CN114544833A - Construction method of nasosinusitis resuscitation inducing granule polypnea medicine effect prediction characteristic map - Google Patents
Construction method of nasosinusitis resuscitation inducing granule polypnea medicine effect prediction characteristic map Download PDFInfo
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- CN114544833A CN114544833A CN202210184561.6A CN202210184561A CN114544833A CN 114544833 A CN114544833 A CN 114544833A CN 202210184561 A CN202210184561 A CN 202210184561A CN 114544833 A CN114544833 A CN 114544833A
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Abstract
The invention relates to a characteristic map analysis method of a Chinese patent medicine, in particular to a construction method of a multiple-information efficacy prediction characteristic map of nasosinusitis dredging particles. The method comprises a method for predicting the pharmacodynamic substances of the nasosinusitis resuscitation-inducing granules, preparation of a test solution, preparation of a mixed reference solution, confirmation of chromatographic conditions, a method for integrating multiple information of spectra, determination of characteristic peaks and construction of characteristic spectra. The method disclosed by the invention is simple to operate, good in stability and good in reproducibility, the characteristic map reflecting the pharmacodynamic information is constructed by predicting the pharmacodynamic substance, the quality control evaluation and the pharmacodynamic are more scientifically combined, the map reflecting information obtained by integrating multiple information is more comprehensive, the method can be used for comprehensively evaluating and controlling the quality of the nasosinusitis resuscitation-inducing granules, the uniformity and stability of the quality of the nasosinusitis resuscitation-inducing granules are ensured, and the safety and effectiveness of clinical medication are improved.
Description
Technical Field
The invention relates to a characteristic map analysis method of a Chinese patent medicine, in particular to a construction method of a medicine effect prediction characteristic map of nasosinusitis resuscitation granules.
Background
The nasosinusitis resuscitation granules are a traditional Chinese medicine compound preparation consisting of fourteen traditional Chinese medicinal materials of magnolia flower, fried cocklebur fruit, ephedra herb, mint, baical skullcap root, weeping forsythia, wild chrysanthemum flower, Mongolian snakegourd root, rehmannia root, danshen root, Indian buead, dahurian angelica root, Chinese ligusticum and liquoric root, have the effects of dispelling wind and clearing heat, and freeing lung and promoting resuscitation, and are used for treating acute nasosinusitis (acute nasosinusitis) which belongs to the syndrome of invading lung by exogenous pathogenic factors and has the symptoms of tenderness on forehead or frontal bone, nasal obstruction, sticky white or yellow running nose, headache, fever, thin yellow or white fur and floating pulse. Clinically, it is widely used for treating acute and chronic nasosinusitis, allergic rhinitis and other diseases.
At present, the current component examination of the nasosinusitis dredging granules in one standard of the chinese pharmacopoeia 2020 edition mainly comprises: thin-layer chromatography identification of magnolin, ephedrine hydrochloride, imperatorin, baicalin, fructus forsythiae, phillyrin and salvianolic acid B, and HPLC determination of ephedrine hydrochloride and pseudoephedrine hydrochloride content. The nasosinusitis dredging granule is prepared from fourteen Chinese medicinal materials, has various raw materials and complex components, only uses a thin-layer chromatography to carry out qualitative identification on index components, and adopts a high performance liquid chromatography to measure the contents of ephedrine hydrochloride and pseudoephedrine hydrochloride, which is not enough to comprehensively reflect the quality of the nasosinusitis dredging granule. The network pharmacology integrates the disciplines of system biology, bioinformatics and network science, researches the action mechanism of the medicine from the perspective of network analysis, emphasizes the analysis of the molecular association between the medicine and a treatment object from the perspective of system hierarchy and biological network, and reveals the systemic pharmacology mechanism of the medicine, so as to guide the research and development of new medicines and clinical diagnosis and treatment, effectively predict the pharmacodynamic substances of the traditional Chinese medicine, and be widely applied to the research on the pharmacodynamic substances and the action mechanism of the traditional Chinese medicine and Chinese patent medicine. The traditional Chinese medicine fingerprint spectrum is used as a quality control technology, can comprehensively control the medicine quality, and has the characteristics of systematicness, integrity and stability. The multi-information integration technology can integrate signal data of the spectrums with multiple wavelengths, and the spectrum information with multiple wavelengths is integrated and reflected on one spectrum, so that the problem that the single wavelength cannot reflect the sample information comprehensively can be solved. The method is used for constructing the multiple information pharmacodynamic prediction characteristic map of the nasosinusitis dredging particles, scientifically reflecting pharmacodynamic information in the fingerprint map, has important significance for quality evaluation, quality control, clinical application and the like of the nasosinusitis dredging particles, can optimize the method for enterprises to carry out quality control on the nasosinusitis dredging particles, and reduces cost.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a method for constructing a multiple-information pharmacodynamic prediction characteristic map of nasosinusitis dredging particles by combining pharmacodynamic prediction aiming at the imperfect quality control method of the nasosinusitis dredging particles in the prior art. The method is characterized in that chemical components of nasosinusitis resuscitation granules are analyzed through a network pharmacological method to predict pharmacodynamic substances, chromatographic data of the nasosinusitis resuscitation granules with 3 wavelengths are obtained through HPLC separation detection of a test solution of the nasosinusitis resuscitation granules, multi-information integration of the data is carried out to obtain a multi-information integration map of the nasosinusitis resuscitation granules, and finally a characteristic peak is determined through comparison of a reference substance to construct a multi-information pharmacodynamic prediction characteristic map of the nasosinusitis resuscitation granules.
In order to achieve the above object, the present invention provides the following technical solutions.
The invention provides a nasosinusitis resuscitation inducing particle multi-information efficacy prediction characteristic map, which is characterized by comprising the following components in parts by weight: the characteristic map contains 14 effective components, wherein the peak (1) is chlorogenic acid, the peak (2) is caffeic acid, the peak (3) is ferulic acid, the peak (4) is forsythoside A, the peak (5) is naringin, the peak (6) is hesperidin, the peak (7) is salvianolic acid B, the peak (8) is baicalin, the peak (9) is quercetin, the peak (10) is luteolin, the peak (11) is wogonoside, the peak (12) is linarin, the peak (13) is baicalein, and the peak (14) is chrysin.
The invention also provides a construction method of the multiple-information efficacy prediction characteristic map of the nasosinusitis dredging granules, which is characterized by comprising the following steps of:
(1) predicting the drug effect substances of the nasosinusitis resuscitation granules:
a. screening active substances of nasosinusitis resuscitation granules: excavating main chemical components of the nasosinusitis dredging particles by using an UPLC-Q-TOF-MS/MS technology, and carrying out activity screening by combining a TCMSP database, wherein the screening conditions are that an OB value is more than or equal to 30 percent and a DL value is more than or equal to 0.18, and the components meeting the conditions are regarded as active substances; inquiring whether a literature report has pharmacological action or biological activity or not through a Chinese knowledge network database which does not meet the screening condition, and taking the ingredients reported by the literature report as active substances to synthesize results;
b. predicting potential target spots of nasosinusitis resuscitation granule active substances: b, inquiring the curative effect target point of the active substance in the step a through a drug bank database and a TTD database; b, performing target prediction on the active substances in the step a through an SEA database, and comprehensively predicting the result;
c. and (3) carrying out enrichment analysis on potential target points KEGG (Kegg pathway) of the nasosinusitis dredging particle active substances: performing KEGG pathway enrichment analysis on the potential target spot of the nasosinusitis resuscitation particle active substance in the step b through a DAVID database, and screening a pathway 20 before an enrichment result as a result by taking a 'PValue' value as a condition;
d. the nasosinusitis resuscitation granules 'active substance-target spot-passage' network is constructed: constructing an active substance-target point-pathway network of the nasosinusitis dredging particles by using Cytoscape 3.8.2 software;
e. predicting the drug effect substances of the nasosinusitis resuscitation granules: performing network topology analysis on the active ingredient-target spot-channel network in the step d, and taking the active ingredient contained in the network as the drug effect substance of the nasosinusitis dredging particles;
(2) preparing a test solution:
taking a proper amount of nasosinusitis resuscitation inducing particles, grinding, precisely weighing fine powder, placing in a conical flask with a plug, precisely adding methanol, sealing the plug, weighing mass, performing ultrasonic treatment, cooling, weighing mass, supplementing loss mass with methanol, shaking uniformly, filtering, and taking a subsequent filtrate;
(3) preparing a mixed reference substance solution:
taking appropriate amount of each reference, precisely weighing, adding methanol to obtain mixed reference solution, and storing at 4 deg.C;
(4) determination of chromatographic conditions:
the chromatographic column is Poroshell 120 SB-C18(4.6X 100 mm, 2.7 μm); gradient elution is carried out on a mobile phase A which is 0.1 percent formic acid aqueous solution and a mobile phase B which is methanol solution; the column temperature is 30 ℃; detecting wavelength 286 nm, 300 nm and 330 nm; the volume flow is 0.8 mL/min; the sample injection amount is 5 mu L;
(5) integrating multiple information:
performing multi-information integration on the chromatographic data with three wavelengths of 286 nm, 300 nm and 330 nm by utilizing Matlab software programming to obtain a chromatogram and 1 group of spectral data which are simultaneously integrated with information of 3 wavelengths;
(6) determining characteristic peaks and making a characteristic spectrum:
analyzing and detecting the sample solution and the mixed reference solution in the step (2) and the step (3) according to the chromatographic condition in the step (4), and integrating information according to the multi-information integration method in the step (5); processing with fingerprint software to obtain a sample solution fingerprint and a mixed reference solution fingerprint, comparing the sample solution fingerprint and the mixed reference solution fingerprint, selecting a common peak with good separation degree as a characteristic peak, and constructing the characteristic spectrum.
Further, the prediction of the drug effect substance of the nasosinusitis dredging granules in the step (1) comprises the following steps: ferulic acid, chrysin, hesperidin, tanshinone IIA, salvianolic acid B, scopoletin, glycyrrhetinic acid, liquiritin, glycyrrhizic acid, wogonoside, salidroside, quercetin, baicalin, baicalein, caffeic acid, forsythoside A, chlorogenic acid, linarin, luteolin, and naringin 20 in total.
Further, the test solution in the step (2) is prepared according to the following steps: taking a proper amount of nasosinusitis resuscitation inducing particles, grinding, precisely weighing fine powder, placing the fine powder in a conical flask with a plug, precisely adding 50-70% of methanol, sealing the plug, weighing the mass, carrying out ultrasonic treatment (power of 700W and frequency of 40 KHz) for 60 min, cooling, weighing the mass, complementing the loss mass with 50-70% of methanol, shaking up, filtering, taking a subsequent filtrate, and filtering with a 0.22 mu m microporous filter membrane to obtain the nasosinusitis resuscitation inducing particles.
Further, the mixed reference solution in the step (3) is prepared according to the following steps: taking appropriate amount of each reference substance, precisely weighing, adding methanol to obtain mixed reference substance solution containing drug effect prediction substance, and storing at 4 deg.C.
Further, the gradient elution conditions in step (4) are as follows:
further, in the step (6), a multiple-information efficacy prediction characteristic spectrum of the nasosinusitis resuscitation granules is obtained by using software of a traditional Chinese medicine chromatogram fingerprint similarity evaluation system 2012 recommended by the national pharmacopoeia committee.
Furthermore, the characteristic map contains 14 of 20 effective components as characteristic peaks of the characteristic map of the nasosinusitis dredging granule, wherein the peak (1) is chlorogenic acid, the peak (2) is caffeic acid, the peak (3) is ferulic acid, the peak (4) is forsythoside A, the peak (5) is naringin, the peak (6) is hesperidin, the peak (7) is salvianolic acid B, the peak (8) is baicalin, the peak (9) is quercetin, the peak (10) is luteolin, the peak (11) is wogonoside, the peak (12) is linarin, the peak (13) is baicalein, and the peak (14) is chrysin.
Compared with the prior art, the invention has the beneficial effects.
(1) The invention analyzes and predicts the pharmacodynamic substances of the nasosinusitis resuscitation granules for the first time, and combines the pharmacodynamic information with the fingerprint map to construct the characteristic map reflecting the pharmacodynamic information.
(2) The characteristic spectrum of the nasosinusitis resuscitation inducing granules is created for the first time, the chromatographic conditions, the detection wavelength, the temperature, the flow, the gradient elution program and the like are optimized, the obtained characteristic spectrum has good characteristic peak pattern and high separation degree, and the detection method is simple, convenient and efficient.
(3) The characteristic spectrum and the contrast characteristic spectrum integrated for the nasosinusitis resuscitation granules are created for the first time, the information of the 3-wavelength spectrum of the nasosinusitis resuscitation granules is integrated through the programming of computer software to obtain the spectrum reflecting more information, and then the characteristic spectrum and the contrast characteristic spectrum integrated for the nasosinusitis resuscitation granules are obtained through the analysis of software of '2012 edition of traditional Chinese medicine chromatogram fingerprint spectrum similarity evaluation system', so that a more accurate and comprehensive means for quality evaluation and control can be provided for the nasosinusitis resuscitation granules.
(4) The invention identifies 14 chemical components of chlorogenic acid, caffeic acid, ferulic acid, forsythiaside A, hesperidin, naringin, salvianolic acid B, baicalin, quercetin, luteolin, wogonoside, linarin, baicalin, baicalein and chrysin by comparison with a reference substance, and further illustrates the chemical substance basis of the nasosinusitis resuscitation granules.
(5) The invention provides a method which is simple to operate and rich in information and can be used for evaluating the product quality for enterprises by constructing the multiple-information efficacy prediction characteristic map of the nasosinusitis dredging particles, thereby effectively reducing the production cost and improving the product quality and safety.
Drawings
Fig. 1 is an enrichment map of the nasosinusitis resuscitation particle potential target KEGG pathway.
Figure 2 is a "active substance-target-pathway" of nasosinusitis dredging particles.
FIG. 3 is a 286 nm chromatogram of nasosinusitis dredging granule.
FIG. 4 is a chromatogram of nasosinusitis dredging granule at 300 nm.
FIG. 5 is a 330 nm chromatogram of nasosinusitis dredging granule.
Fig. 6 is a multi-information integration chromatogram of nasosinusitis dredging particles.
FIG. 7 is a multi-information integration chromatogram of 14 characteristic peak mixed controls.
Fig. 8 is an overlay of the multiple information pharmacodynamic prediction characteristic maps of nasosinusitis dredging granules.
FIG. 9 is a control characteristic spectrum of nasosinusitis dredging granule.
Detailed Description
The present invention is further illustrated by the following specific examples, which should be understood that the following examples are only for describing the technical solutions of the present invention, and are not intended to limit the scope of the present invention. It will be apparent to those skilled in the art that modifications and improvements can be made without departing from the spirit of the invention.
Example 1 construction of a multiple-information pharmacodynamic prediction profile of nasosinusitis dredging granules.
1 instrument and reagent.
1.1 Instrument:
agilent 1290 Infinity hplc (Agilent technologies, inc., usa, including high pressure binary gradient pump, autosampler, column oven, DAD detector, Agilent 1290 chromatography workstation).
1.2 reagent:
2 methods and results.
2.1 prediction of drug effect substance of nasosinusitis resuscitation granules.
2.1.1 screening active substances of the nasosinusitis dredging granules:
the method comprises the steps of excavating 102 main components of nasosinusitis dredging particles through ultra performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (UPLC-Q-TOF-MS/MS), and performing activity screening by combining a TCMSP database, wherein the screening conditions are that an OB value is more than or equal to 30% and a DL value is more than or equal to 0.18, and the components meeting the conditions are regarded as active substances. For the components which do not meet the screening conditions, the database of the Chinese knowledge network is inquired whether the literature reports that the components have pharmacological effects or biological activities, and the components reported by the literature are regarded as active substances. And combining the results, screening out 37 active substances of the nasosinusitis dredging granules.
2.1.2 prediction of potential target spots of nasosinusitis dredging particle active substances:
inquiring the therapeutic effect target of the nasosinusitis resuscitation particle active substance through a drug Bank database and a TTD database; and (4) predicting potential targets of the nasosinusitis resuscitation particle active substances through an SEA database. The combined results obtained 233 potential targets of 20 active ingredients of the nasosinusitis dredging granule, 17 of which were not queried for reliable targets.
2.1.3 enrichment analysis of potential target KEGG pathway of nasosinusitis resuscitation granule active substances:
the method comprises the steps of carrying out KEGG pathway enrichment analysis on 233 targets through a DAVID database, and screening the pathway 20 before the enrichment result as a result by taking a 'PValue' value as a condition, wherein the result is shown in figure 1.
2.1.4 construction of an 'active substance-target spot-passage' network of the nasosinusitis dredging particles:
the network of the nasosinusitis resuscitation granules 'active substance-target point-passage' is constructed by using Cytoscape 3.8.2 software, and the result is shown in figure 2. The topology analysis of the Network is carried out by using an "Analyze Network" plug-in the Cytoscape 3.8.2 software, and the analysis result shows that the Network has 139 nodes and 603 edges, wherein the Network comprises 20 active ingredients, 99 potential targets and 20 paths. Since 20 active ingredients are contained in the network and all of them may be the active ingredients of the nasosinusitis dredging granule, the results of predicting the 20 active ingredients as the active ingredients of the nasosinusitis dredging granule are shown in table 1.
TABLE 1 prediction of the effective components of nasosinusitis dredging granule
2.2 chromatographic conditions:
the chromatographic column is Poroshell 120 SB-C18(4.6X 100 mm, 2.7 μm); the mobile phase was 0.1% formic acid in water (a) -methanol (B), gradient elution, see table 2; the column temperature is 30 ℃; detecting wavelength 286 nm, 300 nm and 330 nm; the volume flow is 0.8 mL/min; and the sample size is 5 muL.
TABLE 2 gradient elution conditions
2.3 Multi-message integration method:
ultraviolet full-wavelength (190-400 nm) scanning is carried out on the nasosinusitis resuscitation particle chromatogram by a DAD detector, and more information under 286 nm, 300 nm and 330 nm and mutual complementation are determined according to the scanning result, so that the 3 wavelengths are selected for integration. CSV format data files of 286 nm, 300 nm and 330 nm are respectively exported from an Agilent 1290 chromatographic workstation, Matlab software programming is used for integrating multiple information of the CSV format data, and a chromatogram and 1 group of spectrum data which are simultaneously integrated with 3 wavelength information are obtained, and the chromatogram and the group of spectrum data are shown in attached figures 3-6.
2.4 preparation of test solution:
taking a proper amount of nasosinusitis resuscitation inducing particles, grinding, precisely weighing 5.0 g of fine powder, placing the fine powder in a conical flask with a plug, precisely adding 25 mL of 50% methanol, sealing the conical flask, weighing the mass, carrying out ultrasonic treatment (power of 700W and frequency of 40 KHz) for 60 min, cooling, weighing the mass, complementing the loss mass with 50% methanol, shaking up, filtering, taking the subsequent filtrate, and filtering with a 0.22 mu m microporous membrane to obtain the nasosinusitis resuscitation inducing particles.
2.5 preparation of mixed control solutions:
taking a proper amount of each control product, precisely weighing, adding methanol to prepare a mixed control solution containing 58 mug/mL of chlorogenic acid, 16 mug/mL of caffeic acid, 83 mug/mL of ferulic acid, 228 mug/mL of forsythiaside A, 40 mug/mL of naringin, 47 mug/mL of hesperidin, 90 mug/mL of salvianolic acid B, 691 mug/mL of baicalin, 53 mug/mL of quercetin, 76 mug/mL of luteolin, 180 mug/mL of wogonin, 266 mug/mL of linarin, 12 mug/mL of baicalin and 12 mug/mL of chrysin, and storing at 4 ℃ for later use.
2.6 methodological investigation.
2.6.1 precision investigation:
the same sample solution (batch number: 0022105040) was sampled 6 times under the chromatographic condition of "2.2" for measurement, and the fingerprint was recorded and integrated for several times. The relative retention time and the relative peak area of each characteristic peak were calculated using peak No. 7 (salvianolic acid B) as a reference peak. The results show that the RSD of the relative retention time of 14 characteristic peaks is not more than 0.33 percent, the RSD of the relative peak area is not more than 3.82 percent, and the results show that the precision of the instrument is good.
2.6.2 repeatability tests:
6 parts of nasosinusitis dredging particles (batch number: 0022105040) are respectively weighed, a test solution is prepared according to the method under the item '2.4', the sample introduction and the measurement are carried out according to the chromatographic condition under the item '2.2', and the fingerprint is recorded and the multiple information integration is carried out. The relative retention time and the relative peak area of each characteristic peak were calculated using peak No. 7 (salvianolic acid B) as a reference peak. The results show that the RSD of the relative retention time of 14 characteristic peaks is not more than 0.38%, and the RSD of the relative peak area is not more than 3.74%, which indicates that the method has good repeatability.
2.6.3 stability examination:
taking the same sample solution (batch number: 0022105040), measuring under the chromatographic condition of '2.2' at 0, 2, 4, 8, 12 and 24 h respectively, recording fingerprint and integrating multiple information. The relative retention time and the relative peak area of each characteristic peak were calculated using peak No. 7 (salvianolic acid B) as a reference peak. The results show that the RSD of the relative retention time of 14 characteristic peak areas is not more than 0.32 percent, and the RSD of the relative peak area is not more than 3.91 percent, which indicates that the test solution is stable within 24 hours.
2.7 determination of characteristic peaks:
the sample solution and the mixed control solution in "2.4" and "2.5" were analyzed and detected according to the chromatographic conditions in "2.2", and integrated according to the multi-step integration method of "2.3". And analyzing and processing by software of a traditional Chinese medicine chromatography fingerprint similarity evaluation system 2012 to obtain a sample solution fingerprint and a mixed reference solution fingerprint, comparing the sample solution fingerprint and the mixed reference solution fingerprint, and taking a common peak with better separation degree in the two fingerprints as a characteristic peak. Totally determining 14 characteristic peaks, wherein the peak 1 is chlorogenic acid, the peak 2 is caffeic acid, the peak 3 is ferulic acid, the peak 4 is forsythoside A, the peak 5 is naringin, the peak 6 is hesperidin, the peak 7 is salvianolic acid B, the peak 8 is baicalin, the peak 9 is quercetin, the peak 10 is luteolin, the peak 11 is wogonoside, the peak 12 is linarin, the peak 13 is baicalin, and the peak 14 is chrysin, and the retention time is as follows: 13.256min, 13.895min, 21.61min, 29.207min, 30.141min, 32.047min, 38.911min, 40.897min, 43.583min, 51.104min, 52.696min, 53.579min, 60.263min and 65.741min, as shown in figure 7, 14 ingredients of 20 effective ingredients are contained, accounting for 70%, and the result of the overall effective prediction can be shown in a characteristic map, and other 6 effective ingredients are not used as characteristic peaks in the map, because the absorption in the selected wavelength is weaker or the content is lower.
2.8 construction of a multiple-information efficacy prediction characteristic map and a contrast characteristic map of the nasosinusitis dredging granules:
taking 10 batches of nasosinusitis resuscitation granules, preparing a test solution according to a method under item 2.4, determining according to chromatographic conditions under item 2.2, recording fingerprints, integrating multiple information according to an integration method under item 2.3, introducing the data of the multiple information integration maps of the 10 batches of nasosinusitis resuscitation granules into software of a traditional Chinese medicine chromatographic fingerprint similarity evaluation system 2012 edition, taking S1 as a reference peak, and performing multi-point correction and peak matching by adopting a median method to generate the reference map. Obtaining 10 batches of characteristic spectrum superposition graphs and comparison characteristic spectrums of the nasosinusitis resuscitation granules, which are respectively shown in the attached figures 8 and 9.
And (3) compared with the comparison fingerprint, calculating the similarity, wherein the similarity of the S1-S10 samples is 1, 0.999, 1, 0.999, 1 and 1 respectively. The similarity of the fingerprint spectrums of 10 batches of the nasosinusitis particles is more than 0.99, which shows that the quality of the 10 batches of the nasosinusitis particles is uniform and stable, the difference of chemical substance groups is small, and the formed contrast spectrums can be used as standard reference substances for measuring the quality of the nasosinusitis particles.
Claims (9)
1. A characteristic map for predicting the multi-information efficacy of nasosinusitis resuscitation granules is characterized in that: the characteristic map contains 14 effective components, wherein the peak (1) is chlorogenic acid, the peak (2) is caffeic acid, the peak (3) is ferulic acid, the peak (4) is forsythoside A, the peak (5) is naringin, the peak (6) is hesperidin, the peak (7) is salvianolic acid B, the peak (8) is baicalin, the peak (9) is quercetin, the peak (10) is luteolin, the peak (11) is wogonoside, the peak (12) is linarin, the peak (13) is baicalein, and the peak (14) is chrysin.
2. The method for constructing the characteristic map for predicting the pharmacodynamic effects of the nasosinusitis dredging granules according to claim 1, which comprises the following steps:
(1) predicting the drug effect substances of the nasosinusitis resuscitation granules:
a. screening active substances of nasosinusitis resuscitation granules: excavating main chemical components of the nasosinusitis dredging particles by using an UPLC-Q-TOF-MS/MS technology, and carrying out activity screening by combining a TCMSP database, wherein the screening conditions are that an OB value is more than or equal to 30 percent and a DL value is more than or equal to 0.18, and the components meeting the conditions are regarded as active substances; inquiring whether a literature report has pharmacological action or biological activity or not through a Chinese knowledge network database which does not meet the screening condition, and taking the ingredients reported by the literature report as active substances to synthesize results;
b. predicting potential target spots of nasosinusitis resuscitation granule active substances: b, inquiring the curative effect target point of the active substance in the step a through a drug bank database and a TTD database; b, performing target prediction on the active substances in the step a through an SEA database, and comprehensively predicting the result;
c. and (3) carrying out enrichment analysis on potential target points KEGG (Kegg pathway) of the nasosinusitis dredging particle active substances: performing KEGG pathway enrichment analysis on the potential target spot of the nasosinusitis resuscitation particle active substance in the step b through a DAVID database, and screening a pathway 20 before an enrichment result as an analysis result by taking a 'PValue' value as a condition;
d. the nasosinusitis resuscitation granules 'active substance-target spot-passage' network is constructed: constructing an active substance-target point-pathway network of the nasosinusitis dredging particles by using Cytoscape 3.8.2 software;
e. predicting the drug effect substances of the nasosinusitis resuscitation granules: performing network topology analysis on the active ingredient-target spot-channel network in the step d, and taking the active ingredient contained in the network as the drug effect substance of the nasosinusitis dredging particles;
(2) preparing a test solution:
taking a proper amount of nasosinusitis resuscitation inducing particles, grinding, precisely weighing fine powder, placing in a conical flask with a plug, precisely adding methanol, sealing the plug, weighing mass, performing ultrasonic treatment, cooling, weighing mass, supplementing loss mass with methanol, shaking uniformly, filtering, and taking a subsequent filtrate;
(3) preparing a mixed reference substance solution:
taking appropriate amount of each reference, precisely weighing, adding methanol to obtain mixed reference solution, and storing at 4 deg.C;
(4) determination of chromatographic conditions:
the chromatographic column is Poroshell 120 SB-C18(4.6X 100 mm, 2.7 μm); gradient elution is carried out on a mobile phase A which is 0.1 percent formic acid aqueous solution and a mobile phase B which is methanol solution; the column temperature is 30 ℃; detecting wavelength 286 nm, 300 nm and 330 nm; the volume flow is 0.8 mL/min; the sample injection amount is 5 mu L;
(5) integrating multiple information:
performing multi-information integration on the chromatographic data with three wavelengths of 286 nm, 300 nm and 330 nm by utilizing Matlab software programming to obtain a chromatogram and 1 group of spectral data which are simultaneously integrated with information of 3 wavelengths;
(6) determining characteristic peaks and making a characteristic spectrum:
analyzing and detecting the sample solution and the mixed reference solution in the step (2) and the step (3) according to the chromatographic condition in the step (4), and integrating information according to the multi-information integration method in the step (5); processing with fingerprint software to obtain a sample solution fingerprint and a mixed reference solution fingerprint, comparing the sample solution fingerprint and the mixed reference solution fingerprint, selecting a common peak with good separation degree as a characteristic peak, and constructing the characteristic spectrum.
3. The method for constructing the nasosinusitis resuscitating particle polyprency pharmacodynamic feature map according to claim 2, wherein the nasosinusitis resuscitating particle pharmacodynamic substance prediction in the step (1) comprises the following steps: ferulic acid, chrysin, hesperidin, tanshinone IIA, salvianolic acid B, scopoletin, glycyrrhetinic acid, liquiritin, glycyrrhizic acid, wogonoside, salidroside, quercetin, baicalin, baicalein, caffeic acid, forsythoside A, chlorogenic acid, linarin, luteolin, and naringin 20 in total.
4. The method for constructing the characteristic map for predicting the multi-information medicinal effect of the nasosinusitis dredging granules as claimed in claim 2, wherein the test solution in the step (2) is prepared by the following steps: taking a proper amount of nasosinusitis resuscitation inducing particles, grinding, precisely weighing fine powder, placing the fine powder in a conical flask with a plug, precisely adding 50-70% of methanol, sealing the plug, weighing the mass, carrying out ultrasonic treatment (power of 700W and frequency of 40 KHz) for 60 min, cooling, weighing the mass, complementing the loss mass with 50-70% of methanol, shaking up, filtering, taking a subsequent filtrate, and filtering with a 0.22 mu m microporous filter membrane to obtain the nasosinusitis resuscitation inducing particles.
5. The method for constructing the characteristic map for predicting the multi-information medicinal effect of the nasosinusitis dredging granules as claimed in claim 2, wherein the mixed reference solution in the step (3) is prepared by the following steps: taking a proper amount of each reference substance, precisely weighing, adding methanol to prepare a mixed reference substance solution containing the Chinese medicinal effect prediction substance of claim 2, and storing at 4 ℃ for later use.
6. The method for constructing the characteristic map for predicting the multi-information pharmacodynamic action of the nasosinusitis dredging particles as claimed in claim 2, wherein the gradient elution conditions in the step (4) are as follows:
7. the method for constructing the multiple information pharmacodynamic prediction characteristic spectrum of the nasosinusitis resuscitation-inducing particles according to claim 2 is characterized in that the multiple information pharmacodynamic prediction characteristic spectrum of the nasosinusitis resuscitation-inducing particles is obtained by analyzing the characteristic spectrum in step (6) by using software of a traditional Chinese medicine chromatogram fingerprint similarity evaluation system 2012 recommended by the national pharmacopoeia committee.
8. The characteristic map for predicting the medicine effect of the nasosinusitis dredging granules as claimed in claim 2, is characterized in that: the characteristic map comprises 14 of 20 effective components as characteristic peaks of the characteristic map of the nasosinusitis resuscitation inducing granules, wherein the peak (1) is chlorogenic acid, the peak (2) is caffeic acid, the peak (3) is ferulic acid, the peak (4) is forsythoside A, the peak (5) is naringin, the peak (6) is hesperidin, the peak (7) is salvianolic acid B, the peak (8) is baicalin, the peak (9) is quercetin, the peak (10) is luteolin, the peak (11) is wogonoside, the peak (12) is linarin, the peak (13) is baicalein and the peak (14) is chrysin.
9. A polygenetic pharmacodynamic prediction profile of nasosinusitis dredging particles obtained by the method according to any one of claims 2-8.
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