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. 2023 Dec 15;10(1):e23865. doi: 10.1016/j.heliyon.2023.e23865

An integrated strategy of UPLC-Q-TOF-MS analysis, network pharmacology, and molecular docking to explore the chemical constituents and mechanism of Zixue Powder against febrile seizures

Lingling Song a,1, Jian Xu a,1, Yanqiong Shi b,1, Hemiao Zhao a, Min Zhang a,c, Yuefei Wang a,c, Ying Cui a,c,∗∗, Xin Chai a,c,∗
PMCID: PMC10772254  PMID: 38192830

Abstract

Febrile seizures (FS) are the most common type of seizures for children. As a commonly used representative cold formula for resuscitation, Zixue Powder (ZP) has shown great efficacy for the treatment of FS in clinic, while its active ingredients and underlying mechanism remain largely unclear. This study aimed to preliminarily elucidate the material basis of ZP and the potential mechanism for the treatment of FS through ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS), network pharmacology, and molecular docking. UPLC-Q-TOF-MS was firstly applied to characterize the ingredients in ZP, followed by network pharmacology to explore the potential bioactive ingredients and pathways of ZP against FS. Furthermore, molecular docking technique was employed to verify the binding affinity between the screened active ingredients and targets. As a result, 75 ingredients were identified, containing flavonoids, chromogenic ketones, triterpenes and their saponins, organic acids, etc. Through the current study, we focused on 13 potential active ingredients and 14 key potential anti-FS targets of ZP, such as IL6, STAT3, TNF, and MMP9. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis showed that inflammatory response, EGFR tyrosine kinase inhibitor resistance, AGE-RAGE signaling pathway in diabetic complications, and neuroactive ligand-receptor interaction were the main anti-FS signaling pathways. Licochalcones A and B, 26-deoxycimicifugoside, and hederagenin were screened as the main potential active ingredients by molecular docking. In conclusion, this study provides an effective in-depth investigation of the chemical composition, potential bioactive components, and possible anti-FS mechanism of ZP, which lays the foundation for pharmacodynamic studies and clinical applications of ZP.

Keywords: Zixue powder, Febrile seizures, UPLC-Q-TOF-MS, Network pharmacology, Molecular docking

1. Introduction

Febrile seizures (FS) are the most common convulsive disease for infants and children aged 6 months to 5 years, affecting 2–5% of children worldwide [[1], [2], [3]]. Studies have shown that recurrent FS may increase the risk of temporal lobe epilepsy, psychiatric disease, and even death [4,5]. The occurrence of FS has been linked to inflammation, familial history, and deficiencies of certain elements (iron, selenium, and zinc) [[6], [7], [8], [9]]. In clinical practice, anticonvulsant drugs, such as phenobarbital, diazepam, and valproic acid, have been used to treat FS [10]. According to traditional Chinese medicine (TCM) theory, FS belong to the category of “acute convulsion”, and the curing principle for FS is to relieve spasm, clear heat, eliminate phlegm, relieve shock, and calm wind [11], which has been proved to be effective for FS [12].

Zixue Powder (ZP) is a classical Chinese herbal formula for FS in children, composed of 16 TCMs, including Gypsum Fibrosum (Shigao), Gypsum Rubrum (Beihanshuishi), Talcum (Huashi), Magnetitum (Cishi), Scrophulariae Radix (Xuanshen, XS), Cimicifugae Rhizoma (Shengma, SM), Glycyrrhizae Radix et Rhizoma (Gancao, GC), Caryophylli Flos (Dingxiang, DX), Aucklandiae Radix (Muxiang, MX), Aquilariae Lignum Resinatum (Chenxiang, CX), Natrii Sulfas (Mangxiao), Nitre (Xiaoshi), Powerdered Buffalo Horn Extract (Shuiniujiaonongsuofen), Saigae Tataricae Cornu (Lingyangjiaofen), artificial Mosk (Rengongshexiang), and Cinnabaris (Zhusha) [13]. ZP can clear heat and induce resuscitation, relieve spasmolysis and tranquilize based on TCM theory. Modern pharmacological studies have demonstrated that ZP has antipyretic, sedative, anti-inflammatory, and anticonvulsant effects [14,15]. At present, ZP has been frequently used in the clinical treatment of children with FS, septic shock, etc [16]. However, no comprehensive studies have been conducted to explore the chemical constituents in ZP and the underlying mechanisms for anti-FS.

Network pharmacology is an integrated approach that blends the disciplines of systems biology and bioinformatics and network science to investigate the underlying molecular mechanisms between drugs and therapeutic targets, which has been frequently used in the research of TCM [17]. Meanwhile, molecular docking can be used to screen active ingredients by predicting the binding affinity between ingredients and proteins, which is a method widely used in drug discovery [18,19]. The combination of network pharmacology and molecular docking allows for greater efficiency on screening active ingredients and identifying their potential targets in TCM [20].

In this research, we identified the possible active compounds and the potential molecular action mechanisms of ZP against FS. A fast and effective method was developed for the chemical characterization of ZP based on ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS), and a strategy combined with network pharmacology and molecular docking was further used to explore the underlying action mechanism of ZP against FS. This study provides a reference for clinical practice and further research on ZP.

2. Materials and methods

2.1. Materials and reagents

Reference standards including agarotetrol, isoferulic acid, and harpagide were obtained from National Institutes for Food and Drug Control (Beijing, China). Liquiritigenin, isoliquiritigenin, angoroside C, cimicifugine, glycyrrhizic acid, liquiritin, liquiritin apioside, isoliquiritin, trans-cinnamic acid, harpagoside, sucrose, formononetin, and licochalcone A were purchased from Shanghai Yuanye Bio-Technology Co., Ltd. (Shanghai, China). The purities of all the reference substances were higher than 98 %.

ZP samples were provided by Tianjin Hongrentang Pharmaceutical Co., Ltd. (Tianjin, China). LC-MS grade methanol was purchased from Sigma-Aldrich (St. Louis, MO, USA). Formic acid was purchased from Shanghai Aladdin Biochemical Technology Co., Ltd. (Shanghai, China). Water used for UPLC-Q-TOF-MS analysis was purified by Milli-Q water purification system (Millipore, Billerica, MA, USA).

2.2. Preparation of reference and sample solutions

Sixteen reference compounds were accurately weighed and respectively dissolved in methanol as stock solutions except for sucrose dissolved in water. An appropriate amount of each reference stock solution was accurately transferred in a 10 mL volumetric flask and diluted with 75 % aqueous methanol (v/v) to obtain the mixed solution at the concentrations of 0.0521 mg/mL harpagide, 0.0500 mg/mL liquiritin, 0.0501 mg/mL cimicifugin, 0.0555 mg/mL isoliquiritin, 0.0501 mg/mL agarotetrol, 0.0581 mg/mL apioside liquiritin, 0.0500 mg/mL angoroside C, 0.0503 mg/mL liquiritigenin, 0.0500 mg/mL isoferulic acid, 0.0501 mg/mL isoliquiritigenin, 0.0502 mg/mL harpagoside, 0.0511 mg/mL glycyrrhizic acid, 0.0500 mg/mL trans-cinnamic acid, 0.0304 mg/mL licoricechalcone A, 0.0487 mg/mL sucrose, and 0.0560 mg/mL formononetin.

1.0 g of ZP was accurately weighed and transferred into a conical flask and ultrasonically extracted at 60 °C by 75 % aqueous methanol for 30 min, then cooled to ambient temperature and diluted to scale by adding 75 % aqueous methanol. The extracted solution was centrifuged at 12700 rpm for 10 min to obtain the supernatant. All the standard and sample solutions were stored at 4 °C when not in use.

2.3. UPLC-Q-TOF-MS analysis

Chemical analysis was conducted on an Acquity UPLC I Class system (Waters Corporation, Milford, MA, USA) equipped with XEVO G2-XS quadrupole time-of-flight mass spectrometry (Waters Corp.). UPLC analysis was performed on the Agilent ZORBAX SB-C18 column (4.6 × 100 mm, 1.8 μm, Agilent, Santa Clara, CA, USA) using a flow rate of 0.5 mL/min at 45 °C for chromatographic separation. The mobile phase consisted of methanol (A) and 0.1 % formic acid (B), using a gradient elution of 10–52 % A for 0–8 min, 52–58 % A for 8–12 min, and 58–95 % A for 12–20 min. The injection volume was 2 μL.

The mass spectrometric detection was operated on Waters Xevo G2-XS QTOF mass spectrometer equipped with ESI source in both positive and negative modes from m/z 100–1500 Da. Detailed mass spectrometric parameters were set as follows: the capillary voltage was set at 3.00 kV (ESI+) and −2.5 kV (ESI–); the ion source temperature was 120 °C; the sample cone voltage was 40 V; the cone gas flow was set at 50 L/h; the desolvation temperature was set at 500 °C; the desolvation gas flow was 800 L/h. The low collision energy was 6 eV and the high collision energy was 10∼45 eV.

2.4. Network pharmacology analysis

2.4.1. Target genes of the identified compounds related to FS

The structures of compounds in ZP were downloaded from PubChem (https://pubchem.ncbi.nlm.nih.gov/) and saved in SDF format, which were submitted to SwissTargetPrediction database (http://www.swisstargetprediction.ch/) and Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, https://www.tcmsp-e.com/) for the targets prediction of the focused compounds with the species “Homo sapiens”. All the proteins obtained previously were converted to the official symbol formats for genes using UniProt database (https://www.uniprot.org/). Meanwhile, the FS-related targets were obtained from GeneCards database (https://www.genecards.org/) and Online Mendelian Inheritance in Man database (OMIM, https://www.omim.org/). Then, the common targets between the predicted targets of chemical compounds and FS were obtained by Venny 2.1 platform (https://bioinfogp.cnb.csic.es/tools/venny/index.html), which were considered as potential therapeutic targets. To further illustrate the mechanism, Cytoscape 3.9.1 software was used for the visualization of the interactions among TCMs, ingredients, and targets in ZP. The potential bioactive compounds were screened according to the degree values, which were used as key parameters to reflect the importance of nodes.

2.4.2. Gene Ontology (GO) and Kyoto Encyclopedia of genes and genomes (KEGG) pathway enrichment analysis

The common genes were imported into DAVID database (https://david.ncifcrf.gov/) to perform GO and KEGG pathway enrichment analysis with species restricted to “Homo sapiens”. The results of biological process (BP), cellular component (CC), and molecular function (MF) of GO enrichment analysis, and KEGG pathway analysis were downloaded and saved as TXT, respectively. Go terms and KEGG pathways were visualized by Bioinformatics (http://www.bioinformatics.com.cn/) with P-value <0.05.

2.4.3. Construction of protein-protein interaction (PPI) network

Targets of the common genes were imported into STRING (https://cn.string-db.org/) database. The species was limited to “Homo sapiens” in the operation interface, and the confidence score was set up to 0.70 (higher confidence). Cytoscape 3.9.1 software was used to construct the PPI network, and the core targets were screened by two algorithms of maximal clique centrality (MCC) and degree value, which could improve accuracy of the results.

2.5. Molecular docking verification

The crystal structures of the targets were acquired from the RCSB Protein Data Bank (https://www.rcsb.org/), whose detailed information was displayed in Table 1. The tested compound's structure files (mol2 format) were downloaded from TCMSP database, and then converted into a 3D structure using Chem 3D 19.0. Then, the 3D structures of target proteins and compounds were imported into Discovery Studio 2020 software. The test compounds as ligands were prepared to constrain the optimal conformation. The target proteins were prepared by removing water molecules, adding hydrogen, fixing the missing residues or rings, and defining the active center. The tested compound was then docked to the target by CDocker module, from which the CDocker interaction energy scores were ranked to predict the interaction capacity. Based on the CDocker interaction energy value, the interaction between the ligand and the receptor was quantified, with the lower energy indicating the stronger interaction.

Table 1.

Detailed information on the crystal structures of the targets.

No. Full name of the protein Abbreviation PDB ID Resolution (Å) References
1 Serine/threonine kinase 1 AKT1 3O96, 6HHF 2.70, 2.90 [21,22]
2 Epidermal growth factor receptor EGFR 2GS6 2.60 [23]
3 Fibroblast growth factor 2 FGF2 5X1O 1.90 [24]
4 Ierleukin-1β IL1B 5R88 1.48 [25]
5 Ierleukin-2 IL2 1M49 2.00 [26]
6 Matrix metalloproteinase-9 MMP9 2OVZ, 4XCT 2.00, 1.30 [27,28]
7 Protein tyrosine phosphatase non-receptor type 11 PTPN11 3MOW 2.30 [29]
8 Signal transducer and activator of transcription 3 STAT3 5AX3, 6NUQ 2.98, 3.15 [30,31]
9 Toll-like receptor 4 TLR4 4G8A 2.40 [32]
10 Tumor necrosis factor TNF 2AZ5 2.10 [33]
11 Tumor protein 53 TP53 4BV2 3.30 [34]
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2.6. Data processing

The information of chemical compounds from ZP was collected, which was combined with the UNIFI Chinese medicine database to establish the chemical constituents database of ZP. The MS data were collected in MSE mode and analyzed by MassLynx 4.1 software and the UNIFI database (Waters Corp.). The compounds were characterized based on the fragments matching with errors less than 10 ppm. Furthermore, the standards and related literatures were used to check the accuracy and reliability of identified results. The common targets between the predicted targets of chemical compounds and FS were visualized by Venny 2.1 platform. The visual analysis of network pharmacology was completed by Cytoscape 3.9.1 software. The docked complexes were visualized and converted in PDB files for 2D-3D interactive visualization study with the help of PyMol 2.4.1 software.

3. Results and discussion

3.1. Chemical profiling of ZP by UPLC-Q-TOF-MS

A method for the separation and identification of the chemical constituents of ZP was established based on UPLC-Q-TOF-MS technology. The total ion chromatograms of ZP and related standards in the positive and negative ion modes were presented in Fig. 1A–D. A total of 75 compounds in ZP were identified, including 19 triterpenoids and their glycosides, 17 flavonoids, 13 chromones, six organic acids, six sesquiterpenes, five phenylpropanoids, four monoterpenes, and five other compounds.

Fig. 1.

Fig. 1

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Total ion chromatograms of ZP in the positive (A) and negative ion modes (B) and reference compounds in the positive (C) and negative ion modes (D).

Among them, 16 compounds (compounds 2, 7, 11, 12, 14, 17, 20, 21, 22, 28, 32, 36, 37, 40, 64, and 67) were confirmed by comparing the retention times and mass fragments with that of authentic standards. While other compounds were preliminarily characterized based on their highly accurate quasi-molecular ions/adduct ions such as [M+H]+, [M−H]−, [M−H+HCOOH]−, and [M+Na]+ as well as mass fragmentation recorded in the self-built ZP UNIFI database. For 75 compounds, the detailed informations including retention time (tR), molecular formula, nomenclature, calculation and determination of molecular weight, fragment ions, and source were provided in Table 2.

Table 2.

Information of the identified compounds from Zixue Powder by UPLC-Q-TOF-MS.

No. Compounds Formula tR (min) Quasi-molecular ion/adduct ion Measured mass (MW) Calculated mass (MW) Error (ppm) Fragment ions (m/z) Source
1 Mokko lactone C15H20O2 1.54 [M+H]+ 232.1459 232.1463 −1.7 189.0972, 161.0955 MX
2* D-(+)-sucrose C12H22O11 1.86 [M+Na]+ 342.1156 342.1162 −1.7 277.0887, 203.0519, 185.0409 XS
3 Fukiic acid C11H12O8 3.74 [M+Na]+ 272.0522 272.0532 −3.6 209.0433, 191.0329, 163.0381, 135.0424 SM
4 Cimidahurinie C14H20O8 4.61 [M+Na]+ 316.1153 316.1158 −1.6 177.0534, 127.0314 SM
5 Piscidic acid C11H12O7 4.67 [M−H]– 256.0572 256.0580 −4.1 183.0668, 147.0428, 119.0478, 91.0526 SM
6 Ningpogenin C9H14O3 4.68 [M−H]– 170.0931 170.0943 −5.9 107.0477, 91.0526, 77.0368 XS
7* Harpagide C15H24O10 4.73 [M+Na]+ 364.1358 364.1370 −3.0 279.0926, 167.0695, 135.0429, 123.0426 XS
8 4-Methoxycinnamic acid C10H10O3 4.97 [M+NH4]+ 178.0621 178.0630 −4.8 165.0545, 119.0475 XS
9 Verbascoside C29H36O15 6.80 [M+Na]+ 624.2063 624.2054 1.4 569.1922, 485.1601, 279.0928, 201.0454 XS
10 Shomaside B C27H30O15 7.04 [M+H]+ 594.1564 594.1585 −3.6 577.1509, 457.1156, 273.0752, 163.0377 SM
11* Liquiritin apioside C26H30O13 8.33 [M+Na]+ 550.1681 550.1686 −0.9 413.1201, 163.0381 GC
12* Liquiritin C21H22O9 8.43 [M+Na]+ 418.1261 418.1264 −0.7 257.0808, 239.0697, 163.0379, 137.0224 GC
13 Prim-O-glucosylcimifugin C22H28O11 8.60 [M+H]+ 468.1628 468.1632 −0.7 451.1621, 409.1106, 307.1171 SM
14* Agarotetrol C17H18O6 8.64 [M+H]+ 318.1100 318.1103 −0.3 301.0944, 283.0865, 255.0901, 227.5403 CX
15 Cimicifugamide C25H31NO10 8.67 [M+H]+ 505.1946 505.1948 −0.4 407.0903, 255.1011 SM
16 Sophoraflavone B C21H20O9 8.86 [M+H]+ 416.1097 416.1107 −2.5 255.0645, 133.0839, 89.0578 GC
17* Isoferulic acid C10H10O4 9.00 [M+H]+ 194.0571 194.0579 −3.9 177.0539, 149.0589, 134.0351, 105.0317 XS
18 Sibirioside A C21H28O12 9.14 [M+Na]+ 472.1559 472.1581 −4.4 333.0943, 273.0753, 131.0481 XS
19 Isoliquiritin apioside C26H30O13 9.31 [M+H]+ 550.1684 550.1686 −0.2 429.2017, 417.1950, 255.1152, 135.0328 GC
20* Angoroside C C36H48O19 9.62 [M+Na]+ 784.2805 784.2790 1.9 507.1853, 339.1065, 177.0539 XS
21* Neoisoliquritin C26H30O13 9.81 [M+H]+ 550.1684 550.1686 −0.4 419.1330, 257.0802, 147.0434 GC
22* Cimicifugin C16H18O6 9.88 [M+H]+ 306.1099 306.1103 −1.3 273.0752, 259.0595, 235.0594 SM
23 8-O-Feruloylharpagide C25H32O13 10.07 [M+Na]+ 540.1863 540.1843 3.6 419.1330, 285.0737, 249.0372, 137.0221 XS
24 Neoliquiritin isomer C21H22O9 10.11 [M+H]+ 418.1260 418.1264 −0.9 257.0805, 239.0691, 177.0535, 137.0222 GC
25* Angoroside C isomer C36H48O19 10.13 [M+Na]+ 784.2792 784.2790 0.3 661.2090, 419.1330, 339.1094, 177.0535 XS
26 Ononin C22H22O9 10.41 [M−H]– 430.1263 430.1264 −0.1 291.0618, 269.0806, 97.0268 GC
27 Licochalcone B C16H14O5 10.61 [M+H]+ 286.0836 286.0841 −1.8 257.0805, 163.0384, 137.0222, 111.0425 GC
28* Liquiritigenin C15H12O4 10.64 [M−H]– 256.0733 256.0736 −1.0 163.0384, 137.0222, 119.0477 GC
29 Cimicifugic acid E C21H20O10 11.08 [M+Na]+ 432.1046 432.1057 −2.3 283.0860, 177.0534, 137.0585 SM
30 Norcimifugin C15H16O6 11.72 [M+H]+ 292.0941 292.0947 −2.0 257.0800, 233.0447, 137.0587 SM
31 Daidzein C15H10O4 12.15 [M+H]+ 254.0574 254.0579 −2.1 149.0585, 137.0212, 121.0296 GC
32* trans-Cinnamic acid C9H8O2 12.36 [M+H]+ 148.0523 148.0524 −0.8 131.0479, 103.0524, 77.0365 XS
33 Medicarpin C16H14O4 12.49 [M+H]+ 270.0882 270.0892 −3.6 229.0845, 163.0741, 107.0483 GC
34 (23R,24S)-25-O-acetyl-ciminol-3-O-β-D-xylopyranoside C37H58O10 12.50 [M+Na]+ 662.4034 662.4030 8.7 535.2902, 527.2986, 185.1321 SM
35 Alantolactone C15H20O2 13.81 [M+H]+ 232.1458 232.1463 −2.4 187.1474, 161.0592, 149.0979 MX
36* Isoliquiritigenin C15H12O4 14.02 [M+H]+ 256.0733 256.0736 −1.1 239.0695, 163.0374, 137.0220, 119.0477 GC
37* Harpagoside C24H30O11 14.42 [M+Na]+ 494.1788 494.1788 −0.1 369.1153, 327.1224, 149.0588 XS
38 6-Methoxy-2-[2- (3′-methoxy-4′-hydroxyphenyl) ethyl] chromone C19H18O5 14.44 [M+H]+ 326.1151 326.1154 −1.1 187.0782, 149.0588, 121.0632 CX
39 2′-O-Acetyl-cimicifugoside H-1 C37H54O10 14.57 [M+H]+ 658.3695 658.3717 −3.3 521.2921, 469.3361, 261.1303 SM
40* Formononetin C16H12O4 15.50 [M+H]+ 268.0732 268.0736 −1.4 253.0500, 237.0542, 107.0473 GC
41 Cimicifugoside H2 C35H54O10 15.64 [M+H]+ 634.3714 634.3717 −0.4 481.2959, 469.3310, 161.0954 SM
42 Isoalantolactone C15H20O2 15.74 [M+H]+ 232.1450 232.1463 −5.7 205.1569, 187.0458, 91.0530 MX
43 Glabrolide C30H44O4 18.26 [M+H]+ 468.3235 468.3240 −1.0 451.3193, 317.2129, 271.2055, 235.1659 GC
44 26-Deoxycimicifugoside C37H54O10 16.30 [M+H]+ 658.3691 658.3717 −4.0 451.3199, 379.2615, 335.2384, 171.1164 SM
45 7β-Hydroxy-cimigenol xyloside C35H56O10 16.33 [M+Na]+ 636.3877 636.3874 0.5 339.1992, 311.1679, 297.1521 SM
46 Cimigemol C30H48O5 16.41 [M+Na]+ 488.3521 488.3502 3.8 453.3345, 315.1972, 189.1628, 159.1168 SM
47 Cimiside A C35H56O10 16.70 [M+H]+ 636.3870 636.3874 −0.5 619.3841, 511.3408, 451.3189, 315.1589 SM
48 Hederagenin C30H48O4 16.79 [M+Na]+ 472.3573 472.3553 4.1 337.2548, 241.1969, 233.1541, 149.1311 CX
49 6-Hydroxy-2-[2-(4′-methoxyphenyl) ethyl] chromone C18H16O4 16.93 [M+H]+ 296.1041 296.1049 −2.5 121.0639 CX
50 (3β,12β,16β)-12-Acetoxy-16,23-epoxy-9,19-cyclolanolin-22- en-24-one-3-O-β-D-pyranoside C37H56O9 17.10 [M+Na]+ 644.3869 644.3924 −8.1 547.3284, 495.3454, 313.1796 SM
51 (−)-Bornyl ferulate C20H26O4 17.10 [M+Na]+ 330.1823 330.1831 −2.3 313.1795, 293.1736, 105.0685 CX
52 6-Hydroxy-2- (2-phenylethyl) chromone C17H14O3 17.17 [M+H]+ 266.0940 266.0943 −1.0 177.0522, 147.0455, 91.0528 CX
53 Cimidahuside J C37H56O11 17.24 [M–H+HCOOH]– 676.3784 676.3823 −5.4 645.3246, 339.1992, 311.1678, 297.1521 SM
54 Baimuxinfuranic acid C15H24O3 17.32 [M+Na]+ 252.1743 252.1725 6.4 189.1652, 175.1108, 121.0999 CX
55 12β-Hydroxycimigenol C30H48O6 17.72 [M+Na]+ 504.3443 504.3451 −1.4 453.3365, 371.2265, 259.2075, 123.0787 SM
56 Licobenzofuran C21H22O5 17.75 [M+H]+ 354.1450 354.1467 −4.9 341.1392, 299.0885, 121.0639 GC
57 6,7-Dimethoxy-2-[2-(p-methoxyphenyl) ethyl] chromone C20H20O5 17.78 [M+H]+ 340.1302 340.1311 −2.5 299.0885, 181.0476, 121.0639 CX
58 6,7-Dimethoxy-2-phenethylchromone C19H18O4 18.00 [M+H]+ 310.1203 310.1205 −0.6 205.0486, 181.0484, 91.0531 CX
59 7α,15-Dihydroxydehydroabietic acid C20H28O4 18.18 [M+H]+ 332.1984 332.1988 −1.2 315.1945, 251.1813, 201.1290, 161.1310 CX
60 Isoglabrolide C30H44O4 18.26 [M+H]+ 468.3235 468.3240 −1.0 451.3193, 317.2129, 271.2055, 235.1659 GC
61 Uralsaponin N C42H62O17 18.26 [M+H]+ 838.4001 838.3987 1.7 645.3631, 469.3304, 451.3193, 297.1841 GC
62 Cimifoetiside Ⅴ C47H76O19 18.38 [M–H+HCOOH]– 944.4986 944.4981 0.5 793.4013, 711.3939, 483.3102, 185.0093 SM
63 AH14 C34H30O8 18.53 [M+H]+ 566.1928 566.1941 −2.2 383.1503, 337.1065, 145.0626 CX
64* Licochalcone A C21H22O4 18.56 [M+H]+ 338.1514 338.1518 −1.1 323.1304 237.0588, 205.1206, 121.0268 GC
65 Licoricone C22H22O6 18.57 [M+H]+ 382.1413 382.1416 −0.9 327.0855, 283.0586, 191.0703, 137.0224 GC
66 2-(2-Phenylethyl) chromone C17H14O2 18.62 [M+H]+ 250.0992 250.0994 −0.6 133.1007, 107.0844, 91.0527 CX
67* Glycyrrhizinic acid C42H62O16 18.62 [M−H]– 822.4054 822.4038 2.0 759.3958, 645.3623, 351.0560, 193.0342 GC
68 24-O-Acetyl-7,8 - dihydro-lignoside C37H58O11 18.72 [M+Na]+ 678.3976 678.3979 −0.5 565.3153, 451.3191, 393.2807, 195.1205 SM
69 Cimidahuside I C37H58O11 18.76 [M–H+HCOOH]– 678.3982 678.3979 0.4 645.3265, 339.1994, 311.1681, 297.1525 SM
70 6-Methoxy-2-(2-phenylethyl) chromone C18H16O3 19.26 [M+H]+ 280.1099 280.1099 −0.2 131.0841, 57.0696 CX
71 Cimicifoetiside A C37H58O10 19.35 [M+Na]+ 662.4034 662.4030 0.6 609.3958, 565.3703, 229.1406 SM
72 Cimifoetiside IV isomer C43H68O15 19.50 [M+Na]+ 824.4574 824.4558 1.9 807.4518, 645.3987, 453.3349, 221.1374 SM
73 Cimiside D C43H70O16 19.67 [M+Na]+ 842.4681 842.4664 2.0 283.2030, 163.1113 SM
74 Isocostic acid C15H22O2 19.73 [M+H]+ 234.1614 234.1620 −2.3 163.0753 107.0838, 77.0371 MX
75 Cimifoetiside IV C43H68O15 19.94 [M+Na]+ 824.4581 824.4558 2.7 761.4809, 665.4278, 221.1371, 117.0899 SM
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Notes: MW: Molecular Weight; MX: Muxiang; XS: Xuanshen; SM: Shengma; GC: Gancao; CX: Chenxiang, *: compared with reference standards.

3.2. Network analysis for FS-related targets of ZP

ZP is a representative cold formula for resuscitation, which is known as the three most effective formulas for warm disease together with Angong Niuhuang pill and Zhibao Dan. It shows favorable clinical effects on FS in children [35], which also has been demonstrated to exert therapeutic effects on collagen-induced arthritis injury in rats through anti-inflammatory and antioxidant effects [15]. Additionally, ZP can play an anticonvulsive role by reducing the content of the excitatory neurotransmitter glutamate in the cerebral cortex of convulsive mice [36].

In this study, 786 protein targets from 75 compounds in ZP were obtained from the SwissTargetPrediction and TCMSP databases. The targets of each compound were provided in Table S1. Through the Genecards and OMIM databases, 2194 FS-associated targets were obtained, and the pertinent data were shown in Table S2. As shown in Venn diagram, 206 common genes were identified as candidate targets of ZP in the treatment of FS. In order to further screen the active compounds in ZP, the “TCMs-compounds-targets” network was constructed (Fig. 2). Cytoscape plug-in was used to perform topological analysis, and active compounds were screened according to degree >20. As a result, ZP might exhibit therapeutic effects on FS through 21 potential bioactive compounds, such as 2-(2-phenylethyl) chromone, isoliquiritin, 6-hydroxy-2-(2-phenylethyl) chromone, licochalcone A, liquiritigenin, cimicifugine, and so on. More details were included in Table S3. These compounds may be the key active compounds in ZP for treating FS. Besides, the results also revealed that different compounds from ZP might exert a therapeutic effect by targeting the same genes.

Fig. 2.

Fig. 2

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“TCMs-compounds-targets” network. (The V-shaped, circular, and pink diamond-shaped nodes represent TCMs, chemical compounds, and 206 potential targets, respectively.). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

3.3. GO and KEGG pathway enrichment analysis

GO terms (BP, CC, and MF) and KEGG pathways were enriched from the 206 common targets (P < 0.05) via DAVID database. The top ten terms for BP, CC, and MF were respectively visualized in Fig. 3A. The main enrichment items of BP included cytokines mediation, inflammatory response, positive regulation of MAPK cascade pathway, hypoxia response, positive regulation of MAP kinase activity, and so on. CC terms were concentrated on the cytoplasm membrane and cell surface. MF terms mainly focused on identical protein binding and protein tyrosine kinase. The results of GO analysis showed that the active compounds in ZP could regulate protein binding and protein tyrosine kinase activity by affecting cellular component on the plasma membrane and cell surface, and might participate in signal pathways such as cytokines, inflammatory response, positive regulation of MAPK cascade pathway so as to exert antipyretic and anticonvulsant effects.

Fig. 3.

Fig. 3

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The top 30 enriched GO terms (A) and top 20 enriched KEGG pathways (B) of ZP candidate targets. (The different color represents the significance of target gene enrichment.). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Furthermore, the top 20 enriched KEGG pathways were presented in Fig. 3B. The results showed that the EGFR tyrosine kinase inhibitor resistance was the main pathway, which is closely associated with excessive inflammatory response after central nervous system injury [[37], [38], [39]]. The inhibitors of EGFR signaling pathway can alleviate the inflammatory response and prevent the progression of inflammatory injury to improve the prognosis of secondary central nervous system injury, ultimately [40,41]. Additionally, the inflammatory response is involved in other pathways, including human cytomegalovirus infection, hepatitis B, and Kaposi sarcoma-associated herpesvirus infection. In view of this, it is hypothesized that ZP may play an anti-inflammatory role through EGFR tyrosine kinase inhibitor pathway to achieve treatment of FS.

3.4. The analysis of PPI network and construction of the “compounds-targets-pathways-disease” network

As the PPI network results displayed in Fig. 4A, the 14 genes that coexisted in two algorithms were regarded as core targets, including IL6, STAT3, TNF, MMP9, VEGFA, IL1B, AKT1, CXCL8, FGF2, EGFR, TP53, TLR4, PTPN11, and IL2. Among these genes, STAT3 is a functional protein involved in cell proliferation, differentiation, apoptosis, angiogenesis, inflammation, and immune response [42]. IL-6 is an important inflammatory cytokine, which can activate the STAT3 pathway, whereas IL-6/STAT3 is an essential pathway to mediate inflammatory signal transduction in cells [43,44]. In addition, EFGR, TNF, and other genes are associated with the regulation of inflammation, some of which are demonstrated closely relating to FS [45,46]. The levels of IL-6 in plasma and cerebrospinal fluid in patients with FS were higher than those in the control group [47]. It was also found that the injection of TNF-α into the hippocampus could reduce the time of seizures in the mouse epileptic model [48].

Fig. 4.

Fig. 4

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The PPI network (A) and “compounds-targets-pathways-disease” network (B).

To further clarify the relationship between ZP and FS, a “compounds-targets-pathways-disease” network was constructed. Searching the targets of 21 components by the network discovered that only 13 components were closely associated with the 14 core targets, such as 6-hydroxy-2-(2-phenylethyl) chromone (compound 49), licochalcones B (compound 27), and 26-deoxycimicifugoside (compound 44). As shown in Fig. 4B, multiple components were connected to the focused targets, which were associated with the related pathways, indicating the characteristics of multi-ingredient, multi-target, and multi-pathway of TCM. It was reported that all the five derivatives of 2-(2-phenylethyl) chromone could effectively inhibit the production of nitric oxide by RAW264.7 cells due to lipopolysaccharide stimulation, which led to the inhibition of inflammation [49]. Moreover, the anti-inflammatory effects exerted by isoliquiritigenin and liquiritigenin in licorice [50]. Meanwhile, the results indicated that 14 targets such as IL6, STAT3, TNF, TLR4, VEGFA, and CXCL8 may play the important roles in the treatment of FS with ZP.

3.5. Molecular docking analysis

Molecular docking is a novel technology in the computer-aided molecular design field, which is widely applied to discover potential bioactive compounds and clarify the potential mechanisms between proteins and compounds [51,52]. To provide further evidence to support the identification of the potential active compounds, the binding of 13 compounds to 14 core targets was respectively verified by molecular docking. The original ligand was docked with the protein as the standard to evaluate the binding affinity and mode, respectively. According to the results shown in Fig. 5A and Table S4, most of the original ligands showed excellent binding activity with the target proteins and the scores ranging from −51.51 to −100.69 kcal/mol except for TNF (PDB ID: 2AZ5), IL1B (PDB ID: 5R88), and STAT3 (PDB ID: 5AX3), which showed moderate binding affinity. The results suggest that this mode was suitable for affinity prediction. Comparing with the original ligands, licochalcone B (compound 27) and licochalcone A (compound 64) showed good interactions with all target proteins (CDocker interaction energy ranging from −41.99 to −66.42 kcal/mol), which may demonstrate the multi-targeted interactions for treatment of FS. Additionally, 26-deoxycimicifugoside (compound 44) and hederagenin (compound 48) showed good binding with most of the target proteins. Notably, compound 44 bound to AKT1 (PDB ID: 3O96) with the score at −79.55 kcal/mol, which was superior to the original ligand with the score at −75.68 kcal/mol. While AKT1 (PDB ID: 3O96) exhibited the excellent binding interaction with most of the tested compounds, which was considered as the essential protein in anti-inflammation [53]. Meanwhile, compound 44 bound to EGFR (PDB ID: 2GS6) with the score at −62.32 kcal/mol, which was the key protein of the EGFR tyrosine kinase inhibitor pathway [54], showing the strong interaction comparing with the original ligand (score at −77.23 kcal/mol). Therefore, take compound 44 docked with AKT1 (PDB ID: 3O96) as an example. As shown in Fig. 5B, compound 44 bound to AKT1 (PDB ID: 3O96) mainly forming hydrogen bonds, which interacted with the protein at residues Arg-2732.6, Gly-2942.8, and Trp-802.8. The stability of the protein–ligand complex is predominantly determined by the hydrogen bonds, which are the major stabilizers for the complexes [55].

Fig. 5.

Fig. 5

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Heat map of molecular docking results arranged in blue-red order by –CDocker interaction energy values (A) and binding map of 26-deoxycimicifugoside and 3O96 protein (B). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Licochalcones A and B have been demonstrated to have a wide range of biological effects, including antioxidant stress, anti-inflammatory, anticancer, antimicrobial, anti-Alzheimer's disease, and neuroprotective effects [56]. Hederagenin belongs to triterpenoid acid compounds, with various pharmacological effects such as anti-tumor, anti-depression, antibacterial, anti-inflammatory, and anti-diabetes [57]. Docking results indicate that ZP may have an important function in the treatment of FS by acting on 14 gene targets, primarily through the four compounds indicated above.

4. Conclusion

In this study, taking advantage of UPLC-Q-TOF-MS, 75 chemical components in ZP were characterized. Network pharmacology analysis explored 13 potential active ingredients and 14 key potential anti-FS targets relating to inflammatory response, EGFR tyrosine kinase inhibitor resistance, AGE-RAGE signaling pathway in diabetic complications, and neuroactive ligand-receptor interaction. Futhermore, licochalcones A and B, 26-deoxycimicifugoside, and hederagenin were screened as the main potential active ingredients by molecular docking. In summary, this study provides a reference for further research into the pharmacodynamic material basis and mechanism of ZP for the treatment of FS.

Data availability statement

Data will be made available on request.

CRediT authorship contribution statement

Lingling Song: Writing - review & editing, Writing - original draft, Visualization, Software, Investigation, Formal analysis, Data curation. Jian Xu: Writing - review & editing, Software, Formal analysis, Data curation. Yanqiong Shi: Writing - review & editing, Validation, Formal analysis. Hemiao Zhao: Writing - review & editing, Software, Formal analysis. Min Zhang: Writing - review & editing, Software, Formal analysis. Yuefei Wang: Writing - review & editing, Supervision, Project administration, Funding acquisition. Ying Cui: Writing - review & editing, Supervision, Project administration, Investigation. Xin Chai: Writing - review & editing, Supervision, Project administration, Investigation, Funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This study was supported by the Science and Technology Project of Haihe Laboratory of Modern Chinese Medicine [No. 22HHZYSS00007 and 22HHZYJC00007], the Science and Technology Program of Tianjin [No. 22ZYJDSS00100], and the National Key Research and Development Program of China [No. 2021YFC1712905].

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2023.e23865.

Contributor Information

Ying Cui, Email: CQL8179270@tjutcm.edu.cn.

Xin Chai, Email: chaix0622@tjutcm.edu.cn.

Appendix A. Supplementary data

The following is the Supplementary data to this article:

Multimedia component 1
mmc1.docx (87.6KB, docx)

References

  • 1.Forsgren L., Sidenvall R., Blomquist H.K., Heijbel J. A prospective incidence study of febrile convulsions. Acta Paediatr. Scand. 1990;79:550–557. doi: 10.1111/j.1651-2227.1990.tb11510.x. [DOI] [PubMed] [Google Scholar]
  • 2.Hauser W.A. The prevalence and incidence of convulsive disorders in children. Epilepsia. 1994;35 doi: 10.1111/j.1528-1157.1994.tb05932.x. [DOI] [PubMed] [Google Scholar]
  • 3.Leung A.K., Hon K.L., Leung T.N. Febrile seizures: an overview. Drugs Context. 2018;7 doi: 10.7573/dic.212536. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Vestergaard M., Pedersen C.B., Sidenius P., Olsen J., Christensen J. The long-term risk of epilepsy after febrile seizures in susceptible subgroups. Am. J. Epidemiol. 2007;165:911–918. doi: 10.1093/aje/kwk086. [DOI] [PubMed] [Google Scholar]
  • 5.Dreier J.W., Pedersen C.B., Cotsapas C., Christensen J. Childhood seizures and risk of psychiatric disorders in adolescence and early adulthood: a Danish nationwide cohort study. Lancet Child Adolesc. Health. 2019;2019:99–108. doi: 10.1016/S2352-4642(18)30351-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Zare-shahabadi A., Soltani S., Ashrafi M.R., Shahrokhi A., Zoghi S., Pourakbari B., et al. Rezaei N. Association of IL4 single-nucleotide polymorphisms with febrile seizures. J. Child Neurol. 2015;30:423–428. doi: 10.1177/0883073814551389. [DOI] [PubMed] [Google Scholar]
  • 7.Tarhani F., Nezami A., Heidari G., Dalvand N. Factors associated with febrile seizures among children. Ann. Med. Surg (Lond). 2022;75 doi: 10.1016/j.amsu.2022.103360. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Sharif M.R., Kheirkhah D., Madani M., Kashani H.H. The relationship between iron deficiency and febrile convulsion: a case-control study. Glob. J. Health Sci. 2015;8:185–189. doi: 10.5539/gjhs.v8n2p185. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Bakri A.H., Hassan M.H., Ahmed A.E., Halim P.R., El-Sawy S.A., Mohamed M.M., et al. Biochemical assessments of neurotrophin-3 and zinc involvement in the pathophysiology of pediatric febrile seizures: biochemical markers in febrile seizures. Biol. Trace Elem. Res. 2022;200:2614–2619. doi: 10.1007/s12011-021-02886-w. [DOI] [PubMed] [Google Scholar]
  • 10.Subcommittee on febrile seizures, American academy of pediatrics, Neurodiagnostic evaluation of the child with a simple febrile seizure. Pediatrics. 2011;127:389–394. doi: 10.1542/peds.2010-3318. [DOI] [PubMed] [Google Scholar]
  • 11.Chen H., Wang S.C. Treatment of pediatric febrile seizure based on syndromes identification of “Heat, Phlegm, Fright and Wind”. J. Nanjing Univ. Tradit. Chin. Med. 2021;37:290–293. doi: 10.14148/j.issn.1672-0482.2021.0290. [DOI] [Google Scholar]
  • 12.Wang E.J. A review on treating infant febrile convulsions in TCM medicine. Clin. J. Chin. Med. 2014;6:147–148. doi: 10.3969/j.issn.1674-7860.2014.31.081. [DOI] [Google Scholar]
  • 13.Chinese Pharmacopoeia Commission . China Medical Science Press; Beijing: 2020. Pharmacopoeia of People's republic of China. [Google Scholar]
  • 14.Wang J.Y., Liu J.L., Liu P. Comparison of pharmacological effects of Zixue Powder suspension in different ways of administration. Pharmacol. Clin. Chin. Mater. Med. 1998:11–12. [Google Scholar]
  • 15.Li L., Liu J., Zhang J., Ying S.L. Therapeutic effect of Zixue Powder on collagen-induced arthritis in rats and its mechanism. Drug & Clin. 2016;31:1135–1140. doi: 10.7501/j.issn.1674-5515.2016.08.003. [DOI] [Google Scholar]
  • 16.Zhang Y.P., Lu Y.L., Sun D., Chen Z.M. Clinical observation on prevention of recurrent febrile seizure in children by short-term administration of Zixue, Inner Mongolia. J. Tradit. Chin. Med. 2022;41:1–3. doi: 10.16040/j.cnki.cn15-1101.2022.05.007. [DOI] [Google Scholar]
  • 17.Li J., Wang Y., Wang R., Wu M.Y., Shan J., Zhang Y.C., et al. Study on the molecular mechanisms of tetrandrine against pulmonary fibrosis based on network pharmacology, molecular docking and experimental verification. Heliyon. 2022;8 doi: 10.1016/j.heliyon.2022.e10201. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Li J.X., Han Z.X., Cheng X., Zhang F.L., Zhang J.Y., Su Z.J., et al. Combinational study with network pharmacology, molecular docking and preliminary experiments on exploring common mechanisms underlying the effects of weijing decoction on various pulmonary diseases. Heliyon. 2023;9 doi: 10.1016/j.heliyon.2023.e15631. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Pinzi L., Rastelli G. Molecular docking: shifting paradigms in drug discovery. Int. J. Mol. Sci. 2019;20:4331. doi: 10.3390/ijms20184331. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Jiao X., Jin X., Ma Y., Yang Y., Li J., Liang L., et al. A comprehensive application: molecular docking and network pharmacology for the prediction of bioactive constituents and elucidation of mechanisms of action in component-based Chinese medicine. Comput. Biol. Chem. 2021;90 doi: 10.1016/j.compbiolchem.2020.107402. [DOI] [PubMed] [Google Scholar]
  • 21.Wu W.I., Voegtli W.C., Sturgis H.L., Dizon F.P., Vigers G.P., Brandhuber B.J. Crystal structure of human AKT1 with an allosteric inhibitor reveals a new mode of kinase inhibition. PLoS One. 2010;5 doi: 10.1016/10.1371/journal.pone.0012913. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Weisner J., Landel I., Reintjes C., Uhlenbrock N., Trajkovic-Arsic M., Dienstbier N., et al. Preclinical efficacy of covalent-allosteric AKT inhibitor borussertib in combination with trametinib in KRAS-mutant pancreatic and colorectal cancer. Cancer Res. 2019;79:2367–2378. doi: 10.1158/0008-5472. [DOI] [PubMed] [Google Scholar]
  • 23.Zhang X., Gureasko J., Shen K., Cole P.A., Kuriyan J. An allosteric mechanism for activation of the kinase domain of epidermal growth factor receptor. Cell. 2006;125:1137–1149. doi: 10.1016/j.cell.2006.05.013. [DOI] [PubMed] [Google Scholar]
  • 24.Tsao Y., Liu J., Kuo J., Huang W., Wu W. Pi(4,5)P2 lipid binding induces a reorientation of FGF2 molecules near membrane surface to facilitate the unconventional oligomerization-dependent secretion process as revealed by a combined FTIR/NMR/X-Ray study. Biophys. J. 2015;108 doi: 10.1016/j.bpj.2014.11.1410. 255a-255a. [DOI] [Google Scholar]
  • 25.Nichols C., Ng J., Keshu A., Kelly G., Conte M.R., Marber M.S., et al. Mining the PDB for tractable cases where X-ray crystallography combined with fragment screens can be used to systematically design protein-protein inhibitors: two test cases illustrated by IL1β-IL1R and p38α-TAB1 complexes. J. Med. Chem. 2020;63:7559–7568. doi: 10.1021/acs.jmedchem. [DOI] [PubMed] [Google Scholar]
  • 26.Arkin M.R., Randal M., DeLano W.L., Hyde J., Luong T.N., Oslob J.D., et al. Binding of small molecules to an adaptive protein-protein interface. Proc. Natl. Acad. Sci. USA. 2003;100:1603–1608. doi: 10.1073/pnas.252756299. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Tochowicz A., Maskos K., Huber R., Oltenfreiter R., Dive V., Yiotakis A., et al. Crystal structures of MMP-9 complexes with five inhibitors: contribution of the flexible Arg424 side-chain to selectivity. J. Mol. Biol. 2007;371:989–1006. doi: 10.1016/j.jmb.2007.05.068. [DOI] [PubMed] [Google Scholar]
  • 28.Nuti E., Cantelmo A.R., Gallo C., Bruno A., Bassani B., Camodeca C., et al. N-O-isopropyl sulfonamido-based hydroxamates as matrix metalloproteinase inhibitors: hit selection and in vivo antiangiogenic activity. J. Med. Chem. 2015;58:7224–7240. doi: 10.1021/acs.jmedchem.5b00367. [DOI] [PubMed] [Google Scholar]
  • 29.Zhang Z.Y., Liu S., Yu Z., Yu X. Crystal structure of SHP2 in complex with a tautomycetin analog TTN D-1, doi: 10.2210/pdb3mow/pdb.
  • 30.Kinoshita T., Sugiyama H., Mori Y., Takahashi N., Tomonaga A. Identification of allosteric ERK2 inhibitors through in silico biased screening and competitive binding assay. Bioorg. Med. Chem. Lett. 2016;26:955–958. doi: 10.1016/j.bmcl.2015.12.056. [DOI] [PubMed] [Google Scholar]
  • 31.Bai L., Zhou H., Xu R., Zhao Y., Chinnaswamy K., McEachern D., et al. A potent and selective small-molecule degrader of STAT3 achieves complete tumor regression in vivo. Cancer Cell. 2019;36:498–511. doi: 10.1016/j.ccell.2019.10.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Ohto U., Yamakawa N., Akashi-Takamura S., Miyake K., Shimizu T. Structural analyses of human Toll-like receptor 4 polymorphisms D299G and T399I. J. Biol. Chem. 2012;287:40611–40617. doi: 10.1074/jbc.M112.404608. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.He M.M., Smith A.S., Oslob J.D., Flanagan W.M., Braisted A.C., Whitty A., et al. Small-molecule inhibition of TNF-alpha. Science. 2005;310:1022–1025. doi: 10.1126/science.1116304. [DOI] [PubMed] [Google Scholar]
  • 34.Gertz M., Fischer F., Nguyen G.T., Lakshminarasimhan M., Schutkowski M., Weyand M. Ex-527 inhibits sirtuins by exploiting their unique nad+-dependent deacetylation mechanism. Proc. Natl. Acad. Sci. USA. 2013;110:E2772–E2781. doi: 10.1073/pnas.1303628110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Yi C.Y. Study on the treatment of infantile febrile convulsion with integrated Chinese and Western medicine. J. Med. Theor. & Prac. 2014;27:3039–3040. doi: 10.19381/j.issn.1001-7585.2014.22.062. [DOI] [Google Scholar]
  • 36.Zhang H.Y., Hu J., Han X., Zhang L., Zhu X.J., Song W., et al. Anticonvulsant effect of different processes of Zixue Powder in mice and its mechanism. Drug Eval. Res. 2022;45:84–89. doi: 10.7501/j.issn.1674-6376.2022.01.010. [DOI] [Google Scholar]
  • 37.Sabbah D.A., Hajjo R., Sweidan K. Review on epidermal growth factor receptor (EGFR) structure, signaling pathways, interactions, and recent updates of EGFR inhibitors. Curr. Top Med. Chem. 2020;20:815–834. doi: 10.2174/1568026620666200303123102. [DOI] [PubMed] [Google Scholar]
  • 38.Li Z.W., Zhao J.J., Li S.Y., Cao T.T., Wang Y., Guo Y., et al. Blocking the EGFR/p38/NF-κB signaling pathway alleviates disruption of BSCB and subsequent inflammation after spinal cord injury. Neurochem. Int. 2021;150 doi: 10.1016/j.neuint.2021.105190. [DOI] [PubMed] [Google Scholar]
  • 39.Cai X.J., Jiao Z., Ren B.Y., Lu Y., Zhang X.H., Wen H., et al. Epidermal growth factor receptor signaling pathway activation mediating inflammatory damage in central nervous system. Chin. J. Neuromed. 2018;17:199–204. doi: 10.1007/s12035-010-8115-7. [DOI] [Google Scholar]
  • 40.Wu M., Zhang P. EGFR-mediated autophagy in tumourigenesis and therapeutic resistance. Cancer Lett. 2020;469:207–216. doi: 10.1016/j.canlet.2019.10.030. [DOI] [PubMed] [Google Scholar]
  • 41.Li Z.W., Jiang D.M., Han J., Tu J.M., Wang Q. Protective effect of EGFR inhibitor on blood spinal barrier damage induced by oxygen glucose deprivation/re-oxygenation. Neural Injury and Functional Reconstruction. 2021;16:63–66+89. doi: 10.16780/j.cnki.sjssgncj.20201240. [DOI] [Google Scholar]
  • 42.Subramaniam A., Shanmugam M.K., Perumal E., Li F., Nachiyappan A., Dai X., et al. Potential role of signal transducer and activator of transcription (STAT)3 signaling pathway in inflammation, survival, proliferation and invasion of hepatocellular carcinoma. Biochim. Biophys. Acta. 2013;1835:46–60. doi: 10.1016/j.bbcan.2012.10.002. [DOI] [PubMed] [Google Scholar]
  • 43.Yu H., Pardoll D., Jove R. STATs in cancer inflammation and immunity: a leading role for STAT3. Nat. Rev. Cancer. 2009;9:798–809. doi: 10.1038/nrc2734. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Hirano T. IL-6 in inflammation, autoimmunity and cancer. Int. Immunol. 2021;33:127–148. doi: 10.1093/intimm/dxaa078. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Choi J., Min H.J., Shin J.S. Increased levels of HMGB1 and pro-inflammatory cytokines in children with febrile seizures. J. Neuroinflammation. 2011;8:135. doi: 10.1186/1742-2094-8-135. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Li X., Guo S., Liu K., Zhang C., Chang H., Yang W., et al. GABRG2 deletion linked to genetic epilepsy with febrile seizures plus affects the expression of GABAA receptor subunits and other genes at different temperatures. Neuroscience. 2020;438:116–136. doi: 10.1016/j.neuroscience.2020.04.049. [DOI] [PubMed] [Google Scholar]
  • 47.Virta M., Hurme M., Helminen M. Increased plasma levels of pro- and anti-inflammatory cytokines in patients with febrile seizures. Epilepsia. 2002;43:920–923. doi: 10.1046/j.1528-1157.2002.02002.x. [DOI] [PubMed] [Google Scholar]
  • 48.Balosso S., Ravizza T., Perego C., Peschon J., Campbell I.L., et al. Tumor necrosis factor-alpha inhibits seizures in mice via p75 receptors. Ann. Neurol. 2005;57:804–812. doi: 10.1002/ana.20480. [DOI] [PubMed] [Google Scholar]
  • 49.Huo H.X., Gu Y.F., Sun H., Zhang Y.F., Liu W.J., Zhu Z.X., et al. Anti-inflammatory 2-(2-phenylethyl)chromone derivatives from Chinese agarwood. Fitoterapia. 2017;118:49–55. doi: 10.1016/j.fitote.2017.02.009. [DOI] [PubMed] [Google Scholar]
  • 50.Babu V., Kapkoti D.S., Binwal M., Bhakuni R.S., Shanker K., Singh M., et al. Liquiritigenin, isoliquiritigenin rich extract of Glycyrrhiza glabra roots attenuates inflammation in macrophages and collagen-induced arthritis in rats. Inflammopharmacology. 2023;31:983–996. doi: 10.1007/s10787-023-01152-w. [DOI] [PubMed] [Google Scholar]
  • 51.Attala K., Eissa M.S., El-Henawee M.M., El-Hay S.S.A. Application of quality by design approach for HPTLC simultaneous determination of amlodipine and celecoxib in presence of process-related impurity. Microchem. J. 2020;162 doi: 10.1016/j.microc.2020.105857. [DOI] [Google Scholar]
  • 52.Attala K., Elsonbaty A., Mostafa A.E., Eissa M.S., Hadad G.M., Abdelshakour M.A., et al. In-Silico analytical chemistry contributions to analytical and bio-analytical applications in spectroscopic and chromatographic techniques: molecular, mechanical and quantum insights. Rec. Pharm. Biomed. Sci. 2023;7:125–144. doi: 10.21608/rpbs.2023.190923.1211. [DOI] [Google Scholar]
  • 53.Zhao C., Yang X., Su E.M., Huang Y., Li L., Matthay M.A., et al. Signals of vagal circuits engaging with AKT1 in α7 nAChR+CD11b+ cells lessen E. coli and LPS-induced acute inflammatory injury. Cell Discov. 2017;3 doi: 10.1038/celldisc.2017.9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Kobayashi K., Tan A.C. Unraveling the impact of intratumoral heterogeneity on EGFR tyrosine kinase inhibitor resistance in EGFR-mutated NSCLC. Int. J. Mol. Sci. 2023;24:4126. doi: 10.3390/ijms24044126. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Zhou A., Li X., Zou J., Wu L., Cheng B., Wang J. Discovery of potential quality markers of Fritillariae thunbergii bulbus in pneumonia by combining UPLC-QTOF-MS, network pharmacology, and molecular docking. Mol. Divers. 2023;26:1–18. doi: 10.1007/s11030-023-10620-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Maria Pia G.D., Sara F., Mario F., Lorenza S. Biological effects of licochalcones. Mini Rev. Med. Chem. 2019;19:647–656. doi: 10.2174/1389557518666180601095420. [DOI] [PubMed] [Google Scholar]
  • 57.Zeng J., Huang T., Xue M., Chen J., Feng L., Du R., et al. Current knowledge and development of hederagenin as a promising medicinal agent: a comprehensive review. RSC Adv. 2018;8:24188–24202. doi: 10.1039/c8ra03666g. [DOI] [PMC free article] [PubMed] [Google Scholar]

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Supplementary Materials

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Data Availability Statement

Data will be made available on request.

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