阅读说明：本技术 一种优化肝复乐抗肝癌组方药理学分析方法和系统 (Optimized Ganfule anti-liver cancer formula pharmacological analysis method and system ) 是由 李顺祥 潘宇 李娟� 徐菲 黄丹 肖利辉 于 2019-10-17 设计创作，主要内容包括：本发明涉及一种优化肝复乐抗肝癌组方药理学分析方法和系统。一种优化肝复乐抗肝癌组方药理学分析方法,包括步骤：S1、构建多药物-化合物-靶点-途径-疾病相互作用网络模型：整合肝复乐的所有组方药物的小分子化合物数据库、抗肝癌靶点蛋白数据库、信号通路数据库、相关疾病数据库,搭建方剂抗肝癌活性物质筛选平台,筛选得到最佳分子组；S2、优化组方。本方法构建了原发性肝癌的药材-化合物-靶点-途径-疾病复杂网络模型,初步阐明肝复乐治疗肝癌的活性成分与作用机制,建立临床病例与用药规律之间的内在关联,并根据最佳分子组,匹配最佳药材组,优化组方。(The invention relates to a method and a system for optimizing the pharmacological analysis of a liver rehabilitation anti-liver cancer prescription. An optimized Ganfule anti-liver cancer prescription pharmacological analysis method comprises the following steps: s1, constructing a multi-drug-compound-target-pathway-disease interaction network model: integrating a small molecule compound database, an anti-liver cancer target protein database, a signal path database and a related disease database of all the medicines of the prescription of Ganfule, building a prescription anti-liver cancer active substance screening platform, and screening to obtain an optimal molecule group; and S2, optimizing the formula. The method constructs a drug-compound-target-pathway-disease complex network model of primary liver cancer, preliminarily clarifies active ingredients and action mechanisms of Ganfule for treating liver cancer, establishes internal association between clinical cases and medication rules, matches the optimal drug groups according to the optimal molecular groups, and optimizes the formula.)

1. An optimized Ganfule anti-liver cancer prescription pharmacological analysis method is characterized by comprising the following steps:

s1, constructing a multi-drug-compound-target-pathway-disease interaction network model: integrating a small molecule compound database, an anti-liver cancer target protein database, a signal path database and a related disease database of all the medicines of the prescription of Ganfule, building a prescription anti-liver cancer active substance screening platform, and screening to obtain an optimal molecule group;

s2, optimizing the formula: matching corresponding medicinal materials in the small molecular compound database according to the optimal molecular group, and forming an optimized formula according to the corresponding medicinal materials.

2. The optimized ganfule anti-liver cancer prescription pharmacological analysis method according to claim 1, wherein the step S1 comprises:

s11, constructing a target-pathway-disease network: acquiring related information of target genes and proteins of drugs, carrying out standardized naming on the target genes and the proteins, carrying out online analysis on the target proteins and the target proteins by utilizing a network database, and establishing a target database, a signal channel database and a disease database;

s12, constructing a multi-medicinal material-compound network: acquiring the chemical structure and relative molecular mass of a corresponding compound of each medicinal material in the Ganfule prescription, and constructing a small molecular compound database;

s13, screening: carrying out drug-like property screening and availability screening on the small molecule compound database, and screening a candidate molecule group;

s14, butt joint test: and carrying out chemical structure optimization on each candidate molecule of the candidate molecule group, carrying out hydrogenation, giving charges, applying a force field, generating a plurality of conformations, then carrying out butt joint on the candidate molecule and the target protein in the target database by using a ligand rapid butt joint algorithm, carrying out consistency scoring on the butt joint, forming a drug-compound-target-pathway-disease network, and screening the optimal molecule group.

3. The optimized ganfule anti-liver cancer prescription pharmacological analysis method according to claim 2, wherein the ligand rapid docking algorithm is a genetic algorithm or a simulated annealing algorithm.

4. The optimized ganfule anti-liver cancer prescription pharmacological analysis method according to claim 2, wherein the consistency score is a score using two or more of a binding energy algorithm, a libdockscore algorithm, a ligacore-1 algorithm, a ligacore-2 algorithm, a PLP-1 algorithm, and a ligacore PLP-2 algorithm together.

5. The optimized Ganfule anti-liver cancer prescription pharmacological analysis method according to claim 4, wherein in step S14, the screening is: and ranking according to scores of a plurality of consistency scores, and forming the optimal molecular group by using small molecular compounds with high scores.

6. The optimized ganfule anti-liver cancer prescription pharmacological analysis method according to claim 1, further comprising the steps of:

s3, pharmacodynamic verification of an optimized formula: and respectively preparing experimental medicaments by using the optimized formula and the control formula, respectively detecting expression influence levels on proliferation, migration and invasion of liver cancer target cells and target protein expression influence levels, and verifying the efficacy of the optimized formula.

7. The optimized Ganfule anti-liver cancer prescription pharmacological analysis method according to claim 6, wherein the experimental medicament is a plurality of extractum with potential pharmacodynamic activity prepared by respectively separating the optimized prescription and the control prescription into a volatile oil part, an alcohol-soluble part and an alcohol-precipitated part by adopting a water vapor extraction method and a water extraction and alcohol precipitation method.

8. The optimized Ganfule anti-liver cancer prescription pharmacological analysis method according to any one of claims 1 to 7, wherein the optimized prescription comprises a spleen-invigorating and qi-regulating group, a blood-activating and stasis-removing group and a heat-clearing and toxicity-removing group.

9. An optimized ganfule anti-hepatoma formulation pharmacological assay system for use in the method of any of claims 1 to 8, comprising: the system comprises a data network module, a screening module and a docking module;

the data network module comprises a capturing unit, a disease database, a target database, a signal channel database and a multi-molecule database;

the screening module comprises a drug-like property screening unit and a utilization degree screening unit;

the docking module is used for docking test of molecular data and target data.

Technical Field

The invention relates to the field of traditional Chinese medicine pharmacology, in particular to a method and a system for optimizing pharmacological analysis of a liver rehabilitation anti-liver cancer formula.

Background

The Chinese herbal compound 'Ganfule' is the first three new Chinese herbal medicines (the national standard Z10940066) for treating liver diseases in the national level, has been awarded first-grade science and technology progress of the State administration of traditional Chinese medicine in 1992, and is listed as an antitumor medicine in the national catalog of basic insurance medical medicines in 2004. Currently, Ganfule is on the market for 20 years, clinical medication mainly comprises decoction, tablets and capsules, and the annual sale reaches 2.0 hundred million yuan. The prescription consists of 21 traditional Chinese medicines such as barbed skullcap herb, largehead atractylodes rhizome, astragalus root, pilose asiabell root, tangerine peel, rhubarb, Indian buead, coix seed, nutgrass galingale rhizome, Chinese thorowax root, Chinese eaglewood, akebia stem, turtle shell, ground beeltle, peach seed, sappan wood oyster shell, turmeric root-tuber, Chinese paris rhizome, dahurian patrinia herb, virgate wormwood herb and the like, and has the traditional Chinese medicine compound recipe for strengthening spleen, regulating vital energy, removing. It can be used for treating primary liver cancer with the symptoms of liver stasis and spleen deficiency, such as abdominal lump, hypochondriac pain, listlessness, anorexia, abdominal distention, vexation, irritability, bitter taste, dry throat, etc.

Although Ganfule is used as a famous medical prescription and has a definite curative effect on liver cancer, the related researches on the substance basis and the action mechanism of the drug effect and the action target point are in a weak position for a long time, and the related researches at home and abroad are less. The chemical substance basis and the pharmacodynamic substance basis are unclear, the action target and the mechanism are unclear, and the scientific connotation of the prescription cannot be clearly explained, which becomes a key technical bottleneck for restricting the further development of the liver recovery.

The integrated pharmacology of traditional Chinese medicine is a new field of pharmacology research, and the rule of interaction between substance entities and organisms of traditional Chinese medicine formulas is one of the key scientific problems of integrated pharmacology research, and is a cross discipline integrating multiple disciplines of traditional Chinese medicine chemistry, pharmacokinetics, pharmacology, system biology, computational science and the like. The integrated pharmacology is used as a subject for researching the interaction between the multi-component medicine and the body and the integration rule and action principle thereof, and provides a solution strategy, a method and a technology for analyzing a complex action system of the traditional Chinese medicine. The proposal of the integrated pharmacology strategy is beneficial to overcoming the problems of insufficient correlation between chemical components, in-vivo process, pharmacological activity and disease effect, research tendency of fragmentation and the like in modern research of the traditional Chinese medicine, and forming a pharmacology evaluation system and a research method with the characteristics of the traditional Chinese medicine.

Patent document No. 201310031897.X discloses a traditional Chinese medicine system pharmacology analysis platform and analysis method, but the optimization of a prescription for matching multiple medicines in multiple proportions cannot be realized. Therefore, there is a need in the art for an optimization method that provides a new research idea based on the multi-target and multi-pathway action mechanism characteristics of compound traditional Chinese medicine, establishes a high-throughput screening system conforming to the multi-component, multi-target and multi-pathway characteristics of traditional Chinese medicine, screens a new effective material basis for efficiently treating liver cancer from Ganfule, defines the effective chemical components and bioactive components of Ganfule against liver cancer cells, studies the interaction relationship among effective chemical component groups, screens key components for inhibiting liver cancer activity, and optimizes the composition.

Disclosure of Invention

In view of the defects of the prior art, the invention aims to provide an optimized ganfule anti-liver cancer formula pharmacological analysis method and system, which can construct a pharmacological network of multiple medicinal materials, compounds, targets, approaches and diseases, can match the optimal medicinal materials by using the pharmacological network and optimize the ganfule formula.

In order to achieve the purpose, the invention adopts the following technical scheme:

an optimized Ganfule anti-liver cancer prescription pharmacological analysis method comprises the following steps:

s1, constructing a multi-drug-compound-target-pathway-disease interaction network model: integrating a small molecule compound database, an anti-liver cancer target protein database, a signal path database and a related disease database of all the medicines of the prescription of Ganfule, building a prescription anti-liver cancer active substance screening platform, and screening to obtain an optimal molecule group;

s2, optimizing the formula: matching corresponding medicinal materials in the small molecular compound database according to the optimal molecular group, and forming an optimized formula according to the corresponding medicinal materials.

Preferably, the optimized ganfule anti-liver cancer prescription pharmacological analysis method comprises the following steps of S1:

s11, constructing a target-pathway-disease network: acquiring related information of target genes and proteins of drugs, carrying out standardized naming on the target genes and the proteins, carrying out online analysis on the target proteins and the target proteins by utilizing a network database, and establishing a target database, a signal channel database and a disease database;

s12, constructing a multi-medicinal material-compound network: acquiring the chemical structure and relative molecular mass of a corresponding compound of each medicinal material in the Ganfule prescription, and constructing a small molecular compound database;

s13, screening: carrying out drug-like property screening and availability screening on the small molecule compound database, and screening a candidate molecule group;

s14, butt joint test: and carrying out chemical structure optimization on each candidate molecule of the candidate molecule group, carrying out hydrogenation, giving charges, applying a force field, generating a plurality of conformations, then carrying out butt joint on the candidate molecule and the target protein in the target database by using a ligand rapid butt joint algorithm, carrying out consistency scoring on the butt joint, forming a drug-compound-target-pathway-disease network, and screening the optimal molecule group.

Preferably, the optimized Ganfule anti-liver cancer prescription pharmacological analysis method is characterized in that the ligand rapid docking algorithm is a genetic algorithm or a simulated annealing algorithm.

Preferably, the optimized ganfule anti-liver cancer prescription pharmacological analysis method is characterized in that the consistency scoring is carried out by using more than two of a binding energy algorithm, a libdockscore algorithm, a ligacore-1 algorithm, a ligacore-2 algorithm, a PLP-1 algorithm and a ligacore PLP-2 algorithm together.

Preferably, in the optimized ganfule anti-liver cancer prescription pharmacological analysis method, in step S14, the screening is as follows: and ranking according to scores of a plurality of consistency scores, and forming the optimal molecular group by using small molecular compounds with high scores.

The optimized Ganfule anti-liver cancer prescription pharmacological analysis method further comprises the following steps:

s3, pharmacodynamic verification of an optimized formula: and respectively preparing experimental medicaments by using the optimized formula and the control formula, respectively detecting expression influence levels on proliferation, migration and invasion of liver cancer target cells and target protein expression influence levels, and verifying the efficacy of the optimized formula.

Preferably, the experimental medicament is a plurality of extractums with potential pharmacodynamic activity prepared by respectively separating the optimized formula and the control formula into a volatile oil part, an alcohol-soluble part and an alcohol-precipitated part by adopting a water vapor extraction method and a water extraction and alcohol precipitation method.

The optimized Ganfule anti-liver cancer prescription pharmacological analysis method preferably comprises a spleen-invigorating and qi-regulating group, a blood-activating and stasis-dissolving group and a heat-clearing and detoxifying group.

An optimized ganfule anti-liver cancer formulation pharmacological analysis system for use in the method, comprising: the system comprises a data network module, a screening module and a docking module;

the data network module comprises a capturing unit, a disease database, a target database, a signal channel database and a multi-molecule database;

the screening module comprises a drug-like property screening unit and a utilization degree screening unit;

the docking module is used for docking test of molecular data and target data.

Compared with the prior art, the optimized ganfule anti-liver cancer prescription pharmacological analysis method and system provided by the invention construct a primary liver cancer medicinal material-compound-target-pathway-disease complex network model, preliminarily clarify the active ingredients and action mechanism of ganfule for treating liver cancer, establish the internal association between clinical cases and medication rules, match the optimal medicinal material group according to the optimal molecular group, and optimize the prescription.

Drawings

FIG. 1 is a flow chart of the pharmacological analysis method of the optimized Ganfule anti-liver cancer prescription provided by the invention;

FIG. 2 is a block diagram of the structure of an optimized Ganfule anti-liver cancer prescription pharmacological analysis system provided by the present invention;

FIG. 3 is a strategy for a cyber-pharmacological study of Ganfule provided by the present invention;

FIG. 4 is a schematic network diagram of the multi-drug-compound-target-pathway-disease provided by the present invention;

fig. 5 shows the morphological changes of HepG-2 cells with different concentrations of the alcohol-soluble portion of ganfule for 48 hours (x 200,

n＝3)；

FIG. 6 shows the effect of the alcohol-soluble fraction of Ganfule on HepG-2 cell proliferation (x 200,

n＝3)；

figure 7 is a graph of the effect of the alcohol-soluble fraction of ganfulan on HepG-2 cell migration provided by the present invention (x 200,

n＝3)；

fig. 8 shows the effect of the alcohol-soluble portion of ganfule on the invasion of HepG-2 cells (x 200,

n＝3)。

Detailed Description

In order to make the objects, technical solutions and effects of the present invention clearer and clearer, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

Referring to fig. 1-8, a method for optimizing the pharmacological analysis of a ganfule anti-liver cancer formulation, comprising the steps of:

s1, constructing a multi-drug-compound-target-pathway-disease interaction network model: integrating a small molecule compound database, an anti-liver cancer target protein database, a signal path database and a related disease database of all the medicines of the prescription of Ganfule, building a prescription anti-liver cancer active substance screening platform, and screening to obtain an optimal molecule group;

s2, optimizing the formula: matching corresponding medicinal materials in the small molecular compound database according to the optimal molecular group, and forming an optimized formula according to the corresponding medicinal materials.

Preferably, in this embodiment, the step S1 includes:

s11, constructing a target-pathway-disease network: acquiring related information of target genes and proteins of drugs, carrying out standardized naming on the target genes and the proteins, carrying out online analysis on the target proteins and the target proteins by utilizing a network database, and establishing a target database, a signal channel database and a disease database;

s12, constructing a multi-medicinal material-compound network: acquiring the chemical structure and relative molecular mass of a corresponding compound of each medicinal material in the Ganfule prescription, and constructing a small molecular compound database;

s13, screening: carrying out drug-like property screening and availability screening on the small molecule compound database, and screening a candidate molecule group;

s14, butt joint test: and carrying out chemical structure optimization on each candidate molecule of the candidate molecule group, carrying out hydrogenation, giving charges, applying a force field, generating a plurality of conformations, then carrying out butt joint on the candidate molecule and the target protein in the target database by using a ligand rapid butt joint algorithm, carrying out consistency scoring on the butt joint, forming a drug-compound-target-pathway-disease network, and screening the optimal molecule group.

Specifically, please refer to fig. 2, the invention also provides an optimized ganfule anti-liver cancer prescription pharmacological analysis system for the method, comprising: the system comprises a data network module 1, a screening module 2 and a butt joint module 3;

the data network module 1 comprises a capturing unit 11, a disease database 12, a target database 13, a signal channel database 14 and a multi-molecule database 15;

the screening module 2 comprises a drug-like property screening unit and a utilization degree screening unit;

the docking module 3 is used for docking test of molecular data and target data.

The multidrug-compound-target-pathway-disease was constructed according to the method steps provided by the invention with the above system, using the database and software in table 1.

Table 1 main database and software required for the construction of pharmacological networks according to the invention

Database and software	Website address
		TCM-ID (Chinese herbal medicine database)	http://www.megabionet.org/tcmid/
TCM Database @ taiwan (Chinese herbal medicine Database)	http://tcm.cmu.edu.tw/
		NCBI PubChem (Compound Structure database))	http://pubchem.ncbi.nlm.nih.gov/
ChemSider (Compound structure database)	http://www.chemspider.com
		OMIM (human disease gene database)	http://www.ncbi.nlm.nih.gov/omim
RCSB (protein crystal structure database)	http://www.pdb.org
		Uniprot (amino acid sequence information database)	http://www.uniprot.org
Swiss-Model (protein homology modeling database)	http://swissmodel.expasy.org/
		STRING9.05 (protein correlation database)	http://string-db.org/
Reactome (Signal path database)	http://www.reactome.org/
		KEGG (Signal path database)	http://www.genome.jp
CambrigeSoft ChemOffice 2012	http://www.cambridgesoft.com/
		Discovery Studio 2.55	http://accelrys.com/
Cytoscape 3.1	http://www.cytoscape.org/

Constructing a target-pathway-disease network:

the information related to the drug target genes and proteins is obtained by searching from disease databases (OMIM, http:// www.omim.org /), DisGeNET (http:// www.disgenet.org /) "with Hepatocellular Carcinoma" as the disease key word, and the target genes and proteins are standardized and named by Uniprot (https:// www.uniprot.org /), to obtain Table 2.

TABLE 2 Primary liver cancer target database

Please focus on fig. 3, a multi-drug-compound network was constructed:

the Ganfule is divided into 4 groups (herba Patriniae, rhizoma paridis, herba Scutellariae Barbatae and herba Artemisiae Scopariae) with heat and toxic materials clearing away, 7 groups (carapax Trionycis, Eupolyphaga Seu Steleophaga, semen Persicae, lignum sappan, Concha Ostreae, radix Curcumae and radix et rhizoma Rhei) with blood circulation promoting and blood stasis removing, and 10 groups (radix Codonopsis, Atractylodis rhizoma, radix astragali, Poria, Coicis semen, rhizoma Cyperi, pericarpium Citri Tangerinae, bupleuri radix, lignum Aquilariae Resinatum and caulis Akebiae) with spleen invigorating and qi regulating effects. The 21 medicinal materials are respectively input into a Chinese herbal medicine online Database in a Chinese pinyin mode, all small molecular compounds of each medicinal material are collected from a TCM-ID (http:// www.megabionet.org/tcmid /), TCM Database @ taiwan (http:// tcm.cmu.edu.tw /) Chinese medicine Database, then repeated data are sorted, normalized and eliminated, the name of the Chinese medicine is normalized, and finally 974 small molecular compounds are obtained. Finally, the 2D and 3D chemical structures (mol file format) of the finished small molecule compounds were obtained from NCBI PubChem (http:// PubChem. NCBI. nlm. nih. gov /) database and ChemSepider (http:// www.chemspider.com). Constructing a small molecular compound database of Ganfule, grouping Ganfule according to the whole formula efficacy, and grouping 4 groups (herba Patriniae, rhizoma paridis, herba Scutellariae Barbatae and herba Artemisiae Scopariae) of heat-clearing and detoxifying groups, 7 groups (carapax Trionycis, Eupolyphaga Seu Steleophaga, semen Persicae, lignum sappan, Concha Ostreae, radix Curcumae and radix et rhizoma Rhei) of blood circulation-promoting and blood stasis-removing groups, and 10 groups (radix Codonopsis, Atractylodis rhizoma, radix astragali, Poria, Coicis semen, rhizoma Cyperi, pericarpium Citri Tangerinae, bupleuri radix, lignum Aquilariae Resinatum and caulis Akebiae) of spleen-invigorating and qi. The 21 medicinal materials are respectively input into a Chinese herbal medicine online Database in a Chinese pinyin mode, all small molecular compounds of each medicinal material are collected from a TCM-ID (http:// www.megabionet.org/tcmid /), TCM Database @ taiwan (http:// tcm.cmu.edu.tw /) Chinese medicine Database, then repeated data are sorted, normalized and eliminated, the name of the Chinese medicine is normalized, and finally 974 small molecular compounds are obtained. Finally, the 2D and 3D chemical structures (mol file format) of the finished small molecule compounds were obtained from NCBI PubChem (http:// PubChem. NCBI. nlm. nih. gov /) database and ChemSepider (http:// www.chemspider.com).

113 biological action paths related to 8 target proteins are obtained from Reactome and KEGG, and 65 related diseases are obtained from OMIM. And finally, utilizing cytoscape3.12 software to jointly construct a complex network action model of multiple medicinal materials-compound-target-pathway-disease by using 3 efficacy groups of Ganfule, 21 medicinal materials, 69 small molecular compounds, 8 liver cancer target proteins, 113 biological action pathways and 65 related diseases.

Screening:

carrying out drug-like property screening and availability screening on the small molecule compound database, and screening a candidate molecule group; the method comprises the steps of constructing an anti-primary liver cancer potential activity small molecule database with good drug metabolism property, carrying out primary rapid screening on 974 small molecule compounds of three efficacy formula groups of Ganfule by using a Lipinski class 5 rule in discovery studio software to obtain 726 small molecule compounds, then further predicting ADME properties of the compounds, and screening 198 candidate small molecule compounds.

Specifically, the drug-like property screening is carried out in the first step, and the drug-like property screening conditions comprise that the molecular weight is 160-480, the logP is-0.4-5.6, the hydrogen bond donor is 0-5, and the hydrogen bond acceptor is 0-5; the utilization screening is a second screening step, the conditions of the utilization screening comprise ADME prediction, and the screening conditions comprise that the intestinal absorption of a human body is 6.1< ALop <9.6, the solubility of 25 degrees is-4.0 < log (Sw) -2.0, and the blood brain barrier penetration is-0.52 < BBB < 0.0.

And (3) butt joint test:

and carrying out chemical structure optimization on each candidate molecule of the candidate molecule group, carrying out hydrogenation, giving charges, applying a force field, generating a plurality of conformations, then carrying out butt joint on the candidate molecule and the target protein in the target database by using a ligand rapid butt joint algorithm, carrying out consistency scoring on the butt joint, forming a drug-compound-target-pathway-disease network, and screening the optimal molecule group. The 198 compounds screened by the steps are subjected to chemical structure optimization by CambrigeSoft ChemOffice 2012, and then the structures of the compounds are subjected to hydrogenation, charge endowment, generation of various conformations, application of force fields and the like by using a "Prepare Ligand and protocol of Discovery Studio 2.55 software. Then, the Protein structure of 8 target points is hydrogenated, the missing side chains are filled up, the pH value of the amino acid residue is adjusted to be 7.0 and the like by utilizing a 'Prepare Protein' (receptor molecule pretreatment) protocol. Finally, molecular Docking is carried out on the optimized compounds and target proteins by using a Ligand Docking protocol in Discovery Studio 2.55, a plurality of scoring functions such as binding energy, libdockscore, ligscore-1, ligscore-2, PLP-1 and ligscore PLP-2 are operated to carry out consistent scoring, the Ligand with the best affinity is selected, and 69 small molecular compounds possibly with strong potential activity are screened to form the optimal molecular group.

Specifically, the ligand rapid docking algorithm is a genetic algorithm or a simulated annealing algorithm; the consistency scoring is performed by using more than two of the binding energy algorithm, the libdockstone algorithm, the ligscore-1 algorithm, the ligscore-2 algorithm, the PLP-1 algorithm and the ligscore PLP-2 algorithm together.

Optimized formula

Matching corresponding medicinal materials in the small molecular compound database according to the optimal molecular group, and forming an optimized formula according to the corresponding medicinal materials to utilize the screened optimal molecular group; specifically, the optimal molecular group is used for matching corresponding medicinal materials in a multi-medicinal material-compound network, and the corresponding medicinal materials are used for matching an optimized formula.

As a preferable scheme, in this embodiment, the method further includes the steps of:

s3, pharmacodynamic verification of an optimized formula: and respectively preparing experimental medicaments by using the optimized formula and the control formula, respectively detecting expression influence levels on proliferation, migration and invasion of liver cancer target cells and target protein expression influence levels, and verifying the efficacy of the optimized formula.

The experimental medicament is a plurality of extractums with potential pharmacodynamic activity prepared by respectively separating the optimized formula and the control formula into a volatile oil part, an alcohol-soluble part and an alcohol-precipitated part by adopting a water vapor extraction method and a water extraction and alcohol precipitation method.

The optimized formula comprises a spleen-invigorating and qi-regulating group, a blood-activating and stasis-dissolving group and a heat-clearing and detoxifying group.

Please refer to fig. 5-8, the steps of detecting the expression effect of the ganfule full-formula group and the efficacy-resolving formula group on the proliferation, migration and invasion of human liver cancer HepG-2 cells and the expression levels of PIK3CA and CASP8 proteins include:

1) grouping experiments: experiment was carried out with a blank control group (5. mu.l/mL, 10. mu.l/mL, hereinafter abbreviated as KB) and a positive control group (5. mu.g/mL, 10. mu.g/mL, hereinafter abbreviated as AM) of doxorubicin, and 4 drug groups were: heat-clearing and detoxifying alcohol-soluble parts (5 mu l/mL and 10 mu l/mL, hereinafter referred to as QR), blood-activating and stasis-removing alcohol-soluble parts (5 mu l/mL and 10 mu l/mL, hereinafter referred to as HX), spleen-invigorating and qi-regulating alcohol-soluble parts (5 mu l/mL and 10 mu l/mL, hereinafter referred to as JP), and whole-prescription alcohol-soluble parts (5 mu l/mL and 10 mu l/mL, hereinafter referred to as ZF).

The 4 medicine components are respectively prepared according to the following steps:

all the medicinal materials are pretreated, the medicinal materials are ground into coarse powder (sieved by a 10-mesh sieve), the coarse powder is dried at the constant temperature of 45 ℃ for 30min, and then the coarse powder and 10 times of water solution are fully mixed, and then the mixture is subjected to ultrasonic cold soaking for 0.5 h.

Extracting volatile oil parts (G1, bright yellow oily liquid) from 8 medicinal materials such as bighead atractylodes rhizome (60G), dried orange peel (60G), dahurian patrinia herb (60G), turmeric root-tuber (60G), oriental wormwood (60G), nutgrass galingale rhizome (60G), Chinese thorowax root (30G) and agilawood (12G) by a steam distillation method for 4-5 h, and reserving distilled water solution for other use.

Decocting the residue with the rest 13 kinds of medicinal materials including Coicis semen (60g), Poria (60g), lignum sappan (60g), caulis Clematidis Armandii (60g), radix Codonopsis (60g), rhizoma paridis (60g), Concha Ostreae (60g), radix astragali (60g), herba Scutellariae Barbatae (60g), semen Persicae (40g), carapax Trionycis (20g), Eupolyphaga Seu Steleophaga (20g), and radix et rhizoma Rhei (10g) with electric heating jacket with electronic temperature control for 2 times, keeping slightly boiling for 2 hr and 1.5 hr for the second time, mixing decoctions, filtering, mixing the filtrate with the distilled water solution after extracting volatile oil, and concentrating with vacuum rotary evaporator (water bath temperature 70 deg.C, rotation speed 65RPM) to obtain fluid extract.

Processing the fluid extract by water extraction and alcohol precipitation, performing ultrasonic treatment, rapidly adding 99.7% anhydrous ethanol at a ratio of 1:1, mixing, refrigerating and standing for 48h, collecting supernatant, recovering ethanol, and concentrating into soft extract. Centrifuging the ethanol precipitate (3000RPM for 5min), mixing the supernatant with the above supernatant to obtain ethanol soluble fraction, and vacuum drying at low temperature (55 deg.C, 10 hr) to obtain ethanol soluble crude extract (G2, dark brown). Collecting precipitate, and vacuum drying at low temperature (55 deg.C, 10 hr) to obtain crude extract (G3, red brown).

2) The CCK-8 method is used for detecting the proliferation influence of the HepG-2 cells: HepG-2 cells in the logarithmic growth phase with good growth are taken and inoculated in a 96-well plate for cell culture. The HepG-2 cells were grouped, and 0.1% DMSO culture solution was added to the cells to culture, 3 duplicate wells were provided for each group, 10 μ l of CCK-8 solution was slowly dropped into each well cell at time points of 0, 24, 48, 72, 96h, and the like in culture, and absorbance (OD value) at 450nm was measured using a microplate reader, and the cell proliferation inhibition ratio (%) for each group was calculated to be (1-experimental OD value/control OD value) × 100%.

3) The Transwell method is used for detecting the migration capacity of HepG-2 cells: grouping HepG-2 cells, incubating for 24h, performing conventional digestion, and adjusting the cell density to 3 × 105 cells/mL; 200 ul of cells are respectively inoculated in the Transwell chamber, and then 500 ul of 10% FBS RPMI-1640 culture medium is added into the lower chamber to be incubated for 24 h; observing the morphology of the cells attached to the lower surface of the basement membrane of the cell using an inverted fluorescence microscope; at a wavelength of 450nm, the number of cells migrated was measured using a microplate reader (OD value, and the number of migrated cells was indirectly calculated (%) -cell mobility (%) -number of migrated cells in control group-number of migrated cells in experimental group)/number of migrated cells in control group) × 100%.

4) The invasion capacity of HepG-2 cells is detected by a Transwell method: preparing a Transwell chamber, dividing cells into groups, digesting each group of cells conventionally, incubating at room temperature, adjusting the cell concentration to be 1 × 105 cells/mL, and adding 800 μ l of DMEM nutrient medium containing 15% FBS into the lower chamber of the Transwell; after 48h incubation the cells were observed through the polyester fiber membrane using an inverted microscope (200 x), and 3 different fields were randomly selected for each group for cell counting. Cell invasion (%) - (1-number of transmembrane cells in experimental group/number of transmembrane cells in negative control group) × 100%.

5) The Western Blot method is used for detecting the expression level of CASP8 and PIK3CA proteins: experiments are divided into groups, HepG-2 cells of 48 hours after treatment of each group are collected, cracked for 10min, and total protein in the cells is extracted. Quantifying protein by using a BCA detection kit, separating the protein by adopting polyacrylamide gel electrophoresis, transferring a membrane by a semidry method, diluting a primary antibody to a proper concentration (1:1000) by using 5% skimmed milk, diluting a secondary antibody to a proper concentration (1:5000) by using a TBST buffer solution, incubating for 1-2 h at room temperature, washing for three times, carrying out chemiluminescence reaction, preparing an ECL detection solution, culturing the membrane for 5min, developing and exposing. The analysis was carried out by using QuantityOne software, labeling with GAPDH (glyceraldehyde-3-phosphate dehydrogenase), and performing gray scale analysis using a gel image processing system (the lower the gray scale, the higher the expression level of protein in cells).

Firstly, a theoretical method of network pharmacology and system biology is applied to characterize the interaction process of the Ganfule medicine and the multi-scale network of the organism. The method firstly constructs a complex network model of medicinal materials, chemical components, targets, signal paths, diseases, multi-medicinal materials, multi-components, multi-targets, multi-paths and multi-diseases of the primary liver cancer, researches the mutual relations among different levels and different objects, preliminarily clarifies the active components and the action mechanism of the liver cancer treatment by Ganfule, establishes the internal relation between clinical cases and medication rules, and discloses a key molecular regulation path of the pathological process of the primary liver cancer.

Secondly, the inventor extracts and prepares the full formula of Ganfule and the efficacy group of a de-formula into a volatile oil part, an alcohol-soluble part and an alcohol-precipitated part to respectively perform pharmacological and pharmacodynamic comparative study on 12 parts with different polarities, takes a Ganfule whole formula and the efficacy group as study carriers, combines computer virtual pharmacological experiments with in-vitro pharmacodynamic experiments of two levels of cells and target proteins, verifies each other, and compares the optimal action concentration and action time of different extraction parts and the expression difference between the Ganfule whole formula group and the efficacy group.

The scheme can develop exploratory attempts for predicting the network target of the effective substances of the compound traditional Chinese medicine and researching the whole action and mechanism of the organism, provides a basis for the deep research of the action mechanism of the compound traditional Chinese medicine, and provides a support for the secondary upgrade development of the compound traditional Chinese medicine and the development of new medicines.

It should be understood that equivalents and modifications of the technical solution and inventive concept thereof may occur to those skilled in the art, and all such modifications and alterations should fall within the scope of the appended claims.