Confident and sensitive profiling of lipid nanoparticle raw materials and impurity identification using a LC/CAD/HRAM-MS method with inverse gradient

Importance of the Topic

The purity and structural integrity of lipid nanoparticle raw materials critically influence the safety and efficacy of mRNA therapeutics and vaccines. Regulatory guidelines emphasize strict control of excipient impurities and PEGylated lipid characteristics to ensure consistent LNP performance.

Objectives and Study Overview

This study evaluates the combined use of Thermo Scientific Vanquish Inverse Gradient LC with charged aerosol detection (CAD) and Thermo Scientific Orbitrap Exploris 120 high-resolution accurate-mass mass spectrometry (HRAM-MS) for sensitive quantitation and comprehensive identification of impurities in two key LNP components: DC-Chol and DSPE-mPEG.

Methodology and Instrumentation

• Chromatography: Vanquish Inverse Gradient LC system with dual pumps, split sampler, Hypersil GOLD C8 column (2.1×50 mm, 1.9 µm), ternary gradient (water/methanol/isopropanol with 5 mM ammonium formate), and optimized inverse gradient timing (0.58 min offset) for uniform CAD response.
• Detection: Charged aerosol detector (evaporation T 35 °C, power function 1.0, 5 Hz) in tandem with Orbitrap Exploris 120 MS (OptaMax NG H-ESI, 120,000 resolution at m/z 200, data-dependent Top4 ddMS2, stepped HCD).
• Software: Xcalibur 4.5 and Freestyle 1.8 SP2 for acquisition, Chromeleon 7.3.2 CDS for CAD quantitation, Compound Discoverer 3.3 for impurity detection and structure elucidation, Xtract algorithm for deconvolution of PEGylated lipids.

Main Results and Discussion

• DC-Chol impurity profiling: Four main impurities identified across vendors, including desaturation of cholesterol or DC moieties (–H2), methylation (+CH2), chloromethylation (+CHCl), and oxidation (+O). MS2 fragmentation localized modifications and distinguished isomeric forms. Vendor-dependent impurity levels ranged up to ~2 %.
• Trace co-eluting impurities: HRAM-MS uncovered additional low-level species masked in CAD, demonstrating the advantage of combined detection.
• DSPE-mPEG characterization: CAD trace of vendor A showed a single dominant peak. Deconvolution via Xtract revealed Gaussian distribution of PEG chain lengths (22–54 units), with the predominant species at 44 repeat units (MW 2742.74 Da). MS2 confirmed the DSPE glyceride fragment.

Benefits and Practical Applications

• The integrated LC/CAD/HRAM-MS approach enables accurate quantitation and confident identification of lipid excipient impurities, supporting quality control and regulatory compliance in LNP production.
• Inverse gradient improves CAD sensitivity across diverse lipid chemistries.
• Data-dependent MS2 and Compound Discoverer workflows accelerate structural elucidation of known and unknown impurities.
• Deconvolution of PEGylated lipid polydispersity informs critical quality attributes of PEG chain distribution.

Future Trends and Opportunities

• Extension to broader lipid libraries and novel ionizable lipid classes.
• High-throughput screening and automation for real-time QC in biomanufacturing.
• Integration of AI-based spectral interpretation to enhance impurity annotation and reduce manual review.
• Standardization of LNP impurity profiling workflows to meet evolving regulatory frameworks.

Conclusion

The presented methodology demonstrates a robust, streamlined workflow for simultaneous quantitation and structural elucidation of impurities in lipid nanoparticle raw materials. By combining charged aerosol detection with high-resolution accurate-mass MS and advanced data analysis, critical quality attributes of LNP excipients can be comprehensively assessed to support vaccine and gene therapy development.

Reference

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