High-throughput and sensitive HPLC analysis of lipids used in LNP formulations with evaporative light scattering detection
Applications | 2024 | KNAUERInstrumentation
Lipid nanoparticles (LNPs) are central to the delivery of mRNA vaccines against COVID-19 and are poised to revolutionize the administration of diverse active pharmaceutical ingredients (APIs). High-performance liquid chromatography (HPLC) combined with evaporative light scattering detection (ELSD) offers a versatile approach to verify lipid identity, purity and content in LNP formulations. Rapid, sensitive lipid analysis ensures robust quality control in research, clinical trials and large-scale vaccine manufacturing.
This study aimed to develop high-throughput HPLC-ELSD methods for characterizing lipid mixtures used in Moderna and BioNTech-Pfizer mRNA vaccines. Key goals included:
Sample Preparation:
Chromatographic Conditions:
Stationary Phase Comparison:
Eluent and Modifier Optimization:
ELSD Temperature:
Analytical Performance:
Advances in LNP research will expand the use of mRNA and other nucleic acid therapeutics. Future directions include:
The developed HPLC-ELSD methods demonstrate rapid, sensitive separation of vaccine-relevant lipids on phenyl-hexyl phases. Gradient and isocratic protocols on core-shell and fully porous columns achieved full resolution in as little as 2 min, with LODs down to 0.8 µg/ml. These workflows support robust quality control of LNP formulations and can be adapted to emerging therapeutic applications.
HPLC, Consumables, LC columns
IndustriesLipidomics
ManufacturerKNAUER
Summary
Significance of the Topic
Lipid nanoparticles (LNPs) are central to the delivery of mRNA vaccines against COVID-19 and are poised to revolutionize the administration of diverse active pharmaceutical ingredients (APIs). High-performance liquid chromatography (HPLC) combined with evaporative light scattering detection (ELSD) offers a versatile approach to verify lipid identity, purity and content in LNP formulations. Rapid, sensitive lipid analysis ensures robust quality control in research, clinical trials and large-scale vaccine manufacturing.
Objectives and Study Overview
This study aimed to develop high-throughput HPLC-ELSD methods for characterizing lipid mixtures used in Moderna and BioNTech-Pfizer mRNA vaccines. Key goals included:
- Assessing retention behavior on phenyl-hexyl stationary phases (core-shell and fully porous).
- Optimizing gradient and isocratic elution protocols to shorten run times.
- Establishing method transferability between quaternary and binary pump systems.
- Evaluating analytical sensitivity in terms of limits of detection (LOD) and quantification (LOQ).
Methodology
Sample Preparation:
- Lipids from Cayman Chemical LNP-0315 (BNT162b2) and LNP-SM102 (mRNA-1273) kits dissolved in ethanol:water (90:10 v/v).
- Stock concentrations between 25–100 µg/ml; dilutions for calibration prepared in the same solvent ratio.
Chromatographic Conditions:
- Core-shell phenyl-hexyl column (50×2.1 mm, 1.7 µm) and fully porous phenyl-hexyl column (50×2.1 mm, 1.8 µm).
- Mobile phases: water with 10 mM ammonium acetate (modifier), acetonitrile, methanol.
- Gradient and isocratic programs optimized for 2–4 min separations at 40–55 °C.
Used Instrumentation
- AZURA P 6.1L LPG quaternary pump
- AZURA AS 6.1L autosampler
- SEDEX 100LT evaporative light scattering detector
- AZURA CT 2.1 column thermostat
Main Results and Discussion
Stationary Phase Comparison:
- Core-shell material offered high efficiency and moderate backpressure, resolving lipids within 3–4 min using combined acetonitrile:methanol gradients.
- Fully porous phase increased retention and separation power, enabling baseline resolution in 2 min under isocratic conditions.
Eluent and Modifier Optimization:
- Pure acetonitrile or methanol gradients yielded suboptimal peak shapes or incomplete elution of PEGylated lipids.
- A mixed acetonitrile:methanol gradient with constant ammonium acetate enabled sharp, symmetric peaks for all analytes.
- Formic acid or absence of modifier deteriorated peak quality or prevented elution of ionizable lipids.
ELSD Temperature:
- Optimal evaporation temperature was 35 °C; lower settings increased noise due to incomplete solvent removal; higher settings reduced sensitivity.
Analytical Performance:
- Linear calibration (quadratic fit) achieved R² > 0.999 for all lipids.
- Core-shell methods: LODs 1.0–3.0 µg/ml (4–15 ng), LOQs 0.9–6.2 µg/ml (5–32 ng).
- Fully porous methods further improved LODs to 0.8–2.5 µg/ml and LOQs to 0.9–3.7 µg/ml.
Benefits and Practical Applications
- High-throughput lipid profiling supports rapid quality control in vaccine development and manufacturing.
- Short isocratic runs reduce solvent consumption and instrument idle time.
- Method transferability to binary pump systems ensures broad applicability.
- Sensitivity in the low-nanogram range facilitates impurity monitoring and stability studies.
Future Trends and Potential Applications
Advances in LNP research will expand the use of mRNA and other nucleic acid therapeutics. Future directions include:
- Integration with LC-MS workflows for simultaneous lipid identification and mass confirmation.
- Automation of sample preparation and data processing for higher throughput.
- Development of multi-dimensional separations to resolve complex lipid mixtures and degradation products.
- Miniaturized HPLC-ELSD platforms for point-of-care or on-line process monitoring.
Conclusion
The developed HPLC-ELSD methods demonstrate rapid, sensitive separation of vaccine-relevant lipids on phenyl-hexyl phases. Gradient and isocratic protocols on core-shell and fully porous columns achieved full resolution in as little as 2 min, with LODs down to 0.8 µg/ml. These workflows support robust quality control of LNP formulations and can be adapted to emerging therapeutic applications.
Reference
- Albertsen H. et al. Adv Drug Deliv Rev. 2022;188:114416.
- USP Draft Guidelines: Analytical Procedures for mRNA Vaccine Quality. 2024.
- Schneider S. Agilent Application Notes 2022–2023.
- Higuchi A. et al. Polymer Reviews. 2023;63:394–436.
- Kinsey C. et al. Electrophoresis. 2022;43:1091–1100.
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