A parameter optimization method of a microneedle coextrusion process

Abstract

The application discloses a kind of parameter optimization methods of microneedle paste forming process, and relates to the field of microneedle manufacturing.The application includes: high drug-loading material rheological property characterization and constitutive modeling, viscosity gradient space-time matching parameter curve generation, bubble nucleation critical threshold prediction, segmented variable-temperature gradient solidification process implementation, online quality detection and feedback, process parameter iterative optimization six steps.The application establishes time sequence matching vacuum, centrifugal acceleration and temperature control curve by coupling constitutive equation of drug loading, viscosity and forming parameter, uses segmented variable-temperature strategy to solve the contradiction between forming quality and drug activity, accurately controls parameter boundary using bubble critical threshold algorithm, realizes the dynamic adaptation of matrix viscosity and filling process, improves the needle tip forming rate of high drug-loading microneedle and drug activity.

Description

Technical Field

[0001] This invention belongs to the field of microneedle manufacturing technology, and in particular relates to a parameter optimization method for microneedle patch forming process. Background Technology

[0002] Microneedle patches, as a novel transdermal drug delivery carrier, are characterized by being painless, highly effective, and safe, and are widely used in fields such as biomedicine and cosmetics. The molding quality of microneedle patches directly affects their transdermal drug delivery efficacy and mechanical properties, and the rational setting of process parameters is crucial to ensuring the molding quality of microneedles.

[0003] In existing technologies, the molding of soluble microneedles generally adopts a process route of "vacuum degassing + centrifugal filling + isothermal curing". This route is suitable for low drug loading ( Microneedles have good effects, but the drug loading capacity is limited. High-drug-loaded microneedles; due to the high drug loading, the material viscosity increases sharply, resulting in severe insufficient cavity filling. When the drug loading is increased from 15% to... At that time, the viscosity of the hyaluronic acid-drug composite matrix changed from... Soaring to Viscosity increase This means that traditional single-parameter vacuum + centrifugation processes cannot drive high-viscosity materials to completely fill the micron-scale microneedle cavity, especially the tip, resulting in a needle tip formation rate of only [a fraction of the original value]. Numerous microneedles exhibit defects such as "flat-headed" and "broken" ends. Simultaneously, degassing of high-viscosity materials is difficult, resulting in a persistently high bubble defect rate. In highly drug-loaded materials, numerous drug particles not only increase viscosity but also become heterogeneous nucleation sites for bubbles, lowering the bubble nucleation threshold by more than 40%. Traditional constant vacuum degassing processes cannot effectively remove microbubbles from high-viscosity materials, and new cavitation is easily generated during centrifugation, leading to a high bubble defect rate in the final product. .

[0004] Therefore, there is an urgent need to develop a drug loading method specifically designed for drug loading. A method for optimizing the process parameters of soluble microneedle molding has been developed, breaking through existing technical bottlenecks. Summary of the Invention

[0005] The purpose of this invention is to provide a parameter optimization method for microneedle patch molding process. By synchronously matching the time-series control curves of vacuum degree, centrifugal acceleration and temperature at each stage, dynamic adaptation of matrix viscosity and filling process is achieved. This solves the problem that existing technologies use a single static process parameter, which cannot adapt to the high viscosity rheological characteristics of high drug-loaded composite matrix, resulting in insufficient microneedle tip filling and low molding rate.

[0006] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution: This invention relates to a parameter optimization method for a microneedle patch forming process, comprising the following steps: The rheological properties of the drug composite matrix of the microneedle patch were tested, and the coupled constitutive equation of the drug loading, viscosity and molding parameters of the microneedle patch was constructed. Based on the coupled constitutive equation, parameter control curves that are time-matched for vacuum degree, centrifugal acceleration and temperature are generated; For the non-Newtonian fluid characteristics of high drug loading materials, calculate the parameter safety boundary for each molding stage to prevent bubble defects; The filling phase is performed according to the timing parameter curve, and a segmented variable temperature gradient curing strategy is adopted after the filling is completed. The forming rate, bubble defect rate, and drug activity of the formed microneedles are detected online, and the quality deviation is calculated. The timing parameter curves are corrected based on the quality deviation, thereby achieving closed-loop iterative optimization of process parameters.

[0007] Optionally, the specific steps for constructing the coupled constitutive equations include: The drug raw materials were weighed according to the target drug loading of the microneedle patch, and multi-stage homogenization was performed to prepare a drug composite matrix sample. Multiple sets of temperature conditions and multiple sets of shear rate conditions were set to conduct comprehensive rheological property tests on drug composite matrix samples and collect viscosity response data under different conditions. Based on the collected rheological test data, the influencing factors of drug loading on the rheological behavior of materials were extracted, and the contribution of drug particles to viscosity changes was quantitatively analyzed. Establish the coupled constitutive relationship between drug loading, temperature, shear rate and material viscosity, and construct a special rheological constitutive model suitable for high drug loading scenarios; The parameters in the constitutive model are fitted and optimized to obtain the optimal combination of model parameters; The accuracy of the constructed constitutive model is verified by calculating the error between the model's predicted values ​​and the experimentally measured values. The model is confirmed to be effective when the error meets a preset threshold.

[0008] Optionally, the specific steps for constructing a dedicated rheological constitutive model suitable for high drug loading scenarios are as follows: Zero-shear viscosity, limiting viscosity, and relaxation time are all set as variables related to drug loading, and a drug loading index influence term is introduced; A coupling exponential term for temperature and drug loading is added to the zero-shear viscosity and relaxation time to describe the nonlinear regulatory effect of temperature on viscosity under high drug loading. By integrating all variables, a four-variable coupled constitutive model is formed, which includes three independent variables (shear rate, temperature, and drug loading) and one dependent variable (viscosity).

[0009] Optionally, the four-variable coupled constitutive model equations are: ; in, This is a zero-shear viscosity coupling term; To relax time coupling; In the formula, For the predicted viscosity of the composite matrix, For shear rate, For temperature, The drug loading mass fraction Let be the limiting viscosity at infinite shear rate, and be a function of drug loading C; Zero shear viscosity; is a coupling function of temperature and drug loading. The infinite shear limiting viscosity, Let be the relaxation time, and be the coupling function between temperature and drug loading. The width parameter represents the rheological behavior. Non-Newtonian exponents The activation energy for viscous flow. Let be the ideal gas constant. The effect coefficient of drug loading on zero shear viscosity. This is the relaxation time pre-factor. The activation energy is the relaxation time. The effect coefficient of drug loading-relaxation time.

[0010] Optionally, the specific steps for generating a time-series matched parameter control curve for vacuum degree, centrifugal acceleration, and temperature include: The complete cavity filling process is divided into three consecutive time-series stages: initial inflow, cavity filling, and tip forming. The filling progress range and core forming target of each stage are defined. Establish a coupled rheological constitutive relationship, and determine the optimal viscosity control range of the composite matrix for each stage based on the molding target and flow resistance characteristics. Using the optimal viscosity range of each stage as a constraint, the driving forces of vacuum degassing, centrifugal filling, and temperature and viscosity regulation are simultaneously matched to solve the feasible domain of parameters such as vacuum degree, centrifugal acceleration, and temperature under each stage, and the optimal parameter combination is selected. A smooth transition range of parameters is set at the junction of two adjacent stages, and a time-series control curve of vacuum degree, centrifugal acceleration and temperature changing synchronously with time is generated by continuous interpolation. The generated timing control curve was simulated and verified using a non-Newtonian fluid filling numerical simulation method. The overall cavity filling rate and needle tip fullness were calculated, and the final parameter curve was output after confirming that the preset accuracy requirements were met.

[0011] Optionally, the specific steps for calculating the parameter safety boundary to prevent bubble defects at each molding stage include: The particle size distribution, volume fraction, and surface wettability parameters of drug particles in the composite matrix were collected. The effect of drug particles as heterogeneous nucleation sites on reducing the nucleation barrier of bubbles was quantitatively analyzed, and the heterogeneous nucleation influence coefficient was obtained. By integrating material viscosity, surface tension, temperature, dissolved gas content, and heterogeneous nucleation influence coefficient, a calculation model for the critical pressure of bubble nucleation suitable for high-drug-loaded non-Newtonian fluids is established. The three time stages of cavity filling are corresponding to each stage. The temperature, shear rate and viscosity parameters of each stage are substituted into the equation to solve the critical vacuum value and the upper limit of centrifugal acceleration that do not induce bubble nucleation in each stage, thus forming the safe operating range of parameters for each stage. The safety boundaries of parameters at each stage are embedded as hard constraints into the time-series parameter control curves, and a safety margin coefficient is set to ensure that the actual operating parameters are always within the safe range. At the same time, a parameter out-of-bounds warning threshold is generated. The non-Newtonian fluid gas-liquid two-phase simulation method was used to simulate and verify the bubble nucleation and growth behavior of the entire filling process. It was confirmed that there was no risk of bubble generation throughout the process. If there was a risk, the process was returned to the parameter adjustment area.

[0012] Optionally, the specific steps for establishing a calculation model for the critical pressure of bubble nucleation suitable for high-drug-loaded non-Newtonian fluids include: Based on the viscosity and temperature parameters of the composite matrix, the saturated vapor pressure and surface tension at the corresponding temperature are calculated. Combined with the gas diffusion coefficient, the critical pressure for homogeneous nucleation is preliminarily calculated. The correction factor is calculated by substituting the heterogeneous nucleation barrier reduction coefficient, drug particle volume fraction, and average particle size into the heterogeneous nucleation comprehensive correction factor formula. By correcting the homogeneous nucleation critical pressure, the true heterogeneous nucleation critical pressure of the high drug loading system is obtained; By combining the inhibitory effect of viscosity on bubble growth and supplementing the cavitation critical conditions under centrifugal shearing conditions, a complete bubble nucleation critical threshold model is formed.

[0013] Optionally, the segmented temperature gradient curing process specifically includes: After the cavity filling process is completed, the vacuum and temperature conditions corresponding to the filling endpoint are maintained and the mold is left to stand to maintain its shape, allowing the high-drug-loaded composite matrix in the cavity to fully relax its stress. The system temperature is gradually increased at a preset low heating rate, while the vacuum is gradually decreased according to the matching ratio to control the uniform and slow diffusion and evaporation of moisture inside the matrix. After the temperature reaches the target curing temperature, the constant temperature condition is maintained to promote the cross-linking reaction of molecular chains and gradually form a stable three-dimensional network structure. Once the degree of curing reaches the preset threshold, the mechanical shaping of the microneedle structure is completed. After the curing reaction is completed, a step-by-step cooling strategy is used to gradually reduce the system temperature, with a corresponding holding time set for each cooling step to gradually release the residual thermal stress generated during the curing process. After the system temperature drops to room temperature, the vacuum is gradually broken to restore normal pressure, and the mold is left to stand so that the microneedle structure is fully balanced with the ambient temperature and humidity. Then, the demolding process of the microneedle patch is completed.

[0014] Optionally, when calculating the quality deviation, the three test results of needle tip forming rate, bubble defect rate and drug activity retention rate are compared with the preset quality standard values ​​one by one to calculate the corresponding relative quality deviation values. The deviation levels are divided according to the magnitude of the three deviations, and three feedback strategies are generated accordingly: parameter fine-tuning, parameter recalculation and shutdown warning. The deviation data and the corresponding strategies are synchronously output to the process parameter optimization module.

[0015] The present invention has the following beneficial effects: This invention overcomes the technical defects of existing technologies that use single static process parameters and cannot adapt to the high viscosity rheological properties of high drug-loaded composite matrices, resulting in insufficient microneedle tip filling and low forming rate, by using a parameter optimization scheme of three-stage cavity filling and spatiotemporal matching of viscosity gradient. Compared with the traditional vacuum centrifugal forming scheme, this invention achieves dynamic adaptation of matrix viscosity and filling process by synchronously matching the time-series control curves of vacuum degree, centrifugal acceleration and temperature at each stage, which significantly improves the tip forming rate of high drug-loaded microneedles and reduces forming defects such as incomplete filling.

[0016] This invention overcomes the shortcomings of existing technologies that rely on homogeneous nucleation theory to calculate safety parameters, resulting in severely distorted predictions of bubble defects in high-drug-loaded microneedles and persistently high bubble defect rates in actual production. This is achieved through a bubble nucleation critical threshold prediction scheme that considers the heterogeneous nucleation effect of drug particles. Compared to traditional processes that use a fixed vacuum threshold, this invention significantly reduces the internal bubble defect rate of high-drug-loaded microneedles by quantitatively correcting the reduction effect of drug particles on the nucleation barrier, solving the parameter safety boundary in stages, and embedding time-series control. This ensures full utilization of the filling driving force.

[0017] This invention overcomes the technical defects of existing technologies that use constant temperature rapid curing, which easily leads to surface skinning, internal hollowing, and brittle cracking of microneedles, as well as long high-temperature exposure time and severe activity loss of drugs. Compared with traditional constant temperature curing schemes, this invention significantly improves drug activity retention rate and shortens the overall curing cycle by using a multi-stage synergistic control of shape stabilization and pressure holding, gradient temperature pre-curing, constant temperature cross-linking, and stepwise slow cooling.

[0018] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This invention provides a parameter optimization method for a microneedle patch forming process; Figure 2 This is a graph showing the changes in particle characteristics during the three-stage homogenization process. Figure 3 Steady-state rheological curves of high drug-loaded composite matrices at different temperatures; Figure 4 This is a timing control curve diagram of process parameters for the full filling process; Figure 5 The time series curves show the critical pressure of the bubble versus the safety boundary parameters during the fully filled process. Detailed Implementation

[0021] 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 embodiments of the present invention, and not all embodiments. 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.

[0022] Furthermore, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0023] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figure 1-5The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0024] Please see Figure 1 As shown, this invention provides a parameter optimization method for a microneedle patch forming process, comprising the following steps: Step S1: Rheological properties of the drug composite matrix of the microneedle patch are tested, and a coupled constitutive equation of drug loading, viscosity and molding parameters of the microneedle patch is constructed. Step S2: Based on the coupled constitutive equation, generate parameter control curves that time-match the vacuum degree, centrifugal acceleration, and temperature; Step S3: Calculate the safety boundary parameters for each molding stage to prevent bubble defects, taking into account the non-Newtonian fluid characteristics of high drug loading materials. Step S4: Perform the filling stage according to the timing parameter curve, and after the filling is completed, adopt a segmented variable temperature gradient curing strategy; Step S5: Perform online detection of tip formation rate, bubble defect rate, and drug activity of the formed microneedles, and calculate the quality deviation; Step S6: Correct the timing parameter curves based on the quality deviation to achieve closed-loop iterative optimization of process parameters.

[0025] In step S1, the specific steps for constructing the coupled constitutive equations are as follows: Step S11, Material preparation and multi-stage homogenization: The hyaluronic acid matrix and drug raw materials are accurately weighed according to the drug loading amount of the microneedle patch, and multi-stage homogenization is carried out to eliminate the drug particle agglomeration effect and prepare a uniform and stable drug composite matrix sample. like Figure 2 As shown in the embodiments of the present invention, a laser particle size analyzer is used to test the initial particle size distribution of the drug raw materials to obtain the average particle size. and particle size distribution standard deviation Substitute into the formula Calculate the initial dispersion uniformity index as the baseline value before treatment; weigh the raw materials according to the target drug loading, and then perform three-stage homogenization treatment: low-speed stirring premixing (input energy) High-pressure microjets homogenize (input energy) ), ultrasonic degassing treatment (input energy) ), through formula The total input energy was accumulated; after homogenization, the particle size distribution of the drug particles in the sample was tested again, and the dispersion uniformity index after treatment was calculated. ;judge If the condition is met, the sample is deemed qualified and proceeds to the next test; if the condition is not met, the high-pressure homogenization and ultrasonic degassing steps are repeated until the dispersion uniformity meets the standard. Specifically, the dispersion uniformity index The loudness dispersion characterizes the particle size distribution of the drug particles. The smaller the value, the more uniform the particles and the weaker the aggregation effect. The total input energy is the sum of the energy of the three-stage treatment. By quantitatively controlling the energy input, the degradation of hyaluronic acid molecular chains is avoided while fully deaggregating. The process parameters for the three-stage homogenization treatment—low-speed stirring premixing, high-pressure micro-jet homogenization, and ultrasonic degassing—are shown in Table 1 below. Table 1 shows the process parameters for the three-stage homogenization process. Step S12, Multi-condition rheological property test: Set multiple sets of temperature conditions and multiple sets of shear rate conditions to conduct a comprehensive rheological property test on the composite matrix sample and collect viscosity response data under different conditions; like Figure 3 As shown in the embodiment of the present invention, the temperature test range and shear rate test range are determined by combining the actual process range of microneedle vacuum centrifugation molding, and are set according to equal interval gradients. Group temperature and Group shear rate, expressed by the formula: ; In the formula, For the first Group test temperature, For the first Group test shear rate, For the number of temperature groups, The number of shear rate groups; Build A rheological test matrix is ​​established; the qualified sample prepared in step S11 is placed in a cone-plate rheometer, and steady-state rheological tests are performed point by point according to the test matrix, collecting the apparent viscosity under each condition. Substitute the average particle size of the drug particles obtained in step S11 With target drug loading Through the formula: ; In the formula, To the corrected true viscosity, The apparent viscosity is measured by a rheometer. This is the wall slip correction factor. The average particle size of the drug is... The drug loading mass fraction Rheometer cone radius.

[0026] For each set of apparent viscosity data, wall slip correction was performed to obtain the true viscosity under the corresponding conditions. All corrected data are organized to form a rheological dataset in which temperature, shear rate and true viscosity are corresponded one-to-one. This eliminates the test error caused by wall slippage at the contact surface between the sample and the cone plate during the rheological test, and restores the true rheological properties of the raw material.

[0027] Step S13, Extraction of drug loading influencing factors: Based on the collected rheological test data, extract the influencing factors of drug loading on the rheological behavior of the material, and quantitatively analyze the contribution of drug particles to viscosity changes. In this embodiment of the invention, the viscosity of a pure hyaluronic acid matrix was tested under the same temperature and shear rate conditions as in S12 to obtain a baseline viscosity dataset. The true viscosity of the composite matrix obtained by S12 Substituting the reference viscosity under the corresponding conditions into the formula: Calculate the relative viscosity under each set of operating conditions. Based on the density of the drug and the matrix, the mass drug loading is converted into a volume fraction. Substitute the relative viscosity data into the formula: Independent influencing factors of drug loading under different operating conditions were isolated by linear regression. The effects of temperature and shear rate on viscosity were eliminated; the variation of drug loading factors with temperature and shear rate was analyzed to form a sequence of influencing factors.

[0028] Step S14: Establishing Coupled Constitutive Relationships: Establish the coupled constitutive relationships between drug loading, temperature, shear rate and material viscosity, and construct a dedicated rheological constitutive model suitable for high drug loading scenarios; In this embodiment of the invention, the classic Carreau-Yasuda model is used as the basic framework, preserving the influence of shear rate on viscosity's shear-thinning effect. Zero-shear viscosity, limiting viscosity, and relaxation time are all set as variables related to drug loading, and a drug loading exponential influence term is introduced. A coupling exponential term of temperature and drug loading is added to zero-shear viscosity and relaxation time to describe the nonlinear regulatory effect of temperature on viscosity under high drug loading. All variables are integrated to form a four-variable coupled constitutive model containing three independent variables (shear rate, temperature, and drug loading) and one viscosity dependent variable. The physical meaning and preliminary value range of all undetermined parameters in the model are clarified.

[0029] It should be noted that the calculation formula for the four-variable coupled constitutive model is: ; The expression for the corresponding coupling term is as follows: Zero-shear viscosity coupling term: ; Relaxation time coupling term: .

[0030] In the formula, For the predicted viscosity of the composite matrix, For shear rate, For temperature, The drug loading mass fraction Let be the limiting viscosity at infinite shear rate, and be a function of drug loading C; Zero shear viscosity; is a coupling function of temperature and drug loading. The infinite shear limiting viscosity, Let be the relaxation time, and be the coupling function between temperature and drug loading. The width parameter represents the rheological behavior. Non-Newtonian exponents The activation energy for viscous flow. Let be the ideal gas constant. The effect coefficient of drug loading on zero shear viscosity. This is the relaxation time pre-factor. The activation energy is the relaxation time. The effect coefficient of drug loading-relaxation time.

[0031] Step S15, Nonlinear Parameter Identification and Fitting: The nonlinear parameter identification method is used to fit and optimize the parameters in the constitutive model to obtain the optimal combination of model parameters; In this embodiment of the invention, upper and lower limits and physical meaning constraints are set for all undetermined parameters, and parameter solutions without physical meaning are excluded; the entire rheological experimental dataset obtained in S12 is substituted into the formula: A nonlinear optimization problem is constructed with model parameters as optimization variables and prediction error as the objective. An improved Levenberg-Marquardt algorithm with constraints is used for iterative solution. At each iteration, the parameters are checked to see if they meet the constraints. When the change in the objective function value of the iteration step is less than the preset convergence threshold, the iteration stops and the corresponding optimal combination of model parameters is output. The fitting residual results are output to preliminarily evaluate the fitting effect.

[0032] In the formula, For the first Model prediction of viscosity under various operating conditions For the first The experimentally measured viscosity under the group operating conditions, The formula aims to find the optimal model parameters by minimizing the sum of squared errors between the predicted and measured values, thus achieving the best fit between the model and the experimental data, where the total number of experimental data sets is denoted as .

[0033] Step S16, Model Validation and Error Calibration: The accuracy of the constructed constitutive model is verified by cross-validation. The error between the model prediction and the experimental measured value is calculated. When the error meets the preset threshold, the model is confirmed to be effective. It should be noted that all The experimental data were split using the leave-one-out method, with one set of data set set aside as the validation set each time, and the remaining data set set set aside as the validation set. The training set is used as the training set; each time, the model parameters are refitted using the training set according to the S15 method, and then the obtained model is used to predict the viscosity value of the validation set, using the formula... Calculate the relative error of the validation set; repeat leave-one-out validation N times to obtain the validation error for all data points, using the formula: ; Calculate the overall average relative error Determine the average relative error Is the accuracy less than the preset accuracy threshold (usually set to 5%)? If the threshold requirement is met, the constitutive model is confirmed to be valid; if not, return to S12 to supplement more rheological test data for different working conditions and repeat the modeling and verification process. Step S16 performs weighted accuracy verification on the core process range of microneedle molding, focusing on ensuring the prediction accuracy within the range of commonly used process parameters, rather than the average accuracy across the entire range, so that the model better fits the actual production optimization needs.

[0034] Step S2, which involves generating a time-series matched parameter control curve for vacuum level, centrifugal acceleration, and temperature, includes the following specific steps: Step S21: Based on the geometric structural characteristics of the microneedle cavity and the filling flow law of the high drug-loaded composite matrix, the complete cavity filling process is divided into three consecutive time-series stages: initial inflow, cavity filling, and needle tip forming. The filling progress range and core forming target of each stage are clearly defined. In this embodiment of the invention, a three-dimensional geometric model of the microneedle cavity is imported, and the total cavity volume and volume distribution at different cross-sections are calculated. Combining the high viscosity and shear-thinning characteristics of the high-drug-loaded composite matrix, the variation of flow resistance with filling depth during the filling process is analyzed. Based on the abrupt change nodes of flow resistance and the cross-sectional changes of the cavity geometry, the filling process is divided into three time-series stages through the filling progress, and the filling progress threshold and core objectives for each stage are determined: the initial inflow stage completes the spreading and inflow of the matrix towards the cavity inlet; the cavity filling stage completes the complete filling of the microneedle column; and the needle tip forming stage completes the full forming of the micron-level tip. The division nodes of the three stages and the forming objectives for each stage are output.

[0035] It should be noted that the formula for representing the filling progress is: ; In the formula, for The cavity filling progress at any given moment. for The volume of the cavity that has been filled at any given time. This refers to the total volume of the cavity; The threshold determination rules for stage division are as follows: Initial inflow phase: (Corresponding to the filling of the large-diameter inlet area of ​​the cavity,) Usually taken ); Cavity filling stage: (Corresponding to the microneedle column area filling,) Usually taken ); Needle tip forming stage: (Corresponding to the microneedle tip area filling).

[0036] The logic of the stage division is shown in Table 2 below: For high-viscosity non-Newtonian fluids with a drug loading of more than 30%, the traditional process of dividing the stages according to time is abandoned. Instead, the filling stages are divided based on the geometric characteristics of the cavity and the change of flow resistance. This ensures that the parameter control of each stage accurately corresponds to the actual filling state, thus solving the problem of asynchronous filling progress and parameter control of high-viscosity fluids. Table 2 shows the stages of microneedle cavity filling and their characteristic parameters. Step S22: Based on the coupled rheological constitutive relationship established in step S1, determine the optimal viscosity control range of the composite matrix for each stage, taking into account the molding target and flow resistance characteristics. In this embodiment of the invention, based on the cavity feature dimensions and target filling time at each stage, the formula is used to... ; Calculate the equivalent shear rate for each stage; where, For the first The equivalent shear rate of the stage. This represents the average matrix flow velocity during this stage. The equivalent feature size of the cavity at this stage is given; combining the driving force and flow resistance at each stage, the maximum allowable viscosity for effective filling at this stage is calculated. Simultaneously, considering the requirements for drug activity retention, the minimum viscosity corresponding to the lowest permissible temperature at this stage was determined. The optimal viscosity control range is formed; the equivalent shear rate and target drug loading are substituted into the coupled constitutive equation obtained in step S1 to establish the correspondence between temperature and viscosity, and to clarify the temperature control range corresponding to the viscosity range; the optimal viscosity control range and corresponding shear rate conditions for each of the three stages are output.

[0037] It should be noted that the viscosity interval inverse equation is based on the coupled constitutive model: In the formula, , Let these be the lower and upper limits of viscosity allowed in stage i. The coupled rheological constitutive model of drug loading, temperature, and shear rate is established for step S1.

[0038] Step S23: Using the optimal viscosity range of each stage as a constraint, simultaneously match the driving force of vacuum degassing, the driving force of centrifugal filling and the effect of temperature and viscosity control, solve for the feasible domain of parameters of vacuum degree, centrifugal acceleration and temperature under each stage, and select the optimal parameter combination. In this embodiment of the invention, the viscosity ranges of each stage obtained in S22 are used as the core constraints, combined with equipment capacity constraints, drug activity temperature constraints, and bubble critical pressure constraints, through the formula: Construct a model for solving the feasible region of parameters; where, For the total filling driving force, The pressure difference caused by the vacuum degree For matrix density, Centrifugal acceleration, For fill depth, For cavity wall resistance, For viscous flow resistance; within the feasible region, traverse the parameter combinations of vacuum degree, centrifugal acceleration, and temperature, and verify the filling capacity of each parameter group through the driving force balance equation; through the comprehensive evaluation criterion of the optimal parameter group, select the optimal parameter combination with the highest filling efficiency and the lowest bubble risk from all feasible parameter combinations as the steady-state parameter values ​​for the corresponding stage; output the optimal steady-state parameters (vacuum degree, centrifugal acceleration, and temperature) for each of the three stages.

[0039] Step S24: Set a smooth transition interval for parameters at the junction of two adjacent stages, and generate a time-series control curve that synchronously changes the vacuum degree, centrifugal acceleration and temperature over time by continuous interpolation, so as to achieve spatiotemporal matching between viscosity gradient and cavity filling process. In this embodiment of the invention, the transition duration between adjacent stages is determined, and is typically set to the duration of the corresponding stage. To avoid excessively fast or slow transitions, a cubic smoothing interpolation algorithm is used for the three parameters of vacuum degree, centrifugal acceleration, and temperature to calculate the parameter values ​​at each time point within the transition interval, ensuring that the parameter curves are continuous, smooth, and without abrupt changes. The parameter sequence of the transition interval is substituted into the coupled constitutive model, and the continuity of viscosity changes during the transition process is verified using a viscosity continuity verification formula to ensure that the viscosity mutation rate is less than a preset threshold. The parameter data of the three steady-state stages and the two transition intervals are integrated to generate a time-series control curve for the entire filling process, with a time resolution matching the equipment control precision. A complete time-series parameter control curve is output.

[0040] It should be noted that the interpolation formula for smooth transition of parameters between stages is: ; In the formula, Within the transition interval The parameter values ​​at any given time (which can represent vacuum level, centrifugal acceleration, and temperature). These are the parameter values ​​at the end of the previous stage. These are the parameter values ​​at the beginning of the next stage. The transition time is given by the formula, which is a cubic spline smoothing interpolation that ensures the continuity of the first derivative of the parametric curve. Viscosity continuity verification formula: ; In the formula, , These are the viscosity values ​​before and after the transition, respectively. This is the threshold for allowing a sudden change in viscosity, typically set at 5%. Please see Figure 4 As shown, the diagram visually illustrates the synchronous changes of three core process parameters—vacuum, centrifugal acceleration, and temperature—with filling time during the full filling process of the microneedle cavity. This provides the core control basis for achieving spatiotemporal matching between viscosity gradient and cavity filling process. The diagram contains three time-series control curves: vacuum time-series curve, centrifugal acceleration time-series curve, and temperature time-series curve. These three curves work together to precisely control the matrix viscosity in stages. The parameter design logic and corresponding filling states for each stage are as follows: Initial inflow stage ( The vacuum level linearly decreased from atmospheric pressure (0 MPa) to -0.060 MPa, the centrifugal acceleration linearly increased from 0 g to 500 g, and the temperature was maintained constant at 20.0℃. During this stage, the matrix spreads and flows into the cavity inlet platform area, where flow resistance is relatively low, requiring no excessive driving force. A gradual loading driving method and low-temperature isothermal setting are used to avoid air entrainment caused by strong suction in the initial stage and to minimize the duration of drug heating, ensuring the activity of protein and peptide drugs. During this stage, the matrix viscosity is stably controlled at... The range satisfies the initial liquidity requirements for deployment.

[0041] First transition zone ( The vacuum level smoothly increases from -0.060 MPa to -0.095 MPa, the centrifugal acceleration smoothly increases from 500 g to 1200 g, and the temperature gradually increases from 20.0℃ to 21.0℃. This stage is the transition from the initial inflow stage to the cavity filling stage. A cubic smooth interpolation algorithm is used to achieve continuous parameter changes without abrupt jumps, avoiding the sudden changes in fluid flow state caused by traditional step parameter switching, and fundamentally reducing the risk of bubble entrainment and cavitation. During the transition, the matrix viscosity decreases steadily without abrupt inflection points, ensuring the continuity and stability of the filling flow.

[0042] Cavity filling stage ( The vacuum level was maintained at -0.095 MPa, the centrifugal acceleration at 1200 g, and the temperature at a constant 21.0℃. This stage corresponds to the bulk filling of the microneedle column region. The flow resistance is within a stable range, and constant steady-state parameters provide a continuous and uniform filling driving force, ensuring the matrix fills the column cavity at a uniform speed. Under this parameter combination, the matrix viscosity remains stable at... The range, which balances filling efficiency and drug activity, represents the optimal parameter matching for the main filling stage.

[0043] Second transition zone ( The vacuum level is maintained at -0.095 MPa, the centrifugal acceleration is smoothly increased from 1200g to 1800g, and the temperature is gradually increased from 21.0℃ to 23.0℃. This stage is the transition from the cavity filling stage to the needle tip forming stage. Addressing the characteristics of narrowed flow channels and sharply increased flow resistance in the microneedle tip region, the centrifugal driving force and temperature are simultaneously increased, gradually reducing the matrix viscosity to adapt to the high-resistance filling of the needle tip region. Parameters remain smoothly and continuously changed to avoid stagnation or bubble encapsulation caused by abrupt parameter changes in high-viscosity fluid within the narrow cavity.

[0044] Needle tip forming stage ( The vacuum level was maintained at -0.095 MPa, the centrifugal acceleration at 1800 g, and the temperature at a constant 23.0℃. This stage corresponds to the final formation of the micron-sized needle tip and is crucial for ensuring tip fullness and formation rate. High centrifugal acceleration provides a strong filling driving force, while moderate heating reduces the matrix viscosity to a minimum. To ensure the matrix can completely fill the microcavities at the tip, the temperature is strictly controlled to not exceed the drug safety threshold of 25°C, so as to ensure the quality of needle tip formation while maximizing the preservation of drug activity.

[0045] Step S25: Use a non-Newtonian fluid filling numerical simulation method to simulate and verify the generated timing control curve, calculate the overall cavity filling rate and needle tip fullness, and output the final parameter curve after confirming that the preset accuracy requirements are met.

[0046] In this embodiment of the invention, the timing control curve generated in step S24 is used as a boundary condition and imported into the non-Newtonian fluid filling numerical simulation model. The material rheology model adopts the coupled constitutive equation established in step S1. The filling process simulation is run to obtain the cavity filling shape after filling. The overall cavity filling rate and needle tip fullness are calculated by formula. The calculation results are compared with preset thresholds (fill rate ≥ 99%, needle tip fullness ≥ 95%). If the requirements are met, the final parameter curve is output. If not, the process returns to S23 to readjust the parameter combination and repeat the optimization process. The final timing parameter control curve that has passed verification is output.

[0047] It should be noted that the formula for calculating the cavity filling rate is as follows: In the formula, The final filling volume obtained from the simulation, This represents the theoretical total volume of the cavity.

[0048] Formula for evaluating needle tip fullness: In the formula, To simulate the needle tip filling height, This is the theoretical height of a needle tip.

[0049] The specific steps for calculating the parameter safety boundary to prevent bubble defects at each molding stage include: Step S31, Characterization of heterogeneous nucleation characteristics of high drug loading matrix: Collect particle size distribution, volume fraction and surface wettability parameters of drug particles in composite matrix, quantitatively analyze the effect of drug particles as heterogeneous nucleation sites on reducing the nucleation barrier of bubble, and obtain the heterogeneous nucleation influence coefficient. In this embodiment of the invention, a laser particle size analyzer is used to test the particle size distribution of drug particles to obtain the average particle size. The drug particle volume fraction is obtained by combining the target drug loading and material density. The solid-liquid contact angle between the drug particle surface and the hyaluronic acid matrix was tested using the seated drop method. Substitute into the formula: ; Calculate the heteronucleation barrier reduction factor Quantify the degree to which heterogeneous nucleation weakens the nucleation barrier; determine the effective wettability coefficient by combining the particle wettability state. Substitute into the formula: ; Calculate the effective heterogeneous nucleation site density per unit volume By integrating the barrier reduction coefficient and site density, a comprehensive heterogeneous nucleation influence coefficient is obtained, which serves as the basis for subsequent modeling corrections.

[0050] Step S32, Construction of critical threshold model for bubble nucleation: Integrating material viscosity, surface tension, temperature, dissolved gas content and heterogeneous nucleation influence coefficient, a calculation model for critical pressure of bubble nucleation suitable for high drug-loaded non-Newtonian fluids is established to clarify the critical conditions for bubble generation; In this embodiment of the invention, the saturated vapor pressure at the corresponding temperature is calculated based on the viscosity and temperature parameters of the composite matrix obtained in S1. With surface tension Based on the gas diffusion coefficient, the critical pressure for homogeneous nucleation was initially calculated. Substituting the heterogeneous nucleation barrier reduction coefficient, drug particle volume fraction, and average particle size obtained in S31 into the heterogeneous nucleation comprehensive correction factor formula, the correction factor was calculated. The critical pressure for homogeneous nucleation was corrected by using the formula for heterogeneous nucleation in high-drug-loaded systems, thus obtaining the true critical pressure for heterogeneous nucleation in high-drug-loaded systems. Combined with the inhibitory effect of viscosity on bubble growth, the cavitation critical condition under centrifugal shearing was supplemented to form a complete critical threshold model for bubble nucleation.

[0051] It should be noted that the preliminary formula for calculating the critical pressure for homogeneous nucleation is as follows: In the formula, The critical pressure for homogeneous nucleation. This represents the saturated vapor pressure of the matrix at the corresponding temperature. For the surface tension of the matrix, The critical bubble core radius; The formula for the modified critical pressure of heterogeneous nucleation in high-drug-loaded systems is: ; The expression for the heterogeneous nucleation comprehensive correction factor is as follows: ; In the formula, This is the critical pressure for heterogeneous nucleation. As a comprehensive correction factor for heterogeneous nucleation, The volume fraction influence coefficient. is the characteristic constant of particle size.

[0052] The formula for the critical bubble radius is as follows: ; In the formula, For matrix viscosity, The gas diffusion coefficient is... Due to environmental pressures, This is for system pressure.

[0053] Step S33, Solving the safety boundary of parameters in stages: For the three time stages of cavity filling, substitute the temperature, shear rate and viscosity parameters corresponding to each stage, and solve the critical vacuum value and the upper limit of centrifugal acceleration that do not cause bubble nucleation in each stage, forming the safe operating range of parameters for each stage. In this embodiment of the invention, corresponding to the three stages of initial inflow, cavity filling, and tip forming defined in S2, the temperature, viscosity, and filling depth parameters of each stage are extracted; the parameters of each stage are substituted into the critical threshold model established in S32 to calculate the heterogeneous nucleation critical pressure corresponding to each stage. ; Calculate the upper limit of vacuum for each stage, i.e. the minimum allowable absolute pressure, to ensure that the vacuum does not fall below the critical value and cause bubble nucleation; Combine the local pressure distribution at the tip of the cavity under the centrifugal field to calculate the upper limit of centrifugal acceleration for each stage to avoid high centrifugal force causing excessively low local pressure and cavitation bubbles; Integrate the upper and lower limits of vacuum and centrifugal acceleration to form the safe operating range of parameters for each stage.

[0054] It should be noted that the formula for calculating the safe upper limit of vacuum degree at each stage is: ; In the formula, For the first The maximum allowable vacuum level (lower limit of absolute pressure) for a given stage. Let be the critical pressure for heterogeneous nucleation in stage i. This refers to the pressure safety margin factor. The formula for calculating the safe upper limit of centrifugal acceleration at each stage is: ; In the formula, For the first The maximum permissible centrifugal acceleration for a given stage. For the first Local static pressure at the tip of the stage cavity, For matrix density, This represents the effective fill depth for this stage.

[0055] Step S34, Couple parameter safety constraints with time series curves: Embed the parameter safety boundaries of each stage as hard constraints into the time series parameter control curve, set safety margin coefficients to ensure that the actual operating parameters are always within the safe range, and generate parameter out-of-bounds warning thresholds at the same time. In this embodiment of the invention, the three-stage steady-state safety boundaries obtained in S33 are combined with the parameter smooth transition interval in S24 to generate a safety boundary curve that changes continuously with time through interpolation; the safety boundary is used as a hard constraint to verify the time-series parameter curve generated in S2. If there is a parameter out-of-bounds, the parameter setting value of the corresponding stage is adjusted to ensure that all parameters in the process are within the safety range; the parameter warning threshold at each time is calculated to generate a warning curve for real-time monitoring and alarm in the production process; the final time-series parameter control curve and warning threshold curve with embedded safety constraints are output.

[0056] It should be noted that the timing parameter safety constraint determination rules are as follows: ; In the formula, , for The actual vacuum level and centrifugal acceleration at a given time , Let t be the safety boundary threshold.

[0057] Formula for calculating parameter warning threshold: ; In the formula, , As the warning threshold, The early warning margin coefficient is typically taken as 5%. like Figure 5 As shown, the three curves from top to bottom are the critical pressure line, the early warning line, and the actual operating line. The actual operating curve is always within the safe range, demonstrating the effectiveness of the safety constraints.

[0058] Step S35, Full-process bubble risk simulation verification: The non-Newtonian fluid gas-liquid two-phase simulation method is used to simulate and verify the bubble nucleation and growth behavior of the full filling process to confirm that there is no risk of bubble generation in the whole process. If there is a risk, return to the parameter adjustment area. It should be noted that the time-series parameter curves with embedded safety constraints are used as boundary conditions and imported into the gas-liquid two-phase non-Newtonian fluid simulation model. The material rheology model adopts the coupled constitutive equation of S1, and the nucleation model adopts the heterogeneous nucleation model of S32. The full filling process simulation is run to calculate the bubble nucleation number density at each time and position, and to simulate the growth and movement of bubbles. After filling, the total volume of bubbles in the microneedle cavity is counted, and the predicted bubble defect rate is calculated. It is determined whether the predicted bubble defect rate meets the quality requirement of ≤2%. If it does, the parameters are confirmed to be safe and effective. If not, the process returns to S33 to tighten the parameter safety boundary and repeats the optimization process. The verified parameter safety constraint scheme is output.

[0059] It should be noted that the formula for calculating the bubble nucleation number density is: ; In the formula, The number of bubble nucleations per unit volume. Boltzmann's constant, Absolute temperature; The formula for predicting the bubble defect rate is as follows: ; In the formula, This represents the bubble defect rate. This represents the total volume of air bubbles inside the microneedle. This represents the total volume of a single microneedle.

[0060] Step S4, the segmented temperature gradient curing process specifically includes the following sub-steps: Step S41, Stable pressure maintenance at the filling endpoint: After the cavity filling process is completed, maintain the vacuum and temperature conditions corresponding to the filling endpoint and keep it stationary to maintain the shape, so that the high drug-loaded composite matrix in the cavity can fully relax the stress, eliminate the residual internal stress generated by the filling flow, and avoid elastic rebound deformation of the needle tip structure. In this embodiment of the invention, the temperature conditions at the filling endpoint are extracted from the time-series parameter curve of step S2, and combined with the coupled constitutive model of step S1, the intrinsic relaxation time of the pure hyaluronic acid matrix at that temperature is calculated. Substitute the target drug loading amount into the formula. Calculate the actual stress relaxation time of the high drug loading composite matrix In the formula, This represents the relaxation time of pure hyaluronic acid matrix at the corresponding temperature. This is the drug loading enhancement factor. This represents the drug loading mass fraction. Calculate the minimum required conformation time. The final shape retention time is set in conjunction with the process cycle; after filling, the final vacuum and temperature are kept constant, and the shape is left to stand for the set time to ensure that the residual stress is fully released.

[0061] Step S42, Low-speed gradient heating pre-curing: Gradually increase the system temperature at a preset low-speed heating rate, and simultaneously gradually decrease the vacuum degree according to the matching ratio to control the uniform and slow diffusion and evaporation of the internal moisture of the matrix, avoid the rapid loss of water and crusting of the matrix surface to form a closed shell, and prevent the internal moisture from being retained and forming hollow defects. In this embodiment of the invention, the water diffusion coefficient of the pure hyaluronic acid matrix is ​​obtained by querying the filling endpoint temperature. Substitute the target drug loading amount into the formula. ; Calculate the effective water diffusion coefficient of the high drug loading matrix. Combining the geometry of the microneedles with the critical water content of the crust, the formula is used to... The maximum allowable temperature rise rate without inducing surface crust formation is calculated by reverse calculation. Based on this, the actual heating rate is set; according to the total heating amplitude and the set rate, the total duration of the pre-curing stage is calculated, and a vacuum recovery curve that is synchronized with the temperature is generated to ensure that the moisture evaporation rate and diffusion rate are balanced; heating and depressurization are performed according to the synchronous curve to complete the pre-curing and dehydration process.

[0062] Step S43, Constant Temperature Crosslinking Curing and Shaping: After heating to the target curing temperature, maintain constant temperature conditions to promote the crosslinking reaction of hyaluronic acid molecular chains and gradually form a stable three-dimensional network structure. After the degree of curing reaches the preset threshold, the mechanical shaping of the microneedle structure is completed. In this embodiment of the invention, the pre-curing endpoint temperature is used as the cross-linking curing temperature, and the reaction rate constant at this temperature is calculated using the Arrhenius formula; the target drug loading and steric hindrance inhibition coefficient are substituted to establish the relationship between the degree of curing and the isothermal time; based on the target degree of curing threshold, the minimum required isothermal curing time is calculated, and the final curing time is determined in combination with the drug thermal stability requirements; the curing temperature is kept constant and held for the set time to complete the cross-linking and shaping.

[0063] Step S44, Stepped slow cooling stress release: After the curing reaction is completed, a step-by-step cooling strategy is adopted to gradually reduce the system temperature. Each cooling step is set with a corresponding heat preservation time to gradually release the residual thermal stress generated during the curing process and avoid microneedles from brittle cracking or dimensional shrinkage deformation due to sudden temperature drop. In this embodiment of the invention, the elastic modulus, linear expansion coefficient and fracture stress threshold of the cured high drug-load matrix are tested; the maximum allowable cooling range in a single step is calculated, and temperature nodes for stepped cooling are divided based on this; for each temperature node, the corresponding holding time is calculated to ensure that the thermal stress in that temperature range is fully released; the temperature is gradually reduced according to the stepped temperature curve, and each temperature segment is held for a set time before moving to the next segment, until the temperature drops to near room temperature.

[0064] Step S45, Room Temperature Equilibrium Demolding: After the system temperature drops to room temperature, the vacuum is gradually broken to restore normal pressure. The microneedle structure is allowed to fully balance with the ambient temperature and humidity before the microneedle patch is demolded.

[0065] In this embodiment of the invention, the maximum allowable peeling force during the demolding process is determined based on the mechanical strength parameters of the microneedle tip. The maximum allowable depressurization rate is calculated in reverse, and the total duration of gradual depressurization is set based on this. The vacuum is broken at a uniform rate according to the set rate to restore normal pressure. The microneedles are left to stand at room temperature to reach equilibrium with the ambient humidity, and then demolded, as detailed in Table 3 below: Table 3 shows the detailed process parameters for each stage of the segmented temperature-controlled curing process. Step S5, online quality detection and feedback, specifically includes the following sub-steps: Step S51, Multi-dimensional online image acquisition: Using a combination of surface microscopic vision and optical coherence tomography, surface morphology images and internal cross-sectional images of the microneedle array are acquired simultaneously, covering the detection range of needle tip shape defects and internal bubble defects. Step S52, Quantitative identification of molding quality features: Feature extraction and identification are performed on the acquired images, and the tip height, tip curvature radius, internal bubble size and number of microneedles are counted for each microneedle. The tip forming rate and bubble defect rate of the entire batch of microneedles are calculated respectively. Step S53, Rapid online detection of drug activity: Using the near-infrared spectroscopy non-destructive detection method, the drug activity prediction model dedicated to the high drug loading system is called to quickly detect the drug activity retention rate of each microneedle patch; Step S54, Quantitative calculation of quality deviation: The three test results of needle tip forming rate, bubble defect rate and drug activity retention rate are compared with the preset quality standard values ​​one by one, and the corresponding relative quality deviation values ​​are calculated. Step S55, Deviation Level Judgment and Feedback Trigger: Based on the magnitude of the three deviations, the deviation levels are divided, and three feedback strategies are generated accordingly: parameter fine-tuning, parameter recalculation, and shutdown warning. The deviation data and the corresponding strategies are synchronously output to the process parameter optimization module.

[0066] Step S6, the iterative optimization of process parameters specifically includes the following sub-steps: Step S61, Deviation-Parameter Mapping Relationship Matching: Based on the coupled rheological constitutive model established in step S1 and the parameter safety boundary obtained in step S3, the corresponding mapping relationship between the three mass deviations and the three types of process parameters, namely vacuum degree, centrifugal acceleration and temperature, is established through sensitivity analysis, and the parameter adjustment direction and adjustment weight corresponding to each deviation are determined. In this embodiment of the invention, based on the rheological constitutive model of step S1, the sensitivity coefficients of vacuum degree, centrifugal acceleration, and temperature on the tip formation rate are calculated respectively; based on the bubble critical threshold model of step S3, the sensitivity coefficients of the three types of parameters on the bubble defect rate are calculated; similarly, the sensitivity coefficients on drug activity are calculated; all sensitivity coefficients are integrated to construct a coupling weight matrix. Each element in the matrix represents the adjustment weight of the corresponding quality deviation on the corresponding parameter; substitute the quality deviation vector output by S54 into the formula. The optimal adjustment amounts for the three types of process parameters are obtained through matrix operations.

[0067] It should be noted that the formula for the multi-bias-multi-parameter coupled correction weight matrix is ​​as follows: ; In the formula: This is a vector for adjusting process parameters, corresponding to the adjustments for vacuum degree, centrifugal acceleration, and temperature, respectively. , which are quality deviation vectors, corresponding to needle tip forming rate deviation, bubble defect rate deviation, and drug activity deviation, respectively; It is a 3×3 coupling weight matrix, and the matrix elements are pre-calibrated by sensitivity analysis of the coupled constitutive model of step S1 and the bubble critical threshold model of step S3.

[0068] Step S62, Segmented Correction of Timing Curves: Based on the parameter adjustment amount obtained from the mapping relationship, the timing parameter curves of the three stages of initial inflow, cavity filling and tip forming are segmented and corrected. The correction process uses the safety boundary of the parameters in each stage as a hard constraint to ensure that the parameters after correction are always within the safe operating range. Step S63, Pre-verification of the corrected effect: Substitute the corrected timing parameter curve into the filling molding simulation model and the curing simulation model, quickly pre-calculate the predicted value of the corrected molding quality, and confirm that the correction is effective after confirming that the quality deviation can converge to the qualified range. Step S64, Incremental Iterative Update of Process Model: The actual process parameters and corresponding quality inspection results of this production are used as new sample data. The parameters of the rheological constitutive model and the bubble nucleation critical threshold model are updated by incremental learning to improve the prediction accuracy of the model for subsequent batches. Step S65, Optimization parameter distribution and archiving: The verified optimized timing parameter curves are distributed to the molding equipment for the next batch of production. At the same time, the process parameters and quality data of this batch are stored in the process database to form a continuous iterative closed-loop optimization mechanism.

[0069] It is worth noting that in the above system embodiments, the various units are divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0070] Furthermore, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware, and the corresponding program can be stored in a computer-readable storage medium.

[0071] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for optimizing parameters in a microneedle patch forming process, characterized in that, Includes the following steps: The rheological properties of the drug composite matrix of the microneedle patch were tested, and the coupled constitutive equation of the drug loading, viscosity and molding parameters of the microneedle patch was constructed. Based on the coupled constitutive equation, parameter control curves that are time-matched for vacuum degree, centrifugal acceleration and temperature are generated; For the non-Newtonian fluid characteristics of high drug loading materials, calculate the parameter safety boundary for each molding stage to prevent bubble defects; The filling phase is performed according to the timing parameter curve, and a segmented variable temperature gradient curing strategy is adopted after the filling is completed. The forming rate, bubble defect rate, and drug activity of the formed microneedles are detected online, and the quality deviation is calculated. The timing parameter curves are corrected based on the quality deviation, thereby achieving closed-loop iterative optimization of process parameters.

2. The method of claim 1, wherein the parameters of the microneedle coform process are optimized by, The specific steps for constructing coupled constitutive equations include: The drug raw materials were weighed according to the target drug loading of the microneedle patch, and multi-stage homogenization was performed to prepare a drug composite matrix sample. Multiple sets of temperature conditions and multiple sets of shear rate conditions were set to conduct comprehensive rheological property tests on drug composite matrix samples and collect viscosity response data under different conditions. Based on the collected rheological test data, the influencing factors of drug loading on the rheological behavior of materials were extracted, and the contribution of drug particles to viscosity changes was quantitatively analyzed. Establish the coupled constitutive relationship between drug loading, temperature, shear rate and material viscosity, and construct a special rheological constitutive model suitable for high drug loading scenarios; The parameters in the constitutive model are fitted and optimized to obtain the optimal combination of model parameters; The accuracy of the constructed constitutive model is verified by calculating the error between the model's predicted values ​​and the experimentally measured values. The model is confirmed to be effective when the error meets a preset threshold.

3. The method of claim 2, wherein the parameters of the microneedle coform process are optimized by, The specific steps for constructing a dedicated rheological constitutive model suitable for scenarios with high drug loading are as follows: Zero-shear viscosity, limiting viscosity, and relaxation time are all set as variables related to drug loading, and a drug loading index influence term is introduced; A coupling exponential term for temperature and drug loading is added to the zero-shear viscosity and relaxation time to describe the nonlinear regulatory effect of temperature on viscosity under high drug loading. By integrating all variables, a four-variable coupled constitutive model is formed, which includes three independent variables (shear rate, temperature, and drug loading) and one dependent variable (viscosity).

4. The method of claim 3, wherein the parameters of the microneedle coform process are optimized by, The equations of the four-variable coupled constitutive model are: ； wherein is a zero shear viscosity coupling term; is a relaxation time coupling term; In the formula, For the predicted viscosity of the composite matrix, For shear rate, For temperature, The drug loading mass fraction Let be the limiting viscosity at infinite shear rate, and be a function of drug loading C; Zero shear viscosity; is a coupling function of temperature and drug loading. The infinite shear limiting viscosity, Let be the relaxation time, and be the coupling function between temperature and drug loading. The width parameter represents the rheological behavior. Non-Newtonian exponents The activation energy for viscous flow. Let be the ideal gas constant. The effect coefficient of drug loading on zero shear viscosity. This is the relaxation time pre-factor. The activation energy is the relaxation time. The effect coefficient of drug loading-relaxation time.

5. The parameter optimization method for a microneedle patch forming process according to claim 1, characterized in that, The specific steps for generating a time-matched parameter control curve for vacuum degree, centrifugal acceleration, and temperature include: The complete cavity filling process is divided into three consecutive time-series stages: initial inflow, cavity filling, and tip forming. The filling progress range and core forming target of each stage are defined. Establish a coupled rheological constitutive relationship, and determine the optimal viscosity control range of the composite matrix for each stage based on the molding target and flow resistance characteristics. Using the optimal viscosity range of each stage as a constraint, the driving forces of vacuum degassing, centrifugal filling, and temperature and viscosity regulation are simultaneously matched to solve the feasible domain of parameters such as vacuum degree, centrifugal acceleration, and temperature under each stage, and the optimal parameter combination is selected. A smooth transition range of parameters is set at the junction of two adjacent stages, and a time-series control curve of vacuum degree, centrifugal acceleration and temperature changing synchronously with time is generated by continuous interpolation. The generated timing control curve was simulated and verified using a non-Newtonian fluid filling numerical simulation method. The overall cavity filling rate and needle tip fullness were calculated, and the final parameter curve was output after confirming that the preset accuracy requirements were met.

6. The parameter optimization method for a microneedle patch forming process according to claim 1, characterized in that, The specific steps for calculating the parameter safety boundary to prevent bubble defects at each molding stage include: The particle size distribution, volume fraction, and surface wettability parameters of drug particles in the composite matrix were collected. The effect of drug particles as heterogeneous nucleation sites on reducing the nucleation barrier of bubbles was quantitatively analyzed, and the heterogeneous nucleation influence coefficient was obtained. By integrating material viscosity, surface tension, temperature, dissolved gas content, and heterogeneous nucleation influence coefficient, a calculation model for the critical pressure of bubble nucleation suitable for high-drug-loaded non-Newtonian fluids is established. The three time stages of cavity filling are corresponding to each stage. The temperature, shear rate and viscosity parameters of each stage are substituted into the equation to solve the critical vacuum value and the upper limit of centrifugal acceleration that do not induce bubble nucleation in each stage, thus forming the safe operating range of parameters for each stage. The safety boundaries of parameters at each stage are embedded as hard constraints into the time-series parameter control curves, and a safety margin coefficient is set to ensure that the actual operating parameters are always within the safe range. At the same time, a parameter out-of-bounds warning threshold is generated. The non-Newtonian fluid gas-liquid two-phase simulation method was used to simulate and verify the bubble nucleation and growth behavior of the entire filling process. It was confirmed that there was no risk of bubble generation throughout the process. If there was a risk, the process was returned to the parameter adjustment area.

7. The parameter optimization method for a microneedle patch forming process according to claim 6, characterized in that, The specific steps for establishing a calculation model for the critical pressure of bubble nucleation applicable to high-drug-loaded non-Newtonian fluids include: Based on the viscosity and temperature parameters of the composite matrix, the saturated vapor pressure and surface tension at the corresponding temperature are calculated. Combined with the gas diffusion coefficient, the critical pressure for homogeneous nucleation is preliminarily calculated. The correction factor is calculated by substituting the heterogeneous nucleation barrier reduction coefficient, drug particle volume fraction, and average particle size into the heterogeneous nucleation comprehensive correction factor formula. By correcting the homogeneous nucleation critical pressure, the true heterogeneous nucleation critical pressure of the high drug loading system is obtained; By combining the inhibitory effect of viscosity on bubble growth and supplementing the cavitation critical conditions under centrifugal shearing conditions, a complete bubble nucleation critical threshold model is formed.

8. The parameter optimization method for a microneedle patch forming process according to claim 1, characterized in that, The segmented temperature gradient curing process specifically includes: After the cavity filling process is completed, the vacuum and temperature conditions corresponding to the filling endpoint are maintained and the mold is left to stand to maintain its shape, allowing the high-drug-loaded composite matrix in the cavity to fully relax its stress. The system temperature is gradually increased at a preset low heating rate, while the vacuum is gradually decreased according to the matching ratio to control the uniform and slow diffusion and evaporation of moisture inside the matrix. After the temperature reaches the target curing temperature, the constant temperature condition is maintained to promote the cross-linking reaction of molecular chains and gradually form a stable three-dimensional network structure. Once the degree of curing reaches the preset threshold, the mechanical shaping of the microneedle structure is completed. After the curing reaction is completed, a step-by-step cooling strategy is used to gradually reduce the system temperature, with a corresponding holding time set for each cooling step to gradually release the residual thermal stress generated during the curing process. After the system temperature drops to room temperature, the vacuum is gradually broken to restore normal pressure, and the mold is left to stand so that the microneedle structure is fully balanced with the ambient temperature and humidity. Then, the demolding process of the microneedle patch is completed.

9. The parameter optimization method for a microneedle patch forming process according to claim 1, characterized in that, When calculating the quality deviation, the test results of the three items—needle tip forming rate, bubble defect rate, and drug activity retention rate—are compared with the preset quality standard values ​​one by one to calculate the corresponding relative quality deviation values. The deviation levels are divided according to the magnitude of the three deviations, and three feedback strategies—parameter fine-tuning, parameter recalculation, and shutdown warning—are generated accordingly. The deviation data and the corresponding strategies are synchronously output to the process parameter optimization module.

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