AQbD-Based Method Development for the Analysis of Lipid-Based Nucleic Acid Delivery Systems Using ChromSwordAuto Software

Significance of the Topic

The emergence of lipid-based nucleic acid delivery systems has revolutionized mRNA therapeutics and vaccines. Robust chromatographic methods are critical to ensure identity, purity, and performance of these complex formulations. An Analytical Quality by Design (AQbD) framework supports systematic development of reliable, regulatory-compliant assays for pharmaceutical quality control.

Study Objectives and Overview

The article reports the development of a UHPLC method for analyzing lipid components in various delivery vehicles, including liposomes, lipoplexes, stabilized plasmid-lipid particles, and lipid nanoparticles (LNPs). Using ChromSwordAuto software and smart algorithms, a Design of Experiment (DoE) approach was applied to define an Analytical Target Profile (ATP) and to establish a robust Method Operable Design Region (MODR).

Methodology and Experimental Design

• Stage 1: Knowledge gathering on lipid structures and properties to define ATP (resolution >2, run time <30 min).
• Stage 2: Screening via DoE with quaternary gradients varying stationary phase chemistry, solvent composition (MeOH, IPA, ACN), temperature and flow rate.
• Stage 3: Optimization using rapid algorithms for large molecules, comparing 50×3 mm and 100×3 mm ZORBAX StableBond CN columns.
• Stage 4: Robustness testing with AutoRobust to map the MODR considering critical process parameters (CPPs) and known impurities.
• Stage 5: Verification of predicted retention times and resolution across selected conditions, achieving R² of 0.9999.

Instrumentation

• Agilent 1290 Infinity II Bio LC system with quaternary and binary pumps, multisampler, multicolumn thermostat, and ELSD detector.
• Agilent ZORBAX StableBond CN columns (100×3 mm, 50×3 mm).
• ChromSwordAuto 5.1 with DataSystem, Scout, Developer, and AutoRobust modules; Agilent OpenLab CDS 2.7.

Main Results and Discussion

• Screening identified CN and CS-C18 phases as top performers, favoring H₂O/MeOH/ACN gradients.
• Optimization on the 100 mm CN column delivered baseline separation of all lipid standards, uncovering two minor impurities from DSPC and DOTAP.
• MODR defined for flow rate (0.5±0.05 mL/min), temperature (25±5 °C), B% gradient start/end (27–90 ±5%), ensuring resolution >2 even in worst-case impurity scenarios.
• Verification experiments confirmed predicted retention times and achieved minimum resolution >3.7 across real formulations (SPLP, liposome, lipoplex, LNP).

Benefits and Practical Applications

This AQbD-based workflow enables accelerated method development with minimal manual intervention, improves understanding of critical parameters, and delivers a resilient assay suitable for regulatory submissions and routine quality control of lipid-based nucleic acid products.

Future Trends and Applications

• Integration of predictive modeling and machine learning for enhanced method prediction and adaptive control.
• Extension of AQbD principles to other complex biologics, including oligonucleotide therapeutics and protein–lipid conjugates.
• Real-time monitoring and inline analytics for continuous manufacturing environments.

Conclusion

An automated AQbD strategy using ChromSwordAuto and Agilent UHPLC platforms achieved robust, high-resolution separation of diverse lipid components in nucleic acid delivery systems. The defined MODR and verification data provide confidence in method performance, supporting consistent quality assessment of emerging mRNA and gene therapy formulations.

References

1. Knezevic I et al. Vaccines. 2021;9(2):81. Development of mRNA Vaccines: Scientific and Regulatory Issues.
2. Liu MA et al. Emerg Microbes Infect. 2022;11(1):384–391. Regulatory Considerations for RNA-Based Vaccines.
3. Daniel S et al. Trends Biotechnol. 2022. Quality by Design for RNA Platform Processes.
4. Peraman R et al. Int J Anal Chem. 2015. Analytical Quality by Design and Regulatory Flexibility.
5. Reid GL et al. Am Pharm Rev. 2013. AQbD in Pharmaceutical Development.