo biodegradation, long-term tissue accumulation, and potential chronic toxicity remain major limitations for further clinical translation. To address these issues, self-immolative LNPs have been developed by incorporating cleavable chemical bonds and cascade-degradable architectures into lipid backbones, enabling programmed disassembly under intracellular reductive and enzymatic microenvironments. This strategy embodies a design concept of “inverse regulation of delivery performance via programmed degradation”, thereby simultaneously optimizing delivery efficiency and biocompatibility. This review classifies self-degradable lipid nanoparticles (LNPs) according to the chemical mechanisms underlying lipid degradation and variations in lipid components, and summarizes recent advances in their application for mRNA delivery. Although this strategy has demonstrated considerable promise, several critical challenges continue to hinder its clinical translation, including the lack of design principles for degradable linkages that combine high specificity with temporally controlled degradation, the absence of robust quantitative models correlating degradation kinetics with delivery performance, and the limited systemic safety and long-term metabolic data available from large-animal studies. Future efforts are expected to integrate multi-responsive molecular design, high-throughput screening, and artificial intelligence-assisted optimization to enable predictable and programmable delivery systems, thereby advancing their translation in precision gene therapy and personalized medicine.