CHARACTERIZING LIPID NANOPARTICLE VACCINES USING CDMS AND FFF-MALS: A MULTI-DIMENSIONAL APPROACH

Significance of the topic

Lipid nanoparticle (LNP) systems are central to delivery of nucleic acid therapeutics such as mRNA vaccines. Reliable characterization of critical quality attributes — particle mass, size, payload loading, and heterogeneity — is essential for development, manufacturing control, and stability/potency assessment. Ensemble techniques (e.g., DLS, FFF-MALS) provide population-averaged metrics but can mask low-abundance or compositionally distinct subpopulations. Integrating single-particle mass analysis with ensemble separations gives a more complete view of LNP architecture and loading-related changes.

Objectives and study overview

This work applied a multidimensional analytical workflow combining Charge Detection Mass Spectrometry (CDMS) and Field-Flow Fractionation with Multi-Angle Light Scattering detection (FFF-MALS) to characterize LNP vaccine formulations. Goals were to (1) measure single-particle mass and charge distributions, (2) compare those metrics with ensemble size/mass measures, and (3) demonstrate how payload loading and formulation differences (examples: empty vs full LNPs; Spikevax vs Comirnaty) affect particle composition and heterogeneity.

Methods and methodology

Samples were buffer-exchanged into 20 mM ammonium acetate (pH 7.4) prior to CDMS analysis. CDMS measurements used nano-electrospray ionization on a benchtop Xevo CDMS instrument. Mass and charge events were processed using the CDMS Toolkit within waters_connect and analyzed statistically with Python scripts. Mass histograms were generated using common logarithmic bins spanning roughly 1×10^7 to 3×10^8 Da.
FFF separations used neat samples on an Eclipse FFF system with a 350 µm fixed-height short channel. PBS served as mobile phase. Online detectors included DAWN MALS, an Optilab differential refractometer, and a UV detector at 260 nm. FFF control and data analysis were performed with VISION and ASTRA software. Reported uncertainties derive from triplicate measurements.

Instrumentation used

• Xevo CDMS (Waters) — single-particle mass and charge detection via nano-ESI
• Eclipse Field-Flow Fractionation (short 350 µm channel) with HPLC pump and autosampler
• DAWN Multi-Angle Light Scattering (MALS) detector
• Optilab differential refractometer (dRI)
• UV detector (260 nm)
• Slide-A-Lyzer MINI Dialysis Devices for buffer exchange
• Software: CDMS Toolkit in waters_connect, ASTRA, VISION; Python for statistical scripting

Main results and discussion

CDMS detected clear and substantial mass increases and greater mass heterogeneity upon payload loading that were only modestly reflected in ensemble hydrodynamic size measures (DLS) or population-averaged FFF-MALS metrics.
Key quantitative findings (representative values reported):

• Empty LNPs: median mass ≈ 3.67×10^7 Da (IQR 1.89×10^7), DLS radius ≈ 49.2 nm, PDI ≈ 0.146.
• Full (payload-loaded) LNPs: median mass ≈ 6.95×10^7 Da (IQR 3.75×10^7), DLS radius ≈ 53.7 nm, PDI ≈ 0.112.
• Spikevax: median mass ≈ 1.17×10^8 Da (IQR 4.11×10^7), DLS radius ≈ 75.4 nm, PDI ≈ 0.24.
• Comirnaty: median mass ≈ 6.49×10^7 Da (IQR 1.99×10^7), DLS radius ≈ 38.4 nm, PDI ≈ 0.26.

Interpretation:

• Payload loading roughly doubled the typical single-particle mass and markedly increased mass heterogeneity, while DLS reported only a modest hydrodynamic radius increase and small PDI changes. This implies loading primarily modifies internal composition rather than overall hydrodynamic size.
• Comparing two commercial vaccine formulations showed distinct single-particle mass distributions and absolute heterogeneity despite superficially similar distribution shapes. Spikevax displayed larger median mass and greater heterogeneity than Comirnaty, consistent with formulation-dependent loading differences.
• FFF-MALS delivers ensemble-averaged size, molar mass and concentration profiles under native solution conditions, providing orthogonal context for CDMS. However, only CDMS resolves single-particle mass/charge and detects low-abundance or compositionally distinct subpopulations that ensemble measures can mask.

Benefits and practical applications of the method

The combined CDMS + FFF-MALS workflow offers complementary strengths: CDMS provides single-particle mass and charge resolution that reveals loading-dependent composition changes and heterogeneity; FFF-MALS supplies size/mass distributions and concentration under native solution conditions suited to routine comparability and formulation profiling. Practical applications include process development, batch-to-batch comparison, formulation screening, and identification of subpopulations relevant to potency or stability.

Future trends and potential applications

• Correlate CDMS-derived single-particle mass distributions with encapsulation efficiency, biological potency, and long-term stability metrics to establish mass-based critical quality attributes.
• Deploy the CDMS–FFF-MALS workflow in manufacturing control and release testing to improve detection of low-abundance variants and compositional drift.
• Expand analyses to diverse LNP formulations and different nucleic acid payloads (siRNA, saRNA, DNA) to map formulation–mass relationships.
• Incorporate charge-resolved CDMS metrics and advanced data integration (multivariate or machine-learning models) to probe particle architecture and predict functional outcomes.

Conclusion

Integrating single-particle CDMS with ensemble FFF-MALS yields a multidimensional characterization that uncovers loading-driven mass increases and heterogeneity not apparent from ensemble size measures alone. Single-particle mass and heterogeneity emerge as critical quality attributes for LNP characterization, with direct implications for formulation development, quality control, and comparability assessment of mRNA-LNP vaccines and related therapeutics.

References

1. D'Esposito RJ, McAllister E, Kurnik M, et al. High-Resolution Characterization of Lipid Nanoparticles Using Xevo CDMS. Application Note, Waters Corporation; 2026.
2. Kurnik M. Multi-Attribute Quantification of LNP-mRNA Therapeutics by FFF-MALS and DLS. Application Note AN2615, Waters Corporation; 2023.