ADVANCES IN CHARGED AEROSOL DETECTION FOR QUANTITATIVE ANALYSIS OF BIOPHARMACEUTICAL EXCIPIENTS

Significance of the topic

Charged aerosol detection (CAD) addresses a recurring analytical need in biopharmaceutical development: reliable, broadly applicable quantification of non-volatile excipients and formulation components that lack chromophores or consistent ionization. CAD provides an essentially universal mass-related response for non-volatile species, making it particularly valuable for quantifying polysorbates, lipids, and lipid nanoparticle (LNP) components where conventional UV or single-mode MS detection is limited. Optimizing CAD operating parameters and chromatographic support directly improves sensitivity, linear dynamic range, precision, and limits of detection — attributes critical for formulation development, QC assays, and regulatory submissions.

Study objectives and overview

This work evaluates CAD performance improvements for quantifying polysorbates and LNP lipids. Key aims were to (1) characterize how ion-trap voltage influences CAD linearity and baseline noise, (2) examine the impact of power function values (PFV) on detector response, (3) validate accuracy and precision via spike–recovery of polysorbates (PS20, PS80) in a monoclonal antibody matrix, and (4) demonstrate chromatographic strategies, including a specialized lipid column, to increase sensitivity and expand dynamic range for LNP components.

Methods

The study combined LC separations with CAD under systematically varied detector and chromatographic conditions. Core method elements included:

• Evaluation of ion-trap voltage settings across the CAD manufacturer range (example steps from 20 V up to 600 V) while keeping PFV fixed in some experiments.
• Use of power function values (PFVs) tailored to analyte classes; some analyses monitored multiple PFVs in a single run to optimize sensitivity for different components.
• Spike–recovery experiments using the well characterized NISTmAb matrix with three concentration levels each injected in replicate (n=6) to assess accuracy and precision for polysorbates.
• Comparative chromatography for LNP components using two columns (ACQUITY Premier CSH Phenyl-Hexyl and Waters GTxResolve Lipid Phenyl-Hexyl+ RP) to evaluate peak shape, height and resulting limits of detection.

Used instrumentation

The principal instrumentation and settings reported were:

• LC system: ACQUITY Premier System.
• Columns: ACQUITY Premier CSH Phenyl-Hexyl (1.7 µm, 130 Å, 2.1 × 50 mm); Waters GTxResolve Lipid Phenyl-Hexyl+ RP (MaxPeak Premier, SPP, 1.6 µm, 230 Å, 2.1 × 50 mm); Oasis MAX trap column for polysorbates work (80 Å, 30 µm, 2.1 × 20 mm).
• Column temps: 30–50 °C depending on assay; sample temperature 10 °C.
• Injection volumes: polysorbate assay ~30 µL; LNP assay 3 µL.
• Mobile phases: various — for lipids 0.1% formic acid in water (A) and 0.1% formic acid in ACN or 50:50 MeOH:ACN (B); polysorbate method used 2.0% formic acid in water (A) and 2.0% formic acid in isopropanol (B).
• CAD settings: sampling rate typically 10 Hz (some experiments at 5 Hz), time constant Normal, evaporator temperature ~35–40 °C, ion-trap voltages explored from 20 V to 600 V, PFV values varied by analyte.

Main results and discussion

Key empirical findings were:

• Ion-trap voltage optimization. Raising the ion-trap voltage improved calibration linearity and reduced baseline noise. Calibration of PS20 across 0.005–0.75 mg/mL showed progressive improvement in R2 and reduced residual sum of squares (RSS) at higher voltages, with the highest voltage tested (600 V) yielding the best linearity and lowest RSS.
• Polysorbate accuracy and precision. Spike–recovery experiments for PS80 and PS20 in the NISTmAb matrix at three concentration levels demonstrated excellent accuracy (measured concentrations close to nominal) and high precision (relative standard deviations below 1% reported for some levels), indicating CAD methods can provide reliable quantitation of polysorbates in protein matrices after appropriate method design.
• Chromatography-driven sensitivity gains. The GTxResolve Lipid Phenyl-Hexyl+ RP column substantially improved peak performance for DMG‑PEG (a PEGylated lipid component), achieving an 86% reduction in peak width and a five-fold increase in peak height compared with the ACQUITY Premier CSH Phenyl-Hexyl column. These gains translated into expansion of the assay dynamic range and lowering of limits of detection, enabling confident detection of several additional lowconcentration levels for LNP component assays.
• Power function monitoring. Utilizing different PFVs within a single analysis enabled further sensitivity tuning for disparate lipid components, helping to detect components across wide concentration ranges within a single run.

Benefits and practical applications

Practical advantages and applications emerging from the study:

• Universal detection for non-volatile excipients: CAD eliminates the need for chromophores or stable ionization, simplifying assays for polysorbates, PEGylated lipids, cholesterol, phospholipids and other LNP constituents.
• Robust quantitative performance: With optimized ion trap voltage and PFV settings, CAD delivers strong linearity, low baseline noise, and tight precision suitable for formulation development and QC workflows.
• Chromatography + CAD synergy: Specialized lipid columns paired with CAD markedly boost sensitivity for low-abundance lipid species, enabling expanded dynamic range and lower LODs without changing detector hardware.
• Method flexibility: The ability to monitor multiple PFVs and tune ion-trap filtering allows a single-platform approach to a chemically diverse panel of excipients and delivery-system components.

Limitations and practical considerations

While CAD offers broad utility, the poster highlights practical constraints:

• CAD response is mass-related but not inherently compound-identifying; coelution can compromise quantitation without adequate chromatographic separation.
• Detector response and baseline are sensitive to aerosol production and excess small ions; ion-trap voltage optimization and evaporator conditions must be tailored to the mobile phase and analyte set.
• Mobile-phase volatility and solvent composition influence aerosol formation and detector stability; methods must use volatile solvents and appropriate additives.

Future trends and potential applications

Projected directions and opportunities for CAD in biopharmaceutical analytics include:

• Method automation and informatics integration: automation of CAD workflows with chromatography data systems (e.g., Empower CDS) to support routine QC and high-throughput screening.
• Predictive method design: use of modeling frameworks (such as inverse power function approaches) to predict linear operating ranges and reduce empirical method development time.
• Advanced column chemistries: further adoption of “supercharged” superficially porous particle (SPP) columns tailored for lipid separations to continue driving down LODs for LNP components.
• Multiparameter CAD strategies: concurrent PFV monitoring and adaptive ion-trap control to widen dynamic range within single analyses and support complex formulation panels.
• Broader regulatory adoption: standardized CAD-based assays for excipient quantitation in release testing and stability studies as the technique matures and is validated across platforms.

Conclusions

Optimization of CAD parameters substantially enhances quantitative analysis of biopharmaceutical excipients. Increasing ion-trap voltage improves linearity and reduces baseline noise; PFV selection and the ability to monitor multiple PFVs enable sensitivity tuning across analyte classes. Chromatographic advances — exemplified by the GTxResolve Lipid Phenyl-Hexyl+ RP column — significantly improve peak shape and signal for lipid components, expanding dynamic range and lowering LODs. Combined, these advances position CAD as a robust, near-universal detector for polysorbates and LNP lipids in formulation development and QC contexts.

References

1. Bigos P, Birdsall RE, Du X, Han D, Bhiwankar N. Quantification of Polysorbates Using the Waters Charged Aerosol Detector. Waters Application Note. July 2026. 720009502EN.
2. Birdsall RE, Du X, Bigos P, Han D, Bhiwankar N. Automating Charged Aerosol Detection (CAD) Analysis with Empower CDS Using a Single-Vendor Integrated LC Platform. Waters Application Note. April 2026. 720009297EN.
3. Birdsall RE, Du X, Bigos P, Han D, Simeone J, Bhiwankar N. Predicting Linear Operating Range for Charged Aerosol Detection Using an Inverse Power Function Framework. Waters Application Note. June 2026. 720009440EN.