Preparation process optimization method and system of compound lidocaine nanogel

Abstract

The invention relates to the technical field of process optimization, and discloses a preparation process optimization method and system of compound lidocaine nanogel. The method comprises the following steps: carrying out difference determination on carrier binding affinity of a lidocaine main drug and an auxiliary drug in compound lidocaine to obtain a drug feeding sequence table; carrying out surface potential pre-adjustment on the nano-carrier according to the drug feeding sequence table to obtain a drug-carrying pretreatment carrier; step-by-step drug loading is carried out according to the drug feeding sequence table based on the drug loading pretreatment carrier, and saturation data in the drug loading process is obtained; the saturation data are synchronously input into a drug loading process regulation and control system for pH and temperature optimization, and multi-component balanced drug loading particles are obtained; layered gel cross-linking is performed according to the multi-component balanced drug-loading particles to obtain nanogel, drug-loading stability and release consistency verification is performed on the nanogel, cross-linking optimization process parameters are generated, and the adaptability and the drug-loading efficiency of the drug-loading process are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of process optimization, and in particular to a preparation process optimization method and system for a compound lidocaine nanogel. Background Art

[0002] Traditional drug-loading processes for compound preparations often employ a one-time mixing and dosing method, where the primary drug, lidocaine, and various auxiliary drugs are simultaneously added to the carrier system for encapsulation. However, due to the varying molecular weights, polarity, and charge characteristics of different drug molecules, their binding affinities with nanocarriers vary significantly, leading to a selective drug loading bias during the loading process. This bias results in preferential encapsulation of drug components that are easily loaded, while the encapsulation efficiency of components that are difficult to load is low, ultimately resulting in an imbalance in the loading ratios of the various drug components within the carrier. Existing drug-loading processes lack effective means to regulate the competitive loading behavior of multi-component drugs, making it impossible to optimize the process based on the loading characteristics of different drug molecules. Simplified treatment of the carrier surface chemical environment makes it difficult to achieve optimal loading conditions for multiple drugs, particularly for auxiliary drugs with low loading affinities, as it lacks sufficient driving force for loading. Furthermore, traditional processes lack real-time monitoring and dynamic control mechanisms for the loading process. Relying on empirical evidence to determine the loading endpoint, they are unable to accurately identify the loading equilibrium state, resulting in insufficient control precision during the loading process. Summary of the Invention

[0003] The present invention provides a method and system for optimizing the preparation process of a compound lidocaine nanogel, which improves the adaptability and efficiency of the drug loading process.

[0004] In a first aspect, the present invention provides a method for optimizing the preparation process of a compound lidocaine nanogel, the method comprising: The carrier binding affinity of lidocaine main drug and auxiliary drug in compound lidocaine was differentially determined to obtain the drug feeding sequence table; Pre-adjusting the surface potential of the nanocarrier according to the drug feeding sequence table to obtain a drug-loaded pretreated carrier; Based on the drug-loaded pretreated carrier, drug loading is performed step by step according to the drug feeding sequence table to obtain saturation data during the drug loading process; The saturation data is synchronously input into the drug loading process control system to optimize pH and temperature to obtain multi-component balanced drug loading particles; Layered gel cross-linking is performed on the multi-component balanced drug-loaded particles to obtain nanogels, and the drug loading stability and release consistency of the nanogels are verified to generate cross-linking optimization process parameters.

[0005] In combination with the first aspect, in a first implementation of the first aspect of the present invention, the differential binding affinity of the lidocaine main drug and the auxiliary drug in the compound lidocaine is measured to obtain a drug feeding sequence table, including: Isothermal titration calorimetry was performed on the main drug lidocaine and each auxiliary drug in the lidocaine compound with the nanocarrier to obtain the carrier binding affinity value of each auxiliary drug; Calculating the drug loading competition coefficient between each auxiliary drug based on the carrier binding affinity value; According to the drug loading competition coefficient, the drug with a binding affinity lower than a first preset value is marked as a drug loading difficult component, and the drug with a binding affinity higher than a second preset value is marked as a drug loading easy component; The first feeding sequence is set according to the difficult drug loading component, and the second feeding sequence is set according to the lidocaine main drug and the easy drug loading component; A drug feeding sequence table is created based on the first feeding sequence and the second feeding sequence.

[0006] In combination with the first aspect, in a second implementation of the first aspect of the present invention, the isothermal titration calorimetry is performed on the lidocaine main drug and each auxiliary drug in the lidocaine compound with the nanocarrier to obtain the carrier binding affinity value of each auxiliary drug, including: Isothermal titration calorimetry was used to prepare drug solutions of lidocaine and each auxiliary drug, and carrier suspensions of nanocarriers. The carrier suspension is used as a sample pool solution, and each drug solution in the drug solution group is used as a titrant to continuously titrate under a constant temperature condition to obtain a titration heat flow signal and a binding saturation curve; Integrating the titration heat flow signal to obtain a binding enthalpy change value, and performing nonlinear fitting on the binding saturation curve to obtain a binding constant; The carrier binding free energy value of each auxiliary drug is calculated based on the binding constant and the binding enthalpy change value, and then the carrier binding free energy value is converted into a carrier binding affinity value expressed in a molar concentration unit.

[0007] In combination with the first aspect, in a third implementation of the first aspect of the present invention, the step of pre-adjusting the surface potential of the nanocarrier according to the drug feeding sequence table to obtain the drug-loaded pretreated carrier comprises: Reading the drug molecular charge density and molecular polarity data marked as drug-loading difficult components in the drug feeding sequence table, and calculating the carrier surface potential enhancement amplitude required for each drug-loading difficult component based on the drug molecular charge density and the molecular polarity data; Calculating the buffer pH value required for the nanocarrier to reach a target surface potential based on the carrier surface potential enhancement amplitude, and determining the buffer pH adjustment parameter based on the buffer pH value; Dispersing the nanocarrier in a phosphate buffer corresponding to the pH adjustment parameter, and adding a polyethylene glycol surfactant to perform a temperature gradient treatment to obtain a modified carrier; The surface potential of the modified carrier is measured using electrophoretic light scattering technology to obtain a surface potential measurement value. When the surface potential measurement value is within a preset voltage range and the deviation from the calculated target value is less than the target value, pre-adjustment is stopped to obtain a drug-loaded pretreated carrier.

[0008] In combination with the first aspect, in a fourth implementation of the first aspect of the present invention, reading the drug molecule charge density and molecular polarity data marked as drug-loading-difficult components in the drug feeding sequence table, and calculating the carrier surface potential enhancement amplitude required for each drug-loading-difficult component based on the drug molecule charge density and the molecular polarity data, includes: Extracting the molecular structure data of each drug marked as a drug-loading difficulty component from the drug feeding sequence table, and calculating the drug molecular charge density and molecular polarity data of each drug-loading difficulty component based on the molecular structure data of each drug; Calculating the effective charge radius of each drug-loading-difficult component in aqueous solution based on the dipole moment value of each drug-loading-difficult component in the molecular polarity data, and calculating the electrostatic interaction strength between each component and the carrier surface according to the effective charge radius; Calculating the carrier surface potential difference required to achieve optimal drug loading efficiency for each drug-load-difficult component based on the electrostatic interaction strength combined with the drug molecule charge density; The difference between the surface potential difference of the carrier and the original surface potential of the nanocarrier is calculated to obtain a target difference. When the target difference is a positive number, the target difference is used as the carrier surface potential enhancement amplitude. When the target difference is a negative number, the carrier surface potential enhancement amplitude is set to zero.

[0009] In combination with the first aspect, in a fifth implementation of the first aspect of the present invention, the step-by-step drug loading based on the drug-loaded pretreated carrier according to the drug feeding sequence table is performed to obtain saturation data during the drug loading process, including: According to the first feeding sequence in the drug feeding sequence table, the auxiliary drug of the drug-loading difficult component is added to the suspension of the drug-loading pretreatment carrier to perform the first stage of drug loading treatment, thereby obtaining the first drug loading reaction system for the encapsulation process of the drug-loading difficult component; Determining the carrier particle size change rate of the first drug loading reaction system to detect a first concentration change rate of the free drug in the supernatant, determining that the first stage drug loading is saturated when the carrier particle size change rate is lower than the first change rate value and the first concentration change rate is lower than the second change rate value, and obtaining a first saturation degree and a first stage drug loading completion signal; Based on the first-stage drug loading completion signal, the lidocaine main drug and the drug loading component are added into the first drug loading reaction system according to the second feeding sequence in the drug feeding sequence table to perform a second-stage drug loading process to obtain a second drug loading reaction system with multi-component mixed drug loading; measuring a surface potential change amplitude of the second drug loading reaction system and monitoring a second concentration change rate of the free drug in each component, determining that the second stage drug loading is saturated when the surface potential change amplitude is less than a third change rate value and the second concentration change rate is less than a fourth change rate value, and obtaining a second saturation degree; The first saturation and the second saturation are used as saturation data during the drug loading process.

[0010] In combination with the first aspect, in a sixth implementation of the first aspect of the present invention, synchronously inputting the saturation data into a drug loading process control system for pH and temperature optimization to obtain multi-component balanced drug loaded particles includes: Synchronously inputting the saturation data into the drug loading process control system, calculating a drug loading balance deviation value, and generating a drug loading balance deviation signal and a drug loading process control trigger instruction based on the drug loading balance deviation value; Based on the drug loading balance deviation signal, a pH gradient control algorithm is started to generate pH dynamic adjustment parameters and buffer pH optimization instructions; Initiate a temperature program control algorithm according to the drug loading process control trigger instruction to generate temperature dynamic adjustment parameters and reaction temperature optimization instructions; The drug loading rate and the corresponding coefficient of variation of each component corresponding to the drug loading reaction system processed by the pH optimization instruction and the reaction temperature optimization instruction are measured. When the coefficient of variation is lower than the first percentage and the drug loading rate of each component is greater than the second percentage, it is determined that the drug loading balance is completed, and multi-component balanced drug-loaded particles are obtained.

[0011] In combination with the first aspect, in a seventh implementation of the first aspect of the present invention, starting a temperature program control algorithm according to the drug loading process control trigger instruction to generate a temperature dynamic adjustment parameter and a reaction temperature optimization instruction includes: Starting the temperature program control algorithm according to the drug loading process control trigger instruction, extracting the drug loading saturation change curve and calculating the drug loading kinetic constant; Based on the comparison between the drug loading kinetic constant and a preset drug loading kinetic threshold range, a temperature adjustment direction instruction is obtained, and a temperature dynamic adjustment parameter including an adjustment amplitude value and a change rate value is calculated according to the temperature adjustment direction instruction; The target reaction temperature is determined by superimposing the adjustment amplitude value in the temperature dynamic adjustment parameter with the current reaction temperature, and the temperature adjustment time process is formulated based on the change rate value in the temperature dynamic adjustment parameter to obtain a reaction temperature optimization instruction including the target reaction temperature and the adjustment time process.

[0012] In combination with the first aspect, in an eighth implementation of the first aspect of the present invention, performing layered gel crosslinking based on the multi-component balanced drug-loaded particles to obtain a nanogel, verifying the drug loading stability and release consistency of the nanogel, and generating crosslinking optimization process parameters, includes: Determining the release half-life of each drug component in the multi-component balanced drug-loaded particles, and calculating the deviation between the release half-life and the target release half-life; Determining a cross-linking degree distribution scheme for drug-loading-difficult components and drug-loading-easy components according to the deviation value, and preparing a dual cross-linker system based on the cross-linking degree distribution scheme to obtain a nanogel; Detecting the drug loading rate of each drug component in the nanogel and calculating the relative standard deviation of the drug loading rate, and simultaneously determining the cumulative release curve of each component in the nanogel and calculating the release similarity factor; Determine drug loading stability and release consistency based on the relative standard deviation of the drug loading rate and the release similarity factor to obtain a verification result; According to the verification results, the cross-linking agent concentration ratio, cross-linking time parameters, cross-linking temperature parameters and pH adjustment parameters that meet the qualified standards are obtained from the process parameter data table to obtain standard cross-linking process parameters, and the nanogel is optimized according to the standard cross-linking process parameters to obtain cross-linking optimized process parameters.

[0013] In a second aspect, the present invention provides a system for optimizing the preparation process of a compound lidocaine nanogel, the system comprising: The difference determination module is used to perform difference determination on the carrier binding affinity between the main drug lidocaine and the auxiliary drug in the compound lidocaine to obtain the drug feeding sequence table; A surface potential pre-adjustment module, used to pre-adjust the surface potential of the nanocarrier according to the drug feeding sequence table to obtain a drug-loaded pretreated carrier; A step-by-step drug loading module is used to perform step-by-step drug loading based on the drug loading pretreatment carrier according to the drug feeding sequence table to obtain saturation data during the drug loading process; A pH and temperature optimization module is used to synchronously input the saturation data into the drug loading process control system to optimize pH and temperature to obtain multi-component balanced drug-loaded particles; The layered gel cross-linking module is used to perform layered gel cross-linking according to the multi-component balanced drug-loaded particles to obtain nanogels, verify the drug loading stability and release consistency of the nanogels, and generate cross-linking optimization process parameters.

[0014] In the technical solution provided by the present invention, a drug loading competition relationship evaluation system is established by isothermal titration calorimetry, and drug loading difficult components and drug loading easy components are accurately identified. A step-by-step drug loading strategy with priority given to drug loading difficult components is adopted to avoid excessive occupation of carrier sites by drug loading easy components, and significantly improve the drug loading balance of multi-component drugs. Based on the molecular charge characteristics of drug loading difficult components, the surface potential of the carrier is differentially pre-adjusted to provide personalized drug loading microenvironment for different drug molecules, thereby improving drug loading efficiency. A quantitative identification standard for drug loading saturation state is established by joint monitoring of dynamic light scattering technology and ultraviolet spectrophotometry, and a dynamic control mechanism of pH and temperature based on drug loading saturation data feedback is combined to achieve adaptive regulation of the drug loading process. A layered gel cross-linking strategy based on differences in drug release kinetics is adopted, and the release rates of different drug components are compensatorily adjusted by a double cross-linker system and differentiated cross-linking conditions to achieve synchronization of multi-component drug release. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 A schematic diagram of a process for optimizing the preparation process of the compound lidocaine nanogel provided in an embodiment of the present application; Figure 2 This is a schematic block diagram of the structure of the preparation process optimization system for the compound lidocaine nanogel provided in the examples of the present application. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0018] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may change based on actual circumstances.

[0019] It should also be understood that the terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit the present application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0020] It should be further understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0021] The following describes some embodiments of the present application in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments may be combined with each other.

[0022] See also Figure 1 , Figure 1 A schematic diagram of the process flow of the optimized method for preparing the compound lidocaine nanogel provided in the embodiment of the present application is shown in FIG. Figure 1 As shown, the method for optimizing the preparation process of the compound lidocaine nanogel provided in the embodiment of the present application includes steps S100 to S500.

[0023] Step S100, performing differential determination of the carrier binding affinity of the lidocaine main drug and the auxiliary drug in the lidocaine compound to obtain a drug feeding sequence table; It is understandable that the execution subject of the present invention can be a preparation process optimization system for compound lidocaine nanogel, or a terminal or a server, which is not limited here. The embodiment of the present invention is described by taking the server as the execution subject as an example.

[0024] Specifically, affinity tests were conducted on the main drug component and auxiliary drug components in the lidocaine compound. Isothermal titration calorimetry was used. By recording the heat of change in the titration reaction between different drug molecules and nanocarriers at a constant temperature, key thermodynamic parameters such as the binding enthalpy, binding constant, and number of reaction sites between the drug molecules and the carrier were quantitatively analyzed. The binding affinity values ​​for each drug component and the nanocarrier were then derived, and a data set of binding affinity distributions between the lidocaine main drug and various auxiliary drugs was constructed. Based on the carrier binding affinity values, the drug loading competition coefficients between each auxiliary drug were calculated. The drug loading competition coefficient, which uses the difference in affinity between drug molecules for the same carrier binding site as a core parameter and is evaluated by combining the ratio of the reaction rate constant to the binding equilibrium constant, reflects the intensity of competition between different drugs for the nanocarrier during actual drug loading. Drugs with higher competition coefficients are more likely to occupy active sites during carrier binding, while drugs with lower competition coefficients are more difficult to encapsulate. A competitive ranking was constructed by comparing the competition coefficients of all auxiliary drugs. Based on the drug loading competition coefficients, drugs were grouped according to preset affinity thresholds. Among them, the auxiliary drug with a binding affinity lower than the first preset value (such as lower than 10 -5 mol / L) as the drug loading difficulty component, and the binding affinity higher than the second preset value (such as higher than 10 -3 mol / L) of auxiliary drugs are labeled as easy-to-load components. Based on the grouping results, the feeding sequence is set, prioritizing the encapsulation of difficult-to-load components to ensure full utilization of their binding sites on the nanocarrier. Then, the "main drug plus lidocaine" and "easy-to-load components" are added to prevent the latter from crowding out the former due to their stronger affinity. The sorted drug components and their feeding sequence are structured and organized to create a drug feeding sequence table, clearly listing the feeding stages, time windows, and concentration ranges for each drug.

[0025] Step S200, pre-adjusting the surface potential of the nanocarrier according to the drug feeding sequence table to obtain a drug-loaded pretreated carrier; Specifically, the structural information corresponding to all drug molecules marked as "difficult-to-load components" is extracted from the drug feeding sequence table, and the charge density and molecular polarity data of each drug molecule are read. These data are obtained through molecular simulation databases or experimental methods such as multivariate mass spectrometry, dipole moment analysis, and potential map reconstruction, and serve as key physicochemical indicators affecting the ability of drug molecules to interact with the nanocarrier surface. A surface potential regulation model is established based on the directional adsorption behavior of drug molecules under different electric field environments. The drug molecule charge density and polarity parameters are input into the surface potential regulation model to calculate the surface potential enhancement required for each difficult-to-load component under the premise of effective adsorption. The corresponding surface potential target value is then derived, and a potential regulation requirement dataset is formed. After obtaining the carrier surface potential enhancement, the buffer pH conditions corresponding to the target surface potential of the nanocarrier are inferred based on the known Zeta potential-environmental pH response relationship of the carrier material. The inverse process is achieved through a potential-pH fitting equation or empirical curve. The target surface potential is substituted into the model to calculate the buffer pH target value. To ensure accurate adjustment, the buffer's buffering capacity, volume, and adjustment gradient are determined simultaneously. This generates pH adjustment parameters that match the target buffer pH. These parameters, including the type of acidic and base components, molar concentration ratio, and order of addition, serve as guidance for buffer preparation during actual preparation. The nanocarriers are dispersed in a phosphate buffer corresponding to the pH adjustment parameters to ensure sufficient prewetting and charge reconstruction of the nanocarriers in an environment close to the target pH. A polyethylene glycol-based nonionic surfactant is introduced at a controlled concentration (e.g., 0.1%-0.3%) to enhance the fluidity and compliance of the hydrophobic regions on the nanocarrier surface without interfering with ionic strength. A temperature gradient treatment is then applied: the dispersion is subjected to a temperature ramp from 25°C to 35°C, held for a period of time, and then cooled to 28°C. This promotes the reconstruction of the active surface structure of the carrier, resulting in a modified carrier with a specific polarity response. To verify that the modification process achieved the desired effect, the surface zeta potential of the modified carrier was measured using electrophoretic light scattering. The surface potential was then measured and the deviation between the measured surface potential and the target surface potential was calculated. If the measured value falls within the preset target potential range (e.g., -15 mV to -25 mV) and the deviation between the measured value and the theoretical value is lower than the allowable error threshold (e.g., ±1 mV), the surface potential adjustment of the carrier is determined to be complete, and further processing is stopped. The obtained nanocarrier is defined as a drug-loaded pretreated carrier.

[0026] Step S300: loading the drug in steps according to the drug feeding sequence table based on the drug pretreatment carrier to obtain saturation data during the drug loading process; Specifically, according to the first feeding sequence in the drug feeding sequence table, the auxiliary drug, which is a component that is difficult to load, is slowly and uniformly added to the dispersed suspension system formed by the pretreated carrier to construct the first-stage drug loading reaction system. The stirring speed, temperature, and pH value of the first-stage drug loading reaction system are maintained within the preset optimal parameter range to maintain the highly active binding sites on the carrier surface and maximize the adsorption and encapsulation of the auxiliary drug molecules. During the first-stage drug loading process, two indicators are monitored in real time: the rate of change of the carrier particle size and the rate of change of the free drug concentration in the supernatant. The particle size change is obtained by dynamic light scattering technology. Data is collected at fixed time intervals and the particle size growth rate is calculated. When the particle size growth rate per unit time is observed to be lower than the first change rate threshold (for example, 5% / 5 minutes), it indicates that the carrier particles have basically stabilized. Simultaneously, the free concentration of the auxiliary drug in the reaction supernatant is continuously monitored using UV spectrophotometry, and its rate of concentration decrease per unit time is calculated. If the rate of concentration change is below a second preset value (e.g., 0.5% / minute), it indicates that the drug molecules are essentially adsorbed or encapsulated by the carrier, with no significant inclusion activity occurring. When both of these conditions are met, the first-stage drug loading process is confirmed to have reached thermodynamic and kinetic saturation. A first-stage drug loading completion signal is output, and the current encapsulation capacity is recorded and a first saturation value is calculated to reflect the effective loading level of the auxiliary drug. Based on the first-stage drug loading completion signal, the second-stage drug loading process is initiated without interrupting the reaction system. In the second stage, lidocaine, the main drug, and the auxiliary drug previously designated as the most readily loaded component are added proportionally to the first-stage drug loading reaction system according to the second feeding sequence, constructing a second drug loading reaction system in a multi-component mixed environment. Because a large number of binding sites have been consumed in the first stage, the second-stage drug encapsulation process is limited by the remaining carrier surface structure and charge state. Therefore, the magnitude of the surface potential change and the rate of change of the newly added drug component in solution are monitored. The surface potential is continuously measured using zeta potential analysis. When its change over a continuous period falls below a third preset rate of change (e.g., 2mV / 5 minutes), it indicates that the carrier surface has reached a new charge equilibrium state and binding activity has essentially ceased. Simultaneously, the concentration changes of the lidocaine active ingredient and the drug-loading component are monitored. When the concentration change rate of all components falls below a fourth rate of change threshold (e.g., 0.5% / minute), the second stage is deemed to have reached saturation. Second saturation data is generated, marking the completion of the second stage of drug loading. The first and second saturation data obtained in each stage are integrated to form a saturation dataset for the drug loading process.

[0027] Step S400: synchronously inputting the saturation data into the drug loading process control system to optimize pH and temperature to obtain multi-component balanced drug loading particles; Specifically, the saturation data is synchronously input into the drug loading process control system. The drug loading process control system has a built-in drug loading balance evaluation module, which compares and analyzes the drug loading efficiency from different drug components, and calculates the drug loading balance deviation value between them through statistical methods. The drug loading balance deviation value represents the uniformity index of the actual encapsulation degree of multi-component drugs in the same batch of nanoparticles. Its core basis is the ratio of the standard deviation to the mean between the encapsulation rates of each drug component. If the ratio deviates from the set ideal range, it means that there is a significant imbalance in the current drug loading distribution state. Based on this, a drug loading balance deviation signal is generated, and a drug loading process control trigger instruction is automatically issued. The pH gradient control algorithm is started based on the drug loading balance deviation signal. The pH gradient control algorithm automatically derives the pH dynamic adjustment parameters, including the target pH value, adjustment range, adjustment rate and duration, based on the direction and intensity of the current drug loading deviation, combined with the established pH effect curve on the encapsulation of different components. The dynamic pH adjustment parameters are converted into executable buffer pH optimization instructions and sent to the automated pH control unit of the drug-loading reaction system. The pH of the reaction medium is adjusted by adding preconfigured acid and base components, thereby improving the carrier surface's binding capacity for certain polar or ionic drug molecules and promoting the secondary adsorption of weaker components. Simultaneously, based on the drug-loading process control trigger, a temperature program control algorithm is activated to intelligently and dynamically adjust the temperature conditions of the current reaction system. The temperature control module combines the diffusion rate of drug molecules with the structural response characteristics of the carrier material to generate dynamic temperature adjustment parameters, including the target temperature, heating or cooling rate, and hold time. These parameters are then converted into actual reaction temperature optimization instructions, which are then implemented step-by-step through the reactor's temperature control device. During the simultaneous optimization of pH and temperature parameters, the drug loading status at each time point is continuously monitored. Specifically, the actual loading rate of each drug component is sampled and analyzed, and its coefficient of variation is calculated to assess encapsulation uniformity within the system. When the measured coefficient of variation falls below a first percentage threshold (e.g., 15%) and the drug loading rates of all drug components exceed a second percentage threshold (e.g., 85%), the current operating conditions are determined to have met the drug loading balance target. At this point, all control operations are terminated and a completion flag is output. The resulting particles are defined as multi-component balanced drug-loaded particles, achieving optimal matching in particle size distribution, charge balance, and drug encapsulation structure.

[0028] Step S500: performing layered gel crosslinking based on the multi-component balanced drug-loaded particles to obtain nanogels, verifying the drug loading stability and release consistency of the nanogels, and generating crosslinking optimization process parameters.

[0029] Specifically, the drug release behavior of multi-component balanced drug-loaded particles was quantitatively measured. Using an in vitro release assay, the cumulative release data of the primary drug, lidocaine, and each auxiliary drug component were recorded under standard physiological conditions (e.g., pH 7.4, 37°C). The release half-life of each component was fitted based on a release rate model and compared with the preset target release half-life. Deviations were calculated to determine the degree of deviation from the expected synchrony of each drug's release behavior. Based on the half-life deviation results, the target drug components requiring release rate control were identified, and a cross-linking degree allocation strategy was developed accordingly. Components with release half-lives significantly below the target were classified as difficult to load due to excessive release, and a higher cross-linking degree was assigned to the corresponding gel regions to increase diffusion resistance. Conversely, regions corresponding to easy to load components with release half-lives close to or slightly above the target were assigned a lower cross-linking degree to maintain their release rate. After determining the cross-linking distribution scheme, a dual-crosslinker system was used to construct the gels. The primary crosslinker, such as glutaraldehyde, formed the basic network structure, with a concentration range of 0.5% to 1.5%. A secondary crosslinker, such as carbodiimide, compensated for charge distribution and local compactness, with a concentration range of 0.2% to 0.8%. By implementing differentiated crosslinking times (e.g., 10 minutes for the first layer and 15 minutes for the second layer) and temperatures (e.g., 25°C and 30°C) for the different component regions, a layered crosslinking process was achieved, generating structurally matched nanogels. The nanogels were evaluated for performance. The final drug loading rates of each drug component in the gel system were determined, and the relative standard deviations (RSDs) were calculated to assess the uniformity of drug loading distribution. Furthermore, the release similarity factor (f2) between the components was calculated, combining high-performance liquid chromatography (HPLC) and cumulative release curve analysis, to assess the temporal consistency of drug release. When the RSD value was less than 8% and the f2 value was greater than 50, the gel system exhibited good drug loading stability and release consistency, preliminarily concluding that the process parameter configuration was effective. The validation results, comprised of the RSD and f² values ​​obtained from the evaluation, were matched and analyzed against historical batch data from the process parameter database. All combinations of cross-linker concentrations, cross-linking time parameters, cross-linking temperature parameters, and pH adjustment parameters that met the qualification criteria were extracted. The parameters with the highest stability and good reproducibility were selected as standard cross-linking process parameters. Using multidimensional modeling methods such as regression analysis and principal component analysis, the standard cross-linking process parameters were locally optimized and adjusted to better suit the current drug components and carrier structure characteristics, resulting in a set of optimized cross-linking process parameters for actual production.

[0030] In a specific embodiment, the process of executing step S100 may specifically include the following steps: Isothermal titration calorimetry was performed on the main drug lidocaine and each auxiliary drug in the lidocaine compound with the nanocarrier to obtain the carrier binding affinity value of each auxiliary drug; The drug loading competition coefficient between each auxiliary drug is calculated based on the carrier binding affinity value; According to the drug loading competition coefficient, the drug with a binding affinity lower than a first preset value is marked as a drug loading difficult component, and the drug with a binding affinity higher than a second preset value is marked as a drug loading easy component; The first feeding sequence is set according to the component that is difficult to load, and the second feeding sequence is set according to the main drug lidocaine and the component that is easy to load; A drug feeding sequence table is created based on the first feeding sequence and the second feeding sequence.

[0031] Specifically, an isothermal titration calorimeter was used as the core instrument platform. This instrument, with its ability to highly sensitively detect heat changes in reactions, is suitable for evaluating the non-covalent interactions between drug molecules and nanocarrier materials, including a combination of mechanisms such as electrostatic attraction, hydrophobic interactions, hydrogen bonding, and van der Waals forces. During the experiment, the primary drug, lidocaine, and each adjuvant were prepared into titration solutions at standard concentrations and added dropwise to a reaction cell containing pre-dispersed nanocarriers. The minute thermal effect generated by each addition was recorded. By integrating the heat and titration volume, thermodynamic parameters such as the binding constant (K), binding enthalpy change (ΔH), entropy change (ΔS), and number of binding sites (n) were fitted. The reciprocal of the binding constant, K, is used to characterize binding affinity; higher K values ​​indicate stronger binding between the drug and carrier, and thus stronger affinity. A binding affinity dataset was constructed by collating and standardizing the K values ​​for all adjuvant-carrier interactions. On this basis, in order to quantify the relative advantages of drug molecules when competing for the same nanocarrier binding sites, the drug loading competition coefficient is introduced. The drug loading competition coefficient not only takes into account a single K value, but also combines factors such as the molar volume, polarity, diffusion rate and spatial arrangement ability of the drug molecules. By establishing a multi-variable linear regression model, the ability of different auxiliary drugs to compete for effective carrier binding sites under fixed carrier concentration and the same system conditions is evaluated. The competition coefficient is defined as the relative ratio of the probability of a single drug molecule successfully occupying an effective site on the carrier surface per unit time. The stronger the competitiveness of the drug, the higher its coefficient, and vice versa. All auxiliary drugs are sorted from low to high according to the competition coefficient, and at the same time, a double index screening is performed in combination with the affinity K value to make group judgments. When the K value of a certain auxiliary drug is lower than the first preset value (for example, 10 -5 mol / L), and when the competition coefficient is in the lower range in the overall ranking, it is defined as a "difficult drug loading component" because it is easily repelled or inhibited by other molecules during the actual drug loading process and is not easy to bind to the carrier surface. -3mol / L), and the auxiliary drugs with a high competition coefficient are judged to have strong carrier binding ability and competitive advantage, so they are classified as "easy drug loading components". Based on the mechanism principle that the encapsulation capacity of nanocarriers is limited and the drug loading sites are dominant first, the feeding sequence is formulated to improve the final drug loading uniformity and component balance. The "difficult drug loading component" is given priority as the first feeding sequence. The purpose of feeding is to provide an exclusive binding time window before the carrier surface is occupied by a large amount of high-affinity drugs to improve its encapsulation efficiency. The first feeding stage is set as single-component feeding. Each auxiliary drug is independently dissolved at the set concentration and mixed with the pretreated carrier before entering the reaction system. A medium stirring rate and adapted pH conditions are used to maintain the stable reaction so that the drug molecules are in full contact with the carrier until the adsorption is stable. According to the experimental schedule and stability assessment results, the second feeding sequence is started, and the lidocaine main drug and all the "easy drug loading components" are added. Since the drugs in the second stage have a high affinity, they can still effectively bind and maintain a high encapsulation rate even under the condition of relatively reduced binding sites on the carrier surface, and will not exclude or replace the already bound difficult drug loading components. Based on the feeding content, sequence and time intervals of the above two stages, a drug feeding sequence table is created. The drug feeding sequence table indicates the specific feeding order of each component, and also includes key process parameters such as the time parameters of each feeding node, drug concentration range, mixing intensity setting, pH control requirements and reaction environment temperature, forming a standardized operation template.

[0032] In a specific embodiment, the step of performing isothermal titration calorimetry on the lidocaine main drug and each auxiliary drug in the lidocaine compound with the nanocarrier to obtain the carrier binding affinity value of each auxiliary drug can specifically include the following steps: Isothermal titration calorimetry was used to prepare drug solutions of lidocaine and each auxiliary drug, and carrier suspensions of nanocarriers. The carrier suspension is used as the sample cell solution, and each drug solution in the drug solution group is used as a titrant to perform continuous titration under constant temperature conditions to obtain a titration heat flow signal and a binding saturation curve; The titration heat flow signal is integrated to obtain the binding enthalpy change value, and the binding constant is obtained by nonlinear fitting based on the binding saturation curve. The carrier binding free energy value of each auxiliary drug is calculated based on the binding constant and binding enthalpy change values, and then the carrier binding free energy value is converted into a carrier binding affinity value expressed in molar concentration units.

[0033] Specifically, the main drug lidocaine and each auxiliary drug are dissolved separately in a unified buffer system to form multiple drug solution groups with consistent concentrations. The buffer system is consistent with the nanocarrier dispersion environment to avoid nonspecific thermal signal interference caused by changes in solution pH or ionic strength. At the same time, the nanocarrier material is pre-dispersed to ensure that it forms a suspension with uniform particle size and good stability, which serves as the receptor solution in the sample cell of the isothermal titration calorimeter. When the isothermal titration calorimetry experiment is officially carried out, the pre-treated carrier suspension is injected into the calorimeter sample cell and maintained at a constant temperature to eliminate the influence of environmental thermal fluctuations. One of the drugs in the drug solution group is selected as the titrant and is gradually dripped into the sample cell at set time intervals and in slightly increasing volumes. After each titration, the drug molecules undergo physical adsorption or chemical binding reaction with the carrier surface, generating a weak endothermic or exothermic phenomenon. The calorimeter records the heat flow signal in real time, and the titration heat flow curve is plotted with time as the horizontal axis and thermal power as the vertical axis. After multiple titrations, the binding saturation process is obtained. As the binding between the drug and the carrier gradually approaches saturation, the recorded heat flow signal gradually decreases until it approaches zero. This process constitutes the characteristic morphology of the binding saturation curve. The titration heat flow signal curve is integrated, accumulating the heat released or absorbed during each titration to obtain the total thermal effect of the drug-carrier binding per unit volume of solution. The binding enthalpy change is then extracted, reflecting the thermodynamic direction and intensity of the drug-carrier binding reaction. Positive values ​​represent endothermic reactions, while negative values ​​represent exothermic reactions. The absolute value of the binding enthalpy change is related to the driving force of the reaction. The heat flow signal integration results, combined with the corresponding drug concentration gradient, are then input into a nonlinear fitting model along with the titration volume. Curve fitting is performed using either the classical ligand-receptor binding model or a modified model. The fitting process simultaneously solves for the binding constant and the number of binding sites. The binding constant is a key parameter reflecting the strength of the binding affinity during the titration; a larger value indicates a more stable binding between the drug molecule and the nanocarrier. Based on the binding constant and binding enthalpy change, the binding free energy of each adjuvant drug-carrier binding reaction is calculated, revealing the spontaneity of the reaction process in the energy dimension. The entropy change during the binding process is further derived from the Gibbs free energy expression to determine whether the binding reaction is accompanied by a change in the degree of molecular order. For example, electrostatic adsorption is characterized by a negative entropy change, while hydrophobic binding is characterized by a positive entropy change. The binding free energy value is converted to a conventional affinity expression, expressed in units of molar concentration. The free energy value is then converted to a binding dissociation constant, the reciprocal of which is the binding affinity. A higher affinity value indicates a greater tendency for the drug molecule to bind to the nanocarrier, indicating that the drug is preferentially adsorbed and encapsulated during the loading process.

[0034] In a specific embodiment, the process of executing step S200 may specifically include the following steps: Read the drug molecular charge density and molecular polarity data marked as drug-loading difficult components in the drug feeding sequence table, and calculate the carrier surface potential enhancement amplitude required for each drug-loading difficult component based on the drug molecular charge density and molecular polarity data; Calculating the buffer pH value required for the nanocarrier to reach the target surface potential based on the carrier surface potential enhancement amplitude, and determining the buffer pH adjustment parameter based on the buffer pH value; The nanocarriers are dispersed in a phosphate buffer corresponding to the pH adjustment parameters, and polyethylene glycol surfactant is added to perform temperature gradient treatment to obtain modified carriers; The surface potential of the modified carrier is measured using electrophoretic light scattering technology to obtain a surface potential measurement value. When the surface potential measurement value is within a preset voltage range and the deviation from the calculated target value is less than the target value, pre-adjustment is stopped to obtain a drug-loaded pretreated carrier.

[0035] Specifically, based on the drug dosing sequence, molecular-level parameter readings were performed for drugs marked as difficult-to-load components, extracting their charge density and molecular polarity data. Charge density was derived from the electrostatic potential map calculated after molecular structure optimization, while molecular polarity was quantitatively assessed based on dipole moment, molecular polarizability, and the position of the distributed charge center. These parameters reflect the drug molecule's responsiveness to the electric field environment in solution and its binding surface preference. The charge density and molecular polarity data were input into the carrier interface regulation model. The predictive model calculated the theoretical binding force of each difficult-to-load component under different surface potential conditions, and a mapping relationship between binding probability and potential strength was established. By analyzing the inflection point position and the adsorption stability range in the mapping curve, the minimum surface potential required for stable adsorption of this type of drug molecule, i.e., the surface potential target value, was reversely deduced. The surface potential target value was compared with the current potential state of the nanocarrier to determine the required potential enhancement amplitude. A larger potential enhancement amplitude indicates a higher degree of regulation required on the carrier surface, and the corresponding buffer pH value in actual regulation should be closer to the extreme endpoint. Based on the magnitude of the surface potential enhancement and the potential response curve of the carrier material under different pH environments, the specific pH value required to achieve the target potential is calculated. The potential response curve is obtained from preliminary experiments, and its corresponding relationship is approximately linear within a certain pH range. By searching or fitting the potential response curve, the pH value of the buffer solution that meets the target potential is derived. Based on this, the configuration parameters of the phosphate buffer system are determined, including the molar ratio of phosphate to conjugate base, total concentration range, adjustment step size, and control accuracy, forming a pH adjustment parameter set. A standardized phosphate buffer is prepared according to the above adjustment parameters, and the nanocarriers are dispersed in the standardized phosphate buffer in an appropriate proportion so that they are uniformly suspended under conditions close to the target pH. During this process, a polyethylene glycol non-ionic surfactant is added to adjust the hydrophobicity and flexible structure of the carrier surface. The surfactant concentration is controlled between 0.1% and 0.3% to ensure that the carrier morphology is not destroyed while improving dispersion stability. The temperature gradient treatment process is initiated, and the temperature is maintained at 25 degrees Celsius for 30 minutes to complete the initial potential adjustment in the pH environment. The temperature is then raised to 35 degrees Celsius and maintained for 15 minutes to activate the surface charge rearrangement mechanism. The temperature is then slowly lowered to 28 degrees Celsius to stabilize the carrier structure and lock the surface charge state to obtain a preliminary modified carrier. The surface potential of the modified carrier is measured with high precision using an electrophoretic light scattering instrument. The instrument analyzes the migration rate of the carrier in an external electric field and calculates its Zeta potential value to obtain the current actual surface potential value. The actual surface potential value is compared and analyzed with the target surface potential. If the measured value falls within the preset potential tolerance range (e.g., -15mV to -25mV) and the deviation from the theoretical target value is less than the preset maximum deviation value (e.g., ±1mV), the surface potential adjustment is determined to be complete, and a drug-loaded pretreated carrier that meets the drug adsorption requirements is obtained.

[0036] In a specific embodiment, the process of executing the step of reading the drug molecular charge density and molecular polarity data marked as drug loading difficulty components in the drug feeding sequence table, and calculating the carrier surface potential enhancement amplitude required for each drug loading difficulty component based on the drug molecular charge density and molecular polarity data can specifically include the following steps: Extracting the molecular structure data of each drug marked as a drug-loading difficulty component from the drug feeding sequence table, and calculating the drug molecular charge density and molecular polarity data of each drug-loading difficulty component based on the molecular structure data of each drug; The effective charge radius of each drug-loading-difficult component in aqueous solution was calculated based on the dipole moment value of each drug-loading-difficult component in the molecular polarity data, and the electrostatic interaction strength between each component and the carrier surface was calculated based on the effective charge radius; The surface potential difference of the carrier required to achieve the optimal drug loading efficiency for each difficult drug loading component is calculated based on the electrostatic interaction strength and the charge density of the drug molecules; The difference between the carrier surface potential difference and the original surface potential of the nanocarrier is calculated to obtain a target difference. When the target difference is positive, the target difference is used as the carrier surface potential enhancement amplitude. When the target difference is negative, the carrier surface potential enhancement amplitude is set to zero.

[0037] Specifically, the molecular structure data for each drug marked as a difficult-to-load component is extracted from the drug feed sequence table, and the corresponding molecular structure data files are accessed. This data is sourced from a drug molecular structure database, quantum chemistry modeling platform, or molecular simulation program. The structural data is presented in a standardized three-dimensional coordinate format and contains information such as atomic composition, bond lengths, bond angles, conjugated structure, and substituent arrangement. Based on this molecular structure data, the charge density and polarity parameters are quantitatively calculated for each drug molecular configuration. Charge density is reconstructed from the molecular orbital distribution using the electrostatic potential mapping method. The distribution of electron cloud density around each atomic site is used as a metric to reflect the electric field gradient formed in space by the molecule and its ability to attract the surrounding environment. Molecular polarity is expressed as the dipole moment. The dipole moment is determined by the product of the distance between the positive and negative charge centers and the charge. It has a well-defined directionality and dimension and is a key physical quantity for determining the direction of movement, orientation, and binding characteristics of drug molecules in polar solvents. For drugs with complex structures or multiple functional groups, the magnitude and direction of the dipole moment also determine how they respond to changes in the carrier surface potential. Based on the dipole moment value, the effective charge radius of each drug-load-difficult component in aqueous solution is derived. The effective charge radius is not equivalent to the physical geometric radius, but refers to the average size of the equipotential shell formed by the drug molecule in the polar medium. It is the electric field distribution boundary formed after solvation and effectively reflects the range of action of the molecule under the action of the electric field. The larger the dipole moment, the stronger the polarity of the molecule, the wider the orientation electric field it generates in the solution, and thus the larger its effective charge radius; conversely, it corresponds to a more limited area of ​​action. After determining the effective charge radius of each molecule, an electrostatic interaction model between each molecule and the carrier is established to simulate its binding ability under specific distance and potential conditions. According to the electrostatic interaction strength model, the molecular polarity, effective charge radius and carrier surface charge density are combined to calculate the electrostatic adsorption force strength acting on each drug molecule per unit area under specific conditions, and derive the minimum surface electric field strength required for each drug-load-difficult component while maintaining thermodynamic adsorption stability. That is, for each molecule to achieve optimal binding, the required carrier surface potential difference is different. The larger the potential difference, the poorer the molecule's binding ability under low surface potential conditions, and effective adsorption requires increasing the carrier potential. If a molecule requires a smaller potential difference, it means it is insensitive to changes in the carrier surface and can meet the binding requirements at the current potential state. The target potential difference required by each molecule is calculated based on the difference with the current original surface potential of the nanocarrier. If the target potential difference is greater than the current potential, that is, the difference is positive, it means that the potential needs to be enhanced, and the absolute value of the difference is the surface potential enhancement amplitude. If the difference is negative, it means that the current potential has met or even exceeded the drug binding requirements. At this time, avoid performing meaningless potential enhancement operations and set the enhancement amplitude to zero to ensure that the stability of the reaction system is not destroyed.

[0038] In a specific embodiment, the process of executing step S300 may specifically include the following steps: According to the first feeding sequence in the drug feeding sequence table, the auxiliary drug of the drug-loading difficult component is added to the suspension of the drug-loading pretreatment carrier to perform the first stage of drug loading treatment, thereby obtaining the first drug loading reaction system for the encapsulation process of the drug-loading difficult component; Determining the carrier particle size change rate of the first drug loading reaction system to detect the first concentration change rate of the free drug in the supernatant, determining that the first stage drug loading is saturated when the carrier particle size change rate is lower than the first change rate value and the first concentration change rate is lower than the second change rate value, and obtaining a first saturation degree and a first stage drug loading completion signal; Based on the first-stage drug loading completion signal, the lidocaine main drug and the drug loading component are added into the first drug loading reaction system according to the second feeding sequence in the drug feeding sequence table to perform the second-stage drug loading process, thereby obtaining a second drug loading reaction system with multi-component mixed drug loading; measuring the surface potential change amplitude of the second drug loading reaction system and monitoring the second concentration change rate of each component, and determining that the second stage drug loading is saturated when the surface potential change amplitude is less than the third change rate value and the second concentration change rate is less than the fourth change rate value, thereby obtaining a second saturation degree; The first saturation and the second saturation are used as saturation data during the drug loading process.

[0039] Specifically, based on the first feeding sequence in the drug feeding sequence table, confirm the list of all auxiliary drugs that are difficult to load, and add these drugs in sequence to the drug-loaded pre-treated carrier suspension that has completed surface potential pre-adjustment according to the established ratio, concentration and dissolution order. Before adding auxiliary drugs, ensure that the environmental conditions in the carrier suspension fully meet the preset standards, including pH value, temperature and stirring speed, to maintain the carrier particles in a stable dispersed state and avoid interference factors such as early agglomeration or structural collapse. Start the dynamic monitoring program of the first stage drug loading reaction system, use a dynamic light scattering instrument to measure the carrier particle size in real time, and perform differential processing on the particle size data every five minutes to obtain the particle size change rate; at the same time, combine ultraviolet spectrophotometry to quantitatively analyze the concentration of drug molecules in the free state in the reaction supernatant, collect a set of continuous concentration data according to the detection frequency, and calculate its concentration change rate. When the carrier particle size change rate is lower than the first change rate value, for example, 5% per five minutes, it indicates that the carrier surface is essentially saturated and the particle size is stabilizing. Conversely, when the first concentration change rate is lower than the second change rate value, for example, 0.5% per minute, it indicates that the concentration of residual unbound drug in the reaction solution is slowing down and entering a kinetic plateau phase. When both change rate indicators meet the threshold conditions, the first stage of drug loading is determined to have reached saturation. The monitoring system automatically outputs a first-stage drug loading completion signal and calculates the first saturation level as an indicator of drug loading efficiency. Based on the first-stage drug loading completion signal, the second stage of drug introduction begins. According to the second feeding sequence in the drug feeding sequence table, the main drug, lidocaine, and all drugs that are easily loaded are sequentially added to the first drug loading reaction system. At this point, the carrier particles in the reaction system have partially encapsulated the auxiliary drug molecules, and the surface binding sites are partially occupied. Therefore, the second stage drug dosage, concentration control strategy, and stirring intensity need to be appropriately adjusted to prevent further encapsulation efficiency reduction due to competition for binding sites. After adding the second-stage drug, the monitoring system continues to continuously collect the surface potential of the second drug-loaded reaction system after the multi-component mixture is mixed. A zeta potential analyzer is used to obtain a curve of the particle surface charge state. By analyzing the continuous change in surface potential, the interfacial reconstruction state of the carrier particles after binding with the newly added drug molecules is determined. If the surface potential change amplitude is detected to be lower than the third rate of change value, for example, 2 millivolts per five minutes, it indicates that the carrier surface charge has reached equilibrium and the drug molecule binding process has stabilized. Simultaneously, ultraviolet spectroscopy is used to separately measure the free concentration of all drug components in the reaction supernatant, and the rate of change of concentration of each component is calculated. Only when the second concentration change rate of all components is lower than the fourth rate of change value, for example, 0.5% per minute, is the second-stage drug loading process considered to be fully saturated. Based on this, a second-stage drug loading saturation state signal is output, and a second saturation value is calculated to reflect the encapsulation efficiency and stability of the lidocaine main drug and other components in the final system.The first saturation and the second saturation are used as saturation data during the drug loading process.

[0040] In a specific embodiment, the process of executing step S400 may specifically include the following steps: The saturation data is synchronously input into the drug loading process control system, the drug loading balance deviation value is calculated, and a drug loading balance deviation signal and a drug loading process control trigger instruction are generated based on the drug loading balance deviation value; Based on the drug loading balance deviation signal, the pH gradient control algorithm is started to generate pH dynamic adjustment parameters and buffer pH optimization instructions; Start the temperature program control algorithm according to the drug loading process control trigger instruction, generate temperature dynamic adjustment parameters and reaction temperature optimization instructions; The drug loading rate and the corresponding coefficient of variation of each component of the drug-loaded reaction system processed by the pH optimization instruction and the reaction temperature optimization instruction are measured. When the coefficient of variation is lower than the first percentage and the drug loading rate of each component is greater than the second percentage, it is determined that the drug loading balance is completed, and multi-component balanced drug-loaded particles are obtained.

[0041] Specifically, the saturation data obtained during the first and second drug loading processes are input into the drug loading process control system in a structured format. The drug loading process control system is embedded with a balance calculation module for evaluating the consistency of drug loading distribution. The module reads the actual saturated drug loading level of each component and compares it with the preset theoretical drug loading ratio to calculate the deviation amplitude between different drug components, thereby forming a drug loading balance deviation value. The drug loading balance deviation value is used to measure the distribution stability and overall consistency of the current drug loading state in the multi-component system. If the deviation is large, it means that some drug components are over-encapsulated or under-encapsulated, affecting the synchronization and stability of the final release. Based on the drug loading balance deviation value, a drug loading balance deviation signal is generated to reflect the intensity of the process correction requirement according to the deviation range, change trend and fluctuation rate. At the same time, a drug loading process control trigger instruction is generated. After receiving the drug loading balance deviation signal, the pH gradient control algorithm is activated, and combined with the binding ability response curve of the drug molecules under different pH conditions, a set of pH dynamic adjustment parameters for optimizing the current carrier surface potential environment is generated. Dynamic pH adjustment parameters include the actual buffer pH value, the target value, the adjustment step size, the adjustment direction, and the adjustment time window. Based on the drug's isoelectric point, solubility behavior, and ionic state variations, a buffer pH optimization command is generated for on-site execution and pushed to the automatic liquid dosing module or acid-base titration controller connected to the reactor in the control terminal for adjustment, ensuring that the pH of the reaction environment smoothly approaches the optimal value during continuous adjustment. Simultaneously, a temperature program control algorithm is initiated in response to the drug loading process control trigger command. Based on thermodynamic analysis results, the temperature dependence of drug molecular diffusion rate, temperature-sensitive parameters of carrier surface aggregation stability, and the overall heat capacity of the system, the temperature program control algorithm generates a set of dynamic temperature adjustment parameters. These dynamic temperature adjustment parameters include the target temperature, heating or cooling rate, hold time, and temperature control period, and are adjusted in real time based on the current stirring rate and carrier concentration. The dynamic temperature adjustment parameters are converted into reaction temperature optimization commands and implemented by the heating or cooling modules of the reaction system, completing the maintenance and optimization of the entire reaction temperature control environment during uninterrupted operation. After the execution of the pH optimization instruction and the temperature optimization instruction is completed, the feedback detection stage is entered. The actual drug loading rate of each drug component in the reaction system is quantitatively determined using high-performance liquid chromatography, and the intra-group coefficient of variation is calculated based on the drug loading rate data of each component. The intra-group coefficient of variation is a statistical indicator reflecting the balanced level of drug loading. The lower its value, the closer the distribution of drug encapsulation is to the ideal equilibrium state. When it is detected that the coefficient of variation is lower than the set first percentage threshold and the actual drug loading rate of each component is greater than the second percentage threshold, for example 85%, it is determined that the current drug loading reaction system has reached the balanced drug loading condition. At this time, the system automatically issues a completion flag and defines this batch of nanocarriers as multi-component balanced drug-loaded particles.

[0042] In a specific embodiment, the execution step of starting the temperature program control algorithm according to the drug loading process control trigger instruction and generating the temperature dynamic adjustment parameters and the reaction temperature optimization instruction may specifically include the following steps: According to the trigger instruction of drug loading process control, the temperature program control algorithm is started to extract the drug loading saturation change curve and calculate the drug loading kinetic constant; Based on the comparison between the drug loading kinetic constant and the preset drug loading kinetic threshold range, a temperature adjustment direction instruction is obtained, and the temperature dynamic adjustment parameter including the adjustment amplitude value and the change rate value is calculated according to the temperature adjustment direction instruction; The target reaction temperature is determined by superimposing the adjustment amplitude value in the temperature dynamic adjustment parameter with the current reaction temperature, and the temperature adjustment time process is formulated based on the change rate value in the temperature dynamic adjustment parameter to obtain a reaction temperature optimization instruction including the target reaction temperature and the adjustment time process.

[0043] Specifically, a temperature program control algorithm is activated based on the drug loading process control trigger command to extract the saturation curve recorded in the current drug loading reaction system. This saturation curve, collected by the real-time monitoring system, reflects the temporal evolution of the encapsulation efficiency during the binding process between the drug molecules and the nanocarrier surface. The temperature program control algorithm then fits the drug loading saturation curve and extracts the kinetic constant of the current drug loading reaction based on the time constant and slope characteristics of the saturation process. The kinetic constant reflects the rate at which drug molecules migrate to the nanocarrier surface and bind to form a stable complex structure per unit time, and indirectly reflects the overall impact of the current temperature conditions on molecular diffusivity and binding activity. A larger kinetic constant indicates a faster drug loading reaction and near-saturation of the system; a smaller kinetic constant indicates unfavorable temperature conditions for molecular activity or poor affinity for the carrier surface, resulting in low binding efficiency. The temperature program control algorithm compares the extracted kinetic constant with a preset kinetic threshold range in the system. This threshold range is derived from historical process data and the thermodynamic behavior of drug molecules and represents the reasonable rate range that the drug loading reaction should achieve within the optimal temperature window. The comparison results serve as the core basis for determining temperature adjustment. If the current kinetic constant is below the lower limit, the reaction environment temperature is insufficient to support efficient drug loading, and a temperature increase instruction is generated. Conversely, if the constant is above the upper limit, it indicates that the excessively high temperature has caused excessive migration of drug molecules and even compromised structural stability, and a temperature decrease instruction is output. If the constant is within the threshold range, no temperature adjustment is required, and the current operating conditions are maintained. Based on the temperature adjustment direction, the current drug loading kinetic deviation and the system response sensitivity parameters are combined to calculate the specific temperature dynamic adjustment parameters, including the adjustment amplitude and the change rate. The adjustment amplitude is the temperature increase or decrease value based on the current reaction temperature. The value is determined by the difference between the kinetic constant and the threshold, the slope of the control curve, and the historical adjustment response efficiency. The change rate is the rate of temperature increase or decrease per unit time, which is calculated by comprehensively considering the system heat capacity, the reactor heating or cooling capacity, and the thermal stability of the drug structure. The adjustment amplitude value in the temperature dynamic adjustment parameters is superimposed on the current reaction temperature to obtain the target reaction temperature. If the current temperature is the set base temperature and the system output adjustment range is positive, the target reaction temperature is the base temperature adjusted upward. If the adjustment range is negative, the target temperature is the adjusted downward. If the adjustment range is zero, no temperature change is required. The temperature adjustment timeline is established based on the rate of change value in the temperature dynamic adjustment parameter. This means that the temperature increase or decrease needs to be completed in several stages. The duration, temperature increment, and hold time of each stage are broken down and written into the instruction set of the temperature control program.All parameters including the target reaction temperature and the temperature adjustment time process are integrated into a structured reaction temperature optimization instruction, and the reaction temperature optimization instruction is pushed to the reaction control system interface to drive the temperature control module to adjust the temperature gradient according to the adjustment rate and target value.

[0044] In a specific embodiment, the process of executing step S500 may specifically include the following steps: Determine the release half-life of each drug component in the multi-component balanced drug-loaded particles and calculate the deviation between the release half-life and the target release half-life; Determining the cross-linking degree distribution scheme for the drug-loading difficult component and the drug-loading easy component according to the deviation value, and preparing a double cross-linker system based on the cross-linking degree distribution scheme to obtain a nanogel; The drug loading rate of each drug component in the nanogel was detected and the relative standard deviation of the drug loading rate was calculated. At the same time, the cumulative release curve of each component in the nanogel was measured and the release similarity factor was calculated. The drug loading stability and release consistency were determined based on the relative standard deviation of drug loading rate and release similarity factor to obtain the verification results; According to the verification results, the cross-linking agent concentration ratio, cross-linking time parameters, cross-linking temperature parameters and pH adjustment parameters that meet the qualified standards are obtained from the process parameter data table to obtain the standard cross-linking process parameters. The nanogel parameters are optimized according to the standard cross-linking process parameters to obtain the cross-linking optimized process parameters.

[0045] Specifically, based on the structure and drug distribution of multi-component balanced drug-loaded particles, a standardized in vitro release test platform was used to independently test the release behavior of each drug component under simulated physiological conditions. Real-time data acquisition and curve modeling were performed on the release curve to extract the release half-life of each drug under the current carrier structure. The release half-life of each drug component was compared with the preset target release half-life, and the target value was determined by the drug dosage form design requirements or clinical release schedule planning. By comparing the numerical difference between the actual and target release half-lives, the release half-life deviation of each component was calculated to reflect the degree of time course incoordination during the release of different drugs. If the release half-life of a component is much lower than the target value, it means that it is released too quickly and the diffusion inhibition of the component needs to be strengthened structurally; conversely, if the release half-life is significantly higher than the target value, it means that its release process is restricted and the cross-linking strength needs to be moderately weakened structurally to accelerate the release rate. Based on the direction and magnitude of the release half-life deviation, a cross-linking degree allocation scheme was established. Within this scheme, drugs that release too quickly are labeled as difficult-to-load components, and their carrier microregions are assigned a higher degree of cross-linking. Drugs with slower release rates or those near the target value are labeled as easy-to-load components, and these are assigned to regions with medium or low cross-linking degrees to ensure smooth release. Based on this cross-linking degree allocation scheme, a dual-crosslinker formulation was developed, in which a primary cross-linker such as glutaraldehyde is used to establish a network structure framework, and an auxiliary cross-linker such as carbodiimide is used to control the local chemical cross-linking density. Based on the specific values ​​of the release half-life deviations for each component, the concentration ratio, action time, and spatial distribution of the two types of cross-linkers were set. A differentiated dual-crosslinker system was configured and subjected to a cross-linking reaction to obtain nanogels with matched structural responsiveness. The drug loading rates of each drug component in the nanogels were quantitatively tested, and the relative standard deviation of the loading rates of each component was calculated to assess the uniformity of drug loading distribution. Under the same release conditions, the cumulative release curves of each drug component were measured. By analyzing the degree of fit between the release curves, the release similarity factors between the components were calculated. The closer the release similarity factor is to the reference value, the more synchronized the multi-component release behavior is. A comprehensive analysis of the two indicators, the relative standard deviation of the drug loading rate and the release similarity factor, was performed. When both indicators met the control threshold set by the system, it was considered that the nanogel under the current cross-linking conditions had good drug loading stability and release consistency, and a verification qualified signal was output; if any indicator did not meet the standard, the cross-linking degree allocation strategy needed to be revised. Based on the verification results, the process parameter database was called in the system, and the index parameters were compared with the batch records to obtain all the cross-linker concentration ratios, cross-linking time settings, cross-linking temperature programs, and pH control parameters that have been verified as qualified in the history to form a standard cross-linking process parameter set. Association rule mining and multivariate regression modeling were performed on the standard cross-linking process parameter set to extract a set of cross-linking conditions that best matched the current drug structure, carrier type, and release requirements as the structural control benchmark for the current batch of nanogels.These parameters are re-entered into the process optimization module as input variables, and fine-tuned in combination with the current release data and structural stability trends to ultimately obtain the cross-linking optimized process parameters after structural optimization.

[0046] See also Figure 2 , Figure 2 This is a schematic block diagram of the structure of the preparation process optimization system of the compound lidocaine nanogel provided in the embodiment of the present application, as shown in FIG. Figure 2 As shown, the preparation process optimization system of compound lidocaine nanogel includes: The difference determination module 211 is used to perform a difference determination on the carrier binding affinity of the lidocaine main drug and the auxiliary drug in the lidocaine compound to obtain a drug feeding sequence table; The surface potential pre-adjustment module 212 is used to pre-adjust the surface potential of the nanocarrier according to the drug feeding sequence table to obtain a drug-loaded pre-treated carrier; The step-by-step drug loading module 213 is used to perform step-by-step drug loading according to the drug feeding sequence table based on the drug pretreatment carrier to obtain saturation data during the drug loading process; pH and temperature optimization module 214, used to synchronously input saturation data into the drug loading process control system to optimize pH and temperature to obtain multi-component balanced drug-loaded particles; The layered gel cross-linking module 215 is used to perform layered gel cross-linking based on the multi-component balanced drug-loaded particles to obtain nanogels, verify the drug loading stability and release consistency of the nanogels, and generate cross-linking optimization process parameters.

[0047] Through the synergistic cooperation of the above components, the binding affinity of each drug molecule to the carrier is quantitatively determined by isothermal titration calorimetry, and a drug loading competition relationship evaluation system based on the principle of thermodynamic equilibrium is established. It can accurately identify components that are difficult to load and components that are easy to load. A step-by-step drug loading strategy is adopted in which the components that are difficult to load are given priority to avoid excessive occupation of carrier sites by components that are easy to load, ensuring that auxiliary drugs that are difficult to load have priority access to drug loading opportunities, significantly improving the drug loading balance of multi-component drugs, and overcoming the drug loading ratio imbalance problem caused by the traditional synchronous feeding method. Based on the molecular charge characteristics of the components that are difficult to load, the surface potential of the carrier is targetedly adjusted, and the binding affinity of the components that are difficult to load and the carrier is enhanced through electrostatic interaction, providing personalized drug loading microenvironments for drug molecules with different characteristics, and improving the adaptability and efficiency of the drug loading process. Through the joint monitoring of dynamic light scattering technology and ultraviolet spectrophotometry, a quantitative identification standard for the drug loading saturation state was established, and precise control of the drug loading process was achieved. A dynamic regulation mechanism of pH and temperature based on the feedback of drug loading saturation data was established. The drug loading environment conditions were automatically optimized through a closed-loop control system, and adaptive regulation of the drug loading process was achieved. A hierarchical cross-linking strategy based on differences in drug release kinetics was adopted. Through a double cross-linker system and differentiated cross-linking conditions, compensatory adjustments were made to the release rates of different drug components, achieving synchronized release of multi-component drugs and solving the release timing mismatch problem caused by the traditional unified cross-linking method.

[0048] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0049] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0050] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for optimizing the preparation process of compound lidocaine nanogel, characterized in that: include: The carrier binding affinity of lidocaine main drug and auxiliary drug in compound lidocaine was differentially determined to obtain the drug feeding sequence table; Pre-adjusting the surface potential of the nanocarrier according to the drug feeding sequence table to obtain a drug-loaded pretreated carrier; Based on the drug-loaded pretreated carrier, drug loading is performed step by step according to the drug feeding sequence table to obtain saturation data during the drug loading process; The saturation data is synchronously input into the drug loading process control system to optimize pH and temperature to obtain multi-component balanced drug loading particles; Layered gel cross-linking is performed on the multi-component balanced drug-loaded particles to obtain nanogels, and the drug loading stability and release consistency of the nanogels are verified to generate cross-linking optimization process parameters.

2. The method for optimizing the preparation process of the compound lidocaine nanogel according to claim 1, wherein: The differential determination of the carrier binding affinity between the lidocaine main drug and the auxiliary drug in the lidocaine compound is performed to obtain a drug feeding sequence table, including: Isothermal titration calorimetry was performed on the main drug lidocaine and each auxiliary drug in the lidocaine compound with the nanocarrier to obtain the carrier binding affinity value of each auxiliary drug; Calculating the drug loading competition coefficient between each auxiliary drug based on the carrier binding affinity value; According to the drug loading competition coefficient, the drug with a binding affinity lower than a first preset value is marked as a drug loading difficult component, and the drug with a binding affinity higher than a second preset value is marked as a drug loading easy component; The first feeding sequence is set according to the difficult drug loading component, and the second feeding sequence is set according to the lidocaine main drug and the easy drug loading component; A drug feeding sequence table is created based on the first feeding sequence and the second feeding sequence.

3. The method for optimizing the preparation process of the compound lidocaine nanogel according to claim 2, wherein: The isothermal titration calorimetry is performed on the lidocaine main drug and each auxiliary drug in the lidocaine compound with the nanocarrier to obtain the carrier binding affinity value of each auxiliary drug, including: Isothermal titration calorimetry was used to prepare drug solutions of lidocaine and each auxiliary drug, and carrier suspensions of nanocarriers. The carrier suspension is used as a sample pool solution, and each drug solution in the drug solution group is used as a titrant to continuously titrate under a constant temperature condition to obtain a titration heat flow signal and a binding saturation curve; Integrating the titration heat flow signal to obtain a binding enthalpy change value, and performing nonlinear fitting on the binding saturation curve to obtain a binding constant; The carrier binding free energy value of each auxiliary drug is calculated based on the binding constant and the binding enthalpy change value, and then the carrier binding free energy value is converted into a carrier binding affinity value expressed in a molar concentration unit.

4. The method for optimizing the preparation process of the compound lidocaine nanogel according to claim 1, wherein: The method of pre-adjusting the surface potential of the nanocarrier according to the drug feeding sequence table to obtain the drug-loaded pretreated carrier comprises: Reading the drug molecular charge density and molecular polarity data marked as drug-loading difficult components in the drug feeding sequence table, and calculating the carrier surface potential enhancement amplitude required for each drug-loading difficult component based on the drug molecular charge density and the molecular polarity data; Calculating the buffer pH value required for the nanocarrier to reach a target surface potential based on the carrier surface potential enhancement amplitude, and determining the buffer pH adjustment parameter based on the buffer pH value; Dispersing the nanocarrier in a phosphate buffer corresponding to the pH adjustment parameter, and adding a polyethylene glycol surfactant to perform a temperature gradient treatment to obtain a modified carrier; The surface potential of the modified carrier is measured using electrophoretic light scattering technology to obtain a surface potential measurement value. When the surface potential measurement value is within a preset voltage range and the deviation from the calculated target value is less than the target value, pre-adjustment is stopped to obtain a drug-loaded pretreated carrier.

5. The method for optimizing the preparation process of the compound lidocaine nanogel according to claim 4, wherein: The step of reading the drug molecular charge density and molecular polarity data marked as drug-loading-difficult components in the drug feeding sequence table, and calculating the carrier surface potential enhancement amplitude required for each drug-loading-difficult component based on the drug molecular charge density and the molecular polarity data, includes: Extracting the molecular structure data of each drug marked as a drug-loading difficulty component from the drug feeding sequence table, and calculating the drug molecular charge density and molecular polarity data of each drug-loading difficulty component based on the molecular structure data of each drug; Calculating the effective charge radius of each drug-loading-difficult component in aqueous solution based on the dipole moment value of each drug-loading-difficult component in the molecular polarity data, and calculating the electrostatic interaction strength between each component and the carrier surface according to the effective charge radius; Calculating the carrier surface potential difference required to achieve optimal drug loading efficiency for each drug-load-difficult component based on the electrostatic interaction strength combined with the drug molecule charge density; The difference between the surface potential difference of the carrier and the original surface potential of the nanocarrier is calculated to obtain a target difference. When the target difference is a positive number, the target difference is used as the carrier surface potential enhancement amplitude. When the target difference is a negative number, the carrier surface potential enhancement amplitude is set to zero.

6. The method for optimizing the preparation process of the compound lidocaine nanogel according to claim 1, wherein: The step-by-step drug loading based on the drug-loaded pretreated carrier according to the drug feeding sequence table to obtain saturation data during the drug loading process includes: According to the first feeding sequence in the drug feeding sequence table, the auxiliary drug of the drug-loading difficult component is added to the suspension of the drug-loading pretreatment carrier to perform the first stage of drug loading treatment, thereby obtaining the first drug loading reaction system for the encapsulation process of the drug-loading difficult component; Determining the carrier particle size change rate of the first drug loading reaction system to detect a first concentration change rate of the free drug in the supernatant, determining that the first stage drug loading is saturated when the carrier particle size change rate is lower than the first change rate value and the first concentration change rate is lower than the second change rate value, and obtaining a first saturation degree and a first stage drug loading completion signal; Based on the first-stage drug loading completion signal, the lidocaine main drug and the drug loading component are added into the first drug loading reaction system according to the second feeding sequence in the drug feeding sequence table to perform a second-stage drug loading process to obtain a second drug loading reaction system with multi-component mixed drug loading; measuring a surface potential change amplitude of the second drug loading reaction system and monitoring a second concentration change rate of the free drug in each component, determining that the second stage drug loading is saturated when the surface potential change amplitude is less than a third change rate value and the second concentration change rate is less than a fourth change rate value, and obtaining a second saturation degree; The first saturation and the second saturation are used as saturation data during the drug loading process.

7. The method for optimizing the preparation process of the compound lidocaine nanogel according to claim 1, characterized in that: The step of synchronously inputting the saturation data into a drug loading process control system to optimize pH and temperature to obtain multi-component balanced drug loading particles comprises: Synchronously inputting the saturation data into the drug loading process control system, calculating a drug loading balance deviation value, and generating a drug loading balance deviation signal and a drug loading process control trigger instruction based on the drug loading balance deviation value; Based on the drug loading balance deviation signal, a pH gradient control algorithm is started to generate pH dynamic adjustment parameters and buffer pH optimization instructions; Initiate a temperature program control algorithm according to the drug loading process control trigger instruction to generate temperature dynamic adjustment parameters and reaction temperature optimization instructions; The drug loading rate and the corresponding coefficient of variation of each component corresponding to the drug loading reaction system processed by the pH optimization instruction and the reaction temperature optimization instruction are measured. When the coefficient of variation is lower than the first percentage and the drug loading rate of each component is greater than the second percentage, it is determined that the drug loading balance is completed, and multi-component balanced drug-loaded particles are obtained.

8. The method for optimizing the preparation process of the compound lidocaine nanogel according to claim 7, wherein: The method of starting a temperature program control algorithm according to the drug loading process control trigger instruction to generate temperature dynamic adjustment parameters and reaction temperature optimization instructions includes: Starting the temperature program control algorithm according to the drug loading process control trigger instruction, extracting the drug loading saturation change curve and calculating the drug loading kinetic constant; Based on the comparison between the drug loading kinetic constant and a preset drug loading kinetic threshold range, a temperature adjustment direction instruction is obtained, and a temperature dynamic adjustment parameter including an adjustment amplitude value and a change rate value is calculated according to the temperature adjustment direction instruction; The target reaction temperature is determined by superimposing the adjustment amplitude value in the temperature dynamic adjustment parameter with the current reaction temperature, and the temperature adjustment time process is formulated based on the change rate value in the temperature dynamic adjustment parameter to obtain a reaction temperature optimization instruction including the target reaction temperature and the adjustment time process.

9. The method for optimizing the preparation process of the compound lidocaine nanogel according to claim 1, wherein: The step of performing layered gel crosslinking on the multi-component balanced drug-loaded particles to obtain nanogels, verifying drug loading stability and release consistency of the nanogels, and generating crosslinking optimization process parameters includes: Determining the release half-life of each drug component in the multi-component balanced drug-loaded particles, and calculating the deviation between the release half-life and the target release half-life; Determining a cross-linking degree distribution scheme for drug-loading-difficult components and drug-loading-easy components according to the deviation value, and preparing a dual cross-linker system based on the cross-linking degree distribution scheme to obtain a nanogel; Detecting the drug loading rate of each drug component in the nanogel and calculating the relative standard deviation of the drug loading rate, and simultaneously determining the cumulative release curve of each component in the nanogel and calculating the release similarity factor; Determine drug loading stability and release consistency based on the relative standard deviation of the drug loading rate and the release similarity factor to obtain a verification result; According to the verification results, the cross-linking agent concentration ratio, cross-linking time parameters, cross-linking temperature parameters and pH adjustment parameters that meet the qualified standards are obtained from the process parameter data table to obtain standard cross-linking process parameters, and the nanogel is optimized according to the standard cross-linking process parameters to obtain cross-linking optimized process parameters.

10. A preparation process optimization system for compound lidocaine nanogel, characterized in that: A method for optimizing the preparation process of the compound lidocaine nanogel according to any one of claims 1 to 9, comprising: The difference determination module is used to perform difference determination on the carrier binding affinity between the main drug lidocaine and the auxiliary drug in the compound lidocaine to obtain the drug feeding sequence table; A surface potential pre-adjustment module, used to pre-adjust the surface potential of the nanocarrier according to the drug feeding sequence table to obtain a drug-loaded pretreated carrier; A step-by-step drug loading module is used to perform step-by-step drug loading based on the drug loading pretreatment carrier according to the drug feeding sequence table to obtain saturation data during the drug loading process; A pH and temperature optimization module is used to synchronously input the saturation data into the drug loading process control system to optimize pH and temperature to obtain multi-component balanced drug-loaded particles; The layered gel cross-linking module is used to perform layered gel cross-linking according to the multi-component balanced drug-loaded particles to obtain nanogels, verify the drug loading stability and release consistency of the nanogels, and generate cross-linking optimization process parameters.

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