Method for identifying core components and overall target spectrum of traditional Chinese medicine compound

Abstract

The invention discloses a method for identifying a core component and an overall target spectrum of a traditional Chinese medicine compound. The method comprises the following steps: screening metabolic active components of the traditional Chinese medicine compound, acquiring target information of the active components, calculating key targets of the whole formula, determining core active components of the traditional Chinese medicine compound, regulating a network through the key targets, analyzing a path and the like, so that the core components and the key targets of the whole formula of the traditional Chinese medicine compound are determined; the potential mechanism of the compound traditional Chinese medicine for treating different diseases at the same time is further analyzed, and the synergistic effect of the compound is disclosed. The invention provides an innovative thought for material basis and action mechanism research of traditional Chinese medicine complex chemical system intervention of complex diseases, and lays a methodological basis for clinical precise positioning of classical famous prescriptions and new drug development.

Description

Technical Field

[0001] The present invention belongs to the field of traditional Chinese medicine, and particularly relates to a method for identifying the core components and overall target spectrum of a traditional Chinese medicine compound. Background Art

[0002] Traditional Chinese medicine compounds, as an important part of traditional Chinese medicine, are widely used in the treatment of various diseases. Different from single-component drugs, traditional Chinese medicine compounds play a therapeutic role through the interaction of multiple components, which conforms to the theories of "holistic concept" and "treatment based on syndrome differentiation", and reflects the unique treatment concept of traditional Chinese medicine. However, the components of the compound are complex and diverse, and how to scientifically reveal the core components and their action mechanisms has become a difficult problem in current traditional Chinese medicine research. Current research mainly focuses on pharmacological induction and clinical observation, lacking a systematic method to accurately predict the core components and key targets of the compound at the molecular level; while existing target prediction methods are mostly limited to the analysis of single components, ignoring the synergistic effects and overall effects among components. Therefore, accurately identifying the core components and targets of the compound has become the key to deeply understanding its action mechanism and optimizing the treatment effect.

[0003] With the development of modern medicine, the concept of "treating different diseases with the same therapy" has gradually attracted attention. Treating different diseases with the same therapy means treating different diseases through common treatment mechanisms or targets. The application of traditional Chinese medicine compounds in treating different diseases with the same therapy is particularly prominent, and it shows therapeutic effects in multiple diseases by regulating multiple targets and pathways. However, in the analysis of the mechanism of treating different diseases with the same therapy, existing research still lacks an effective computational model to comprehensively consider the relevance and common targets among different diseases. Therefore, there is an urgent need for a new method to reveal the core components and key targets of traditional Chinese medicine compounds, especially the analysis of their action mechanisms in cross-disease treatment. An ideal research method should not only be able to predict the targets of each component in the compound, but also reveal the interactions among components, the relationships among targets, and the cross-disease treatment mechanisms, providing a theoretical basis for the precise application of traditional Chinese medicine compounds.

[0004] Constructing a principle interpretation system that reflects the holistic characteristics of traditional Chinese medicine is a key basic scientific research project. Against the background of the rapid development of multi-omics, multi-modal traditional Chinese medicine big data, and artificial intelligence technologies, the key to scientifically interpreting the principles of traditional Chinese medicine lies in establishing original inference and prediction algorithms for analyzing the complex action system of traditional Chinese medicine, especially complex relationships. Currently, there are mainstream methods for predicting the "multi-component - multi-target - multi-pathway" disease association network of traditional Chinese medicine compounds: first, collecting the biological target spectra of the chemical components contained in the prescription, then collecting the target spectra related to the disease, and using a Venn diagram to analyze the intersection of the two to obtain the overall target spectrum of the prescription. However, this method has certain limitations, mainly reflected in the following aspects:

[0005] In theory, a chemical component of a prescription may have multiple direct targets, and the relationship between these direct targets and the component is not the same. Even for direct targets, their affinity with chemical components varies, resulting in different accuracy of target prediction. Given the differences in the prediction accuracy of targets, the simple intersection method of obtaining the target of the entire prescription treats these targets as equally important, and therefore cannot effectively identify the contribution and role of these targets in the overall drug mechanism.

[0006] Traditional intersection methods simplify the relationship between drug targets and disease targets into a binary linear relationship of "existence" or "non-existence". However, a network target can appear in multiple Chinese medicine ingredients at the same time, and such a target is defined as an important target of the entire prescription. Correspondingly, the chemical components that combine multiple important targets are defined as the key components of the entire prescription. Therefore, simply finding the intersection to obtain the target of the entire prescription ignores the overlapping characteristics of the targets, and the overlapping characteristics of the targets happen to reflect the multivariate nonlinear relationship of Chinese medicine of "multiple components with the same target" and "one component with multiple targets", so it is impossible to effectively identify the contribution and role of these key pharmacodynamic components in the overall drug mechanism.

[0007] Finally, the multiple components of traditional Chinese medicine compound prescriptions often have synergistic effects, that is, different components may act on the same or different targets through different mechanisms, thereby exerting a stronger therapeutic effect. Simple intersection analysis cannot reveal this synergistic effect, nor can it accurately reflect the comprehensive effect of the drug on the disease. For example, some targets may play a minor role in multiple components of the drug, but by seeking intersection, they may be mistakenly excluded, missing out on potential therapeutic targets.

[0008] In actual analysis, the rank of the target is of great significance, because the top-ranked target in each component represents its most likely target. Therefore, in order to improve the accuracy and credibility of network pharmacology analysis, it is necessary to calculate the probability of occurrence of each target in all components and combine its ranking information in each component to more comprehensively evaluate the role of each target in the entire compound, and then infer a more reliable drug-target interaction network.

[0009] Based on this, this application proposes a method for identifying the core ingredients and target spectra of traditional Chinese medicine compound prescriptions based on the SARA algorithm. This method can accurately identify the core ingredients and key targets in the compound prescriptions by comprehensively considering the specificity and correlation between each ingredient and target in the compound prescription, and provide a scientific basis for analyzing the mechanism of treating different diseases with the same method. Summary of the invention

[0010] One of the purposes of the present invention is to provide a method for identifying the core components and overall target spectrum of a traditional Chinese medicine compound, comprising the following steps:

[0011] S1: Screening active ingredients of traditional Chinese medicine compound: Analyze the components of traditional Chinese medicine compound and its metabolites by liquid chromatography tandem high-resolution mass spectrometry, and identify the active ingredients and blood components in the traditional Chinese medicine compound through data processing;

[0012] The metabolites can be blood, urine, feces, etc.

[0013] During the screening process, comprehensively consider factors such as the pharmacological effects, molecular structure characteristics, presence frequency and concentration of the components in the compound, and finally screen out the core components that play a key role in the therapeutic efficacy of the traditional Chinese medicine compound and can exert a synergistic effect on specific disease targets.

[0014] S2: Obtaining the target information of the whole formula: According to the molecular structure data of the active ingredients, obtain the target information of the active ingredients of the traditional Chinese medicine compound.

[0015] Use professional databases (such as SEASearch Sever, PharmMapper, etc.) to obtain potential target information related to the active ingredients of the traditional Chinese medicine compound. These target information include known drug target molecules, such as proteins, enzymes, receptors, etc.

[0016] S3: Calculating the key targets of the whole formula: Construct a relationship network between the components and targets of the traditional Chinese medicine compound based on the active ingredient information in S1 and the target information in S2. The nodes in the network represent the components or targets of the traditional Chinese medicine compound, and the edges represent the mutual relationship between the components and targets. Use the specific association recognition algorithm, combined with the scarcity and global network relevance of the components and targets, to score the importance of the components and targets of the traditional Chinese medicine compound. According to the scoring results, screen out the targets with higher importance scores as the key targets of the traditional Chinese medicine compound.

[0017] In the relationship network, the nodes represent targets, and the edges represent the interactions between components and targets. Based on this network, apply the specific association recognition (SARA) algorithm, comprehensively consider the scarcity of components and targets and their relevance in the global network, and score the importance of the components and targets in the compound; The SARA algorithm quantifies the role of components and targets in the overall network by spreading the influence between targets, simulating the interactions between targets in the network, and updating the "importance" value of each target through multiple iterations. Combine the relationship strength of the targets with the propagated influence, calculate the comprehensive evaluation score of each target, and fuse this information through weighted averaging. Normalization processing ensures that the scores of the targets are within the same scale, and the targets are sorted according to the score, and potential key targets are screened out. Through this comprehensive evaluation, both the direct relationship between components and targets and the relative importance between targets are considered, so as to realize the recognition of specific associations and provide a prediction basis for the core active ingredients and action mechanisms of the compound.

[0018] S4: Determine the core active ingredients of the traditional Chinese medicine compound: Based on the probability values of each target in the active ingredients of the traditional Chinese medicine compound in S3, the influence of each target is weighted and adjusted. Combining the ranking weight and the probability value of the target, calculate the comprehensive distribution entropy of each ingredient in the traditional Chinese medicine compound, and obtain the core active ingredients of the traditional Chinese medicine compound according to the calculation results.

[0019] In this process, to improve the accuracy of the calculation, ranking weights are introduced to weight and adjust the influence of each target. The ranking weights are assigned according to the relative importance of the targets in the compound (the higher the relative importance of the target, the greater the weight assigned to it). By combining the ranking weight and the probability value of the target, calculate the optimized comprehensive distribution entropy (CDE), so that the results can more accurately reflect the importance of the ingredients. Finally, infer the core active ingredients of the traditional Chinese medicine compound based on the comprehensive distribution entropy value and determine their key roles in the compound.

[0020] S5: Analysis of the key target regulatory network and pathways: Use gene set enrichment analysis to analyze the biological functions and signaling pathways of the key targets obtained in S3, and use the human disease target database to analyze the potential diseases of action.

[0021] After obtaining the sorted list of target genes, the R language clusterProfiler package can be used for gene set enrichment analysis, and cnetplot can be used to display the association network between the enriched gene sets and genes. Then, use human disease target databases such as DisGeNET and OMIM to analyze the potential diseases of action.

[0022] In a specific embodiment of the present invention, the above method steps are used to specifically analyze the Banxia Xiexin Decoction, and the specific process is as follows:

[0023] S1: Screen the active ingredients of the traditional Chinese medicine compound

[0024] The Thermo QE plus liquid chromatography tandem high-resolution mass spectrometry was used to analyze traditional Chinese medicine compound and its metabolites efficiently and accurately. The liquid chromatography conditions were as follows: an ACQUITY UPLC HSS column (2.1×100mm, 1.8μm) was used as the chromatographic column, the column temperature was set at 35°C, the injection volume was 10 μL, the flow rate was 0.3 mL / min, and gradient elution was carried out using mobile phase A (deionized water containing 0.1% formic acid) and mobile phase B (acetonitrile containing 0.1% formic acid); for the mass spectrometry part, a Q Exactive Orbitrap high-resolution mass spectrometer was used for data acquisition, scanning was carried out in positive and negative modes respectively. Subsequently, Compound Discoverer 3.2 software was used to extract characteristic peaks, perform elemental matching, predict molecular formulas and match isotope distributions on the original Raw mass spectrometry data, and the mean value of the mass deviation was set within 5 ppm, so as to analyze the active ingredients of traditional Chinese medicine compound efficiently and accurately. A total of 25 blood-metabolized components of Banxia Xiexin Decoction were identified in this scheme.

[0025] S2: Obtain the target information of the whole formula

[0026] Based on the molecular structure data of the active ingredients, the target information of the active ingredients was obtained using a public database. In this scheme, a total of 498 known druggable targets of 25 blood components of Banxia Xiexin Decoction were identified using the SEA database.

[0027] S3: Calculate the key targets of the whole formula

[0028] An ingredient-target association network was constructed according to the results of S1 and S2. The nodes in the network represent traditional Chinese medicine compound ingredients or targets, and the edges represent the mutual relationship between ingredients and targets. The SARA algorithm was applied to calculate the local influence and global influence of each target in each ingredient of Banxia Xiexin Decoction in the association network, evaluate the core role of the target in the efficacy transmission path, identify rare targets highly related to the specific efficacy of Banxia Xiexin Decoction by analyzing the scarcity relationship between traditional Chinese medicine compound ingredients and targets, comprehensively consider the specificity and relevance between targets, calculate the target score, and a score above 0.5 indicates that the target is more likely to be a key target of traditional Chinese medicine compound. When the selected key targets frequently co-occur in different chemical components, it reflects the potential biological synergistic effect between different components; an adjacency matrix of the mutual association between key targets was constructed using open-source network visualization software such as Cytoscape and Gephi. The size of the nodes reflects the frequency of occurrence of the key targets, and the thickness of the connecting edges between nodes reflects the strength of the association between the two targets. In this scheme, Gephi software was used to screen out that IL2, VEGFA, P4HB, etc. play a core role in the 36-key drug target network of 25 blood components of Banxia Xiexin Decoction.

[0029] S4: Determine the core active ingredients of traditional Chinese medicine compound

[0030] Using the probability values of each target obtained in S3 in the traditional Chinese medicine compound, introduce the ranking weight, weighted-adjust the influence of each target, combine the ranking weight and the probability value of the target, calculate the comprehensive distribution entropy (CDE) of each ingredient in the traditional Chinese medicine compound. A score above 0.5 indicates that the ingredient is more likely to be the core ingredient of the traditional Chinese medicine compound. This solution screened out 15 key blood-entry ingredients of Banxia Xiexin Decoction in total.

[0031] S5: Key target network and pathway analysis

[0032] Based on the key targets of the obtained traditional Chinese medicine compound, conduct functional enrichment analysis on its biology and intervened diseases. In this solution, the R language clusterProfiler package was used to find that 15 core blood-entry ingredients and 36 key targets of Banxia Xiexin Decoction mainly regulate biological pathways such as the core complex of proteasome, hydrolase activity, and growth factor receptor binding. The DisGeNET human disease target database package was used to find that 15 core blood-entry ingredients and 36 key targets mainly intervene in diseases such as tumors, atherosclerosis, stroke, and contact dermatitis.

[0033] The method of the present invention can quantitatively evaluate the importance of targets; consider the interaction and repeated occurrence of targets in multiple ingredients of the compound, and assign different weights to different targets; solve the deficiencies of traditional intersection analysis through an optimized algorithm, and more comprehensively reveal the overall target spectrum of the prescription drug. This method combines target importance ranking and probability weighting algorithm, can achieve more accurate prediction of compound drug targets, and further realize the analysis of the mechanism of treating different diseases with the same formula according to the weight of the target and the gene set enrichment algorithm.

[0034] The present invention provides a method for identifying the core ingredients and overall target spectrum of traditional Chinese medicine compound. Through this method, the core active ingredients and key targets in the compound can be accurately identified, and a scientific basis is provided for the analysis of the mechanism of treating different diseases with the same formula. The present invention combines the SARA algorithm and the CDE model, successfully predicts the effective ingredients of multiple traditional Chinese medicine compounds, and this method has achieved good results in the prediction of the effective ingredients of Banxia Xiexin Decoction. By comprehensively considering the specificity and relevance of each ingredient and target in the traditional Chinese medicine compound, combining the synergistic effect between ingredients and the mutual relationship between targets, the present invention overcomes the limitations of single ingredient analysis and static network construction in traditional methods, provides a dynamic and comprehensive target prediction method, and provides new ideas for the precise application of traditional Chinese medicine compounds and the in-depth understanding of treatment mechanisms.

[0035] The SARA-CDE algorithm significantly improves the accuracy and stability of target prediction by comprehensively calculating the probabilities of targets from multiple perspectives. This algorithm accurately captures the complex relationships between components and targets, enhancing the accuracy of component importance scoring. By introducing a correlation-driven optimization strategy, SARA-CDE demonstrates higher prediction accuracy and robustness in complex network environments, especially suitable for the analysis of the synergistic effects of multiple components and multiple targets in traditional Chinese medicine compound prescriptions, and can deeply reveal the internal relationships between components and targets.

[0036] The beneficial effects of the present invention are as follows:

[0037] 1. The method of the present invention comprehensively analyzes the influence of targets from both global and local levels by identifying the most relevant and important targets in specific components, which can not only reveal the specificity of targets but also reflect their relationships with other targets. It can more accurately capture the complex and multi-dimensional interactions between components and targets. In addition, the model can still maintain a high prediction accuracy when facing datasets of different scales and complexities. This method not only improves the prediction accuracy but also enhances its interpretability, and is a more efficient and reliable target prediction method.

[0038] 2. The method of the present invention comprehensively evaluates the importance of components through distribution entropy, considering the probability distribution and relative importance of each target in the component, thus avoiding the limitations of a single indicator. In addition, this method can dynamically reflect the synergistic effects between targets in the component, and is particularly suitable for the complexity analysis of traditional Chinese medicine compound prescriptions. By effectively identifying potential key components, it helps to screen out the most active components from multi-component compound prescriptions, thereby improving the stability and reliability of the prediction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is the target score distribution diagram of the blood components of the traditional Chinese medicine compound Banxia Xiexin Decoction.

[0040] Figure 2 It is the adjacency matrix diagram of the key targets of the traditional Chinese medicine compound Banxia Xiexin Decoction.

[0041] Figure 3 It is the gene set and gene association network of the top 3 significantly enriched blood core components of Banxia Xiexin Decoction.

[0042] Figure 4 It is the algorithm logic flow chart of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0043] The preferred embodiments of the present invention will be described in detail below in conjunction with examples. It should be understood that the following examples are given only for illustrative purposes and are not intended to limit the scope of the present invention. Those skilled in the art can make various modifications and substitutions to the present invention without departing from the purpose and spirit of the present invention.

[0044] Unless otherwise specified, the experimental methods used in the following examples are all conventional methods.

[0045] Unless otherwise specified, the materials, reagents, etc. used in the following examples can all be obtained from commercial channels.

[0046] The technical solution of the present invention will be described in detail below by taking Banxia Xiexin Decoction as a compound model drug.

[0047] Example 1

[0048] The preparation process of the serum containing Banxia Xiexin Decoction in this example is as follows:

[0049] The cut pieces of Banxia Xiexin Decoction (Pinellia ternata 12 g, dried ginger 9 g, Scutellaria baicalensis 9 g, Coptis chinensis 3 g, ginseng 9 g, Chinese date 4 pieces, roasted licorice 9 g) are added with 8 times the amount (mL / g) of water. After soaking at room temperature for 1 h, it is decocted twice, each time for 1.5 h; the decoction liquids are combined, centrifuged at 4000 rpm for 30 min to take the supernatant, and concentrated under reduced pressure to 2.7 g of crude drug amount / mL.

[0050] Six- to eight-week-old healthy male SD rats are gavaged at 54 g of crude drug amount / kg and continuously gavaged for one week. Blood is collected from the abdominal aorta 1 h after the last administration, the serum is separated, inactivated at 56 °C for 30 min, and filtered to remove bacteria. The control group is given an equal volume of distilled water every day to prepare blank control serum.

[0051] S1. Analysis of the chemical components and blood components of the Banxia Xiexin Decoction solution

[0052] The chemical components and blood components of the Banxia Xiexin Decoction solution are analyzed by using a Thermo QE plus liquid chromatography tandem high-resolution mass spectrometer and Compound discover data processing software. Taking the chemical components of the decoction solution as a reference, the blood components of the Banxia Xiexin Decoction are characterized.

[0053] Specifically, the liquid chromatography conditions were as follows: an ACQUITY UPLC HSS column (2.1×100 mm, 1.8 μm) was used as the chromatographic column, the column temperature was set at 35 °C, the injection volume was 10 μL, the flow rate was 0.3 mL / min, and gradient elution was performed using mobile phase A (deionized water containing 0.1% formic acid) and mobile phase B (acetonitrile containing 0.1% formic acid). The elution conditions are shown in Table 1; for the mass spectrometry part, a QExactive Orbitrap high-resolution mass spectrometer was used for data acquisition, and scans were performed in both positive and negative modes. Subsequently, Compound Discoverer 3.2 software was used to extract characteristic peaks, perform elemental matching, predict molecular formulas, and match isotope distributions from the original Raw mass spectrometry data. The mean mass deviation was set within 5 ppm to efficiently and accurately analyze the active ingredients of the traditional Chinese medicine compound formula.

[0054] The results are shown in Table 2. A total of 25 blood-metabolized components of Banxia Xiexin Decoction were identified using this method.

[0055] Table 1 Gradient elution conditions

[0056] Time Flow rate (mL / min) Proportion of mobile phase A (%) Proportion of mobile phase B (%) 0 0.3 100 0 10 0.3 70 30 25 0.3 60 40 30 0.3 50 50 40 0.3 30 70 45 0.3 0 100 60 0.3 0 100 60.5 0.3 100 0 70 0.3 100 0

[0057] Table 2 Blood-metabolized components of Banxia Xiexin Decoction

[0058]

[0059]

[0060] S2. Prediction of targets of blood components of Banxia Xiexin Decoction

[0061] Through the molecular structure data of the active ingredients, techniques such as three-dimensional conformational matching of ligand similarity, pharmacophore matching, or chemical genomics were used to explore the interactions between the entire chemical space and the entire genomic space.

[0062] In this example, SEA Search Sever (https: / / sea.bkslab.org / ) was used to predict the protein targets of the blood components of Banxia Xiexin Decoction and obtain the component-target interaction matrix. The results are shown in Table 3. A total of 498 known druggable targets were identified for the 25 blood-metabolized components of Banxia Xiexin Decoction.

[0063] Table 3 Number of protein targets of all blood components of Banxia Xiexin Decoction

[0064]

[0065]

[0066] S3. Calculate the key target spectrum of Banxia Xiexin Decoction

[0067] First, construct a relationship network between components and targets based on the results of steps S1 and S2, calculate the association frequency and relationship strength between components and targets, and sort the calculation results in descending order. The specific calculation formulas are as follows:

[0068]

[0069] Among them, C i is a component in the traditional Chinese medicine compound, T j is the target in the component, count(C i , T j ) represents the association frequency between the component and the target, N is the total number of components, count j represents the number of times the target appears in the component. A ij is the relationship strength between the component and the target.

[0070] Set the initial weight for each target T j as

[0071]

[0072] where M is the total number of targets in the traditional Chinese medicine compound components, is the initial importance of the target.

[0073] After the initial weight setting is completed, update the weights of the targets through multiple iterations to simulate the interaction and influence between targets. At the (t + 1)-th iteration, the updated weight j of the target T is calculated by the following formula:

[0074]

[0075] Among them, represents the updated weight of the target T j at the (t + 1)-th iteration; d is the damping factor, usually taking a value of 0.85, which is used to control the influence range of propagation; N j is the set of all component nodes connected to the target T j ; A ij represents the relationship strength between the component C i and the target T j ; is the weight of the component C i at the t-th iteration.

[0076] Calculate the probability value of the target, and its score range is between 0 and 1. A score greater than 0.5 is a key target:

[0077]

[0078] Among them, A j is the relationship strength between the component and the target, which is the normalized value of the relationship strength between the component and the target. B j is the weight of the target after stacking is completed, which is the normalized value of the weight of the target after iteration is completed. Finally, by integrating the normalized values of the two, P j is obtained, that is, the probability value of the target.

[0079] Construct an adjacency matrix between key targets through open-source network visualization software such as Cytoscape and Gephi. The size of the nodes reflects the frequency of occurrence of the key targets, and the thickness of the connecting edges between the nodes reflects the correlation strength between the two targets. This co-occurrence relationship may reflect the potential biological synergistic effect between the components of the traditional Chinese medicine compound.

[0080] Such as Figure 1 shown, in this embodiment, a scoring ranking graph of 498 known druggable targets is calculated, among which there are 36 key targets with a score above 0.5 (shown in Table 4); use Gephi software to draw a target adjacency matrix graph, as Figure 2 shown, among which nodes such as IL2, VEGFA, and P4HB have higher connectivity and centrality, indicating that they play a core role in the target spectrum of Banxia Xiexin Decoction. In this embodiment, the prototype Oroxylin A (aesculetin A) and Luteolin are detected in Banxia Xiexin Decoction, and the corresponding glucuronic acid conjugate metabolites Oroxylin A-7-O-β-D-glucuronide and Luteolin-7-glucuronide are detected. The former can act on IL2 and the latter can act on VEGFA. Therefore, the two core metabolic components may be synergistic components.

[0081] Table 4 Calculation results of key targets of 25 blood components in Banxia Xiexin Decoction (Score>0.5)

[0082]

[0083]

[0084]

[0085] S4. Analysis of key serum chemical components in Banxia Xiexin Decoction

[0086] Calculate the comprehensive distribution entropy (CDE) of the components based on the target scores obtained in step S3. To improve the accuracy of the calculation, ranking weights are introduced to weight and adjust the influence of each target. The ranking weights are assigned according to the relative importance of the targets in the compound prescription (the higher the relative importance of a target, the greater the weight assigned to it). By combining the ranking weights with the probability values of the targets, the optimized comprehensive distribution entropy (CDE) is calculated, enabling the result to more accurately reflect the importance of the components. Finally, the core active components of the traditional Chinese medicine compound prescription are inferred based on the comprehensive distribution entropy value, and their key roles in the compound prescription are determined.

[0087] The calculation method of the core active components of the traditional Chinese medicine compound prescription is as follows:

[0088]

[0089] where M is the number of targets in the component, P j the probability of the j-th target, log(p j ) the logarithm of the probability of the j-th target (the purpose is to balance the contribution of different probability values to the entropy value), e -k(j-1) the exponential function represents the weight of the j-th target, j - 1 is the ranking of the j-th target, and k controls the rate of weight decrease. The factor for normalizing all targets, making the sum of all weights equal to 1. m represents the ranking index of the target, ranging from 1 to N. The CDE score range is 0 - 1, and components with a CDE score greater than 0.5 are core components.

[0090] As shown in Table 5, a total of 15 core blood - entering components were screened out.

[0091] Table 5 Calculation results of the core blood - entering components of Banxia Xiexin Decoction (score > 0.5)

[0092]

[0093]

[0094] S5. Biological function and mechanism analysis of the same treatment for different diseases of the serum metabolic profile of Banxia Xiexin Decoction

[0095] According to the results in Table 4, use the clusterProfiler package in R language for gene set enrichment analysis, and use cnetplot to display the association network between the enriched gene sets and genes. Utilize public human disease target databases such as DisGeNET to enrich the potential diseases of the key targets.

[0096] Such as Figure 3As shown, the 15 core components and 36 key targets of Banxia Xiexin Decoction mainly regulate biological pathways such as the core complex of the proteasome, hydrolase activity, and growth factor receptor binding; taking the false discovery rate FDR < 0.01 as the threshold standard, it is obtained that the 15 core components and 36 key targets of Banxia Xiexin Decoction can intervene in diseases such as tumors, atherosclerosis, stroke, contact dermatitis, etc. (as shown in Table 6).

[0097] Table 6. Prediction of diseases affected by the key target spectrum of the core components of Banxia Xiexin Decoction entering the blood (FDR < 0.01)

[0098]

Claims

1. A method for identifying the core components and overall target spectrum of a traditional Chinese medicine compound, characterized in that: The steps include: S1: Screening of active ingredients in traditional Chinese medicine compound: Liquid chromatography-tandem high-resolution mass spectrometry is used to analyze the components of traditional Chinese medicine compound and its metabolites, and to identify the active ingredients and blood-entering components in the traditional Chinese medicine compound; S2: Obtaining the target information of the whole prescription: According to the molecular structure data of the active ingredients in S1, the target information of the active ingredients of the traditional Chinese medicine compound is obtained; S3: Calculate the key targets of the whole prescription: Based on the active ingredient information of S1 and the target information of S2, a relationship network between the ingredients and targets of the Chinese medicine compound is constructed. The nodes in the network represent the ingredients or targets of the Chinese medicine compound, and the edges represent the mutual relationships between the ingredients and targets. The importance of the ingredients and targets of the Chinese medicine compound is scored by using a specific correlation recognition algorithm, combined with the scarcity of the ingredients and targets and the correlation in the global network. According to the scoring results, the targets with higher importance scores are screened out as the key targets of the Chinese medicine compound; S4: Determine the core active ingredients of the Chinese medicine compound: According to the probability value of each target in the Chinese medicine compound in S3, the influence of each target is weighted and adjusted. Combined with the ranking weight and the probability value of the target, the comprehensive distribution entropy of each ingredient in the Chinese medicine compound is calculated to obtain the core active ingredients of the Chinese medicine compound; S5: Key target regulatory network and pathway analysis: Gene set enrichment analysis is used to analyze the biological functions and signal pathways of the key targets obtained in S3, and the human disease target database is used to analyze potential disease effects.

2. The method for identifying the core components and overall target spectrum of a traditional Chinese medicine compound according to claim 1, characterized in that: The Chinese medicinal compound is Banxia Xiexin Decoction.

3. The method for identifying the core components and overall target spectrum of a traditional Chinese medicine compound according to claim 2, characterized in that: In the liquid chromatography-tandem high-resolution mass spectrometry used in S1, the liquid chromatography conditions were as follows: the chromatographic column was an ACQUITY UPLC HSS column, the column temperature was set to 35°C, the injection volume was 10 µL, the flow rate was 0.3 mL / min, the mobile phase A was deionized water containing 0.1% formic acid, and the mobile phase B was acetonitrile containing 0.1% formic acid, with gradient elution; the mass spectrometry part used a Q Exactive Orbitrap high-resolution mass spectrometer for data acquisition, scanning in positive and negative modes respectively.

4. The method for identifying the core components and overall target spectrum of a traditional Chinese medicine compound according to claim 2, characterized in that: In S2, the SEA database was used to obtain the target information of active ingredients.

5. The method for identifying the core components and overall target spectrum of a traditional Chinese medicine compound according to claim 2, characterized in that: The specific association identification algorithm in S3 evaluates the core role of each target in the drug delivery pathway by calculating the local influence and global influence of each target in the Banxia Xiexin Decoction ingredients in the association network.

6. The method for identifying the core components and overall target spectrum of a traditional Chinese medicine compound according to claim 2, characterized in that: The specific correlation identification algorithm in S3 identifies rare targets that are highly correlated with the specific therapeutic effects of Banxia Xiexin Decoction by analyzing the scarcity relationship between the active ingredients of the Chinese herbal compound and the targets.

7. The method for identifying the core components and overall target spectrum of a traditional Chinese medicine compound according to claim 2, characterized in that: The specific association recognition algorithm in S3 comprehensively considers the specificity and association between targets and calculates the target score. A score above 0.5 indicates that the target is likely to become a key target for a traditional Chinese medicine compound.

8. The method for identifying the core components and overall target spectrum of a traditional Chinese medicine compound according to claim 2, characterized in that: In S4, the probability value of each target in the TCM compound obtained in S3 is used to introduce ranking weights, and the influence of each target is weighted and adjusted. The comprehensive distribution entropy of each component in the TCM compound is calculated by combining the ranking weights with the probability value of the target. A score above 0.5 indicates that the component is likely to be the core component of the TCM compound.

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