Affinity profiling of monoclonal antibody and antibody-drug-conjugate preparations by coupled liquid chromatography-surface plasmon resonance biosensing.

Monoclonal antibodies (mAbs) and antibody-drug conjugates (ADCs) are highly potent biopharmaceuticals designed for targeted cancer therapies. mAbs and ADCs can undergo modifications during production and storage which may affect binding to target receptors, potentially altering drug efficacy. In this work, liquid chromatography was coupled online to surface plasmon resonance (LC-SPR) to allow label-free affinity evaluation of mAb and ADC sample constituents (size and charge variants), under near-native conditions. Trastuzumab and its ADC trastuzumab emtansine (T-DM1) were used as a test sample and were analyzed by aqueous size-exclusion chromatography (SEC)-SPR before and after exposure to aggregate-inducing conditions. SEC-SPR allowed separation of the formed aggregates and measurement of their affinity towards the ligand-binding domain of the human epidermal growth factor receptor 2 (HER2) receptor immobilized on the surface of the SPR sensor chip. The monomer and aggregates of the mAb and ADC were shown to have similar antigen affinity. Conjugation of drugs to trastuzumab appeared to accelerate the aggregate formation. In addition, cation-exchange chromatography (CEX) was coupled to SPR enabling monitoring the maximum ligand-analyte binding capacity (Rmax) of individual charge variants present in mAbs. Deamidated species and lysine variants in trastuzumab sample were separated but did not show different binding affinities to the immobilized HER2-binding domain. In order to allow protein variant assignment, parallel MS detection was added to the LC-SPR setup using a column effluent split. The feasibility of the LC-MS/SPR system was demonstrated by analysis of trastuzumab and T-DM1 providing information on antibody glycoforms and/or determination of the drug-to-antibody ratio (DAR), while simultaneously monitoring binding of eluting species to HER2. Graphical abstract ᅟ.