Meconopsis integrifolia liver protection pharmacodynamic component group screening and verification method and system

Abstract

The invention discloses a meconopsis integrifolia liver-protecting pharmacodynamic component group screening and verification method and system, and belongs to the field of pharmaceutical analys.The method mainly comprises the steps that meconopsis integrifolia total flavonoid components and total alkaloid components are extracted; measuring the content of total flavonoid components and total alkaloid components of meconopsis integrifolia; cCl4 is adopted to induce HepG2 human hepatoma carcinoma cell injury for modeling, the expression level of a corresponding liver injury index is detected, and in-vitro screening is carried out on a pharmacodynamic component group; cCl4 is adopted to induce acute liver injury of KM mice for modeling, corresponding liver injury indexes and the expression level of inflammatory factors are detected, and screened pharmacodynamic component groups are subjected to in-vivo verification. According to the invention, in vitro screening is carried out on the medicinal component groups with the liver protection effect in meconopsis integrifolia, then the liver protection effects of different component groups are verified at the in vivo level, the screening accuracy of the medicinal component groups is ensured, and meanwhile, a modern infrared spectroscopic analysis technology is combined to characterize and predict the disease characteristics to a certain extent, so that the screening accuracy of the medicinal component groups is improved. And rapid identification and disease prediction are facilitated.

Description

Technical Field

[0001] The present invention relates to the field of drug analysis, and in particular to a method and system for screening and verifying a group of liver-protecting medicinal components of Meconopsis integrifolia. Background Art

[0002] The multi-component and multi-target characteristics of traditional Chinese medicine determine that it is difficult to express its quality well by using only a single component as a detection indicator. Therefore, the research direction of relevant researchers has gradually turned to the pharmacodynamic component groups of traditional Chinese medicine. The research purpose of the pharmacodynamic component groups of traditional Chinese medicine is mainly to study the basic principles of pharmacodynamic substances and serve clinical efficacy, and its screening and identification are the main research methods. In recent years, with the widespread application of fingerprint technology in the field of drug analysis, screening pharmacodynamic component groups through the analysis of "spectrum-effect relationship" has gradually become one of the most important screening methods. It clarifies the relationship between fingerprints and efficacy through multiple statistical analyses to identify the pharmacodynamic component groups of traditional Chinese medicine, which provides important reference value for the quality evaluation of medicinal materials. However, the "spectrum-effect" analysis lacks animal models that can better reflect the characteristics of traditional Chinese medicine syndromes and recognized, exclusive, and highly sensitive evaluation indicators. Summary of the invention

[0003] The purpose of the present invention is to overcome the technical problems existing in the prior art. Taking Meconopsis integrifolia as an example, from the perspective of actual drug efficacy, through in vitro cell level screening, in vivo animal model verification, and combined with modern infrared spectroscopy analysis technology to assist in characterizing and predicting the disease characteristics of the model, a method and system for screening and verifying the liver-protecting effective component groups of Meconopsis integrifolia is provided.

[0004] The objective of the present invention is achieved through the following technical solutions:

[0005] In a first aspect, a method for screening and verifying a group of hepatoprotective medicinal components of Meconopsis integrifolia is provided, comprising the following steps:

[0006] S1. Extracting total flavonoids and total alkaloids from Meconopsis integron;

[0007] S2. Determine the contents of total flavonoids and total alkaloids in Meconopsis integron;

[0008] S3, CCl4 was used to induce HepG2 human liver cancer cell damage to establish a model, the expression levels of corresponding liver damage indicators were detected, and the efficacy component groups were screened in vitro;

[0009] S4. CCl4 was used to induce acute liver injury in KM mice, and the expression levels of corresponding liver injury indicators and inflammatory factors were detected, and the screened pharmacological component groups were verified in vivo.

[0010] In some embodiments, the method for extracting the total flavonoid components in step S1 comprises:

[0011] The total flavonoids of Meconopsis integrifolia were extracted by alcohol extraction, and the polarity distribution of compounds and organic chemical reagents was further used to purify the components. The solvent was evaporated by rotary evaporator to obtain the extract of the corresponding total flavonoid components. Specifically, 200g of Meconopsis integrifolia crude powder was weighed, 75% ethanol was added to a round-bottom flask, and condensation reflux extraction was performed twice, each time for 1.5 hours; the filtrate was combined and collected, and the ethanol was recovered until there was no alcohol taste, and the residue was diluted with an appropriate amount of distilled water, and then extracted with petroleum ether several times, and the petroleum ether layer was discarded; the water layer was recovered and extracted with ethyl acetate several times, and the ethyl acetate layers were combined and evaporated to dryness, and the residue was the total flavonoid extract of Meconopsis integrifolia.

[0012] In some embodiments, the method for extracting the total alkaloid components in step S1 comprises:

[0013] The total flavonoids of Meconopsis integrifolia were extracted by alcohol extraction, and the polarity distribution of compounds and organic chemical reagents was further used to purify the components. The solvent was evaporated by rotary evaporator to obtain the extract of the corresponding total flavonoid components. Specifically, 200 g of Meconopsis integrifolia crude powder was weighed, 95% ethanol was condensed and refluxed for 2 times, each time for 1.5 hours, filtered, and the filtrate was collected; the ethanol was recovered under reduced pressure until there was no alcohol taste, the residue was degreased by shaking with petroleum ether, 1% hydrochloric acid was added to adjust the pH value to 2-3, and filtered; the filtrate was taken and concentrated ammonia was added to adjust the pH value to 9-10, and then extracted several times with chloroform, the chloroform layers were combined, the solvent was recovered, and the residue was the total alkaloid extract of Meconopsis integrifolia.

[0014] In some embodiments, in step S2, ultraviolet spectrophotometry is used to determine the contents of total flavonoids and total alkaloids in the total flavonoid component and the total alkaloid component of Meconopsis integrifolia, respectively.

[0015] In some embodiments, the step S3 uses CCl4 to induce HepG2 human liver cancer cell damage for modeling, including:

[0016] Mix CCl4 and anhydrous ethanol in a ratio of 1:1, add an appropriate amount of DMEM complete culture medium, and mix using a vortex mixer to prepare CCl4 injury solution; then take the CCl4 injury solution and dilute it into injury solutions with different concentration gradients of 0.01%, 0.05%, 0.1%, 0.5% and 1%.

[0017] In some embodiments, the step S3 detects the expression level of the corresponding liver injury indicator and performs in vitro screening on the pharmacodynamic component group, including:

[0018] Drug concentration toxicity experiment: HepG2 human liver cancer cells in the logarithmic growth phase were digested with trypsin and diluted to 5×10 cells with an appropriate amount of DMEM complete medium. 4The cells were inoculated at a density of 100 μg / well in a 96-well plate, with 6 replicate wells in each group. After 24 hours, the culture medium was injected with different target dosing concentrations of 5 μg / mL, 10 μg / mL, 20 μg / mL, 50 μg / mL, 100 μg / mL, and 200 μg / mL. After 24 hours, the culture medium was discarded and the cell viability was measured using the MTT method.

[0019] Comparison of in vitro liver protection activity of drugs: HepG2 human liver cancer cells in the logarithmic growth phase were digested with trypsin and diluted to 5×10 cells with an appropriate amount of DMEM complete medium. 4 The cells were inoculated at a density of cells / ml in a 96-well plate, with 6 replicate wells in each group. After 24 hours, a blank control group, a 0.5% CCl4+5ug / ml total flavonoids administration group, a 0.5% CCl4+5ug / ml total alkaloids administration group, a 0.5% CCl4+5ug / ml positive control group, and a 0.5% CCl4 model group were set up respectively. After 12 hours of administration of the corresponding drugs, the cell viability was determined using the MTT method.

[0020] In some embodiments, the step S4 uses CCl4 to induce acute liver injury in KM mice for modeling, comprising:

[0021] Animal grouping: 70 SPF male KM mice were randomly divided into 7 groups, 10 mice in each group, namely blank control group A, model group B, flavonoid low-dose group C, flavonoid high-dose group D, alkaloid low-dose group E, alkaloid high-dose group F, and bifendate positive drug control group G;

[0022] Animal modeling: All groups were given ordinary feed, the blank control group A and the model group B were not treated, and were given normal saline; the low-dose flavonoids group C, the high-dose flavonoids group D, the low-dose alkaloids group E, the high-dose alkaloids group F, and the bifendate positive drug control group G were given aqueous solutions containing the corresponding drugs; each group of KM mice was intragastrically administered once a day, with a dosage of 0.1mL / 10g, for 7 consecutive days. After the last administration, the mice were fasted but not watered. After 12 hours, except for the blank control group A, which was intraperitoneally injected with an equal volume of normal saline, the KM mice in the other groups were intraperitoneally injected with 0.15% CCl4 olive oil solution; 24 hours after modeling, the eyeballs were removed and blood was collected. The blood was centrifuged for 10 minutes to separate the serum, and the upper serum was taken and frozen at -20°C for later use; after the KM mice were killed, the liver was quickly opened and removed, and then the left lobe of the liver was cut and fixed with cell tissue fixative, routinely paraffin-embedded and sectioned, and stained with hematoxylin-eosin (HE). The pathological changes of liver tissue were observed under an optical microscope, and the remaining liver tissue was quickly frozen with liquid nitrogen and then frozen at -80°C for later use.

[0023] In some embodiments, the method further comprises:

[0024] The infrared spectral characteristics of the liver of KM mice after administration of different pharmacological component groups to CCl4-induced acute liver injury were analyzed, including:

[0025] (1) ATR infrared spectra of the livers of KM mice in different groups were collected using a Fourier transform infrared spectrometer;

[0026] (2) collecting the ATR spectrum of the liver homogenate of KM mice, and performing one-dimensional difference spectrum and one-dimensional spectrum correlation analysis based on the ATR spectrum;

[0027] Preferably, the analysis of infrared spectral characteristics of the liver of KM mice after administration of different pharmacological component groups to CCl4-induced acute liver injury also includes:

[0028] The disease prediction model was established by combining infrared spectra and biochemical indicators of the liver of KM mice after administration of different pharmacological component groups to CCl4-induced acute liver injury, including:

[0029] (1) Outlier removal: The Mahalanobis distance (MD) and principal component analysis (PCA) methods were used to remove outliers from the ATR spectrum;

[0030] (2) Sample set division: A portion of the samples corresponding to the biochemical indicators was taken as the external validation set at a ratio of 5:1, and the remaining samples were randomly divided into the calibration set and the validation set at a ratio of 3:1;

[0031] (3) Spectral preprocessing: The spectrum is imported into TQ Analyst modeling software for spectrum preprocessing;

[0032] (4) Modeling band selection: The modeling bands were selected using the correlation coefficient method (CC), full band and principal component analysis (PCA) methods recommended by TQ Analyst software;

[0033] (5) Quantitative model establishment: partial least squares regression model (PLS) and principal component regression model (PCR) were established by combining mouse biochemical indicators with the corresponding ATR spectra;

[0034] (6) Model prediction: Take unknown samples and bring them into the established model to calculate the prediction rate.

[0035] In the second aspect, a screening and verification system for the hepatoprotective medicinal component group of Meconopsis integrifolia is provided, comprising:

[0036] Component extraction module, used to extract total flavonoid components and total alkaloid components from Meconopsis integron;

[0037] Component content determination module, used to determine the content of total flavonoid components and total alkaloid components of Meconopsis integron;

[0038] The drug efficacy component group screening module is used to use CCl4 to induce HepG2 human liver cancer cell damage to model, detect the expression level of corresponding liver damage indicators, and screen the drug efficacy component group;

[0039] The pharmacodynamic component group verification module is used to model acute liver injury in KM mice induced by CCl4, detect the expression levels of corresponding liver injury indicators and inflammatory factors, and verify the screened pharmacodynamic component group.

[0040] It should be further explained that the technical features corresponding to the above options can be combined or replaced with each other to form a new technical solution if there is no conflict.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] (1) The screening and verification method of the liver-protecting medicinal components of Meconopsis integrifolia of the present invention is a systematic research process. First, the target sample is extracted and concentrated by traditional methods, and the contents of the corresponding total flavonoids and total alkaloids are determined. Subsequently, a hepatocyte injury model is constructed to simulate liver injury, and the liver-protecting effects of different components are evaluated. Finally, a suitable animal model is selected to conduct a verification experiment of the liver-protecting components by inducing liver injury through chemical drugs. Specifically, the present invention studies the in vitro screening of the medicinal component groups with liver-protecting effects in Meconopsis integrifolia. The results show that the alkaloid components in Meconopsis integrifolia have the best efficacy and are the target medicinal component groups. Subsequently, the liver-protecting efficacy of its different component groups is verified at the in vivo level. The results show that the total alkaloid components in Meconopsis integrifolia have the best therapeutic effect, which is consistent with the in vitro screening results, ensuring the accuracy of the group screening.

[0043] (2) The present invention uses modern infrared spectroscopy analysis technology to assist in analyzing and predicting the disease characteristics of animal models and establish an infrared evaluation method. Specifically, the ATR infrared spectra of the liver of KM mice in the blank control group, model group and each drug-treated group were analyzed by combining modern infrared spectroscopy analysis technology. The infrared fingerprints of the blank control group, model group and blank group were slightly different. To further analyze the difference, the ATR spectra of the model group and the blank group were subjected to one-dimensional difference spectrum analysis, which showed that the ATR spectra of the liver of the two groups of KM mice had a peak at 3344 cm -1 With 1637cm -1The study used ATR spectra to analyze the secondary structure characteristics of KM mouse proteins in each group. The results showed that there were certain differences in the amide I band region that characterizes the secondary structure of proteins between each drug-treated group and its model group and the blank group. Specifically, compared with the blank group, the content of β-folding and random coiling in each drug-treated group and the model group increased, the content of β-turn decreased, and the relative content of α-helix was 0. That is, infrared spectroscopy can help relevant researchers quickly identify the disease characteristics of animal models in real life.

[0044] (3) The ATR infrared spectra of each group of KM mice were collected, and a quantitative model was established in combination with their biochemical index data, hoping to characterize and predict disease characteristics to a certain extent through modern analytical methods. In the modeling process, the spectral preprocessing and modeling band selection were optimized to obtain the optimal models of TNF-α, IL-6, IL-1β, SOD, GSH and MDA. The ATR spectra of the external validation set samples were imported into the model to obtain the predicted values. The results showed that the external validation prediction rates of the six biochemical index models were all above 50%, among which the IL-1β prediction rate reached 83.23%, indicating that the established model can understand and predict new samples to a certain extent. That is, infrared spectroscopy technology can help relevant researchers quickly identify, screen and predict diseases, especially when the disease onset mechanism is complex and there are large differences between different individuals. Modern infrared spectroscopy analysis technology can assist in providing a new disease prediction method. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a flow chart of a method for screening and verifying a group of liver-protecting medicinal components of Meconopsis integrifolia shown in an embodiment of the present invention;

[0046] Figure 2 The standard curve for determining the total flavonoid content shown in the embodiment of the present invention;

[0047] Figure 3 The standard curve for determining the total alkaloid content shown in the embodiment of the present invention;

[0048] Figure 4 The effects of different concentrations of anhydrous ethanol on the viability of HepG2 human liver cancer cells shown in the examples of the present invention (Note: n = 5; compared with the blank control Con group, * indicates P < 0.05, ** indicates P < 0.01);

[0049] Figure 5 The effects of different concentration gradients of CCl4 damaging solutions on the viability of HepG2 human liver cancer cells shown in the examples of the present invention (Note: n = 5; compared with the blank control Con group, * indicates P < 0.05, ** indicates P < 0.01);

[0050] Figure 6Schematic diagram of drug concentration toxicity experiment shown in the embodiment of the present invention, wherein (A) the effect of different concentrations of total flavonoid components on the viability of HepG2 human liver cancer cells (B) the effect of different concentrations of total alkaloid components on the viability of HepG2 human liver cancer cells (Note: n = 5; compared with the blank control Con group, *** indicates P < 0.001);

[0051] Figure 7 The effects of different drugs on the cell viability of HepG2 human liver cancer cells damaged by CCl4 shown in the examples of the present invention (Note: n = 5; Con is a blank control group, A is a drug group with total flavonoids added, B is a drug group with total alkaloids added, C is a positive control bifendate group, and Model is a 0.5% CCl4 model group; compared with the blank control Con group, *** indicates P < 0.001);

[0052] Figure 8 Comparison of the biochemical index activities of different pharmacodynamic components shown in the examples of the present invention (Note: n=3; Con is a blank control group, A is a positive control bifendate group, B is a drug group after adding total flavonoids, C is a drug group after adding total alkaloids, and Model is a 0.5% CCl4 model group; compared with the blank control Con group, * indicates P<0.05, *** indicates p<0.001 compared with the Con group; # indicates P<0.05 compared with the model group, and ### indicates p<0.001 compared with the model group);

[0053] Fig. 9 The liver morphology of different groups shown in the examples of the present invention (Note: A is a blank group, B is a model group, C is a low-dose flavonoid group, D is a high-dose flavonoid group, E is a low-dose alkaloid group, and G is a positive control bifendate group);

[0054] Fig.10 HE staining pathological images of liver tissues of different groups shown in the embodiments of the present invention (×200) (Note: A is a blank group, B is a model group, C is a low-dose flavonoid group, D is a high-dose flavonoid group, E is a low-dose alkaloid group, and G is a positive control bifendate group);

[0055] Fig.11 This is a schematic diagram of comparing inflammation indicators according to an embodiment of the present invention;

[0056] Fig.12 This is a schematic diagram showing a comparison of liver injury indicators according to an embodiment of the present invention;

[0057] Fig.13 Schematic diagram of oxidative stress index comparison shown in the embodiment of the present invention; (Note: Figure 11-13n=6; A is blank control group, B is model group, C is low-dose flavonoid group, D is high-dose flavonoid group, E is low-dose alkaloid group, F is high-dose alkaloid group, G is positive control bifendate group; compared with blank control group A, * indicates p<0.05, ** indicates P<0.01, *** indicates P<0.001; compared with model group B, # indicates p<0.05, ## indicates P<0.01, ### indicates p<0.001);

[0058] Fig.14 ATR fingerprints of all mouse liver tissues shown in the examples of the present invention (Note: A is a blank control group, B is a model group, C is a low-dose flavonoid group, D is a high-dose flavonoid group, E is a low-dose alkaloid group, F is a high-dose alkaloid group, and G is a positive control bifendate group); Fig.15 The one-dimensional difference spectrum of ATR between the normal group and the model group of mice shown in the embodiment of the present invention;

[0059] Fig.16 The peak fitting of the amide I band of the infrared of different groups of mouse liver shown in the embodiment of the present invention (Note: A is the blank control group, B is the model group, C is the low-dose flavonoid group, D is the high-dose flavonoid group, E is the low-dose alkaloid group, F is the high-dose alkaloid group, and G is the positive control bifendate group; Fit Peak1 is the β-folding region, Fit Peak2 is the random curling region, and Fit Peak3 is the β-turn region);

[0060] Fig.17 The abnormal value judgment result of mouse liver ATR spectrum shown in the embodiment of the present invention;

[0061] Fig.18 The principal component analysis recommended modeling bands shown in the embodiment of the present invention (A is a graph of the contribution of each principal component, and B is a schematic diagram of the contribution value of different wave numbers to PC1);

[0062] Fig.19 Schematic diagram of different biochemical index quantitative models shown in the embodiments of the present invention (Note: Figure a is the PCR model of TNF-α, Figure b is the PCR model of IL-6, Figure c is the PLS model of IL-1β, Figure d is the PCR model of SOD, Figure e is the PLS model of GSH, and Figure f is the PLS model of MDA). DETAILED DESCRIPTION

[0063] The technical solution of the present invention is clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the accompanying drawings can be arranged and designed in various configurations. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0064] It should be noted that the defects existing in the solutions in the above-mentioned prior art are the results obtained by the inventor after practice and careful research. Therefore, the discovery process of the above-mentioned problems and the solutions proposed in the embodiments of the present application for the above-mentioned problems below should all be the contributions made by the inventor to the present application in the process of invention and creation, and should not be understood as technical contents known to technical personnel in this field.

[0065] In view of the technical problems pointed out in the background technology, the embodiments provided by the present invention are as follows:

[0066] Example 1

[0067] In an exemplary embodiment, a method for screening and verifying a group of hepatoprotective medicinal components of Meconopsis integron is provided, referring to Figure 1 , the method comprises the following steps:

[0068] S1. Extracting total flavonoids and total alkaloids from Meconopsis integron;

[0069] S2. Determine the contents of total flavonoids and total alkaloids in Meconopsis integron;

[0070] S3, CCl4 was used to induce HepG2 human liver cancer cell damage to establish a model, the expression levels of corresponding liver damage indicators were detected, and the efficacy component groups were screened in vitro;

[0071] S4. CCl4 was used to induce acute liver injury in KM mice, and the expression levels of corresponding liver injury indicators and inflammatory factors were detected, and the screened pharmacological component groups were verified in vivo.

[0072] Specifically, the present invention mainly studies the in vitro screening of the effective component groups with liver protection effect in Meconopsis integrifolia, and screens out the components with the best efficacy in Meconopsis integrifolia. Subsequently, the liver protection efficacy of different component groups is verified at the in vivo level to ensure the accuracy of group screening.

[0073] Furthermore, the method also combines modern infrared spectroscopy analysis technology to analyze the infrared spectral characteristics of the liver of KM mice after different groups of pharmacological components were administered to CCl4-induced acute liver injury. By collecting the ATR infrared spectra of each group of KM mice and combining their biochemical index data to establish a quantitative model, modern analytical methods were used to characterize and predict disease characteristics to a certain extent.

[0074] Example 2

[0075] Based on the method of Example 1, a specific experimental process is given in this example, including the following parts:

[0076] 1. Instruments and Reagents

[0077] 1.1 Instruments: UV spectrophotometer (Agilent Technologies, USA), electric constant temperature water bath (Beijing Kewei Yongxing Instrument Co., Ltd., Beijing, China), electronic analytical balance (Mettler-Toledo Co., Ltd., Shanghai, China), rotary evaporator (Shanghai Ailang Instrument Co., Ltd., Shanghai, China), automatic enzyme-linked immunosorbent assay (model: Epoch2, BioTek, USA), inverted phase contrast microscope (model: DMI1, Leica, Germany), constant temperature CO2 cell culture incubator (model: ICP500, Memmert, Germany), microplate constant temperature oscillator (model: BE-9010, Haimen, Jiangsu, China). Linbel Instrument Manufacturing Co., Ltd.), Eppendorf refrigerated high-speed centrifuge (Model: 5430R, Eppendorf, Germany), ultrasonic cell crusher (Model: Scientz-ⅡD, Ningbo Xinzhi Technology Co., Ltd., Zhejiang), ultra-low temperature refrigerator (Model: DW-86L578J, Qingdao Haier Co., Ltd.), pressure steam sterilizer (Model: BXM-30R, Shanghai Boxun Industrial Co., Ltd. Medical Equipment Factory), double-person single-sided clean workbench (Model: SW-CJ-1C, Shanghai Hujing Medical Equipment Co., Ltd.), panoramic slice scanner (Model: PANNORAMIC DESK / MIDI / 250 / 1000, 3DHISTECH, Hungary), Fourier transform infrared spectrometer (Model: IS 50; Thermo Fisher Scientific Inc., USA) equipped with mid-infrared attenuated total reflection accessories.

[0078] 1.2 Reagents: Papaverine hydrochloride reference substance (Chengdu Aifa Biotechnology Co., Ltd., batch number: 171214-201906), rutin reference substance (Chengdu Aifa Biotechnology Co., Ltd., batch number: AD1520-1000), anhydrous ethanol, petroleum ether, ethyl acetate, chloroform, concentrated ammonia, hydrochloric acid, aluminum nitrate, sodium nitrite, and sodium hydroxide are all analytical grade. HepG2 human hepatoma cells (Hubei Wuhan Punuosai Life Science Technology Co., Ltd.), DMEM high glucose medium (Hubei Wuhan Punuosai Life Science Technology Co., Ltd.), penicillin-streptomycin double antibody solution (Hubei Wuhan Punuosai Life Science Technology Co., Ltd.), 0.25%-EDTA pancreatin (Hubei Wuhan Punuosai Life Science Technology Co., Ltd.), Sijiqing fetal bovine serum (Zhejiang Tianhang Biotechnology Co., Ltd.), olive oil (batch number: O815210, Shanghai MacLean Technology Co., Ltd.), PBS (Hubei Wuhan Punuosai Life Science Technology Co., Ltd.), carbon tetrachloride (batch number: C805325, purity: 99.6%, Shanghai MacLean Biochemical Technology Co., Ltd. Co., Ltd.), bifendate (batch number: D843898, purity 98%, Shanghai MacLean Biochemical Technology Co., Ltd.), BCA protein concentration assay kit (Shanghai Biyuntian Biotechnology Co., Ltd.), alanine aminotransferase (ALT) test kit (Nanjing Jiancheng Bioengineering Institute), aspartate aminotransferase (AST) test kit (Nanjing Jiancheng Bioengineering Institute), malondialdehyde (MDA) assay kit (Nanjing Jiancheng Bioengineering Institute), superoxide dismutase (SOD) assay kit (Nanjing Jiancheng Bioengineering Institute), reduced glutathione (GSH) assay kit (Nanjing Jiancheng Bioengineering Institute).

[0079] 1.3 Experimental animals: SPF male Kunming KM mice (Beijing Sibeifu Biotechnology Co., Ltd., license number: SCXK2019-0010) weighing 18g-22g, KM mice were kept in a clean animal room with appropriate temperature and humidity, and adapted to feeding for one week, during which they were free to eat and drink. Relevant experiments followed the regulations of the Experimental Animal Ethics Committee.

[0080] 2. Experimental Methods

[0081] 2.1 Extraction of total flavonoids and total alkaloids from Meconopsis integron

[0082] (1) Total flavonoid component extraction method: Weigh 200 g of Meconopsis crude powder, add 75% ethanol into a round-bottom flask, condense and reflux extraction twice (1500 ml for the first time and 1000 ml for the second time), each time for 1.5 h, combine and collect the filtrates, recover the ethanol until there is no alcohol taste, dilute the residue with an appropriate amount of distilled water, then extract with petroleum ether several times, discard the petroleum ether layer, recover the water layer and extract with ethyl acetate several times, combine the ethyl acetate layers, evaporate to dryness, and the residue is the total flavonoid extract of Meconopsis.

[0083] (2) Total alkaloid component extraction method: Weigh 200 g of Meconopsis crude powder, extract twice with 95% ethanol condensation reflux (1500 ml for 12 h, 1000 ml for the second time), each time for 1.5 h, filter, collect the filtrate, and recover the ethanol under reduced pressure until there is no alcohol taste. After the residue is shaken and defatted with petroleum ether, add 1% hydrochloric acid to adjust the pH value to 2-3, filter, take the filtrate and add concentrated ammonia water to adjust the pH value to 9-10, extract several times with chloroform, combine the chloroform layers, recover the solvent, and the residue is the Meconopsis total alkaloid extract.

[0084] 2.2 Determination of the content of total flavonoids and total alkaloids in Meconopsis integron

[0085] (1) Determination of total flavonoids content in Meconopsis integron (ultraviolet spectrophotometry):

[0086] ① Preparation of reference solution: Weigh 50 mg of rutin reference substance, dissolve it in 50 mL of 75% ethanol, and dilute to a 50 mL volumetric flask to obtain a 1 mg / mL reference substance stock solution for later use.

[0087] ② Preparation of test solution: Weigh an appropriate amount of the extracted sample extract, dissolve it in 75% ethanol, transfer it to a 10mL volumetric flask to make up to volume to obtain a 1mg / mL extract test solution; at the same time, take a certain amount of the stock solution and dilute it to a stock solution equivalent to 50mg / mL.

[0088] ③ Drawing of standard curve: Take 5 10mL volumetric flasks and add 1.0mL, 3.0mL, 5.0mL, 7.0mL, and 9.0mL of the mother solution of the reference substance, and then dilute to the scale with 75% ethanol to obtain 0.1mg / mL, 0.3mg / mL, 0.5mg / mL, 0.7mg / mL, and 0.9mg / mL of the reference substance solution. Take 2.0mL of the reference substance solution of different mass concentrations after diluting with 75% ethanol to 25mL volumetric flasks, add 1.0mL of 5% sodium nitrate and 10% aluminum nitrate solution, shake well and let stand for 10 minutes. Take 75% ethanol as the blank control, measure the absorbance value at 500nm, use the reference substance concentration as the horizontal axis and the absorbance value as the vertical axis, and calculate the regression equation.

[0089] ④Content determination: Take the sample to be tested and determine it according to the above determination method.

[0090] ⑤Precision test: Pipette 2.0 mL of the same test solution, measure the absorbance according to the above method, measure 5 times continuously, and calculate the RSD value (relative standard deviation).

[0091] ⑥Stability test: Take the same sample, prepare it according to the test solution preparation method, accurately pipette 4 mL, process it according to the "drawing of standard curve" method, measure the absorbance at 0h, 0.5h, 1.0h, 1.5h, and 2.0h, and calculate the RSD value.

[0092] ⑦ Repeatability test: Accurately pipette 2.0 mL of the same test solution, measure the absorbance, and calculate the RSD value.

[0093] (2) Determination of total alkaloid content in Meconopsis integron (ultraviolet spectrophotometry):

[0094] ① Accurately weigh 50 mg of papaverine hydrochloride reference substance, dilute to 10 mL volumetric flask with pure water, shake well and set aside, accurately pipette 5.0 mL of the solution into a 25 mL volumetric flask, add 1% hydrochloric acid solution and dilute to the scale line of the volumetric flask to obtain a papaverine hydrochloride reference substance solution with a concentration of 1 mg / mL.

[0095] ② Dilution of the test solution: Take an appropriate amount of extract sample and add pure water to make up to 10mL volumetric flask, shake well and set aside, use a pipette to accurately draw 10.0mL of the solution into a 50ml volumetric flask, add 1% hydrochloric acid to completely dissolve it, make up to volume, and obtain 0.2mg / mL of the target test solution for use; at the same time, take a certain amount of the stock solution and dilute it to a stock solution equivalent to 25mg / mL of the test solution for use.

[0096] ③ Drawing of standard curve: Accurately draw 1.0mL, 3.0mL, 5.0mL, 7.0mL, and 9.0mL of papaverine hydrochloride reference substance mother solution, dilute with water to obtain 0.001mg / mL, 0.01mg / mL, 0.03mg / mL, 0.05mg / mL, 0.07mg / mL, and 0.09mg / mL reference substance solutions, respectively, place in a separating funnel, add 5.0mL of acetate buffer and 2.0mL of bromocresol green in sequence, shake well, let stand for 5min, then add 10.0mL of chloroform and shake thoroughly, take the chloroform layer and measure its absorbance at 410nm, and use 1% hydrochloric acid as a blank control. Calculate the regression equation with the reference substance concentration as the horizontal axis and the absorbance value as the vertical axis.

[0097] ④Content determination: Take the test sample for detection after color development, substitute the absorbance value into the regression equation to calculate the total alkaloid content in the sample.

[0098] ⑤Precision test: Pipette 10.0 mL of the same test solution, determine the absorbance according to the above method, measure 5 times continuously, and calculate the RSD value.

[0099] ⑥Stability test: Take 10.0 mL of the same test solution, accurately pipette it, and process it according to the “standard curve drawing” method. Measure the absorbance at 0 h, 0.5 h, 1.0 h, 1.5 h, and 2.0 h, and calculate the RSD value.

[0100] ⑦ Repeatability test: Accurately pipette 10.0 mL of the same test solution, prepare it according to the test solution preparation method, measure the absorbance, and calculate the RSD value.

[0101] 2.3 Screening of liver-protective active components of Meconopsis integron

[0102] CCl4 was used to induce HepG2 human liver cancer cell damage to establish a model, detect the expression levels of corresponding liver damage indicators, and screen the drug efficacy component groups. The specific experimental methods are as follows:

[0103] (1) Cell culture

[0104] ① Cell recovery: Take out the HepG2 cells stored in liquid nitrogen, quickly thaw them in a 37℃ water bath, add the thawed cell fluid to a sterile centrifuge tube containing 9mL of culture medium, centrifuge at 1200rpm / min for 3 to 5min, remove the supernatant after centrifugation, resuspend the cells with an appropriate amount of complete culture medium corresponding to the cells, transfer them to a 10mL cell culture dish, and then add complete culture medium to make the total volume reach 10mL, and continue to culture. Experiments can be carried out after at least 3 passages after cell recovery.

[0105] ② Cell culture and passaging: HepG2 cells were cultured in complete medium containing 10% FBS and 1% double antibody solution in a 10 mL cell culture dish in a 37°C constant temperature humidified incubator containing 5% CO2. The medium was changed every 2 to 3 days. When the cells were fused to about 70%-80%, they were passaged. The original complete medium was discarded, and the cells were washed twice with 3 mL PBS. 1 mL of trypsin (0.25% trypsin-0.01% EDTA) solution was added. When the cells were observed to shrink and become round under the microscope, the trypsin was discarded, and 2 mL of fresh complete medium was added to terminate the digestion. The cells were gently blown into a single cell suspension, and transferred to a new cell culture bottle at a ratio of 1:3 for continued culture.

[0106] ③ Cell freezing: Take HepG2 cells in the logarithmic growth phase, digest them with trypsin to make a single cell suspension, transfer them to a centrifuge tube and centrifuge at 800 rpm for 5 min, discard the supernatant, add cell freezing solution, blow to make a single cell suspension, and transfer it to a cryopreservation tube (1-5×10 6 cells / 1.5mL / tube). First place at 4℃ for 0.5h, then at -20℃ for 2h, at -80℃ for 12-24h, and finally place in liquid nitrogen for long-term storage.

[0107] (2) Establishment of cell model

[0108] ① Toxicity test of anhydrous ethanol solvent: HepG2 cells in the logarithmic growth phase were digested with trypsin and diluted to 5×10 cells with an appropriate amount of DMEM complete medium. 4 The cells were inoculated at a density of 5 × 10 cells / well in a 96-well plate, with 8 replicates per group. After 24 hours, the cells were cultured with 0.1%, 0.5%, 1%, 5% and 10% anhydrous ethanol, respectively. After 24 hours, the cell viability was measured using the MTT method. The MTT colorimetric method was used to determine cell viability. The principle is that MTT can be reduced to purple water-insoluble formazan by succinate dehydrogenase in living cells, thereby indirectly reflecting the number of living cells. The specific method is: take cells in the logarithmic growth phase and inoculate them in a 96-well plate (5 × 10 4 / well), incubate at 37℃ for 24h, then discard the culture medium, add culture medium containing different concentrations of target drugs and culture for 24h, then add 10μL / well (5mg / mL) of MTT solution to each well, put it into the incubator for further culture, take out the culture plate after 4h, aspirate the culture medium, add 150μL dimethyl sulfoxide (DMSO) and incubate for 10min, and measure the absorbance (Absorbance, A) of each well at 490nm using an automatic enzyme-linked analyzer. Calculate cell viability as follows:

[0109] Cell viability = (Aexperimental - Ablank / Acontrol - Ablank) × 100%.

[0110] ②CCl4 modeling: Mix CCL4 and anhydrous ethanol in a ratio of 1:1, add an appropriate amount of DMEM complete culture medium, and mix using a vortex mixer to prepare CCl4 injury liquid; then take the CCl4 injury liquid and dilute it into injury liquids with different concentration gradients of 0.01%, 0.05%, 0.1%, 0.5% and 1.0% for use.

[0111] ③Drug concentration toxicity experiment: HepG2 human liver cancer cells in the logarithmic growth phase were digested with trypsin and diluted to 5×10 cells with an appropriate amount of DMEM complete medium. 4 The cells were inoculated at a density of 100 μg / well in a 96-well plate, with 6 replicate wells in each group. After 24 hours, the culture medium was administered with different target dosing concentrations of 5ug / mL, 10ug / mL, 20ug / mL, 50ug / mL, 100ug / mL, and 200ug / mL. After 24 hours, the culture medium was discarded and the cell viability was measured using the MTT method.

[0112] ④Comparison of in vitro liver protection activity of drugs: HepG2 human liver cancer cells in the logarithmic growth phase were digested with trypsin and diluted to 5×10 cells with an appropriate amount of DMEM complete medium. 4The cells were inoculated at a density of 100 / ml in a 96-well plate, with 6 replicate wells in each group. After 24 hours, a blank control group, a 0.5% CCl4+5ug / ml total flavonoids administration group, a 0.5% CCl4+5ug / ml total alkaloids administration group, a 0.5% CCl4+5ug / ml positive control group, and a 0.5% CCl4 model group were set up respectively. After 6 hours of administration of the corresponding drugs, the cell viability was determined using the MTT method.

[0113] (3) Determination of biochemical indices: Determine the corresponding biochemical indices according to the instructions of the kit.

[0114] 2.4 Validation of the pharmacodynamic component groups

[0115] CCl4 was used to induce acute liver injury in KM mice to establish a model, and the expression levels of corresponding liver injury indicators and inflammatory factors were detected to verify the target pharmacodynamic component group. The specific experimental methods are as follows:

[0116] (1) Animal grouping: 70 SPF male Kunming KM mice were randomly divided into 7 groups, with 10 mice in each group, namely blank control group A, model group B, low-dose flavonoids group (100 mg / kg / d) C, high-dose flavonoids group (300 mg / kg / d) D, low-dose alkaloids group (100 mg / kg / d) E, high-dose alkaloids group (300 mg / kg / d) F, and bifendate positive drug control group (200 mg / kg / d) G.

[0117] (2) Animal modeling: All groups were given ordinary feed, while groups A and B were not treated but given normal saline. Groups C, D, E, F and G were given extracts respectively. The KM mice in each group were gavaged once a day with a volume of 0.1 mL / 10 g for 7 consecutive days. After the last dose, the mice were fasted but not watered. After 12 h, except for the control group, which was injected intraperitoneally with an equal volume of normal saline, the KM mice in the other groups were injected intraperitoneally with 0.15% CCl4 olive oil solution (0.1 mL / 10 g). 24 hours after modeling, the eyeballs were removed and blood was collected. The blood was centrifuged (3000r / min) for 10min to separate the serum. The upper serum was taken and frozen at -20℃ for later use. After the KM mice were killed, the liver was quickly removed by laparotomy, and then the left lobe of the liver was cut and fixed with cell tissue fixative. The liver was embedded in conventional paraffin and sectioned. The liver was stained with hematoxylin-eosin (HE). The pathological changes of liver tissue were observed under an optical microscope. The remaining liver tissue was quickly frozen with liquid nitrogen and then frozen at -80℃ for later use.

[0118] (3) Determination of biochemical indicators:

[0119] ① Detect the activity levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) in mouse serum according to the instructions of the activity detection kit.

[0120] ② According to the instructions of the activity detection kit, the activities of glutathione (GSH), superoxide dismutase (SOD), and malondialdehyde (MDA) in mouse liver tissue were determined, and the levels of TNF-α, IL-6, and IL-1β in liver tissue were detected by ELISA.

[0121] (6) Data processing: t-test and one-way analysis of variance (ANOVA) were used to determine statistical significance, and P < 0.05 was considered statistically significant. All data are expressed as mean ± SD (standard deviation).

[0122] 2.5 Analysis of infrared spectral characteristics of liver of KM mice after administration of different active components of Meconopsis integrifolia on acute liver injury induced by CCl4

[0123] (1) Sample pretreatment: 67 livers from each group of mice were cut, weighed, and homogenized. The entire process was carried out on ice and then kept at 4°C for later use.

[0124] (2) ATR spectrum acquisition: ATR spectra of mouse liver homogenate tissue were collected using a Fourier transform infrared spectrometer, with background interference subtracted and a resolution of 4 cm -1 The spectra were collected under the conditions of 32 acquisition times. Each sample was collected 3 times and the average spectrum was taken.

[0125] (3) Protein secondary structure analysis: The ATR average spectra of liver tissues from different groups of mice were baseline corrected, Gaussian deconvolution, and second-order derivative fitting were performed using Peak fit (4.12) software. The ratio of each secondary structure was then calculated based on the peak area, and then peak fitting and plotting were performed using Origin (2022) software.

[0126] (4) Model establishment: Combine biochemical index data with corresponding ATR infrared spectrum information and try to characterize and predict diseases through infrared technology. The specific experimental methods are as follows:

[0127] ①Outlier removal: The Mahalanobis distance (MD) and principal component analysis (PCA) methods were used to remove outliers from the ATR spectrum.

[0128] ② Sample set division: A portion of the samples corresponding to the biochemical indicators was taken out as the external validation set in a ratio of 6:1, and the remaining samples were randomly divided into the calibration set and the validation set in a ratio of 3:1.

[0129] ③ Spectral preprocessing: The spectrum was preprocessed by TQ Analyst modeling software (including baseline correction, derivative and derivative smoothing), and an orthogonal experiment with three factors and three levels was designed to select the best spectral preprocessing method, see Table 1 for details.

[0130] Table 1. Levels of factors in orthogonal test for spectral pretreatment

[0131]

[0132] ④ Modeling band selection: The modeling band was selected using the correlation coefficient method (CC), full band and principal component analysis (PCA) method recommended by TQ Analyst software.

[0133] ⑤ Quantitative model establishment: The partial least squares regression model (PLS) and the principal component regression model (PCR) were established by combining the biochemical indicators of mice with the corresponding ATR spectra, and the root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP), calibration set correlation coefficient (Rc), and prediction set correlation coefficient (Rp) were used as evaluation indicators of the model. Generally speaking, the closer the Rc and Rp values ​​are to 1, the closer and lower the RMSEC and RMSEP values ​​are, the better the effect of the model.

[0134] 3. Results and Discussion

[0135] 3.1 Determination of total flavonoids and total alkaloids in Meconopsis integron

[0136] (1) Linear range investigation: The standard curve was drawn with the concentration of rutin reference solution as the horizontal axis and the absorbance as the vertical axis. The linear regression equation was Y = 0.8962X-0.003 (R 2 =0.9995), and the concentration and absorbance of the rutin reference solution showed a good linear relationship in the linear range of 0.1-0.9 mg / mL. Figure 2 The standard curve was drawn with the concentration of papaverine hydrochloride reference solution as the horizontal axis and the absorbance as the vertical axis. The linear regression equation was Y=0.0501X-0.0105(R 2 =0.9997), and the concentration and absorbance of papaverine hydrochloride reference solution showed a good linear relationship in the linear range of 0.001-0.09 mg / mL. Figure 3 shown.

[0137] (2) Content determination and method validation

[0138] As shown in Table 2, the total flavonoid content of the sample increased from 0.25% to 19.04% after extraction and concentration, and the RSD value of the methodological verification was less than 5%, and the results were reliable. Table 3 shows the results of the determination of the alkaloid content of the sample. It can be seen that the total alkaloid content after extraction and concentration increased from 0.18% to 3.97%, and the RSD value of the methodological verification was less than 5%. Therefore, after the extraction and concentration of the extract of the target sample, the total flavonoid and total alkaloid contents of the sample increased to a certain extent, and can be used for subsequent experiments.

[0139] Table 2 Determination of total flavonoid content in samples

[0140]

[0141]

[0142] Table 3 Determination of total alkaloid content of samples

[0143]

[0144] 3.2 Screening of pharmacodynamic component groups

[0145] (1) Solvent toxicity test: Figure 4 It can be seen that with the increase of anhydrous ethanol concentration (0.1% ~ 10%), the activity of HepG2 cells gradually decreased, showing concentration dependence. Anhydrous ethanol had no obvious toxic effect on the activity of HepG2 cells within the concentration range of 1%. Therefore, the concentration of anhydrous ethanol should be controlled within the range of 1% in subsequent experiments.

[0146] (2) CCL4 modeling concentration experiment: Figure 5 It can be seen that different concentrations of CCl4 damaging fluid all caused certain toxicity to cell viability and showed dose-dependency. When the concentration of the damaging fluid was 0.5%, the cell viability dropped to about 50%. Therefore, the CCl4 modeling concentration of 0.5% was selected.

[0147] (3) Drug concentration toxicity test: Figure 6 It can be seen that the total flavonoids and total alkaloids had no significant effect on cell viability within the range of 5ug / ml, and then with the increase of concentration, they produced obvious toxicity to cell viability and showed dose-dependency. Therefore, the safe dosing range was confirmed to be 5ug / ml.

[0148] (4) Comparison of in vitro liver protection activity: Figure 7 It can be seen that after adding 0.5% CCl4, the cell viability decreased to 50%, while after the model group was given total flavonoids, total alkaloids and positive drug bifenthrin, the cell viability increased significantly, indicating that total flavonoids and total alkaloid extracts have a protective effect on CCl4-induced cell damage.

[0149] (5) Determination of biochemical indicators: Figure 8 As shown in the figure, the activities of ALT (a), AST (b) and MDA (e) in the model group were significantly increased compared with the blank control group, and the model was successfully established. The activities of the positive control group and group C were decreased to a certain extent compared with the model group. Among them, the flavonoids administration group in group B and the alkaloid administration group in group C were compared with the model group, and the alkaloid C group had a significant therapeutic effect. Compared with the blank group, the activities of GSH (c) and SOD (d) in the model group were significantly decreased, and the model was successfully established. The activities of the three administration groups were increased to a certain extent compared with the model group, and the alkaloid administration group had the best therapeutic effect.

[0150] 3.3 Validation of drug efficacy groups

[0151] 3.3.1 Liver morphology observation

[0152] like Fig. 9 As shown in the figure, the liver of the blank group was smooth and ruddy, while the liver of the model group mice induced by CCl4 was whitish and had white spots of lesions. In terms of morphology, the liver of the model group mice showed visible damage to the naked eye, and the degree of lesions in the liver of the mice in each group after drug treatment was reduced compared with the liver of the model group mice. However, it is impossible to clearly compare the differences in drug treatment among the groups in morphology, so further in-depth research is needed through pathological sections and biochemical indicators.

[0153] 3.3.2 HE staining of liver tissue

[0154] like Fig.10HE staining results showed that the liver cells of mice in the blank control group had normal structure and were arranged neatly, and were roughly radial with the central vein as the center, with a small amount of vascular congestion, and no obvious inflammatory cell infiltration (Figure A); mice in the model group had large-scale liver cell necrosis with the central vein as the center, nuclear condensation or dissolution, eosinophilic red staining or vacuolar cytoplasm, accompanied by a small amount of lymphocytes and granulocytes infiltration (Figure B). Figure CG is the staining result of the liver tissue of mice in the corresponding drug-treated groups. Compared with the model group, it can be seen that different drug-treated groups have a certain improvement effect on CCl4-induced liver tissue structural damage and inflammatory response to a certain extent. Among them, the liver cells in the flavonoids-treated group (Figures C and D) showed a small area of ​​regional necrosis with the central vein of the hepatic lobule as the center, accompanied by a very small amount of granulocytes and lymphocytes infiltration; the alkaloid-treated group (Figures E and F), especially the high-dose alkaloid group, had only a small amount of liver cell necrosis, and occasionally the liver cells swelled to balloon-like, without obvious inflammatory cell infiltration. According to the results of pathological studies, although the flavonoids-administered group and the positive control group showed more serious pathological changes, compared with the model group, the necrotic area was reduced, the inflammatory response was also improved to a certain extent, and the degree of lesions in the high-dose flavonoids group was less than that in the low-dose group; in contrast, the alkaloids-administered group had more significant treatment, with milder lesions compared with the blank control group, indicating that the treatment effect of the Meconopsis integrifolia alkaloids-administered group was better than that of the flavonoids-administered group, and the high-dose alkaloids group had a more significant protective effect on the liver, which was consistent with the subsequent results of related biochemical indicators.

[0155] 3.3.3 Determination of biochemical indicators

[0156] (1) Inflammatory indicators

[0157] like Fig.11 As shown in (a)(b)(c), compared with the blank group, the inflammatory response of mice in the CCl4-induced acute liver injury model group was obvious, and the levels of inflammatory factors IL-1β, IL-6 and TNF-α were significantly increased. Compared with the model group, the levels of inflammatory factors IL-1β, IL-6 and TNF-α in the other groups after administration of the corresponding drugs decreased to a certain extent, and overall, the decrease in the expression level of inflammatory factors in the alkaloid treatment group was better than that in the flavonoids treatment group.

[0158] (2) Liver damage indicators

[0159] Compared with the blank control group, mice in the CCl4-induced acute liver injury model group had significant damage, and the levels of ALT and AST were significantly increased (e.g. Fig.12 (a)(b)). Compared with the model group, the levels of ALT and AST in different drug administration groups were significantly reduced, among which the alkaloid administration group had the best therapeutic effect.

[0160] (3) Oxidative stress indicators

[0161] like Fig.13 As shown in (a)(b)(c), the levels of oxidative stress indicators GSH, SOD and MDA in the model group were significantly different from those in the blank group. After treatment with different dosing groups, the levels of GSH, SOD and MDA improved to a certain extent, among which the alkaloid administration group had the best therapeutic effect.

[0162] 3.4 Analysis of infrared spectral characteristics of different active components of Meconopsis integrifolia after drug intervention on acute liver injury induced by CCl4

[0163] 3.4.1 ATR spectral analysis of liver tissue

[0164] A total of 67 ATR average spectra of KM mouse liver were scanned and obtained ( Fig.14 ), including 10 blank groups ( Fig.14 -A), model group 10 ( Fig.14 -B), 9 samples of low-dose flavonoids group ( Fig.14 -C), 10 servings of flavonoid high-dose group ( Fig.14 -D), alkaloid low dose 8 parts ( Fig.14 -E), alkaloid high dose 10 portions ( Fig.14 -F), positive control group 10 ( Fig.14 -G). The ATR spectra of all KM mouse livers were analyzed. It can be seen from the figure that the ATR spectra of the livers of each group of mice are different, but it is impossible to intuitively distinguish the difference between the normal group and the model group. Therefore, the ATR one-dimensional spectra of the corresponding mouse liver tissues were subjected to difference spectrum and correlation analysis. The absorption peaks of the mouse liver ATR spectrum were analyzed, and there were 4 main absorption peaks, which were concentrated at 3339cm -1 、2115cm -1 、1636cm -1 With 422cm -1 Nearby, they are respectively attributed to the broad peaks generated by the association phenomenon of hydroxyl compounds, triple bond and cumulative double bond regions, amide I band, and fingerprint region; among them, the amide I band (1600-1700cm -1 ) contains rich secondary structure information of proteins, such as α-helix, β-fold, β-turn, random coil structure, etc., so the subsequent analysis will focus on the amide I band.

[0165] 3.4.2 One-dimensional ATR difference spectrum and correlation analysis of livers from different groups of KM mice

[0166] The model group was KM mice with liver damage induced by CCl4. To further analyze the difference between the model group and the blank control group, the ATR one-dimensional spectra of the normal group and the model group KM mice were analyzed by differential spectrum analysis ( Fig.15The results showed that there were differences in the ATR one-dimensional spectra of the normal group KM mice and the model group KM mice, and the differences were mainly concentrated at 3344cm -1 With 1637cm -1 At the same time, the correlation analysis of the ATR one-dimensional spectra of the blank group and other groups was performed (Table 4). The results showed that the correlation coefficient of the one-dimensional spectra between the blank group and the model group was 0.9767, the correlation coefficient of the one-dimensional spectra between the blank group and the low-dose flavonoid group was 0.9880, the correlation coefficient with the high-dose flavonoid group was 0.9915, the correlation coefficient with the low-dose alkaloid group was 0.9901, the correlation coefficient with the high-dose alkaloid group was 0.9943, and the correlation coefficient with the positive control group was 0.9942. Among them, the correlation coefficient of the one-dimensional spectra between the blank control group and the model group was the lowest, indicating that the ATR one-dimensional infrared spectra of the blank control group and the model group were the most different. Through the correlation analysis of the one-dimensional spectra of different groups with the blank group and the difference spectrum analysis between the normal group and the model group, the results showed that there were significant differences in the ATR spectra of the normal group and the model group, which further indicated that the CCl4-induced acute liver injury model of KM mice was successfully established in the early stage.

[0167] Table 4 Correlation coefficients of ATR one-dimensional spectra of mice in each group and the blank group

[0168]

[0169] 3.4.3 Protein secondary structure analysis

[0170] The secondary structure of proteins contains important information. Fourier transform infrared spectroscopy is one of the main analytical techniques used to determine the secondary structure of proteins. A typical protein infrared spectrum contains nine characteristic bands of the amide main chain and amino acid side chains, namely amide bands A, B, I, II, III, IV, V, VI, and VII. Among them, amide I band (1600-1700cm -1 ) mainly reflects the stretching vibration of C=O and the hydrogen bonding between C=O and NH groups, which is directly related to the main chain conformation and is commonly used in infrared spectroscopy measurement of most protein secondary structures. The main secondary structures of amide I band include α-helix, β-fold, β-turn and random curl. The hydrogen bonds between the above four secondary structures are different, so their absorption peak positions in amide I band are also different. The absorption peaks are attributed as follows: β-fold (1600-1640cm -1 ), β-angle (1660-1700cm -1 ), α-helix (1650-1660cm -1 ), irregular curl (1640-1650cm -1 The ATR spectrum of the original KM mouse liver was imported into PeakFit (4.12) software, and the amide I band (1600-1700 cm-1 ) area were subjected to baseline correction, Gaussian deconvolution and second-order derivative fitting, and the peak area and the ratio of each secondary structure were calculated, and the results are shown in Table 5. It can be seen that compared with the blank group, the content of β-fold and random coil in the model group and each drug-treated group increased, the content of β-turn decreased, and the relative content of α-helix was 0, while the content of β-fold, random coil and β-turn in each drug-treated group did not change compared with the model group. Related studies have shown that hydrogen bonds maintain the stability of protein secondary structure, and external factors will change protein secondary structure by destroying hydrogen bonds. Therefore, compared with the blank group, the changes in the content of protein secondary structure in the model group and different drug-treated groups may be related to the destruction of hydrogen bonds, which indicates that the protein secondary structure of the liver tissue of KM mice has undergone a certain degree of structural change after CCl4 induction.

[0171] Table 5 Secondary structure content of liver proteins in different groups of KM mice

[0172]

[0173] Fig.16 Figure 2 is the peak fitting diagram of ATR amide I band in liver of KM mice in different groups. It can be seen that compared with the blank group ( Fig.16 A) compared with the model group ( Fig.16 B) decreased to a certain extent, while the other groups ( Fig.16 C- Fig.16 F) The peak absorbance did not change compared with the blank control group. Generally speaking, when the protein structure is completely destroyed, the absorption peak will change significantly. The above phenomenon indicates that CCl4 has destroyed and changed the secondary structure of the liver protein of KM mice to a certain extent, further illustrating the success of the KM mouse acute liver injury model established in the early stage.

[0174] 3.4.4 Establishment of ATR quantitative model based on biochemical indicators

[0175] (1) Removal of spectral outliers:

[0176] There are 63 ATR spectra corresponding to TNF-α, 67 ATR spectra of IL-6, 67 ATR spectra of IL-1β, 65 ATR spectra of SOD, 62 ATR spectra of GSH, and 60 ATR spectra of MDA. The spectra were eliminated with principal component analysis (PCA) and Mahalanobis distance (MD), respectively. When the Mahalanobis distance (MD) exceeds 3 and the samples in the principal component analysis (PCA) exceed the 95% confidence interval, they can be regarded as outliers. Fig.17 A is the abnormal value discrimination result of spectral MD analysis corresponding to different biochemical indicators. The six indicators ( Fig.17 A-1 to Fig.17There are no abnormal values ​​in the spectra corresponding to A-4-17A-6). Fig.17 B is the abnormal value discrimination result of PCA analysis of spectra corresponding to different biochemical indicators. The abnormal spectra identified by PCA in TNF-α are K2, K5, Y8 and M2 ( Fig.17 B-1); The abnormal spectra identified by PCA in IL-6 are K2, K5, K6, Y8 and M2 ( Fig.17 B-2); The abnormal spectra identified by PCA in IL-1β are K2, K5, K6, Y8 and M2 ( Fig.17 B-3); The abnormal spectra identified by PCA in SOD are K2, K5, K6, Y8 and M2 ( Fig.17 B-4); the abnormal spectra identified by PCA in GSH are K2, K5, K6, Y8 and M2 ( Fig.17 B-5); the abnormal spectra identified by PCA in MDA are K2, K5, K6, Y8 and M2 ( Fig.17 B-6). The detailed information of outlier removal of the spectra corresponding to each index is summarized, as shown in Table 6. TNF-α finally removed 4 outlier spectra, IL-6, IL-1β, SOD, GSH and MDA finally removed 5 outlier spectra, which constituted the final modeling sample sets.

[0177] Table 6 Detailed information on the removal of outliers in the spectra of various biochemical indicators

[0178]

[0179] (2) Sample set division: A portion of the samples corresponding to the biochemical indexes was taken out as the external validation set at a ratio of 6:1, and the remaining samples were randomly divided into the calibration set and the validation set at a ratio of 3:1. The detailed information on the sample set division of each biochemical index model is shown in Table 7.

[0180] Table 7 Detailed information on the removal of outliers in the spectra of various biochemical indicators

[0181]

[0182]

[0183] (3) Spectral pretreatment: Orthogonal test analysis of different spectral pretreatment methods of biochemical indicators based on PLS and PCR modeling methods was performed, and the results showed that the best pretreatment method was selected based on the Rp value. The best spectral pretreatment combination of TNF-α was: 2D+SG smoothing+Constant+PCR, the best spectral pretreatment combination of IL-6 was: 1D+SG smoothing+MSC+PCR, the best spectral pretreatment combination of IL-1β was: 2D+Norris smoothing+MSC+PLS, the best spectral pretreatment combination of SOD was: 2D+SG smoothing+MSC+PCR, the best spectral pretreatment combination of GSH was: Spectrum+unsmoothed+SNV+PLS, and the best spectral pretreatment combination of MDA was: 2D+Norris smoothing+SNV+PLS.

[0184] (4) Modeling band selection:

[0185] The full band, CC method and PCA were used to optimize the modeling band under the best spectral preprocessing conditions, and the Rp value was used as an important reference index to screen the best modeling band. PCA was used to recommend the modeling bands such as Fig.18 A total of 7 principal components were obtained ( Fig.18 -A), the cumulative contribution rate reaches more than 95%, of which the first principal component (PC1) reaches 80%, which can explain 80% of the spectral features. Therefore, PC1 is selected to establish the X-loading diagram of PC1 ( Fig.18 -B) to characterize the contribution of each band to PC1. The bands with contribution values ​​> 0.1 were selected for modeling, and the 750-400cm -1 and 3500~3100cm -1 The bands are recommended for modeling.

[0186] The modeling bands recommended by CC method vary with the pretreatment methods and different biochemical index models. Under the best pretreatment method, the best modeling band for TNF-α is 1962-1958 cm -1 2050~2042cm -1 The best modeling band for IL-6 is 1745-460 cm -1 2025~1830cm -1 The optimal modeling band of IL-1β is 480-475 cm -1 、680~675cm -1 2053~2048cm -1 The best modeling band for SOD is 415-407 cm -1 1985~1755cm -1 The best modeling band of GSH is 1681-461 cm -1; The best modeling band of MDA is 490~435cm -1 2020~635cm -1 .

[0187] Table 8 shows the detailed information of the optimization of modeling bands for different biochemical indicators, and the Rp value is used as an important reference index to screen the best modeling bands. The PCA method is the best for TNF-α modeling band, with an Rp value of 0.6123; the CC method is the best for IL-6 modeling band, with an Rp value of 0.6776; the PCA method is the best for IL-1β modeling band, with an Rp value of 0.5086; the CC method is the best for SOD modeling band, with an Rp value of 0.5244; the CC method is the best for GSH modeling band, with an Rp value of 0.5101; the CC method is the best for MDA modeling band, with an Rp value of 0.4579.

[0188] Table 8 Optimization of modeling bands for different biochemical index models

[0189]

[0190] (5) Quantitative model establishment: The sample ATR spectrum is combined with different biochemical indicators, and the quantitative model is established using TQ Aanlyst software, such as Fig.19 Generally speaking, the smaller the RMSEC and RMSEP values ​​of the model, and the closer the Rc and Rv values ​​are to 1, the better the model effect. Fig.19 It is known that TNF-α( Fig.19 a) IL-6 ( Fig.19 b) IL-1β ( Fig.19 c) and SOD ( Fig.19 The model effect of e) is not good, and its RMSEC value and RMSEP value are both above 30. Fig.19 d) and MDA( Fig.19 f) The modeling effect is better than the previous four models. The RMSEC, Rc, RMSEP and Rv values ​​of GSH are 3.7700, 0.3312, 9.7100 and 0.5101, and the RMSEC, Rc, RMSEP and Rv values ​​of MDA are 1.9500, 0.5444, 4.5600 and 0.4579, respectively. Overall, the quantitative models of the six biochemical indicators need to be further improved.

[0191] Generally, the primary factor affecting the effect of infrared spectroscopy models is the degree of correlation between the spectrum and the research target. The stronger the correlation, the easier it is to build a model. Secondly, the quality of the collected spectrum and the accuracy of the reference value will also significantly affect the prediction effect of the model. In addition, the choice of modeling algorithm, optimization of model parameters, sample quantity, sample distribution, etc. are also important factors affecting the modeling effect. Therefore, the poor effect of the above model may be due to the following reasons:

[0192] ① The biochemical index detection kit, especially the test kit, has the characteristics of high sensitivity, and the modeling data has a certain volatility, which in turn affects the final prediction effect of the model. ② The correlation between the ATR spectrum of the liver of KM mice and the corresponding biochemical indicators is not high enough, resulting in underfitting of the model. ③ The algorithm of the built model may not match the corresponding indicator data, and the model building still needs to be further improved. Subsequent work can find a better model based on the original model or further study the development of the algorithm. ④ The number of modeling samples is small. The number of modeling samples should be representative, and the number should be wide enough, so that the generalization ability and robustness of the model are also good. The establishment of the biochemical index model this time is based on the previous work, and the number of samples is relatively limited, which also has a certain impact on the model effect. ④ The quality of the collected spectrum and the choice of preprocessing method. The spectral data obtained in the actual analysis process contains valid information but also contains some redundant and interfering information (such as noise, baseline drift, light source scattering, etc.), which will affect the quality of the collected spectrum and the final modeling effect of the model. Different preprocessing methods and their combinations have different effects on data analysis. In practical applications, choosing an appropriate preprocessing analysis strategy is crucial to model establishment.

[0193] (6) External validation of the model: Using the quantitative model of the established biochemical indexes, the ATR spectra of the external validation set samples were imported to obtain the predicted values ​​and the prediction rates were calculated. The results are shown in Table 9. As can be seen from the table, the external validation prediction rates of the six biochemical index models were all above 50%, among which the prediction rate of IL-1β reached 83.23%. The established model can understand and predict new samples to a certain extent.

[0194] Table 9 External validation of models constructed by different biochemical indicators

[0195]

[0196]

[0197] In general, the application of infrared spectroscopy technology in the characterization and prediction of diseases has certain research significance, and can help relevant researchers quickly identify and predict diseases in real life. But at the same time, due to the complexity of the disease onset mechanism and the differences between different individuals, the future development of this research direction is also challenging. Infrared spectroscopy technology has the advantages of being fast and non-destructive. With the vigorous development of computer science and chemometrics, various model algorithms are emerging in an endless stream. Combining chemometrics with infrared spectroscopy technology and disease prediction has certain feasibility and research significance. Whether it is the precision and sensitivity of spectral instruments or the flexibility of model algorithms and their adaptability to data, they are all directions worthy of further development in the future.

[0198] IV. Conclusion

[0199] (1) The study conducted in vitro screening for the hepatoprotective efficacy of the active components of Meconopsis integron. The results showed that the alkaloid component of Meconopsis integron had the best efficacy and was the target active component. Subsequently, the hepatoprotective efficacy of its different component groups was verified at the in vivo level. The results showed that the total alkaloid component of Meconopsis integron had the best therapeutic effect, which was consistent with the in vitro screening results.

[0200] (2) Combining modern infrared spectroscopy analysis technology, the ATR infrared spectra of the liver of KM mice in the blank control group, model group and other drug-treated groups were analyzed. The infrared fingerprints of the KM mice were slightly different. To further analyze the difference, the ATR spectra of the model group and the blank group were analyzed by one-dimensional difference spectrum analysis. The results showed that the ATR spectra of the liver of the KM mice in the two groups were at 3344 cm -1 With 1637cm -1 The study used ATR spectra to analyze the secondary structure characteristics of KM mouse proteins in each group. The results showed that there were certain differences in the amide I band region that characterizes the secondary structure of proteins between each drug-treated group and its model group and the blank group. Specifically, compared with the blank group, the content of β-folding and random coiling in each drug-treated group and the model group increased, the content of β-turn decreased, and the relative content of α-helix was 0.

[0201] (3) The ATR infrared spectra of each group of KM mice were collected and combined with their biochemical index data to establish a quantitative model, hoping to characterize and predict disease characteristics to a certain extent through modern analytical methods. In the modeling process, the spectral preprocessing and modeling band selection were optimized to obtain the optimal models of TNF-α, IL-6, IL-1β, SOD, GSH and MDA. The ATR spectra of the external validation set samples were imported into the model to obtain the predicted values. The results showed that the external validation prediction rates of the six biochemical index models were all above 50%, among which the IL-1β prediction rate reached 83.23%, indicating that the established model can understand and predict new samples to a certain extent.

[0202] Example 3

[0203] Based on the same inventive concept as Example 1, a screening and verification system for a group of hepatoprotective medicinal components of Meconopsis integrifolia is provided, comprising:

[0204] Component extraction module, used to extract total flavonoid components and total alkaloid components from Meconopsis integron;

[0205] Component content determination module, used to determine the content of total flavonoid components and total alkaloid components of Meconopsis integron;

[0206] The drug efficacy component group screening module is used to use CCl4 to induce HepG2 human liver cancer cell damage to model, detect the expression level of corresponding liver damage indicators, and screen the drug efficacy component group;

[0207] The pharmacodynamic component group verification module is used to model acute liver injury in KM mice induced by CCl4, detect the expression levels of corresponding liver injury indicators and inflammatory factors, and verify the screened pharmacodynamic component group.

[0208] It should be noted that each module of the system utilizes the means and methods used in each step of the aforementioned method to achieve corresponding functions, which will not be elaborated here.

[0209] The above specific implementation methods are detailed descriptions of the present invention. It cannot be determined that the specific implementation methods of the present invention are limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions and substitutions can be made without departing from the concept of the present invention, which should be regarded as belonging to the protection scope of the present invention.

Claims

1. A method for screening and verifying a group of hepatoprotective medicinal components of Meconopsis integrifolia, characterized in that: The following steps are involved: S1. Extracting total flavonoids and total alkaloids from Meconopsis integron; S2. Determine the contents of total flavonoids and total alkaloids in Meconopsis integron; S3, CCl4 was used to induce HepG2 human liver cancer cell damage to establish a model, the expression levels of corresponding liver damage indicators were detected, and the efficacy component groups were screened in vitro; S4. CCl4 was used to induce acute liver injury in KM mice, and the expression levels of corresponding liver injury indicators and inflammatory factors were detected, and the screened pharmacological component groups were verified in vivo.

2. The method for screening and verifying the liver-protecting medicinal components of Meconopsis integrifolia according to claim 1, characterized in that: The method for extracting the total flavonoid components in step S1 comprises: The total flavonoids from Meconopsis integrifolia were extracted by alcohol extraction, and the components were purified by further utilizing the polar distribution of compounds and organic chemical reagents. The solvent was evaporated by rotary evaporator to obtain the extract of the corresponding total flavonoid components.

3. The method for screening and verifying the hepatoprotective medicinal components of Meconopsis integrifolia according to claim 1, characterized in that: The method for extracting the total alkaloid components in step S1 comprises: The total alkaloids from Meconopsis integrifolia were extracted by alcohol extraction, and the components were further purified by utilizing the polarity distribution of the compounds and organic chemical reagents. The solvent was evaporated by a rotary evaporator to obtain the corresponding total alkaloid component extract.

4. The method for screening and verifying the liver-protecting medicinal components of Meconopsis integrifolia according to claim 1, characterized in that: In the step S2, ultraviolet spectrophotometry is used to respectively determine the contents of total flavonoid components and total alkaloid components of Meconopsis integron.

5. The method for screening and verifying the liver-protecting medicinal components of Meconopsis integrifolia according to claim 1, characterized in that: In step S3, CCl4 is used to induce HepG2 human liver cancer cell damage for modeling, including: (1) HepG2 cell culture: HepG2 cells were cultured by adherent culture; (2) Prepare CCl4 damage solution; (3) HepG2 cells in the logarithmic growth phase were treated with CCl4 damaging solution to induce chemical damage.

6. The method for screening and verifying the liver-protecting medicinal components of Meconopsis integrifolia according to claim 1, characterized in that: The step S3 detects the expression level of the corresponding liver injury index and screens the pharmacodynamic component group in vitro, including: (1) Screening of drug concentration: HepG2 cells in the logarithmic growth phase were treated with different concentration gradients of total flavonoids and total alkaloids. After 12 h, the activity was measured using the MTT method to select the drug concentration; (2) Cell grouping and drug administration: A blank control group, a total flavonoids administration group, a total alkaloids administration group, a positive control bifendate group, and a model group were set up. The corresponding drugs were induced and CCl4 injury solution was applied. After 12 hours, the cells were extracted and the test kit was ready for use. (3) Determination of biochemical indicators: According to the instructions of the kit, different groups of cells were taken to measure the expression levels of corresponding indicators and compare the drug treatment effects of each group.

7. The method for screening and verifying the hepatoprotective medicinal components of Meconopsis integrifolia according to claim 1, characterized in that: In step S4, CCl4 is used to induce acute liver injury in KM mice for modeling, including: (1) Male KM mice were randomly divided into blank control group A, model group B, flavonoid low-dose group C, flavonoid high-dose group D, alkaloid low-dose group E, alkaloid high-dose group F, and bifendate positive drug control group. Each group of mice was gavaged with the corresponding drug once a day, with an interval of 24 hours, for 7 consecutive days; (2) After the last administration, mice were fasted but not watered. Twelve hours later, except for the control group, which was intraperitoneally injected with an equal volume of normal saline, mice in other groups were intraperitoneally injected with 0.15% CCl4 olive oil solution to induce acute liver injury. (3) In accordance with the ethical requirements of experimental animals, mice in each group were killed and their serum and liver were collected for later use; (4) The left lobe of the liver of each group of mice was taken for HE staining to compare the pathological effects of mice in different groups; (5) The right lobe of the liver and serum of each group of mice were taken according to the kit instructions to measure the expression levels of the corresponding indicators of different groups of mice, and the therapeutic effects of different drugs were compared.

8. The method for screening and verifying the hepatoprotective medicinal components of Meconopsis integrifolia according to claim 7, characterized in that: Also includes: The infrared spectral characteristics of the liver of KM mice after administration of different pharmacological component groups to CCl4-induced acute liver injury were analyzed, including: (1) ATR infrared spectra of the livers of KM mice in different groups were collected using a Fourier transform infrared spectrometer; (2) The ATR spectrum of the liver homogenate of KM mice was collected, and the one-dimensional difference spectrum, one-dimensional spectrum correlation and protein secondary structure were analyzed based on the ATR spectrum.

9. The method for screening and verifying the hepatoprotective medicinal components of Meconopsis integrifolia according to claim 8, characterized in that: The infrared spectral characteristics of the liver of KM mice after administration of different pharmacological component groups to CCl4-induced acute liver injury are analyzed, and further include: The disease prediction model was established by combining infrared spectra and biochemical indicators of the liver of KM mice after administration of different pharmacological component groups to CCl4-induced acute liver injury, including: (1) Outlier removal: The Mahalanobis distance (MD) and principal component analysis (PCA) methods were used to remove outliers from the ATR spectrum; (2) Sample set division: A portion of the samples corresponding to the biochemical indicators was taken as the external validation set at a ratio of 5:1, and the remaining samples were randomly divided into the calibration set and the validation set at a ratio of 3:1; (3) Spectral preprocessing: The spectrum is imported into TQ Analyst modeling software for spectrum preprocessing; (4) Modeling band selection: The modeling bands were selected using the correlation coefficient method (CC), full band and principal component analysis (PCA) methods recommended by TQ Analyst software; (5) Quantitative model establishment: partial least squares regression model (PLS) and principal component regression model (PCR) were established by combining mouse biochemical indicators with the corresponding ATR spectra; (6) Model prediction: Take unknown samples and bring them into the established model to calculate the prediction rate.

10. A screening and verification system for the hepatoprotective medicinal component groups of Meconopsis integrifolia, characterized in that: include: Component extraction module, used to extract total flavonoid components and total alkaloid components from Meconopsis integron; Component content determination module, used to determine the content of total flavonoid components and total alkaloid components of Meconopsis integron; The drug efficacy component group screening module is used to use CCl4 to induce HepG2 human liver cancer cell damage to model, detect the expression level of corresponding liver damage indicators, and screen the drug efficacy component group; The pharmacodynamic component group verification module is used to model acute liver injury in KM mice induced by CCl4, detect the expression levels of corresponding liver injury indicators and inflammatory factors, and verify the screened pharmacodynamic component group.

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