化学
质谱法
抗体-药物偶联物
有效载荷(计算)
结合
还原(数学)
电荷(物理)
质子
色谱法
分析化学(期刊)
抗体
单克隆抗体
核物理学
计算机网络
免疫学
数学
计算机科学
网络数据包
数学分析
几何学
物理
生物
量子力学
作者
Linda Lieu,Cynthia Nagy,Jingjing Huang,Christopher Mullen,Graeme C. McAlister,Vlad Zabrouskov,Kristina Srzentić,Kenneth R. Durbin,Rafael D. Melani,Luca Fornelli
标识
DOI:10.1021/acs.analchem.4c03872
摘要
Antibody-drug conjugates (ADCs) represent a novel class of immunoconjugates with growing therapeutic relevance, since they combine the efficacy of cytotoxic drugs with the specificity of antibodies. However, by design, ADCs introduce structural features into the monoclonal antibody scaffold that complicate their analysis. Payload attachment to cysteine or lysine residues can often result in product heterogeneity, regarding both the number of attached drug molecules and their conjugation site, necessitating the use of state-of-the-art MS instrumentation to elucidate their complexity. In middle-down mass spectrometry (MD MS), the gas-phase sequencing of ∼25 kDa ADC subunits with different ion activation techniques generally produces rich fragmentation mass spectra; however, spectral congestion can cause some fragment ions to go undetected, including those that can pinpoint the exact location of payload conjugation sites. Proton transfer charge reduction (PTCR) can substantially simplify fragment ion spectra, thereby unveiling the presence of product ions whose signals were previously suppressed. Herein, we present an MD MS strategy relying on the use of PTCR to investigate a cysteine-based ADC mimic with a variable drug-to-antibody ratio, targeting the unambiguous localization of payload conjugation sites. Unlike traditional tandem MS experiments (MS2), which could not provide a complete map of conjugation sites, a single PTCR-based experiment (MS3) proved to be sufficient to achieve this goal across all variably modified ADC subunits, including isomeric ones. Combining the results obtained from orthogonal ion activation techniques followed by PTCR further strengthened the confidence in the assignments.
科研通智能强力驱动
Strongly Powered by AbleSci AI