Development of a platform UPLC-CAD method for high-throughput lipid quantitation and characterization in novel mRNA LNPs

Title : Development of a platform UPLC-CAD method for high-throughput lipid quantitation and characterization in novel mRNA LNPs

Abstract:

Accurate lipid quantitation is critical for the development and manufacturing of mRNA lipid nanoparticle (LNP) drug products. This work describes the development of a robust, high-throughput UPLC-CAD platform method for lipid identification and quantitation in complex LNP formulations. The method was optimized to achieve separation of at least 20 lipid species, including novel ionizable, helper, PEGylated, and stabilizing lipids. Systematic optimization of mobile phase composition, gradient profiles, and autosampler parameters minimized matrix interference and improved peak shape, resolution, and reproducibility. The final method achieved full separation within a 21-minute runtime, demonstrating greater than 90% recovery and acceptable precision across multiple dilution schemes. The method has shown consistent reproducibility even with complex matrices. This platform assay provides a scalable, GMP-compatible solution for comprehensive lipid profiling in mRNA-LNP therapeutics, supporting clinical and commercial batch release for current and future LNP-based products.

Biography:

Dr. Muzulu studied Chemistry at Brown University, USA and graduated with her MSc degree in 2021. Upon graduation, she joined Sanofi’s mRNA Center of Excellence in Waltham, USA, where she works as an mRNA Analytical Development Scientist. Her research focuses on the development and optimization of UPLC-CAD methods for lipid quantitation and identification in lipid nanoparticle (LNP) formulations. She also applies her background in organic synthesis to investigate lipid degradation pathways and support the development of robust analytical methods for mRNA vaccine formulations.