Manufacturing method and application of tumor targeting peptide kit based on oral cancer

Abstract

The invention relates to the technical field of preparation processes of targeting peptide kits, in particular to a preparation method and application of a tumor targeting peptide kit based on oral cancer, and the preparation method comprises the following steps: on the basis of combination of targeting peptide and markers, setting pH value, temperature and ion concentration, and monitoring reaction parameters and adjusting reaction rate to obtain the tumor targeting peptide kit based on oral cancer. And target peptide reaction conditions are obtained. According to the method, the optimal reaction path is screened out based on the data of the reaction rate and the product purity by adopting nonlinear model predictive control, and the variables are subjected to cross comparison, so that the accuracy and the reliability of the target peptide synthesis conditions are improved, the limitation of condition setting is avoided, the variables can be dynamically adjusted, and the target peptide synthesis efficiency is improved. According to the present invention, the optimal reaction effect can be ensured under different reaction conditions, the combination efficiency of the targeting peptide and the oral cancer marker is analyzed by using the particle swarm optimization algorithm, the combination efficiency of the targeting peptide and the marker is further optimized through multiple induction and analysis, and the sensitivity and the specificity of the kit are enhanced.

Description

Technical Field

[0001] The invention relates to the technical field of preparation technology of a targeting peptide kit, and in particular to a preparation method and application of a tumor targeting peptide kit based on oral cancer. Background Art

[0002] The field of targeted peptide kit preparation process technology focuses on the development of kits and methods for detecting and identifying tumor markers in the body, helping to achieve early cancer diagnosis, monitor disease progression and evaluate treatment effects. Tumor marker detection technology uses biochemistry, molecular biology and immunology to bind to specific antibodies or ligands to tumor markers to detect the presence and concentration of markers.

[0003] The method for preparing a tumor targeting peptide kit based on oral cancer is intended to prepare a kit for detecting specific markers of oral cancer, with the aim of providing a standardized, stable and easy-to-use product that can help users quickly and accurately detect tumor markers associated with oral cancer, providing a convenient tool for laboratory testing and clinical applications.

[0004] Traditional peptide synthesis methods rely on preset reaction conditions and lack precise adjustment of dynamic variables during the reaction process, resulting in low product purity and reaction efficiency. The conditional screening process of traditional methods lacks effective algorithm support, making it difficult to achieve the optimal binding efficiency of peptides and markers, affecting the detection sensitivity and specificity of the kit, which may lead to unstable reaction conditions and poor repeatability of results, affecting the actual application effect of the kit and increasing uncertainty and complexity in clinical applications. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a preparation method and application of a tumor targeting peptide kit based on oral cancer.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme: a method for preparing a tumor targeting peptide kit based on oral cancer, comprising the following steps:

[0007] Step 1: Based on the combination of oral cancer targeting peptide and marker, the pH value, temperature and ion concentration are set, and the reaction conditions set of the targeting peptide are obtained by adjusting and monitoring the changes of each reaction parameter and gradually adjusting the reaction rate in the synthesis process of the targeting peptide;

[0008] Step 2: Based on the set of targeted peptide reaction conditions, the intermediate products in the peptide generation process are screened step by step, and the binding efficiencies of the peptides generated under different conditions are summarized to identify the efficient binding conditions in turn, and obtain the targeted peptide activity analysis results;

[0009] Step 3: Based on the activity analysis results of the targeted peptide, the intermediate products in each reaction step are analyzed, the reaction rate and product purity data are extracted, the optimal reaction path is screened, and nonlinear model prediction and control are adopted to perform cross comparison of each variable and complete calculation to obtain the targeted peptide synthesis condition scheme;

[0010] Step 4: Based on the targeting peptide synthesis condition scheme, extract the intermediate products produced in the targeting peptide generation process, gradually record the purity and generation time of each product, and obtain the targeting peptide purification condition set by comparing the reaction time and ion concentration of different products;

[0011] Step 5: Based on the targeted peptide purification condition set, each product generated during the purification process is extracted, analyzed one by one, and impurities are removed in combination with the component ratio and impurity content of each product to complete the screening and separation of components and obtain the targeted peptide purification product information;

[0012] Step 6: Based on the information of the purified targeted peptide products, a particle swarm optimization algorithm is used to summarize the binding efficiency of each targeted peptide with the oral cancer marker multiple times, the binding efficiency and component ratio are recorded, the assembly and combination of the targeted peptides are completed, and an oral cancer targeted peptide kit is generated.

[0013] As a further solution of the present invention, based on the combination of oral cancer targeting peptide and marker, the pH value, temperature and ion concentration are set, and each reaction condition is adjusted one by one, and the initial reaction parameter set is obtained by monitoring the real-time change of each variable;

[0014] Based on the initial reaction parameter set, by monitoring the peptide synthesis process, gradually adjusting the reaction rate, sequentially calculating the peptide generation rate and verifying its binding with the marker, the result of dynamic adjustment of the reaction conditions is obtained;

[0015] Based on the dynamic adjustment results of the reaction conditions, the fluctuations of various variables are comprehensively analyzed, and the optimal reaction state is achieved by adjusting the variables. The optimal value of each variable is determined in turn to obtain a set of targeted peptide reaction conditions.

[0016] As a further embodiment of the present invention, the specific steps of generating the target peptide activity analysis results are:

[0017] Based on the set of targeted peptide reaction conditions, the intermediate products in the peptide generation are gradually extracted, the generation efficiency of each product is recorded in turn, and the peptide generation efficiency analysis results are obtained in combination with the binding ability during its generation;

[0018] Based on the peptide generation efficiency analysis results, the binding efficiencies of peptides generated under different conditions are summarized, the binding effects are gradually ranked, efficient binding conditions are identified, and the binding efficiency summary results are obtained;

[0019] Based on the binding efficiency summary results, the optimal range of effective binding conditions is confirmed in turn, the reaction conditions are adjusted one by one for verification, the optimal combination of conditions is determined, and the targeting peptide activity analysis results are obtained.

[0020] As a further embodiment of the present invention, the specific steps of generating the targeting peptide synthesis condition scheme are:

[0021] Based on the target peptide activity analysis results, extract the intermediate products in each reaction step, separate the products by centrifugation, gradually determine the generation time and purity of each intermediate product, record the purity change, and generate the intermediate product purity record;

[0022] Based on the intermediate product purity record, the reaction rate is extracted, and by monitoring the rate change of the generated product, combining the comparison between the reaction conditions and the generation rate, an efficient reaction path is screened out, and the corresponding rate change of the variable is recorded to generate the optimal reaction path data;

[0023] Based on the optimal reaction path data, nonlinear model predictive control is used to perform cross comparisons among variables, and the combination of variables is gradually optimized by adjusting the reaction time and purity parameters. The optimal combination of generation conditions is calculated in turn to obtain the targeted peptide synthesis condition scheme.

[0024] As a further solution of the present invention, the nonlinear model predictive control is according to the formula:

[0025]

[0026] Where: α is the influence coefficient of reaction time on the output result, β is the magnification coefficient of target purity, y(k) is the predicted output value of the current time step k, r(k) is the target value, λ is the weight coefficient of the controller, Δu(k) represents the adjustment amount of the input variable, γ is the by-product suppression coefficient, P(k) represents the amount of by-products generated at time step k, δ is the adjustment weight of the reaction temperature, and T(k) represents the reaction temperature at time step k.

[0027] As a further embodiment of the present invention, the specific steps of generating the target peptide purification condition set are:

[0028] Based on the targeting peptide synthesis condition scheme, extract the intermediate products in the generation of the targeting peptide, record the generation time and purity of each intermediate product by time separation method, measure the ion concentration in turn, and generate the intermediate product generation record;

[0029] Based on the intermediate product generation record, the reaction time and ion concentration of different intermediate products are compared, the generation efficiency is verified through multiple tests, and the generation efficiency under different concentration conditions is recorded one by one by extracting and screening the products, so as to generate product efficiency analysis results;

[0030] Based on the product efficiency analysis results, a comparative analysis of purity and generation time was performed, the variables involved in the purification process were extracted, and the generation path was gradually adjusted by optimizing the concentration and generation conditions to obtain a set of targeted peptide purification conditions.

[0031] As a further embodiment of the present invention, the specific steps of generating the targeted peptide purification product information are:

[0032] Based on the target peptide purification condition set, each product generated during the purification process is extracted, the component ratio of each product is analyzed in turn, excess impurities are removed by centrifugal separation, and a purified product component ratio record is generated;

[0033] Based on the recorded proportions of the purified products, the impurity content in each product is compared, and components that do not meet the purity requirements are eliminated through step-by-step filtering and testing to generate component impurity screening results;

[0034] Based on the component impurity screening results, complete the component screening and separation of each product, record the purification status of each component, and obtain the targeted peptide purification product information.

[0035] As a further embodiment of the present invention, the specific steps of producing the oral cancer targeting peptide kit are:

[0036] Based on the information of the purified products of the targeting peptides, the binding efficiency of each targeting peptide with the oral cancer marker is tested one by one, and the binding efficiency is summarized and its change trend is recorded to generate the targeting peptide binding efficiency data;

[0037] Based on the targeting peptide binding efficiency data, the component ratios of the targeting peptides are compared, and the optimal combination is determined by analyzing the binding efficiency and component ratio to generate a targeting peptide component combination scheme;

[0038] Based on the targeting peptide component combination scheme, a particle swarm optimization algorithm is used to complete the assembly of the targeting peptides, and a unified assembly structure is formed by combining the component ratios of each targeting peptide to generate an oral cancer targeting peptide kit.

[0039] As a further solution of the present invention, the particle swarm optimization algorithm is according to the formula:

[0040]

[0041] Where: v i (t+1) represents the velocity of the i-th particle at time t+1, ω is the inertia weight, v i (t) is the velocity of the ith particle at the tth moment, c1 is the individual learning factor, r1 is a random number, p i is the individual optimal position of the ith particle, xi (t) is the position of the i-th particle at the t-th moment, c2 is the group learning factor, r2 is a random number, g is the global optimal position, α is the environmental factor adjustment coefficient, and f i is the influencing factor of the targeting peptide component, β is the time decay coefficient, is the time decay function, t represents the current number of iterations, and T is the maximum number of iterations of the algorithm.

[0042] As a further solution of the present invention, the application method of the tumor targeting peptide kit based on oral cancer is as follows:

[0043] Prepare the test kit and targeting peptide reagents in advance. Take out the reagents containing fluorescently labeled FAP targeting peptides and NGO nanomaterials from the -20°C freezing environment 2 hours before surgery, and dissolve them in the dark at 4°C. 15 minutes before specimen collection, drop the melted reagents into the lower chamber of the test kit and store them in the dark to prevent premature activation of the reagents. The test kit is designed as an upper and lower chamber structure connected by a hollow microneedle system. When in use, the reagents are squeezed into the test tissue by mechanical pressing.

[0044] After the main tumor tissue is removed, confirm whether there is oral cancer cell invasion in the marginal tissue, and collect suspicious tissue samples from 8 sites at the surgical margin intermittently. The diameter and thickness of the collected tissue samples are 2-3mm, and they are numbered in sequence and placed in the 8 numbered sites corresponding to the upper chamber of the reagent kit;

[0045] After the specimen is placed, the lid of the box is mechanically pressed to allow the reagent to enter the tissue to be tested from the lower chamber through the hollow microneedle system. The fluorescently labeled targeting peptide will bind to the FAP tumor marker in the tissue, the fluorescence quenching effect will be released, and the fluorescence will reappear. The reaction time is set to 20 minutes to ensure that the reagent fully reacts with the marker in the tissue.

[0046] After the reaction is finished, the upper chamber of the kit is taken out and placed in a near-infrared fluorescence instrument to detect the fluorescence signal; the device analyzes the fluorescence intensity and defines the boundary of the invaded tissue by detecting the fluorescence signal after the target peptide binds to the tumor marker;

[0047] By interpreting the fluorescent signal, the boundary of the tumor tissue is determined and a decision is made as to whether to further remove the marginal tissue. The entire detection and analysis process is completed in a relatively short time.

[0048] Compared with the prior art, the advantages and positive effects of the present invention are:

[0049] In the present invention, by adopting nonlinear model prediction control, the optimal reaction path is screened out based on the data of reaction rate and product purity, and each variable is cross-compared, so as to improve the accuracy and reliability of the synthesis conditions of the targeted peptide, avoid the limitation of condition setting, dynamically adjust the variables, ensure that the best reaction effect can be obtained under different reaction conditions, adopt particle swarm optimization algorithm to analyze the binding efficiency of the targeted peptide and the oral cancer marker, further optimize the binding efficiency of the targeted peptide and the marker through multiple inductive analyses, enhance the sensitivity and specificity of the kit, enable each reaction step to be carried out under the optimized conditions, improve the overall performance of the targeted peptide kit, and ensure the purity of the components and the reaction efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a schematic diagram of the main steps of the present invention. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0052] Embodiment 1

[0053] See also Figure 1 The present invention provides a technical solution: a method for preparing a tumor targeting peptide kit for oral cancer, comprising the following steps:

[0054] Step 1: Based on the combination of oral cancer targeting peptide and marker, the pH value, temperature and ion concentration are set, and the reaction conditions set of the targeting peptide are obtained by adjusting and monitoring the changes of each reaction parameter and gradually adjusting the reaction rate in the synthesis process of the targeting peptide;

[0055] Step 2: Based on the set of targeted peptide reaction conditions, the intermediate products in the peptide generation process are screened step by step. By summarizing the binding efficiency of peptides generated under different conditions, the efficient binding conditions are identified in turn to obtain the analysis results of the targeted peptide activity;

[0056] Step 3: Based on the results of the activity analysis of the targeted peptide, the intermediate products in each reaction step are analyzed, the data of the reaction rate and product purity are extracted, the optimal reaction path is screened out, and nonlinear model prediction and control are used to perform cross-comparison of various variables and complete the calculation to obtain the synthetic condition plan of the targeted peptide;

[0057] Step 4: Based on the targeted peptide synthesis condition scheme, extract the intermediate products produced in the process of generating the targeted peptide, gradually record the purity and generation time of each product, and obtain the targeted peptide purification condition set by comparing the reaction time and ion concentration of different products;

[0058] Step 5: Based on the targeted peptide purification condition set, extract each product generated during the purification process, analyze them one by one, combine the component ratio and impurity content of each product, remove impurities, complete the screening and separation of components, and obtain the targeted peptide purification product information;

[0059] Step 6: Based on the information of the purified products of the targeted peptides, the particle swarm optimization algorithm is used to summarize the binding efficiency of each targeted peptide with the oral cancer marker multiple times, and the binding efficiency and component ratio are recorded to complete the assembly and combination of the targeted peptides to generate an oral cancer targeted peptide kit.

[0060] The specific steps to generate a set of targeted peptide reaction conditions are:

[0061] Based on the combination of oral cancer targeting peptides and markers, the pH value, temperature and ion concentration are set, and each reaction condition is adjusted one by one. By monitoring the real-time changes of each variable, the initial reaction parameter set is obtained;

[0062] Based on the initial reaction parameter set, the peptide synthesis process is monitored, the reaction rate is gradually adjusted, the peptide generation rate is calculated in turn, and its binding with the marker is verified to obtain the dynamic adjustment result of the reaction conditions;

[0063] Based on the results of dynamic adjustment of reaction conditions, the fluctuations of various variables are comprehensively analyzed, and the optimal reaction state is achieved by adjusting the variables, and the optimal value of each variable is determined in turn to obtain a set of targeted peptide reaction conditions;

[0064] Based on the combination of the targeting peptide and the marker, the pH value, temperature and ion concentration were set, and the gradient progressive adjustment algorithm was used to gradually adjust the reaction conditions. The initial parameters were set to pH 7.2, temperature 37°C, and ion concentration 150mM. By real-time monitoring the changes in each reaction variable, the differential monitoring algorithm was used to record the value changes every minute and analyze the fluctuations of each variable to generate an initial reaction parameter set. The adjusted value of each variable was recorded in the database. The database was updated every 10 seconds. Batch update operations were performed by calling SQL stored procedures, and real-time monitoring data tables were generated.

[0065] Based on the initial reaction parameter set, an adaptive rate control algorithm was used to adjust the reaction rate in the peptide synthesis process. The parameters were set to an initial rate of 0.5 mol / min and a reaction time interval of 1 minute. Combined with the rate change feedback from the real-time monitoring system, the rate at each time node was updated. The update conditions were that when the rate deviation exceeded 5%, the rate was adjusted to 0.4 mol / min, when the deviation was less than 3%, it remained unchanged, and when the deviation exceeded 10%, the rate was increased to 0.6 mol / min. The peptide generation rates were calculated in turn, and the matrix operation function was used to analyze the generation rates one by one to verify their binding with the marker. The rate and binding efficiency were fitted by the least squares method to generate the dynamic adjustment results of the reaction conditions.

[0066] Based on the results of dynamic adjustment of reaction conditions, the fluctuations of various variables were comprehensively analyzed, and the standard deviation of each variable was calculated using the coefficient of variation analysis method. The standard deviation parameter was set to ±0.01. If the fluctuation range was within the set standard deviation, the original variable value was kept unchanged. If it exceeded the set range, the variable was readjusted. By adjusting the temperature, pH value and ion concentration, the optimization parameters were set to pH 7.0, temperature 36.5°C, and ion concentration 140mM. The optimal value of each variable was determined in turn to generate a set of targeted peptide reaction conditions.

[0067] The specific steps to generate the targeted peptide activity analysis results are:

[0068] Based on the set of targeted peptide reaction conditions, the intermediate products in peptide generation are gradually extracted, the generation efficiency of each product is recorded in turn, and the peptide generation efficiency analysis results are obtained by combining the binding ability during generation;

[0069] Based on the peptide generation efficiency analysis results, the binding efficiency of peptides generated under different conditions is summarized, the binding effects are gradually ranked, and the efficient binding conditions are identified to obtain the binding efficiency summary results;

[0070] Based on the binding efficiency summary results, the optimal range of effective binding conditions was confirmed in turn, and the reaction conditions were adjusted one by one for verification, and the optimal combination of conditions was determined to obtain the analysis results of the activity of the targeted peptide;

[0071] Based on the set of targeted peptide reaction conditions, a stepwise extraction algorithm was used to extract the intermediate products in the peptide generation process. The execution interval of each extraction step was 2 minutes, and the extraction volume was 0.5 ml. The generation efficiency of each intermediate product was recorded by the automatic acquisition system. The generation efficiency was calculated by the rate monitoring module. The calculation formula was the generation volume divided by the time. The binding capacity at the time of generation was analyzed. The binding capacity was calculated by the ratio of the marker concentration to the peptide concentration to generate the peptide generation efficiency analysis results.

[0072] Based on the analysis results of peptide generation efficiency, a cluster analysis algorithm was used to summarize the binding efficiency of peptides generated under different conditions. The binding efficiency of each peptide was calculated by the mean method. The mean calculation formula was the binding capacity of each sample divided by the total number of samples. The binding effects were sorted step by step. The sorting algorithm used the bubble sort method to compare each binding efficiency from large to small, identify efficient binding conditions, and generate a summary of binding efficiency.

[0073] Based on the binding efficiency summary results, the interval optimization algorithm was used to confirm the optimal range of effective binding conditions. The interval range was set to a binding efficiency between 80% and 90%. Each reaction condition was adjusted one by one for verification. The verification method was to fix other variables, adjust the target variable separately, and adjust the pH value, temperature and ion concentration in turn. The binding efficiency after each adjustment was recorded through the verification module. Finally, the optimal combination of conditions was determined to generate the targeted peptide activity analysis results.

[0074] The specific steps for generating the targeted peptide synthesis condition scheme are:

[0075] Based on the results of the activity analysis of the targeted peptide, the intermediate products in each reaction step are extracted, each product is separated by centrifugation, the generation time and purity of each intermediate product are gradually determined, and the purity changes are recorded to generate intermediate product purity records;

[0076] Based on the purity record of the intermediate product, the reaction rate is extracted. By monitoring the rate change of the generated product and comparing the reaction conditions with the generation rate, the efficient reaction path is screened out, and the corresponding rate change of the variable is recorded to generate the optimal reaction path data;

[0077] Based on the optimal reaction path data, nonlinear model predictive control is used to perform cross-comparisons among the variables. By adjusting the reaction time and purity parameters, the variable combination is gradually optimized, and the optimal combination of the generation conditions is calculated in turn to obtain the targeted peptide synthesis condition plan;

[0078] Based on the results of the targeted peptide activity analysis, the intermediate products in each reaction step were extracted by centrifugation. The centrifugal speed was set to 12,000 rpm and the centrifugal time was set to 10 minutes. The products were separated layer by layer by centrifugal force. The generation time and purity of each intermediate product were determined by quantitative analysis. The generation time was calculated by the difference between the reaction start time and the centrifugation end time. The purity was determined by spectral absorption method with the spectral wavelength set to 280 nm. The purity changes of each intermediate product were gradually recorded to generate intermediate product purity records.

[0079] Based on the purity records of intermediate products, the gradient rate monitoring algorithm is used to extract the reaction rate. The rate is calculated by the ratio of product generation to time. The time interval is set to every 5 minutes. Combined with the comparison of reaction conditions and generation rate, efficient reaction paths are screened out. The screening condition is that when the rate change is greater than 5%, the path is marked as an efficient path, and the optimal reaction path data is generated by recording the corresponding rate changes of each variable.

[0080] Based on the optimal reaction path data, a nonlinear model predictive control algorithm was used to cross-compare the variables. The model parameters were set as reaction time adjusted every 10 minutes and purity variation range of ±0.02. By comparing the changes in reaction time and purity parameters, the variable combination was gradually optimized. The combination of each variable was fitted and calculated using the least squares method, and the optimal combination of generation conditions was calculated in turn to generate the targeted peptide synthesis condition scheme.

[0081] Nonlinear model predictive control, according to the formula:

[0082]

[0083] Where: α is the influence coefficient of reaction time on the output result, β is the magnification coefficient of target purity, y(k) is the predicted output value of the current time step k, r(k) is the target value, λ is the weight coefficient of the controller, Δu(k) represents the adjustment amount of the input variable, γ is the by-product suppression coefficient, P(k) represents the amount of by-products generated at time step k, δ is the adjustment weight of reaction temperature, and T(k) represents the reaction temperature at time step k;

[0084] Execution process: First, α is the coefficient of reaction time on the output result, and the influence of different reaction times on the purity of peptide synthesis is determined. The optimal α value is obtained by experimental fitting. β is the amplification factor of the target purity, which is used to improve the priority of the target purity in the synthesis of targeted peptides. β is obtained by analyzing the expected purity requirements. Then, λ is the weight coefficient of the controller, which controls the amplitude of the input variable change to ensure the stability of the reaction process. Δu(k) is the change in the input variable adjustment, which represents the adjustment of reaction conditions such as temperature and time at each step. Then, γ is the by-product inhibition coefficient, which is used to control the negative impact of by-product generation on the reaction. Through experimental settings, it is ensured that by-product generation is minimized. δ is the reaction temperature adjustment weight, which adjusts the effect of temperature on reaction efficiency and by-product generation. T(k) is the reaction temperature at each time step. Too high or too low temperature will affect the synthesis of targeted peptides. The appropriate temperature range is selected through experimental data. Finally, y(k) represents the current predicted output value, such as reaction purity and time, and r(k) is the set target value. The difference between the two is minimized by the formula to optimize the reaction conditions so that the synthesis of targeted peptides reaches the optimal combination.

[0085] The specific steps to generate a set of targeted peptide purification conditions are:

[0086] Based on the targeted peptide synthesis condition scheme, the intermediate products in the generation of the targeted peptide are extracted, the generation time and purity of each intermediate product are recorded by the time separation method, and the ion concentration is measured in turn to generate the intermediate product generation record;

[0087] Based on the intermediate product generation records, the reaction time and ion concentration of different intermediate products are compared, the generation efficiency is verified through multiple tests, and the generation efficiency under different concentration conditions is recorded one by one by extracting and screening the products, generating product efficiency analysis results;

[0088] Based on the results of product efficiency analysis, a comparative analysis of purity and generation time was conducted to extract the variables involved in the purification process. By optimizing the concentration and generation conditions, the generation path was gradually adjusted to obtain a set of purification conditions for the targeted peptide.

[0089] Based on the targeted peptide synthesis condition scheme, the time separation method was used to extract the intermediate products in the generation of the targeted peptide. The time separation parameters were set to extract once every 5 minutes, and the extraction volume was 1 ml each time. The generation time was calculated using the time difference, and the generation time was calculated by the difference between the start time and the extraction time. The purity was determined by ultraviolet spectrophotometry, and the wavelength was set to 280 nm. The purity of each intermediate product was measured one by one, and the ion concentration was determined in turn. The concentration was determined by the conductivity method to generate the intermediate product generation record.

[0090] Based on the intermediate product generation records, the reaction time and ion concentration of different intermediate products were compared by multiple test method. The test number was set to 3 repetitions for each test group. The generation efficiency was verified by linear regression model, and the regression coefficient was set to 0.95. The products under different concentration conditions were extracted and screened. The concentration range was set to 100mM to 200mM. The generation efficiency under different conditions was recorded one by one to generate product efficiency analysis results.

[0091] Based on the results of product efficiency analysis, the purity and generation time were analyzed by comparative analysis. The analysis formula was the ratio of purity to generation time. The variables involved in the purification process were extracted, including ion concentration and reaction time, and the generation path was gradually adjusted. The dynamic optimization algorithm was used to optimize the generation path. The optimization parameters were set so that the fluctuation range of the generation rate and purity did not exceed ±0.02, and a set of targeted peptide purification conditions was generated.

[0092] The specific steps to generate targeted peptide purification product information are:

[0093] Based on the target peptide purification condition set, each product generated during the purification process is extracted, the component ratio of each product is analyzed in turn, and excess impurities are removed by centrifugation to generate a record of the component ratio of the purified product;

[0094] Based on the records of the proportion of purified product components, the impurity content in each product is compared, and components that do not meet the purity requirements are eliminated through step-by-step filtering and testing to generate component impurity screening results;

[0095] Based on the results of component impurity screening, complete the component screening and separation of each product, record the purification status of each component, and obtain the information of the targeted peptide purification product;

[0096] Based on the target peptide purification condition set, density gradient centrifugation was used to extract each product generated during the purification process. The centrifugation speed was set to 12,000 rpm and the time was set to 15 minutes. The component ratio of each product was analyzed by ultraviolet spectrophotometry. The wavelength was set to 280 nm. The absorbance value of each component was recorded in turn. The component ratio was calculated using Excel batch processing tools to generate a record of the component ratio of the purified product.

[0097] Based on the records of the proportion of purified product components, a gradient filtering algorithm was used to compare the impurity content in each product, and the impurity content threshold was set to 5%. Products with content exceeding the threshold were filtered step by step using a filtering device, and the filtering parameters were set to a pore size of 0.22 microns. Impurity detection was performed using a gas chromatograph, and the detection temperature was set to 180°C and the detection time was set to 30 minutes. Components that did not meet the purity requirements were eliminated to generate component impurity screening results;

[0098] Based on the results of component impurity screening, high performance liquid chromatography was used to complete the component screening and separation of each product. The mobile phase was set to acetonitrile and water, the flow rate was 1.0 ml / min, and the separation process time was set to 45 minutes. The purification status of each component was recorded one by one to generate the targeted peptide purification product information.

[0099] The specific steps for generating the oral cancer targeting peptide kit are:

[0100] Based on the information of the purified products of the targeted peptides, the binding efficiency of each targeted peptide with the oral cancer markers was tested one by one, and the binding efficiency was summarized and its changing trend was recorded to generate the data of the binding efficiency of the targeted peptides;

[0101] Based on the targeting peptide binding efficiency data, the component ratios of each targeting peptide are compared, and the optimal combination is determined through the analysis of binding efficiency and component ratio to generate a targeting peptide component combination plan;

[0102] Based on the combination scheme of targeted peptide components, the particle swarm optimization algorithm is used to complete the assembly of targeted peptides. By combining the component ratios of each targeted peptide, a unified assembly structure is formed to generate an oral cancer targeted peptide kit;

[0103] Based on the information of the purified products of the targeting peptides, the stepwise test method was used to test the binding efficiency of each targeting peptide with the oral cancer marker. The test conditions were set at 37°C and pH 7.4. The binding efficiency was determined by enzyme-linked immunosorbent assay (ELISA). The wavelength was set at 450nm and the test volume was 100μl each time. The changes in binding efficiency were recorded and trend analysis was performed by the inductive algorithm of binding efficiency. The inductive algorithm used linear fitting to generate the targeting peptide binding efficiency data.

[0104] Based on the targeting peptide binding efficiency data, the component ratios of each targeting peptide were compared using the multivariate comparison method. The component ratios were calculated by mass ratio, and the binding efficiency was calculated by ratio analysis. The correlation between each targeting peptide component and the binding efficiency was analyzed, and the Pearson correlation coefficient was used to analyze the correlation between the component and the binding efficiency. The correlation coefficient threshold was set to 0.8, and the optimal combination of the component and the binding efficiency was gradually determined to generate a targeting peptide component combination scheme.

[0105] Based on the targeting peptide component combination scheme, the particle swarm optimization algorithm was used to optimize the assembly of the targeting peptides. The number of particles was set to 100, the number of iterations was 500, the search space was a floating point number with a component ratio between 0.1 and 1.0, and the speed update formula was set to the weight difference between the historical optimal value of the particle and the global optimal value. The component ratio of each targeting peptide was gradually adjusted to generate a unified assembly structure and an oral cancer targeting peptide kit.

[0106] Particle swarm optimization algorithm, according to the formula:

[0107]

[0108] Where: v i (t+1) represents the velocity of the i-th particle at time t+1, ω is the inertia weight, v i (t) is the velocity of the ith particle at the tth moment, c1 is the individual learning factor, r1 is a random number, p i is the individual optimal position of the ith particle, x i (t) is the position of the i-th particle at the t-th moment, c2 is the group learning factor, r2 is a random number, g is the global optimal position, α is the environmental factor adjustment coefficient, and f i is the influencing factor of the targeting peptide component, β is the time decay coefficient, is the time decay function, t represents the current number of iterations, and T is the maximum number of iterations of the algorithm;

[0109] Execution process: First, initialize the particle swarm, each particle represents a combination of targeted peptide components, and then adjust the particle speed v according to the inertia weight ω i (t+1), ensuring that it retains a certain continuity in the search space. Subsequently, the individual learning factor c1 and the random number r1 guide the particle to its individual optimal position p i Move, adjust the proportion of each targeted peptide component, the group learning factor c2 and the random number r2 ensure that the particles converge toward the global optimal position g, and adjust the coefficient α and the impact factor f of the targeted peptide component through the newly added environmental factors i Combined with the experimental environment, the composition ratio is adjusted. Finally, the time decay coefficient β and function The control search process gradually converges over time, so that the particles are more concentrated on the optimal solution in the later stage. After multiple iterations, the optimal combination of targeting peptide components is generated, and the preparation of the oral cancer targeting peptide kit is completed.

[0110] The application method of the tumor targeting peptide kit based on oral cancer is as follows:

[0111] Prepare the test kit and targeting peptide reagents in advance. Take out the reagents containing fluorescently labeled FAP targeting peptides and NGO nanomaterials from the -20°C freezing environment 2 hours before surgery, and dissolve them in the dark at 4°C. 15 minutes before specimen collection, drop the melted reagents into the lower chamber of the test kit and store them in the dark to prevent premature activation of the reagents. The test kit is designed as an upper and lower chamber structure connected by a hollow microneedle system. When in use, the reagents are squeezed into the test tissue by mechanical pressing.

[0112] After the main tumor tissue is removed, confirm whether there is oral cancer cell invasion in the marginal tissue, and collect suspicious tissue samples from 8 sites at the surgical margin intermittently. The diameter and thickness of the collected tissue samples are 2-3mm, and they are numbered in sequence and placed in the 8 numbered sites corresponding to the upper chamber of the reagent kit;

[0113] After the specimen is placed, the lid of the box is mechanically pressed to allow the reagent to enter the tissue to be tested from the lower chamber through the hollow microneedle system. The fluorescently labeled targeting peptide will bind to the FAP tumor marker in the tissue, the fluorescence quenching effect will be released, and the fluorescence will reappear. The reaction time is set to 20 minutes to ensure that the reagent fully reacts with the marker in the tissue.

[0114] After the reaction is finished, the upper chamber of the kit is taken out and placed in a near-infrared fluorescence instrument to detect the fluorescence signal; the device analyzes the fluorescence intensity and defines the boundary of the invaded tissue by detecting the fluorescence signal after the target peptide binds to the tumor marker;

[0115] By interpreting the fluorescent signal, the boundary of the tumor tissue is determined and a decision is made as to whether to further remove the marginal tissue. The entire detection and analysis process is completed in a relatively short time.

[0116] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for preparing a tumor targeting peptide kit for oral cancer, characterized in that: The following steps are involved: Step 1: Based on the combination of oral cancer targeting peptide and marker, the pH value, temperature and ion concentration are set, and the reaction conditions set of the targeting peptide are obtained by adjusting and monitoring the changes of each reaction parameter and gradually adjusting the reaction rate in the synthesis process of the targeting peptide; Step 2: Based on the set of targeted peptide reaction conditions, the intermediate products in the peptide generation process are screened step by step, and the binding efficiencies of the peptides generated under different conditions are summarized to identify the efficient binding conditions in turn, and obtain the targeted peptide activity analysis results; Step 3: Based on the activity analysis results of the targeted peptide, the intermediate products in each reaction step are analyzed, the reaction rate and product purity data are extracted, the optimal reaction path is screened, and nonlinear model prediction and control are adopted to perform cross comparison of each variable and complete calculation to obtain the targeted peptide synthesis condition scheme; Step 4: Based on the targeting peptide synthesis condition scheme, extract the intermediate products produced in the targeting peptide generation process, gradually record the purity and generation time of each product, and obtain the targeting peptide purification condition set by comparing the reaction time and ion concentration of different products; Step 5: Based on the targeted peptide purification condition set, each product generated during the purification process is extracted, analyzed one by one, and impurities are removed in combination with the component ratio and impurity content of each product to complete the screening and separation of components and obtain the targeted peptide purification product information; Step 6: Based on the information of the purified targeted peptide products, a particle swarm optimization algorithm is used to summarize the binding efficiency of each targeted peptide with the oral cancer marker multiple times, the binding efficiency and component ratio are recorded, the assembly and combination of the targeted peptides are completed, and an oral cancer targeted peptide kit is generated.

2. The method for preparing the oral cancer-based tumor targeting peptide kit according to claim 1, characterized in that: The specific steps of generating the target peptide reaction condition set are: Based on the combination of oral cancer targeting peptides and markers, the pH value, temperature and ion concentration are set, and each reaction condition is adjusted one by one. By monitoring the real-time changes of each variable, the initial reaction parameter set is obtained; Based on the initial reaction parameter set, by monitoring the peptide synthesis process, gradually adjusting the reaction rate, sequentially calculating the peptide generation rate and verifying its binding with the marker, the result of dynamic adjustment of the reaction conditions is obtained; Based on the dynamic adjustment results of the reaction conditions, the fluctuations of various variables are comprehensively analyzed, and the optimal reaction state is achieved by adjusting the variables. The optimal value of each variable is determined in turn to obtain a set of targeted peptide reaction conditions.

3. The method for preparing the oral cancer-based tumor targeting peptide kit according to claim 1, characterized in that: The specific steps of generating the targeting peptide activity analysis results are: Based on the set of targeted peptide reaction conditions, the intermediate products in the peptide generation are gradually extracted, the generation efficiency of each product is recorded in turn, and the peptide generation efficiency analysis results are obtained in combination with the binding ability during its generation; Based on the peptide generation efficiency analysis results, the binding efficiencies of peptides generated under different conditions are summarized, the binding effects are gradually ranked, efficient binding conditions are identified, and the binding efficiency summary results are obtained; Based on the binding efficiency summary results, the optimal range of effective binding conditions is confirmed in turn, the reaction conditions are adjusted one by one for verification, the optimal combination of conditions is determined, and the targeting peptide activity analysis results are obtained.

4. The method for preparing the oral cancer-based tumor targeting peptide kit according to claim 1, characterized in that: The specific steps for generating the targeting peptide synthesis condition scheme are: Based on the target peptide activity analysis results, extract the intermediate products in each reaction step, separate the products by centrifugation, gradually determine the generation time and purity of each intermediate product, record the purity change, and generate the intermediate product purity record; Based on the intermediate product purity record, the reaction rate is extracted, and by monitoring the rate change of the generated product, combining the comparison between the reaction conditions and the generation rate, an efficient reaction path is screened out, and the corresponding rate change of the variable is recorded to generate the optimal reaction path data; Based on the optimal reaction path data, nonlinear model predictive control is used to perform cross comparisons among variables, and the combination of variables is gradually optimized by adjusting the reaction time and purity parameters. The optimal combination of generation conditions is calculated in turn to obtain the targeted peptide synthesis condition scheme.

5. The method for preparing the oral cancer-based tumor targeting peptide kit according to claim 1, characterized in that: The nonlinear model predictive control is based on the formula: Where: α is the influence coefficient of reaction time on the output result, β is the magnification coefficient of target purity, y(k) is the predicted output value of the current time step k, r(k) is the target value, λ is the weight coefficient of the controller, Δu(k) represents the adjustment amount of the input variable, γ is the by-product suppression coefficient, P(k) represents the amount of by-products generated at time step k, δ is the adjustment weight of the reaction temperature, and T(k) represents the reaction temperature at time step k.

6. The method for preparing the oral cancer-based tumor targeting peptide kit according to claim 1, characterized in that: The specific steps for generating the target peptide purification condition set are: Based on the targeting peptide synthesis condition scheme, extract the intermediate products in the generation of the targeting peptide, record the generation time and purity of each intermediate product by time separation method, measure the ion concentration in turn, and generate the intermediate product generation record; Based on the intermediate product generation record, the reaction time and ion concentration of different intermediate products are compared, the generation efficiency is verified through multiple tests, and the generation efficiency under different concentration conditions is recorded one by one by extracting and screening the products, so as to generate product efficiency analysis results; Based on the product efficiency analysis results, a comparative analysis of purity and generation time was performed, the variables involved in the purification process were extracted, and the generation path was gradually adjusted by optimizing the concentration and generation conditions to obtain a set of targeted peptide purification conditions.

7. The method for preparing the oral cancer-based tumor targeting peptide kit according to claim 1, characterized in that: The specific steps for generating the targeted peptide purification product information are: Based on the target peptide purification condition set, each product generated during the purification process is extracted, the component ratio of each product is analyzed in turn, excess impurities are removed by centrifugal separation, and a purified product component ratio record is generated; Based on the recorded proportions of the purified products, the impurity content in each product is compared, and components that do not meet the purity requirements are eliminated through step-by-step filtering and testing to generate component impurity screening results; Based on the component impurity screening results, complete the component screening and separation of each product, record the purification status of each component, and obtain the targeted peptide purification product information.

8. The method for preparing the oral cancer-based tumor targeting peptide kit according to claim 1, characterized in that: The specific steps of generating the oral cancer targeting peptide kit are: Based on the information of the purified products of the targeting peptides, the binding efficiency of each targeting peptide with the oral cancer marker is tested one by one, and the binding efficiency is summarized and its change trend is recorded to generate the targeting peptide binding efficiency data; Based on the targeting peptide binding efficiency data, the component ratios of the targeting peptides are compared, and the optimal combination is determined by analyzing the binding efficiency and component ratio to generate a targeting peptide component combination scheme; Based on the targeting peptide component combination scheme, a particle swarm optimization algorithm is used to complete the assembly of the targeting peptides, and a unified assembly structure is formed by combining the component ratios of each targeting peptide to generate an oral cancer targeting peptide kit.

9. The method for preparing the oral cancer-based tumor targeting peptide kit according to claim 1, characterized in that: The particle swarm optimization algorithm is based on the formula: Where: v i (t+1) represents the velocity of the i-th particle at time t+1, ω is the inertia weight, v i (t) is the velocity of the ith particle at the tth moment, c1 is the individual learning factor, r1 is a random number, p i is the individual optimal position of the ith particle, x i (t) is the position of the i-th particle at the t-th moment, c2 is the group learning factor, r2 is a random number, g is the global optimal position, α is the environmental factor adjustment coefficient, and f i is the influencing factor of the targeting peptide component, β is the time decay coefficient, is the time decay function, t represents the current number of iterations, and T is the maximum number of iterations of the algorithm.

10. The application method of the oral cancer-based tumor targeting peptide kit according to claim 1, characterized in that: The application method is as follows: Prepare the test kit and targeting peptide reagents in advance. Take out the reagents containing fluorescently labeled FAP targeting peptides and NGO nanomaterials from the -20°C freezing environment 2 hours before surgery, and dissolve them in the dark at 4°C. 15 minutes before specimen collection, drop the melted reagents into the lower chamber of the test kit and store them in the dark to prevent premature activation of the reagents. The test kit is designed as an upper and lower chamber structure connected by a hollow microneedle system. When in use, the reagents are squeezed into the test tissue by mechanical pressing. After the main tumor tissue is removed, confirm whether there is oral cancer cell invasion in the marginal tissue, and collect suspicious tissue samples from 8 sites at the surgical margin intermittently. The diameter and thickness of the collected tissue samples are 2-3mm, and they are numbered in sequence and placed in the 8 numbered sites corresponding to the upper chamber of the reagent kit; After the specimen is placed, the lid of the box is mechanically pressed to allow the reagent to enter the tissue to be tested from the lower chamber through the hollow microneedle system. The fluorescently labeled targeting peptide will bind to the FAP tumor marker in the tissue, the fluorescence quenching effect will be released, and the fluorescence will reappear. The reaction time is set to 20 minutes to ensure that the reagent fully reacts with the marker in the tissue. After the reaction is finished, the upper chamber of the kit is taken out and placed in a near-infrared fluorescence instrument to detect the fluorescence signal; the device analyzes the fluorescence intensity and defines the boundary of the invaded tissue by detecting the fluorescence signal after the target peptide binds to the tumor marker; By interpreting the fluorescent signal, the boundary of the tumor tissue is determined and a decision is made as to whether to further remove the marginal tissue. The entire detection and analysis process is completed in a relatively short time.

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