Applying UHPLC-HRAM MS technology to characterize and quantify lipid components in vivo to support new LNP development

Significance of the Topic

The delivery of nucleic acid therapeutics via lipid nanoparticles (LNPs) has become critical for vaccines and gene therapies. Accurate quantification and metabolic profiling of synthetic ionizable lipids such as SM-102 are essential to understand clearance pathways, ensure safety, and accelerate development of next-generation biodegradable LNPs.

Objectives and Study Overview

This study aimed to develop a single UHPLC-HRAM MS method that combines data-dependent MS/MS and targeted MS/MS to (1) quantify SM-102 in complex biological matrices with high sensitivity and selectivity and (2) identify its in vivo metabolites to support LNP pharmacokinetic and biodistribution studies.

Methodology and Instrumentation

• Formulation: LNPs composed of SM-102, DSPC, cholesterol, and PEG-lipid, prepared by microfluidic mixing and dialysis. DLS measured 169 nm size, PDI 0.068, and 96.8% mRNA encapsulation.
• Sample preparation: IM administration of fLuc mRNA-LNP to mice; plasma and tissues harvested at 1–24 h; homogenization; lipid extraction with methanol/chloroform mixtures; reconstitution in IPA/methanol.
• Chromatography: Vanquish Horizon UHPLC with Accucore C30 column (2.1 × 150 mm), 24 min gradient of aqueous ACN and IPA mobile phases.
• Mass spectrometry: Thermo Scientific Orbitrap Exploris 480 operated in a combined ddMS2-tMS2 mode; high resolution (60 000 for full MS, 30 000 for MS/MS) and targeted isolation for m/z 710.6642.

Main Results and Discussion

• Quantification: SM-102 calibration curve linear from 1 to 10 000 pg on column (R2 0.994) with LOQ at 1 pg, CV < 5%.
• In vivo clearance: Liver SM-102 decreased from ~3.7 µg/g at 1 h to <0.1 µg/g at 24 h; spleen showed >99% clearance by 2 h; muscle retained higher levels but exhibited steady decline.
• Metabolite profiling: Compound Discoverer 3.3 predicted phase I/II biotransformations and annotated MS/MS fragments with FISh scoring, revealing potential SM-102 metabolites in liver with mass errors <1 ppm and concentration decay over time.

Benefits and Practical Applications of the Method

• Simultaneous sensitive quantification and structural characterization in a single run reduces analysis time and sample consumption.
• High mass accuracy and targeted detection enhance confidence in metabolite identification and pharmacokinetic profiling.
• Approach supports rapid screening of novel ionizable lipids for safety and efficacy in preclinical LNP development.

Future Trends and Opportunities

• Integration of high-throughput UHPLC-HRAM workflows with bioinformatics for comprehensive ADME and PK/PD modeling of LNP components.
• Application to emerging biodegradable and stimuli-responsive lipids to optimize in vivo clearance and reduce toxicity.
• Extension of method to multi-omics studies combining lipidomics, proteomics, and transcriptomics for systems-level understanding of LNP interactions.

Conclusion

The developed LC ddMS2-tMS2 platform enables robust, high-sensitivity quantification and confident metabolite identification of LNP ionizable lipids in vivo. This combined approach accelerates preclinical assessment of clearance kinetics and supports the design of next-generation biodegradable lipid nanoparticles.

Reference

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