Automating Charged Aerosol Detection (CAD) Analysis with Empower™ CDS Using a Single-Vendor Integrated LC Platform

Automating Charged Aerosol Detection (CAD) Analysis with Empower CDS — Executive Summary

Importance of the topic

Liquid chromatography coupled with charged aerosol detection (CAD) addresses a key analytical gap for non‑chromophoric, nonvolatile or semi‑volatile components such as lipid constituents of lipid nanoparticles (LNPs). Reliable, sensitive, and automation‑friendly quantitation of such species is critical across biopharmaceutical development and manufacturing for product quality, batch‑to‑batch consistency, and regulatory compliance. Integrating CAD within a single‑vendor LC and chromatography data system (Empower CDS) enables streamlined, traceable workflows suited to regulated environments.

Objectives and overview of the study

This application note describes a data‑driven, Empower CDS–based workflow that integrates Waters’ new CAD with optical and mass detectors to:

• enable real‑time acquisition correction of CAD’s nonlinearity via selectable power function values (PFVs),
• facilitate rapid, objective selection of PFV and regression/weighting schemes using in‑CDS statistical metrics, and
• demonstrate the approach using LNP lipid component analysis as a representative, analytically challenging case study.

Methodology and used instrumentation

Samples and separation

• Lipids (cholesterol, DSPC, DMG‑PEG2000, SM‑102) prepared in methanol; dilutions in 90:10 methanol/water (v/v).
• Separation performed on an ACQUITY Premier UPLC system using a prototype RP 230 Å Phenyl‑hexyl+ column (1.6 µm, 2.1 × 50 mm) at 50 °C with a 6‑minute gradient and 3 µL injection volume; flow 0.400 mL/min.
• Mobile phases: A = 0.1% formic acid in water; B = 50:50 MeOH:MeCN with 0.1% formic acid.

Detection and data system

• Charged Aerosol Detector (Waters CAD) fully integrated and controlled inside Empower 3.9.0 CDS, used alongside UV (TUV at 200/280 nm) and optionally MS for orthogonal detection.
• Key CAD acquisition settings: sampling rate 10 Hz, time constant = normal, ion trap 20 V, evaporation temperature 35 °C. CAD supports four user‑selectable PFV settings to correct nonlinearity during acquisition.

Processing workflow

• Calibration series: 10 concentration points in triplicate for each analyte; processing methods created to test four PFV values and duplicate methods with/without weighting under linear regression.
• All acquisition, processing, calibration, and reporting conducted in Empower CDS to maintain traceability and enable automated comparison of figures of merit (R2, residuals, RSS, % deviation, %RSD, S/N).

Main results and discussion

Detector linearization

• CAD’s native non‑linear response follows a power law; selecting an appropriate PFV during acquisition reduces curvature and produces near‑linear calibration behavior without post‑acquisition transforms. An example: applying PFV=1.4 improved linear regression R2 from ~0.9899 (PFV=1.0) to ~0.9998.

Chromatography and sensitivity gains

• Column particle technology improvements (prototype RP 230 Å Phenyl‑hexyl+) substantially sharpened the low‑abundance, polydisperse DMG‑PEG peak relative to the prior “gold” standard column — reported ~86% reduction in peak width and ~5× increase in peak height — leading to improved sensitivity and lower LOQs.

Integrated, objective method selection

• By processing combinations of PFV and regression/weighting methods in Empower, the study generated and evaluated a large dataset (example: ~1,440 data points) and used CDS reporting filters to identify the best conditions (e.g., highest R2 and lowest RSS for SM102).
• Empower’s built‑in metrics (correlation coefficients, residuals, RSS) and additional diagnostics (% deviation, %RSD, S/N) enable automated flagging of outliers and objective exclusion or re‑evaluation of suspect points, reducing subjective manual curation.

Automated compositional control

• Optimized acquisition/processing settings combined with predefined acceptance thresholds for concentration and molar ratios permit automated determination of whether LNP composition meets specification; out‑of‑spec components are flagged in reports to streamline release decisions in manufacturing environments.

Benefits and practical applications

• Compliant‑ready, single‑vendor workflow: Consolidating acquisition, processing, calibration, and reporting in Empower reduces risk, improves traceability, and simplifies validation for regulated labs.
• Faster method development: Real‑time PFV correction removes the need for post‑acquisition transform workflows (e.g., log–log), accelerating calibration and decision making.
• Improved quantitative robustness: Sharper chromatographic peaks and PFV linearization increase sensitivity, lower LOQs, and enable more reliable automated processing for low‑abundance analytes.
• Scalability for manufacturing QA/QC: The semi‑automated CDS workflow supports routine screening of large sample sets with objective pass/fail logic and reporting suitable for release testing.

Future trends and potential applications

• Broader adoption for LNP and other non‑chromophoric analytes: CAD integrated workflows will likely expand across mRNA and nucleic acid therapeutic analytics where many species lack UV activity.
• Multi‑detector fusion: Combining CAD, UV, and MS data within unified processing pipelines will enable richer identity, purity, and quantitation information and better risk mitigation.
• Automated optimization and AI: Data‑driven PFV selection could be further automated with model‑based or machine learning approaches to accelerate method setup and adapt to lot‑to‑lot variability.
• Regulatory integration: Formalization of CAD workflows and in‑CDS statistical reporting should facilitate regulatory acceptance for release testing of complex drug‑delivery excipients.

Conclusion

Integrating Waters CAD into an Empower CDS‑centric workflow provides a practical strategy to address CAD’s inherent nonlinearity while preserving regulatory traceability. Real‑time PFV correction coupled with robust chromatographic improvements and in‑CDS statistical assessment enables faster, more objective method development and routine quantitation of challenging lipids in LNPs. The combined approach enhances sensitivity, reduces manual data handling, and supports deployment in manufacturing and regulated environments.

References

1. Han D., DeLaney K., Alden B., Birdsall R., Yu Y. Lipid Nanoparticle Analysis: Leveraging MS to Reduce Risk. Waters Application Note 720007716, 2022.
2. Fekete S., Doneanu C., Addepalli B., et al. Challenges and emerging trends in liquid chromatography‑based analyses of mRNA pharmaceuticals. Journal of Pharmaceutical and Biomedical Analysis. 2023;224:115174. DOI:10.1016/j.jpba.2022.115174.
3. DeLaney K., Han D., Birdsall R., Yu Y. Optimized ELSD Workflow for Improved Detection of Lipid Nanoparticle Components. Waters Application Note 720007740, 2022.
4. Han D., Birdsall R., Simeone J., Fogwill M., Yu Y. Comparing ELSD and CAD Performance on Polysorbate Quantification in Infliximab Drug Products. Waters Application Note 720007501, 2022.