Vitamin B6 pathway regulated and controlled anti-ovarian cancer nano-drug regulation and control method

Abstract

The invention relates to the technical field of nano-drug regulation and control, in particular to a vitamin B6 pathway regulated and controlled anti-ovarian cancer nano-drug regulation and control method, which comprises the following steps: acquiring and comparing metabolic enzyme activity, pH value, oxygen concentration and temperature of a tumor area, screening abnormal areas and marking characteristics, extracting data of the abnormal areas to compare carrier characteristics, screening release conditions, and determining the ovarian cancer resistance of the nano-drug. Optimizing a carrier material and a structure, adjusting a drug connection mode, calculating a release rate and adjusting a time node, tracking a release effect and correcting arrangement, and generating a release area division and adjustment result. According to the present invention, by collecting the vitamin B6 metabolic enzyme activity, the pH value, the oxygen concentration, the temperature and other parameters in the tumor region, the tumor microenvironment characteristic region is accurately identified, the drug release condition conforming to the physiological state is screened, and the effect of the drug and the nano-carrier is optimized; the release rate is dynamically adjusted, the release process is accurately controlled, the adjustment effect is tracked in real time, accurate drug delivery is ensured, the treatment effect is improved, and side effects and waste are reduced.

Description

Technical Field

[0001] This invention relates to the field of nanomedicine regulation technology, and in particular to a method for regulating anti-ovarian cancer nanomedicine through vitamin B6 pathway regulation. Background Technology

[0002] The field of nanomedicine regulation technology encompasses research directions that utilize nanocarriers to control the release, distribution, and metabolic processes of drugs in vivo. Core components include the selection and modification of nanomaterials, drug encapsulation and stability regulation within nanostructures, targeted drug delivery to specific tissues or cells, and biocompatibility and degradation characteristics at the cellular level. Overall, the field of nanomedicine regulation technology systematically covers a research framework ranging from nanoparticle preparation, surface modification, drug molecule binding mechanisms, and delivery pathway design to response mechanisms in biological environments. It aims to fundamentally construct drug regulation processes through multi-level nanoscale interventions.

[0003] One approach involves regulating the vitamin B6 pathway in anti-ovarian cancer nanomedicines. This method targets key aspects of the vitamin B6 metabolic pathway within the context of ovarian cancer-related cell metabolism, combining this with nanocarriers to achieve complex drug regulation. The technical aspects covered include the selection of key vitamin B6 metabolic enzyme activities and determination of their sites of action, the construction of drug molecule-nanomaterial binding mechanisms, the responsive design of nanomedicines in specific pathway environments, and the regulatory modes during intracellular and extracellular transport. Specifically, this involves introducing drug molecules capable of recognizing and binding to vitamin B6 pathway-related factors onto nanocarriers. The assembly of nanoscale structures enables targeted drug release and local regulation within this pathway, thus constructing a pathway-guided nanomedicine regulation system.

[0004] Existing technologies struggle to address the dynamic changes in the tumor microenvironment during drug release. Most technologies focus on static design, failing to adapt in real-time to variations in tumor pH, enzyme activity, and oxygen concentration. This lack of consideration for tumor heterogeneity can lead to uneven drug distribution across different regions, impacting treatment efficacy. Furthermore, drug release is typically based on static conditions, which cannot effectively adapt to changes in the microenvironment, potentially resulting in over- or under-release in certain areas, affecting treatment effectiveness and increasing side effects. Summary of the Invention

[0005] To address the technical problems existing in the prior art, this invention provides a method for regulating anti-ovarian cancer nanomedicines through vitamin B6 pathway regulation. The technical solution is as follows:

[0006] A method for regulating vitamin B6 pathway in anti-ovarian cancer nanomedicine includes the following steps:

[0007] S1: Collect the activity of vitamin B6 metabolic enzymes, pH value, oxygen concentration and temperature value in the tumor area, compare and screen the areas with abnormal metabolic enzyme activity, mark the corresponding pH value range and oxygen concentration characteristics, and generate tumor microenvironment characteristic differentiation results.

[0008] S2: Based on the tumor microenvironment characteristics, extract the pH value and oxygen concentration range of the abnormal metabolic enzyme activity region, compare the pH response and oxygen triggering characteristics of the nanocarrier, screen suitable condition combinations, eliminate conditions that do not meet the range, and generate drug release condition range.

[0009] S3: Based on the drug release condition range, select low pH value and high enzyme activity depolymerization material, optimize carrier components and surface structure, adjust the drug-carrier connection method to enable it to react accurately under target conditions, and generate carrier design scheme and structural characteristics.

[0010] S4: Based on the carrier design scheme and structural characteristics, combined with the temperature and oxygen concentration changes in the target area, calculate the release rate under different conditions, adjust the release time nodes, form a differentiated release mode, and generate a dynamic drug release schedule;

[0011] S5: Based on the dynamic drug release schedule, combined with real-time enzyme activity, pH value and temperature changes in the tumor region, track the release effect and adjust the release schedule, complete the monitoring and correction, and generate the drug release area division and adjustment results.

[0012] As a further aspect of the present invention, the tumor microenvironment feature differentiation results include regions with abnormal metabolic enzyme activity, pH range, and oxygen concentration characteristics; the drug release condition range includes a suitable pH range for release, a suitable oxygen concentration range for release, and a range of invalid conditions to be eliminated; the carrier design scheme and structural characteristics include material type, surface structure, and the connection method between the drug and the carrier; the drug dynamic release arrangement includes release rate distribution, release time nodes, and differentiated release characteristics; and the drug release region division and adjustment results include release region division, regional release effect records, and release arrangement adjustment schemes.

[0013] As a further aspect of the present invention, the steps for obtaining the tumor microenvironment feature differentiation results are as follows:

[0014] S101: Collect the activity level of vitamin B6 metabolic enzymes, local pH value, oxygen concentration and temperature value within the tumor area, match the collected metabolic enzyme activity level with its corresponding regional location, and classify different regions according to the matched metabolic enzyme activity level data to generate metabolic enzyme activity zoning values.

[0015] S102: Call the metabolic enzyme activity partition value, compare the metabolic enzyme activity level of each region with the corresponding pH range and oxygen concentration one by one, screen the regions with abnormal metabolic enzyme activity during the comparison process, and record the corresponding pH range and oxygen concentration data to generate the characteristic comparison value of abnormal metabolic enzyme regions.

[0016] S103: Based on the comparison values ​​of the abnormal regions of metabolic enzymes, the pH range and oxygen concentration characteristics of the regions with abnormal metabolic enzyme activity are classified and statistically analyzed. Combined with the metabolic enzyme activity level corresponding to the region, specific parameters for distinguishing tumor microenvironment features are established to obtain the results of tumor microenvironment feature distinction.

[0017] As a further aspect of the present invention, the step of obtaining the drug release condition range is as follows:

[0018] S201: Based on the tumor microenvironment characteristics differentiation results, extract the pH value and oxygen concentration range of the abnormal metabolic enzyme activity region, group and classify the extracted pH value and oxygen concentration range data, and mark the metabolic enzyme activity characteristics corresponding to each group to generate the characteristic range value of the abnormal metabolic enzyme region.

[0019] S202: Call the abnormal region characteristic range value of the metabolic enzyme, compare the pH response characteristics and oxygen concentration triggering characteristics of the nanocarrier for the pH range and oxygen concentration range of each data set, remove the data range that does not meet the nanocarrier response, and retain the condition combination that meets the requirements to generate the nanocarrier response condition combination value.

[0020] S203: Based on the combined response conditions of the nanocarrier, statistical analysis is performed on the pH range and oxygen concentration range in the retained combination of conditions, and combined with the characteristics of the nanocarrier release triggering conditions, specific conditions suitable for drug release are screened out to obtain the drug release condition range.

[0021] As a further aspect of the present invention, the steps for obtaining the carrier design scheme and structural characteristics are as follows:

[0022] S301: Based on the drug release condition range, select materials that can depolymerize under low pH and high enzyme activity conditions, match and screen the materials according to their depolymerization characteristics and target condition range, eliminate data that do not meet the target conditions, and mark the material characteristic parameters that meet the conditions to generate applicable material characteristic parameters;

[0023] S302: Call the applicable material characteristic parameters to optimize the component type and surface structure of the selected materials, adjust the surface morphology according to the component characteristics of the materials, and adjust the surface structure parameters in combination with the target condition range so that the surface characteristics of the materials can adapt to the release conditions and generate optimized surface structure parameters.

[0024] S303: Based on the optimized surface structure parameters, adjust the connection method between the drug molecule and the carrier, determine the range of connection parameters based on the surface characteristics of the material and the characteristics of the drug molecule, and verify the reactivity of the connection method under the target conditions to obtain the carrier design scheme and structural characteristics.

[0025] As a further aspect of the present invention, the step of obtaining the dynamic drug release schedule is as follows:

[0026] S401: Using the carrier design scheme and structural characteristics, combined with the temperature change range and oxygen concentration distribution data in the target area, calculate the release rate of the carrier under different temperature and oxygen concentration conditions, mark the rate value range under different conditions according to the change trend of the release rate, and generate the release rate distribution range.

[0027] S402: Based on the release rate distribution range, adjust the time nodes of the rate values ​​under different oxygen concentration conditions, determine the time node grouping of the release rate according to the distribution trend of oxygen concentration, and match the grouped time nodes with the rate values ​​to generate release node values ​​under oxygen concentration conditions.

[0028] S403: Call the oxygen concentration condition release node value, analyze the change characteristics of the release rate at different time nodes, combine the time node distribution of the release rate, construct a drug release mode with differentiated release characteristics, and obtain the dynamic drug release arrangement.

[0029] As a further aspect of the present invention, the steps for obtaining the drug release region division and adjustment results are as follows:

[0030] S501: Based on the aforementioned dynamic drug release schedule, combined with real-time enzyme activity, pH value, and temperature data within the tumor region, the changes in drug concentration during the release process are tracked by region, the deviation between the release amount in each region and the real-time environmental data is calculated, and regions with insufficient or excessive release are marked to generate a distribution value of release deviation regions.

[0031] S502: Based on the distribution value of the release deviation area, adjust the release arrangement within the marked area, call up the enzyme activity, pH value and temperature change data of the deviation area, recalculate the release rate and time distribution of each area, and match the adjusted release rate to the corresponding area to generate the regional release adjustment rate value;

[0032] S503: Call the regional release adjustment rate value, monitor the adjusted release schedule, dynamically compare the corrected release rate distribution with the environmental data of each region, verify the release adjustment effect, record the release status of each region, and obtain the drug release region division and adjustment results.

[0033] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0034] In this invention, by collecting parameters such as vitamin B6 metabolic enzyme activity, pH value, oxygen concentration, and temperature within the tumor region, the characteristic areas of the tumor microenvironment can be accurately identified, and drug release conditions can be screened based on these parameters. This method makes the drug release conditions more closely match the physiological state of the tumor, optimizes the interaction between the drug and the nanocarrier, and improves the drug's targeting in the tumor region. The drug release rate is dynamically adjusted according to changes in temperature and oxygen concentration, allowing for precise control of the drug release process under different environments. Furthermore, real-time tracking and adjustment of the drug release effect ensures that the drug is accurately delivered to the target area, maximizing therapeutic efficacy and minimizing side effects and waste. Attached Figure Description

[0035] Figure 1 This is a flowchart of the method of the present invention;

[0036] Figure 2 This is a flowchart illustrating the process of obtaining the tumor microenvironment feature differentiation results of the present invention.

[0037] Figure 3 This is a flowchart illustrating the process of obtaining the drug release condition range of the present invention.

[0038] Figure 4 This is a flowchart illustrating the process of obtaining the carrier design scheme and structural characteristics of the present invention.

[0039] Figure 5 This is a flowchart illustrating the process of obtaining the dynamic drug release schedule of the present invention.

[0040] Figure 6 This is a flowchart illustrating the process of obtaining the drug release region division and adjustment results of this invention. Detailed Implementation

[0041] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0042] refer to Figures 1 to 6 A digital simulation method for a logistics production system based on PFEP (Powered Processing Epitaxy), referencing Figure 1 It includes the following steps:

[0043] S1: Collect the activity level of vitamin B6 metabolic enzymes, local pH value, oxygen concentration and temperature value in the tumor area, compare each collected value one by one, screen out the tumor areas with abnormal metabolic enzyme activity, and mark the corresponding pH value range and oxygen concentration characteristics to obtain the tumor microenvironment characteristic differentiation results.

[0044] S2: Based on the tumor microenvironment characteristics, the pH value and oxygen concentration range of the abnormal metabolic enzyme activity area were extracted. The pH response characteristics and oxygen concentration triggering characteristics of the nanocarrier were compared to screen out suitable release condition combinations. At the same time, unsuitable condition ranges were eliminated to obtain the drug release condition range.

[0045] S3: Combining the drug release condition range, select materials that can depolymerize under low pH and high enzyme activity conditions, optimize the component type and surface structure of the nanocarrier, adjust the connection mode between drug molecules and carrier, so that they can achieve accurate reaction within the target condition range, and generate carrier design scheme and structural characteristics.

[0046] S4: Utilizing the carrier design scheme and structural characteristics, combined with the temperature and oxygen concentration variation range in the target area, calculate the release rate of the carrier under different conditions, adjust the time node of the release rate according to the oxygen concentration distribution, form a drug release mode with differentiated release characteristics, and obtain the dynamic drug release arrangement.

[0047] S5: Based on the dynamic drug release schedule, combined with real-time enzyme activity, pH value and temperature changes within the tumor area, the release effect is tracked and recorded in different regions. For regions with insufficient or excessive release, the release schedule is readjusted to complete the monitoring and correction of the release process and generate the results of drug release area division and adjustment.

[0048] The tumor microenvironment characteristics differentiation results include regions with abnormal metabolic enzyme activity, pH range, and oxygen concentration characteristics; drug release condition ranges include suitable pH ranges for release, suitable oxygen concentration ranges for release, and ranges of ineffective conditions to be eliminated; carrier design schemes and structural characteristics include material type, surface structure, and drug-carrier connection method; drug dynamic release arrangement includes release rate distribution, release time nodes, and differentiated release characteristics; and drug release area division and adjustment results include release area division, regional release effect records, and release arrangement adjustment schemes.

[0049] Please see Figure 2 The steps for obtaining the tumor microenvironment feature differentiation results are as follows:

[0050] S101: Collect the activity level of vitamin B6 metabolic enzymes, local pH value, oxygen concentration and temperature value within the tumor area, match the collected metabolic enzyme activity level with its corresponding regional location, and classify different regions according to the matched metabolic enzyme activity level data to generate metabolic enzyme activity zoning values.

[0051] The activity levels of vitamin B6 metabolic enzymes, local pH, oxygen concentration, and temperature within the tumor region were collected. Raw data were collected from 10 randomly positioned monitoring points (numbered P01 to P10) within the targeted tumor region using a multi-probe microelectrode system. The unit for vitamin B6 metabolic enzyme activity was enzyme activity per milligram of protein (U / mg). Local pH was not measured in units. Oxygen concentration was measured in millimeter-high mercury (mmHg), and temperature was measured in degrees Celsius (°C). The three-dimensional coordinates (x, y, z) of each monitoring point were correlated with the corresponding vitamin B6 metabolic enzyme activity. Precise matching of the activity levels is performed. For example, the coordinates (1.2, 3.4, 0.5) of monitoring point P01 are bound to the metabolic enzyme activity level of 45.2 U / mg at that point, forming a location-activity dataset. A baseline value for metabolic enzyme activity is set. The process for establishing this baseline value is as follows: 50 pathologically confirmed adjacent normal tissue samples are obtained, and their vitamin B6 metabolic enzyme activity levels are measured. The arithmetic mean and standard deviation are calculated. The mean plus three times the standard deviation is taken as the baseline value. Specifically, if the measured mean value is 10.5 U / mg and the standard deviation is 1.5 U / mg, then the baseline value is... Based on the baseline value of U / mg, the tumor region is divided into three categories: normal metabolic enzyme zone (activity level ≤ 15.0 U / mg), low-abnormal metabolic enzyme zone (15.0 U / mg < activity level ≤ 30.0 U / mg), and high-abnormal metabolic enzyme zone (activity level > 30.0 U / mg). The 30.0 U / mg threshold is a high-level cutoff value set based on clinical statistics showing that 75% of malignant tumor tissue samples have metabolic enzyme activity levels exceeding this value. Each monitoring point is matched to the corresponding zone category according to its metabolic enzyme activity level. For example, the activity level of monitoring point P01 is 45.2 U / mg. Since it is greater than 30.0 U / mg, it is classified as a high-abnormal metabolic enzyme zone. The zone category label (e.g., 2 for high-abnormal zone, 1 for low-abnormal zone, and 0 for normal zone) is bound to the monitoring point. The location of all monitoring points, the original activity data, and the corresponding zone category label are output to generate the metabolic enzyme activity zone value.

[0052] S102: Call the metabolic enzyme activity partition value, compare the metabolic enzyme activity level of each region with the corresponding pH range and oxygen concentration one by one, screen the regions with abnormal metabolic enzyme activity during the comparison process, and record the corresponding pH range and oxygen concentration data to generate the characteristic comparison value of abnormal metabolic enzyme regions.

[0053] The metabolic enzyme activity partition values ​​are retrieved, and data from all monitoring points with partition category labels of 1 (low-level abnormality) and 2 (high-level abnormality) are extracted. Monitoring points with partition category label 0 (normal) are ignored. For each extracted monitoring point with abnormal metabolic enzyme activity, the corresponding pH value and oxygen concentration data at the same location and time point are retrieved from the original data collection. These are matched and recorded one by one. For example, the data of monitoring point P01 (activity level 45.2 U / mg) which was identified as a high-level abnormality zone in the previous process is retrieved. In the original dataset, the corresponding pH value of P01 is found to be 6.5 and the oxygen concentration is 8.1 mmHg. These three data points (45.2 U / mg, 6.5, 8.1 mmHg) are recorded as a data unit. Similarly, for monitoring points identified as high-level abnormality zones, the data is recorded one by one. For monitoring point P05 (activity level 25.8 U / mg) in the low-abnormality area of ​​metabolic enzymes, retrieve its corresponding pH value of 6.8 and oxygen concentration of 15.3 mmHg. Record the data (25.8 U / mg, 6.8, 15.3 mmHg). Iterate through all monitoring points in partition categories 1 and 2, and repeat the above data retrieval and matching operation. The activity level, pH range, and oxygen concentration data of all screened abnormal metabolic enzyme activity areas are structured and integrated to form a feature list containing five fields: monitoring point number, metabolic enzyme activity level, partition category, local pH value, and oxygen concentration. Each row in this list represents a region with abnormal metabolic enzyme activity and its key environmental parameters, generating a feature comparison value for abnormal metabolic enzyme areas.

[0054] S103: Based on the comparison values ​​of abnormal metabolic enzyme regions, the pH range and oxygen concentration characteristics of abnormal metabolic enzyme activity regions are classified and statistically analyzed. Combined with the corresponding metabolic enzyme activity levels in these regions, specific parameters for distinguishing tumor microenvironment features are established to obtain the results of tumor microenvironment feature distinction.

[0055] Based on the comparison values ​​of abnormal metabolic enzyme regions, the data in the feature list were grouped according to the partition category labels. All data belonging to the low-grade abnormal metabolic enzyme region (label 1) were grouped together, and all data belonging to the high-grade abnormal metabolic enzyme region (label 2) were grouped together. Then, independent statistical analysis was performed on the data within each group. Specifically, in the high-grade abnormal metabolic enzyme region data group, the pH values ​​of all monitoring points were extracted, and their maximum and minimum values ​​were determined to define the pH range of that region. For example, if there are 5 points in this group... The pH values ​​of monitoring points P01, P02, P03, P04, and P08 are 6.5, 6.4, 6.6, 6.5, and 6.4, respectively. Therefore, the pH range for this area is determined to be [6.4, 6.6]. Simultaneously, the oxygen concentration values ​​of all monitoring points within this group are extracted, which are 8.1, 9.5, 7.8, 8.5, and 9.2 mmHg, respectively, determining the oxygen concentration range to be [7.8, 9.5] mmHg. The average activity level of metabolic enzymes in this group is calculated. For example, the average activity level of the five monitoring points is... The three statistical results (average activity level 49.86 U / mg, pH range [6.4, 6.6], and oxygen concentration range [7.8, 9.5] mmHg) were used together as specific parameters to define the "highly abnormal microenvironment of metabolic enzymes". The same processing method was used to analyze the data set of the low-abnormality area of ​​metabolic enzymes to obtain the specific parameter set corresponding to the region. The parameter sets of the two regions were integrated to form the parameter list, as shown in Table 1 below.

[0056] Table 1. Parameters for Differentiating Tumor Microenvironment Characteristics

[0057] Microenvironment types Metabolic enzyme activity level (U / mg) pH range Oxygen concentration range (mmHg) Highly abnormal area of ​​metabolic enzymes > 30.0 $6.4,6.6] $7.8,9.5] Low-grade abnormality area of ​​metabolic enzymes (15.0, 30.0] $6.7,6.9] $12.0,18.5]

[0058] As shown in Table 1, by classifying and statistically analyzing the environmental parameters of different abnormality levels and defining their ranges, the results of distinguishing tumor microenvironment characteristics were obtained.

[0059] Please see Figure 3 The steps for obtaining the drug release condition range are as follows:

[0060] S201: Based on the tumor microenvironment characteristics, extract the pH and oxygen concentration ranges of regions with abnormal metabolic enzyme activity, group and classify the extracted pH and oxygen concentration range data, and label the metabolic enzyme activity characteristics of each group to generate characteristic range values ​​of abnormal metabolic enzyme regions.

[0061] Based on the tumor microenvironment characteristics, namely the previously identified "highly abnormal metabolic enzyme zone" and "lowly abnormal metabolic enzyme zone" and their corresponding parameters, the pH range [6.4, 6.6] and oxygen concentration range [7.8, 9.5] mmHg of the "highly abnormal metabolic enzyme zone" were extracted and associated with the feature "metabolic enzyme activity level > 30.0 U / mg" to form the first type of abnormal region characteristic data. Similarly, the pH range [6.7, 6.9] and oxygen concentration range [12.0, 18.5] mmHg of the "lowly abnormal metabolic enzyme zone" were extracted and associated with the feature "metabolic enzyme activity level between (15.0, 30.0) U / mg" to form the second type of abnormal region characteristic data. The two types of abnormal region characteristic data are structured and integrated. Each type of data includes a pH range, an oxygen concentration range, and a description of the corresponding metabolic enzyme activity characteristics. For example, the first type of data is represented as {metabolic enzyme characteristics: "highly abnormal (>30.0 U / mg)", pH range: [6.4, 6.6], oxygen concentration range: [7.8, 9.5]}, and the second type of data is represented as {metabolic enzyme characteristics: "lowly abnormal (15.0-30.0 U / mg)", pH range: [6.7, 6.9], oxygen concentration range: [12.0, 18.5]}. In this way, the microenvironment parameters obtained in the previous steps are classified and labeled to generate the characteristic range values ​​of the abnormal metabolic enzyme regions.

[0062] S202: Call the abnormal region characteristic range value of metabolic enzyme, compare the pH response characteristics and oxygen concentration triggering characteristics of the nanocarrier for the pH range and oxygen concentration range of each data set, remove the data range that does not meet the nanocarrier response, and retain the condition combination that meets the requirements to generate the nanocarrier response condition combination value.

[0063] The abnormal region characteristic range value of the metabolic enzyme is invoked, and the response characteristics of the nanocarrier, which have been determined in advance through in vitro experiments, are introduced. The key threshold of this response characteristic is set in the following way: setting the pH response characteristic threshold. The experimental verification process involved testing the cumulative drug release rate over 4 hours at pH values ​​of 7.4, 7.0, 6.8, 6.7, and 6.5 under an oxygen concentration of 20.9 mmHg. The results were 8.5%, 15.2%, 25.1%, 52.3%, and 75.8%, respectively. The pH value at which the cumulative release rate first exceeded 50% was taken as the threshold, hence the setting... Secondly, set the oxygen concentration trigger characteristic threshold. The baseline release rate was set at 8.5% at pH 7.4 and an oxygen concentration of 20.9 mmHg. The oxygen concentration at which the release rate first exceeded five times the baseline value was used as the threshold. The experiment involved fixing the pH at 6.7 and testing the release rates at oxygen concentrations of 20.9 mmHg, 15.0 mmHg, 10.0 mmHg, and 8.0 mmHg, obtaining rates of 52.3%, 53.0%, 54.5%, and 78.2%, respectively. The factors relative to the baseline value were calculated. , , , Since the increase in oxygen concentration to 10.0 mmHg is 6.41 times, which exceeds 5 times, the setting is... mmHg was compared with the characteristic data of the first type of abnormal region {pH range: [6.4, 6.6], oxygen concentration range: [7.8, 9.5]}, and the upper limit of its pH range of 6.6 was compared with... Comparison, because The pH conditions meet the response requirements, and the upper limit of its oxygen concentration range of 9.5 mmHg is compared with... Comparison, because The oxygen concentration condition also meets the triggering requirements; therefore, this condition combination is fully preserved. Then, a comparison is made with the characteristic data of the second type of anomaly region {pH range: [6.7, 6.9], oxygen concentration range: [12.0, 18.5]}, and the upper limit of the pH range of 6.9 is not met. The requirements are not met, and the lower limit of its oxygen concentration range of 12.0 mmHg is not satisfied. The requirement of mmHg meant that this condition combination was eliminated. All the retained condition combinations were then summarized to generate the nanocarrier response condition combination value.

[0064] S203: Based on the combination values ​​of response conditions of the nanocarrier, statistical analysis is performed on the pH range and oxygen concentration range in the retained combination of conditions, and combined with the characteristics of the release triggering conditions of the nanocarrier, specific conditions suitable for drug release are screened out to obtain the drug release condition range.

[0065] Based on the combined response conditions of the nanocarrier, specifically the retained characteristic data of the first type of anomalous region {pH range: [6.4, 6.6], oxygen concentration range: [7.8, 9.5] mmHg}, a deeper statistical analysis was conducted on this retained combination of conditions. Referring to specific in vitro release data points, the subset of conditions with the highest drug release efficiency was screened. A high-efficiency release threshold was set as a cumulative release rate of no less than 80% over 4 hours. According to experimental data, the release rate was 80.1% when the pH was 6.5 and the oxygen concentration was 10.0 mmHg, and 92.4% when the pH was 6.5 and the oxygen concentration was 8.0 mmHg. All parameters meet the requirements for efficient drug release. Based on this, the "optimal release conditions" most suitable for drug release are defined as the combination of pH value in the range of [6.4, 6.5] and oxygen concentration in the range of [7.8, 8.5] mmHg. Under these conditions, a drug release efficiency of over 85% is expected. At the same time, the complete pH range of [6.4, 6.6] and oxygen concentration range of [7.8, 9.5] mmHg are defined as the "allowable release conditions," with an expected release rate of over 50% in 4 hours. By integrating the optimal release conditions and the allowable release conditions, the environmental parameters required for drug release are clarified, and the range of drug release conditions is obtained.

[0066] Please see Figure 4 The steps for obtaining the carrier design scheme and structural characteristics are as follows:

[0067] S301: Based on the drug release condition range, select materials that can depolymerize under low pH and high enzyme activity conditions. Match and screen the materials according to their depolymerization characteristics and target condition range, eliminate data that do not meet the target conditions, and mark the material characteristic parameters that meet the conditions to generate applicable material characteristic parameters.

[0068] Combining the drug release condition range, namely the "allowable release condition" pH range [6.4, 6.6] and the "optimal release condition" pH range [6.4, 6.5], and the "highly anomalous" metabolic enzyme activity level (>30.0 U / mg) in the target region, three candidate polymer materials (Material M1: polycaprolactone-polyethylene glycol block copolymer, Material M2: pH-responsive polyurethane, Material M3: polyhistidine-dextrin graft copolymer) were screened. The screening basis was the depolymerization characteristics of the materials under the target conditions, and two core screening criteria were set: First, the pH depolymerization threshold (… The particle size distribution must fall within the range of [6.4, 6.6]. This threshold is determined by laser particle size analysis, specifically the critical pH value at which the particle size undergoes a sudden change and the absolute value of the zeta potential decreases by more than 80% after the material has been incubated in different pH buffers for 1 hour. Secondly, the enzyme degradation rate (… Under the condition of a vitamin B6 metabolic enzyme concentration of 40 U / mg, the mass loss within 2 hours must be higher than 50%. This degradation rate is calculated by measuring the change in molecular weight using gel permeation chromatography. The specific screening process involves experimental testing on three materials, and the test results are shown in Table 4. The experimental data in Table 4 are compared one by one with the set criteria. Material M1... The value is 7.0, which does not meet the pH criterion and is therefore rejected. Material M2... The content was 35%, which did not meet the enzyme degradation criterion and was therefore rejected. Material M3... It is 6.5, and The yield is 62%, both criteria are met, therefore material M3 is selected, and its key characteristic parameters are recorded, including... The value is 6.5, the degradation rate at a concentration of 40 U / mg enzyme is 62% in 2 hours, and its molecular weight is 25 kDa, generating applicable material characteristic parameters.

[0069] Table 2 Screening Table for the Properties of Candidate Carrier Materials

[0070] Material Number pH depolymerization threshold Enzyme degradation rate (% / 2h) M1 7.0 15 M2 6.6 35 M3 6.5 62

[0071] As shown in Table 2, this table lists the experimental data of pH depolymerization threshold and enzyme degradation rate of three alternative materials under specific conditions.

[0072] S302: Call the applicable material property parameters to optimize the component type and surface structure of the selected materials, adjust the surface morphology according to the component characteristics of the materials, and adjust the surface structure parameters in combination with the target condition range so that the surface characteristics of the materials can adapt to the release conditions and generate optimized surface structure parameters.

[0073] By calling upon the applicable material characteristic parameters, namely the core parameters of the selected material M3 (polyhistidine-dextrin graft copolymer), the component type and surface structure of this material are optimized. The component ratio, namely the molar ratio of polyhistidine segments (pHis) to dextrin backbone (Dex), is adjusted to fine-tune its pH response point so that it is precisely aligned with the midpoint of the pH range of "optimal release conditions" at 6.45. The initial ratio was 1:1. The initial value was 6.5. The molar ratio of pHis to Dex was adjusted to 1.2:1, and the mixture was synthesized again and its concentration was determined. , get new The value was 6.46, which falls within the range of [6.4, 6.5]. Therefore, the determined molar ratio of the components was 1.2:1. The surface structure parameter, namely the grafting density of the dextrin side chains (defined as the number of polyhistidine chains grafted onto every 100 glucose units), was adjusted. This parameter directly affects the particle size and stability of the formed nanocarriers. The target particle size range was set to [100, 150] nm. The experimental data for this range were based on dynamic light scattering experiments, which showed that over 60% of carriers with a particle size less than 100 nm aggregated within 4 hours in an in vitro serum stability test. In the cell uptake experiment, the uptake rate of carriers with a particle size greater than 150 nm was less than 20% within 2 hours, which did not meet the requirements. The grafting density was controlled by adjusting the amount of activator in the reaction. When the grafting density was 5, the particle size of the formed nanocarrier was 85 nm, which was not within the range. When the grafting density was 15, the particle size was 170 nm, which was also not within the range. When the grafting density was 10, the average hydrated particle size of the formed nanocarrier was measured to be 125 nm, which was within the range of [100, 150] nm. Therefore, the optimal grafting density was determined to be 10, and the optimized surface structure parameters were generated.

[0074] S303: Based on the optimized surface structure parameters, adjust the connection method between drug molecules and the carrier, determine the range of connection parameters based on the surface characteristics of the material and the characteristics of the drug molecules, and verify the reactivity of the connection method under the target conditions to obtain the carrier design scheme and structural characteristics.

[0075] Based on optimized surface structure parameters, namely the determined component molar ratio of 1.2:1 and grafting density of 10, the connection mode between the drug molecules (doxorubicin, DOX) and the carrier was adjusted. Acid-sensitive hydrazone bonds were chosen as the connection mode, as these bonds break at pH below 6.8, releasing the drug. The connection parameters were determined based on drug loading (DLC), defined as the mass percentage of drug in the carrier. The target DLC was set at 10%. This value was set because a DLC below 8% did not show significant tumor inhibition in animal pharmacodynamic experiments, while a DLC above 12% resulted in drug crystallization during synthesis, affecting the uniformity of the carrier formulation. Based on the molecular structure of the carrier (polyhistidine-dextrin molar ratio 1.2:1, grafting density 10) and the molecular weight of doxorubicin (543.52 g / mol), the required reaction molar ratio of doxorubicin to the connectable sites on the carrier (hydroxyl groups on the dextrin chain) to achieve 10% DLC was calculated. The calculation process is as follows: assuming the total mass of the nanocarrier is... Then the quality of the drug Carrier quality The required molecular weight ratio was calculated by converting the molecular weights of the components, and the molar ratio of doxorubicin to dextrin units was determined to be 1:5. After synthesis, the actual DLC was measured to be 10.3% by UV spectrophotometry, which met the target range. The reactivity of this linkage method was verified under the target conditions. The drug-loaded nanocarrier was placed in a buffer solution at pH 6.5 and sampled every 30 minutes under the environment of 40 U / mg vitamin B6 metabolic enzyme to detect the drug release. After 4 hours, the cumulative release rate reached 91.5%, while in the control group at pH 7.4, the cumulative release rate was only 12.8% after 4 hours. This confirmed the high reactivity of this linkage method under the target conditions, and the carrier design scheme and structural characteristics were obtained.

[0076] Please see Figure 5 The steps to obtain the dynamic drug release schedule are as follows:

[0077] S401: Utilizing the carrier design scheme and structural characteristics, combined with the temperature change range and oxygen concentration distribution data within the target area, calculate the release rate of the carrier under different temperature and oxygen concentration conditions, mark the rate value range under different conditions according to the trend of release rate change, and generate the release rate distribution range.

[0078] Using a carrier design and structural characteristics—specifically, a polyhistidine-dextrin molar ratio of 1.2:1, a grafting density of 10, and doxorubicin loaded via hydrazone bonds—and incorporating temperature variation data [38.5℃, 40.0℃] and oxygen concentration distribution data [7.8, 9.5 mmHg] within the target region, and under constant conditions of pH 6.5 and vitamin B6 metabolic enzyme activity of 40 U / mg, the drug release rate of the carrier under different environmental combinations was calculated. This calculation was based on a baseline release rate of 15% / hr measured at 38.5℃ and 9.5 mmHg oxygen concentration, and a temperature influence coefficient was introduced. Influence coefficient with oxygen concentration ,in Based on experimental data, the rate increases by 10% for every 0.75℃ increase in temperature; therefore, at 40.0℃... It is 1.21, while Based on hypoxia response experimental data, the rate of increase was 17% when the oxygen concentration decreased from 9.5 mmHg to 7.8 mmHg. Therefore, at 7.8 mmHg... The value is 1.17. Based on this, the release rate at 40.0℃ and 7.8 mmHg is calculated to be... The rates were calculated using the same method under different combinations of temperature and oxygen concentration, resulting in a set of rate values ​​ranging from 15.00% / hr to 21.23% / hr. These rate values ​​were then divided into intervals, and rate thresholds were set. and The two values ​​are set with reference to the 25th and 75th percentiles of all calculated rates. Release rates below 17.5% / hr are marked as "low rate range", those between 17.5% / hr and 19.5% / hr are marked as "medium rate range", and those above 19.5% / hr are marked as "high rate range", thus generating a release rate distribution range.

[0079] S402: Based on the release rate distribution range, adjust the time nodes of the rate values ​​under different oxygen concentration conditions, determine the time node grouping of the release rate according to the distribution trend of oxygen concentration, and match the grouped time nodes with the rate values ​​to generate release node values ​​under oxygen concentration conditions.

[0080] Based on the release rate distribution range, the rate values ​​under different oxygen concentration conditions were adjusted at specific time points. A 6-hour dosing cycle was set and divided into three time points: time point one (0-2 hours), time point two (2-4 hours), and time point three (4-6 hours). Following the increasing trend of intratumoral oxygen concentration from the core to the periphery, the lowest oxygen concentration of 7.8 mmHg was assigned to time point one, the middle concentration of 8.65 mmHg to time point two, and the highest concentration of 9.5 mmHg to time point three. After grouping the time points, the rate values ​​under each oxygen concentration condition were matched. Specifically, the following steps were taken: For each oxygen concentration, the arithmetic mean of multiple release rates caused by temperature changes is used as the representative rate for that condition. For example, for an oxygen concentration of 7.8 mmHg, the calculated rate values ​​at different temperatures are 17.55% / hr, 19.31% / hr, and 21.23% / hr, with an average of approximately 19.36% / hr. Using the same processing method, the representative rate at an oxygen concentration of 8.65 mmHg is 17.87% / hr, and the representative rate at an oxygen concentration of 9.5 mmHg is 16.55% / hr. By mapping each time point to the calculated representative rate value, the release node values ​​for each oxygen concentration condition are generated.

[0081] S403: Call the oxygen concentration condition release node value, analyze the change characteristics of the release rate at different time nodes, combine the time node distribution of the release rate, construct a drug release mode with differentiated release characteristics, and obtain the dynamic drug release arrangement.

[0082] Using the oxygen concentration conditions for release node values—namely, 19.36% / hr for time node one (0-2 hours), 17.87% / hr for time node two (2-4 hours), and 16.55% / hr for time node three (4-6 hours)—we analyzed the variation characteristics of the release rate at different time nodes and calculated the rate change gradient between adjacent nodes. From node one to node two, the rate decreased by [missing value]. From node two to node three, the rate decreased. This decreasing rate of release forms the basis for differentiated release. Based on this change characteristic and the distribution of time points, a drug release method with differentiated release characteristics is constructed. This method is executed in three stages: in the first stage (0-2 hours), the drug release rate is set at 19.36% / hr; in the second stage (2-4 hours), the drug release rate is adjusted to 17.87% / hr; and in the third stage (4-6 hours), the release rate is further adjusted to 16.55% / hr. The release rates of these three stages are integrated with the corresponding time windows to form a step-decreasing release scheme that changes over time, resulting in a dynamic drug release schedule.

[0083] Please see Figure 6 The steps for obtaining the drug release area division and adjustment results are as follows:

[0084] S501: Based on the dynamic drug release schedule, combined with real-time enzyme activity, pH value and temperature data in the tumor area, the drug concentration changes during the release process are tracked in different regions, the deviation between the release amount in each region and the real-time environmental data is calculated, and regions with insufficient or excessive release are marked to generate the release deviation region distribution value.

[0085] Based on a dynamic drug release schedule—a stepped release pattern: 19.36% / hr at time point one (0-2 hours), 17.87% / hr at time point two (2-4 hours), and 16.55% / hr at time point three (4-6 hours)—and combined with real-time data on enzyme activity, pH, and temperature in three different tumor regions (Region A: hypoxic core, Region B: transition zone, Region C: periphery oxygen-rich zone) collected by microsensors, the system tracks drug concentration changes. Within the first time point (0-2 hours), the system uses the preset release rate of 19.36% / hr as a baseline to calculate the theoretical drug release amount for each region. Simultaneously, by monitoring changes in the fluorescence signal of drug concentration in each region, the actual drug release amount is calculated and compared with the theoretical value to determine the deviation. A deviation threshold is set, which is based on the following reference: according to the pharmacokinetic model, if the drug concentration deviates from the target value by more than [a certain amount]... At this time, the treatment effect will deviate significantly from the expected result. Therefore, the cumulative release deviation threshold within 2 hours is set as follows: (Reserving a 1% safety margin), meaning the release amount exceeds [ , The range of [condition] was then determined to be abnormal. The specific calculation process was as follows: the real-time environmental data for region A (enzyme activity 45 U / mg, pH 6.4, temperature 39.8℃) was better than the average conditions, resulting in an actual release rate of 44.5%, with a deviation of [value missing]. This value exceeded the +4% upper limit, so region A was marked as "over-released". The real-time environmental data for region B was close to the average conditions, with an actual release rate of 39.1%, and a deviation of [missing value]. Within the threshold range, it was marked as "normal release". The real-time environmental data for region C (enzyme activity 31 U / mg, pH 6.6, temperature 38.6℃) were worse than the average conditions, with an actual release of 33.2%, and a deviation of [missing information]. Since the value is below the lower limit of -4%, it is marked as "insufficient release". The marking results of these three regions are summarized to generate the regional distribution value of release deviation.

[0086] S502: Based on the distribution value of the release deviation area, adjust the release arrangement within the marked area, call up the enzyme activity, pH value and temperature change data of the deviation area, recalculate the release rate and time distribution of each area, and match the adjusted release rate to the corresponding area to generate the regional release adjustment rate value;

[0087] Based on the distribution of release deviation regions—region A marked as "excessive release," region B as "normal release," and region C as "insufficient release"—the release schedule for the next time point (2-4 hours) is adjusted differentially. Region A, marked as "excessive release," is processed by retrieving its current real-time environmental data (enzyme activity 45 U / mg, pH 6.4, temperature 39.8℃) and recalculating the theoretical release rate under this specific environment, yielding 22.5% / hr. To compensate for the +5.78% excess release generated in the first 2 hours, a compensation coefficient is set. The principle for setting this coefficient is to completely offset the excess release within the remaining 4-hour treatment cycle; that is, an additional reduction is needed per hour. Therefore, the target release rate for region A at time node two (2-4 hours) is reduced from the originally planned 17.87% / hr to [missing information]. For region C marked as "insufficient release," its current real-time environmental data (enzyme activity 31 U / mg, pH 6.6, temperature 38.6℃) was retrieved, and its theoretical release rate was recalculated to be 15.5% / hr. To compensate for the -5.52% release shortfall in the first two hours, a compensation coefficient was also set, requiring an additional increase every hour. Therefore, the target release rate for region C at time node two (2-4 hours) is adjusted upward from the originally planned 17.87% / hr to [missing information]. For region B marked as "normal release", the original plan will be maintained, and its target release rate at time node two (2-4 hours) will still be 17.87% / hr. The adjusted release rates of these three regions will be matched to form a new, regionally differentiated release plan and generate regional release adjustment rate values.

[0088] S503: Call the regional release adjustment rate value, monitor the adjusted release schedule, dynamically compare the corrected release rate distribution with the environmental data of each region, verify the release adjustment effect, record the release status of each region, and obtain the drug release region division and adjustment results.

[0089] The system calls upon the regional release adjustment rate values, with target rates of 16.425% / hr for region A, 17.87% / hr for region B, and 19.25% / hr for region C. During time node two (2-4 hours), the adjusted release schedule is continuously monitored. Specifically, the system continuously collects real-time environmental data for each region and monitors the actual changes in drug concentration in each region. At the end of the fourth hour, the actual cumulative release amount in each region during this time period (2-4 hours) is dynamically compared with the newly set target values. For example, the target release amount for region A is... The actual release rate detected was 33.1%, with a deviation of +0.25%. This deviation is within... Within the threshold range, the adjustment is deemed effective, and the target release amount for region C is [value missing]. The actual release rate was 38.1%, with a deviation of -0.4%, which was also within the threshold range, indicating that the adjustment was effective. The system recorded the release status of each region after this adjustment cycle. For example, the status of region A was updated to "corrected", the status of region B remained "continuously normal", and the status of region C was updated to "corrected". These statuses were bound to the identifiers of each region to form a comprehensive data set including region division, adjusted rate, and verification status, thus obtaining the drug release region division and adjustment results.

[0090] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for regulating anti-ovarian cancer nanodrugs of vitamin B6 pathway, characterized in that, Comprise the following steps: S1: Collect tumor area vitamin B6 metabolic enzyme activity, pH value, oxygen concentration and temperature value, compare each item screening metabolic enzyme activity abnormal area, mark corresponding pH value range and oxygen concentration characteristics, generate tumor microenvironment characteristic distinction result; S2: According to the tumor microenvironment characteristic distinction result, extract the pH value and oxygen concentration range of the metabolic enzyme activity abnormal area, compare the pH response and oxygen trigger characteristics of the nano carrier, screen the suitable condition combination, eliminate the conditions that do not meet the range, generate the drug release condition range; S3: Combine the drug release condition range, select low pH value and high enzyme activity depolymerization material, optimize the carrier component and surface structure, adjust the drug and carrier connection mode, so that it accurately reacts under the target condition, generate the carrier design scheme and structure characteristics; S4: Based on the carrier design scheme and structure characteristics, combined with the temperature and oxygen concentration change of the target area, calculate the release rate under different conditions, adjust the release time node, form the differential release mode, generate the drug dynamic release arrangement; S5: Based on the drug dynamic release arrangement, combined with the real-time enzyme activity, pH value and temperature change of the tumor area, track the release effect and adjust the release arrangement, complete the monitoring and correction, generate the drug release area division and adjustment result.

2. The method for regulating anti-ovarian cancer nanomedicine through vitamin B6 pathway regulation according to claim 1, characterized in that: The tumor microenvironment characteristic distinction result includes metabolic enzyme activity abnormal area, pH value range, oxygen concentration characteristics, the drug release condition range includes suitable release pH range, suitable release oxygen concentration range, eliminated invalid condition range, the carrier design scheme and structure characteristics include material type, surface structure, drug and carrier connection mode, the drug dynamic release arrangement includes release rate distribution, release time node, differential release characteristics, the drug release area division and adjustment result includes release area division, area release effect record, release arrangement adjustment scheme.

3. The method for regulating anti-ovarian cancer nanomedicine through vitamin B6 pathway regulation according to claim 1, characterized in that: The acquisition steps of the tumor microenvironment characteristic distinction result are: S101: Collect the vitamin B6 metabolic enzyme activity level, local pH value, oxygen concentration and temperature value in the tumor area, match the collected metabolic enzyme activity level with its corresponding area position, classify different areas according to the matched metabolic enzyme activity level data, generate metabolic enzyme activity zoning value; S102: Call the metabolic enzyme activity zoning value, compare each region's metabolic enzyme activity level with the corresponding pH value range and oxygen concentration one by one, screen the metabolic enzyme activity abnormal area in the comparison process, and record its corresponding pH value range and oxygen concentration data, generate metabolic enzyme abnormal area characteristic comparison value; S103: According to the metabolic enzyme abnormal area characteristic comparison value, classify and count the pH value range and oxygen concentration characteristics of the metabolic enzyme activity abnormal area, and combine the metabolic enzyme activity level corresponding to the area, establish the specific parameters of tumor microenvironment characteristic distinction, get the tumor microenvironment characteristic distinction result.

4. The method for regulating anti-ovarian cancer nanomedicine through vitamin B6 pathway regulation according to claim 1, characterized in that: The acquisition steps of the drug release condition range are: S201: According to the tumor microenvironment feature classification result, the pH value and oxygen concentration range of the metabolic enzyme activity abnormal area are extracted, the extracted pH value and oxygen concentration range data are grouped and classified, and the metabolic enzyme activity characteristics corresponding to each group are marked to generate metabolic enzyme abnormal area characteristic range value; S202: Call the metabolic enzyme abnormal area characteristic range value, compare the pH response characteristics and oxygen concentration trigger characteristics of the nano carrier for each group of data pH value range and oxygen concentration range, eliminate the data range that does not meet the nano carrier response, and keep the required condition combination to generate the nano carrier response condition combination value; S203: According to the nano carrier response condition combination value, statistical analysis is performed on the pH value range and oxygen concentration range of the retained condition combination, and combined with the characteristics of the nano carrier release trigger condition, the specific conditions suitable for drug release are screened to obtain the drug release condition range.

5. The method for regulating anti-ovarian cancer nanomedicine through vitamin B6 pathway regulation according to claim 1, characterized in that: The steps of obtaining the carrier design scheme and structural characteristics are: S301: Combine the drug release condition range, select materials that can depolymerize under low pH value and high enzyme activity conditions, match and screen according to the depolymerization characteristics of the materials and the target condition range, eliminate data that do not meet the target conditions, and mark the material characteristic parameters that meet the conditions to generate applicable material characteristic parameters; S302: Call the applicable material characteristic parameters, optimize the component type and surface structure of the screened materials, adjust the surface morphology according to the component characteristics of the materials, and adjust the surface structure parameters according to the target condition range, so that the surface characteristics of the materials can adapt to the release conditions, and generate optimized surface structure parameters; S303: According to the optimized surface structure parameters, adjust the connection mode between the drug molecules and the carrier, determine the connection parameter range according to the surface characteristics of the materials and the characteristics of the drug molecules, and verify the reactivity of the connection mode under the target conditions to obtain the carrier design scheme and structural characteristics.

6. The method for regulating anti-ovarian cancer nanomedicine through vitamin B6 pathway regulation according to claim 1, characterized in that: The steps of obtaining the drug dynamic release arrangement are: S401: Use the carrier design scheme and structural characteristics, combine the temperature variation amplitude and oxygen concentration distribution data in the target area, calculate the release rate of the carrier under different temperature and oxygen concentration conditions, mark the rate value interval under different conditions according to the change trend of the release rate, and generate the release rate distribution interval; S402: According to the release rate distribution interval, adjust the time node of the rate value under different oxygen concentration conditions, determine the time node grouping of the release rate according to the distribution trend of the oxygen concentration, and match the grouped time node and rate value to generate the oxygen concentration condition release node value; S403: Call the oxygen concentration condition release node value, analyze the change characteristics of the release rate at different time nodes, combine the time node distribution of the release rate, construct a drug release mode with differentiated release characteristics, and obtain the drug dynamic release arrangement.

7. The method for regulating anti-ovarian cancer nanomedicine through vitamin B6 pathway regulation according to claim 1, characterized in that: The steps of obtaining the drug release area division and adjustment result are: S501: Based on the dynamic drug release arrangement, combined with real-time enzyme activity, pH value and temperature data in the tumor region, the change amplitude of drug concentration in the release process is tracked regionally, the deviation amount of the release amount of each region from the real-time environmental data is calculated, and the regions with insufficient or excessive release are marked to generate the release deviation region distribution value; S502: According to the release deviation region distribution value, the release arrangement in the marked region is adjusted, the enzyme activity, pH value and temperature change data of the deviation region are called, the release rate and time distribution of each region are recalculated, and the adjusted release rate is matched to the corresponding region to generate the region release adjustment rate value; S503: Call the region release adjustment rate value to monitor the adjusted release arrangement, dynamically compare the corrected release rate distribution with the environmental data of each region, verify the release adjustment effect, and record the release state of each region to obtain the drug release region division and adjustment result.

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