High-Throughput LNP Compositional Analysis Using GTxResolve™ RP 230 Å PH+ Columns: Method Development Considerations
Applications | 2026 | WatersInstrumentation
Liquid chromatography methods capable of robustly resolving diverse lipid classes are critical for characterization and quality control of lipid nanoparticles (LNPs) used in drug delivery and vaccines. Accurate compositional analysis supports identification and quantification of critical quality attributes (CQAs), stability testing, raw material assessment and regulatory submissions. Highly hydrophobic and ionizable lipid species present particular chromatographic challenges that require tailored stationary phase chemistry and pragmatic method development strategies to achieve high throughput and reproducible performance.
This application note introduces and evaluates the GTxResolve RP 230 Å PH+ column, a wide-pore superficially porous particle (SPP) column with a phenyl‑hexyl ligand and an acid-activated positively charged particle surface. The aims were to: (1) demonstrate the column design benefits for rapid LNP compositional analysis, (2) define practical method development rules based on ionic strength and mobile phase organic modifiers, and (3) illustrate application to a challenging LNP formulation containing highly hydrophobic ionizable lipids.
Samples: Individual lipid standards (e.g., ALC‑0315, DOTAP, cholesterol, DSPC, DMG‑PEG2000) prepared as methanolic stocks and LNP samples (commercial Spikevax and a proprietary formulation termed LNP2) deformulated by dilution into methanol.
Chromatography and detection: ACQUITY Premier System (UPLC) with short 2.1 × 50 mm columns, operated at 1.0 mL/min and 40 °C, short gradients (high‑throughput workflows: tg = 2 min, total runtime ~4 min). Injection volumes 0.3–3 µL. Mobile phases contained 0.1% formic acid (aqueous and organic), with ammonium formate salt varied 0–20 mM as an ionic strength modifier. Mobile phase B typically MeCN with controlled additions of protic organic modifiers (notably MeOH at up to 30%). Detection employed ELSD (SEDEX 85 LT) and tunable UV at 205 nm; CAD considerations discussed in cited references.
Columns evaluated: GTxResolve Lipid Phenyl‑Hexyl+ RP, SPP 1.6 µm, 230 Å (charged surface activated under acidic conditions) compared against a standard phenyl‑hexyl column (120 Å). Other system consumables and wash solvents were specified to ensure reproducible sample handling and carryover control.
Charged surface benefits: The GTxResolve PH+ column shows improved peak shape, symmetry and higher peak capacity for ionizable (cationic) lipids versus an uncharged phenyl‑hexyl reference. The column’s acid‑activated positive surface (activated at pH < ~5–6 using volatile acids) selectively modulates retention of cationic lipids through electrostatic interactions, while neutral lipids remain minimally affected.
Ionic strength as a selectivity switch: Adding ammonium formate modulates electrostatic repulsion between cationic analytes and the positively charged stationary phase, increasing retention of ionizable lipids. Retention increases with salt concentration over 0–10 mM and plateaus above ~10 mM. In the 0–10 mM range the retention dependence follows a logarithmic trend (R2 > 0.98), enabling empirical prediction of retention times from a salt‑free baseline (RT0) with small errors (average ΔRT ≈ 0.017 min; max ΔRT ≈ 0.037 min in test cases). This provides a straightforward, experimentally validated workflow to tune separation of cationic lipids relative to neutral species.
Organic modifier (alcohol) effects: Screening of 10% v/v alcohols showed that protic short alcohols (MeOH, EtOH) sharpen PEGylated lipid peaks and reduce heterogeneity‑related band broadening, with MeOH giving the best improvement. Optimizing to 30% MeOH in MPB (MeCN/MeOH 70/30) delivered pronounced peak sharpening for PEG lipids and DSPC without compromising elution strength or ionizable lipid separation when MeOH was added only to MPB. More hydrophobic alcohols (propanols, butanol) and aprotic solvents were less effective. However, adding organic modifiers reduced overall peak capacity by ~20–30% relative to the no‑additive case, introducing a tradeoff between peak shape for late‑eluting heterogeneous species and global resolving power.
Application to a challenging LNP: For the proprietary LNP2 containing a very hydrophobic ionizable lipid, a combination of MPB = MeCN/MeOH 70/30 and 10 mM ammonium formate in both mobile phases produced baseline separation of key components (ionizable lipid, PEG lipid, cholesterol, DSPC) with Rs > 3.3. These changes also increased ELSD signal intensity for cholesterol and DSPC, improving detection sensitivity. The example establishes a concise development path: start with a high‑throughput generic method, apply MeOH in MPB to sharpen problematic peaks, then adjust ionic strength (up to ~10 mM) to resolve coeluting cationic species.
Limitations and practical notes: Organic modifiers decreased peak capacity so they should be applied selectively when peak shape of late eluting components is prioritized. Analysis of negatively charged lipids may require elevated ionic strength and/or higher column temperature to elute efficiently under conditions that activate the positive surface. Changes in mobile phase composition also affect ELSD/CAD response and must be evaluated during method validation. Longer columns or extended gradients can increase peak capacity for lipidomics workflows but require collective optimization of flow rate, gradient time and column length.
The GTxResolve RP 230 Å PH+ column couples a phenyl‑hexyl ligand and an acid‑activated positively charged SPP surface to deliver predictable, tunable separations of hydrophobic and ionizable lipids. Practical method‑development principles—using ionic strength (0–10 mM ammonium formate) as a selectivity switch for cationic lipids and applying MeOH (30% in MPB) to sharpen PEG and late‑eluting lipid peaks—enable rapid, high‑throughput LNP compositional analyses. The approach balances throughput, selectivity and sensitivity and is broadly applicable across typical LNP formulations; further optimization (column length, temperature, gradient profiling) can be used to tailor methods for lipidomics or heightened resolving power needs.
1. Antonara L.; et al. A review, Lipid‑based drug delivery systems: concepts and recent advances in transdermal applications. Nanomaterials. 2025;15:1326.
2. Sandra K.; dos Santos Pereira A.; Vanhoenacker G.; David F.; Sandra P. Comprehensive blood plasma lipidomics by liquid chromatography/quadrupole time‑of‑flight mass spectrometry. J Chromatogr A. 2010;1217:4087–4099.
3. U.S. Food and Drug Administration. Guidance for Industry, Liposome Drug Products: Chemistry, Manufacturing, and Controls; Human Pharmacokinetics and Bioavailability; and Labeling Documentation. FDA; 2018.
4. Ovčačíková M.; Lísa M.; Cífková E.; Holčapek M. Retention behavior of lipids in reversed‑phase ultrahigh‑performance liquid chromatography–electrospray ionization mass spectrometry. J Chromatogr A. 2016;1450:76–85.
5. Fan Y.; Marioli M.; Zhang K. A review, Analytical characterization of liposomes and other lipid nanoparticles for drug delivery. J Pharm Biomed Anal. 2021;192:113642.
6. Schad G.J.; Fujisaki S. Streamlined method development for efficient and reliable lipid nanoparticle analysis. LC GC Europe. 2025;21:19–23.
7. Imiolek M.; Koza S.M. High throughput LNP Compositional Analysis using GTxResolve™ RP 230 Å PH+ Columns: Method Development Considerations. Waters Application Note. 2026;720009424.
8. Alden B.A.; Isaac G.; Chen W.; Lauber M.A. Lipid nanoparticle compositional analysis using charged surface hybrid phenyl‑hexyl separation with evaporative light scattering detection. Waters Application Note. 2021;720007331.
9. Iraneta P.C.; Wyndham K.D.; McCabe D.R.; Walter T.H. A review of Waters hybrid particle technology. Part 3: Charged surface hybrid (CSH) technology and its use in liquid chromatography. Waters White Paper. 2011;720003929.
10. DeLaney K.; Han D.; Birdsall R.E.; Yu Y.Q. Optimized ELSD workflow for improved detection of lipid nanoparticle components. Waters Application Note. 2022;720007740.
11. Birdsall R.; Du X.; Bigos P.; Han D.; Bhiwankar N. Automating charged aerosol detection (CAD) analysis with Empower CDS software using a single‑vendor integrated LC platform. Waters Application Note. 2026;720009297.
HPLC, Consumables, LC columns
IndustriesLipidomics
ManufacturerWaters
Summary
Significance of the topic
Liquid chromatography methods capable of robustly resolving diverse lipid classes are critical for characterization and quality control of lipid nanoparticles (LNPs) used in drug delivery and vaccines. Accurate compositional analysis supports identification and quantification of critical quality attributes (CQAs), stability testing, raw material assessment and regulatory submissions. Highly hydrophobic and ionizable lipid species present particular chromatographic challenges that require tailored stationary phase chemistry and pragmatic method development strategies to achieve high throughput and reproducible performance.
Objectives and overview of the study
This application note introduces and evaluates the GTxResolve RP 230 Å PH+ column, a wide-pore superficially porous particle (SPP) column with a phenyl‑hexyl ligand and an acid-activated positively charged particle surface. The aims were to: (1) demonstrate the column design benefits for rapid LNP compositional analysis, (2) define practical method development rules based on ionic strength and mobile phase organic modifiers, and (3) illustrate application to a challenging LNP formulation containing highly hydrophobic ionizable lipids.
Methodology and instrumentation
Samples: Individual lipid standards (e.g., ALC‑0315, DOTAP, cholesterol, DSPC, DMG‑PEG2000) prepared as methanolic stocks and LNP samples (commercial Spikevax and a proprietary formulation termed LNP2) deformulated by dilution into methanol.
Chromatography and detection: ACQUITY Premier System (UPLC) with short 2.1 × 50 mm columns, operated at 1.0 mL/min and 40 °C, short gradients (high‑throughput workflows: tg = 2 min, total runtime ~4 min). Injection volumes 0.3–3 µL. Mobile phases contained 0.1% formic acid (aqueous and organic), with ammonium formate salt varied 0–20 mM as an ionic strength modifier. Mobile phase B typically MeCN with controlled additions of protic organic modifiers (notably MeOH at up to 30%). Detection employed ELSD (SEDEX 85 LT) and tunable UV at 205 nm; CAD considerations discussed in cited references.
Columns evaluated: GTxResolve Lipid Phenyl‑Hexyl+ RP, SPP 1.6 µm, 230 Å (charged surface activated under acidic conditions) compared against a standard phenyl‑hexyl column (120 Å). Other system consumables and wash solvents were specified to ensure reproducible sample handling and carryover control.
Main results and discussion
Charged surface benefits: The GTxResolve PH+ column shows improved peak shape, symmetry and higher peak capacity for ionizable (cationic) lipids versus an uncharged phenyl‑hexyl reference. The column’s acid‑activated positive surface (activated at pH < ~5–6 using volatile acids) selectively modulates retention of cationic lipids through electrostatic interactions, while neutral lipids remain minimally affected.
Ionic strength as a selectivity switch: Adding ammonium formate modulates electrostatic repulsion between cationic analytes and the positively charged stationary phase, increasing retention of ionizable lipids. Retention increases with salt concentration over 0–10 mM and plateaus above ~10 mM. In the 0–10 mM range the retention dependence follows a logarithmic trend (R2 > 0.98), enabling empirical prediction of retention times from a salt‑free baseline (RT0) with small errors (average ΔRT ≈ 0.017 min; max ΔRT ≈ 0.037 min in test cases). This provides a straightforward, experimentally validated workflow to tune separation of cationic lipids relative to neutral species.
Organic modifier (alcohol) effects: Screening of 10% v/v alcohols showed that protic short alcohols (MeOH, EtOH) sharpen PEGylated lipid peaks and reduce heterogeneity‑related band broadening, with MeOH giving the best improvement. Optimizing to 30% MeOH in MPB (MeCN/MeOH 70/30) delivered pronounced peak sharpening for PEG lipids and DSPC without compromising elution strength or ionizable lipid separation when MeOH was added only to MPB. More hydrophobic alcohols (propanols, butanol) and aprotic solvents were less effective. However, adding organic modifiers reduced overall peak capacity by ~20–30% relative to the no‑additive case, introducing a tradeoff between peak shape for late‑eluting heterogeneous species and global resolving power.
Application to a challenging LNP: For the proprietary LNP2 containing a very hydrophobic ionizable lipid, a combination of MPB = MeCN/MeOH 70/30 and 10 mM ammonium formate in both mobile phases produced baseline separation of key components (ionizable lipid, PEG lipid, cholesterol, DSPC) with Rs > 3.3. These changes also increased ELSD signal intensity for cholesterol and DSPC, improving detection sensitivity. The example establishes a concise development path: start with a high‑throughput generic method, apply MeOH in MPB to sharpen problematic peaks, then adjust ionic strength (up to ~10 mM) to resolve coeluting cationic species.
Limitations and practical notes: Organic modifiers decreased peak capacity so they should be applied selectively when peak shape of late eluting components is prioritized. Analysis of negatively charged lipids may require elevated ionic strength and/or higher column temperature to elute efficiently under conditions that activate the positive surface. Changes in mobile phase composition also affect ELSD/CAD response and must be evaluated during method validation. Longer columns or extended gradients can increase peak capacity for lipidomics workflows but require collective optimization of flow rate, gradient time and column length.
Benefits and practical applications
- Enables high‑throughput (minutes per run) compositional profiling of LNPs and broad lipid classes while preserving the ability to fine‑tune selectivity.
- Positively charged particle surface provides an orthogonal selectivity parameter (ionic strength) to resolve ionizable lipids independently of neutral lipids.
- Phenyl‑hexyl ligand and controlled addition of protic modifiers (notably MeOH) improve peak shape for PEGylated and late‑eluting lipids, enhancing quantification and sensitivity especially with ELSD/CAD.
- Workflows and empirical rules (e.g., RT prediction from RT0 across 0–10 mM salt) accelerate method development and reduce trial‑and‑error screening.
Future trends and potential applications
- Adoption of longer columns and extended gradients for comprehensive lipidomics where increased peak capacity is required.
- Integration of empirical models and chromatographic software to automate prediction of retention shifts with ionic strength and organic modifiers, shortening development cycles.
- Further stationary phase engineering (ligand chemistries and pore architectures) to enhance separation of structurally similar lipid impurities and isomers.
- Combined detector strategies (ELSD, CAD, MS) and optimized workflows to improve sensitivity and quantitative robustness for diverse lipid matrices.
- Method transfer and regulatory alignment for QC of therapeutic LNP products, including validation paths that incorporate the ionic strength and modifier‑driven selectivity levers described here.
Conclusion
The GTxResolve RP 230 Å PH+ column couples a phenyl‑hexyl ligand and an acid‑activated positively charged SPP surface to deliver predictable, tunable separations of hydrophobic and ionizable lipids. Practical method‑development principles—using ionic strength (0–10 mM ammonium formate) as a selectivity switch for cationic lipids and applying MeOH (30% in MPB) to sharpen PEG and late‑eluting lipid peaks—enable rapid, high‑throughput LNP compositional analyses. The approach balances throughput, selectivity and sensitivity and is broadly applicable across typical LNP formulations; further optimization (column length, temperature, gradient profiling) can be used to tailor methods for lipidomics or heightened resolving power needs.
Reference
1. Antonara L.; et al. A review, Lipid‑based drug delivery systems: concepts and recent advances in transdermal applications. Nanomaterials. 2025;15:1326.
2. Sandra K.; dos Santos Pereira A.; Vanhoenacker G.; David F.; Sandra P. Comprehensive blood plasma lipidomics by liquid chromatography/quadrupole time‑of‑flight mass spectrometry. J Chromatogr A. 2010;1217:4087–4099.
3. U.S. Food and Drug Administration. Guidance for Industry, Liposome Drug Products: Chemistry, Manufacturing, and Controls; Human Pharmacokinetics and Bioavailability; and Labeling Documentation. FDA; 2018.
4. Ovčačíková M.; Lísa M.; Cífková E.; Holčapek M. Retention behavior of lipids in reversed‑phase ultrahigh‑performance liquid chromatography–electrospray ionization mass spectrometry. J Chromatogr A. 2016;1450:76–85.
5. Fan Y.; Marioli M.; Zhang K. A review, Analytical characterization of liposomes and other lipid nanoparticles for drug delivery. J Pharm Biomed Anal. 2021;192:113642.
6. Schad G.J.; Fujisaki S. Streamlined method development for efficient and reliable lipid nanoparticle analysis. LC GC Europe. 2025;21:19–23.
7. Imiolek M.; Koza S.M. High throughput LNP Compositional Analysis using GTxResolve™ RP 230 Å PH+ Columns: Method Development Considerations. Waters Application Note. 2026;720009424.
8. Alden B.A.; Isaac G.; Chen W.; Lauber M.A. Lipid nanoparticle compositional analysis using charged surface hybrid phenyl‑hexyl separation with evaporative light scattering detection. Waters Application Note. 2021;720007331.
9. Iraneta P.C.; Wyndham K.D.; McCabe D.R.; Walter T.H. A review of Waters hybrid particle technology. Part 3: Charged surface hybrid (CSH) technology and its use in liquid chromatography. Waters White Paper. 2011;720003929.
10. DeLaney K.; Han D.; Birdsall R.E.; Yu Y.Q. Optimized ELSD workflow for improved detection of lipid nanoparticle components. Waters Application Note. 2022;720007740.
11. Birdsall R.; Du X.; Bigos P.; Han D.; Bhiwankar N. Automating charged aerosol detection (CAD) analysis with Empower CDS software using a single‑vendor integrated LC platform. Waters Application Note. 2026;720009297.
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