Multi-attribute quantification of LNP-mRNA therapeutics by FFF-MALS and DLS

Significance of the topic

Accurate, high-resolution characterization of lipid nanoparticle (LNP)–mRNA formulations is critical across development, manufacturing and quality control for mRNA therapeutics. Particle size, particle concentration, lipid and mRNA content, and the size-resolved mRNA payload all influence delivery efficiency, stability, dose accuracy and safety. Methods that provide multi-attribute quantification in a single, automatable experiment reduce analyst workload, improve resolution of subpopulations, and support robust batch-to-batch comparisons required for regulatory compliance.

Objectives and overview of the study

This application note demonstrates how field-flow fractionation coupled with multi-angle light scattering (FFF-MALS) and complementary detectors (UV and differential refractive index, dRI) can deliver multi-attribute quantification (MAQ) of LNP-mRNA therapeutics. The work uses two commercially available bivalent COVID-19 vaccines (Comirnaty and Spikevax) as case studies to quantify: size distributions, particle concentrations, lipid and mRNA concentrations, molar mass distributions, and size-dependent mRNA payload (mRNA per particle). The study also compares FFF-MALS results with rapid batch dynamic light scattering (DLS) screening and highlights the advantages of high-resolution, size-resolved analysis.

Methodology

FFF-MALS workflow

- Samples: Thawed, handled according to manufacturer instructions; empty LNP standards were prepared with matching lipid composition for UV scattering corrections.
- Separation: Asymmetrical-flow field-flow fractionation (Eclipse FFF instrument with 350 µm short channel) using PBS as mobile phase; autosampler and HPLC pump enabled automated runs.
- Online detection: Multi-angle light scattering (DAWN MALS) for size and molar mass; Optilab differential refractometer (dRI) for concentration of optically active components; UV detection at 260 nm for nucleic acid signal. Data acquisition via VISION and ASTRA software. UV scattering corrections were generated experimentally using the empty LNP samples and processed with the ASTRA LNP Analysis Module.
- DLS screening: Batch hydrodynamic radius and coarse polydispersity assessment by DynaPro NanoStar (data processed with DYNAMICS software) for rapid sample triage.
- Calculations: ASTRA was used to derive size distributions (geometric radius), molar mass distributions (Mw, Mn, Đ), total lipid and mRNA concentrations, and size-resolved mRNA weight fractions. Published nucleotide sequences and estimated molar mass of the vaccine mRNA sequences were used to convert measured mRNA mass into average mRNA molecules per LNP.

Instrumentation

• Eclipse asymmetrical-flow FFF instrument (short 350 µm channel).
• DAWN multi-angle light scattering (MALS) detector.
• Optilab differential refractometer (dRI).
• UV detector (260 nm).
• DynaPro NanoStar dynamic light scattering instrument for batch screening.
• ASTRA LNP Analysis Module and VISION software for acquisition and processing; DYNAMICS software for DLS data.

Main results and discussion

Comparison with DLS

- Batch DLS provided quick screening and suggested that Spikevax contained larger hydrodynamic radii than Comirnaty and similar polydispersity indices. Regularization indicated two populations (a major small population and a minor larger population) but provided only low-resolution mass fractions.

High-resolution FFF-MALS findings

- Size ranges: Both vaccines exhibited broad particle size distributions spanning roughly 20–150 nm geometric radius; however, Spikevax had a substantially higher weight fraction of larger particles.
- Molar mass: Weight-average molar mass (Mw) was ~95.4 ± 2.3 MDa for Comirnaty and ~269.8 ± 5.1 MDa for Spikevax, consistent with Spikevax containing a greater fraction of high-mass particles.
- Dispersity: Spikevax showed higher dispersity (Đ = Mw/Mn ≈ 5.01 ± 0.11) than Comirnaty (Đ ≈ 2.58 ± 0.08), a difference not captured by cumulants DLS analysis.
- Total mRNA and lipid concentrations: Measured mRNA concentrations were 0.106 ± 0.002 mg/mL (Comirnaty) and 0.086 ± 0.001 mg/mL (Spikevax), matching manufacturer specs (~0.1 mg/mL). Measured lipid concentrations were 2.06 ± 0.02 mg/mL (Comirnaty) and 1.97 ± 0.01 mg/mL (Spikevax), close to published values.
- mRNA weight fraction: Overall mRNA accounted for ~4.9 ± 0.1 % w/w in Comirnaty and ~4.2 ± 0.1 % w/w in Spikevax, consistent with labeling (4–5% w/w). Crucially, the mRNA-to-lipid ratio varied with particle size rather than being uniform across the distribution.
- Size-dependent payload: Converting mRNA mass to molecule counts showed that average mRNA per LNP varied strongly with radius. Small particles (~20 nm) had similar mRNA counts in both vaccines; mid-sized Comirnaty particles (~25–50 nm) tended to contain fewer mRNA molecules per particle than corresponding Spikevax particles. Large Spikevax particles (>50 nm) carried substantially more mRNA per particle. Nonetheless, the very largest particles contributed <1% of particle number, emphasizing that high payload per particle can coexist with low numerical prevalence.

Implications of the findings

- FFF-MALS reveals heterogeneity in payload distribution that would be missed by ensemble or fraction-then-breakage workflows. Such size-dependent payload information is important for understanding delivery performance, dosing, and safety, and for guiding formulation or process optimization.

Benefits and practical applications

• Single-run, multi-attribute readout: Simultaneous size, molar mass, concentration, composition (lipid vs mRNA) and size-resolved payload eliminates multiple orthogonal wet-chemistry assays.
• High-resolution subpopulation analysis: Resolves weight- and number-based heterogeneity and enables targeted strategies to enrich desirable particle classes.
• Automatable and regulatory-ready: Workflow and software support 21 CFR Part 11 compliance and do not require external calibration curves for accurate quantitation.
• Utility across development lifecycle: Useful for formulation development, process monitoring, batch release assessment, stability studies and troubleshooting.

Future trends and potential applications

• Broader QC adoption: As regulatory expectations for particulate and payload characterization rise, FFF-MALS could be incorporated into release testing and stability protocols.
• Method standardization: Improved experimental determination of optical constants (dn/dc, extinction coefficients) for lipids and nucleic acids will reduce residual systematic error and improve cross-laboratory comparability.
• Integration with orthogonal analytics: Coupling FFF-MALS outputs with cryo-EM, mass spectrometry and biological assays will strengthen structure–activity relationships and mechanistic insights.
• High-throughput and automation: Miniaturized or parallelized FFF workflows and optimized software pipelines could enable higher sample throughput for screening formulations and storage conditions.
• Real-time process control: Adaptations that enable near-line or in-process monitoring of particle size and payload could improve manufacturing consistency.

Conclusions

FFF-MALS with UV and dRI detection provides a powerful, automatable platform for multi-attribute quantification of LNP-mRNA therapeutics. Compared with batch DLS and offline dye-binding assays, the approach yields higher-resolution size, molar mass, composition and payload-per-particle data in a single experiment. The case study comparing Comirnaty and Spikevax highlights substantial differences in size distributions, dispersity and size-resolved mRNA loading that are important for product understanding and optimization. The method supports regulatory-compliant workflows and offers clear advantages for development, manufacturing control and formulation optimization of LNP-mRNA drug products.

References

1. Hou X, Zaks T, Langer R, Dong Y. Lipid nanoparticles for mRNA delivery. Nature Reviews Materials. 2021;6(12):1078–1094.
2. Corbett KS, et al. SARS-CoV-2 mRNA vaccine design enabled by prototype pathogen preparedness. Nature. 2020;586(7830):567–571.
3. Polack FP, et al. Safety and Efficacy of the BNT162b2 mRNA Covid-19 Vaccine. New England Journal of Medicine. 2020;383(27):2603–2615.
4. Podzimek S. Light scattering, size exclusion chromatography and asymmetric flow field-flow fractionation: powerful tools for the characterization of polymers, proteins and nanoparticles. John Wiley & Sons; 2011.
5. Mildner R, et al. Improved multidetector asymmetrical-flow field-flow fractionation method for particle sizing and concentration measurements of lipid-based nanocarriers for RNA delivery. European Journal of Pharmaceutics and Biopharmaceutics. 2021;163:252–265.
6. Parot J, et al. Physical characterization of liposomal drug formulations using multi-detector asymmetrical-flow field flow fractionation. Journal of Controlled Release. 2020;320:495–510.
7. World Health Organization. Messenger RNA encoding the full-length SARS-CoV-2 spike glycoprotein. Document 11889; 2020.
8. Jeong D-E, et al. Assemblies of putative SARS-CoV-2-spike-encoding mRNA sequences for vaccines BNT-162b2 and mRNA-1273. Version 0.21 Beta. 2021.
9. Zhang X, Kenrick S. Measuring size, concentration, and zeta Potential of LNPs with the DynaPro ZetaStar. Application note.
10. Kurnik M. High-throughput freeze-thaw stability studies with the DynaPro Plate Reader. Application note.
11. COMIRNATY Full Prescribing Information (12 years of age and older). Pfizer; retrieved 2024.
12. Moderna. Fact Sheet for Healthcare Providers Administering Vaccine: EUA of Moderna COVID-19 Vaccine, Bivalent (Original and Omicron BA.4/BA.5). Retrieved 2024.
13. Jia X, et al. Enabling online determination of the size-dependent RNA content of lipid nanoparticle-based RNA formulations. Journal of Chromatography B. 2021;1186:123015.