A quality testing method for sustained-release performance of sustained-release pharmaceutical excipients

Abstract

The invention discloses a quality detection method for the sustained-release performance of sustained-release pharmaceutical excipients. The method comprises the following steps: collecting and fitting the diffusion images of excipients to construct a three-dimensional model of excipient distribution, substituting the model into a sustained-release performance analysis model for colorimetric analysis, comprehensively analyzing the sustained-release performance of the excipients, and then constructing an excipient performance influence model to explore the joint action mechanism of the dissolution medium of the pharmaceutical excipients and the properties of the excipients on the sustained-release performance of the excipients, thereby ensuring the comprehensiveness of the data influence analysis and improving the detection work efficiency; through a sustained-release drug design model and based on the actual needs of drug application, a prefabricated drug of the sustained-release drug is designed, and then through a drug production verification model, the deviation of the excipient performance influence model is compared and analyzed, drug production verification is achieved and the model is optimized, thereby improving the accuracy of the sustained-release performance of the excipients of the prefabricated drug in the actual application process, ensuring the drug sustained-release effect of the sustained-release drug, and thus improving the drug production quality.

Description

Technical Field

[0001] The invention relates to the technical field of pharmaceutical excipient detection, and in particular to a quality detection method for the sustained-release performance of a sustained-release pharmaceutical excipient. Background Art

[0002] The release performance of sustained-release drugs is affected by a variety of test conditions and material characteristics, for example, the dissolution medium and the properties of the drug itself;

[0003] From the perspective of the dissolution medium of experimental drugs, the increase in temperature usually increases the kinetic energy of molecules, promotes dissolution and diffusion, and high temperature can also reduce the viscosity of the dissolution medium, thereby accelerating the drug release rate; the degree of drug ionization is affected by pH, which mainly affects water solubility and release rate; high-concentration organic solvents affect the solubility of drug pigments, reduce their available concentration in the dissolution medium, and thus change the drug release rate; therefore, when the temperature, pH and concentration of the excipient dissolution medium change, it will affect the sustained release performance;

[0004] From the perspective of the properties of the experimental drugs themselves, smaller particles usually have a larger surface area, which can increase the release rate of the drug, while larger particles may form precipitation, affecting the overall drug release process; the roughness of the particle surface will also affect the drug release rate. The higher the roughness, the more voids there are on the particle surface, which will increase the drug release rate; therefore, the different particle sizes and roughness of the experimental drugs will also affect the sustained release performance;

[0005] However, the existing sustained-release performance quality testing process for sustained-release pharmaceutical excipients has the defects of low testing efficiency, insufficient comprehensiveness and accuracy of sustained-release performance analysis, and the existing technology mainly relies on dissolution rate to evaluate sustained-release performance, while the sustained-release performance of excipients is easily affected by the dissolution medium of the excipients and the properties of the excipients themselves, resulting in insufficient comprehensiveness of sustained-release performance analysis, low efficiency of sustained-release experimental testing, and difficulty in accurately designing preformed drugs of sustained-release drugs based on drug application requirements, and the sustained-release drugs produced thereby may have problems of poor drug sustained-release effect and low drug production quality;

[0006] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0007] The purpose of the present invention is to solve the defects of low detection efficiency and insufficient comprehensiveness and accuracy of sustained-release performance analysis in the existing quality detection process of sustained-release performance of sustained-release pharmaceutical excipients, by collecting excipient diffusion images and constructing an excipient distribution three-dimensional model and an excipient performance influence model, comprehensively analyzing the excipient sustained-release performance, realizing intelligent periodic automatic detection, improving detection efficiency, exploring the joint action mechanism of the dissolution medium and excipient properties of pharmaceutical excipients on the excipient sustained-release performance, and through a sustained-release drug design model based on the actual needs of drug application, custom-designing preformed drugs of sustained-release drugs, thereby realizing the adjustment and preset of the excipient property parameters of the preformed drugs, and ensuring the comprehensiveness of data impact analysis.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] A method for quality testing of sustained-release performance of sustained-release pharmaceutical excipients, comprising the following steps:

[0010] S1, collecting excipient diffusion images and constructing an excipient distribution three-dimensional model: preparing an excipient experimental drug for a sustained-release drug and placing it in an organic solvent to perform an excipient diffusion release detection process, obtaining N0 excipient diffusion images through an image acquisition device, and constructing an excipient distribution three-dimensional model by fitting;

[0011] S2, establish a sustained-release performance analysis model: perform colorimetric analysis on the three-dimensional model of adjuvant distribution, evaluate the static colorimetric grade and dynamic diffusion degree of the three-dimensional model of adjuvant distribution, and then comprehensively evaluate the sustained-release performance of adjuvant;

[0012] S3, establish the excipient performance influence model: set the parameter collection period Tc to collect the dissolution medium parameters and excipient property parameters, analyze and obtain the dissolution medium influence function and the excipient property influence function, and then output the comprehensive influence coefficient of the excipient performance;

[0013] S4, establish a sustained-release drug design model: obtain the required value of the excipient sustained-release performance index and the required value of the dissolution medium parameter through the actual demand of drug application, substitute it into the excipient performance influence model to obtain the required value of the excipient property parameter, so as to obtain the preformulated drug of the sustained-release drug;

[0014] S5, establish a drug production verification model: substitute the prefabricated drug into the sustained-release performance analysis model for diffusion analysis, obtain the test value of the excipient sustained-release performance index of the prefabricated drug, and compare the test value of the excipient sustained-release performance index of the prefabricated drug with the required value to determine the deviation of the excipient performance impact model, thereby optimizing the excipient performance impact model.

[0015] Furthermore, the specific process of constructing the three-dimensional model of adjuvant distribution is as follows:

[0016] By replacing the original drug with pigment and combining it with excipients and additives to make medicine, we can obtain the excipient experimental drug for sustained-release drugs;

[0017] The adjuvant drug diffusion images are collected by an image acquisition device to obtain adjuvant drug diffusion images at N0 viewing angles, wherein the viewing angles of the N0 adjuvant drug diffusion images are coaxially dispersed;

[0018] A reference axis L is set and marked in the adjuvant diffusion image, the position of the reference point o is marked from the reference axis L, the reference axis L and the reference point o of N0 adjuvant diffusion images are overlapped, and the reference point o is taken as the center point, so as to fit the N0 adjuvant diffusion images and construct an adjuvant distribution three-dimensional model.

[0019] Furthermore, the specific process of establishing the sustained-release performance analysis model is as follows:

[0020] A1: Extract any adjuvant diffusion image of the adjuvant distribution three-dimensional model and mark it as Pi, and perform static analysis of adjuvant diffusion. The specific process of static analysis is as follows:

[0021] Divide the adjuvant diffusion image Pi into n0 feature blocks, mark any feature block as Z, mark n1 feature points of the feature block Z, mark any feature point as z, collect the chromaticity value SDz of the feature point z, and then comprehensively evaluate the chromaticity coefficient XsdZ of the feature block Z;

[0022] Set n2 chromaticity intervals, mark any chromaticity interval as r, and obtain the instantaneous chromaticity index ZsdP of the adjuvant diffusion image Pi through the chromaticity coefficients XsdZ of n0 feature blocks and their corresponding chromaticity intervals r;

[0023] Extract N0 adjuvant diffusion images of the same image acquisition period Tx, and analyze the corresponding instantaneous chromaticity index ZsdP of the N0 adjuvant diffusion images, and then comprehensively obtain the chromaticity static evaluation coefficient XJT of the adjuvant distribution stereo model;

[0024] Set the evaluation interval of the chromaticity static evaluation coefficient XJT, and determine the static chromaticity level of the auxiliary drug distribution three-dimensional model by interval comparison;

[0025] A2: Set the image acquisition cycle Tx, construct a three-dimensional model of adjuvant distribution corresponding to N1 image acquisition cycles, and perform dynamic analysis of adjuvant diffusion. The specific process of dynamic analysis is as follows:

[0026] Obtain the fluctuation coefficient σsz of the feature block Z through the chromaticity values ​​SDz of the n1 feature points z, compare the fluctuation coefficient σsz of the n0 feature blocks with the chromaticity coefficient XsdZ, and integrate to obtain N3 feature color block groups, and mark any feature color block group as V;

[0027] The volume of the characteristic block Z is marked as a to obtain the volume Av of the characteristic color block group V, and then construct a variation curve Sav between the volume Av of the characteristic color block group V and the image acquisition period Tx;

[0028] Extract n4 points of the change curve Sav, calculate the vertical coordinate difference between two adjacent points and mark it as the volume difference A, by measuring n4 points of the change curve Sav, n5 volume differences are obtained. A, and then calculate the volume difference The average value Apj, floating coefficient Afd and peak-to-valley difference Afg of A are used to evaluate the diffusion coefficient XslV of the characteristic color block group V;

[0029] Through the diffusion coefficients XslV corresponding to the N3 characteristic color block groups, the dynamic diffusion coefficients XDT of the pigments in the three-dimensional model of adjuvant distribution are comprehensively obtained;

[0030] Set the evaluation interval of the pigment dynamic diffusion coefficient XDT, and determine the dynamic diffusion degree of the auxiliary drug distribution three-dimensional model by interval comparison;

[0031] A3: Combine the pigment static evaluation coefficient XJT and the pigment dynamic diffusion coefficient XDT to obtain and output the excipient sustained-release performance index Zhs of the excipient distribution three-dimensional model.

[0032] Furthermore, the specific process of establishing the adjuvant performance influence model is as follows:

[0033] B1: Construct the curve analysis sub-model. The specific process is as follows:

[0034] Input the change curve Se into the curve analysis model, obtain N3 points of the change curve Se, and mark the peak points and valley points of the change curve Se;

[0035] Extract the characteristic curve segment between the peak point and the valley point in the change curve Se, obtain N4 points of the characteristic curve segment, and analyze and obtain the average increase rate Ke and peak-to-valley difference of the characteristic curve segment e and coefficient of variation σe;

[0036] The average increase rate Ke and peak-to-valley difference of the characteristic curve segment e and the variation coefficient σe are combined to obtain the influence coefficient Xse of the variation curve Se;

[0037] B2: By detecting the organic solvent in the excipient experimental environment, the dissolution medium parameters are collected, and by analyzing the changing relationship between the excipient sustained-release performance and the dissolution medium parameters, the dissolution medium influence function F1 is obtained;

[0038] B3: By testing the excipient experimental drugs of sustained-release drugs, the excipient property parameters are collected, and by analyzing the relationship between the excipient sustained-release performance and the excipient property parameters, the excipient property influence function F2 is obtained;

[0039] B4: Then, by combining the dissolution medium influence function F1 and the excipient property influence function F2, the comprehensive influence coefficient FZ of the excipient performance is output to evaluate the comprehensive influence of the dissolution medium parameters and the excipient property parameters on the excipient sustained-release performance;

[0040] B5: By combining the comprehensive influence coefficient of adjuvant performance FZ and the adjuvant sustained-release performance index Zhs, the combined effect change curve Slh of the adjuvant sustained-release performance is constructed, and the combined effect change function F0 is constructed by fitting.

[0041] Furthermore, the specific process of obtaining the dissolution medium influence function F1 is as follows:

[0042] Dissolution medium parameters include temperature Tj, pH and concentration Cj of organic solvent;

[0043] Constructing the variation curve Stj between the auxiliary drug sustained-release performance index Zhs and the temperature Tj, constructing the variation curve Sph between the auxiliary drug sustained-release performance index Zhs and the pH value pH, and constructing the variation curve Scj between the auxiliary drug sustained-release performance index Zhs and the concentration Cj;

[0044] Substitute the change curve Stj, the change curve Sph and the change curve Scj into the curve analysis model in turn, obtain the influence coefficients of the corresponding change curves, and mark them as Xstj, Xsph and Xscj respectively;

[0045] Then, the corresponding weight coefficients of the temperature Tj, pH and concentration Cj of the organic solvent are obtained and marked as η1, η2 and η3 respectively;

[0046] The dissolution medium influence function F1 is comprehensively obtained through the temperature Tj, pH and concentration Cj of the organic solvent and their corresponding weight coefficients, and the influence of the dissolution medium parameters on the excipient sustained-release performance index Zhs is evaluated.

[0047] Furthermore, the specific process of obtaining the adjuvant property influence function F2 is as follows:

[0048] The excipient property parameters include the particle size Ky and roughness Gy of the excipient experimental drug;

[0049] Constructing the variation curve Sky between the auxiliary drug sustained-release performance index Zhs and the particle size Ky, and constructing the variation curve Sgy between the auxiliary drug sustained-release performance index Zhs and the roughness Gy;

[0050] Substitute the change curve Sky and the change curve Sgy into the curve analysis model in turn, obtain the influence coefficients of the corresponding change curves, and mark them as Xsky and Xsgy respectively;

[0051] Then, the corresponding weight coefficients of the particle size Ky and roughness Gy of the excipient experimental drug are obtained and marked as η4 and η5 respectively;

[0052] Through the particle size Ky and roughness Gy of the excipient experimental drug and their corresponding weight coefficients, the excipient property influence function F2 is comprehensively obtained to evaluate the influence of the excipient property parameters on the excipient sustained-release performance index Zhs.

[0053] Furthermore, the specific process of establishing a sustained-release drug design model is as follows:

[0054] The demand value of the excipient sustained-release performance index of the preformed drug is marked as the sustained-release performance demand index E1 of the preformed drug; the demand value of the dissolution medium parameter includes the temperature demand value Tj0, the pH demand value pH and the concentration demand value Cj0;

[0055] Substituting the required value of the dissolution medium parameter into the dissolution medium influence function F1, obtaining the predicted influence degree F1y of the required value of the dissolution medium parameter on the sustained-release performance requirement index E1;

[0056] Substitute the predicted influence degree F1y of the dissolution medium parameters and the sustained-release performance requirement index E1 of the preformed drug into the comprehensive influence coefficient FZ of the excipient performance to obtain the predicted influence degree F2y of the excipient property parameters on the excipient sustained-release performance index Zhs;

[0057] Through the predicted influence degree F2y of the excipient property parameters, the required value of the excipient property parameters is obtained, and the preformed drug for sustained-release drugs is produced.

[0058] Furthermore, the specific process of establishing a drug production verification model is as follows:

[0059] Perform diffusion release detection on the preformed drug, collect the diffusion image of the preformed drug and fit it to build a three-dimensional distribution model of the preformed drug, substitute it into the sustained-release performance analysis model for diffusion analysis, output the auxiliary drug sustained-release performance index of the three-dimensional distribution model of the preformed drug, and mark it as the sustained-release performance detection index E2 of the preformed drug;

[0060] Through the sustained-release performance demand index E1 and the sustained-release performance test index E2 of the preformed drug, the deviation index ZPC of the excipient performance influence model is analyzed and obtained;

[0061] The comparison interval of the deviation index ZPC is set and marked as [c1, c2], and the corresponding optimization signal is generated through comparison.

[0062] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0063] The invention constructs a three-dimensional model of adjuvant distribution by collecting adjuvant diffusion images and fitting them, and substitutes them into the sustained-release performance analysis model for chromaticity analysis. From the two perspectives of static chromaticity and dynamic pigment diffusion, the sustained-release performance of the adjuvant is comprehensively analyzed. Then, by constructing an adjuvant performance influence model, the joint action mechanism of the dissolution medium of pharmaceutical excipients and the properties of adjuvants on the sustained-release performance of the adjuvant is explored, the comprehensiveness of data influence analysis is ensured, intelligent periodic automatic detection is realized, and the detection work efficiency is improved.

[0064] The present invention designs preformed drugs of controlled-release drugs based on the actual needs of drug application through a controlled-release drug design model, thereby realizing customized design of property parameters of excipients of preformed drugs, and through a drug production verification model, comparing and analyzing the deviation of the excipient performance impact model, realizing drug production verification and performing model optimization, thereby improving the accuracy of the excipient sustained-release performance of preformed drugs in actual application, ensuring the drug sustained-release effect of controlled-release drugs, and thus improving the drug production quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 The overall process diagram of the present invention is shown;

[0066] Figure 2 A schematic flow chart of the adjuvant performance influence model of the present invention is shown. DETAILED DESCRIPTION

[0067] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0068] Embodiment 1:

[0069] like Figure 1-2 As shown, a quality testing method for sustained-release performance of sustained-release pharmaceutical excipients comprises the following steps:

[0070] S1, collecting excipient diffusion images and constructing an excipient distribution three-dimensional model: preparing an excipient experimental drug for a sustained-release drug and placing it in an organic solvent to perform an excipient diffusion release detection process, obtaining N0 excipient diffusion images through an image acquisition device, and constructing an excipient distribution three-dimensional model by fitting;

[0071] By replacing the original drug with pigment and combining it with adjuvants and additives to make medicine, we can obtain the excipient experimental drug of sustained-release drug; among them, additives include fillers and lubricants; fillers include starch, sucrose and lactose; lubricants include magnesium stearate, micro powder silica gel and talcum powder;

[0072] The adjuvant drug diffusion images are collected by an image acquisition device to obtain adjuvant drug diffusion images at N0 viewing angles, wherein the viewing angles of the N0 adjuvant drug diffusion images are coaxially dispersed;

[0073] A reference axis L is set and marked in the adjuvant diffusion image, the position of the reference point o is marked from the reference axis L, the reference axis L and the reference point o of N0 adjuvant diffusion images are overlapped, and the reference point o is taken as the center point, thereby fitting the N0 adjuvant diffusion images to construct an adjuvant distribution three-dimensional model;

[0074] It should be noted that the coaxial dispersion setting refers to taking the experimental drug as the center point, the center point is the reference point o, and drawing a line from the reference point o to take the axis. For example, drawing a line in the vertical direction, the vertical axis is the reference axis L. Taking the reference axis L as the center line, N0 vertical planes are obtained. The vertical plane is the auxiliary drug diffusion image, and the distribution of N0 vertical planes is coaxial distribution. The dispersion setting refers to the distribution of N0 vertical planes in the same angle and uniform dispersion setting. For example, if the value of N0 is 8, the angle between two adjacent vertical planes is 45°, and the auxiliary drug diffusion images of 8 viewing angles are uniformly dispersed according to the same angle.

[0075] S2, establish a sustained-release performance analysis model: perform chromaticity analysis on the three-dimensional model of adjuvant distribution, evaluate the static chromaticity level and dynamic diffusion degree of the three-dimensional model of adjuvant distribution, and then comprehensively evaluate the sustained-release performance of the adjuvant. The specific process of establishing the sustained-release performance analysis model is as follows:

[0076] A1: Extract any adjuvant diffusion image of the adjuvant distribution three-dimensional model and mark it as Pi, and perform static analysis of adjuvant diffusion. The specific process of static analysis is as follows:

[0077] Divide the adjuvant diffusion image Pi into n0 feature blocks, mark any feature block as Z, mark n1 feature points of feature block Z, mark any feature point as z, collect the chromaticity value SDz of feature point z, and then comprehensively evaluate the chromaticity coefficient XsdZ of feature block Z: ;

[0078] Wherein, α0 refers to the conversion coefficient of the average value of the chromaticity value, and α0 is greater than 0; the conversion coefficient α0 refers to the conversion constant value of the chromaticity coefficient XsdZ of the feature block Z after averaging the chromaticity values ​​SDz of n1 feature points, and the conversion coefficient α0 is obtained by preset; when the chromaticity value SDz of the feature point z is higher, the chromaticity coefficient XsdZ of the feature block Z is higher, and the chromaticity severity level of the feature block Z is evaluated to be heavier;

[0079] By setting n2 chromaticity intervals for the chromaticity coefficient XsdZ of the feature block Z, marking any chromaticity interval as r, and combining the chromaticity coefficients XsdZ of n0 feature blocks, the instantaneous chromaticity index ZsdP of the adjuvant diffusion image Pi is obtained: ;

[0080] Among them, λr refers to the weight factor coefficient of the chromaticity interval r, and λr is greater than 0; when the chromaticity coefficient XsdZ of the feature block Z is in the chromaticity interval r, the weight factor coefficient λr is assigned to the chromaticity coefficient XsdZ of the feature block Z, and when the chromaticity coefficient XsdZ is higher, the corresponding preset value of the weight factor coefficient λr is higher; therefore, when the chromaticity coefficient XsdZ of the n0 feature blocks is higher, the instantaneous chromaticity index ZsdP of the adjuvant diffusion image Pi is higher;

[0081] Extract N0 adjuvant diffusion images of the same image acquisition period Tx, analyze the corresponding instantaneous chromaticity index ZsdP, and then comprehensively obtain the chromaticity static evaluation coefficient XJT of the adjuvant distribution stereo model: ;

[0082] Set the evaluation interval of the chromaticity static evaluation coefficient XJT, and determine the static chromaticity level of the auxiliary drug distribution three-dimensional model by interval comparison;

[0083] There are M1 evaluation intervals of the preset chromaticity static evaluation coefficient XJT, and any evaluation interval is marked as φm, wherein m is the serial number of the evaluation interval and 1≤m≤M1. When the chromaticity static evaluation coefficient XJT is located in the evaluation interval φm, the static chromaticity level of the three-dimensional model of the evaluation of the excipient distribution is m-level, and an m-level static prompt signal is generated; when the chromaticity static evaluation coefficient XJT is lower, the level m of the static prompt signal is lower, indicating that the static chromaticity of the three-dimensional model of the excipient distribution is lighter, and the evaluation drug dissolution rate is lower; conversely, when the chromaticity static evaluation coefficient XJT is higher, the level m of the static prompt signal is higher, indicating that the static chromaticity of the three-dimensional model of the excipient distribution is heavier, and the evaluation drug dissolution rate is higher;

[0084] A2: Set the image acquisition cycle Tx, construct a three-dimensional model of adjuvant distribution corresponding to N1 image acquisition cycles, and perform dynamic analysis of adjuvant diffusion. The specific process of dynamic analysis is as follows:

[0085] The fluctuation coefficient σsz of the feature block Z is obtained by the chromaticity value SDz of n1 feature points z:

[0086] ;

[0087] Among them, α1 refers to the conversion coefficient of the standard deviation of the chromaticity value, and α1 is greater than 0; the conversion coefficient α1 refers to the conversion constant value of the fluctuation coefficient σsz of the feature block Z after the standard deviation of the chromaticity value SDz of n1 feature points is calculated, and the conversion coefficient α1 is obtained by preset; when the fluctuation coefficient σsz is higher, it means that the floating difference of the chromaticity value SDz of the feature point z is larger;

[0088] The fluctuation coefficient σsz and the chromaticity coefficient XsdZ of the n0 feature blocks are compared, and the error ranges of the fluctuation coefficient σsz and the chromaticity coefficient XsdZ are preset to be ±φ1 and ±φ2 respectively;

[0089] Determine the consistent feature blocks and integrate them into the feature color block group V. The specific process is as follows:

[0090] Extract feature block Z1 and feature block Z2, mark the fluctuation coefficient and chromaticity coefficient of feature block Z1 as σsz1 and XsdZ1 respectively, and mark the fluctuation coefficient and chromaticity coefficient of feature block Z2 as σsz2 and XsdZ2 respectively;

[0091] Get the coefficient of fluctuation difference w1: ;

[0092] Get the chromaticity coefficient difference w2: ;

[0093] If the volatility difference w1<±φ1 and the chromaticity coefficient difference When w2<±φ2, it is determined that the feature block Z1 and the feature block Z2 are consistent; it means that the chromaticity fluctuation degree of the feature points of the two feature blocks is similar and is within the error range of ±φ1; and the overall chromaticity level of the feature points of the two feature blocks is similar and is within the error range of ±φ2;

[0094] Then, by comparing adjacent feature blocks, all consistent feature blocks are integrated and marked as a feature color block group V. The feature color block group V is preset to have N2 feature blocks, which means that the chromaticity fluctuation degree of the feature points of the feature blocks of the feature color block group V is similar, and the overall chromaticity level of the feature points of the feature blocks is similar;

[0095] The chromaticity gradient of the characteristic color block group V is set and marked as STv, thereby obtaining N3 characteristic color block groups;

[0096] The volume of the feature block Z is marked as a, and the volume of the feature color block group V is Av: ;

[0097] With the volume Av of the characteristic color block group V as the ordinate and the image acquisition period Tx as the abscissa, a change curve Sav between the volume Av of the characteristic color block group V and the image acquisition period Tx is constructed, and n4 points of the change curve Sav are collected, and any point and its coordinates are marked as q1 (Xq1, Yq1), and the adjacent point of the marked point q1 is q2 (Xq2, Yq2);

[0098] Calculate the vertical coordinate difference between two adjacent points and mark it as the volume difference A:

[0099] ;

[0100] By measuring n4 points of the change curve Sav, n5 volume differences are obtained. A, where ;

[0101] Then calculate n5 volume differences The average value Apj, floating coefficient Afd and peak-to-valley difference Afg of A;

[0102] Among them, the average value Apj is: , floating coefficient Afd: ;

[0103] Then, by comparing n5 volume differences A is sorted in descending order to obtain n5 volume differences The maximum value of A is marked as peak Afz, and n5 volume differences are obtained. The minimum value of A is marked as the valley value Agz, and the peak-to-valley difference Afg is: ;

[0104] Thus, the diffusion coefficient XslV of the characteristic color block group V is evaluated: ;

[0105] Among them, β1 and β2 are weight factor coefficients of the floating coefficient Afd and the peak-to-valley difference Afg, respectively, and both β1 and β2 are greater than 0; when the floating coefficient Afd and the peak-to-valley difference Afg are higher, the diffusion coefficient XslV of the characteristic color block group V is higher, indicating that the diffusion degree of the characteristic color block group is higher;

[0106] Through the diffusion coefficients XslV corresponding to the N3 characteristic color block groups, the dynamic diffusion coefficient XDT of the pigment of the auxiliary drug distribution three-dimensional model is comprehensively obtained: ;

[0107] Set the evaluation interval of the pigment dynamic diffusion coefficient XDT, and determine the dynamic diffusion degree of the auxiliary drug distribution three-dimensional model by interval comparison;

[0108] There are M2 evaluation intervals of the preset pigment dynamic diffusion coefficient XDT, and any evaluation interval is marked as φu, where u is the serial number of the evaluation interval and 1≤u≤M2. When the pigment dynamic diffusion coefficient XDT is in the evaluation interval φu, the dynamic diffusion degree of the three-dimensional model of adjuvant distribution is evaluated as u level, and a u-level dynamic prompt signal is generated; when the pigment dynamic diffusion coefficient XDT is lower, the level u of the dynamic prompt signal is lower, indicating that the dynamic diffusion degree of the three-dimensional model of adjuvant distribution is lower; conversely, when the pigment dynamic diffusion coefficient XDT is higher, the level u of the dynamic prompt signal is higher, indicating that the dynamic diffusion degree of the three-dimensional model of adjuvant distribution is higher;

[0109] A3: Combine the pigment static evaluation coefficient XJT and the pigment dynamic diffusion coefficient XDT to obtain and output the adjuvant sustained-release performance index Zhs of the adjuvant distribution three-dimensional model: ;

[0110] Among them, ω1 and ω2 are weight factor coefficients of the pigment static evaluation coefficient XJT and the pigment dynamic diffusion coefficient XDT, respectively, and both ω1 and ω2 are greater than 0; when the pigment static evaluation coefficient XJT and the pigment dynamic diffusion coefficient XDT are higher, the adjuvant sustained-release performance index Zhs of the adjuvant distribution stereoscopic model is higher, indicating that the adjuvant sustained-release performance is lower;

[0111] Through image acquisition and colorimetric analysis, the sustained-release performance of adjuvants is comprehensively analyzed from two perspectives: static colorimetric lightness and dynamic pigment diffusion, realizing intelligent periodic automatic detection and improving detection efficiency.

[0112] S3, establish the excipient performance influence model: set the parameter collection period Tc to collect the dissolution medium parameters and excipient property parameters, analyze and obtain the dissolution medium influence function and the excipient property influence function, and then output the comprehensive influence coefficient of the excipient performance;

[0113] The specific process of establishing the adjuvant performance influence model is as follows:

[0114] B1: Construct the curve analysis sub-model. The specific process is as follows:

[0115] Input the change curve Se into the curve analysis model, obtain N3 points of the change curve Se, mark any point and its coordinates as ε1 (Xε1, Yε1), mark the adjacent point of point ε1 as ε2 (Xε2, Yε2), where Xε2>Xε1;

[0116] Get the slope value Kε1 of point ε1 through the coordinates of point ε1 and point ε2: ;

[0117] When the slope value Kε1 is greater than 0, it means that the change curve Se shows an upward trend at point ε1;

[0118] When the slope value Kε1 is less than 0, it means that the change curve Se shows a downward trend at point ε1;

[0119] When the slope value Kε1 is equal to 0, the slope value Kε2 of point ε2 is obtained by measurement, so as to judge point ε1: if the slope value Kε2 is greater than 0, point ε1 is judged to be a valley point; if the slope value Kε2 is less than 0, point ε1 is judged to be a peak point;

[0120] Extract the characteristic curve segment between the peak point and the valley point in the change curve Se, obtain N4 points of the characteristic curve segment, and analyze and obtain the average increase rate Ke and peak-to-valley difference of the characteristic curve segment e and coefficient of variation σe;

[0121] The average increase rate Ke is obtained by averaging the slope values ​​of N4 points; the peak-to-valley difference is obtained by calculating the difference between the ordinates of the peak point and the valley point. e; obtain the coefficient of variation σe by calculating the variance of the ordinates of N4 points;

[0122] The average increase rate Ke and peak-to-valley difference of the characteristic curve segment e and the variation coefficient σe to obtain the influence coefficient Xse of the variation curve Se: ;

[0123] Among them, ρ1, ρ2 and ρ3 are the average increase rate Ke, peak-to-valley difference of the characteristic curve segment respectively. e and the weight factor coefficient of the coefficient of variation σe, and ρ1, ρ2 and ρ3 are all greater than 0; when the average increase rate Ke of the characteristic curve segment and the peak-to-valley difference When e and the coefficient of variation σe are higher, the influence coefficient Xse of the variation curve Se is higher, which means that the overall increase rate of the variation curve Se is large, the growth amplitude is large, and the degree of variation difference is large;

[0124] B2: Collect dissolution medium parameters by testing the organic solvents in the excipient experimental environment;

[0125] B2-1: The dissolution medium parameters include the temperature Tj, pH and concentration Cj of the organic solvent; the temperature Tj of the organic solvent is collected by a temperature sensor, the pH of the organic solvent is collected by a pH meter, and the concentration Cj of the organic solvent is collected by a concentration test;

[0126] By analyzing the relationship between the sustained-release performance of excipients and the dissolution medium parameters, the dissolution medium influence function is obtained:

[0127] With temperature Tj as the abscissa and adjuvant sustained-release performance index Zhs as the ordinate, a variation curve Stj between adjuvant sustained-release performance index Zhs and temperature Tj is constructed;

[0128] With pH as the horizontal coordinate and the excipient sustained-release performance index Zhs as the vertical coordinate, a change curve Sph between the excipient sustained-release performance index Zhs and pH is constructed;

[0129] With the concentration Cj as the horizontal coordinate and the auxiliary drug sustained-release performance index Zhs as the vertical coordinate, a change curve Scj between the auxiliary drug sustained-release performance index Zhs and the concentration Cj is constructed;

[0130] B2-2: Substitute the change curve Stj, the change curve Sph and the change curve Scj into the curve analysis model in turn, obtain the influence coefficients of the corresponding change curves, and mark them as Xstj, Xsph and Xscj respectively;

[0131] Then, the corresponding weight coefficients of the temperature Tj, pH and concentration Cj of the organic solvent are obtained and marked as η1, η2 and η3 respectively;

[0132] in, , , ;

[0133] When the weight coefficient η1 is higher, it means that the temperature Tj of the organic solvent has a higher degree of influence on the excipient sustained-release performance index Zhs; when the weight coefficient η2 is higher, it means that the pH of the organic solvent has a higher degree of influence on the excipient sustained-release performance index Zhs; when the weight coefficient η3 is higher, it means that the concentration Cj of the organic solvent has a higher degree of influence on the excipient sustained-release performance index Zhs;

[0134] B2-3: The dissolution medium influence function F1 is obtained comprehensively through the temperature Tj, pH and concentration Cj of the organic solvent and their corresponding weight coefficients: ;

[0135] Substitute the temperature Tj, pH and concentration Cj of the organic solvent into the dissolution medium influence function F1 to obtain the influence of the dissolution medium parameters on the excipient sustained release performance index Zhs;

[0136] B3: Collect the property parameters of excipients by testing the excipients of sustained-release and controlled-release drugs;

[0137] B3-1: The excipient property parameters include the particle size Ky and roughness Gy of the excipient experimental drug, wherein the roughness of the excipient experimental drug is collected by an existing laser surface roughness meter; the surface area and volume of the excipient experimental drug are collected by an optical measuring instrument; the particle size Ky refers to the ratio of the surface area to the volume of the excipient experimental drug;

[0138] By analyzing the relationship between the sustained-release performance of adjuvants and the parameters of adjuvant properties, the influence function of adjuvant properties can be obtained.

[0139] With the particle size Ky as the horizontal coordinate and the auxiliary drug sustained-release performance index Zhs as the vertical coordinate, a change curve Sky between the auxiliary drug sustained-release performance index Zhs and the particle size Ky is constructed;

[0140] With roughness Gy as the horizontal coordinate and adjuvant sustained-release performance index Zhs as the vertical coordinate, a change curve Sgy between adjuvant sustained-release performance index Zhs and roughness Gy is constructed;

[0141] B3-2: Substitute the change curve Sky and the change curve Sgy into the curve analysis model in turn, obtain the influence coefficients of the corresponding change curves, and mark them as Xsky and Xsgy respectively;

[0142] Then, the corresponding weight coefficients of the particle size Ky and roughness Gy of the excipient experimental drug are obtained and marked as η4 and η5 respectively; among them, , ;

[0143] When the weight coefficient η4 is higher, it means that the particle size Ky of the excipient experimental drug has a higher degree of influence on the excipient sustained-release performance index Zhs; when the weight coefficient η5 is higher, it means that the roughness Gy of the excipient experimental drug has a higher degree of influence on the excipient sustained-release performance index Zhs;

[0144] B3-3: Through the particle size Ky and roughness Gy of the excipient experimental drug and their corresponding weight coefficients, the excipient property influence function F2 is comprehensively obtained: ;

[0145] Substitute the particle size Ky and roughness Gy of the excipient experimental drug into the excipient property influence function F2 to obtain the influence of the excipient property parameters on the excipient sustained-release performance index Zhs;

[0146] B4: Then, by combining the dissolution medium influence function F1 and the excipient property influence function F2, the comprehensive influence coefficient FZ of the excipient performance is output: ;

[0147] Among them, υ1 and υ2 are the weight factor coefficients of the dissolution medium influence function F1 and the excipient property influence function F2, respectively, and υ1 and υ2 are both greater than 0; when the dissolution medium influence function F1 and the excipient property influence function F2 are higher, the excipient performance comprehensive influence coefficient FZ is higher, indicating that the comprehensive influence of the dissolution medium parameters and the excipient property parameters on the excipient sustained-release performance is higher;

[0148] B5: Through the comprehensive influence coefficient FZ of adjuvant performance and the sustained-release performance index Zhs of adjuvant, the combined effect change curve Slh of adjuvant sustained-release performance is constructed, and the combined effect change function F0 is generated by fitting:

[0149] , Substitute the comprehensive influence coefficient FZ of the adjuvant performance into the combined action change function F0, and then obtain the adjuvant sustained-release performance index Zhs under the influence of the combined action;

[0150] By exploring the combined action mechanism of dissolution medium and excipient properties on the sustained-release performance of excipients, the comprehensiveness of data analysis and processing is ensured;

[0151] S4, establish a sustained-release drug design model: obtain the required value of the excipient sustained-release performance index and the required value of the dissolution medium parameter through the actual demand of drug application, substitute it into the excipient performance influence model to obtain the required value of the excipient property parameter, so as to obtain the preformulated drug of the sustained-release drug. The specific process of establishing the sustained-release drug design model is as follows:

[0152] The actual demand for drug application includes the demand for sustained-release drug application and the physical conditions of applicable patients;

[0153] According to the sustained release requirement of drug application, the demand value of the sustained release performance index of the auxiliary drug is preset; the demand value of the sustained release performance index of the prefabricated drug is marked as the sustained release performance demand index E1 of the prefabricated drug;

[0154] Substitute the sustained-release performance demand index E1 of the preformed drug into the combined action variation function F0, and reversely calculate the demand value FZ0 of the comprehensive influence coefficient of the excipient performance of the preformed drug;

[0155] By applying the patient's physical signs, the required values ​​of the dissolution medium parameters are preset; wherein, the applicable patient's physical signs include parameter indicators of body fluid solutes such as gastric juice and intestinal juice, and the required values ​​of the dissolution medium parameters are preset by collecting the patient's gastric juice and intestinal juice and performing detection and analysis, and the required values ​​of the dissolution medium parameters include the temperature required value Tj0, the pH required value pH and the concentration required value Cj0;

[0156] Substitute the required values ​​of the dissolution medium parameters into the dissolution medium influence function F1 to obtain the predicted influence degree F1y of the required values ​​of the dissolution medium parameters on the sustained release performance requirement index E1:

[0157] ;

[0158] Substitute the predicted influence degree F1y of the dissolution medium parameters and the required value FZ0 of the comprehensive influence coefficient of the excipient performance of the preformed drug into the comprehensive influence coefficient of the excipient performance FZ to obtain the predicted influence degree F2y of the excipient property parameters on the excipient sustained-release performance index Zhs: ;

[0159] The demand values ​​of the adjuvant property parameters include the particle size demand value Ky0 and the roughness demand value Gy0 of the pre-prepared drug. The demand values ​​of the adjuvant property parameters are preset to obtain the predicted influence degree F2y of the adjuvant property parameters. The demand values ​​of the adjuvant property parameters need to satisfy the predicted influence degree F2y of the adjuvant property parameters, that is:

[0160] ;

[0161] Thus, according to the particle size requirement value Ky0 and roughness requirement value Gy0 of the prefabricated drug, the prefabricated drug of the sustained-release drug is produced and manufactured, and the property parameters of the prefabricated drug adjuvant are adjusted and preset;

[0162] S5, establish a drug production verification model: substitute the prefabricated drug into the sustained-release performance analysis model for diffusion analysis, obtain the test value of the excipient sustained-release performance index of the prefabricated drug, compare the test value of the excipient sustained-release performance index of the prefabricated drug with the required value, determine the deviation of the excipient performance influence model, and thus optimize the excipient performance influence model. The specific process of establishing the drug production verification model is as follows:

[0163] Perform diffusion release detection on the preformed drug, collect the diffusion image of the preformed drug and fit it to build a three-dimensional distribution model of the preformed drug, substitute it into the sustained-release performance analysis model for diffusion analysis, output the auxiliary drug sustained-release performance index of the three-dimensional distribution model of the preformed drug, and mark it as the sustained-release performance detection index E2 of the preformed drug;

[0164] Compare the sustained-release performance demand index E1 and the sustained-release performance test index E2 of the preformed drug, and analyze and obtain the deviation index ZPC of the excipient performance influence model: ;

[0165] When the deviation index ZPC is higher, the deviation of the model for determining the influence of excipient performance is higher, indicating that the gap between the test results of the preformulated drug and the actual demand is larger, which further indicates that the sustained-release performance of the drug is inaccurate and the drug production quality is insufficient;

[0166] Set the comparison interval of the deviation index ZPC and mark it as [c1, c2], and generate the corresponding optimization signal through comparison. The specific process is as follows:

[0167] When the deviation index ZPC of the adjuvant drug performance influence model is higher than the comparison interval [c1, c2], the deviation degree of the adjuvant drug performance influence model is determined to be severe, and a first-level optimization signal is generated. When the first-level optimization signal is received, the weight coefficient of the adjuvant drug performance influence model is synchronously calculated and updated through the adjuvant sustained-release performance index of the prefabricated drug distribution three-dimensional model, so as to realize the overall algorithm optimization of the adjuvant drug performance influence model;

[0168] When the deviation index ZPC of the excipient performance influence model is in the comparison interval [c1, c2], the deviation of the excipient performance influence model is determined to be moderate, and a secondary optimization signal is generated. When the secondary optimization signal is received, the excipient sustained-release performance index of the prefabricated drug distribution three-dimensional model is used to sequentially perform feedback verification on the dissolution medium influence function F1, the excipient property influence function F2, and the excipient performance comprehensive influence coefficient FZ, so as to accurately correct the weight coefficient of the influence function that is incorrectly calculated;

[0169] When the deviation index ZPC of the adjuvant performance influence model is lower than the comparison interval [c1, c2], the deviation of the adjuvant performance influence model is judged to be mild, and a third-level optimization signal is generated. When the third-level optimization signal is received, no processing is performed.

[0170] In summary, the present invention constructs a three-dimensional model of adjuvant distribution by collecting adjuvant diffusion images and fitting them, and substitutes them into the sustained-release performance analysis model for chromaticity analysis. From the two perspectives of static chromaticity and dynamic pigment diffusion, the sustained-release performance of the adjuvant is comprehensively analyzed. Then, by constructing an adjuvant performance influence model, the joint action mechanism of the dissolution medium of pharmaceutical excipients and the properties of adjuvants on the sustained-release performance of the adjuvant is explored, the comprehensiveness of data impact analysis is ensured, intelligent periodic automatic detection is realized, and the detection efficiency is improved.

[0171] The present invention designs preformed drugs of controlled-release drugs based on the actual needs of drug applications through a controlled-release drug design model, thereby realizing the adjustment and preset of the property parameters of the excipients of the preformed drugs, and through a drug production verification model, comparing and analyzing the deviation of the excipient performance impact model, realizing drug production verification and performing model optimization, thereby improving the accuracy of the excipient sustained-release performance of the preformed drugs in the actual application process, ensuring the drug sustained-release effect of the controlled-release drugs, and thus improving the drug production quality.

[0172] The size of the interval and threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technical personnel in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0173] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0174] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A quality testing method for the sustained-release performance of sustained-release pharmaceutical excipients, characterized in that: The following steps are involved: S1, collecting excipient diffusion images and constructing an excipient distribution 3D model: by preparing an excipient experimental drug of a sustained-release drug and placing it in an organic solvent, an excipient diffusion release detection process is performed, N0 excipient diffusion images are obtained through an image acquisition device, and an excipient distribution 3D model is constructed by fitting; S2, establish a sustained-release performance analysis model: perform colorimetric analysis on the three-dimensional model of adjuvant distribution, evaluate the static colorimetric grade and dynamic diffusion degree of the three-dimensional model of adjuvant distribution, and then comprehensively evaluate the sustained-release performance of adjuvant; S3, establish the excipient performance influence model: set the parameter collection period Tc to collect the dissolution medium parameters and excipient property parameters, analyze and obtain the dissolution medium influence function and the excipient property influence function, and then output the comprehensive influence coefficient of the excipient performance, and fit and construct the combined effect change function; S4, establish a sustained-release drug design model: obtain the required value of the excipient sustained-release performance index and the required value of the dissolution medium parameter through the actual demand of drug application, substitute it into the excipient performance influence model to obtain the required value of the excipient property parameter, so as to obtain the preformulated drug of the sustained-release drug; S5, establishing a drug production verification model: substituting the prefabricated drug into the sustained-release performance analysis model for diffusion analysis, obtaining the test value of the excipient sustained-release performance index of the prefabricated drug, and comparing the test value of the excipient sustained-release performance index of the prefabricated drug with the required value to determine the deviation of the excipient performance influence model, thereby optimizing the excipient performance influence model; The specific process of establishing the sustained-release performance analysis model is as follows: A1: Extract any adjuvant diffusion image of the adjuvant distribution three-dimensional model and mark it as Pi, and perform static analysis of adjuvant diffusion. The specific process of static analysis is as follows: Divide the adjuvant diffusion image Pi into n0 feature blocks, mark any feature block as Z, mark n1 feature points of the feature block Z, mark any feature point as z, collect the chromaticity value SDz of the feature point z, and then comprehensively evaluate the chromaticity coefficient XsdZ of the feature block Z; Set n2 chromaticity intervals, mark any chromaticity interval as r, and obtain the instantaneous chromaticity index ZsdP of the adjuvant diffusion image Pi through the chromaticity coefficients XsdZ of n0 feature blocks and their corresponding chromaticity intervals r; Extract N0 adjuvant diffusion images of the same image acquisition period Tx, and analyze the corresponding instantaneous chromaticity index ZsdP of the N0 adjuvant diffusion images, and then comprehensively obtain the chromaticity static evaluation coefficient XJT of the adjuvant distribution stereo model; Set the evaluation interval of the chromaticity static evaluation coefficient XJT, and determine the static chromaticity level of the auxiliary drug distribution three-dimensional model by interval comparison; A2: Set the image acquisition cycle Tx, construct a three-dimensional model of adjuvant distribution corresponding to N1 image acquisition cycles, and perform dynamic analysis of adjuvant diffusion. The specific process of dynamic analysis is as follows: Obtain the fluctuation coefficient σsz of the feature block Z through the chromaticity values ​​SDz of the n1 feature points z, compare the fluctuation coefficient σsz of the n0 feature blocks with the chromaticity coefficient XsdZ, and integrate to obtain N3 feature color block groups, and mark any feature color block group as V; The volume of the characteristic block Z is marked as a to obtain the volume Av of the characteristic color block group V, and then construct a variation curve Sav between the volume Av of the characteristic color block group V and the image acquisition period Tx; Extract n4 points of the change curve Sav, calculate the vertical coordinate difference between two adjacent points and mark it as the volume difference A, by measuring n4 points of the change curve Sav, n5 volume differences are obtained. A, and then calculate the volume difference The average value Apj, floating coefficient Afd and peak-to-valley difference Afg of A are used to evaluate the diffusion coefficient XslV of the characteristic color block group V; Through the diffusion coefficients XslV corresponding to the N3 characteristic color block groups, the dynamic diffusion coefficients XDT of the pigments in the three-dimensional model of adjuvant distribution are comprehensively obtained; Set the evaluation interval of the pigment dynamic diffusion coefficient XDT, and determine the dynamic diffusion degree of the auxiliary drug distribution three-dimensional model by interval comparison; A3: Combine the pigment static evaluation coefficient XJT and the pigment dynamic diffusion coefficient XDT to obtain and output the adjuvant sustained-release performance index Zhs of the adjuvant distribution three-dimensional model; The specific process of establishing the adjuvant performance influence model is as follows: B1: Construct the curve analysis sub-model. The specific process is as follows: Input the change curve Se into the curve analysis model, obtain N3 points of the change curve Se, and mark the peak points and valley points of the change curve Se; Extract the characteristic curve segment between the peak point and the valley point of the change curve Se, and obtain N4 points of the characteristic curve segment and their coordinates; Analyze and obtain the average increase rate Ke and peak-to-valley difference of characteristic curve segments e and the variation coefficient σe, and then obtain the influence coefficient Xse of the variation curve Se; B2: By detecting the organic solvent in the excipient experimental environment, the dissolution medium parameters are collected and substituted into the curve analysis sub-model to analyze the relationship between the sustained-release performance of the excipient and the dissolution medium parameters, thereby obtaining the dissolution medium influence function F1; B3: By testing the excipient experimental drugs of sustained-release drugs, the excipient property parameters are collected and substituted into the curve analysis sub-model to analyze the relationship between the excipient sustained-release performance and the excipient property parameters, thereby obtaining the excipient property influence function F2; B4: Then, by combining the dissolution medium influence function F1 and the excipient property influence function F2, the comprehensive influence coefficient FZ of the excipient performance is output to evaluate the comprehensive influence of the dissolution medium parameters and the excipient property parameters on the excipient sustained-release performance; B5: By combining the comprehensive influence coefficient of adjuvant performance FZ and the adjuvant sustained-release performance index Zhs, the combined effect change curve Slh of the adjuvant sustained-release performance is constructed, and the combined effect change function F0 is constructed by fitting; The specific process of establishing a sustained-release drug design model is as follows: C1: The required values ​​of the dissolution medium parameters include the temperature required value Tj0, the pH required value pH and the concentration required value Cj0. The required values ​​of the dissolution medium parameters are substituted into the dissolution medium influence function F1 to obtain the predicted influence degree F1y of the required values ​​of the dissolution medium parameters on the sustained-release performance of the excipient of the preformed drug; C2: marking the required value of the excipient sustained-release performance index of the preformed drug as the sustained-release performance required index E1 of the preformed drug; C3: Substitute the predicted influence degree F1y of the dissolution medium parameters and the sustained-release performance requirement index E1 of the preformed drug into the comprehensive influence coefficient FZ of the excipient performance to obtain the predicted influence degree F2y of the excipient property parameters on the excipient sustained-release performance index Zhs; C4: Through the predicted influence degree F2y of the excipient property parameters, the required value of the excipient property parameters is obtained, and the preformed drug for sustained-release drugs is produced.

2. The method for quality testing of sustained-release performance of a sustained-release pharmaceutical excipient according to claim 1, characterized in that: The specific process of constructing the three-dimensional model of adjuvant distribution is as follows: By replacing the original drug with pigment and combining it with excipients and additives to make medicine, we can obtain the excipient experimental drug for sustained-release drugs; The adjuvant drug diffusion images are collected by an image acquisition device to obtain adjuvant drug diffusion images of N0 viewing angles; the reference axis L is set and marked by the adjuvant drug diffusion images, the position of the reference point o is marked from the reference axis L, the reference axis L and the reference point o of the N0 adjuvant drug diffusion images are overlapped, and the reference point o is used as the center point to fit the N0 adjuvant drug diffusion images to construct an adjuvant drug distribution three-dimensional model.

3. The method for quality testing of sustained-release performance of a sustained-release pharmaceutical excipient according to claim 2, characterized in that: The specific process of obtaining the dissolution medium influence function F1 is: B2-1: Dissolution medium parameters include temperature Tj, pH and concentration Cj of organic solvent; Constructing a variation curve Stj between the adjuvant sustained-release performance index Zhs and temperature Tj, a variation curve Sph between the adjuvant sustained-release performance index Zhs and pH, and a variation curve Scj between the adjuvant sustained-release performance index Zhs and concentration Cj; B2-2: Substitute the change curve Stj, the change curve Sph and the change curve Scj into the curve analysis model in turn, obtain the influence coefficients of the corresponding change curves, and mark them as Xstj, Xsph and Xscj respectively; Then, the corresponding weight coefficients of the temperature Tj, pH and concentration Cj of the organic solvent are obtained and marked as η1, η2 and η3 respectively; B2-3: Through the temperature Tj, pH and concentration Cj of the organic solvent and their corresponding weight coefficients, the dissolution medium influence function F1 is comprehensively obtained to evaluate the influence of the dissolution medium parameters on the excipient sustained-release performance index Zhs.

4. The method for quality testing of sustained-release performance of sustained-release pharmaceutical excipients according to claim 3, characterized in that: The specific process of obtaining the auxiliary drug property influence function F2 is: B3-1: The excipient property parameters include the particle size Ky and roughness Gy of the excipient experimental drug; Constructing the variation curve Sky between the excipient sustained-release performance index Zhs and the particle size Ky, and the variation curve Sgy between the excipient sustained-release performance index Zhs and the roughness Gy; B3-2: Substitute the change curve Sky and the change curve Sgy into the curve analysis model in turn, obtain the influence coefficients of the corresponding change curves, and mark them as Xsky and Xsgy respectively; Then, the corresponding weight coefficients of the particle size Ky and roughness Gy of the excipient experimental drug are obtained and marked as η4 and η5 respectively; B3-3: Through the particle size Ky and roughness Gy of the excipient experimental drug and their corresponding weight coefficients, the excipient property influence function F2 is comprehensively obtained to evaluate the influence of the excipient property parameters on the excipient sustained-release performance index Zhs.

5. The method for quality testing of sustained-release performance of sustained-release pharmaceutical excipients according to claim 4, characterized in that: The specific process of establishing a drug production verification model is as follows: D1: Perform diffusion release detection on the preformed drug, collect the diffusion image of the preformed drug and fit it to build a three-dimensional distribution model of the preformed drug, and substitute it into the sustained-release performance analysis model for diffusion analysis, output the excipient sustained-release performance index of the three-dimensional distribution model of the preformed drug, and mark it as the sustained-release performance detection index E2 of the preformed drug; D2: Analyze and obtain the deviation index ZPC of the excipient performance influence model through the sustained release performance demand index E1 and the sustained release performance test index E2 of the preformed drug; D3: Set the comparison interval of the deviation index ZPC and mark it as [c1, c2], and generate the corresponding optimization signal through comparison.

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