Automated method optimization for drug-to-antibody ratio determination using hydrophobic interaction chromatography

Significance of the Topic

Antibody–drug conjugates (ADCs) represent a rapidly expanding class of targeted cancer therapies. The average drug-to-antibody ratio (DAR) is a critical quality attribute affecting efficacy, pharmacokinetics, and safety of ADCs. Hydrophobic interaction chromatography (HIC) remains the reference approach to quantify DAR and drug distribution in cysteine-linked ADCs. Reliable, robust, and automated optimization of HIC methods is essential for consistent quality control and regulatory compliance in biopharmaceutical development.

Objectives and Study Overview

This application note presents an automated analytical quality by design (AQbD) workflow to optimize HIC methods for determining DAR in three commercial cysteine-linked ADCs: brentuximab vedotin, disitamab vedotin, and polatuzumab vedotin. Using a Vanquish Flex UHPLC system and Fusion QbD software, the study aimed to identify method operable design regions (MODR) that satisfy predefined criteria for resolution, peak shape, and reproducibility, in compliance with ICH Q14 and USP <1220> guidelines.

Methodology and Instrumentation

The method development combined design-of-experiment (DoE) studies with mathematical modeling to explore key chromatographic parameters. Instrumentation and software included:

• Thermo Scientific Vanquish Flex UHPLC with binary pump, split sampler, column compartment, and biocompatible detector cell
• MAbPac HIC-Butyl column (4.6 × 100 mm, 5 µm) for separation of DAR species
• Chromeleon 7.3.1 CDS for data acquisition and baseline subtraction
• Fusion QbD software for DoE design, data analysis, and MODR generation

Chromatographic variables investigated included column temperature (25–40 °C), flow rate (0.7–0.9 mL/min), gradient time (10–20 min), and initial organic modifier percentage (0–20 % IPA). A high-salt eluent (1.5 M ammonium sulfate, 50 mM phosphate, pH 7.0) with IPA was used to elute DAR 6 and DAR 8 species under non-denaturing conditions.

Main Results and Discussion

• IPA Content Optimization: Less than 20 % IPA failed to elute DAR 8; 20 % IPA balanced elution and column pressure.
• DoE and MODR: Fusion QbD generated a 30-run design, consuming ~42 h of instrument time. MODR plots identified temperature–gradient regions ensuring ≥1.5 resolution and adequate peak symmetry. Allowing slight relaxation of resolution for minor unknown peaks broadened the MODR.
• Baseline Subtraction: Chromeleon’s subtraction of blank injections removed baseline drifts caused by high salt gradients and vendor-dependent salt purity.
• Robustness: Over 150 sequential injections, retention time RSD was <0.9 % and peak area RSD was <2 %, confirming system and method stability.
• DAR Determination: Calculated average DAR values—3.90 for brentuximab vedotin, 3.91 for disitamab vedotin, and 3.43 for polatuzumab vedotin—agreed with high-resolution MS data and literature reports.

Benefits and Practical Applications of the Method

• Automated AQbD workflow reduces trial-and-error and accelerates method development.
• Fusion QbD-driven MODR ensures robust performance across defined operating ranges.
• Chromeleon baseline correction simplifies data processing under high-salt conditions.
• Method reproducibility supports routine QC testing for ADC production.

Future Trends and Opportunities for Application

Advances in two-dimensional chromatography and on-line mass spectrometry coupling will further enhance DAR characterization and unknown impurity profiling. Integration of machine learning algorithms could refine predictive modeling of chromatographic behavior. The AQbD approach demonstrated here can be extended to newer payload linkers, antibody formats, and high-throughput automated platforms for comprehensive ADC analytics.

Conclusion

The combination of a fully biocompatible UHPLC system, MAbPac HIC-Butyl column, and an AQbD-based optimization workflow enables rapid, reproducible, and regulatory-compliant DAR determination for cysteine-linked ADCs. The identified MODR and validated method deliver consistent peak resolution, minimal baseline interference, and strong robustness, supporting reliable quality control in ADC development and manufacturing.

References

1. Nguyen TD, Bordeau BM, Balthasar JP. Mechanisms of ADC toxicity and strategies to increase ADC tolerability. Cancers. 2023;15(3):713–747.
2. Baek J, Liu X. High-resolution separation of cysteine-linked antibody-drug conjugate mimics using hydrophobic interaction chromatography. Thermo Fisher Scientific Application Note; 2020.
3. International Council for Harmonisation. ICH Q14 Analytical Procedure Development. 2022.
4. United States Pharmacopeia. General Chapter <1220> Analytical Procedure Lifecycle. USP–NF; 2021.
5. Tome T, Žigart N, Časar Z et al. Development and optimization of LC analytical methods by using AQbD principles. Org Process Res Dev. 2019;23(9):1784–1802.
6. Cusumano A, Guillarme D, Beck A et al. Practical method development for separation of mAbs and ADCs in HIC: Optimization of the phase system. J Pharm Biomed Anal. 2016;121:161–173.
7. Younes A, Yasothan U, Kirkpatrick P. Brentuximab vedotin. Nat Rev Drug Discov. 2012;11(1):19–20.
8. Jiang J, Li S, Shan X et al. Preclinical safety profile of disitamab vedotin: A novel anti-HER2 ADC. Toxicol Lett. 2020;324:30–37.
9. Burke JM, Morschhauser F, Andorsky D et al. ADCs for previously treated aggressive lymphomas: Focus on polatuzumab vedotin. Expert Rev Clin Pharmacol. 2020;13(10):1073–1083.