Hydrophobic Interaction Chromatography Framework for Drug-to-Antibody Ratio Assessment of a High DAR Cysteine-Linked ADC

Accurate quantitation of the drug-to-antibody ratio (DAR) using Quality Control (QC)-friendly analytical methods is critical for characterizing antibody–drug conjugates (ADCs) and ensuring product quality. Hydrophobic interaction chromatography (HIC) is increasingly recognized as the industry standard for DAR measurement of cysteine- and engineered-cysteine–linked ADCs in the biopharmaceutical industry. While low-DAR species (DAR ≈ 2–4) are readily resolved using standard HIC conditions, high-DAR species (DAR ≈ 6–8) present significant challenges due to coelution, peak broadening, and adsorption driven by increased hydrophobicity and sample heterogeneity. Systematic strategies for developing robust HIC methods tailored to high-DAR ADCs are limited, underscoring the need to understand the interplay of separation parameters that govern selectivity and resolution. Herein, a practical workflow for HIC method development specifically designed for high-DAR ADCs using readily available LC technology is introduced. The method development strategy begins with the screening of stationary phases and salt systems, to balance retention and resolution across the DAR distribution, with a particular emphasis on selectivity for highly hydrophobic conjugates. Secondary parameters including column type, temperature and organic modifier content are then optimized to modulate analyte–stationary phase interactions and improve peak shapes without compromising ADC integrity. Building on these findings, we developed a simplified two-segment linear gradient: an initial gentle segment for effective elution of low- to mid-DAR species with adequate dispersion, followed by a shallower segment optimized for high-DAR components that require stronger desorption conditions. Additionally, in silico software-based capabilities were successfully employed to map the separation landscape of ADC multicomponent mixtures using polynomial regression models, to predict optimal operating windows and assess robustness prior to QC deployment. When applying the multifactorial modeling of retention times for high-DAR ADCs with diverse linker–payload chemistries, this workflow delivered reproducible resolution of adjacent species and critical pairs, accurate DAR quantitation, and enhanced assay transferability. This analytical framework provides a clear pathway towards streamlined HIC assay development to reliably characterize high-DAR ADCs, while also introducing in silico robustness for reliable deployment across late-stage product development.