Determining Four Components in a Lipid Nanoparticle RNA Delivery System by Liquid Chromatography Combined with Evaporative Light Scattering Detector

• Published in: Journal of visualized experiments no. 219
• Main Authors: Zheng, Jia, Jiang, Huijuan, Huang, Junqi, Liu, Qiaoxia, Hao, Hongyuan
• Format: Journal Article
• Language: English
• Published: United States 30.05.2025
• Subjects: Chromatography, Liquid - methods; Lipids - analysis; Lipids - chemistry; Nanoparticles - chemistry; RNA - administration & dosage; RNA - chemistry; Scattering, Radiation
• ISSN: 1940-087X
• DOI: 10.3791/67711

This article presents a method for the analysis of lipid nanoparticle (LNP) components. LNP, serving as a pivotal vector for RNA-based drugs, primarily consists of cholesterol, PEG with modifications, ionizable lipids, and helper lipids. These components exhibit weak polarity, leading to strong retention and difficulty in separation using reverse-phase chromatography, as well as lacking distinct ultraviolet absorption characteristics. In order to address this challenge, a liquid chromatography system was coupled with an evaporative light-scattering detector (ELSD). By systematically adjusting the type of chromatographic column and optimizing the gradient elution program of the mobile phase, rapid and complete baseline separation of the four critical components was accomplished. Utilizing the latest ELSD technology enhanced detection sensitivity significantly and expanded the linear range of the method. Experimental results demonstrated that within a concentration range of 5 µg/mL to 250 µg/mL, the four components of LNP displayed excellent linearity with correlation coefficients all greater than 0.999, and the accuracy ranged from 94.2% to 108.0%. In precision experiments, the relative standard deviations of both retention time and peak area for a 10 µg/mL standard solution were below 0.1% and 2%, respectively. When using this method to analyze different LNP samples, all components are successfully separated, and their respective contents are accurately quantified, highlighting the robust adaptability of this analytical approach.