1434 results about "Target drug" patented technology

The invention relates to a dialkyl imidazole bionic lipid compound and a preparation method and application thereof.The structure of the dialkyl imidazole bionic lipid compound imitates natural phospholipid design, alkyl with no less than 10 carbon atoms is modified on the fourth site and the fifth site of imidazole, and primary amines with different alkyl chain lengths are modified on the second site; the dialkyl imidazole bionic lipid compound is mixed with a therapeutic drug and other auxiliary materials to prepare lipid nanoparticles loaded with the therapeutic drug, and the lipid nanoparticles can be used as a drug delivery system for preparation of brain-targeted drugs. The dialkyl imidazole bionic lipid has the function of dynamically regulating and controlling the blood-brain barrier, and can realize reversible opening of the blood-brain barrier; the formed drug-loaded lipid composition can pass through a blood brain barrier and well realize brain-targeted delivery of loaded therapeutic drugs; the drug-loaded lipid composition composed of the dialkyl imidazole bionic lipid belongs to a new generation of bionic nano-drug carriers, and provides a new strategy for realizing brain-targeted delivery of different types of drugs.

The invention discloses a drug resistance prediction method and system based on comparative learning and multi-modal fusion, and the method comprises the steps: firstly generating a molecular map and a molecular fingerprint based on the SMILES of a target drug, and extracting the molecular features of the drug through a comparative learning model constructed through combining a map attention network and a map convolution network; and then, acquiring protein expression, gene expression and metabolic expression data from the target tissue cells, extracting modal features through a deep convolutional network, a Transform encoder and a multi-dimensional attention network, and realizing adaptive fusion of the multi-modal features through a heterogeneous interactive attention mechanism. And finally, jointly inputting the fused multi-modal features and drug molecular features into a multi-layer sensor to realize high-precision prediction of the drug resistance of cells to drugs. By introducing a contrast learning and multi-modal feature fusion mechanism, the characterization capability and prediction precision of the model are effectively improved, and efficient and reliable support can be provided for drug screening and clinical decision making.

The invention discloses a blockchain-based intelligent tracing and risk early warning system for medicine supply data, and relates to the technical field of data processing, and the system comprises the steps: carrying out the circulation node extraction of a medicine supply chain, and obtaining a plurality of medicine supply participants; performing distributed block deployment to obtain a medicine supply block chain; acquiring a medicine database, and acquiring a medicine supply data set; performing classification identification and mapping encryption, and uploading to the medicine supply block chain to obtain a block chain distributed account book; obtaining an access smart contract, and obtaining target drug supply data; and performing risk response early warning on the target drug based on the target drug supply data. The technical problems that in the prior art, traditional Chinese medicine supply data tracing is difficult and inaccurate, and potential risks of medicine supply are difficult to find and accurately evaluate in advance are solved, and the technical effects that accurate and rapid tracing of target medicine supply data is achieved, the potential supply risks are found in time, and effective response and early warning are carried out are achieved.

The present application relates to the technical field of medical equipment and smart alarm clocks, and discloses a smart auxiliary medication reminder method, system, device and medium, wherein the method includes: obtaining a target scheme for a target drug object; determining whether the current time is within the reminder time period of the target scheme, and obtaining a first result; if the first result is yes, obtaining the detection data of the target space corresponding to the target drug object, and determining whether a human body exists according to the detection data, and obtaining a second result; if the second result is yes, performing a medication reminder according to a preset first multi-level reminder strategy; if the second result is no, performing a medication reminder according to a preset second multi-level reminder strategy. This effectively solves the problem in the prior art that only ringing reminders based on the set time often cannot ensure that the information is accurately transmitted to the user in actual applications, and greatly improves the effectiveness of medication reminders.

The invention discloses a drug crushing method and device based on particle size control, and relates to the technical field of drug crushing, and the method comprises the following steps: obtaining crushing basic information of a target drug; connecting target crushing equipment, and collecting a first crushing control parameter and first environment control information; generating a first predicted particle size characteristic and a first particle size dispersion characteristic; when the first predicted particle size characteristic does not meet the expected particle size constraint interval and / or the first particle size dispersion characteristic does not meet the expected particle size dispersion characteristic, crushing parameter optimization is carried out; and performing parameter initialization on the target crushing equipment according to the second crushing control parameter and the second environment control information. The technical problems that due to the fact that existing medicine smashing depends on manual experience adjustment, medicine particle size control precision is insufficient, production efficiency is low, product quality is unstable, and expected particle size standards and particle size dispersion characteristics cannot be achieved are solved. The technical effects of improving the particle size control precision, the production efficiency and the product quality stability are achieved.

The invention discloses a neuraminidase-targeted drug-loaded nanoparticle as well as a preparation method and application thereof, and belongs to the technical field of pharmaceutical preparations. Polysialic acid and poly (lactic-co-glycolic acid) are connected through ester bond connection to obtain the drug-loaded nanoparticles with stable structure and performance, and the drug-loaded nanoparticles have the effect of targeting neuraminidase and also have high efficiency of entering cells; the antibiotic drug is further dispersed in the drug-loaded nanoparticles, the inflammation regulating agent is adsorbed on the surface of the drug-loaded nanoparticles, and the drug-loaded nanoparticles can deliver the antibiotic drug and the inflammation regulating agent to the pig Gleisseria in the pig body. According to the present invention, the antibiotic drug and the inflammation regulating agent are released under the acidic environment at the infection site in the pig body, such that the pig Gleisseria is efficiently killed, the inflammation damage caused by bacterial infection and the toxic damage of the antibiotic drug are reduced, and the good application prospect is provided in the preparation of the drug for preventing and / or treating the pig Gleisseria infection.

The invention discloses a drug relocation method and system based on subgraph perception and a mixed graph neural network, and the method comprises the steps: generating a corresponding dynamic drug-disease associated subgraph # imgabs1 # for a target drug-disease pair in a drug-disease associated network # imgabs0 #; aiming at the medicine nodes and the disease nodes in the # imgabs2, respectively constructing a medicine sub-graph # imgabs3 # and a disease sub-graph # imgabs4 #; performing deep feature extraction on # imgabs5 by applying a multi-head dynamic graph attention neural network model, and performing feature extraction on # imgabs6 and # imgabs7 on the basis of fusion of a GCN model and a GAT model to obtain final feature representations of # imgabs8, # imgabs9 and # imgabs10; and fusing the three, inputting the fused three into a drug-disease association prediction model based on a multi-layer perceptron (MLP) network, and predicting the association relationship of the target drug-disease pair. According to the invention, efficient and accurate drug-disease association prediction can be realized.

The invention discloses a colorectal cancer drug relocation method based on multi-omics integration. The system comprises a multi-omics data acquisition and preprocessing module, a tumor microenvironment analysis module, a specific disease network construction module, a multi-dimensional drug relocation module and a result evaluation module. And the tumor microenvironment analysis module comprises cell heterogeneity identification, cell map construction, cell annotation and tumor cell subset annotation. The specific disease network construction module comprises tumor feature expression program extraction, expression program screening, meta-program construction, clinical related meta-program recognition and specific disease protein interaction network construction. And the multi-dimensional drug relocation module comprises a module for identifying diseases by using a random walk algorithm, carrying out drug screening based on disturbance data, carrying out drug screening based on network proximity and carrying out comprehensive drug relocation. From the perspective of single cell data, element programs related to colorectal cancer survival are excavated, corresponding modules are designed, and the efficiency and precision of colorectal cancer targeted drug screening are improved.

The embodiment of the invention discloses an intelligent prediction method and device for adverse drug reaction based on multi-source data fusion, relates to the technical field of drug-gene-adverse drug reaction neural networks, and aims to solve the problem of low ADR prediction performance due to lack of a more effective feature analysis method and design of a more efficient model in the prior art. The method comprises the following steps: acquiring multi-source data; performing standardization processing on the multi-source data, constructing drug descriptor features, and obtaining a drug similarity vector set; constructing adverse drug reaction descriptor features, and obtaining an adverse drug reaction similarity vector set; and training the target adverse drug reaction intelligent prediction model based on the drug similarity vector set and the adverse drug reaction similarity vector set, and after training is completed, predicting an adverse reaction generated when a target drug is applied to a target individual based on the target adverse drug reaction intelligent prediction model.

The invention discloses a drug-drug interaction prediction method based on a drug flow sub-graph, and relates to the technical field of bioinformatics, and the method comprises the steps: data preparation: collecting a reference data set including drug-drug interaction, and introducing an external knowledge graph for adaptability preprocessing; constructing a model: constructing a drug-drug interaction prediction model, and training by using the preprocessed reference data set; and effect prediction: inputting a target drug into the drug-drug interaction prediction model, and outputting a drug-drug interaction prediction result through the drug-drug interaction prediction model. The structure and semantic information of the drug flow sub-graph are fully utilized, accurate prediction of drug interaction is realized, and the prediction efficiency is improved. And the interpretability of the drug-drug interaction prediction model is improved.

The invention discloses a liver-targeting drug liposome and application thereof, and relates to the field of drug delivery, cholate and mannose are used for respectively modifying phospholipid molecules DSPE-PEG2000, two raw materials DSPE-PEG2000-CA and DSPE-PEG2000-MAN composed of the liposome are synthesized, and then the liposome is prepared into the liver-targeting drug liposome. The liposome with liver targeting property is obtained from the liposome, cholesterol, DSPC, DC-cholesterol and a water-soluble therapeutic drug according to a certain proportion, the liposome has relatively high cell safety and liver targeting property, and the therapeutic effect of the corresponding drug can be remarkably improved after the drug is embedded.

The invention discloses a drug target prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: performing dynamic gating fusion on graph structure features and sequence features of a target drug to obtain drug features; performing dynamic feature enhancement on the digitized sequence of the target protein, and further obtaining protein features through context sensing optimization; obtaining a target affinity score of the target drug and the target protein by using a prediction model based on the drug characteristics and the protein characteristics; wherein the prediction model is obtained through feature representation training marked with actual affinity scores on the basis of a neural network. According to the method, the prediction precision of drug target interaction is improved through deep fusion of drug multi-modal features and protein sequence context perception optimization. The method can realize high-precision prediction of the drug target, and can be widely applied to the technical field of drug target prediction.

The present application relates to a kind of dihydrocarbylimidazole biomimetic lipid compound and its preparation method and application, dihydrocarbylimidazole biomimetic lipid compound structure imitates natural phospholipid design, modification is not less than 10 carbon atoms alkyl on the 4th and 5th of imidazole, and modification is different alkyl chain length primary amine on 2nd position;Dihydrocarbylimidazole biomimetic lipid compound is mixed with therapeutic drug and other adjuvant, can be prepared to be loaded in the lipid nanoparticle of therapeutic drug, can be used as drug delivery system for the preparation of brain-targeted drug.Dihydrocarbylimidazole biomimetic lipid has the function of dynamic control blood-brain barrier, can realize the reversible opening of blood-brain barrier;Formed drug-loaded lipid composition can cross blood-brain barrier and better realize the brain-targeted delivery of loaded therapeutic drug;Drug-loaded lipid composition consisting of dihydrocarbylimidazole biomimetic lipid belongs to a new generation of biomimetic nanomedicine carrier, provides new strategy for realizing the brain-targeted delivery of different kinds of drugs.

The invention discloses a tumor cholesterol metabolism regulation microneedle patch and a preparation method, the microneedle patch comprises a needle tip and a backing which are connected, the needle tip comprises a needle tip body and nanoparticles loaded on the needle tip body, the nanoparticle is a manganese ion-doped organic metal framework, the outer layer of the manganese ion-doped organic metal framework is modified with a targeting agent, cholesterol oxidase and superoxide dismutase are carried on the manganese ion-doped organic metal framework, the targeting agent can target a CD44 receptor, and the organic metal framework can carry drugs. The targeted drug can enter tumor cells in a targeted mode, superoxide dismutase catalyzes superoxide anions overexpressed in the tumor cells to generate H2O2 and O2, O2 needed by catalysis is provided for cholesterol oxidase, cholesterol at the tumor site is consumed by the cholesterol oxidase, accumulation of 7-DHC is reduced, meanwhile H2O2 can be generated, and the cholesterol oxidase can be used for catalyzing the tumor site. H2O2 is catalyzed by manganese ions through a Fenton-like reaction to generate OH to induce tumor cells to generate ferroptosis, so that ferroptosis-immune synergistic treatment is realized.

The invention belongs to the technical field of drug screening, and provides a multi-target drug screening and optimizing method based on machine learning, which comprises the following steps: S1, carrying out feature screening on compound molecular descriptors by utilizing a machine learning algorithm, and determining the molecular descriptor which has the most significant influence on biological activity; s2, constructing a quantitative structure-activity relation model, and predicting the biological activity of the compound; s3, based on a multi-objective optimization algorithm, combining absorption, distribution, metabolism, excretion and toxicity properties of the compound; according to the method, feature screening is carried out on compound molecule descriptors by utilizing a machine learning algorithm, a QSAR model is constructed to predict the biological activity, ADMET property classification prediction is combined, the biological activity, ADMET properties and pharmacokinetic characteristics of drugs are comprehensively considered, and the accuracy and accuracy of drug activity prediction are improved. The method can efficiently and accurately screen and optimize drug candidate compounds with potential drug effects and good pharmacokinetic characteristics, so that the success rate of drug research and development is increased, and the research and development cost and time are reduced.

The invention relates to the technical field of exosome loaded drugs, and provides a preparation method and application of exosome vesicles capable of efficiently loading drugs, and the preparation method comprises the following steps: exosome preparation: preparing an exosome sample with enough granularity and purity; pretreatment of exosomes: performing precipitation treatment and collecting the exosomes to prepare exosome precipitates; carrying out loaded medicine pretreatment: treating loaded medicine or a medicine preparation by using a lipid reagent; target drug loading: rapidly treating the exosome precipitate by using a drug lipid component, and then treating by using a buffer system to obtain the exosome load of the drug; and free lipid and drug removal: reducing free lipid and drug residues by re-enriching the exosomes. The problems of high concentration, high purity and low cost in industrial production of the exosome are solved, meanwhile, an efficient and convenient technical method is provided for taking the exosome loaded medicine as a delivery system, and the method can be suitable for the fields of health medical treatment, medical beauty cosmetics and biological medicine products.

The invention discloses an active oxygen scavenging drug-loaded targeted nano delivery platform based on diselenide bond bridging as well as a preparation method and application thereof, and belongs to the technical field of biological medicines. According to the invention, tetra (4-carboxyphenyl) porphyrin (TCPP) and manganese porphyrin (Mn-TCPP) are used as raw materials to synthesize an organic metal framework MOFs, a target drug is loaded on the MOFs through electrostatic adsorption / blending, and amino and diselenide bonds are linked on the MOFs to realize combination with a specific peptide ligand and promote release of a drug oxidative stress microenvironment. The nano delivery platform with specific targeting and ROS response performance is obtained. The specific treatment effect of different diseases is achieved through different selections of the loaded drugs and the specific peptide ligands.

The invention belongs to the technical field of biology, and provides an electroporation buffer solution applied to exosome electroporation, the pH value is 6.8-9.8, the osmotic pressure is 80-320 mosm, water is used as a solvent, and the electroporation buffer solution comprises the following components: 15-25% w / v of iodixanol, 1.035-1.265 mM of KH2PO4, 22.5-27.5 mM of KCl, 0.18-0.22 mM of EDTA-2Na and 18-22 mM of HEPES. The electroporation buffer solution provided by the invention can improve the loading efficiency of the load and reduce damage to an exosome membrane structure and internal bioactive molecules in an electroporation process. The electroporation buffer solution can effectively reduce aggregation and fusion phenomena of exosomes after electroporation, and improves the stability and integrity of the exosomes. The electroporation buffer solution provided by the invention can improve the application degree of the exosome as a targeting drug delivery system.

The embodiment of the invention provides a drug recommendation method. The method comprises the steps of obtaining a current question and a historical dialogue initiated by a target user; carrying out portrait construction on the current question and the historical dialogue to obtain a medical portrait and a potential portrait; performing knowledge retrieval in combination with the portrait to obtain a target drug suitable for the target user; and generating drug recommendation information indicating the target drug according to the potential portrait information. Therefore, a drug recommendation process in combination with the user portrait is realized, the integrity of the user portrait is improved by expanding the multi-view portrait data of the user and mining potential portrait information in combination with user habits, and the user portrait is combined in a knowledge retrieval process, so that the pertinence of question and answer knowledge is ensured, and the user experience is improved. And the accuracy of drug recommendation is improved.

The invention relates to the field of nano medicines, in particular to a targeted medicine as well as a preparation method and application thereof. The invention provides a targeted drug. The targeted drug comprises a tissue-type plasminogen activator and a targeted peptide. Thrombus targeting modification is directly carried out on a thrombolytic drug rtPA which is most widely applied clinically by utilizing a chemical method, the defects of narrow treatment time window, high dosage and high bleeding risk of the rtPA are remarkably improved, the preparation process is simple and controllable, materials required for preparation are easy to obtain, the cost is low, and the application prospect is wide. The toxic effect and degradation problems of materials such as compounds are eliminated, other instruments and equipment are not needed in the treatment process, and clinical transformation application of thrombus targeted rtPA becomes possible.

According to the GPX4 protein degradation agent and the application, the degradation agent serves as molecular glue to induce tumor cell ferroptosis and is different from an existing PROTAC technology, and the molecular glue can induce interaction between E3 ubiquitin ligase and target protein GPX4 and promote ubiquitination of the E3 ubiquitin ligase and the target protein GPX4. Compared with the existing GPX4 degradation agent, the molecular glue has the advantages of small molecular weight, high cell permeability and better druggability. The GPX4 molecular glue disclosed by the invention can be used for effectively degrading GPX4 and has a killing effect on various tumor cell lines. The preparation method is simple in synthesis route and mild in reaction condition, can be used for being developed into a new generation of GPX4 targeting drugs, and has great clinical application value and considerable market potential.

The invention relates to the technical field of information processing, in particular to a medicine conflict automatic detection and prescription optimization system for old multi-disease patients. According to the system, a data management module is used for collecting individual data of patients and group data of co-diseased reference groups and receiving a planned medication scheme; the parameter calculation module is used for identifying a target medicine combination with time overlapping in the patient medication record and the planned medication scheme; determining a basic risk index according to the group medication response data, the patient medication record and the work and rest text data; determining a lag risk coefficient according to the physiological indexes and the drug metabolism data; determining group medication associated risk parameters according to atypical reactions and group medication records in the medication reaction data; the risk fusion module is used for determining a drug use conflict risk value based on the basic risk index, the lagging risk coefficient and the group drug use associated risk parameter; and the prescription optimization module is used for generating an optimal medication scheme, so that the generated optimal scheme is more suitable for the actual life of the patient.

The invention discloses a nanometer delivery device for a gastric cancer targeted drug and a use method of the nanometer delivery device, and belongs to the technical field of drug targeted delivery. The device comprises a molecular recognition and optimization module which is responsible for accurately recognizing a tumor cell specific molecular marker and providing personalized treatment strategy recommendation; the nano-carrier design module is used for designing multifunctional lipid-polymer hybrid nano-particles and carrying out surface targeting ligand modification; the intelligent drug loading module is used for improving the stability and controllability of drug loading based on a pH / temperature sensitive release mechanism and effectively reducing the toxic influence of non-targeted tissues; the molecular imaging and monitoring module integrates a nanoprobe imaging technology and a quantum dot tracing technology, constructs a treatment-imaging integrated system, realizes real-time dynamic monitoring of a treatment process, and provides instant feedback of a molecular level; and the risk assessment module is used for establishing a safety database and providing dynamic risk assessment of the treatment process through a biological safety assessment system and long-term tracking management.

The invention relates to the field of molecular targeted therapy, and discloses an application of an SLC16A5 (MCT6) small-molecule inhibitor MCT6-Ai7-2 in preparation of a medicine for treating acute myeloid leukemia (AML). The inhibitor takes an SLC16A5 protein structure predicted by Alphafold as a target spot, and is obtained through compound database screening, molecular docking and druggability optimization. An in-vitro experiment proves that MCT6-Ai7-2 can remarkably inhibit proliferation of AML cell lines such as U937 and MOLM-13, induce cell apoptosis and retard a cell cycle, has an inhibiting effect on a primary AML patient specimen and is relatively low in toxicity to normal cells; in-vivo experiments show that the compound is effective and has good safety in AML model mice. In addition, the MCT6-Ai7-2 and the vinca can be combined to synergistically enhance the inhibition effect on vinca drug-resistant cells, and by reducing the expression of anti-apoptotic protein MCL-1, the activation of pro-apoptotic factors BIM and tBID is promoted to play a role. The invention provides a novel targeting drug and strategy for treatment of AML (especially drug-resistant or recurrent patients).

The invention discloses a data management system and method based on a large model, and relates to the technical field of big data analysis, and the method comprises the steps: setting a data monitoring management period, enabling the system to collect the stock change information of various types of medicines in a hospital, analyzing the historical warehouse-out data of a department in combination with a time sequence model, and precisely measuring and calculating a first warehouse-out predicted value; and comparing the daily average dose with a standard limited daily dose, deeply mining compatible drug data associated data in an auxiliary drug use scene, analyzing a compatible drug data ex-warehouse trend, and accurately mastering a collaborative consumption rule. By tracking ex-warehouse proportion changes of target drug data and compatible drug data in a historical period, predicting a proportion trend, predicting ex-warehouse quantity in combination with the compatible drug data, dynamically calculating an auxiliary drug use demand, and deeply deconstructing inventory change data, prospective pre-judgment of the inventory change data is realized, hospitals are assisted to optimize an inventory structure, and the workload of the hospitals is reduced. And the accuracy and response efficiency of inventory data management are improved.

The invention relates to the technical field of targeted cargo delivery, in particular to a drug loading and delivery method and a cell carrying method of a core-shell mesoporous micro-nano robot. The drug loading and delivery method is realized based on the drug loading capacity of the mesoporous structure, the magnetism of the iron oxide core and the photothermal effect of the polydopamine shell. The core-shell mesoporous micro-nano robot can adsorb and load drugs through capillary force and electrostatic force generated by the mesoporous structure of the core-shell mesoporous micro-nano robot; then, an external magnetic field is used for driving the device to move; near-infrared light is utilized to irradiate when approaching a targeted area, the temperature of a pore structure rises, and along with thermal vibration, drugs in mesopores are effectively released to the targeted area, so that the drug delivery effect is achieved. When the drug is loaded and delivered, the core-shell mesoporous micro-nano robot has carrying capacity after being clustered under a magnetic field, and cells are carried through an eddy current carrying method and a contact adhesion method. Targeted goods delivery of goods such as targeted drugs and cells can be achieved through the micro-nano robot.

The present invention provides a hypergraph-based drug-disease association prediction method, device and electronic device, which relates to the field of bioinformatics, including: obtaining a drug hypergraph and a disease hypergraph; based on a hierarchical contrastive learning algorithm, inputting the drug hypergraph and the disease hypergraph into a target hypergraph convolution model to obtain an initial drug feature matrix and an initial disease feature matrix; performing interactive information aggregation through the initial drug feature matrix and the initial disease feature matrix to obtain a target drug feature matrix and a target disease feature matrix; performing association prediction through the target drug feature matrix and the target disease feature matrix to obtain a drug-disease potential association matrix. The present invention realizes accurate prediction of drug-disease potential association.

The invention provides a pathogen interpretation method and device and a medium, and the method comprises the steps: carrying out the data preprocessing of received initial sequencing data, and obtaining first sequence data; comparing the first sequence data with a reference genome sequence in a preset pathogen database to obtain a comparison result; according to a comparison result, obtaining a read parameter set of the initial sequencing data; performing pathogen interpretation processing on the read parameter set to obtain a target pathogen type corresponding to the initial sequencing data; performing drug-resistant gene detection on the target pathogen type, and judging whether a target drug-resistant gene corresponding to the target pathogen exists or not; if the target drug-resistant gene exists, analyzing the target drug-resistant gene to obtain drug-resistant data; and generating a target report according to the target pathogen type, the target drug-resistant gene and the drug-resistant data. According to the invention, the type of the infected pathogen can be rapidly and accurately interpreted, and the drug resistance condition of the infected pathogen can be accurately analyzed, so as to assist clinicians in formulating a reasonable target report.

The invention belongs to the technical field of functional nanoprobes and biological detection, and particularly relates to a kit for simultaneously detecting a specific membrane protein marker and a membrane protein dimer and application of the kit. The detection kit comprises a membrane proteome anchoring chain, a recognition probe, an SERS probe and an auxiliary signal amplification reagent, by combining DNA logic sensing based on aptamer recognition and logic response type assembly based on EpCAM protein expression and DNA signal amplification of Met protein dimerization, networked assembly of a recognition probe and an SERS probe is realized, so that specific accurate recognition of tumor cells and high-sensitivity SERS detection and imaging of membrane protein dimers are realized; the method is suitable for monitoring an HGF / Met signal channel based on Met protein dimerization mediation between stromal cells and cancer cells in a complex tumor microenvironment, and can evaluate the inhibition effect of a targeted drug on receptor dimerization in real time.

The invention relates to an application of an intervention SNRK-MTA1 signal channel axis in preparation of a non-small cell lung cancer targeted therapy drug. The nucleotide sequences of the mRNA of the SNRK gene and the mRNA of the MTA1 gene are respectively as shown in SEQ ID NO.1-2. The invention innovatively provides a strategy for treating the non-small cell lung cancer through double-target combined intervention. According to the strategy, SNRK gene expression is improved through exogenous gene overexpression plasmids, and meanwhile MTA1 gene expression is silenced through the siRNA technology. In a non-small cell lung cancer model, the strategy of combined application of the SNRK-OE plasmid and siMTA1 can specifically up-regulate the SNRK mRNA level and knock down the MTA1 mRNA level at the same time, and the combined strategy shows a better anti-tumor effect than single intervention, and can more effectively inhibit the growth and migration of tumor cells. Based on the discovery, the SNRK-MTA1 signal pathway axis can be developed into a novel therapeutic target for non-small cell lung cancer, and is used for designing a drug combination scheme or a composite targeted drug.