Development of Integrated Informatics Workflows for the Automated Assessment of Comparability for Antibody Drug Conjugates (ADCs) Using LC-UV and LC-UV/MS

Significance of the Topic

Antibody–drug conjugates (ADCs) represent a rapidly advancing class of targeted cancer therapies that combine the selectivity of monoclonal antibodies with potent cytotoxic payloads.
Accurate measurement of critical quality attributes (CQAs) such as drug-to-antibody ratio (DAR) and drug load distribution is essential for ensuring batch comparability, safety, and efficacy.
Traditional workflows rely on multiple analytical techniques and manual data handling, which introduce inefficiencies and potential errors.

Study Objectives and Overview

This work demonstrates a fully integrated informatics workflow built on UNIFI Software to automate the acquisition, processing, and reporting of LC-UV and LC-UV/MS data for ADC comparability assessment.
It encompasses cysteine- and lysine-conjugated ADCs, employing hydrophobic interaction chromatography (HIC-UV), native size-exclusion chromatography coupled to MS (SEC-MS), and reversed-phase LC-MS (RP-LC-MS).
The primary goal is to calculate DAR values and drug distribution profiles across multiple batches and analytical platforms with minimal manual intervention.

Methodology and Instrumentation Used

Samples of cysteine- and lysine-linked ADC mimics were prepared at defined concentrations in appropriate buffers (ammonium acetate or phosphate with salt).
Analytical techniques included:

• HIC-UV: ACQUITY UPLC H-Class with Protein-Pak Hi Res HIC column, 20-min salt gradient, UV detection at 280 nm.
• Native SEC-MS: ACQUITY UPLC Protein BEH SEC column with Xevo G2-S QTof, volatile ammonium acetate mobile phase to preserve non-denaturing conformation.
• RP-LC-MS: ACQUITY UPLC Protein BEH C4 column at 80 °C with TFA/FA modifiers for denaturing intact mass analysis of lysine ADCs.

Data collection, deconvolution, component identification, and custom DAR calculations were performed automatically within UNIFI Scientific Information System v1.7, using predefined processing methods and secure user roles.

Main Results and Discussion

Cysteine-conjugated ADCs analyzed by HIC-UV and native SEC-MS yielded consistent drug distribution profiles (peaks for 0, 2, 4, 6, and 8 payloads) and total DAR values across three loading levels.
Average DARs from HIC and SEC-MS agreed within 0.1 units for each batch, demonstrating strong concordance between orthogonal approaches.
Custom UNIFI fields calculated individual and total DARs in real time, and user-defined report templates generated area summaries, statistics, and 3D visualization without manual data handling.
Lysine-conjugated ADCs analyzed by RP-LC-MS produced deconvoluted spectra showing up to 12 drug attachments per antibody; automatically derived DAR values facilitate lot-to-lot comparison while highlighting the need for orthogonal validation.

Benefits and Practical Applications

The integrated workflow eliminates manual data transfer and processing steps, reducing human error and enhancing throughput.
Automated DAR and distribution calculations support rapid comparability assessments during ADC development and QC release.
Role-based security and protected methods ensure compliance with regulated laboratory standards.
Customizable reporting streamlines communication of CQA results across multidisciplinary teams.

Future Trends and Applications

Expansion of integrated informatics to include additional analytical modes (e.g., peptide mapping, ion mobility) will deliver deeper structural insights.
Incorporation of machine-learning algorithms for trend detection, anomaly prediction, and real-time process monitoring may accelerate ADC candidate selection.
Cloud-based data sharing and standardized formats will facilitate collaboration across global R&D networks.

Conclusion

This study illustrates the effective deployment of an end-to-end UNIFI-based informatics workflow that automates CQA measurement for ADCs by LC-UV and LC-UV/MS.
High agreement between HIC-UV, native SEC-MS, and RP-LC-MS results underscores the reliability of automated DAR calculations.
The approach enhances productivity, data integrity, and regulatory readiness for ADC development pipelines.

References

1. Chen J, Yin S, Wu Y, Ouyang J. Native nanoelectrospray MS method for DAR determination of ADCs. Anal Chem. 2013;85(3):1699–1704.
2. Valliere-Douglass JF et al. Native intact mass determination of cysteine-linked ADCs. Anal Chem. 2012;84(6):2843–2849.
3. Neri D. Antibody-drug conjugates for cancer therapy. Nature Tech Poster. 2013.
4. ADC Review Website. ADC Drug Map.