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; Dean Guo; Wenzhi Yang

doi:10.1016/j.jpha.2024.100994

Article Contents

Article Navigation > Journal of Pharmaceutical Analysis > 2024 > Accepted Manu

Lili Hong, Wei Wang, Shiyu Wang, Wandi Hu, Yuyang Sha, Xiaoyan Xu, Xiaoying Wang, Kefeng Li, Hongda Wang, Xiumei Gao, Dean 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 MS2 library: Sishen Formula as a case[J]. Journal of Pharmaceutical Analysis. 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, Dean 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 MS² library: Sishen Formula as a case[J]. Journal of Pharmaceutical Analysis. 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, Dean 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 MS² library: Sishen Formula as a case[J]. Journal of Pharmaceutical Analysis. doi: 10.1016/j.jpha.2024.100994

PDF( 11612 KB)

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 MS² library: 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
,
,},
Dean 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, 10 Poyanghu Road, Tianjin 301617, China;
b. Haihe Laboratory of Modern Chinese Medicine, 10 Poyanghu Road, 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, 10 Poyanghu Road, 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, 501 Haike Road, Shanghai 201203, China

Funds:

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

Received Date: Feb. 11, 2024
Accepted Date: Apr. 29, 2024
Rev Recd Date: Mar. 31, 2024
Available Online: May 10, 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 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 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.

FullText(HTML)

References(46)

References

[1]	H.Y. Xu, Y.Q. Zhang, P. Wang, et al., A comprehensive review of integrative pharmacology-based investigation: a paradigm shift in traditional Chinese medicine, Acta Pharm. Sin. B 11 (2021) 1379-1399.
[2]	R.D. Beger, W. Dunn, M.A. Schmidt, et al., Metabolomics enables precision medicine: “A white paper, community perspective.”, Metabolomics 12 (2016) 149.
[3]	Y.H. Chen, J.H. Bi, M. Xie, et al., Classification-based strategies to simplify complex traditional Chinese medicine (TCM) researches through liquid chromatography-mass spectrometry in the last decade (2011-2020): Theory, technical route and difficulty, J. Chromatogr. A 1651 (2021) 462307.
[4]	M.L. Huang, S.J. Yu, Q. Shao, et al., Comprehensive profiling of Lingzhihuang capsule by liquid chromatography coupled with mass spectrometry-based molecular networking and target prediction, Acupunct. Herb. Med. 2 (2022) 58-67.
[5]	X. Gao, J.J. Wang, X.L. Chen, et al., Reduning Injection prevents carrageenan-induced inflammation in rats by serum and urine metabolomics analysis, Chin. Herb. Med. 14 (2022) 583-591.
[6]	S. Ma, S.K. Chowdhury, Data acquisition and data mining techniques for metabolite identification using LC coupled to high-resolution MS, Bioanalysis 5 (2013) 1285-1297.
[7]	X. Li, J. Liu, T.T. Zuo, et al., Advances and challenges in ginseng research from 2011 to 2020: the phytochemistry, quality control, metabolism, and biosynthesis, Nat. Prod. Rep. 39 (2022) 875-909.
[8]	X.Y. Wang, M.T. Jiang, J. Lou, et al., Pseudotargeted metabolomics approach enabling the classification-induced ginsenoside characterization and differentiation of ginseng and its compound formulation products, J. Agric. Food Chem. 71 (2023) 1735-1747.
[9]	X.Y. Zhang, Z.Z. Jiang, L. Zhang, et al., Identification of prototype compounds and their metabolites in rats’ serum from Xuefu Zhuyu Decoction by UPLC-Q-TOF/MS, Chin. Herb. Med. 15 (2023) 139-150.
[10]	K.Y. Feng, S.M. Wang, L.F. Han, et al., Configuration of the ion exchange chromatography, hydrophilic interaction chromatography, and reversed-phase chromatography as off-line three-dimensional chromatography coupled with high-resolution quadrupole-orbitrap mass spectrometry for the multicomponent characterization of Uncaria sessilifructus, J. Chromatogr. A 1649 (2021) 462237.
[11]	H.D. Wang, C.X. Zhang, T.T. Zuo, et al., In-depth profiling, characterization, and comparison of the ginsenosides among three different parts (the root, stem leaf, and flower bud) of Panax quinquefolius L. by ultra-high performance liquid chromatography/quadrupole-orbitrap mass spectrometry, Anal. Bioanal. Chem. 411 (2019) 7817-7829.
[12]	C.X. Zhang, M.Y. Liu, X.Y. Xu, et al., Application of large-scale molecular prediction for creating the preferred precursor ions list to enhance the identification of ginsenosides from the flower buds of Panax ginseng, J. Agric. Food Chem. 70 (2022) 5932-5944.
[13]	M.X. Sun, X.H. Li, M.T. Jiang, et al., A practical strategy enabling more reliable identification of ginsenosides from Panax quinquefolius flower by dimension-enhanced liquid chromatography/mass spectrometry and quantitative structure-retention relationship-based retention behavior prediction, J. Chromatogr. A 1706 (2023) 464243.
[14]	Z.X. Yan, R. Yan, Improved data-dependent acquisition for untargeted metabolomics using gas-phase fractionation with staggered mass range, Anal. Chem. 87 (2015) 2861-2868.
[15]	F.Y. Zhong, M.Y. Xu, J.J. Zhu, Development and application of time staggered/mass staggered-globally optimized targeted mass spectrometry, J. Chromatogr. B 1120 (2019) 80-88.
[16]	Y. Wang, R.B. Feng, R.B. Wang, et al., Enhanced MS/MS coverage for metabolite identification in LC-MS-based untargeted metabolomics by target-directed data dependent acquisition with time-staggered precursor ion list, Anal. Chim. Acta 992 (2017) 67-75.
[17]	D. Mehta, S. Scandola, R.G. Uhrig, BoxCar and library-free data-independent acquisition substantially improve the depth, range, and completeness of label-free quantitative proteomics, Anal. Chem. 94 (2022) 793-802.
[18]	J.Z. Zhao, Y. Yang, H. Xu, et al., Data-independent acquisition boosts quantitative metaproteomics for deep characterization of gut microbiota, NPJ Biofilms Microbiomes 9 (2023) 4.
[19]	N.H. Anh, Y.C. Yoon, Y.J. Min, et al., Caenorhabditis elegans deep lipidome profiling by using integrative mass spectrometry acquisitions reveals significantly altered lipid networks, J. Pharm. Anal. 12 (2022) 743-754.
[20]	J. Guo, S. Shen, S.P. Xing, et al., DaDIA: Hybridizing data-dependent and data-independent acquisition modes for generating high-quality metabolomic data, Anal. Chem. 93 (2021) 2669-2677.
[21]	Y.X. Qian, D.X. Zhao, H.D. Wang, et al., An ion mobility-enabled and high-efficiency hybrid scan approach in combination with ultra-high performance liquid chromatography enabling the comprehensive characterization of the multicomponents from Carthamus tinctorius, J. Chromatogr. A 1667 (2022) 462904.
[22]	H.D. Wang, H.M. Wang, X.Y. Wang, et al., A novel hybrid scan approach enabling the ion-mobility separation and the alternate data-dependent and data-independent acquisitions (HDDIDDA): Its combination with off-line two-dimensional liquid chromatography for comprehensively characterizing the multicomponents from Compound Danshen Dripping Pill,Anal. Chim. Acta 1193 (2022) 339320.
[23]	W.D. Hu, X.Y. Xu, Y.X. Qian, et al., Integration of a hybrid scan approach and in-house high-resolution MS2 spectral database for charactering the multicomponents of Xuebijing Injection, Arab. J. Chem. 16 (2023) 104519.
[24]	M. Giera, O. Yanes, G. Siuzdak, Metabolite discovery: Biochemistry's scientific driver, Cell Metab. 34 (2022) 21-34.
[25]	H.C. Lan, S.Z. Li, K. Li, et al., In vitro human intestinal microbiota biotransformation of nobiletin using liquid chromatography-mass spectrometry analysis and background subtraction strategy, J. Sep. Sci. 44 (2021) 2046-2053.
[26]	W.L. Wei, H.J. Li, W.Z. Yang, et al., An integrated strategy for comprehensive characterization of metabolites and metabolic profiles of bufadienolides from Venenum Bufonis in rats, J. Pharm. Anal. 12 (2022) 136-144.
[27]	J. Wu, W.F. Gou, Z.Y. Wang, et al., Discovery of the radio-protecting effect of Ecliptae Herba, its constituents and targeting p53-mediated apoptosis in vitro and in vivo, Acta Pharm. Sin. B 13 (2023) 1216-1230.
[28]	H.Q. Lai, Y. Ouyang, G.H. Tian, et al., Rapid characterization and identification of the chemical constituents and the metabolites of Du-zhi pill using UHPLC coupled with quadrupole time-of-flight mass spectrometry, J. Chromatogr. B 1209 (2022) 123433.
[29]	C.X. Zhang, X.Y. Wang, Z.Z. Lin, et al., Highly selective monitoring of in-source fragmentation sapogenin product ions in positive mode enabling group-target ginsenosides profiling and simultaneous identification of seven Panax herbal medicines, J. Chromatogr. A 1618 (2020) 460850.
[30]	H.Y. Wang, H.M. Zhao, Y. Wang, et al., Sishen Wan^® ameliorated trinitrobenzene-sulfonic-acid-induced chronic colitis via NEMO/NLK signaling pathway, Front. Pharmacol. 10 (2019) 170.
[31]	F. Chen, Y.T. Yin, H.M. Zhao, et al., Sishen pill treatment of DSS-induced colitis via regulating interaction with inflammatory dendritic cells and gut microbiota, Front. Physiol. 9 (2020) 801.
[32]	X.L. Su, Y.P. Tang, J. Zhang, et al., Curative effect of warming renal and fortifying spleen recipe on diarrhea-predominant irritable bowel syndrome, J. Tradit. Chin. Med. 33 (2013) 615-619.
[33]	L. Liu, S. Wang, Q.X. Xu, et al., Poly-pharmacokinetic strategy represented the synergy effects of bioactive compounds in a traditional Chinese medicine formula, Si Shen Wan and its separated recipes to normal and colitis rats, J. Sep. Sci. 44 (2021) 2065-2077.
[34]	G. Paglia, P. Angel, J.P. Williams, et al., Ion mobility-derived collision cross section as an additional measure for lipid fingerprinting and identification, Anal. Chem. 87 (2015) 1137-1144.
[35]	J. Liu, Y.T. Li, Y.J. Chen, Advances in high-resolution mass spectrometric-based data-mining technologies for detecting and characterizing the components and metabolites of Chinese materia medica, Acta Pharm. Sin. 56 (2021) 113-129.
[36]	F. Fenaille, P.B. Saint-Hilaire, K. Rousseau, et al., Data acquisition workflows in liquid chromatography coupled to high resolution mass spectrometry-based metabolomics: Where do we stand?, J. Chromatogr. A 1526 (2017) 1-12.
[37]	R. Franski, B. Gierczyk, T. Kozik, et al., Signals of diagnostic ions in the product ion spectra of [M-H]- ions of methoxylated flavonoids, Rapid Commun. Mass Spectrom. 33 (2019) 125-132.
[38]	Y. Gao, S.M. Wu, R.H. Cong, et al., Characterization of lignans in Schisandra chinensis oil with a single analysis process by UPLC-Q/TOF-MS, Chem. Phys. Lipids 218 (2019) 158-167.
[39]	Z.W. Zhang, T.Z. Fang, H.Y. Zhou, et al., Characterization of the in vitro metabolic profile of evodiamine in human liver microsomes and hepatocytes by UHPLC-Q Exactive mass spectrometer, Front. Pharmacol. 9 (2018) 130.
[40]	Y.F. Zhang, Yuan J, Wang Y, et al., LC-MS/MS determination and pharmacokinetics study of puerarin and daidzein in rat plasma after oral administration of Gegenqinlian decoction and Radix Puerariae extract, Pharmacogn. Mag. 10 (2014) 241-248.
[41]	D.W. Lee, Y. Kang, M.J. Kang, et al., Phase I and phase II metabolite identification of rutaecarpine in freshly isolated hepatocytes from male Sprague-Dawley rats, Arch. Pharm. Res. 40 (2017) 972-979.
[42]	H. Ren, S. Guo, Y.Y. Zhang, Metabolite identification and metabolic pathway analysis of Bufei Huoxue Capsules in rats, Chin. Tradit. Herb. Drugs 54 (2023) 1051-1063.
[43]	H.D. Wang, L. Zhang, X.H. Li, et al., Machine learning prediction for constructing a universal multidimensional information library of Panax saponins (ginsenosides), Food Chem. 439 (2024) 138106.
[44]	D.H. Ross, L.B. Xu, Determination of drugs and drug metabolites by ion mobility-mass spectrometry: A review, Anal. Chim. Acta 1154 (2021) 338270.
[45]	C. Chalet, B. Hollebrands, H.G. Janssen, et al., Identification of phase-II metabolites of flavonoids by liquid chromatography-ion-mobility spectrometry-mass spectrometry, Anal. Bioanal. Chem. 410 (2018) 471-482.
[46]	J.-J. Xu, M.-S. Li, Z.-H. Yao, et al. In vitro metabolic mapping of neobavaisoflavone in human cytochromes P450 and UDP-glucuronosyltransferase enzymes by ultra high-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry, J. Pharm. Biomed. Anal. 158 (2018) 351-360.