Native size-exclusion chromatography-mass spectrometry: suitability for antibody-drug conjugate drug-to-antibody ratio quantitation across a range of chemotypes and drug-loading levels

天然尺寸排阻色谱-质谱联用技术:适用于多种化学类型和载药水平的抗体药物偶联物药物抗体比定量分析

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Abstract

Native size-exclusion chromatography-mass spectrometry (nSEC-MS) is an analytical methodology that is appropriate for accurately quantitating the drug-to-antibody ratio (DAR) on a wide variety of interchain cysteine-linked antibody-drug conjugates (ADCs), irrespective of chemotype. In the current preclinical environment, novel ADCs conjugated with unique drug-linkers need to progress toward the clinic as quickly as possible. Platform analytical approaches can reduce time-to-clinic because key process development and optimization activities can be decoupled from the development of bespoke, molecule-specific analytical methods. In this work, we assessed the potential of nSEC-MS as a platformable, quantitative DAR method. The nSEC-MS method was evaluated according to performance characteristics and parameters described in the ICH guideline Validation of Analytical Procedures: Text and Methodology Q2(R1). In order to comprehensively assess the accuracy and bias of nSEC-MS DAR quantitation, ADCs were generated using three different drug-linker chemotypes with DARs ranging from 2 to 8. These molecules were tested by hydrophobic interaction chromatography (HIC) and nSEC-MS, and DARs obtained from both methods were compared to assess the degree to which nSEC-MS quantitation aligned with the HIC release assay. Our results indicated that there is no bias introduced by nSEC-MS quantitation of DAR and that SEC-MS data can be bridged to HIC data without the need for a correction factor or offset. nSEC-MS was also found to be suitable for unbiased DAR quantitation in the other ADC chemotypes that were evaluated. Based on the totality of our work, we conclude that, used as intended, nSEC-MS is well suited for quantitating DAR on a variety of interchain cysteine-linked ADCs in an accurate, unbiased manner.

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