纳米颗粒
化学
纳米技术
固体脂质纳米粒
计算生物学
材料科学
生物
作者
Yichen Yuan,Ying Li,Guo Li,Liqun Lei,Xingxu Huang,Ming Li,Yuan Yao
标识
DOI:10.1021/acs.molpharmaceut.4c00925
摘要
Lipid nanoparticles (LNPs) are an effective delivery system for gene therapeutics. By optimizing their formulation, the physiochemical properties of LNPs can be tailored to improve tissue penetration, cellular uptake, and precise targeting. The application of these targeted delivery strategies within the LNP framework ensures efficient delivery of therapeutic agents to specific organs or cell types, thereby maximizing therapeutic efficacy. In the realm of genome editing, LNPs have emerged as a potent vehicle for delivering CRISPR/Cas components, offering significant advantages such as high in vivo efficacy. The incorporation of machine learning into the optimization of LNP platforms for gene therapeutics represents a significant advancement, harnessing its predictive capabilities to substantially accelerate the research and development process. This review highlights the dynamic evolution of LNP technology, which is expected to drive transformative progress in the field of gene therapy.
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