Hong Lili, Wang Wei, Wang Shiyu, Hu Wandi, Sha Yuyang, Xu Xiaoyan, Wang Xiaoying, Li Kefeng, Wang Hongda, Gao Xiumei, Guo De-An, Yang Wenzhi
National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.
Haihe Laboratory of Modern Chinese Medicine, Tianjin, 301617, China.
J Pharm Anal. 2024 Oct;14(10):100994. doi: 10.1016/j.jpha.2024.100994. Epub 2024 May 3.
Identifying the compound formulae-related xenobiotics in bio-samples is full of challenges. Conventional strategies always exhibit the insufficiencies in overall coverage, analytical efficiency, and degree of automation, and the results highly rely on the personal knowledge and experience. The goal of this work was to establish a software-aided approach, by integrating ultra-high performance liquid chromatography/ion-mobility quadrupole time-of-flight mass spectrometry (UHPLC/IM-QTOF-MS) and in-house high-definition MS library, to enhance the identification of prototypes and metabolites of the compound formulae , taking Sishen formula (SSF) as a template. Seven different MS acquisition methods were compared, which demonstrated the potency of a hybrid scan approach (namely high-definition data-independent/data-dependent acquisition (HDDIDDA)) in the identification precision, MS coverage, and MS spectra quality. The HDDIDDA data for 55 reference compounds, four component drugs, and SSF, together with the rat bio-samples (e.g., plasma, urine, feces, liver, and kidney), were acquired. Based on the UNIFI™ platform (Waters), the efficient data processing workflows were established by combining mass defect filtering (MDF)-induced classification, diagnostic product ions (DPIs), and neutral loss filtering (NLF)-dominated structural confirmation. The high-definition MS spectral libraries, dubbed -SSF and -SSF, were elaborated, enabling the efficient and automatic identification of SSF-associated xenobiotics in diverse rat bio-samples. Consequently, 118 prototypes and 206 metabolites of SSF were identified, with the identification rate reaching 80.51% and 79.61%, respectively. The metabolic pathways mainly involved the oxidation, reduction, hydrolysis, sulfation, methylation, demethylation, acetylation, glucuronidation, and the combined reactions. Conclusively, the proposed strategy can drive the identification of compound formulae-related xenobiotics in an intelligent manner.
在生物样本中识别与化合物分子式相关的外源性物质充满挑战。传统策略在全面覆盖、分析效率和自动化程度方面总是存在不足，而且结果高度依赖个人知识和经验。本研究的目的是以四神方（SSF）为模板，通过整合超高效液相色谱/离子淌度四极杆飞行时间质谱（UHPLC/IM-QTOF-MS）和内部高清质谱库，建立一种软件辅助方法，以增强对化合物分子式原型和代谢物的鉴定。比较了七种不同的质谱采集方法，结果表明混合扫描方法（即高清数据非依赖/数据依赖采集（HDDIDDA））在鉴定精度、质谱覆盖范围和质谱图质量方面具有优势。采集了55种参考化合物、四种组成药物和SSF以及大鼠生物样本（如血浆、尿液、粪便、肝脏和肾脏）的HDDIDDA数据。基于沃特世公司的UNIFI™平台，通过结合质量亏损过滤（MDF）诱导的分类、诊断产物离子（DPI）和以中性丢失过滤（NLF）为主的结构确证，建立了高效的数据处理工作流程。精心构建了名为-SSF和-SSF的高清质谱图库，能够高效、自动地鉴定不同大鼠生物样本中与SSF相关的外源性物质。结果，共鉴定出118种SSF原型和206种代谢物，鉴定率分别达到80.51%和79.61%。代谢途径主要包括氧化、还原、水解、硫酸化、甲基化、去甲基化、乙酰化、葡萄糖醛酸化以及联合反应。总之，所提出的策略能够以智能方式推动对与化合物分子式相关的外源性物质的鉴定。
