Software-aided efficient identification of the components of compound formulae and their metabolites in rats by UHPLC/IM-QTOF-MS and an in-house high-definition MS<sup>2</sup>library: Sishen formula as a case

Lili Hong; Wei Wang; Shiyu Wang; Wandi Hu; Yuyang Sha; Xiaoyan Xu; Xiaoying Wang; Kefeng Li; Hongda Wang; Xiumei Gao; De-an Guo; Wenzhi Yang

doi:10.1016/j.jpha.2024.100994

Volume 14 Issue 10

Oct. 2024

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Lili Hong, Wei Wang, Shiyu Wang, Wandi Hu, Yuyang Sha, Xiaoyan Xu, Xiaoying Wang, Kefeng Li, Hongda Wang, Xiumei Gao, De-an Guo, Wenzhi Yang. Software-aided efficient identification of the components of compound formulae and their metabolites in rats by UHPLC/IM-QTOF-MS and an in-house high-definition MS2library: Sishen formula as a case[J]. Journal of Pharmaceutical Analysis, 2024, 14(10): 100994. doi: 10.1016/j.jpha.2024.100994

Citation:

Lili Hong, Wei Wang, Shiyu Wang, Wandi Hu, Yuyang Sha, Xiaoyan Xu, Xiaoying Wang, Kefeng Li, Hongda Wang, Xiumei Gao, De-an Guo, Wenzhi Yang. Software-aided efficient identification of the components of compound formulae and their metabolites in rats by UHPLC/IM-QTOF-MS and an in-house high-definition MS²library: Sishen formula as a case[J]. Journal of Pharmaceutical Analysis, 2024, 14(10): 100994. doi: 10.1016/j.jpha.2024.100994

Citation:

Lili Hong, Wei Wang, Shiyu Wang, Wandi Hu, Yuyang Sha, Xiaoyan Xu, Xiaoying Wang, Kefeng Li, Hongda Wang, Xiumei Gao, De-an Guo, Wenzhi Yang. Software-aided efficient identification of the components of compound formulae and their metabolites in rats by UHPLC/IM-QTOF-MS and an in-house high-definition MS²library: Sishen formula as a case[J]. Journal of Pharmaceutical Analysis, 2024, 14(10): 100994. doi: 10.1016/j.jpha.2024.100994

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Software-aided efficient identification of the components of compound formulae and their metabolites in rats by UHPLC/IM-QTOF-MS and an in-house high-definition MS²library: Sishen formula as a case

doi: 10.1016/j.jpha.2024.100994

Lili Hong^a,b,
Wei Wang^a,b,
Shiyu Wang^a,b,
Wandi Hu^a,b,
Yuyang Sha^c,
Xiaoyan Xu^a,b,
Xiaoying Wang^d,
Kefeng Li^c,
Hongda Wang^{a,b,d
,
,},
Xiumei Gao^{a,b,d
,
,},
De-an Guo^{a,b,e
,
,},
Wenzhi Yang^{a,b,d
,
,}

a. National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China;
b. Haihe Laboratory of Modern Chinese Medicine, Tianjin, 301617, China;
c. Centre for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, Rua de Luís Gonzaga Gomes, Macao, 999078, China;
d. Key Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China;
e. Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Laboratory for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China

Funds:

This work was financially supported byNational Natural Science Foundation of China (Grant No.:82192914),Tianjin Outstanding Youth Fund (Grant No.:23JCJQJC00030), and the Innovation Team and Talents Cultivation Program ofNational Administration of Traditional Chinese Medicine (Grant No.:ZYYCXTD-C-202009).

Received Date: Feb. 11, 2024
Accepted Date: Apr. 29, 2024
Rev Recd Date: Mar. 31, 2024
Publish Date: May 03, 2024

Abstract

Abstract

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 MS² library, to enhance the identification of prototypes and metabolites of the compound formulae in vivo, taking Sishen formula (SSF) as a template. Seven different MS² 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, MS¹ coverage, and MS² 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^TM 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 MS² spectral libraries, dubbed in vitro-SSF and in vivo-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 vivo in an intelligent manner.

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