Yichen Yuan, Ying Li, Guo Li, Liqun Lei, Xingxu Huang, Ming Li, Yuan Yao
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引用次数: 0
Abstract
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.
期刊介绍：
Molecular Pharmaceutics publishes the results of original research that contributes significantly to the molecular mechanistic understanding of drug delivery and drug delivery systems. The journal encourages contributions describing research at the interface of drug discovery and drug development.
Scientific areas within the scope of the journal include physical and pharmaceutical chemistry, biochemistry and biophysics, molecular and cellular biology, and polymer and materials science as they relate to drug and drug delivery system efficacy. Mechanistic Drug Delivery and Drug Targeting research on modulating activity and efficacy of a drug or drug product is within the scope of Molecular Pharmaceutics. Theoretical and experimental peer-reviewed research articles, communications, reviews, and perspectives are welcomed.
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