A method for mining key pathways of pharmacodynamic substances and pharmacodynamic indicators of traditional Chinese medicine based on PLS-SEM

Abstract

This invention discloses a method for mining critical pathways of pharmacodynamic substance groups and pharmacodynamic index groups in traditional Chinese medicine (TCM) based on PLS-SEM. Using experimental data of TCM pharmacodynamic substances and indices as the foundation, a PLS-SEM model is constructed. Several pharmacodynamic substances in TCM are selected as independent variables, and several pharmacodynamic indexes are selected as dependent variables. Variable grouping is optimized to establish causal path models of reaction-reaction-formation or formation-reaction-formation. Using this method to explore the pharmacodynamic data of different TCM formulas, it can mine groups of pharmacodynamic substances with similar efficacy, groups of pharmacodynamic indexes with similar characteristics, and the optimal pathways for pharmacodynamic substances to act on pharmacodynamic indexes. Experiments show that this method is feasible and effective, providing support for elucidating the principles of TCM and offering a reference for new drug development.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of traditional Chinese medicine formula efficacy mining, and particularly relates to a method for mining a key path of a traditional Chinese medicine efficacy substance group and an efficacy index group based on PLS-SEM. BACKGROUND

[0002] Traditional Chinese medicine has complex chemical components, different components of single herbs are matched, different drugs of compound prescriptions exist, and some drug components exist in the serum of the body. Therefore, traditional Chinese medicine has multiple components, multiple targets, multiple pathways and multiple efficacy effects, which causes the substance components exerting efficacy to be unclear, and the relationship between different traditional Chinese medicine components and different efficacy indexes is unclear, which brings difficulties to the clinical rational use of traditional Chinese medicine.

[0003] Existing researches usually adopt spectrum-effect kurtosis correlation, network pharmacology or system pharmacology, correlation analysis, association analysis, partial least squares method, etc. to study the components related to efficacy as a component group. In the prior art, high performance liquid chromatography (HPLC) is used to analyze the relationship between efficacy components and efficacy indexes, which can reflect the comprehensive action characteristics of multiple efficacy components on efficacy indexes, and is suitable for screening of efficacy-related components of a complex system of traditional Chinese medicine. However, this method needs to use relatively complex HPLC, and the results reflected by the peak area of the chromatogram are not intuitive enough. Although the related traditional Chinese medicine efficacy research methods have made extraordinary achievements in recent years, they still cannot well reflect the overall efficacy, overall regulation and effective intervention on diseases of traditional Chinese medicine. The methodology and methodology of traditional Chinese medicine efficacy research are still the bottleneck problem of traditional Chinese medicine pharmacology.

[0004] The research team led by the inventor Nie Bin previously used orthogonal partial least squares (PLS) to study the relationship between single efficacy components of traditional Chinese medicine and efficacy indexes, and found that there is a complex action relationship. However, it only established a PLS regression equation for single efficacy components, and did not group all efficacy components for research, resulting in the action relationship between single efficacy components and efficacy indexes in the research results being relatively scattered and not having a system and pertinence.

[0005] Partial Least Squares Structural Equation Modeling (PLS-SEM) is a powerful second-generation statistical analysis method, which uses Partial Least Squares (PLS) technology to define a variable explanatory structure with linear combination of variables, and then uses regression principle to test the prediction and explanation relationship between principal components. This method has advantages that other methods do not have: ①It can overcome the influence of small sample, non-normal distribution, multicollinearity, measurement error, ensure convergence, and make statistical estimation more accurate; ②It is suitable for calculation and explanation of complex relationship structure model with multiple independent variables, multiple dependent variables, interaction between variables, direct and indirect path mode, etc. Therefore, the advantages of PLS-SEM are more consistent with the analysis of traditional Chinese medicine efficacy data. SUMMARY

[0006] The purpose of the present application is to provide a method for mining the key path of traditional Chinese medicine efficacy substance group and efficacy index group based on PLS-SEM, which constructs a PLS-SEM model based on the experimental data of efficacy substances and efficacy indexes of traditional Chinese medicine formula. The method can mine the similar efficacy substance group, similar efficacy index group, and the path of efficacy substance acting on efficacy index of different traditional Chinese medicine formulas. If the P value of each parameter is significant, it is considered as a key path. Experimental verification shows that the method is feasible and effective, which supports the elucidation of traditional Chinese medicine principles and provides a reference for the development of new drugs.

[0007] The technical solution of the present application is as follows:

[0008] A method for mining the key path of traditional Chinese medicine efficacy substance group and efficacy index group based on PLS-SEM, which constructs a PLS-SEM model based on the experimental data of efficacy substances and efficacy indexes of traditional Chinese medicine formula, selects several efficacy substances in traditional Chinese medicine as independent variables, several efficacy indexes as dependent variables, optimizes variable grouping, and establishes a causal path model reflecting the formation type or the formation type reflecting the formation type.

[0009] The causal path model sequentially includes an initial causal path model, a grouped causal path model (which can also include an optimized grouped causal path model, a re-optimized grouped causal path model, etc. according to actual needs) according to the modeling steps, which are both composed of corresponding exogenous and endogenous modes. The exogenous mode includes an independent variable exogenous mode and a dependent variable exogenous mode. The independent variable exogenous mode takes corresponding efficacy substances as measurement independent variables and corresponding efficacy substance groups as exogenous latent variables. The dependent variable exogenous mode takes corresponding efficacy indexes as measurement dependent variables and corresponding efficacy index groups as endogenous latent variables. The endogenous mode is composed of corresponding causal paths corresponding to the efficacy substance groups and the efficacy index groups.

[0010] Preferably, the drug efficacy substances are derived from serum medicine components of traditional Chinese medicine compound, medicine compatible with traditional Chinese medicine compound or medicine compatible with components of traditional Chinese medicine; the exogenous mode is reflective or formative, and the endogenous mode is reflective.

[0011] A method for mining a key path of a group of drug efficacy substances and a group of drug efficacy indexes of traditional Chinese medicine based on PLS-SEM, specifically comprising the following steps:

[0012] S1, establishing an initial causal path model, the initial causal path model comprising an exogenous mode 1 and an endogenous mode 1, the exogenous mode 1 comprising an independent variable exogenous mode 1 and a dependent variable exogenous mode 1, the independent variable exogenous mode 1 taking all drug efficacy substances as measured independent variables and taking an initial group of drug efficacy substances as exogenous latent variables, the dependent variable exogenous mode 1 taking all drug efficacy indexes as measured dependent variables and taking an initial group of drug efficacy indexes as endogenous latent variables, the endogenous mode 1 being formed by the initial group of drug efficacy substances and the initial group of drug efficacy indexes corresponding to a cause-and-effect path, and the variables being optimized by selecting a better path through PLS-SEM model calculation;

[0013] S2, establishing a grouped causal path model based on step S1, the causal path model comprising an exogenous mode 2 and an endogenous mode 2, the exogenous mode 2 comprising an independent variable exogenous mode 2 and a dependent variable exogenous mode 2, the independent variable exogenous mode 2 taking each group of drug efficacy substances optimized in step S1 as measured independent variables and taking grouped drug efficacy substance groups as exogenous latent variables, the dependent variable exogenous mode 2 taking each group of drug efficacy indexes optimized in step S1 as measured dependent variables and taking grouped drug efficacy index groups as endogenous latent variables, the endogenous mode 2 being formed by the grouped drug efficacy substance groups and the grouped drug efficacy index groups crossing to form a cause-and-effect path, and the variables being optimized by selecting a better path through PLS-SEM model calculation;

[0014] S3, optimizing based on step S2 to establish an optimized grouped causal path model, the method being the same as that of the grouped causal path model of step S2, the variables being optimized by selecting a better path through PLS-SEM model calculation until optimization is not possible, or stopping according to actual needs;

[0015] S4, arranging the results to obtain groups of drug efficacy substances and groups of drug efficacy indexes with similar efficacy and their key paths.

[0016] Preferably, the variable optimization criteria of the exogenous mode comprise but are not limited to any one or several of the following:

[0017] (1) Check the reliability of each measurement variable: the absolute value of factor loading (indicating the size of correlation), the value is selected above or strong (factor loading≥0.70 is strong, 0.40≤factor loading<0.70 is moderate, 0≤factor loading<0.40 is weak), that is, the absolute value of factor loading is selected to be large, the critical value is 0.7 or 0.4, the different signs are separated, the same signs are combined into a group, and the significance of P value and T value of factor loading is checked;

[0018] (2) Check the combined reliability of each latent variable: CR (the standard of exploratory research, representing internal consistency) is positive (indicating good consistency between variables), and the variables with CR≥0.60 (CR≥0.70 is strong, 0.60≤CR<0.70 is acceptable) are combined into a group, and the variables with negative CR (indicating possible negative correlation between variables) are rechecked for factor loading and adjusted for correlation;

[0019] (3) Check the convergence validity AVE, AVE≥0.5;

[0020] (4) Check the correlation, Cronbach's alpha coefficient is positive;

[0021] (5) Check the discriminant validity, use Fomell-Larcker criterion or cross loading to distinguish and group variables, the Fomell-Larcker criterion is: the root value of AVE of each construct must be greater than the correlation coefficient with other constructs (latent variables); the cross loading is: the highest factor loading of each variable should be in the construct to be measured, and the value will be smaller in other constructs.

[0022] When the outer model is formative, the following criteria also need to be considered:

[0023] (6) Multicollinearity: VIF<5;

[0024] (7) Outer model index weight;

[0025] (8) The significance of P value and T value of outer model index weight.

[0026] Preferably, in step S1, the variable optimization criteria of outer model 1 include: checking the reliability of each variable: the absolute value of factor loading, the value is selected to be large, the critical value is 0.7 or 0.4, the different signs are separated, and the same signs are combined into a group.

[0027] Preferably, the variable optimization criteria of inner model include but are not limited to any one or more of the following:

[0028] ① Check the significance of P value and T value of path coefficient (its size indicates the strength of the relationship between two latent variables);

[0029] ② Check the explanatory power R of each variable 2 , the value is moderate and above (R 2 ≥ 0.670 is strong, 0.333≤R 2 < 0.670 is moderate, 0.190≤R 2 < 0.333 is weak);

[0030] ③ Predictive power and cross-validity verification: Q 2 > 0 (Q 2 , that is, Q2predict, Q 2 ≥ 0.35 is strong, 0.15≤Q 2 < 0.35 is moderate, 0.02≤Q 2 < 0.15 is weak).

[0031] Preferably, the pharmacodynamic substance is derived from the serum drug components of traditional Chinese medicine compound, the traditional Chinese medicine compound is Dachengqi Decoction, and the Dachengqi Decoction includes Dachengqi Decoction original prescription and uniform design Dachengqi Decoction compatibility prescription, and the experimental data is the experimental data of Dachengqi Decoction in treating acute pancreatitis.

[0032] The serum drug components include emodin, rhein, chrysophanol, aloe-emodin, emodin methyl ether, magnolol, honokiol, hesperidin, and hesperetin, and the pharmacodynamic indexes include calcium ions, pancreatic lipase, interleukin-6, interleukin-10, lymphocyte function-associated antigen 1a, amylase, lung index, and survival rate.

[0033] The pharmacodynamic substance group obtained in step S4 is pharmacodynamic substance group 1 or pharmacodynamic substance group 2, the pharmacodynamic substance group 1 includes magnolol and hesperidin, and the pharmacodynamic substance group 2 includes magnolol, honokiol, and hesperidin; the obtained pharmacodynamic index group includes pharmacodynamic index group 1 and pharmacodynamic index group 2, the pharmacodynamic index group 1 includes amylase, interleukin-6, lymphocyte function-associated antigen 1a, and pancreatic lipase, and the pharmacodynamic index group 2 includes interleukin-10 and calcium ions.

[0034] The obtained key path includes: the pharmacodynamic substance group 1 or the pharmacodynamic substance group 2 reduces the index value of the pharmacodynamic index group 1 and increases the index value of the pharmacodynamic index group 2.

[0035] Preferably, the pharmacodynamic substance is derived from the serum drug components of traditional Chinese medicine compound, the traditional Chinese medicine compound is Maxing Shigan Decoction, and the Maxing Shigan Decoction includes Maxing Shigan Decoction original prescription and uniform design Maxing Shigan Decoction compatibility prescription, and the experimental data is the experimental data of Maxing Shigan Decoction in treating fever.

[0036] The serum drug components include ephedrine, pseudoephedrine, methylephedrine, amygdalin, wild black cherry glycoside, glycyrrhizin, glycyrrhizin, glycyrrhizic acid, and the pharmacodynamic indexes include prostaglandin E2, body temperature response index, 6h fever inhibition rate.

[0037] The pharmacodynamic substance group I obtained in step S4 includes ephedrine, methylephedrine and pseudoephedrine, the pharmacodynamic index group obtained includes pharmacodynamic index group I and pharmacodynamic index group II, the pharmacodynamic index group I includes body temperature response index, and the pharmacodynamic index group II includes 6h fever inhibition rate.

[0038] The key path obtained includes that the pharmacodynamic substance group I makes the index value of the pharmacodynamic index group I increase and makes the index value of the pharmacodynamic index group II decrease.

[0039] Preferably, the pharmacodynamic substance is derived from a compatibility drug of a traditional Chinese medicine formula, the traditional Chinese medicine formula is Da Chengqi Decoction, the Da Chengqi Decoction includes Da Chengqi Decoction original formula and uniform design Da Chengqi Decoction compatibility formula, and the experimental data is experimental data of the Da Chengqi Decoction in treating acute pancreatitis.

[0040] The compatibility drug includes rhubarb, magnolia officinalis, immature bitter orange fruit and mirabilite, and the pharmacodynamic indexes include calcium ions, pancreatic lipase, interleukin-6, interleukin-10, lymphocyte function-associated antigen 1a, amylase, lung index and survival rate.

[0041] The pharmacodynamic substance group A obtained in step S4 includes magnolia officinalis, the pharmacodynamic index group obtained includes pharmacodynamic index group A and pharmacodynamic index group B, the pharmacodynamic index group A includes amylase and pancreatic lipase, and the pharmacodynamic index group B includes interleukin-10, calcium ions and survival rate.

[0042] The key path obtained includes that the pharmacodynamic substance group A makes the index value of the pharmacodynamic index group A decrease and makes the index value of the pharmacodynamic index group B increase.

[0043] Preferably, the pharmacodynamic substance is derived from a compatibility drug of a traditional Chinese medicine component, the compatibility drug of the traditional Chinese medicine component is a compatibility drug of Polygonum orientale component, and the experimental data is experimental data of the compatibility drug of the Polygonum orientale component in resisting hypoxia / reoxygenation.

[0044] The compatibility drug of the Polygonum orientale component includes phloridzin, quercetin and vitexin, and the pharmacodynamic indexes include cell viability, leakage rate of lactate dehydrogenase and nitric oxide level.

[0045] The pharmacodynamic substance group a obtained in step S4 includes phloridzin, and the pharmacodynamic index group a obtained includes leakage rate of lactate dehydrogenase and nitric oxide level.

[0046] The key path obtained includes that the pharmacodynamic substance group a makes the index value of the pharmacodynamic index group a increase.

[0047] The present application has the following beneficial effects:

[0048] 1. The method of the present application is to establish a relationship model between the pharmacodynamic substances and the pharmacodynamic indexes, and to select and iteratively calculate quantitatively by PLS-SEM, to obtain the pharmacodynamic substance group, the pharmacodynamic index group with similar efficacy (up or down), and the action path therebetween from numerous component pharmacodynamic indexes and numerous complex actions, which can better reflect the characteristics of the component action pharmacodynamic indexes and their action pathways.

[0049] 2. The present application proposes the concept of the pharmacodynamic substance group, the pharmacodynamic index group with similar efficacy (up or down), and the key path concept from the pharmacodynamic substance group to the pharmacodynamic index group, which is realized by screening the factor loading quantity and the Cronbach's coefficient, and experimental verification shows that the method is feasible and effective, which supports the elucidation of the principles of traditional Chinese medicine and provides a reference for the development of new drugs.

[0050] 3. As can be known from the verification effect of the embodiments, the present application establishes the key path of the pharmacodynamic substance group and the pharmacodynamic index group of the serum drug components of traditional Chinese medicine compound, the compatibility of drugs of traditional Chinese medicine compound, and the compatibility of drugs of traditional Chinese medicine components, which is highly feasible, especially the verification effect of the serum drug components of Dachengqi Decoction is better. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 It is a PLS-SEM model diagram of the reflection type of the present application;

[0052] Figure 2 It is a PLS-SEM model diagram of the reflection type of the present application;

[0053] Figure 3 It is a diagram of the factor loading quantity, the path coefficient, and the explanation ability in the first optimization grouping path model of embodiment 2 of the present application;

[0054] Figure 4 It is the significance result of Bootstrapping in the first optimization grouping model of embodiment 2 of the present application;

[0055] Figure 5 It is a diagram of the factor loading quantity, the path coefficient, and the explanation ability in the initial path model of embodiment 3 of the present application;

[0056] Figure 6 It is the significance result of Bootstrapping in the initial path model of embodiment 3 of the present application;

[0057] Figure 7 It is a diagram of the factor loading quantity, the path coefficient, and the explanation ability in the initial path model of embodiment 4 of the present application;

[0058] Figure 8Figure of factor loading, path coefficient and explanation ability in initial path model of Example 5 of the present application;

[0059] Figure 9 Significant result of Bootstrapping in initial path model of Example 5 of the present application.

[0060] wherein, Figure 1 Figure 1: Rectangular box represents measured variable, circular box represents latent variable, χ represents independent variable, y represents dependent variable, X represents exogenous latent variable explained by independent variable χ, Y represents endogenous latent variable explained by dependent variable y, λ represents factor loading, γ represents path coefficient.

[0061] Figure 2 Figure 2: Each symbol refers to the meaning and Figure 1 the same as Figure 1.

[0062] Figure 3 , 5 , 7, 8: Except that the number in the circular box of pharmacodynamic index group represents the explanation ability of pharmacodynamic index group, the meaning of the remaining numbers, rectangular boxes and circular boxes is the same as Figure 1 Figure 1.

[0063] Figure 4 , 6 , 9: All numbers represent significant P value, the meaning of the remaining rectangular boxes and circular boxes is the same as Figure 1 Figure 1. DETAILED DESCRIPTION

[0064] The technical solutions in the embodiments of the present application will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0065] Unless otherwise specified, the reagents involved in the embodiments of the present application are all commercially available and can be purchased through commercial channels.

[0066] I. Explanation of some special terms in the present application:

[0067] 1. Pharmacodynamic substance group: refers to a collection of a group of pharmacodynamic components (pharmacodynamic substances) with the same or similar effects on pharmacodynamic indexes, mainly refers to pharmacodynamic substance groups with similar functions and indications in the present application.

[0068] 2. Pharmacodynamic index group: refers to a collection of a group of pharmacodynamic indexes with the same or similar effects.

[0069] 3. Key path: the path of the action of the pharmacodynamic substance group on the pharmacodynamic index group, and the path has statistical significance.

[0070] 4. Reflective model: a model in which the latent variable reflects the measured variable, and the latent variable is the cause and the measured variable is the effect. The latent variable is considered to produce the observed variable (for example, the personality characteristics of the latent variable and the attitude of the observed variable).

[0071] 5. Formative model: a model that explores the latent variables that form the cause, and the measured variable is the cause and the latent variable is the effect. Formative indicators produce observable variables that are not part of the theoretical structure (for example, social status structure is produced by occupation, income, and place of residence).

[0072] II. Software and algorithm settings

[0073] The PLS-SEM calculation of the present application uses SmartPLS software, and part of the parameter settings are shown in Table 1 (reference literature "Ringle, C. M., Wende, S., and Becker, J.-M. 2022.", software type "SmartPLS 4." Oststeinbek: SmartPLS GmbH).

[0074] Table 1 PLS-SEM algorithm and Bootstrapping parameter settings

[0075]

[0076]

[0077] III. Key path of traditional Chinese medicine compound serum pharmacodynamic substance group and pharmacodynamic index group based on PLS-SEM

[0078] Example 1 Dachengqi Decoction for the treatment of acute pancreatitis (the optimization criterion for factor loading when grouping the model and optimizing the grouping model is strong ≥ 0.70)

[0079] Based on the experimental data of Dachengqi Decoction serum drug components for the treatment of acute pancreatitis, a PLS-SEM model was constructed, and several serum drug components in Dachengqi Decoction were selected as independent variables, and several pharmacodynamic indexes were selected as dependent variables. The reflective formative path model was established by optimizing the variable grouping, and the schematic diagram of the model is shown in Figure 1 .

[0080] Experimental data of Dachengqi Decoction serum drug components for the treatment of acute pancreatitis:

[0081] The experimental data acquisition method: based on the experimental data of Dachengqi Decoction in existing literature 1 (Jordan, Yu Raya, Yu Xiaojuan, Wan Panting, Liu Xinhui, Nie Bin, Chen Yinfang, Peng Hong, Xu Guoliang. Experimental study on the treatment of acute pancreatitis with Dachengqi Decoction [J]. Liaoning Journal of Traditional Chinese Medicine, 2017, 44(1): 190-192.) drug compatibility, in animal experiment, after the animal is given (Dachengqi Decoction decoction liquid), according to the measurement method in literature 2 (Shen Fengyun, Wei Huizhen, Sun Yongbing, Wang Yuesheng, Lv Shang, Gao Meng, Zeng Lianqing, Rao Yi. UPLC-MS / MS simultaneous determination of nine active ingredients in rat plasma of Dachengqi Decoction [J]. Chinese Journal of Chinese Medicine, 2014, 39(12): 2345-2350.) to measure the serum drug components of Dachengqi Decoction, and the serum drug components of Dachengqi Decoction are corresponding to the efficacy index data in literature 1, to obtain Tables 2 and 3.

[0082] Serum drug components: emodin, rhein, chrysophanol, aloe emodin (AE), emodin methyl ether (EME), magnolol, honokiol, hesperidin, and hesperetin.

[0083] Efficacy index: calcium ion (Ca 2+ ), pancreatic lipase (PL), interleukin-6 (IL6), interleukin-10 (IL10), lymphocyte function associated antigen 1a (LFA1a), amylase (AMY), organ indicators (OI), and survival rate (SR).

[0084] Table 2 Serum drug components of Dachengqi Decoction (dose, g / kg)

[0085]

[0086]

[0087] Table 3 Results of serum drug components of Dachengqi Decoction in the treatment of acute pancreatitis

[0088]

[0089] 1. Establishing the initial causal path model

[0090] (1) Model construction:

[0091] All independent variables and dependent variables participate in modeling.

[0092] Exogenous model: reflective type

[0093] Independent variable exogenous model: emodin, rhein, chrysophanol, aloe-emodin, physcion, magnolol, honokiol, hesperidin, hesperetin as measured independent variables, initial pharmacodynamic substance group as latent variables.

[0094] Dependent variable exogenous model: calcium ions, pancreatic lipase, interleukin-6, interleukin-10, lymphocyte function-associated antigen 1a, amylase, lung index, survival rate as measured dependent variables, initial pharmacodynamic index group as latent variables.

[0095] Endogenous model: formative type

[0096] The initial pharmacodynamic substance group is the cause and the initial pharmacodynamic index group is the effect, forming a cause-effect path.

[0097] (2) Calculation results

[0098] Model and PLS-SEM calculation results are shown in Tables 4-7.

[0099] Table 4 Reliability and convergent validity of the initial path model of Example 1

[0100]

[0101] Table 5 Fomell-Larcke criteria for the initial path model of Example 1

[0102] Initial pharmacodynamic index group Initial pharmacodynamic substance group Initial pharmacodynamic index group 0.735 Initial pharmacodynamic substance group -0.805 0.498

[0103] Table 6 Path coefficients / total effects of the initial path model of Example 1

[0104]

[0105] Table 7 Interpretability R of the initial path model of Example 1 2

[0106] [R 2 ]]> adjusted R 2 ]] Initial pharmacodynamic index group 0.648 0.604

[0107] (3) The results are described and analyzed as follows:

[0108] ① Reliability of the measured variable: the value of the factor loading, positive and negative, large and small. Indicate that there is positive and negative correlation, and the size of the correlation. T value is less than 1.96, which does not reach significance. P value is greater than 0.05, which is not significant.

[0109] ②Combinational reliability of latent variables: CR value is greater than 0.8, indicating that the construct has good internal consistency.

[0110] ③Convergent validity: the AVE value corresponding to the pharmacodynamic index is greater than 0.5, and has convergent validity; the AVE value corresponding to the pharmacodynamic substance is less than 0.5, and has no convergent validity.

[0111] ④Correlation: Cronbach's coefficient term, the pharmacodynamic index is negative, indicating that there may be a negative correlation between the indicators.

[0112] ⑤Discriminant validity: Fomell-Larcker criterion diagonal is the square root value of AVE, and there is no correlation coefficient value greater than the lower one, indicating that there is no discriminant validity.

[0113] ⑥Path coefficient: the path coefficient is not significant.

[0114] ⑦Explanatory power: R 2

[0115] ⑧Predictive ability and cross-validity verification: in the structural equation, the sample size is less than the number of variables, and cannot be 10-fold cross-validated, without Q 2 value.

[0116] (4) Conclusion

[0117] Select independent variables and dependent variables, and group them. Examine the independent variables and dependent variables, select large factor loadings, separate different signs, and combine the same signs; separate the different signs of the Cronbach's coefficient of the dependent variable, and combine the same signs. Get:

[0118] Pharmacodynamic substance group 0': aloe emodin, emodin, rhein, chrysophanol, emodin methyl ether, hesperetin;

[0119] Pharmacodynamic substance group 1': magnolol, honokiol, hesperidin;

[0120] Pharmacodynamic index group 1': amylase, pancreatic lipase, lymphocyte function-associated antigen 1a, interleukin-6;

[0121] Pharmacodynamic index group 2': interleukin-10, calcium ion, survival rate.

[0122] 2. Establish a causal path model for grouping

[0123] (1) Model construction:

[0124]

[0125] ​​Exogenous pattern (reflective): aloe-emodin, emodin, rhein, chrysophanol, physcion, hesperetin as the measured variable, pharmacodynamic substance group 0" as the latent variable. Magnolol, honokiol, hesperidin as the measured variable, pharmacodynamic substance group 1" as the latent variable.

[0126] Endogenous pattern (formative): amylase, pancreatic lipase, lymphocyte function-associated antigen 1a, interleukin-6 as the measured variable, pharmacodynamic index group 1" as the latent variable. Interleukin-10, calcium ion, survival rate as the measured variable, pharmacodynamic index group 2" as the latent variable.

[0127] Endogenous pattern: formative

[0128] Taking pharmacodynamic substance group 0" and pharmacodynamic substance group 1" as the cause, pharmacodynamic index group 1" and pharmacodynamic index group 2" as the effect, cross-constitute two-cause-two-effect paths.

[0129] (2) Calculation results

[0130] The model and PLS-SEM calculation results are shown in Tables 8-11.

[0131] Table 8 Reliability and convergent validity of the grouping path model of Example 1

[0132]

[0133] Table 9 Fomell-Larcke criteria for the grouping path model of Example 1

[0134] Pharmacodynamic index group 1" Pharmacodynamic index group 2" Pharmacodynamic substance group 0" Pharmacodynamic substance group 1" Pharmacodynamic index group 1" 0.802 Pharmacodynamic index group 2" -0.734 0.861 Pharmacodynamic substance group 0" 0.066 -0.399 0.884 Pharmacodynamic substance group 1" -0.746 0.744 -0.224 0.824

[0135] Table 10 Path coefficients / total effects of the grouping path model of Example 1

[0136] Pathway Original sample Sample mean Standard deviation T value P value Pharmacodynamic substance group 0"→ pharmacodynamic index group 1" -0.107 -0.014 0.358 0.298 0.766 Pharmacodynamic substance group 0"→ pharmacodynamic index group 2" -0.245 -0.193 0.330 0.743 0.458 Pharmacodynamic substance group 1"→ pharmacodynamic index group 1" -0.770 -0.727 0.301 2.558 0.011 Pharmacodynamic substance group 1"→ pharmacodynamic index group 2" 0.689 0.690 0.330 2.087 0.038

[0137] Table 11 Interpretability R of the grouping path model of Example 1 2

[0138] [R 2 ]]> adjusted R 2 ]] Pharmacodynamic index group 1" 0.567 0.443 Pharmacodynamic index group 2" 0.611 0.499

[0139] (3) Variable optimization criteria

[0140] The variable optimization criteria of the exogenous pattern include:

[0141] ① Check the reliability of each measurement variable: the absolute value of factor loading (indicating the size of correlation), the value is strong (factor loading ≥ 0.70 is strong, 0.40 ≤ factor loading < 0.70 is moderate, 0 ≤ factor loading < 0.40 is weak), that is, the absolute value of factor loading is large, the critical value is 0.7, the different signs are separated, the same signs are combined into groups, and the significance of P value and T value of factor loading is checked;

[0142] ② Check the combined reliability of each latent variable: CR (standard of exploratory research, representing internal consistency) is positive (indicating good consistency between variables), and variables with CR ≥ 0.60 (CR ≥ 0.70 is strong, 0.60 ≤ CR < 0.70 is acceptable) are combined into groups, and variables with negative CR (indicating possible negative correlation between variables) are rechecked for factor loading and adjusted for correlation;

[0143] ③ Check the convergence validity AVE, AVE ≥ 0.5;

[0144] ④ Check the correlation, Cronbach's alpha coefficient is positive;

[0145] ⑤ Check the discriminant validity, use Fomell-Larcke criterion or cross loading to distinguish and group variables, the Fomell-Larcke criterion is: the square root of the AVE of each construct must be greater than its correlation coefficient with other constructs (latent variables); the cross loading is: the highest factor loading of each variable should be in the construct to be measured, and in other constructs, the value will be smaller.

[0146] The variable optimization criteria of the inner model include:

[0147] ① Check the significance of P value and T value of path coefficient (its size indicates the strength of the relationship between two latent variables);

[0148] ② Check the explanation ability R 2 of each variable (the value is selected as moderate and above (R 2 ≥ 0.670 is strong, 0.333 ≤ R 2 < 0.670 is moderate, 0.190 ≤ R 2 < 0.333 is weak), and combine into groups;

[0149] ③ Predictive ability and cross validity verification: Q 2 > 0 (Q 2 , that is, Q2predict, Q 2 ≥ 0.35 is strong, 0.15 ≤ Q 2 < 0.35 is moderate, 0.02 ≤ Q 2 < 0.15 is weak).

[0150] (4) Main results are described and analyzed as follows:

[0151] ① The reliability of the measured variable: the value of factor loading, positive and negative have been separated. And the value of Houtpu- kui is 0.543 less than 0.7; T value is greater than 1.96, which is significant.

[0152] ② The combination reliability of latent variable: CR value is greater than 0.8, indicating that the profile has good internal consistency.

[0153] ③ Convergent validity: the AVE value corresponding to the group of pharmacodynamic indicators 1" and the group of pharmacodynamic indicators 2" is greater than 0.5, which has convergent validity; the AVE value corresponding to the group of pharmacodynamic substances 0" and the group of pharmacodynamic substances 1" is greater than 0.5, which has convergent validity.

[0154] ④ Correlation: all Cronbach's coefficients are positive.

[0155] ⑤ Discriminant validity: the diagonal of Fomell-Larcker criterion is the square root of AVE, which is greater than the correlation coefficient value below, also indicating that the discriminant validity is good.

[0156] ⑥ Path coefficient: there are two paths with insignificant path coefficients.

[0157] ⑦ Explained ability R 2 : moderate.

[0158] ⑧ Predictive ability and cross validity verification: since the sample size is less than the number of variables, there is no such result.

[0159] The above results show that the significant path coefficients are tested, and it is found that:

[0160] The components of Houpu and Zhishi show that the effect of the group of pharmacodynamic substances 1"→ the group of pharmacodynamic indicators 1" is decreased, and the effect of the group of pharmacodynamic substances 1"→ the group of pharmacodynamic indicators 2" is increased, which is significant.

[0161] The P values of the group of pharmacodynamic substances 0"→ the group of pharmacodynamic indicators 1" and the group of pharmacodynamic substances 0"→ the group of pharmacodynamic indicators 2" are 0.766 and 0.458 respectively, which are not significant. However, other parameters show that the components of Dahuang in the group of pharmacodynamic substances 0" have a reducing effect on the values of the two groups of pharmacodynamic indicators.

[0162] (5) Conclusion

[0163] Select independent variables and dependent variables, group pharmacodynamic indicators with positive and negative correlations. Leave the paths with high reliability and validity and significant path coefficients. Get:

[0164] The group of pharmacodynamic substances 1": Houtpu-kui and orange glycoside;

[0165] Pharmacodynamic index group 1": amylase, pancreatic lipase, lymphocyte function-associated antigen 1a, interleukin-6;

[0166] Pharmacodynamic index group 2": interleukin-10, calcium ion, survival rate.

[0167] 3. Optimized grouping causal path model

[0168] (1) Model construction:

[0169] Exogenous model: reflection type

[0170] Independent variable exogenous model (reflection type): magnolol, hesperidin as measured independent variable, pharmacodynamic substance group 1 as latent variable.

[0171] Dependent variable exogenous model (reflection type): amylase, pancreatic lipase, lymphocyte function-associated antigen 1a, interleukin-6 as measured dependent variable, pharmacodynamic index group 1 as latent variable. Interleukin-10, calcium ion, survival rate as measured dependent variable, pharmacodynamic index group 2 as latent variable.

[0172] Endogenous model: formative type

[0173] Pharmacodynamic substance group 1 as cause, pharmacodynamic index group 1 and pharmacodynamic index group 2 as effect, cross-constitute causal path.

[0174] (2) Calculation results

[0175] Model and PLS-SEM calculation results are shown in Tables 12-16.

[0176] Table 12 Reliability and convergent validity of the optimized grouping path model of Example 1

[0177]

[0178]

[0179] Table 13 Fomell-Larcke criteria for the optimized grouping path model of Example 1

[0180] Pharmacodynamic index group 1 Pharmacodynamic index group 2 Pharmacodynamic substance group 1 Pharmacodynamic index group 1 0.804 Pharmacodynamic index group 2 -0.742 0.862 Pharmacodynamic substance group 1 -0.700 0.756 0.961

[0181] Table 14 Path coefficients / total effects of the optimized grouping path model of Example 1

[0182] Pathway Original sample Sample mean Standard deviation T value P value Pharmacodynamic substance group 1→ pharmacodynamic index group 1 -0.700 -0.726 0.311 2.255 0.024 Pharmacodynamic substance group 1→ pharmacodynamic index group 2 0.756 0.706 0.392 1.927 0.054

[0183] Table 15 Interpretability R of the optimized grouping path model of Example 1 2

[0184] [R 2 ]] adjusted R 2 <!-- 12 -->]]> Pharmacodynamic index group 1 0.490 0.426 Pharmacodynamic index group 2 0.571 0.517

[0185] Table 16 Predictive ability and cross-validity of the optimized grouping path model of Example 1

[0186]

[0187] Note: RMSE stands for root mean square error, MAE stands for mean absolute error, PLS-SEM_RMSE stands for root mean square error of PLS-SEM test, PLS-SEM_MAE stands for mean absolute error of PLS-SEM test, LM_RMSE stands for root mean square error of linear regression test, LM_MAE stands for mean absolute error of linear regression test.

[0188] (3) Variable optimization criteria

[0189] Refer to the aforementioned grouping of the causal path model building process.

[0190] (4) The main results are described and analyzed as follows:

[0191] ① Reliability of measurement variables: factor loadings are all greater than 0.70, T values are all greater than 1.96, and P values are all significant.

[0192] ② Component reliability of latent variables: CR is all greater than 0.70

[0193] ③ Convergent validity: AVE is all greater than 0.50. The effect is very good.

[0194] ④ Correlation: Cronbach's coefficient is all positive.

[0195] ⑤ Discriminant validity: Fomell-Larcker criterion diagonal is the square root of AVE, greater than the correlation coefficient value below, indicating good discriminant validity.

[0196] ⑥ Path coefficient: one path coefficient is close to 0.05, and the other is significant.

[0197] ⑦ Explaining ability: moderate.

[0198] ⑧ Predictive ability and cross-validity: for Q 2 , the measurement mode PL is less than 0, and other indicators are greater than 0, which is acceptable. For Q 2 , all indicators are greater than 0, which is acceptable. PLS-SEM performs better than linear (LM) regression.

[0199] The above results show that for the total pharmacodynamic index, the effect is very good, indicating that Magnolol (magnolol), Hesperidin (hesperidin) is the most useful pharmacodynamic substance group.

[0200] (5) Conclusion

[0201] Pharmacodynamic substance group 1: Magnolol, hesperidin;

[0202] Pharmacodynamic index group 1: Amylase, pancreatic lipase, lymphocyte function-associated antigen 1a, interleukin-6;

[0203] Pharmacodynamic index group 2: Interleukin-10, calcium ion.

[0204] Key path: Pharmacodynamic substance group 1 makes the index value of pharmacodynamic index group 1 decrease, and makes the index value of pharmacodynamic index group 2 increase.

[0205] Example 2: Dachengqi Decoction for the treatment of acute pancreatitis (grouping model, the optimization criterion of factor loading quantity in the optimized grouping model is moderate or above, factor loading quantity ≥0.4)

[0206] The difference between this embodiment and example 1 is that the optimization criterion of factor loading quantity in the grouping model is moderate or above, i.e. factor loading quantity ≥0.4, and the rest remains unchanged.

[0207] 1, Establish the initial causal path model

[0208] The process and results are the same as in example 1.

[0209] 2, Establish the grouped causal path model

[0210] (1) Model construction: the same as in example 1.

[0211] (2) Calculation results: the same as in example 1.

[0212] (3) Variable optimization criterion

[0213] In the variable optimization criterion of the outer model 2, the absolute value of the factor loading quantity is selected to be greater than the critical value of 0.4, and the rest is the same as in example 1.

[0214] (4) Conclusion

[0215] Select independent variables and dependent variables, group, separate pharmacodynamic indexes with positive correlation and negative correlation, and leave paths with high reliability and validity and relatively significant path coefficients. We get:

[0216] Pharmacodynamic substance group 2": Magnolol, honokiol, hesperidin;

[0217] Pharmacodynamic index group 1": Amylase, pancreatic lipase, lymphocyte function-associated antigen 1a, interleukin-6;

[0218] Pharmacodynamic index group 2": Interleukin-10, calcium ion, survival rate.

[0219] 3, Optimize the grouped causal path model

[0220] (I) First modeling

[0221] (1) Model construction:

[0222] Exogenous model: Reflective

[0223] Exogenous model (Reflective): Magnolol, honokiol, hesperidin as the measured independent variable, pharmacodynamic substance group 2 as the latent variable.

[0224] Endogenous model (Reflective): Amylase, pancreatic lipase, lymphocyte function-associated antigen 1a, interleukin-6 as the measured dependent variable, pharmacodynamic index group 1 as the latent variable. Interleukin-10, calcium ion, survival rate as the measured dependent variable, pharmacodynamic index group 2 as the latent variable.

[0225] Endogenous model: Formative

[0226] Pharmacodynamic index group 1 and pharmacodynamic index group 2 as the fruit, cross to form the causal path.

[0227] (2) Calculation results

[0228] The model and PLS-SEM calculation results are shown in Tables 17-21. Figure 3 , 4 It can be seen that the T value P value of the pharmacodynamic index "survival rate" is not obtained, so "survival rate" is removed and the model is rebuilt. Figure 4

[0229] (II) Second modeling

[0230] (1) Model construction:

[0231] Exogenous model: Reflective

[0232] Exogenous model (Reflective): Magnolol, honokiol, hesperidin as the measured independent variable, pharmacodynamic substance group 2 as the latent variable.

[0233] Endogenous model (Reflective): Amylase, pancreatic lipase, lymphocyte function-associated antigen 1a, interleukin-6 as the measured dependent variable, pharmacodynamic index group 1 as the latent variable. Interleukin-10, calcium ion as the measured dependent variable, pharmacodynamic index group 2 as the latent variable.

[0234] Endogenous model: Formative

[0235] Pharmacodynamic index group 1 and pharmacodynamic index group 2 as the fruit, cross to form the causal path.

[0236] (2) Calculation results

[0237] The model and PLS-SEM calculation results are shown in Tables 17-21.

[0238] ​Table 17 Reliability, convergent validity table for the optimized grouping path model of Example 2

[0239]

[0240] Table 18 Fomell-Larcke criteria for the optimized grouping path model of Example 2

[0241] Pharmacodynamic index group 1 Pharmacodynamic index group 2 Pharmacodynamic substance group 2 Pharmacodynamic index group 2 0.802 Pharmacodynamic index group 1 -0.610 0.910 Pharmacodynamic substance group 2 -0.745 0.663 0.824

[0242] Table 19 Path coefficients / total effects for the optimized grouping path model of Example 2

[0243] Pathway Original sample Sample mean Standard deviation T value P value Pharmacodynamic substance group 2→ pharmacodynamic index group 1 -0.745 -0.769 0.293 2.543 0.011 Pharmacodynamic substance group 2→ pharmacodynamic index group 2 0.663 0.633 0.386 1.718 0.086

[0244] Table 20 Explained variance R for the optimized grouping path model of Example 2 2

[0245] [R 2 ]]> adjusted R 2 ]] Pharmacodynamic index group 1 0.555 0.500 Pharmacodynamic index group 2 0.440 0.370

[0246] Table 21 Predictive validity and cross-validity verification for the optimized grouping path model of Example 2

[0247]

[0248] (3) Variable optimization criteria

[0249] In the variable optimization criteria of Outer Model 2, the absolute value of the factor loading was selected to have a critical value of 0.4, and the rest was the same as Example 1.

[0250] (4) Main result description and analysis are as follows:

[0251] ① Reliability of the measurement variable: the factor loading of magnolol was 0.545, and the other indicators were all greater than 0.70, and the T value was greater than 1.96, and the P value was significant.

[0252] ② Component reliability of the latent variable: CR was greater than 0.70.

[0253] ③ Convergent validity: AVE was greater than 0.50. The effect was very good.

[0254] ④ Correlation: the Cronbach's coefficient was the same sign.

[0255] ⑤ Discriminant validity: the diagonal of the Fomell-Larcker criteria was the square root of AVE, which was greater than the correlation coefficient value below, indicating good discriminant validity.

[0256] ⑥ Path coefficient: one was significant, and one was more significant.

[0257] ⑦ Explained variance: moderate.

[0258] ⑧ Predictive validity and cross-validity verification: for Q2 Only the measurement model PLS predicts less than 0, and the other indicators are greater than 0, it is acceptable. PLS-SEM performs better than LM (linear) regression.

[0259] (5) Conclusion

[0260] Pharmacodynamic substance group 2: Magnolol, Honokiol, Hesperidin;

[0261] Pharmacodynamic index group 1: Amylase, Pancreatic lipase, Lymphocyte function-associated antigen 1a, Interleukin-6;

[0262] Pharmacodynamic index group 2: Interleukin-10, Calcium ion.

[0263] Key path: Pharmacodynamic substance group 2 makes the pharmacodynamic index group 1 index value decrease, and makes the pharmacodynamic index group 2 index value increase.

[0264] Example 3 Ma Xing Shi Gan Decoction Antipyretic

[0265] Based on the experimental data of Ma Xing Shi Gan Decoction serum drug ingredients antipyretic, select several serum drug ingredients in Ma Xing Shi Gan Decoction as independent variables, and several pharmacodynamic indexes as dependent variables, optimize variable grouping, and establish a reflective path model, the model schematic diagram is shown in Figure 1 .

[0266] Experimental data of Ma Xing Shi Gan Decoction serum drug ingredients antipyretic:

[0267] Based on the experimental data of Ma Xing Shi Gan Decoction serum drug ingredients antipyretic in existing literature 3 (Cui Yanru, Qu Fei, Xu Jing, etc. Effect of compatibility dosage change on antipyretic effect of Ma Xing Shi Gan Decoction [J]. Chinese Journal of Experimental Prescription Science, 2014, 20(06): 122-126.), measure the serum drug ingredients of Ma Xing Shi Gan Decoction after the animals are given (Ma Xing Shi Gan Decoction decoction liquid) in animal experiments, and correspond the serum drug ingredients of Ma Xing Shi Gan Decoction with the pharmacodynamic index data in literature 3, to obtain Table 22 and Table 23.

[0268] Serum drug ingredients: Ephedrine, Pseudoephedrine, Methylephedrine, Amygdalin, Prunasin, Liquiritin, Liquiritigenin, Glycyrrhetinic Acid.

[0269] Pharmacodynamic indexes: prostaglandin E2 (PGE2), temperature response index (TRI), 6-hour fever suppression rate (ShFSR).

[0270] Table 22 Serum drug components of Maxingshigan Decoction

[0271]

[0272] Table 23 Serum drug fever-reducing experiment results of Maxingshigan Decoction

[0273]

[0274]

[0275] 1. Establishing an initial causal path model

[0276] (1) Model construction:

[0277] Exogenous model: reflecting type

[0278] Independent variable exogenous model: ephedrine, pseudoephedrine, methylephedrine, amygdalin, wild black cherry glycoside, glycyrrhizin, glycyrrhizin, glycyrrhizic acid as measured independent variables, initial pharmacodynamic substance group as latent variables.

[0279] Dependent variable exogenous model: prostaglandin E2, temperature response index, 6-hour fever suppression rate as measured dependent variables, initial pharmacodynamic index group as latent variables.

[0280] Endogenous model: formative type

[0281] Taking the initial pharmacodynamic substance group as the cause and the initial pharmacodynamic index group as the effect, a cause-and-effect path is formed.

[0282] (2) Calculation results

[0283] Model and PLS-SEM calculation results are as follows Figure 5 , 6 .

[0284] (3) Conclusion

[0285] Select independent variables and dependent variables, group them. Examine the independent variables and dependent variables, select large factor loadings, separate different signs, and combine the same signs; separate different signs of Cronbach's coefficient of the dependent variable, and combine the same signs.

[0286] From Figure 5It can be seen that the absolute values of the negative parts of the factor loadings of the left pharmacodynamic substance group are very small, and are discarded; the factor loading of the right pharmacodynamic index group "prostaglandin E2" is very small, and is discarded. The remaining measurement variables, only the positive values of the factor loadings on the left form a group, and the positive and negative values on the right can be divided into two groups, and a new model is established. The following is obtained:

[0287] Pharmacodynamic substance group I': ephedrine, methylephedrine, pseudoephedrine;

[0288] Pharmacodynamic index group I': body temperature response index;

[0289] Pharmacodynamic index group II': 6h fever inhibition rate.

[0290] 2. Establishment of the grouped causal path model

[0291] (1) Model construction:

[0292] Independent variable external pattern (reflective type): ephedrine, methylephedrine, pseudoephedrine as measurement independent variables, and pharmacodynamic substance group I as a latent variable.

[0293] Dependent variable external pattern (reflective type): body temperature response index as a measurement dependent variable, and pharmacodynamic index group I as a latent variable. 6h fever inhibition rate as a measurement dependent variable, and pharmacodynamic index group II as a latent variable.

[0294] Internal pattern: formative type

[0295] Take pharmacodynamic substance group I as the cause, and pharmacodynamic index group I and pharmacodynamic index group II as the effect, and cross to form a causal path.

[0296] (2) Calculation results

[0297] The model and PLS-SEM calculation results are shown in Tables 24-28.

[0298] Table 24 Reliability and convergent validity of the grouped path model of Example 3

[0299]

[0300] Table 25 Fomell-Larcke criteria for the grouped path model of Example 3

[0301] Pharmacodynamic substance group I Pharmacodynamic index group I Pharmacodynamic index group II Pharmacodynamic substance group I 0.994 Pharmacodynamic index group I 0.796 1.000 Pharmacodynamic index group II -0.716 -0.939 1.000

[0302] Table 26 Path coefficients / total effects of the grouped path model of Example 3

[0303]

[0304] Table 27 Interpretability R of the grouped path model of Example 3 2

[0305] [R 2 ]]> adjusted R 2 ]] Pharmacodynamic index group I 0.634 0.600 Pharmacodynamic index group II 0.512 0.468

[0306] Table 28 Predictive ability and cross-validity verification of the path model of Example 3

[0307]

[0308]

[0309] (3) Variable optimization criteria

[0310] Reference Example 1.

[0311] (4) The main results are described and analyzed as follows:

[0312] ① Reliability of measurement variables: factor loadings are all greater than 0.70, T values are all greater than 1.96, and P values are all significant.

[0313] ② Component reliability of latent variables: CRs are all greater than 0.70.

[0314] ③ Convergent validity: AVEs are all greater than 0.50. The effect is very good.

[0315] ④ Correlation: the Cronbach's coefficient is positive.

[0316] ⑤ Discriminant validity: the Fomell-Larcker criterion diagonal is the square root of AVE, which is greater than the correlation coefficient value below, indicating good discriminant validity.

[0317] ⑥ Path coefficient: significant.

[0318] ⑦ Explained ability: R 2 good.

[0319] ⑧ Predictive ability and cross-validity verification: for Q 2 , the measurement model indicators are all greater than 0, and are good and acceptable. For Q 2 , the latent variable model is all greater than 0, and greater than 0.35, indicating strong predictive ability.

[0320] The above results show that ephedrine, methyl ephedrine, and pseudoephedrine are the pharmacodynamic substance group; the body temperature response index and the 6h fever inhibition rate are the pharmacodynamic index group (single); the pharmacodynamic substance group and the pharmacodynamic index group have good reliability and validity, and may be the key path.

[0321] (5) Conclusion

[0322] Pharmacodynamic substance group I: ephedrine, methyl ephedrine, and pseudoephedrine.

[0323] Pharmacodynamic index group I: body temperature response index.

[0324] Pharmacodynamic index group II: 6h fever inhibition rate;

[0325] Key path: pharmacodynamic substance group I makes the index value of pharmacodynamic index group I increase, and makes the index value of pharmacodynamic index group II decrease.

[0326] Four, based on PLS-SEM, excavate the key path of pharmacodynamic substance group and pharmacodynamic index group of traditional Chinese medicine compound compatibility drug

[0327] Based on the experimental data of Dachengqi Decoction compatibility drug in treating acute pancreatitis, PLS-SEM model was constructed, 4 compatibility drugs in Dachengqi Decoction were selected as independent variables, 8 pharmacodynamic indexes were selected as dependent variables, variable grouping was optimized, and a formative reflective formative path model was established, and the schematic diagram of the model is shown in Figure 2 .

[0328] Experimental data of Dachengqi Decoction compatibility drug in treating acute pancreatitis:

[0329] The experimental data comes from existing literature 1 (Qiao J D, Yu R Y, Yu X J, Wan P T, Liu X H, Nie B, Chen Y F, Peng H, Xu G L. Experimental study on Dachengqi Decoction in the treatment of acute pancreatitis [J]. Liaoning Journal of Traditional Chinese Medicine, 2017, 44(1): 190-192.), see Table 29 and Table 30.

[0330] Serum drug components: Radix et Rhizoma Rhei (RRR), Magnoliae Officmalis Cortex (MOC), Fructus Aurantii Immaturus (FAI), Mirabilite.

[0331] Pharmacodynamic indexes: calcium ion (Ca 2+ ), Pancreatic Lipase (PL), Interleukin-6 (IL-6), Interleukin-10 (IL10), Lymphocyte Function Associated Antigen 1Alpha (LFA1a), Amylase (AMY), Organ indicators (OI), Survival rate (SR).

[0332] Table 29 Dachengqi Decoction compatibility drug ratio

[0333] Group Rhubarb (g / kg) Magnolia officinalis (g / kg) Citrus aurantium (g / kg) Mirabilite (g / kg) Original formula 54.00 40.50 40.50 20.25 Proportion 1 21.33 12.64 162.00 9.00 Proportion 2 0.00 28.44 14.22 45.00 Proportion 3 32.00 144.00 0.00 63.00 Proportion 4 48.00 0.00 48.00 54.00 Proportion 5 9.48 8.43 6.32 27.00 Proportion 6 162.00 96.00 72.00 36.00 Proportion 7 14.22 216.00 32.00 18.00 Proportion 9 108.00 18.96 21.33 81.00 Proportion 10 72.00 42.67 9.48 0.00

[0334] Table 30 Results of treating acute pancreatitis with Dachengqi Decoction combined with drugs

[0335]

[0336]

[0337] 1. Establishing an initial causal path model

[0338] (1) Model construction:

[0339] All independent variables and dependent variables participate in modeling.

[0340] Independent variable exogenous pattern (formation type): rhubarb, magnolia officinalis, hovenia dulcis, mirabilite as measurement independent variables, initial pharmacodynamic substance group as latent variables.

[0341] Dependent variable exogenous pattern (reflection type): calcium ions, pancreatic lipase, interleukin-6, interleukin-10, lymphocyte function-associated antigen 1a, amylase, lung index, survival rate as measurement dependent variables, initial pharmacodynamic index group as latent variables.

[0342] Endogenous pattern: formation type

[0343] Taking the initial pharmacodynamic substance group as the cause and the initial pharmacodynamic index group as the effect, a cause-and-effect path is formed.

[0344] (2) Calculation results

[0345] Model and PLS-SEM calculation results are as follows Figure 7 (factor load, path coefficient and explanation ability), and the Bootstrapping modeling did not pass.

[0346] (3) Conclusion

[0347] Select independent variables and dependent variables, group them. Examine the independent variables and dependent variables, select large factor load, separate different signs, and combine the same signs to get:

[0348] Pharmacodynamic substance group A': magnolia officinalis;

[0349] Pharmacodynamic substance group B': rhubarb, hovenia dulcis, mirabilite;

[0350] Pharmacodynamic index group A': amylase, pancreatic lipase, lymphocyte function-associated antigen 1a, interleukin-6, lung index;

[0351] Pharmacodynamic index group B': interleukin-10, calcium ions, survival rate.

[0352] 2. Establishing a grouped causal path model

[0353] (1) Model construction:

[0354] Exogenous pattern (formative) : Magnolia officinalis as a measurement variable, and pharmacological substance group A" as a latent variable. Rhei Radix et Rhizoma, Fructus Aurantii Immaturus, and Mirabilite as measurement variables, and pharmacological substance group B" as a latent variable.

[0355] Endogenous pattern (reflective) : Amylase, pancreatic lipase, lymphocyte function-associated antigen 1a, interleukin-6, and lung index as measurement variables, and pharmacological index group A" as a latent variable. Interleukin-10, calcium ion, and survival rate as measurement variables, and pharmacological index group B" as a latent variable.

[0356] Cross-formative pattern

[0357] Pharmacological substance group A" and pharmacological substance group B" as causes, and pharmacological index group A" and pharmacological index group B" as effects, and cross-formation of two-cause-two-effect paths.

[0358] (2) Calculation results

[0359] The model and PLS-SEM calculation results are shown in Tables 31-37.

[0360] Table 31 Reliability and convergent validity of the grouped path model of Example 4

[0361]

[0362] Table 32 Fomell-Larcke criteria of the grouped path model of Example 4

[0363] Pharmacodynamic substance group A" Pharmacodynamic index group A" Pharmacodynamic index group A" Pharmacodynamic substance group A" 1.000 Pharmacodynamic index group A" -0.812 0.710 Pharmacodynamic index group B" 0.739 -0.819 0.862

[0364] Table 33 Exogenous pattern weights of the grouped path model of Example 4

[0365]

[0366]

[0367] Table 34 Collinearity analysis of the grouped path model of Example 4

[0368]

[0369] Table 35 Path coefficients / total effects of the grouped path model of Example 4

[0370] Pathway Original sample Sample mean Standard deviation T value P value Pharmacodynamic substance group A"→ pharmacodynamic index group A" -0.797 -0.735 0.269 2.967 0.004 Pharmacodynamic substance group A"→ pharmacodynamic index group B" 0.634 0.566 0.326 1.943 0.055 Pharmacodynamic substance group B"→ pharmacodynamic index group A" 0.071 0.190 0.389 0.182 0.856 Pharmacodynamic substance group B"→ pharmacodynamic index group B" -0.49 -0.507 0.382 1.306 0.194

[0371] Table 36 Interpretability R of the grouped path model of Example 4 2

[0372] [R 2 ]] adjusted R 2 ]] Pharmacodynamic index group A" 0.784 0.723 Pharmacodynamic index group B" 0.664 0.568

[0373] Table 37 Prediction ability and cross-validity verification of the grouping path model of Example 4

[0374]

[0375] (3) Variable optimization criteria

[0376] On the basis of Example 1, three optimization criteria for forming the external pattern of the model are added: multivariate collinearity VIF < 5; external pattern index weight; significance of P value and T value of external pattern index weight; the rest is the same as Example 1.

[0377] (4) Conclusion

[0378] Select independent variables and dependent variables, and group. Separate the positive correlation and negative correlation of the efficacy index. Leave the path with high reliability and validity and significant path coefficient (P < 0.05). As can be seen from Table 31, the factor loading of the efficacy substance is greater than 0.7 in absolute value, and is separated into two groups; the positive and negative efficacy indexes can be separated into two groups. The following is obtained:

[0379] Efficacy substance group A'': Magnolia officinalis;

[0380] Efficacy index group A'': Amylase, pancreatic lipase;

[0381] Efficacy index group B'': Interleukin-10, calcium ion, survival rate.

[0382] 3. Optimized causal path model of grouping

[0383] (1) Model construction:

[0384] Independent variable external pattern (formation type): Magnolia officinalis as a measure of independent variable, and efficacy substance group A as a latent variable.

[0385] Dependent variable external pattern (reflection type): amylase and pancreatic lipase as measure of dependent variable, and efficacy index group A as latent variable. Interleukin-10, calcium ion, and survival rate as measure of dependent variable, and efficacy index group B as latent variable.

[0386] Internal pattern: cross formation type

[0387] Taking efficacy substance group A as cause and efficacy index group A and efficacy index group B as effect, a causal path is formed by cross.

[0388] (2) Calculation results

[0389] The model and PLS-SEM calculation results are shown in Tables 38-43.

[0390] Table 38 Reliability and convergent validity table of the optimized grouping path model of Example 4

[0391]

[0392] Table 39 Fomell-Larcke Criteria for the Optimization of the Grouped Path Model of Example 4

[0393] Pharmacodynamic substance group A Pharmacodynamic index group A Pharmacodynamic index group B Pharmacodynamic substance group A 1.000 Pharmacodynamic index group A -0.714 0.852 Pharmacodynamic index group B 0.796 -0.840 0.856

[0394] Table 40 Outer Model Weights for the Optimization of the Grouped Path Model of Example 4

[0395]

[0396] Table 41 Collinearity Analysis for the Optimization of the Grouped Path Model of Example 4

[0397]

[0398] Table 42 Path Coefficients / Total Effects for the Optimization of the Grouped Path Model of Example 4

[0399] Pathway Original sample Sample mean Standard deviation T value P value Pharmacodynamic substance group A→ pharmacodynamic index group A -0.714 -0.715 0.211 3.375 0.001 Pharmacodynamic substance group A→ pharmacodynamic index group B 0.796 0.796 0.223 3.570 0.001

[0400] Table 42 Interpretability R2 for the Optimization of the Grouped Path Model of Example 4

[0401] [R 2 ]]> adjusted R 2 ]]> Pharmacodynamic index group A 0.509 0.448 Pharmacodynamic index group B 0.634 0.588

[0402] Table 43 Predictive Ability and Cross-Validity Verification for the Optimization of the Grouped Path Model of Example 4

[0403]

[0404] (3) Variable Optimization Criteria

[0405] Based on Example 1, three optimization criteria for forming the outer model are added: Multicollinearity VIF < 5; Outer Model Index Weight; Significance of P value, T value of Outer Model Index Weight; the rest is the same as Example 1.

[0406] (4) Results Description and Analysis:

[0407] The above results show that the factor loading values are all greater than 0.7, the combination reliability is greater than 0.800, the convergence validity AVE is greater than 0.7, the path coefficients are -0.714, 0.796 respectively, which are relatively high; the interpretability R 2 is 0.509, 0.634 respectively, indicating that the interpretability is acceptable; P < 0.05, passing the test. The pharmacodynamic substance group A contains Magnolia Bark, the pharmacodynamic index group A contains amylase, pancreatic lipase, and the pharmacodynamic index group B contains interleukin-10, calcium ion, and survival rate.

[0408] The two key paths are the pharmacodynamic substance group A acting on the pharmacodynamic index group A path and the pharmacodynamic substance group B acting on the pharmacodynamic index group B path.

[0409] (5) Conclusion

[0410] Pharmacodynamic substance group A: Magnoliae officinalis Cortex;

[0411] Pharmacodynamic index group A: Amylase, Pancreatic lipase;

[0412] Pharmacodynamic index group B: Interleukin-10, Calcium ion, Survival rate.

[0413] Key path: Pharmacodynamic substance group A makes the index value of pharmacodynamic index group A decrease, and makes the index value of pharmacodynamic index group B increase.

[0414] Five, based on PLS-SEM, excavate the key path of pharmacodynamic substance group and pharmacodynamic index group of traditional Chinese medicine components combined with drugs

[0415] Based on the experimental data of Polygonum orientale components combined with drugs against hypoxia / reoxygenation, a PLS-SEM model was constructed, three components combined with drugs in Dachengqi Decoction were selected as independent variables, three pharmacodynamic indexes were selected as dependent variables, variable grouping was optimized, and a formative reflective formative path model was established. The schematic diagram of the model is shown in Figure 2 .

[0416] Experimental data of Polygonum orientale against hypoxia / reoxygenation:

[0417] The experimental data comes from existing literature (Siqi Wan; Jinfang Zhang; Rong Hou; Minsi Zheng; Liya Liu; Mingshuo Zhang; Zhiyong Li; Xiulan Huang; A strategy for component-based Chinese medicines design approach of Polygonum orientale L. against hypoxia / reoxygenation based on uniform design-stepwise regression-simulated annealing [J]; Biomedicine & Pharmacotherapy, 2021, 135), see Table 44 for details.

[0418] Polygonum orientale components combined with drugs: orientin, quercitrin, vitexin.

[0419] Pharmacodynamic indexes: Cell viability, Leakage rate of LDH, Level of NO.

[0420] Table 44: Red Polygonum component drug compatibility ratio and anti-hypoxia / reoxygenation results

[0421]

[0422] 1. Establishing an initial causal path model

[0423] (1) Model construction:

[0424] All independent variables and dependent variables participate in modeling.

[0425] Independent variable external pattern (formative): formononetin, quercitrin, vitexin as measured independent variables, initial pharmacodynamic substance group as latent variable.

[0426] Dependent variable external pattern (reflective): cell viability, lactate dehydrogenase, nitric oxide level as measured dependent variables, initial pharmacodynamic index group as latent variable.

[0427] Internal pattern: formative

[0428] Taking the initial pharmacodynamic substance group as the cause and the initial pharmacodynamic index group as the effect, a cause-and-effect path is formed.

[0429] (2) Calculation results

[0430] Model and PLS-SEM calculation results are as follows Figure 8 , 9 .

[0431] (3) Conclusion

[0432] Select independent variables and dependent variables, group them. Examine the independent variables and dependent variables, select large factor loadings, separate different signs, and combine the same signs. From Figure 8 , it is known that the cell viability value of the pharmacodynamic index is negative. According to the selection of greater than 0.7 and separation of positive and negative signs, we have:

[0433] Pharmacodynamic substance group a′: quercitrin;

[0434] Pharmacodynamic index group a′: lactate dehydrogenase, nitric oxide level.

[0435] 2. Establishing a grouped causal path model

[0436] (1) Model construction:

[0437] Independent variable external pattern (formative): quercitrin as measured independent variable, pharmacodynamic substance group a as latent variable.

[0438] Dependent variable external pattern (reflective): lactate dehydrogenase, nitric oxide level as measured dependent variable, pharmacodynamic index group a as latent variable.

[0439] Internal pattern: cross-formative

[0440] With the pharmacodynamic substance group a as the cause and the pharmacodynamic index group a as the effect, without cross, the cause-effect path is constituted.

[0441] (2) Calculation results

[0442] The model and PLS-SEM calculation results are shown in Tables 45-51.

[0443] Table 45 Reliability and convergent validity of the grouping path model of Example 5

[0444]

[0445] Table 46 Fomell-Larcke criteria of the grouping path model of Example 5

[0446] Pharmacodynamic index group a Pharmacodynamic index group a 0.912

[0447] Table 47 Exogenous weights of the grouping path model of Example 5

[0448] Latent variable Measured variable Exogenous pattern weight T value P value Pharmacodynamic substance group a Quercitrin 1.000 n / a n / a Pharmacodynamic index group a Lactate dehydrogenase 0.496 1.337 0.169 Pharmacodynamic index group a Nitric oxide level 0.598 1.689 0.092

[0449] Table 48 Collinearity analysis of the grouping path model of Example 5

[0450] VIF Lactate dehydrogenase 1.800 Nitric oxide level 1.800 Quercitrin 1.000

[0451] Table 49 Path coefficients / total effects of the grouping path model of Example 5

[0452] Pathway Original sample Sample mean Standard deviation T value P value Pharmacodynamic substance group a→ pharmacodynamic index group a 0.564 0.507 0.336 1.678 0.094

[0453] Table 50 R of the explanatory power of the grouping path model of Example 5 2

[0454] [R 2 ]] adjusted R 2 ]]> Pharmacodynamic index group a 0.318 0.220

[0455] Table 51 Predictive power and cross-validity verification of the grouping path model of Example 5

[0456]

[0457]

[0458] (3) Variable optimization criteria

[0459] On the basis of Example 1, three optimization criteria for forming the exogenous model are added: multivariate collinearity VIF < 5; exogenous index weight; significance of P value and T value of exogenous index weight; the rest is the same as Example 1.

[0460] (4) The main results are described and analyzed as follows:

[0461] ①The reliability of the measured variables: the factor loading is greater than 0.70, and the T value is greater than 1.96, and the P value is significant.

[0462] ②The composition reliability of the latent variables: CR is greater than 0.70.

[0463] ③Convergent validity: AVE is greater than 0.50.

[0464] ④Correlation: the Cronbach's coefficient is of the same sign and consistent with the function.

[0465] ⑤Discriminant validity: the Fomell-Larcker criterion diagonal is the square root of AVE, which is greater than the correlation coefficient value below.

[0466] ⑥Factor loading weight: P value is not significant.

[0467] ⑦Collinearity analysis: there is no correlation.

[0468] ⑧Path coefficient: 0.094 is not significant, but less than 0.1.

[0469] ⑨Explanatory power R 2 : the value is 0.318, which is acceptable.

[0470] ⑩Predictive power and cross-validity verification: the predictive value of the index is greater than 0. The cross-validation predictive power test is not significant enough, which may be affected by the small sample size, or it may be due to other reasons, and further research is needed.

[0471] The above results show that the factor loading of the outer mode variable is good, the path coefficient is good, the explanatory power is acceptable, there is no correlation between variables, and the cross-validation predictive power is not enough. It has a certain prompting effect on the composition of the group, and the effect of quercitrin on lactate dehydrogenase and nitric oxide level may be the key path.

[0472] (5) Conclusion

[0473] Select independent variables and dependent variables, and group them. Examine the independent variables and dependent variables, select large factor loading, separate different signs and combine the same signs. Positive correlation and negative correlation are separated. Leave the path with high reliability and validity and significant path coefficient.

[0474] Pharmacodynamic substance group a: quercitrin;

[0475] Pharmacodynamic index group a: lactate dehydrogenase and nitric oxide level.

[0476] Key path: pharmacodynamic substance group a makes the pharmacodynamic index group a index value rise.

Claims

1. A method for mining the key path of a traditional Chinese medicine efficacy substance group and an efficacy index group based on PLS-SEM, characterized in that: The PLS-SEM model is constructed based on experimental data of pharmacodynamic substances and pharmacodynamic indexes of traditional Chinese medicine, a plurality of pharmacodynamic substances in traditional Chinese medicine are selected as independent variables, a plurality of pharmacodynamic indexes are selected as dependent variables, variable grouping is optimized, and a causal path model reflecting a formation type or a formation type reflecting a formation type is established; The causal path model sequentially comprises an initial causal path model and a grouped causal path model according to the modeling steps, and both of them are composed of corresponding exogenous and endogenous modes, the exogenous mode comprises an independent variable exogenous mode and a dependent variable exogenous mode, the independent variable exogenous mode takes corresponding pharmacodynamic substances as measurement independent variables and takes corresponding pharmacodynamic substance groups as exogenous latent variables, the dependent variable exogenous mode takes corresponding pharmacodynamic indexes as measurement dependent variables and takes corresponding pharmacodynamic index groups as endogenous latent variables, and the endogenous mode is composed of corresponding pharmacodynamic substance groups and corresponding pharmacodynamic index groups corresponding to a causal path; The method for mining a key path of a pharmacodynamic substance group and a pharmacodynamic index group of traditional Chinese medicine based on PLS-SEM specifically comprises the following steps: S1, an initial causal path model is established, the initial causal path model comprises an exogenous mode 1 and an endogenous mode 1, the exogenous mode 1 comprises an independent variable exogenous mode 1 and a dependent variable exogenous mode 1, the independent variable exogenous mode 1 takes all pharmacodynamic substances as measurement independent variables and takes an initial pharmacodynamic substance group as an exogenous latent variable, the dependent variable exogenous mode 1 takes all pharmacodynamic indexes as measurement dependent variables and takes an initial pharmacodynamic index group as an endogenous latent variable, and the endogenous mode 1 comprises a one-to-one causal path of the initial pharmacodynamic substance group and the initial pharmacodynamic index group, a better path is selected by PLS-SEM model calculation, and variable grouping is optimized; S2, a grouped causal path model is established based on step S1, the causal path model comprises an exogenous mode 2 and an endogenous mode 2, the exogenous mode 2 comprises an independent variable exogenous mode 2 and a dependent variable exogenous mode 2, the independent variable exogenous mode 2 takes each group of pharmacodynamic substances optimized in step S1 as measurement independent variables and takes grouped pharmacodynamic substance groups as exogenous latent variables, the dependent variable exogenous mode 2 takes each group of pharmacodynamic indexes optimized in step S1 as measurement dependent variables and takes grouped pharmacodynamic index groups as endogenous latent variables, and the endogenous mode 2 comprises a causal path of the grouped pharmacodynamic substance groups and the grouped pharmacodynamic index groups, a better path is selected by PLS-SEM model calculation, and variable grouping is optimized; S3, optimization is performed based on step S2, and an optimized grouped causal path model is established, the method is the same as that of the grouped causal path model of step S2, a better path is selected by PLS-SEM model calculation, variable grouping is optimized until optimization is not possible, or the process is stopped according to actual needs; S4, the results are arranged to obtain pharmacodynamic substance groups, pharmacodynamic index groups and key paths with similar effects.

2. The method of claim 1, wherein: The pharmacodynamic substances are derived from serum drug components of traditional Chinese medicine compound, drugs compatible with traditional Chinese medicine compound or drugs compatible with components of traditional Chinese medicine; the exogenous mode is of a reflection type or a formation type, and the endogenous mode is of a reflection type.

3. The method of claim 1, wherein: The variable optimization criteria of the exogenous mode include any one or more of the following: (1) check the reliability of each variable: the absolute value of factor loading, the critical value of which is 0.7 or 0.4, the signs of which are separated, the same signs are combined into groups, and the significance of P value and T value of factor loading is checked; (2) check the combination reliability of each variable: when CR is positive, select CR >= 0.60 to combine into groups, and recheck the factor loading of the variable with negative CR and adjust the correlation; (3) check the convergence validity AVE, AVE >= 0.5; (4) check the correlation, the Cronbach's coefficient is positive; (5) check the discriminant validity, use Fomell-Larcker criterion or cross loading to distinguish and group variables, the Fomell-Larcker criterion is: the root value of AVE of each construct must be greater than the correlation coefficient between it and other constructs; the cross loading is: the highest factor loading of each variable should be in the construct to be measured; (6) multicollinearity: VIF < 5; (7) external model index weight; (8) the significance of P value and T value of external model index weight.

4. The method of claim 1, wherein: The variable optimization criteria of the internal model include any one or several of the following: (1) check the reliability of each variable: the absolute value of factor loading, the critical value of which is 0.7 or 0.4, the signs of which are separated, the same signs are combined into groups.

2. Check the explanatory power R of each variable 2 The values of moderate and above are combined into a group. iii. Predictive ability and out-of-sample validation: Q 2 > 0.

5. The method of claim 3, wherein: The pharmacodynamic substance is derived from serum drug components of traditional Chinese medicine compound, the traditional Chinese medicine compound is Dachengqi Decoction, the Dachengqi Decoction includes Dachengqi Decoction original prescription and uniform design Dachengqi Decoction compatibility prescription, and the experimental data is experimental data of Dachengqi Decoction in treating acute pancreatitis; 6. The method of any one of claims 1-5, wherein: The serum drug components include emodin, rhein, chrysophanol, aloe-emodin, emodin methyl ether, magnolol, honokiol, hesperidin, and hesperetin, and the pharmacodynamic indexes include calcium ion, pancreatic lipase, interleukin-6, interleukin-10, lymphocyte function-associated antigen 1a, amylase, lung index, and survival rate; The obtained pharmacodynamic substance group is pharmacodynamic substance group 1 or pharmacodynamic substance group 2, the pharmacodynamic substance group 1 includes magnolol and hesperidin, and the pharmacodynamic substance group 2 includes magnolol, honokiol and hesperidin; the obtained pharmacodynamic index group includes pharmacodynamic index group 1 and pharmacodynamic index group 2, the pharmacodynamic index group 1 includes amylase, interleukin-6, lymphocyte function-associated antigen 1a and pancreatic lipase, and the pharmacodynamic index group 2 includes interleukin-10 and calcium ion; The obtained key path includes: the pharmacodynamic substance group 1 or the pharmacodynamic substance group 2 reduces the index value of the pharmacodynamic index group 1 and increases the index value of the pharmacodynamic index group 2. The pharmacodynamic substance is derived from serum drug components of traditional Chinese medicine compound, the traditional Chinese medicine compound is Maxing Shigan Decoction, the Maxing Shigan Decoction includes Maxing Shigan Decoction original prescription and uniform design Maxing Shigan Decoction compatibility prescription, and the experimental data is experimental data of Maxing Shigan Decoction in reducing fever; 7. The method of any one of claims 1-5, wherein: ​ The serum drug components include ephedrine, pseudoephedrine, methylephedrine, amygdalin, prunus wild black cherry glycoside, glycyrrhizin, glycyrrhizin, glycyrrhizinic acid, and the pharmacodynamic indexes include prostaglandin E2, body temperature response index, 6h fever inhibition rate; The obtained pharmacodynamic substance group I includes ephedrine, methylephedrine and pseudoephedrine, the obtained pharmacodynamic index group includes pharmacodynamic index group I and pharmacodynamic index group II, the pharmacodynamic index group I includes body temperature response index, and the pharmacodynamic index group II includes 6h fever inhibition rate; The obtained key path includes that the pharmacodynamic substance group I makes the pharmacodynamic index group I index value increase and makes the pharmacodynamic index group II index value decrease.

8. The method of any one of claims 1-5, wherein: The pharmacodynamic substance is derived from a compatibility drug of a traditional Chinese medicine compound, the traditional Chinese medicine compound is Da Chengqi Decoction, the Da Chengqi Decoction includes Da Chengqi Decoction original party and uniform design Da Chengqi Decoction compatibility party, and the experimental data is experimental data of Da Chengqi Decoction in treating acute pancreatitis; The compatibility drug includes rhubarb, magnolia officinalis, citrus aurantium, mirabilite, the pharmacodynamic indexes include calcium ions, pancreatic lipase, interleukin-6, interleukin-10, lymphocyte function-associated antigen 1a, amylase, lung index and survival rate; The obtained pharmacodynamic substance group A includes magnolia officinalis, the obtained pharmacodynamic index group includes pharmacodynamic index group A and pharmacodynamic index group B, the pharmacodynamic index group A includes amylase and pancreatic lipase, and the pharmacodynamic index group B includes interleukin-10, calcium ions and survival rate; The obtained key path includes that the pharmacodynamic substance group A makes the pharmacodynamic index group A index value decrease and makes the pharmacodynamic index group B index value increase.

9. The method of any one of claims 1-5, wherein: The pharmacodynamic substance is derived from a compatibility drug of a traditional Chinese medicine component, the compatibility drug of the traditional Chinese medicine component is a compatibility drug of red reed components, and the experimental data is experimental data of the compatibility drug of red reed components in resisting hypoxia / reoxygenation; The compatibility drug of the red reed components includes isorhamnetin, quercetin and vitexin, and the pharmacodynamic indexes include cell viability, lactate dehydrogenase leakage rate and nitric oxide level; The obtained pharmacodynamic substance group a includes quercetin, and the obtained pharmacodynamic index group a includes lactate dehydrogenase leakage rate and nitric oxide level; The obtained key path includes that the pharmacodynamic substance group a makes the pharmacodynamic index group a index value increase.

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