化学
分辨率(逻辑)
色谱法
分解代谢抑制
液相色谱-质谱法
质谱法
高分辨率
生物化学
地质学
计算机科学
基因
人工智能
突变体
遥感
作者
Lijuan Kang,Raul C. Camacho,Wenyu Li,Katharine D’Aquino,Seo-Hee You,Vanessa Chuo,Naidong Weng,Wenying Jian
标识
DOI:10.1021/acs.analchem.7b00674
摘要
As therapeutic recombinant fusion proteins become more widely applicable for the treatment of various types of diseases, there is an increased demand for universal methods such as liquid chromatography (LC)-mass spectrometry (MS) for the determination of their pharmacokinetic properties, particularly their catabolism. The most common approach of analyzing proteins by LC-MS is to digest them into peptides, which can serve as surrogates of the protein. Alternatively, we have developed a novel high-resolution mass spectrometry (HRMS) based approach for analyzing large-molecule proteins at the intact level in biological samples without digestion. We established an immunoaffinity capture LC-HRMS method to quantify the intact parent molecule while simultaneously identifying catabolites for recombinant fusion proteins. We describe this method using dulaglutide, a glucagon-like peptide 1 (GLP1)-Fc fusion protein. Two proteolytic sites within the GLP1 peptide sequence of dulaglutide were identified using this novel LC-HRMS analysis in vivo in mice. These proteolytic sites were identified with the intact molecule being quantified simultaneously. Together with the trypsin digestion based LC-MS/MS analysis using surrogate peptides from different domains of the analyte, an insightful understanding of the pharmacokinetics and in vivo biotransformation of dulaglutide was obtained. Thus, this method enables simultaneous acquisition of both intact drug concentration and important catabolite information for this recombinant fusion protein, providing valuable insight into the integrity of the molecule and its catabolism in vivo. This is critical for designing and screening novel protein therapeutics and for understanding their pharmacokinetics and pharmacodynamics. With continuing advancement of LC-HRMS and software, this method can be very beneficial in drug discovery and development.
科研通智能强力驱动
Strongly Powered by AbleSci AI