Preparation process optimization method and system of compound lidocaine nanogel

Abstract

The application relates to the technical field of process optimization, and discloses a preparation process optimization method and system of a compound lidocaine nanogel. The method comprises the following steps: differentially determining the carrier binding affinity of lidocaine main drugs and auxiliary drugs in the compound lidocaine, and obtaining a drug feeding sequence table; pre-adjusting the surface potential of the nanocarrier according to the drug feeding sequence table, and obtaining a drug-loaded pretreatment carrier; performing step-by-step drug loading based on the drug-loaded pretreatment carrier according to the drug feeding sequence table, and obtaining saturation data in the drug loading process; synchronously inputting the saturation data into a drug loading process regulation system to optimize pH and temperature, and obtaining multi-component balanced drug-loaded particles; performing layered gel crosslinking according to the multi-component balanced drug-loaded particles, obtaining a nanogel, and verifying the drug loading stability and release consistency of the nanogel, and generating crosslinking optimization process parameters; and the application improves the adaptability and drug loading efficiency of the drug loading process.

Description

Technical Field

[0001] This invention relates to the field of process optimization technology, and in particular to a method and system for optimizing the preparation process of compound lidocaine nanogel. Background Technology

[0002] Traditional drug loading processes for compound formulations often employ a one-time mixing and addition method, simultaneously encapsulating lidocaine and multiple adjuvant drugs into a carrier system. However, due to the different molecular weights, polarities, and charge properties of various drug molecules, their binding affinity to nanocarriers varies significantly. This leads to drug loading selectivity, where the carrier preferentially encapsulates drug components that are easier to load, while components that are more difficult to load have lower encapsulation efficiency, ultimately resulting in an imbalance in the drug loading ratios within the carrier. Existing drug loading processes lack effective means to regulate the competitive behavior of multi-component drugs, and cannot perform personalized process optimization based on the loading characteristics of different drug molecules. The simplistic approach of treating the carrier surface chemical environment is insufficient to simultaneously address the optimal loading conditions for multiple drugs, especially for adjuvant drugs with low affinity, failing to provide sufficient loading driving force. Furthermore, traditional processes lack real-time monitoring and dynamic control mechanisms for the drug loading process, relying on experience to determine the loading endpoint, which cannot accurately identify the drug loading equilibrium state, resulting in insufficient precision in drug loading process control. Summary of the Invention

[0003] This invention provides an optimized method and system for preparing compound lidocaine nanogels, which improves the adaptability and efficiency of drug loading processes.

[0004] In a first aspect, the present invention provides an optimized preparation process method for compound lidocaine nanogels, the optimized preparation process method for compound lidocaine nanogels comprising:

[0005] The carrier binding affinity of lidocaine active drug and excipient drug in compound lidocaine was determined by differential determination, and a drug dosing sequence table was obtained.

[0006] The surface potential of the nanocarrier is pre-adjusted according to the drug loading sequence table to obtain a drug-loaded pretreated carrier.

[0007] Based on the drug-loaded pretreatment carrier, drug is loaded stepwise according to the drug loading sequence table to obtain saturation data during the drug loading process;

[0008] The saturation data is synchronously input into the drug loading process control system for pH and temperature optimization to obtain multi-component balanced drug-loaded particles.

[0009] Layered gel crosslinking 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 optimized crosslinking process parameters.

[0010] In conjunction with the first aspect, in a first implementation of the first aspect of the present invention, the determination of the difference in carrier binding affinity between the lidocaine active ingredient and the adjuvant drug in compound lidocaine to obtain a drug dosing sequence table includes:

[0011] The binding affinity of each adjuvant drug to the nanocarrier was determined by isothermal titration calorimetry using lidocaine as the active ingredient and each adjuvant drug in compound lidocaine.

[0012] The drug loading competition coefficients among the adjuvant drugs were calculated based on the carrier binding affinity values.

[0013] Based on the drug loading competition coefficient, drugs with binding affinity lower than a first preset value are marked as drug-difficult components, and drugs with binding affinity higher than a second preset value are marked as drug-easy components.

[0014] The first feeding sequence is set according to the drug-difficult-to-load component, and the second feeding sequence is set according to the lidocaine active ingredient and the drug-easy-to-load component.

[0015] A drug feeding sequence table is created based on the first feeding sequence and the second feeding sequence.

[0016] In conjunction with the first aspect, in a second implementation of the first aspect of the present invention, the step of performing isothermal titration calorimetry on the lidocaine active ingredient and each auxiliary drug in the compound lidocaine with the nanocarrier to obtain the carrier binding affinity values ​​of each auxiliary drug includes:

[0017] The lidocaine active drug and each auxiliary drug were prepared into drug solution groups by isothermal titration calorimetry, and a carrier suspension was prepared for the nanocarrier.

[0018] The carrier suspension was used as the sample cell solution, and each drug solution in the drug solution group was used as a titrant for continuous titration under constant temperature conditions to obtain the titration heat flow signal and binding saturation curve.

[0019] The binding enthalpy change is obtained by integrating the titration heat flow signal and performing nonlinear fitting on the binding saturation curve to obtain the binding constant.

[0020] Based on the binding constant and the binding enthalpy change, the carrier binding free energy of each auxiliary drug is calculated, and then the carrier binding free energy is converted into a carrier binding affinity value expressed in molar concentration units.

[0021] In conjunction 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 delivery sequence table to obtain a drug-loaded pretreated carrier includes:

[0022] Read the charge density and polarity data of drug molecules marked as difficult-to-load components in the drug delivery sequence table, and calculate the required carrier surface potential enhancement amplitude for each difficult-to-load component based on the charge density and polarity data.

[0023] The pH value of the buffer solution required for the nanocarrier to reach the target surface potential is calculated based on the enhancement amplitude of the carrier surface potential, and the pH adjustment parameters of the buffer solution are determined based on the pH value of the buffer solution.

[0024] The nanocarrier was dispersed in a phosphate buffer solution corresponding to the pH adjustment parameter, and a polyethylene glycol surfactant was added for temperature gradient treatment to obtain the modified carrier.

[0025] The surface potential of the modified carrier is measured using electrophoretic light scattering technology to obtain the surface potential measurement value. When the surface potential measurement value is within the preset voltage range and the deviation from the calculated target value is less than the target value, the pre-adjustment is stopped to obtain the drug-loaded pretreated carrier.

[0026] In conjunction with the first aspect, in a fourth implementation of the first aspect of the present invention, the step of reading the charge density and molecular polarity data of drug molecules marked as drug-difficult-to-load components in the drug delivery sequence table, and calculating the required carrier surface potential enhancement amplitude for each drug-difficult-to-load component based on the drug molecule charge density and molecular polarity data, includes:

[0027] Extract the molecular structure data of each drug molecule marked as a drug-difficult component from the drug feeding sequence list, and calculate the molecular charge density and molecular polarity data of each drug-difficult component based on the molecular structure data.

[0028] Based on the dipole moment values ​​of each drug-difficult-to-load component in the molecular polarity data, the effective charge radius of each drug-difficult-to-load component in the aqueous solution is calculated, and the electrostatic interaction strength between each component and the carrier surface is calculated based on the effective charge radius.

[0029] The surface potential difference of the carrier required to achieve the optimal drug loading efficiency for each difficult-to-load component is calculated based on the electrostatic interaction strength and the charge density of the drug molecules.

[0030] The difference between the surface potential difference of the carrier and the original surface potential of the nanocarrier is calculated to obtain the target difference value. When the target difference value is positive, the target difference value is used as the carrier surface potential enhancement amplitude. When the target difference value is negative, the carrier surface potential enhancement amplitude is set to zero.

[0031] In conjunction with the first aspect, in a fifth implementation of the first aspect of the present invention, the step-by-step loading of the drug-loaded pretreatment carrier according to the drug loading sequence table to obtain saturation data during the drug loading process includes:

[0032] According to the first feeding sequence in the drug feeding sequence table, the auxiliary drug of the drug-difficult-to-load component is added to the suspension of the drug-loading pretreatment carrier for the first stage of drug loading treatment, thereby obtaining the first drug loading reaction system for the encapsulation process of the drug-difficult-to-load component.

[0033] The first drug loading reaction system is measured by measuring the change rate of carrier particle size and detecting the first concentration change rate of free drug in the supernatant. When the change rate of carrier particle size is lower than the first change rate value and the first concentration change rate is less than the second change rate value, the first stage of drug loading is determined to be saturated, and the first saturation and the first stage of drug loading completion signal are obtained.

[0034] Based on the first stage drug loading completion signal, lidocaine active drug and drug-loading components are added to the first drug loading reaction system according to the second feeding sequence in the drug feeding sequence table for the second stage drug loading treatment, resulting in a second drug loading reaction system with multi-component mixed drug loading.

[0035] The surface potential change amplitude of the second drug loading reaction system is measured and the second concentration change rate of each component of the free drug is monitored. 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, the second stage of drug loading saturation is determined, and the second saturation is obtained.

[0036] The first saturation and the second saturation are used as saturation data during the drug loading process.

[0037] In conjunction with the first aspect, in the sixth implementation of the first aspect of the present invention, the step of synchronously inputting the saturation data into the drug loading process control system for pH and temperature optimization to obtain multi-component balanced drug-loaded particles includes:

[0038] The saturation data is synchronously input into the drug loading process control system to calculate the drug loading balance deviation value, and a drug loading balance deviation signal and a drug loading process control trigger command are generated based on the drug loading balance deviation value.

[0039] Based on the drug loading uniformity deviation signal, the pH gradient control algorithm is activated to generate dynamic pH adjustment parameters and buffer pH optimization instructions.

[0040] The temperature program control algorithm is activated according to the drug loading process control trigger command to generate dynamic temperature adjustment parameters and reaction temperature optimization commands.

[0041] The drug loading rate and corresponding coefficient of variation of each component in the drug-loaded reaction system treated by the pH optimization command and the reaction temperature optimization command 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, the drug loading equilibrium is determined to be completed, and multi-component balanced drug-loaded particles are obtained.

[0042] In conjunction with the first aspect, in the seventh implementation of the first aspect of the present invention, the step of activating the temperature program control algorithm according to the drug loading process control trigger instruction to generate dynamic temperature adjustment parameters and reaction temperature optimization instructions includes:

[0043] The temperature program control algorithm is activated according to the drug loading process control trigger command to extract the drug loading saturation change curve and calculate the drug loading kinetic constant.

[0044] Based on the comparison between the drug loading kinetic constant and the preset drug loading kinetic threshold range, a temperature adjustment direction command is obtained, and a temperature dynamic adjustment parameter including the adjustment amplitude value and the change rate value is calculated according to the temperature adjustment direction command.

[0045] The target reaction temperature is determined by superimposing the adjustment amplitude value in the dynamic temperature adjustment parameter with the current reaction temperature, and the temperature adjustment time process is formulated based on the change rate value in the dynamic temperature adjustment parameter, resulting in a reaction temperature optimization command that includes the target reaction temperature and the adjustment time process.

[0046] In conjunction with the first aspect, in the eighth implementation of the first aspect of the present invention, the step of performing layered gel crosslinking based on the multi-component balanced drug-loaded particles to obtain a nanogel, and verifying the drug loading stability and release consistency of the nanogel to generate crosslinking optimization process parameters, includes:

[0047] The release half-life of each drug component in the multi-component balanced drug-loaded particles was determined, and the deviation between the release half-life and the target release half-life was calculated.

[0048] The crosslinking degree allocation scheme of the drug-difficult and drug-easy components is determined based on the deviation value, and a dual crosslinking agent system is prepared based on the crosslinking degree allocation scheme to obtain nanogels.

[0049] 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 determined and the release similarity factor was calculated.

[0050] The drug loading stability and release consistency were determined based on the relative standard deviation of the drug loading rate and the release similarity factor, and the verification results were obtained.

[0051] Based on the verification results, the crosslinking agent concentration ratio, crosslinking time parameter, crosslinking temperature parameter, and pH adjustment parameter that meet the qualification standard are obtained from the process parameter data table to obtain the standard crosslinking process parameters. The nanogel is then optimized based on the standard crosslinking process parameters to obtain the optimized crosslinking process parameters.

[0052] Secondly, the present invention provides a process optimization system for preparing compound lidocaine nanogels, the process optimization system for preparing compound lidocaine nanogels comprising:

[0053] The difference determination module is used to determine the difference in carrier binding affinity between lidocaine active drug and excipient drug in compound lidocaine and obtain the drug dosing sequence table.

[0054] A surface potential pre-adjustment module is used to pre-adjust the surface potential of the nanocarrier according to the drug delivery sequence table to obtain a drug-loaded pretreated carrier.

[0055] The step-by-step drug loading module is used to load drugs step-by-step according to the drug loading sequence table based on the drug loading pretreatment carrier, and to obtain saturation data during the drug loading process.

[0056] The pH and temperature optimization module is used to synchronously input the saturation data into the drug loading process control system for pH and temperature optimization, so as to obtain multi-component balanced drug-loaded particles.

[0057] The layered gel crosslinking module is used to perform layered gel crosslinking based on the multi-component balanced drug-loaded particles to obtain nanogels, and to verify the drug loading stability and release consistency of the nanogels, and generate crosslinking optimization process parameters.

[0058] The technical solution provided by this invention establishes a drug loading competition evaluation system using isothermal titration calorimetry to accurately identify drug-difficult and drug-easy components. A stepwise drug loading strategy prioritizing drug-difficult components is adopted, avoiding excessive occupation of carrier sites by drug-easy components and significantly improving the drug loading balance of multi-component drugs. Differential pre-adjustment of carrier surface potential is performed based on the molecular charge characteristics of drug-difficult components, providing personalized drug loading microenvironments for different drug molecules and improving drug loading efficiency. A quantitative identification standard for drug loading saturation is established through joint monitoring using dynamic light scattering technology and ultraviolet spectrophotometry. Combined with a dynamic pH and temperature control mechanism based on drug loading saturation data feedback, adaptive adjustment of the drug loading process is achieved. A layered gel crosslinking strategy based on differences in drug release kinetics is employed. A dual crosslinking agent system and differentiated crosslinking conditions are used to compensate for the release rates of different drug components, achieving synchronized release of multi-component drugs. Attached Figure Description

[0059] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 A schematic flowchart illustrating the optimized preparation process of compound lidocaine nanogel provided in this application embodiment;

[0061] Figure 2 A schematic block diagram of the structure of the preparation process optimization system for compound lidocaine nanogel provided in the embodiments of this application. Detailed Implementation

[0062] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0063] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the described order. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change based on the actual situation.

[0064] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the 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.

[0065] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0066] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features described herein can be combined with each other.

[0067] Please see Figure 1 , Figure 1 This is a schematic flowchart illustrating the optimized preparation process of the compound lidocaine nanogel provided in the embodiments of this application, as shown below. Figure 1As shown, the optimized preparation process of compound lidocaine nanogel provided in this application includes steps S100 to S500.

[0068] Step S100: The carrier binding affinity of lidocaine active drug and excipient drug in compound lidocaine was determined to obtain the drug dosing sequence table.

[0069] It is understood that the executing entity of this invention can be a process optimization system for the preparation of compound lidocaine nanogels, or it can be a terminal or a server; the specific implementation is not limited here. This embodiment of the invention will be illustrated using a server as the executing entity.

[0070] Specifically, affinity assays were performed on the main and auxiliary drug components of compound lidocaine. An isothermal titration calorimetry method was used. By recording the changes in the heat of titration reaction between different drug molecules and nanocarriers at a constant temperature, key thermodynamic parameters such as binding enthalpy, binding constant, and number of reaction sites between drug molecules and carriers were quantitatively analyzed. This yielded numerical values ​​of the binding affinity between each drug component and the nanocarrier, constructing a dataset of binding affinity distributions between lidocaine and various auxiliary drugs. Based on the carrier binding affinity values, the drug loading competition coefficient among the auxiliary drugs was calculated. The drug loading competition coefficient uses the difference in affinity between drug molecules for the same binding site on the carrier as the core parameter, and is evaluated as the ratio of the binding reaction rate constant to the binding equilibrium constant, thus reflecting the intensity of competition between different drugs for the nanocarrier during actual drug loading. Drugs with higher competition coefficients are more likely to occupy effective sites during carrier binding, while those with lower coefficients are more difficult to encapsulate effectively. A competitive ranking result was constructed by comparing the competition coefficients of all auxiliary drugs. Based on the drug loading competition coefficients, the drugs were grouped according to a preset affinity threshold. Among them, adjuvant drugs with affinity lower than the first preset value (such as lower than 10) will be combined. -5 Components with a binding affinity higher than a second preset value (e.g., higher than 10 mol / L) are labeled as having difficulty loading drugs. -3 The auxiliary drug (mol / L) was labeled as the easily loaded component. Based on the grouping results, the order of drug loading was determined, prioritizing the encapsulation of components with difficult loading to ensure full utilization of their binding sites on the nanocarrier. Then, the "addition of lidocaine" and the "easily loaded component" were added, thus avoiding the latter from crowding out the former's binding process due to its stronger affinity. The sorted drug components and their loading order were structured and organized to create a drug loading sequence table, clearly listing the loading stages, time windows, and concentration ranges for each type of drug.

[0071] Step S200: Pre-adjust the surface potential of the nanocarrier according to the drug feeding sequence table to obtain the drug-loaded pretreated carrier;

[0072] Specifically, the structural information of all drug molecules marked as "drug-difficult-to-load components" was extracted from the drug loading sequence, and their charge density and molecular polarity data were read for each drug molecule. This data was obtained through molecular simulation databases or experimental methods such as multivariate mass spectrometry, dipole moment analysis, and potential map reconstruction, serving as key physicochemical indicators affecting the interaction between drug molecules and the nanocarrier surface. Combining the directional adsorption behavior of drug molecules under different electric fields, a surface potential regulation model was established. By inputting the charge density and polarity parameters of the drug molecules into this model, the required surface potential enhancement for each drug-difficult-to-load component under effective adsorption conditions was calculated, thereby deriving the corresponding target surface potential value and forming a potential regulation requirement dataset. After obtaining the surface potential enhancement amplitude of the carrier, based on the known Zeta potential-environmental pH response relationship of the carrier material, the buffer pH condition corresponding to the nanocarrier reaching the target surface potential was deduced. The deduction process was achieved through a potential-pH fitting equation or empirical curve, substituting the target surface potential into the model to calculate the target buffer pH value. To ensure precise adjustment, the buffer's buffering capacity, volume, and adjustment gradient were determined, generating pH adjustment parameters that matched the target pH value of the buffer. These parameters included the types of required acid and base components, their molar ratios, and the order of addition, guiding the buffer preparation process. The nanocarrier was dispersed in a phosphate buffer solution corresponding to the pH adjustment parameters, ensuring sufficient pre-wetting and charge reconstruction in an environment close to the target pH. A polyethylene glycol-based nonionic surfactant was introduced, with its concentration controlled within a specific range (e.g., 0.1%-0.3%), to enhance the fluidity and flexibility of the hydrophobic regions on the nanocarrier surface without interfering with ionic strength. A temperature gradient treatment was implemented, applying a temperature gradient of 25°C to 35°C and maintaining it for a stable period, followed by cooling to 28°C. This spurred the reconstruction of the active structure on the carrier surface, forming a modified carrier with specific polar response capabilities. To verify whether the modification process achieved the intended effect, the surface Zeta potential of the modified carrier was measured using an electrophoretic light scattering instrument. 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., -15mV to -25mV) and the deviation between the measured value and the theoretical value is less than the allowable error threshold (e.g., ±1mV), then the carrier surface potential adjustment is considered complete, and further processing is stopped. The obtained nanocarrier is defined as the drug-loaded pretreatment carrier.

[0073] Step S300: Based on the drug loading pretreatment carrier, drug is loaded stepwise according to the drug loading sequence table to obtain saturation data during the drug loading process;

[0074] Specifically, according to the first feeding sequence in the drug feeding order table, the auxiliary drug, which is difficult to load, is slowly and uniformly added to the dispersion suspension system formed by the pretreated carrier, constructing 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 keep the binding sites on the carrier surface in a highly active state and to maximize the adsorption and encapsulation of auxiliary drug molecules. During the first-stage drug loading process, two indicators are monitored in real time: the carrier particle size change rate and the concentration change rate of free drug in the supernatant. Particle size change is obtained through 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 (e.g., 5% / 5 minutes), it indicates that the carrier particles have basically stabilized. Simultaneously, the free concentration of the auxiliary drug in the reaction supernatant was continuously monitored using ultraviolet spectrophotometry, and its concentration decrease rate per unit time was calculated. If the concentration change rate was lower than the second preset value (e.g., 0.5% / min), it indicated that the drug molecules were basically adsorbed or encapsulated by the carrier, and no further significant inclusion behavior occurred. When both judgment conditions were met, it was confirmed that the drug loading process in the first stage had reached a thermodynamic and kinetic saturation state, and a drug loading completion signal for the first stage was output. At the same time, the encapsulation capacity at the current moment was recorded, and the first saturation value was calculated to reflect the effective loading level of the auxiliary drug. Based on the drug loading completion signal of the first stage, the drug loading procedure in the second stage was started without interrupting the continuity of the reaction system. In the second stage, lidocaine and the auxiliary drugs previously marked as easily loaded components were added to the drug loading reaction system in the first stage according to the second feeding sequence, constructing a second drug loading reaction system in a multi-component mixed environment. Since a large number of binding sites were consumed in the first stage, the drug encapsulation process in the second stage would be limited by the surface structure and charge state of the remaining carrier. Therefore, the change in surface potential and the concentration change rate of the newly added drug components in the solution were monitored. Surface potential is continuously measured using Zeta potential analysis. When its change over a continuous period is lower than a third preset rate of change (e.g., 2 mV / 5 minutes), it indicates that the carrier surface has reached a new charge balance state, and binding behavior has essentially ceased. Simultaneously, the concentration changes of lidocaine and the drug-loaded components are monitored. When the rate of change of concentration for all components is detected to be lower than a fourth rate of change threshold (e.g., 0.5% / minute), it is determined that the second stage has also reached saturation, generating second saturation data and marking the completion of the second stage of drug loading. The first and second saturation data obtained from the two stages are integrated to form a drug loading process saturation dataset.

[0075] Step S400: Synchronously input the saturation data into the drug loading process control system for pH and temperature optimization to obtain multi-component balanced drug-loaded particles;

[0076] Specifically, saturation data is synchronously input into the drug loading process control system. This system includes a built-in drug loading uniformity assessment module, which compares and analyzes the drug loading efficiency from different drug components and calculates the drug loading uniformity deviation using statistical methods. The drug loading uniformity deviation represents the uniformity of the actual encapsulation degree of multiple drug components within the same batch of nanoparticles. Its core basis is the ratio of the standard deviation to the mean of the encapsulation rates of each drug component. If this ratio deviates from the set ideal range, it indicates a significant imbalance in the current drug loading distribution. Based on this, a drug loading uniformity deviation signal is generated, and a drug loading process control trigger command is automatically issued. The pH gradient control algorithm is then activated based on the drug loading uniformity deviation signal. This algorithm automatically derives dynamic pH 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 and the established pH influence curves on the encapsulation of different components. The dynamic pH adjustment parameters are converted into executable buffer pH optimization commands and sent to the automatic pH control unit of the drug-loaded reaction system. By adding pre-configured acid-base components, the pH of the reaction medium is adjusted, thereby improving the binding capacity of certain polar or ionic drug molecules on the carrier surface and promoting the secondary adsorption process of weaker components. Simultaneously, based on the drug loading process control trigger command, the temperature program control algorithm is activated to intelligently and dynamically correct 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 value, heating or cooling rate, and holding time, and converts them into actual reaction temperature optimization commands, which are implemented step-by-step through the temperature control device of the reactor. During the synchronous optimization of pH and temperature parameters, the drug loading status at each time point is continuously monitored, especially the actual drug loading rate of each drug component is sampled and analyzed, and its coefficient of variation is calculated to assess the encapsulation uniformity within the system. When the measured coefficient of variation is lower than the first percentage threshold (e.g., 15%) and the drug loading rate of all drug components exceeds the second percentage threshold (e.g., 85%), the current operating condition is determined to have met the drug loading balance target. At this point, all control operations are terminated and a completion flag is output. The particles obtained in this way are defined as multi-component balanced drug-loaded particles, which have achieved optimal matching in particle size distribution, charge balance, and drug encapsulation structure.

[0077] Step S500: Perform layered gel crosslinking based on multi-component balanced drug-loaded particles to obtain nanogels, and verify the drug loading stability and release consistency of the nanogels to generate optimized crosslinking process parameters.

[0078] Specifically, the drug release behavior of multi-component balanced drug-loaded particles was quantitatively determined. Using in vitro release experiments, the cumulative release data of lidocaine and each auxiliary drug component were recorded under standard physiological conditions (e.g., pH 7.4, 37℃). The release half-life of each component was fitted based on a release rate model and compared with the preset target release half-life. The deviation value was calculated to obtain the degree of deviation between the release behavior of each drug and the expected synchronicity. Based on the half-life deviation results, the target drug components requiring release rate regulation were identified, and a crosslinking degree allocation scheme was formulated accordingly. Components with release half-lives significantly lower than the target value were classified as drug-difficult-to-load components with excessively rapid release, and their corresponding gel regions were given a higher crosslinking degree to increase diffusion resistance. Conversely, drug-difficult-to-load components with release half-lives close to or slightly higher than the target value were given a lower crosslinking degree in their corresponding regions to maintain their existing release rate. After determining the crosslinking degree allocation scheme, a dual crosslinking agent system was used for gel construction. The primary crosslinking agent, such as glutaraldehyde, was used to form the basic network structure, with a concentration controlled between 0.5% and 1.5%. The secondary crosslinking agent, such as carbodiimide, was used to compensate for charge distribution and local compactness, with a concentration controlled between 0.2% and 0.8%. By implementing differentiated crosslinking times (e.g., 10 minutes for the first layer and 15 minutes for the second layer) and crosslinking temperatures (e.g., 25℃ and 30℃) for different component regions, layered crosslinking was achieved, generating nanogels with structurally matched differences. The performance of the nanogels was evaluated by determining the final drug loading rate of each drug component in the gel system and calculating its relative standard deviation (RSD) to assess the uniformity of drug loading distribution. Simultaneously, combined with high-performance liquid chromatography and cumulative release data curve analysis, the release similarity factor (f2) between each component was calculated to evaluate the temporal consistency of different drugs during release. When the RSD value was less than 8% and the f2 value was greater than 50, it indicated that the gel system possessed good drug loading stability and release consistency, suggesting that the process parameters were preliminarily effective. Based on the validation results derived from the RSD and f2 values ​​obtained from the evaluation, a matching analysis was performed with historical batch data in the process parameter database. This extracted all combinations of crosslinking agent concentration ratios, crosslinking time parameters, crosslinking temperature parameters, and pH adjustment parameters that met the qualification standards. The parameters with the highest stability and good repeatability were then selected as standard crosslinking process parameters. Multidimensional modeling methods, including regression analysis and principal component analysis, were used to locally optimize and adjust the standard crosslinking process parameters to better adapt them to the current drug component and carrier structural characteristics, resulting in a set of optimized crosslinking process parameters for actual preparation.

[0079] In one specific embodiment, the process of performing step S100 may specifically include the following steps:

[0080] The binding affinity of each adjuvant drug to the nanocarrier was determined by isothermal titration calorimetry using lidocaine as the active ingredient and each adjuvant drug in compound lidocaine.

[0081] The drug loading competition coefficients among various adjuvant drugs were calculated based on the numerical value of carrier binding affinity.

[0082] Based on the drug loading competition coefficient, drugs with binding affinity lower than a first preset value are labeled as drug-difficult components, and drugs with binding affinity higher than a second preset value are labeled as drug-easy components.

[0083] The first feeding sequence is set according to the components that are difficult to load with drugs, and the second feeding sequence is set according to the lidocaine active ingredient and the components that are easy to load with drugs.

[0084] A drug feeding sequence table is created based on the first feeding sequence and the second feeding sequence.

[0085] Specifically, an isothermal titration calorimeter was used as the core instrument platform. This calorimeter possesses the ability to detect changes in reaction heat with high sensitivity, making it suitable for evaluating the non-covalent interaction processes between drug molecules and nanoscale carrier materials. These interactions include a combination of mechanisms such as electrostatic attraction, hydrophobic interactions, hydrogen bonding, and van der Waals forces. During the experiment, lidocaine and each auxiliary drug were individually prepared into titration solutions of standard concentrations and added dropwise to a reaction cell containing pre-dispersed nanocarriers. The minute thermal effects generated during each addition were recorded, and thermodynamic parameters such as the binding constant (K), binding enthalpy change (ΔH), entropy change (ΔS), and number of binding sites (n) were fitted by correlating the integrated heat with the titration volume. The reciprocal of the binding constant K characterizes the strength of the binding affinity; a higher K value indicates a stronger binding between the drug and the carrier, thus a stronger affinity. By organizing and standardizing the K values ​​between all auxiliary drugs and the carrier, a binding affinity dataset was constructed. Building upon this, to quantify the relative advantages of drug molecules competing for the same nanocarrier binding sites, a drug loading competition coefficient is introduced. This coefficient considers not only a single K value but also factors such as the molar volume, polarity, diffusion rate, and spatial arrangement ability of the drug molecules. A multivariate linear regression model is then established to assess the ability of different adjuvant drugs to compete for effective carrier binding sites under fixed carrier concentration and identical system conditions. The competition coefficient is defined as the relative ratio of the probability that a single drug molecule successfully occupies an effective site on the carrier surface per unit time; the stronger the competitive ability, the higher the coefficient, and vice versa. All adjuvant drugs are sorted from low to high according to their competition coefficients, and a dual-indicator screening is performed using affinity K values ​​for grouping. When the K value of a certain adjuvant drug is lower than a first preset value (e.g., 10), the grouping is determined. -5When the K value is mol / L and the competition coefficient is in the lower range of the overall ranking, it is defined as a "difficult-to-load component" because it is easily rejected or inhibited by other molecules during actual drug loading and is not easy to bind to the carrier surface. For K values ​​higher than the second preset value (e.g., 10 mol / L), the component is defined as a "difficult-to-load component" because it is easily rejected or inhibited by other molecules during actual drug loading and is not easy to bind to the carrier surface. -3 The auxiliary drugs with a high competition coefficient (mol / L) and strong carrier binding capacity were identified as "easily loaded components." Based on the principle of limited encapsulation capacity and the pre-occupancy advantage of drug loading sites in nanocarriers, a feeding sequence was determined to improve the final drug loading uniformity and component balance. "Difficult-to-load components" were prioritized as the first feeding sequence. Their purpose was to provide a dedicated binding time window before the carrier surface was largely occupied by high-affinity drugs, thereby improving encapsulation efficiency. The first feeding stage was set as single-component feeding. Each auxiliary drug was independently dissolved at a set concentration and mixed with the pretreated carrier before entering the reaction system. A moderate stirring rate and suitable pH conditions were used to maintain stable reaction, ensuring sufficient contact between drug molecules and the carrier until adsorption stabilized. Based on the experimental timeline and stability assessment results, the second feeding sequence was initiated, adding lidocaine and all readily loadable components. Due to its high affinity, the drug in the second stage can effectively bind and maintain a high encapsulation efficiency even with a relatively reduced number of binding sites on the carrier surface, without repelling or displacing the already bound, difficult-to-load components. Based on the feeding content, sequence, and time intervals of the two stages, a drug feeding sequence table was created. This table specifies the exact order of feeding each component and includes key process parameters such as time parameters, drug concentration range, mixing intensity settings, pH control requirements, and reaction temperature for each feeding node, forming a standardized operating template.

[0086] In one specific embodiment, the process of performing isothermal titration calorimetry to determine the carrier binding affinity of each auxiliary drug to the lidocaine active ingredient and each excipient drug in the compound lidocaine with the nanocarrier can specifically include the following steps:

[0087] The lidocaine active drug and each auxiliary drug were prepared into drug solution groups by isothermal titration calorimetry, and a carrier suspension was prepared for the nanocarrier.

[0088] The carrier suspension was used as the sample cell solution, and each drug solution in the drug solution group was used as a titrant for continuous titration under isothermal conditions to obtain the titration heat flow signal and binding saturation curve.

[0089] The binding enthalpy change was obtained by integrating the titration heat flux signal, and the binding constant was obtained by nonlinear fitting of the binding saturation curve.

[0090] The carrier binding free energy values ​​of each adjuvant drug are calculated based on the binding constant and binding enthalpy change values, and then the carrier binding free energy values ​​are converted into carrier binding affinity values ​​expressed in molar concentration units.

[0091] Specifically, lidocaine and each adjuvant drug were separately dissolved in a unified buffer system to form multiple drug solution groups with consistent concentrations. The buffer system was consistent with the dispersion environment of the nanocarrier to avoid non-specific thermal signal interference introduced by changes in solution pH or ionic strength. Simultaneously, the nanocarrier material was pre-dispersed to ensure it formed a suspension with uniform particle size and good stability, serving as the acceptor solution in the sample cell of the isothermal titration calorimeter. During the formal isothermal titration calorimetry experiment, the pre-treated carrier suspension was injected into the calorimeter sample cell, and a constant temperature environment was maintained to eliminate the influence of environmental thermal fluctuations. One drug from each drug solution group was selected as the titrant, and it was gradually added to the sample cell at set time intervals and in incremental volumes. After each titration, the drug molecules underwent physical adsorption or chemical binding reactions with the carrier surface, producing weak endothermic or exothermic phenomena. The calorimeter recorded the heat flow signal in real time, and a titration heat flow curve was plotted with time as the x-axis and thermal power as the y-axis. After multiple titrations, the binding saturation process was obtained. As the binding between the drug and the carrier gradually approaches saturation, the recorded heat flow signal gradually weakens until it approaches zero; this process constitutes the characteristic shape of the binding saturation curve. The titration heat flow signal curve is integrated, and the heat released or absorbed in each titration is accumulated to obtain the total thermal effect of the drug-carrier binding per unit volume of solution. The binding enthalpy change is extracted, reflecting the thermodynamic direction and intensity of the drug-carrier binding reaction; positive values ​​represent endothermic processes, and negative values ​​represent exothermic processes, with the absolute value related to the driving force of the reaction. Combining the integrated heat flow signal results with the corresponding drug concentration gradient, the obtained integrated data and titration volume are input into a nonlinear fitting model. A classical ligand-receptor binding model or a modified model is used for curve fitting. The binding constant and the number of binding sites are simultaneously calculated during the fitting process. The binding constant is a key parameter reflecting the strength of binding affinity during 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 values, the carrier binding free energy values ​​of each auxiliary drug are calculated, revealing the spontaneity of the reaction process in the energy dimension. Further, by combining the Gibbs free energy expression, the entropy change of the binding process is derived to determine whether the binding reaction is accompanied by a change in the degree of molecular order. For example, electrostatic adsorption processes exhibit negative entropy changes, while hydrophobic binding processes exhibit positive entropy changes. The binding free energy value is converted into a conventional form of affinity expression, i.e., an affinity index in molar concentration units. The free energy value is then converted into a binding dissociation constant, and the reciprocal of the binding dissociation constant is the binding affinity. The higher the affinity value, the stronger the tendency of drug molecules to bind to the nanocarrier, indicating that the drug is preferentially adsorbed and encapsulated during drug delivery.

[0092] In one specific embodiment, the process of performing step S200 may specifically include the following steps:

[0093] Read the charge density and polarity data of drug molecules marked as difficult-to-load components in the drug delivery sequence table, and calculate the required carrier surface potential enhancement amplitude for each difficult-to-load component based on the charge density and polarity data.

[0094] The pH value of the buffer solution required for the nanocarrier to reach the target surface potential is calculated based on the enhancement amplitude of the carrier surface potential, and the pH adjustment parameters of the buffer solution are determined based on the pH value of the buffer solution.

[0095] The nanocarrier was dispersed in a phosphate buffer solution with a pH adjustment parameter, and a polyethylene glycol surfactant was added for temperature gradient treatment to obtain the modified carrier.

[0096] The surface potential of the modified carrier was measured using electrophoretic light scattering technology. The surface potential was measured and the pre-adjustment was stopped when the measured surface potential was within the preset voltage range and the deviation from the calculated target value was less than the target value, thus obtaining the drug-loaded pretreated carrier.

[0097] Specifically, based on the drug loading sequence list, molecular-level parameters of drugs marked as difficult-to-load components were read, and their charge density and molecular polarity data were extracted. Charge density was obtained from the electrostatic potential diagram calculated after molecular structure optimization, while molecular polarity was quantitatively evaluated based on dipole moment, molecular polarizability, and the location of distributed charge centers. These parameters reflect the degree of response of drug molecules to the electric field environment in solution and their preference for binding surfaces. The charge density and molecular polarity data were input into the carrier interface regulation model, and the theoretical binding force of each difficult-to-load component under different surface potential conditions was calculated through the prediction model. Based on this, a mapping relationship between binding probability and potential intensity was established. By analyzing the inflection point position and adsorption stability range in the mapping curve, the minimum surface potential value required for stable adsorption of this type of drug molecule was derived, i.e., the target surface potential value. The target surface potential value was compared with the current potential state of the nanocarrier to obtain the required potential enhancement amplitude. The larger the potential enhancement amplitude, the higher the required regulation amplitude of the carrier surface, and the closer the corresponding buffer pH value needs to be to the extreme endpoint in actual regulation. Based on the surface potential enhancement amplitude and the potential response curves of the carrier material under different pH conditions, the specific pH value required to achieve the target potential was calculated. The potential response curves were obtained from preliminary experiments, and their correspondence was approximately linear within a certain pH range. By searching or fitting the potential response curves, the pH value of the buffer solution that satisfies the target potential was derived, and based on this, the configuration parameters of the phosphate buffer system were determined, including the molar ratio of phosphate to conjugate base, the total concentration range, the adjustment step size, and the control precision, forming a set of pH adjustment parameters. Standardized phosphate buffer solutions were prepared according to the above adjustment parameters, and the nanocarrier was dispersed in the standardized phosphate buffer solution in an appropriate proportion, allowing it to be uniformly suspended under conditions close to the target pH. During this process, a polyethylene glycol nonionic surfactant was added, which serves to adjust the hydrophobicity and flexible structure of the carrier surface. The surfactant concentration was controlled between 0.1% and 0.3% to ensure that the carrier morphology was not damaged while improving dispersion stability. A temperature gradient treatment process was initiated, maintaining the temperature at 25°C for 30 minutes to complete initial potential regulation in the pH environment. The temperature was then increased to 35°C and maintained for 15 minutes to activate the surface charge rearrangement mechanism. Finally, the temperature was slowly lowered to 28°C to stabilize the carrier structure and lock the surface charge state, resulting in a pre-modified carrier. The surface potential of the modified carrier was precisely measured using an electrophoretic light scattering instrument. The instrument analyzes the migration rate of the carrier in an applied electric field and calculates its Zeta potential value to obtain the current actual surface potential value. The actual surface potential value was compared with the target surface potential. If the measured value fell within the preset allowable potential range (e.g., -15mV to -25mV), and the deviation from the theoretical target value was less than the preset maximum deviation value (e.g., ±1mV), the surface potential regulation was considered complete, and a drug-loaded pretreated carrier meeting the drug adsorption requirements was obtained.

[0098] In one specific embodiment, the process of reading the charge density and molecular polarity data of drug molecules marked as difficult-to-load components in the drug delivery sequence table, and calculating the required carrier surface potential enhancement amplitude for each difficult-to-load component based on the drug molecule charge density and molecular polarity data, can specifically include the following steps:

[0099] Extract the molecular structure data of each drug molecule marked as a drug-difficult component from the drug feeding sequence list, and calculate the molecular charge density and molecular polarity data of each drug molecule based on the molecular structure data.

[0100] The effective charge radius of each drug-difficult-to-load component in aqueous solution is calculated based on the dipole moment values ​​of each component in the molecular polarity data, and the electrostatic interaction strength between each component and the carrier surface is calculated based on the effective charge radius.

[0101] The surface potential difference of the carrier required to achieve the best drug loading efficiency for each difficult-to-load component is calculated based on the electrostatic interaction strength and the charge density of the drug molecules.

[0102] The difference between the surface potential difference of the carrier and the original surface potential of the nanocarrier is calculated to obtain the target difference. When the target difference is positive, it is used as the enhancement amplitude of the carrier surface potential. When the target difference is negative, the enhancement amplitude of the carrier surface potential is set to zero.

[0103] Specifically, the structural data of each drug molecule marked as a difficult-to-load component is extracted from the drug loading sequence list, and the corresponding molecular structure data files are accessed. This data comes from drug molecule structure databases, quantum chemical modeling platforms, or molecular simulation programs. The structural data is represented in a standardized three-dimensional coordinate format and includes information such as atomic composition, bond lengths, bond angles, conjugated structures, and substituent arrangements. Based on the structural data of each drug molecule, the charge density and polarity parameters of each drug molecule configuration are quantitatively calculated. The charge density is reconstructed based on the molecular orbital distribution using the electrostatic potential mapping method, using the distribution of electron cloud density around each atomic site as a metric to reflect the electric field gradient formed by the molecule in space and its attraction to the surrounding environment. Molecular polarity is expressed by the dipole moment value, which is determined by the product of the distance between the centers of positive and negative charges and the charge. It has a clear directionality and dimensional units and is a key physical quantity for determining the direction of movement, orientation tendency, and binding characteristics of drug molecules in polar solvents. In drugs with complex structures or multiple functional groups, the magnitude and direction of the dipole moment also determine its response to changes in the surface potential of the carrier. Based on the dipole moment values, the effective charge radius of each drug-loaded component in aqueous solution is derived. The effective charge radius is not equivalent to the physical geometric radius, but rather refers to the average size of the equipotential shell formed by drug molecules in a polar medium. It is the boundary of the electric field distribution formed after solvation, effectively reflecting the range of action exhibited by the molecule under the influence of an electric field. A larger dipole moment indicates stronger molecular polarity, resulting in a wider oriented electric field effect in solution, and thus a larger effective charge radius; conversely, a smaller dipole moment 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 their binding capacity under specific distance and potential conditions. Based on the electrostatic interaction strength model, molecular polarity, effective charge radius, and carrier surface charge density are combined to calculate the electrostatic adsorption force per unit area of ​​each drug molecule under specific conditions, and the minimum surface electric field strength required for each drug-loaded component to maintain thermodynamic adsorption stability is derived. Each molecule requires a different surface potential difference on the carrier to achieve optimal binding. A larger potential difference indicates a weaker binding ability of the molecule under low surface potential conditions, requiring an increase in the carrier potential for effective adsorption. If a molecule requires a smaller potential difference, it indicates that it is not sensitive to changes in the carrier surface and its binding requirement can be met at the current potential. The difference between the target potential difference required by each molecule and the current original surface potential of the nanocarrier is calculated. If the target potential difference is greater than the current potential (positive difference), it indicates that the potential needs to be increased, and the absolute value of the difference is the surface potential enhancement amplitude. If the difference is negative, it indicates that the current potential already meets or even exceeds the drug binding requirement. In this case, meaningless potential enhancement operations are avoided, and the enhancement amplitude is set to zero to ensure that the stability of the reaction system is not compromised.

[0104] In one specific embodiment, the process of performing step S300 may specifically include the following steps:

[0105] According to the first feeding sequence in the drug feeding sequence table, the auxiliary drug of the drug-difficult-to-load component is added to the suspension of the drug loading pretreatment carrier for the first stage of drug loading treatment, and the first drug loading reaction system of the drug-difficult-to-load component encapsulation process is obtained.

[0106] The first drug loading reaction system is determined by measuring the change rate of carrier particle size and detecting the first concentration change rate of free drug in the supernatant. When the change rate of carrier particle size is lower than the first change rate value and the first concentration change rate is less than the second change rate value, the first stage of drug loading is determined to be saturated, and the first saturation and the first stage of drug loading completion signal are obtained.

[0107] Based on the first-stage drug loading completion signal, lidocaine active drug and drug-loadable components are added to the first drug loading reaction system according to the second feeding sequence in the drug feeding sequence table for the second-stage drug loading treatment, resulting in a second drug loading reaction system with multi-component mixed drug loading.

[0108] The surface potential change amplitude of the second drug loading reaction system was measured and the second concentration change rate of each component of the free drug was monitored. When the surface potential change amplitude was less than the third change rate value and the second concentration change rate was less than the fourth change rate value, the second stage of drug loading saturation was determined, and the second saturation was obtained.

[0109] The first and second saturations are used as saturation data during the drug loading process.

[0110] Specifically, based on the first feeding sequence in the drug feeding order table, a list of all auxiliary drugs belonging to components with difficult drug loading is identified. These drugs are then added sequentially to the drug-loaded pretreated carrier suspension, which has undergone surface potential pre-adjustment, according to predetermined ratios, concentrations, and dissolution sequences. Before adding the auxiliary drugs, the environmental conditions in the carrier suspension are ensured to fully meet 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 aggregation or structural collapse. The dynamic monitoring program for the first-stage drug loading reaction system is initiated. A dynamic light scattering instrument is used to measure the carrier particle size in real time, and the particle size data every five minutes is differentially processed to obtain the particle size change rate. Simultaneously, ultraviolet spectrophotometry is used to quantitatively analyze the concentration of drug molecules in the free state in the reaction supernatant. A set of continuous concentration data is collected according to the detection frequency, and its concentration change rate is calculated. When the carrier particle size change rate is lower than the first change rate value, such as 5% per five minutes, it indicates that the carrier surface is basically saturated and the particle size tends to stabilize. When the first concentration change rate is lower than the second change rate value, such as 0.5% per minute, it indicates that the change in the concentration of residual unbound drug in the reaction solution is slowing down, entering the kinetic plateau period. When both of these change rate indicators simultaneously meet the threshold conditions, it is determined that the first stage of drug loading process has reached saturation. The monitoring system automatically outputs a signal indicating the completion of the first stage of drug loading and calculates the first saturation as an evaluation index of drug loading efficiency. Based on the signal indicating the completion of the first stage of drug loading, the second stage of drug introduction process begins. According to the second feeding sequence in the drug feeding sequence table, lidocaine and all drugs belonging to the easily loaded components are added to the first drug loading reaction system in sequence. At this time, the carrier particles in the reaction system have encapsulated part of the auxiliary drug molecules, and the surface binding sites are partially occupied. Therefore, the dosage, concentration control strategy, and stirring intensity of the second stage of drug loading need to be appropriately adjusted to prevent a new decrease in encapsulation efficiency due to competition for binding sites. After adding the second-stage drug, the monitoring system was continued, and the surface potential of the second drug-loaded reaction system after multi-component mixing was continuously collected. A Zeta potential analyzer was used to obtain the change curve of the particle surface charge state. By analyzing the continuous change amplitude of the surface potential, the interfacial reconstruction state of the carrier particles after binding with the newly added drug molecules was determined. If the surface potential change amplitude was detected to be lower than the third rate of change value, for example, 2 mV every five minutes, it indicated that the carrier surface charge had basically reached equilibrium, and the binding process of the drug molecules tended to be stable. Simultaneously, ultraviolet spectrophotometry was used to separately detect the free concentration of all drug components in the reaction supernatant, and the concentration change rate of each component was calculated. Only when the second concentration change rate of all components was lower than the fourth rate of change value, for example, 0.5% per minute, was the second-stage drug loading process considered to be saturated. Based on this, the second-stage drug loading saturation state signal was output, and the second saturation value was calculated to reflect the encapsulation efficiency and stability of lidocaine and other components in the final system.The first and second saturations are used as saturation data during the drug loading process.

[0111] In one specific embodiment, the process of performing step S400 may specifically include the following steps:

[0112] The saturation data is synchronously input into the drug loading process control system to calculate the drug loading balance deviation value, and a drug loading balance deviation signal and a drug loading process control trigger command are generated based on the drug loading balance deviation value.

[0113] The pH gradient control algorithm is activated based on the drug loading uniformity deviation signal to generate dynamic pH adjustment parameters and buffer pH optimization instructions.

[0114] The temperature program control algorithm is activated based on the drug loading process control trigger command to generate dynamic temperature adjustment parameters and reaction temperature optimization commands.

[0115] The drug loading rate and corresponding coefficient of variation of each component in the drug-loaded reaction system after pH optimization and reaction temperature optimization 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, the drug loading equilibrium is determined to be completed, and multi-component balanced drug-loaded particles are obtained.

[0116] Specifically, the saturation data obtained during the first and second stages of drug loading are input into the drug loading process control system in a structured format. The system includes an embedded equilibrium calculation module for assessing the consistency of drug loading distribution. This 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, thus generating a drug loading equilibrium deviation value. This deviation value measures the distribution stability and overall consistency of the current drug loading state in the multi-component system. A large deviation indicates that some drug components are over-encapsulated or under-encapsulated, affecting the synchronicity and stability of the final release. Based on the drug loading equilibrium deviation value, a drug loading equilibrium deviation signal reflecting the intensity of process correction needs is generated according to the deviation range, trend, and fluctuation rate. Simultaneously, a drug loading process control trigger command is generated. Upon receiving the drug loading equilibrium deviation signal, a pH gradient control algorithm is activated, combining the drug molecule binding capacity response curves under different pH conditions to generate a set of dynamic pH adjustment parameters for optimizing the current carrier surface potential environment. The dynamic pH adjustment parameters include the current actual pH value of the buffer solution, the target adjustment value, the adjustment step size, the adjustment direction, and the adjustment time window. Simultaneously, based on the drug's isoelectric point, solubility behavior, and ionic state changes, a buffer pH optimization command is generated for on-site execution and pushed to the automatic liquid addition module or acid-base titration controller connected to the reaction vessel in the control terminal for adjustment, ensuring that the pH value of the reaction environment smoothly approaches the optimal value during continuous adjustment. Simultaneously, upon triggering the drug loading process control command, the temperature program control algorithm is activated. Based on thermodynamic analysis results, combined with the drug molecule diffusion rate's dependence on temperature, the temperature-sensitive parameters of the 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. This set of dynamic temperature adjustment parameters covers the target temperature, heating or cooling rate, maintenance time period, and temperature control cycle, and is corrected in real-time based on the current stirring rate and carrier concentration. The dynamic temperature adjustment parameters are converted into a reaction temperature optimization command, implemented through the heating or cooling modules of the reaction system, completing the repair and optimization of the entire reaction temperature control environment under uninterrupted operation. After the pH and temperature optimization commands are executed, the feedback detection phase begins. High-performance liquid chromatography (HPLC) is used to quantitatively determine the actual drug loading rate of each drug component in the reaction system, and the coefficient of variation (COP) within each group is calculated based on the drug loading rate data for each component. The COP serves as a statistical indicator reflecting the level of drug loading equilibrium; the lower the COP, the closer the drug encapsulation distribution is to the ideal equilibrium state. When the COP is detected to be below the set first percentage threshold, and the actual drug loading rate of each component is greater than the second percentage threshold (e.g., 85%), the current drug loading reaction system is determined to have reached equilibrium drug loading conditions. At this point, the system automatically issues a completion flag, defining this batch of nanocarriers as multi-component equilibrium drug-loaded particles.

[0117] In one specific embodiment, the process of executing the temperature program control algorithm based on the drug loading process control trigger command to generate dynamic temperature adjustment parameters and reaction temperature optimization commands can specifically include the following steps:

[0118] The temperature program control algorithm is activated based on the drug loading process control trigger command, the drug loading saturation change curve is extracted and the drug loading kinetic constant is calculated;

[0119] Based on the comparison between the drug loading kinetic constant and the preset drug loading kinetic threshold range, the temperature adjustment direction command is obtained, and the temperature dynamic adjustment parameters including the adjustment amplitude value and the change rate value are calculated according to the temperature adjustment direction command.

[0120] 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, resulting in a reaction temperature optimization command that includes the target reaction temperature and the adjustment time process.

[0121] Specifically, the temperature program control algorithm is activated based on the drug loading process control trigger command to extract the saturation change curve recorded in the current drug loading reaction system. This saturation change curve, collected by a real-time monitoring system, reflects the evolution of encapsulation efficiency over time during the binding of drug molecules to the nanocarrier surface. The temperature program control algorithm fits the drug loading saturation change curve, extracting the kinetic constants of the current drug loading reaction process based on the time constant and slope characteristics of the saturation process. The kinetic constants reflect the rate at which drug molecules migrate to the nanocarrier surface and bind to form a stable composite structure per unit time, and indirectly reflect 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 that the system is close to saturation; a smaller kinetic constant indicates that the temperature is unfavorable for molecular activity or that the carrier surface has poor affinity, resulting in lower binding efficiency. The temperature program control algorithm then compares the extracted drug loading kinetic constants with a preset kinetic threshold range in the system. This kinetic threshold range, obtained from historical process data and statistical analysis of drug molecule thermodynamic behavior, 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 temperature regulation judgment. If the current kinetic constant is found to be below the lower limit, it indicates that the reaction environment temperature is insufficient to support the efficient advancement of the drug loading process, generating a temperature increase instruction. Conversely, if the constant is above the upper limit, it means that excessively high temperatures have caused drug molecules to migrate too quickly or even affect structural stability, outputting a temperature decrease instruction. If the constant is within the threshold range, no temperature adjustment is needed, and the current operating conditions are maintained. Based on the temperature regulation direction, combined with the current degree of drug loading kinetic deviation and system response sensitivity parameters, specific dynamic temperature regulation parameters are calculated, including the regulation amplitude and the rate of change. The regulation amplitude refers to the temperature increase or decrease required based on the current reaction temperature, and its magnitude is determined by the difference between the kinetic constant and the threshold, the slope of the regulation curve, and historical regulation response efficiency. The rate of change refers to the rate of heating or cooling to be applied per unit time, and its calculation comprehensively considers the system's heat capacity, the reactor's heating or cooling capacity, and the thermal stability of the drug structure. The regulation amplitude value in the dynamic temperature regulation parameters is superimposed with the current reaction temperature to obtain the target reaction temperature. If the current temperature is the set reference temperature and the system output adjustment range is positive, then the target reaction temperature is the value after adjusting the reference temperature upwards; if the adjustment range is negative, then the target temperature is the value after adjusting downwards; if it is zero, then no temperature change is required. The temperature adjustment time process is determined based on the rate of change value in the dynamic temperature adjustment parameters, meaning that heating or cooling needs to be completed in several stages. The duration of each stage, the temperature increment, and the holding time are decomposed and written into the instruction set of the temperature control program.All parameters, including the target reaction temperature and the temperature regulation time process, are integrated into a structured reaction temperature optimization command. This command is then pushed to the reaction control system interface to drive the temperature control module to adjust the temperature gradient according to the regulation rate and the target value.

[0122] In one specific embodiment, the process of executing step S500 may specifically include the following steps:

[0123] The release half-life of each drug component in a multi-component balanced drug-loaded particle was determined, and the deviation between the release half-life and the target release half-life was calculated.

[0124] The crosslinking degree distribution scheme of the drug-difficult and drug-easy components was determined based on the deviation value, and a dual crosslinking agent system was prepared based on the crosslinking degree distribution scheme to obtain nanogels.

[0125] 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 determined and the release similarity factor was calculated.

[0126] The drug loading stability and release consistency were determined based on the relative standard deviation of drug loading rate and release similarity factor, and the verification results were obtained.

[0127] Based on the verification results, the crosslinking agent concentration ratio, crosslinking time parameters, crosslinking temperature parameters, and pH adjustment parameters that meet the qualification standards are obtained from the process parameter data table to obtain the standard crosslinking process parameters. Then, the nanogel is optimized according to the standard crosslinking process parameters to obtain the optimized crosslinking process parameters.

[0128] Specifically, based on the structure and drug distribution of multi-component balanced drug-loaded particles, a standardized in vitro release testing platform is used to independently test the release behavior of each drug component under simulated physiological conditions. Real-time data acquisition and curve modeling are performed on the release curves to extract the release half-life of each drug under the current carrier structure. The release half-life of each drug component is compared with a preset target release half-life, the target value of which is determined by drug formulation design requirements or clinical release timeline planning. By comparing the numerical difference between the actual and target release half-lives, the deviation of the release half-life of each component is calculated, reflecting the degree of timeline incoordination in the release processes of different drugs. If the release half-life of a component is much lower than the target value, it indicates that its release is too rapid, requiring structural enhancement to inhibit diffusion of that component; conversely, if the release half-life is significantly higher than the target value, it indicates that its release process is restricted, requiring a moderate reduction in cross-linking strength to accelerate the release rate. Based on the direction and magnitude of the release half-life deviation, a crosslinking degree allocation scheme was established. In this scheme, drugs with excessively rapid release were labeled as components with difficult loading, and their carrier microregions were assigned a higher degree of crosslinking. Conversely, drugs with slower release rates or close to the target value were labeled as components with easy loading, and their medium or low crosslinking degree regions were allocated to ensure smooth release. Based on this scheme, a dual crosslinking agent formulation was developed. The primary crosslinking agent, such as glutaraldehyde, was used to establish the network structure framework, while the secondary crosslinking agent, such as carbodiimide, was used to regulate the local chemical crosslinking density. Based on the specific values ​​of the release half-life deviations of each component, the concentration ratio, reaction time, and spatial distribution of the two types of crosslinking agents were set. A differentiated dual crosslinking agent system was then configured and subjected to a crosslinking reaction to obtain a structure-responsive nanogel. The drug loading rate of each drug component in the nanogel was quantitatively detected, and the relative standard deviation of the drug loading rate of each component was calculated to assess the uniformity of the drug loading distribution. Cumulative release curves of each drug component were measured under the same release conditions. By analyzing the fit between the release curves, the release similarity factor among the components was calculated. The closer the release similarity factor is to the reference value, the more synchronized the release behavior of the multiple components. A comprehensive analysis of the relative standard deviation of drug loading and the release similarity factor was performed. When both met the system's set control thresholds, the nanogel under the current crosslinking conditions was considered to have good drug loading stability and release consistency, thus outputting a verification pass signal. If either indicator did not meet the standard, the crosslinking degree allocation strategy needed to be revised. Based on the verification results, the system accessed the process parameter database. By comparing indexed parameters with batch records, all historically verified crosslinking agent concentration ratios, crosslinking time settings, crosslinking temperature programs, and pH control parameters were obtained, forming a standard crosslinking process parameter set. Association rule mining and multivariate regression modeling were performed on the standard crosslinking process parameter set to extract the set of crosslinking conditions that best matched the current drug structure, carrier type, and release requirements, serving as the structural control benchmark for the current batch of nanogels.These parameters are then re-inputted into the process optimization module as input variables, and fine-tuned by combining the current release data with the structural stability trend, ultimately yielding the optimized crosslinking process parameters after structural optimization.

[0129] Please see Figure 2 , Figure 2 A schematic block diagram of the structure of the system for optimizing the preparation process of compound lidocaine nanogel provided in the embodiments of this application, as shown below. Figure 2 As shown, the system for optimizing the preparation process of compound lidocaine nanogel includes:

[0130] The difference determination module 211 is used to determine the difference in carrier binding affinity between lidocaine active drug and excipient drug in compound lidocaine and obtain the drug dosing sequence table.

[0131] 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 pretreated carrier.

[0132] Step-by-step drug loading module 213 is used to perform step-by-step drug loading based on the drug loading pretreatment carrier according to the drug loading sequence table, and obtain saturation data during the drug loading process;

[0133] pH and temperature optimization module 214 is used to synchronously input saturation data into the drug loading process control system for pH and temperature optimization, so as to obtain multi-component balanced drug-loaded particles.

[0134] The layered gel crosslinking module 215 is used to perform layered gel crosslinking based on multi-component balanced drug-loaded particles to obtain nanogels, and to verify the drug loading stability and release consistency of the nanogels, and generate crosslinking optimization process parameters.

[0135] Through the synergistic cooperation of the aforementioned components, the binding affinity between each drug molecule and the carrier was quantitatively determined using isothermal titration calorimetry. A drug loading competition evaluation system based on the principle of thermodynamic equilibrium was established, enabling precise identification of drug-difficult and easy-to-load components. A stepwise drug loading strategy prioritizing the loading of drug-difficult components avoids excessive occupation of carrier sites by easily loaded components, ensuring that adjuvant drugs with difficulty in loading have priority in obtaining loading opportunities. This significantly improves the drug loading balance of multi-component drugs and overcomes the problem of drug loading ratio imbalance caused by traditional simultaneous loading methods. Based on the molecular charge characteristics of drug-difficult components, the carrier surface potential is specifically adjusted. Electrostatic interactions enhance the binding affinity between drug-difficult components and the carrier, providing personalized drug loading microenvironments for drug molecules with different characteristics, thus improving the adaptability and efficiency of the drug loading process. By combining dynamic light scattering technology and ultraviolet spectrophotometry, a quantitative identification standard for drug loading saturation was established, enabling precise control of the drug loading process. A dynamic pH and temperature regulation mechanism based on drug loading saturation data feedback was established. Through a closed-loop control system, the drug loading environment conditions were automatically optimized, achieving adaptive regulation of the drug loading process. A layered crosslinking strategy based on differences in drug release kinetics was adopted. Through a dual crosslinking agent system and differentiated crosslinking conditions, the release rates of different drug components were compensated and regulated, achieving synchronization of multi-component drug release and solving the problem of release timing mismatch caused by traditional uniform crosslinking methods.

[0136] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0137] If the integrated unit is implemented as 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 this application, in essence, 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0138] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. An optimized preparation process for compound lidocaine nanogel, characterized in that, include: The carrier binding affinity of lidocaine active drug and excipient drug in compound lidocaine was determined by differential determination, and a drug dosing sequence table was obtained. The surface potential of the nanocarrier is pre-adjusted according to the drug loading sequence table to obtain a drug-loaded pretreated carrier. Based on the drug-loaded pretreatment carrier, drug is loaded stepwise according to the drug loading sequence table to obtain saturation data during the drug loading process; The saturation data is synchronously input into the drug loading process control system for pH and temperature optimization to obtain multi-component balanced drug-loaded particles. Layered gel crosslinking 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 optimized crosslinking process parameters.

2. The optimized preparation process of the compound lidocaine nanogel according to claim 1, characterized in that, The differential determination of the carrier binding affinity between the lidocaine active ingredient and the excipient in compound lidocaine yielded a drug dosing sequence table, including: The binding affinity of each adjuvant drug to the nanocarrier was determined by isothermal titration calorimetry using lidocaine as the active ingredient and each adjuvant drug in compound lidocaine. The drug loading competition coefficients among the adjuvant drugs were calculated based on the carrier binding affinity values. Based on the drug loading competition coefficient, drugs with binding affinity lower than a first preset value are marked as drug-difficult components, and drugs with binding affinity higher than a second preset value are marked as drug-easy components. The first feeding sequence is set according to the drug-difficult-to-load component, and the second feeding sequence is set according to the lidocaine active ingredient and the drug-easy-to-load component. A drug feeding sequence table is created based on the first feeding sequence and the second feeding sequence.

3. The optimized preparation process of compound lidocaine nanogel according to claim 2, characterized in that, The method involves isothermal titration calorimetry determination of the carrier binding affinity of each auxiliary drug in compound lidocaine with the lidocaine active ingredient and each auxiliary drug, respectively, using nanocarriers. This determination yields the carrier binding affinity values ​​for each auxiliary drug. The lidocaine active drug and each auxiliary drug were prepared into drug solution groups by isothermal titration calorimetry, and a carrier suspension was prepared for the nanocarrier. The carrier suspension was used as the sample cell solution, and each drug solution in the drug solution group was used as a titrant for continuous titration under constant temperature conditions to obtain the titration heat flow signal and binding saturation curve. The binding enthalpy change is obtained by integrating the titration heat flow signal and performing nonlinear fitting on the binding saturation curve to obtain the binding constant. Based on the binding constant and the binding enthalpy change, the carrier binding free energy of each auxiliary drug is calculated, and then the carrier binding free energy is converted into a carrier binding affinity value expressed in molar concentration units.

4. The optimized preparation process of the compound lidocaine nanogel according to claim 1, characterized in that, The step of pre-adjusting the surface potential of the nanocarrier according to the drug delivery sequence table to obtain a drug-loaded pretreated carrier includes: Read the charge density and polarity data of drug molecules marked as difficult-to-load components in the drug delivery sequence table, and calculate the required carrier surface potential enhancement amplitude for each difficult-to-load component based on the charge density and polarity data. The pH value of the buffer solution required for the nanocarrier to reach the target surface potential is calculated based on the enhancement amplitude of the carrier surface potential, and the pH adjustment parameters of the buffer solution are determined based on the pH value of the buffer solution. The nanocarrier was dispersed in a phosphate buffer solution corresponding to the pH adjustment parameter, and a polyethylene glycol surfactant was added for temperature gradient treatment to obtain the modified carrier. The surface potential of the modified carrier is measured using electrophoretic light scattering technology to obtain the surface potential measurement value. When the surface potential measurement value is within the preset voltage range and the deviation from the calculated target value is less than the target value, the pre-adjustment is stopped to obtain the drug-loaded pretreated carrier.

5. The optimized preparation process of the compound lidocaine nanogel according to claim 4, characterized in that, The step of reading the charge density and polarity data of drug molecules marked as difficult-to-load components in the drug delivery sequence table, and calculating the required carrier surface potential enhancement amplitude for each difficult-to-load component based on the charge density and polarity data, includes: Extract the molecular structure data of each drug molecule marked as a drug-difficult component from the drug feeding sequence list, and calculate the molecular charge density and molecular polarity data of each drug-difficult component based on the molecular structure data. Based on the dipole moment values ​​of each drug-difficult-to-load component in the molecular polarity data, the effective charge radius of each drug-difficult-to-load component in the aqueous solution is calculated, and the electrostatic interaction strength between each component and the carrier surface is 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-to-load component is calculated based on the electrostatic interaction strength and the charge density of the drug molecules. The difference between the surface potential difference of the carrier and the original surface potential of the nanocarrier is calculated to obtain the target difference value. When the target difference value is positive, the target difference value is used as the carrier surface potential enhancement amplitude. When the target difference value is negative, the carrier surface potential enhancement amplitude is set to zero.

6. The optimized preparation process of the compound lidocaine nanogel according to claim 1, characterized in that, The stepwise drug loading based on the drug pretreatment carrier according to the drug loading sequence table, obtaining 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-difficult-to-load component is added to the suspension of the drug-loading pretreatment carrier for the first stage of drug loading treatment, thereby obtaining the first drug loading reaction system for the encapsulation process of the drug-difficult-to-load component. The first drug loading reaction system is measured by measuring the change rate of carrier particle size and detecting the first concentration change rate of free drug in the supernatant. When the change rate of carrier particle size is lower than the first change rate value and the first concentration change rate is less than the second change rate value, the first stage of drug loading is determined to be saturated, and the first saturation and the first stage of drug loading completion signal are obtained. Based on the first stage drug loading completion signal, lidocaine active drug and drug-loading components are added to the first drug loading reaction system according to the second feeding sequence in the drug feeding sequence table for the second stage drug loading treatment, resulting in a second drug loading reaction system with multi-component mixed drug loading. The surface potential change amplitude of the second drug loading reaction system is measured and the second concentration change rate of each component of the free drug is monitored. 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, the second stage of drug loading saturation is determined, and the second saturation is obtained. The first saturation and the second saturation are used as saturation data during the drug loading process.

7. The optimized preparation process of the compound lidocaine nanogel according to claim 1, characterized in that, The step of synchronously inputting the saturation data into the drug loading process control system for pH and temperature optimization to obtain multi-component balanced drug-loaded particles includes: The saturation data is synchronously input into the drug loading process control system to calculate the drug loading balance deviation value, and a drug loading balance deviation signal and a drug loading process control trigger command are generated based on the drug loading balance deviation value. Based on the drug loading uniformity deviation signal, the pH gradient control algorithm is activated to generate dynamic pH adjustment parameters and buffer pH optimization instructions. The temperature program control algorithm is activated according to the drug loading process control trigger command to generate dynamic temperature adjustment parameters and reaction temperature optimization commands. The drug loading rate and corresponding coefficient of variation of each component in the drug-loaded reaction system treated by the pH optimization command and the reaction temperature optimization command 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, the drug loading equilibrium is determined to be completed, and multi-component balanced drug-loaded particles are obtained.

8. The optimized preparation process of the compound lidocaine nanogel according to claim 7, characterized in that, The step of activating the temperature program control algorithm according to the drug loading process control trigger command to generate dynamic temperature adjustment parameters and reaction temperature optimization commands includes: The temperature program control algorithm is activated according to the drug loading process control trigger command 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 command is obtained, and a temperature dynamic adjustment parameter including the adjustment amplitude value and the change rate value is calculated according to the temperature adjustment direction command. The target reaction temperature is determined by superimposing the adjustment amplitude value in the dynamic temperature adjustment parameter with the current reaction temperature, and the temperature adjustment time process is formulated based on the change rate value in the dynamic temperature adjustment parameter, resulting in a reaction temperature optimization command that includes the target reaction temperature and the adjustment time process.

9. The optimized preparation process of the compound lidocaine nanogel according to claim 1, characterized in that, The process involves performing layered gel crosslinking based on the multi-component balanced drug-loaded particles to obtain a nanogel, and verifying the drug loading stability and release consistency of the nanogel to generate optimized crosslinking process parameters, including: The release half-life of each drug component in the multi-component balanced drug-loaded particles was determined, and the deviation between the release half-life and the target release half-life was calculated. The crosslinking degree allocation scheme of the drug-difficult and drug-easy components is determined based on the deviation value, and a dual crosslinking agent system is prepared based on the crosslinking degree allocation scheme to obtain nanogels. 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 determined and the release similarity factor was calculated. The drug loading stability and release consistency were determined based on the relative standard deviation of the drug loading rate and the release similarity factor, and the verification results were obtained. Based on the verification results, the crosslinking agent concentration ratio, crosslinking time parameter, crosslinking temperature parameter, and pH adjustment parameter that meet the qualification standard are obtained from the process parameter data table to obtain the standard crosslinking process parameters. The nanogel is then optimized based on the standard crosslinking process parameters to obtain the optimized crosslinking process parameters.

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

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