Abstract：
Lipid nanoparticles (LNPs) have proven to be promising delivery vehicles for RNA-based vaccines and therapeutics, particularly in LNP formulations containing ionizable cationic lipids that undergo protonation/deprotonation in response to buffer pH changes. These nanoparticles are typically formulated using a rapid mixing technique at low pH, followed by a return to physiological pH that triggers LNP-LNP fusion. A detailed understanding of these dynamic processes is crucial to optimize the overall performance and efficiency of LNPs. However, knowledge gaps persist regarding how particle formation mechanisms impact drug loading and delivery functions. In this work, we employ single-molecule Convex Lens-induced Confinement (CLiC) microscopy in combination with Förster resonance energy transfer (FRET) measurements to study LNP fusion dynamics in relation to various formulation parameters, including lipid concentration, buffer conditions, drug loading ratio, PEG-lipid concentrations, and ionizable lipid selection. Our results reveal a strong correlation between the measured fusion dynamics and the formulation parameters used; these findings are consistent with DLS and Cryo-TEM-based assays. These measurements offer a cost-effective method for characterizing and screening potential drug candidates and can provide additional insights into their design, with opportunities for optimization.
摘要：
脂质纳米颗粒（LNP）已被证明是基于RNA的疫苗和治疗剂的有前途的递送载体，特别是在含有可电离阳离子脂质的LNP制剂中,所述可电离阳离子脂质响应于缓冲液pH变化而经历质子化/去质子化。这些纳米颗粒通常使用快速混合技术在低pH下配制，然后恢复到触发LNP-LNP融合的生理pH。详细了解这些动态过程对于优化LNP的整体性能和效率至关重要。然而,关于颗粒形成机制如何影响药物负载和递送功能的知识差距仍然存在。在这项工作中,我们采用单分子凸透镜诱导的约束（CLiC）显微镜与Förster共振能量转移（FRET）测量相结合，以研究与各种配方参数相关的LNP融合动力学。包括脂质浓度，缓冲条件,载药率,PEG-脂质浓度，和可电离的脂质选择。我们的结果揭示了所测量的融合动力学与所使用的制剂参数之间的强相关性;这些发现与基于DLS和Cryo-TEM的测定一致。这些测量为表征和筛选潜在候选药物提供了一种具有成本效益的方法，并且可以提供对其设计的更多见解。优化的机会。
