Method for dynamically analyzing pharmacodynamic substances based on time dosage

Abstract

The invention discloses a method for dynamically analyzing pharmacodynamic substances based on time and dosage, and relates to the technical field of pharmacodynamic substance analys.The method is characterized by comprising the steps that traditional Chinese medicine and compound time-dependent drug-containing serum or macroporous adsorption resin solvent eluent is measured based on UPLC-Q-TOF / MS, and a time factor time sequence quantity matrix (X, independent variable) is obtained; the method comprises the following steps: acquiring effect information of a time factor time sequence quantity matrix (X, independent variable) of traditional Chinese medicine and a compound by adopting related cell and animal models, and constructing an effect matrix (Y, dependent variable) of dose change; the correlation of [X] and [Y] is analyzed or deeply mined by adopting a PLS or space-time network method to obtain components related to the effect, so that the problem of relinearity between [X] and [Y] is solved, and a new thought is developed for traditional Chinese medicine compound pharmacodynamic substances.

Description

Technical Field

[0001] The present invention relates to the technical field of pharmacological substance analysis, and more particularly to a method for analyzing pharmacological substances based on time-dose dynamics. Background Art

[0002] Traditional Chinese medicine has the characteristics of "multiple components and multiple targets", and the lack of clear active substances has always been a bottleneck restricting its clinical application. In recent decades, domestic and foreign scholars have revealed the active substances of traditional Chinese medicine based on research methods such as cell and molecular biology technology and computer technology, but most of them are static research. Although static analysis is necessary in some aspects (such as the preliminary separation and identification of chemical components), it cannot fully reflect the dynamic changes of the active substance basis of traditional Chinese medicine. For example, traditional research on the basic substance of traditional Chinese medicine often first conducts systematic separation of chemical components, and then screens for biological activity or pharmacodynamic effects. This method is time-consuming and inefficient, and often the chemical components are studied more clearly, but the active ingredients are still not elucidated.

[0003] Due to the inherent connection between variables and the trend of variables changing in the same direction over time, multiple collinearity problems of variables are caused. The present invention uses partial least squares method, based on ordinary multiple regression, the ideas of soft principal component analysis and canonical correlation analysis to perform correlation analysis on the quantity matrix and the effect matrix, so as to obtain the effect components related to efficacy.

[0004] Therefore, the present invention aims to provide a method for dynamically analyzing pharmacological substances based on time and dose to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a method for dynamically analyzing pharmacological substances based on time and dose. The present invention uses UPLC-Q-TOF / MS to measure the drug-containing serum or macroporous adsorption resin solvent eluate of traditional Chinese medicine and compound prescriptions over time to obtain a time factor time series quantity matrix (X, independent variable); uses relevant cells and animal models to obtain the effect information of the time factor time series quantity matrix (X, independent variable) of the traditional Chinese medicine and compound prescriptions, and constructs an effect matrix (Y, dependent variable) of dosage changes; uses PLS or spatiotemporal network methods to analyze or deeply mine the correlation between [X] and [Y], thereby obtaining components related to the effect, solving the problem of heavy linearity between [X] and [Y], and opening up new ideas for pharmacological substances in traditional Chinese medicine compound prescriptions.

[0006] The above technical objectives of the present invention are achieved through the following technical solutions: A method for dynamically analyzing pharmacological substances based on time and dose, comprising the following steps:

[0007] S1. Prepare the test solution of the blank group and the control group, and process the test solution by ultra-high performance liquid chromatography-quadrupole tandem time-of-flight mass spectrometry, take the signal variable of the blank group as the null value, and the variable of the control group that is not the null value as the drug-derived component, and construct the quantity matrix by peak area to obtain the drug-containing serum spectrum and time series quantity matrix of the time factor of the test solution;

[0008] S2. Use relevant cell and animal models to obtain the effect information of the time factor time series matrix, and use the effect information to construct the effect matrix with dose changes;

[0009] S3. Use PLS or spatiotemporal network methods to conduct correlation analysis or deep mining on the quantity matrix and effect matrix to obtain components related to the effect and solve the problem of multilinearity between the quantity matrix and the effect matrix.

[0010] The present invention is further configured as follows: the test solution is prepared by using a time-dependent drug-containing serum method or a macroporous adsorption resin elution method.

[0011] The present invention is further configured as follows: the chromatographic conditions of the ultra-high performance liquid chromatography-quadrupole tandem time-of-flight mass spectrometry adopt ACQUITY UPLC CSH C 18 Column, specifications: 2.1 mm × 100 mm, 1.7 μm, mobile phase: 0.1% formic acid aqueous solution A and acetonitrile B, injection volume: 3 μL, flow rate: 0.3 mL·min -1 , column temperature: 40℃.

[0012] The present invention is further configured as follows: the gradient elution parameters of the mobile phase are:

[0013] Time 0 min, mobile phase A 99%, mobile phase B 1%;

[0014] Time 6min, mobile phase A 50%, mobile phase B 50%;

[0015] Time 20min, mobile phase A 1%, mobile phase B 99%;

[0016] Time 22min, mobile phase A 1%, mobile phase B 99%;

[0017] Time: 22.1 min, mobile phase A: 99%, mobile phase B: 1%.

[0018] Time: 25 min, mobile phase A: 99%, mobile phase B: 1%.

[0019] In summary, the present invention has the following beneficial effects:

[0020] 1. The present invention discloses a method for dynamic analysis of pharmacological substances based on time and dose; by establishing a UPLC-Q-TOF / MS-based method to determine the material information (in terms of peak area) of the drug-containing serum of traditional Chinese medicine and traditional Chinese medicine compound at different time points, a time factor time series quantity matrix (X, independent variable) is obtained; relevant cell and animal models are used to obtain the effect information of the drug-containing serum of traditional Chinese medicine and traditional Chinese medicine compound at different time points, and an effect matrix (Y, dependent variable) of dose changes is constructed; partial least squares method is used to analyze the correlation between [X] and [Y], obtain components related to the effect, and clarify the pharmacological substances of traditional Chinese medicine and compound for improving diseases;

[0021] 2. The present invention introduces a variable projection importance analysis method (VIP) based on partial least squares regression, establishes a regression model to process the relationship between the effect and the effect component, and determines the important effect index according to the explanatory power of regression; overcomes the shortcomings of traditional chemical component separation, explores and establishes a technical system for the research of compound effect components of traditional Chinese medicine that meets the requirements of multi-component, multi-target and efficacy-related, and solves the problem of multicollinearity of variables caused by the inherent connection between variables and the trend of variables changing in the same direction over time;

[0022] 3. The present invention discloses a method for dynamic analysis of pharmacological substances based on time and dose, that is, the PLS method is used to analyze the correlation between [X] and [Y] to obtain the effect component; this method is easy to operate and can reveal the relationship between the efficacy and dose of Yueju Pills in improving IR-L02 cells, providing a new perspective for the study of the dose-effect relationship of traditional Chinese medicine; this method integrates multidisciplinary technologies and promotes interdisciplinary and fusion. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is the total ion current diagram of the drug-containing serum of the Yueju Pills water extract over time in the embodiment of the present invention;

[0024] Figure 2 is a bar graph showing the effects of hypoxia time and insulin solution on glucose consumption of L02 cells in an embodiment of the present invention;

[0025] Figure 3 is a bar graph showing the effects of hypoxia time and insulin solution on L02 cell viability in an embodiment of the present invention;

[0026] Figure 4 Schematic diagram of the effect of different doses of Yueju Pills drug-containing serum on glucose consumption of L02 cells over time in the embodiment of the present invention;

[0027] Figure 5 This is a bar graph showing the effect of the drug-containing serum of Yueju Pills on the cell viability of IR-L02 cells over time in the embodiment of the present invention;

[0028] Figure 6 This is a bar graph showing the effect of the Yueju Pills drug-containing serum on the glucose consumption of IR-L02 cells over time in the embodiment of the present invention;

[0029] Figure 7 It is a bar graph of the VIP values ​​of the chemical components in the drug-containing serum of Yueju Pills over time in the positive ion mode in the embodiment of the present invention;

[0030] Figure 8 It is a bar graph of the VIP values ​​of the chemical components in the drug-containing serum of Yueju Pills over time in the negative ion mode in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The following is combined with Figure 1-8 The present invention is described in further detail.

[0032] The medicinal materials and reagents used in this example are as follows: Atractylodes lancea, Cyperus rotundus, Ligusticum chuanxiong, Gardenia jasminoides (Guangdong Yangxinhe Pharmacy, batch numbers: 220601, 220801, C-220090, C-220162); Shenqu (Chengdu Xingfengrui Chinese Medicine Pieces Co., Ltd., batch number: Z51022411); Gardenia glycoside, baicalin, astragaloside, quercetin reference substances (Sichuan Weikeqi Biotechnology Co., Ltd., batch numbers: wkq23010613, wkq23031004, wkq23010402, wkq22051613, purity: HPLC ≥ 98%); fetal bovine serum (Zhejiang Tianhang Biotechnology Co., Ltd., batch number: 22120704); RPMI Medium 1640 culture medium (Gibco, USA, batch number: 8122136); metformin hydrochloride (McLean reagent, batch number: M90518060); 0.25% trypsin-EDTA, penicillin-streptomycin mixture (Beijing Solebao Company, batch number: 20221012, 20220923); Cell Counting Kit-8 (CCK-8) (Beijing Bioson Biotechnology Co., Ltd., batch number: BB12229069); glucose determination kit (Shanghai Rongsheng Bio-Pharmaceutical Co., Ltd., batch number: 20221206137); water is ultrapure water.

[0033] The instruments and equipment selected are as follows: Waters Acquity UPLC liquid chromatography system, Q-TOF SYNAPT G2HDMS mass spectrometer (Waters, USA); CO2 incubator (Thermo Fisher Scientific, USA); inverted microscope (Olympus, Japan); mobile three-gas incubator (Ningbo Huayi Ningchuang Intelligent Technology Co., Ltd.); full-wavelength microplate reader (Spectra, USA); R-220SE large rotary evaporator (Rotavapor, Switzerland); SL ​​8R desktop high-speed refrigerated centrifuge (Thermo Fisher Scientific (China) Co., Ltd.).

[0034] Cell line: Normal human liver cell L02, provided by Beina Chuanglian Biotechnology Co., Ltd.

[0035] Experimental animals: SPF male SD rats, weight (180±20) g, provided by the Animal Center of Jiangxi University of Traditional Chinese Medicine; free access to food and water, temperature 18-24°C, relative humidity 50%-70%, maintain circadian rhythm of 12 h each. The experimental animal ethics committee of Jiangxi University of Traditional Chinese Medicine has passed the review standards, approval number: JZLLSC20230710.

[0036] Example: A method for analyzing pharmacological substances based on time-dose dynamics

[0037] 1. Obtain the time series matrix of time factors.

[0038] (1.1) Preparation of Yueju Pills water extract: Weigh Atractylodes lancea, Cyperus rotundus, Ligusticum chuanxiong, Gardenia jasminoides, and Shenqu according to the prescription composition ratio (1:1:1:1:1), add 8 times the amount of water to soak for 0.5 h, boil and then simmer for 1 h, filter, add 6 times the amount of water and boil in the same way, filter, mix the liquid obtained by the two filtrations, and concentrate to a crude drug concentration of 1.35 g mL -1 (According to the animal body surface area (human: rat = 1:6.3), the daily clinical dosage is 0.45g / 100g -1 (raw drug amount / body weight), use 3 times the daily clinical dosage), and store in a freezer at -20℃.

[0039] (1.2) Preparation of test solution: 80 SPF male SD rats were adaptively fed for 7 days and randomly divided into blank serum group, 0.5 h group, 1 h group, 1.5 h group, 2 h group, 3 h group, 4 h group, 6 h group, 8 h group, 12 h group, and 24 h group; the time-dependent drug serum group was divided into 10 groups, i.e., 10 blood collection time points, with 5 SD rats in each group. The time-dependent drug serum group was given 1.35 g mL-1 of the drug solution at a gavage dose of 1 mL per 100 g body weight. -1For the aqueous extract of Yueju Pills, the blank group was given normal saline for 7 days, and fasted for 12 hours before the last administration. Blood was collected from the abdominal aorta 2 hours after the last administration, and the blood was allowed to stand, centrifuged at 5000rpm for 15 minutes, and the supernatant was taken. The blood was placed in a 56℃ water bath for 30 minutes, filtered with a 0.22μm sterilizing filter membrane, and dispensed into cryopreservation tubes and stored at -80℃. 100μL of blank serum and drug-containing serum of the aqueous extract of Yueju Pills were accurately pipetted, and then 300μL of methanol was added to fully vortex, allowed to stand, centrifuged at 4℃ and 15000rpm for 15 minutes, and the supernatant was taken. After concentration, 200μL of methanol was added to reconstitute, vortexed, and centrifuged at 4℃ and 15000rpm for 15 minutes. 100μL of the supernatant was taken and placed in a sample injection vial for standby use.

[0040] (1.3) Establishment of the spectrum of blank serum and drug-containing serum of the test product Yueju Pills: Chromatographic conditions: ACQUITY UPLCCSH C18 column (2.1 mm × 100 mm, 1.7 μm), mobile phase: 0.1% formic acid aqueous solution (A)-acetonitrile (B), injection volume: 3 μL, flow rate: 0.3 mL·min -1 , column temperature: 40℃.

[0041] Mass spectrometry conditions: positive ion mode using electrospray ionization source (ESI); capillary voltage 3.0 kV; ionization source temperature 100 °C; cone gas flow rate 50 L·h -1 ; Desolvation temperature 400℃; Flow rate 800L·h -1 ; Cone voltage 40V; mass number collection range 100~1500Da. ESI source, capillary voltage 2.5kV; ionization source temperature 100℃; cone gas flow rate 50L·h -1 ; Desolvation temperature 400℃; Flow rate 800L·h -1 ; Cone voltage 40V; mass number collection range 100~1500Da.

[0042] The mobile phase gradient elution parameters are shown in Table 1:

[0043] Table 1 Mobile phase gradient elution parameters

[0044] Time (min) Mobile phase A (%) Mobile phase B (%) 0 99 1 6 50 50 20 1 99 22 1 99 22.1 99 1 25 99 1

[0045] (1.4) Data acquisition and processing: MassLynx V4.1 software was used to collect data signals from the test solution. The results are shown in Figure 1 , Progenesis QI software was used to preprocess the collected data, including molecular feature extraction, peak alignment, etc., and the .txt format file was exported. The signal variable collected by the blank group was taken as the null value, and the variables in other groups that were not null values ​​were studied as drug-derived components. R(Example 246.1057@1.90) describes the variable compound and uses its peak area to construct the X matrix of the component. The positive ion mode is shown in Table 2:

[0046] Table 2 Positive ion mode - Time-dependent change matrix of Yueju Pill-containing serum group at different time points (X 正 matrix)

[0047]

[0048] Negative ion mode is shown in Table 3:

[0049] Table 3 Negative ion mode - Time-varying matrix of serum group containing Yueju pills at different time points (X 负 matrix)

[0050]

[0051]

[0052]

[0053] 2. Establish an efficacy matrix for dose variation.

[0054] (2.1) Preparation of drug-containing serum: 80 SPF male SD rats were adaptively fed for 7 days and randomly divided into blank serum group, 0.5 h group, 1 h group, 1.5 h group, 2 h group, 3 h group, 4 h group, 6 h group, 8 h group, 12 h group, and 24 h group. The time-dependent drug-containing serum group was divided into 10 groups, i.e., 10 blood collection time points, with 5 SD rats in each group. The time-dependent drug-containing serum group was given 1.35 g mL-1 of 1 mL per 100 g body weight. -1 Yueju Pills water extract: The blank group was given normal saline by gavage for 7 days. The mice were fasted for 12 hours before the last administration. Blood was collected from the abdominal aorta 2 hours after the last administration, and the blood was allowed to stand. The blood was centrifuged at 5000 rpm for 15 minutes, and the supernatant was collected. The blood was bathed at 56°C for 30 minutes, filtered with a 0.22 μm sterilizing filter membrane, and dispensed into cryovials and stored at -80°C.

[0055] (2.2) Establishment of hypoxia-induced IR-L02 cell model: L02 cells were cultured in RPMI 1640 medium (containing 2% fetal bovine serum and 1% penicillin-streptomycin) at 37°C in a CO2 incubator. After three passages, cells in the logarithmic growth phase were selected for the experiment. The normal group was cultured in normoxic conditions (37°C, CO2 incubator), and the model group was placed in a mobile three-gas incubator. When the gas concentration in the culture environment reached the hypoxic condition (94% N2, 5% CO2, 1% O2), the timing began. Four hypoxic time points (12, 24, 36, and 48 h) and three insulin solution concentrations (10 -7 mol·L -1 , 10-8 mol·L -1 , 10 -9 mol·L -1 ), each group was set up with 6 replicate wells, the results are shown in Figure 2 , Figure 3 By investigating the effects of different hypoxia time and different insulin solution concentrations on glucose consumption and cell viability of L02 cells, the statistically significant difference in glucose consumption between the two groups of cells was used as the basis for the success of the model, and the optimal hypoxia time and insulin solution concentration were determined.

[0056] (2.3) Optimal time-dependent drug-containing serum addition: 5 gradients of serum addition were set at 1%, 5%, 10%, 15%, and 20%, according to EC 50 Explore the serum addition amount of drug-containing serum over time, EC 50 ≈10%, so the test was carried out with 10% serum addition, see Figure 4 .

[0057] (2.4) Cell grouping: cell density is 1.3×10 4 The cells were inoculated into 96-well plates and the normal serum group, model group, and drug-containing serum groups at different time points were set up. The normal serum group was cultured in normoxia, while the model group and drug-treated group were cultured in 1% O2 hypoxia. See Table 4 for details.

[0058] Table 4 Experimental grouping table

[0059] Group composition Normal serum group 10% blank serum + 2% FBS culture medium Model Group <![CDATA[10% blank serum + 2% FBS medium + 10 -9 mol·L -1 insulin solution]]> YJP-0.5h drug-containing serum group <![CDATA[10% YJP-0.5h drug-containing serum + 2% FBS medium + 10 -9 mol·L -1 insulin solution]]> YJP-1h drug-containing serum group <![CDATA[10% YJP-1h drug-containing serum + 2% FBS medium + 10 -9 mol·L -1 insulin solution]]> YJP-1.5h drug-containing serum group <![CDATA[10% YJP-1.5h drug-containing serum + 2% FBS medium + 10 -9 mol·L -1 insulin solution]]> YJP-2h drug-containing serum group <![CDATA[10% YJP-2h drug-containing serum + 2% FBS medium + 10 -9 mol·L -1 insulin solution <!-- 7 -->]]> YJP-3h drug-containing serum group <![CDATA[10% YJP-3h drug-containing serum + 2% FBS medium + 10 -9 mol·L -1 insulin solution]]> YJP-4h drug-containing serum group <![CDATA[10% YJP-4h drug-containing serum + 2% FBS medium + 10 -9 mol·L -1 insulin solution]]> YJP-6h drug-containing serum group <![CDATA[10% YJP-6h drug-containing serum + 2% FBS medium + 10 -9 mol·L -1 insulin solution]]> YJP-8h drug-containing serum group <![CDATA[10% YJP-8h drug-containing serum + 2% FBS medium + 10 -9 mol·L -1 insulin solution]]> YJP-12h drug-containing serum group <![CDATA[10% YJP-12h drug-containing serum + 2% FBS medium + 10 -9 mol·L -1 insulin solution]]> YJP-24h drug-containing serum group <![CDATA[10% YJP-24h drug-containing serum + 2% FBS medium + 10 -9 mol·L -1 insulin solution]]>

[0060] Glucose consumption and cell viability were detected 24 h after hypoxia induction, and the mean glucose consumption of each group was used as the effect variable to construct the effect matrix (Y matrix) of dose changes.

[0061] (2.5) Statistical analysis: GraphPad Prism 8.0 statistical software was used for data analysis, and the quantitative data were expressed as mean ± standard deviation. One-way ANOVA was used for comparison among multiple groups, and t-test was used for comparison between two groups. P < 0.05 was considered statistically significant.

[0062] (2.6) Effects of hypoxia time and insulin solution on glucose consumption and cell viability of L02 cells: Compared with the normal group, 10 -9 mol·L -1 The glucose consumption of the insulin solution group at 12, 24, 36, and 48 hours of hypoxia was significantly reduced (P < 0.05, P < 0.01). -7 mol·L -1 , 10 -8 mol·L-1 The glucose consumption of the insulin solution group at 24, 36, and 48 hours of hypoxia was significantly reduced (P < 0.01); the 10 -8 mol·L -1 with 10 -9 mol·L -1 The cell viability of the insulin solution group increased, and the 10 -7 mol·L -1 The cell viability of the insulin solution group increased, with significant differences (P < 0.05, P < 0.01). The cell viability of the three concentrations of insulin solution groups after 48 hours of hypoxia was significantly reduced (P < 0.01). The results are shown in Tables 5, 6, Figure 2 , Figure 3 .

[0063] Table 5 Effects of hypoxia time and insulin solution on glucose consumption of L02 cells

[0064]

[0065] Among them, compared with the normal group, **P<0.01, *P<0.05.

[0066] Table 6 Effects of hypoxia time and insulin solution on glucose consumption of L02 cells

[0067]

[0068]

[0069] Among them, compared with the normal group, **P<0.01, *P<0.05.

[0070] Therefore, the hypoxia time was selected as 24h and 10 -9 mol·L -1 The concentration of insulin solution serves as the optimal hypoxia time and modeling concentration for the hypoxia-induced L02 cell model.

[0071] (2.7) Effects of Yuejuwan time-dependent serum on cell viability and glucose consumption of IR-L02 cells: Compared with the model group, Yuejuwan time-dependent serum groups at different time points and the normal serum group, there was no change in cell viability (P>0.05), see Table 7, Figure 5 .

[0072] Table 7 Effect of Yueju Pills drug-containing serum on cell viability of IR-L02 cells over time

[0073] Group <![CDATA[Relative cell viability (A 450 )]]> Normal serum group 1.00±0.02 Model Group <![CDATA[0.81±0.01 ** ]]> YJP-0.5h <![CDATA[0.82±0.03 ** ]]> YJP-1h <![CDATA[0.86±0.03 ** ]]> YJP-1.5h <![CDATA[0.83±0.04 ** ]]> YJP-2h <![CDATA[0.83±0.04 ** ]]> YJP-3h <![CDATA[0.84±0.04 ** ]]> YJP-4h <![CDATA[0.87±0.03 ** ]]> YJP-6h <![CDATA[0.86±0.05 ** ]]> YJP-8h <![CDATA[0.87±0.01 ** ]]> YJP-12h <![CDATA[0.89±0.02 ** ]]> YJP-24h <![CDATA[0.86±0.03 ** ]]>

[0074] Among them, compared with the normal serum group, **P<0.01.

[0075] Compared with the normal serum group, the glucose consumption of the model group was significantly reduced (P < 0.01), indicating that the model was successfully established; compared with the model group, the glucose consumption at 2h and 12h increased, and the IR was improved (P < 0.05), as shown in Table 8. Figure 6 .

[0076] Table 8 Effect of Yueju Pills drug-containing serum on glucose consumption of IR-L02 cells over time

[0077] Group <![CDATA[Glucose consumption (mmol·L -1 )]]> Normal serum group 5.25±0.70 Model Group <![CDATA[3.28±0.21 ## ]]> YJP-0.5h 3.00±0.53 YJP-1h 3.90±0.39 YJP-1.5h 3.83±0.43 YJP-2h <![CDATA[4.21±0.18 * ]]> YJP-3h 3.23±0.84 YJP-4h 3.82±0.54 YJP-6h 3.78±0.66 YJP-8h 3.70±0.65 YJP-12h <![CDATA[4.22±0.58 * ]]> YJP-24h 4.00±0.66

[0078] Among them, compared with the normal serum group, ##P<0.01; compared with the model group, *P<0.05.

[0079] 3. Correlation analysis of the serum dose-effect matrix of Yueju Pills over time.

[0080] (3.1) Principle of Partial Least Squares: Partial least squares is an innovative multivariate statistical analysis method. Based on ordinary multivariate regression, it softens the ideas of principal component analysis and canonical correlation analysis and solves the problem of multicollinearity between independent variables. By using PLS to analyze the correlation between [X] and [Y], the effect component can be obtained. Assume that effect Y is an observable random variable, which is affected by n non-random factors X1, X2, X3..., X n Therefore, there is the following linear relationship:

[0081] Y1~[k 11 X 11 +k 12 X 12 +k 13 X 13 +……+k 1n X 1n ]

[0082] If the effect Y variable is observed m times, m groups of observation data are obtained. If the effect Y is observed at m doses, the following linear relationship exists (generally m>n):

[0083]

[0084] It can be expressed in matrix form as follows:

[0085]

[0086] Among them, Y is called the observed variable, X is called the design matrix, and k is called the regression coefficient.

[0087] (3.2) Results of the dose-effect matrix correlation analysis: After the [X] and [Y] matrix data were processed by the Min-max standardization method, they were imported into the SIMCA-P software to construct a PLS model to obtain the VIP values ​​of the chemical components in the drug-containing serum of Yueju Pills under positive and negative ion modes. The results are as follows: Figure 7 , Figure 8 As shown. The signal acquisition data of VIP>1 were processed with the help of Progenesis QI software, and the possible compounds were speculated based on the retention time, primary and secondary mass spectrometry fragment information of the compounds, combined with PubChem, MassBank 2.1.6, and HMDB 5.0. Using VIP>1 as the threshold for screening, the PLS models constructed by the positive and negative ion mode data predicted that 4 and 14 effect components had a greater impact on glucose consumption, respectively, suggesting that these components may be related to the drug-containing serum to increase the glucose consumption of IR-L02 cells. The results are shown in Table 9.

[0088] Table 9 Effect component table (VIP>1)

[0089]

[0090]

[0091] The effective components with VIP>1 in the table were verified by standard samples and mass spectrometry secondary fragmentation verification. 389.1821@16.38 and 447.3382@19.83 were identified as gardenia glycoside and baicalin in positive ion mode; 941.274@7.37 and 301.0738@9.85 were identified as astragaloside and quercetin in negative ion mode.

[0092] 4. Verification of the efficacy of 4 compounds in improving IR-L02 cells.

[0093] The screened compounds (gardenia glycoside, baicalin, astragalus glycoside, quercetin) were validated by cell-based pharmacological tests. L02 cells were grown at a cell density of 1.3×10 4 Each well was inoculated with (3, 1, 0.3, 0.1, 0.03 μmol·L -1 ) concentrations of gardenia glycoside, baicalin, astragaloside, and quercetin were cultured for 24 h, and glucose consumption was measured. 50 As a measure of the drug's strength of action, the results are shown in Table 10.

[0094] Table 10 EC values ​​of the compounds 50 value

[0095] Compound <![CDATA[EC 50 ]]> Quercetin 0.30 Gardenia glycoside 0.03 Astragaloside 0.02 Baicalin 4.45

[0096] It can be seen that all four compounds can effectively improve insulin resistance.

[0097] This specific embodiment is merely an explanation of the present invention and is not a limitation of the present invention. After reading this specification, those skilled in the art may make non-creative modifications to the present embodiment as needed. However, as long as they are within the scope of the claims of the present invention, they are protected by the patent law.

Claims

1. A method for analyzing pharmacological substances based on time-dose dynamics, characterized in that: The following steps are involved: S1. Prepare the test solution of the blank group and the control group, and process the test solution by ultra-high performance liquid chromatography-quadrupole tandem time-of-flight mass spectrometry, take the signal variable of the blank group as the null value, and the variable of the control group that is not the null value as the drug-derived component, and construct the quantity matrix by peak area to obtain the drug-containing serum spectrum and time series quantity matrix of the time factor of the test solution; S2. Use relevant cell and animal models to obtain the effect information of the time factor time series matrix, and use the effect information to construct the effect matrix with dose changes; S3. Use PLS or spatiotemporal network methods to conduct correlation analysis or deep mining on the quantity matrix and effect matrix to obtain components related to the effect and solve the problem of multilinearity between the quantity matrix and the effect matrix.

2. The method for analyzing pharmacological substances based on time-dose dynamics according to claim 1, characterized in that: The test solution is prepared by using a time-dependent drug-containing serum method or a macroporous adsorption resin elution method.

3. The method for analyzing pharmacological substances based on time-dose dynamics according to claim 1, characterized in that: The chromatographic conditions of the ultra-high performance liquid chromatography-quadrupole tandem time-of-flight mass spectrometry were ACQUITY UPLC CSH C 18 Column, specifications: 2.1 mm × 100 mm, 1.7 μm, mobile phase: 0.1% formic acid aqueous solution A and acetonitrile B, injection volume: 3 μL, flow rate: 0.3 mL·min -1 , column temperature: 40℃.

4. The method for analyzing pharmacological substances based on time-dose dynamics according to claim 3, characterized in that: The gradient elution parameters of the mobile phase are: time 0 min, mobile phase A 99%, mobile phase B 1%; Time 6 min, mobile phase A 50%, mobile phase B 50%; Time 20min, mobile phase A 1%, mobile phase B 99%; Time 22min, mobile phase A 1%, mobile phase B 99%; Time: 22.1 min, mobile phase A 99%, mobile phase B 1%. Time: 25 min, mobile phase A 99%, mobile phase B 1%.

Images