Quantifying payloads of antibody‒drug conjugates using a postcolumn infused-internal standard strategy with LC‒MS

Antibody‒drug conjugates (ADCs) are innovative biopharmaceutics consisting of a monoclonal antibody, linkers, and cytotoxic payloads. Monitoring circulating payload concentrations has the potential to identify ADC toxicity; however, accurate quantification faces challenges, including low plasma concentrations, severe matrix effects, and the absence of stable isotope-labeled internal standards (SIL-IS) for payloads and their derivatives. Previous studies used structural analogs as internal standards, but different retention times between structural analogs and target analytes may hinder effective matrix correction. Therefore, a more flexible approach is required for precise payload quantification.

We developed an LC‒MS/MS method incorporating a postcolumn-infused internal standard (PCI-IS) strategy for quantifying payloads and their derivatives of trastuzumab emtansine, trastuzumab deruxtecan, and sacituzumab govitecan, including DM1, MCC-DM1, DXd, SN-38, and SN-38G. Structural analogs (maytansine, Lys-MCC-DM1, and exatecan) were selected as PCI-IS candidates, and their accuracy performance was evaluated based on the percentage of samples within 80%-120% quantification accuracy. Compared to the approach without PCI-IS correction, exatecan enhanced the accuracy performance from 30-40%-100% for SN-38 and DXd, while maytansine and Lys-MCC-DM1 showed comparable accuracy for DM1 and MCC-DM1. This validated PCI-IS analytical method showed superior normalization of matrix effect in all analytes compared to the conventional internal standard approach. The clinical application of this approach showed pronounced differences in DXd and SN-38 concentrations before and after PCI-IS correction. Moreover, only DXd concentrations after PCI-IS correction were significantly higher in patients with thrombocytopenia (p = 0.037).

This approach effectively addressed the issue of unavailability of SIL-IS for novel ADC payloads and provided more accurate quantification, potentially yielding more robust statistical outcomes for understanding the exposure-toxicity relationship in ADCs. It is anticipated that this PCI-IS strategy may be extrapolated to quantify payloads and derivatives in diverse ADCs, thereby providing invaluable insights into drug toxicity and fortifying patient safety in ADC usage.