Method for evaluating nano-emulsion type low-density contrast agent

Abstract

The invention discloses a method for evaluating a nano-emulsion type low-density contrast agent, and relates to the technical field of contrast agent analys.The method comprises the steps that based on the preparation requirement of the nano-emulsion type low-density contrast agent, an optimal surfactant combination is selected, single-factor variance analysis is conducted by measuring the particle size and PDI value of each set of samples, and key preparation parameters are obtained; setting experimental combinations of the key preparation parameters under different levels by using a central composite design method, preparing multiple groups of contrast agent samples, measuring particle sizes and PDI values, establishing an initial mathematical model among the key preparation parameters, the particle sizes and the PDI values, and establishing an optimized mathematical model by adding a third-dimensional evaluation index; and determining a target process parameter combination by analyzing a response surface diagram of every two combinations of the key preparation parameters, preparing a verification batch sample, and comparing the verification batch sample with a model prediction value to obtain an evaluation result. The problem that the performance of the contrast agent is difficult to comprehensively evaluate in the prior art is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of contrast agent analysis, in particular to a nanoemulsion low-density contrast agent evaluation method. BACKGROUND

[0002] With the development of medical imaging technology, contrast agents play a crucial role in clinical diagnosis. As a new drug carrier system, nanoemulsion has unique advantages. Nanoemulsion is a thermodynamically stable, isotropic, transparent or translucent homogeneous dispersion system in which one immiscible liquid is dispersed in another liquid in the form of droplets. It is usually divided into O / W (water-in-oil), W / O (oil-in-water) and double-continuous nanoemulsion, with an average particle size of 20-200 nm. Nanoemulsion has shown great potential in drug delivery and imaging applications due to its good appearance, good patient compliance, good physical stability and large-scale production, and is particularly suitable for the development and application of low-density contrast agents.

[0003] In recent years, researchers have begun to try to apply nanoemulsion technology to the preparation of low-density contrast agents, especially liquid paraffin-based nanoemulsion (LP-NE), which has attracted widespread attention. To optimize the preparation process and formula of LP-NE, many researchers have used various experimental design methods for systematic research. Among them, response surface method (RSM) is a highly efficient multivariate statistical technique widely used in such optimization research. This method establishes a mathematical model and uses statistical techniques to study the relationship and interaction between independent variables and response variables, with the significant advantages of less experimental quantity and reliable results, providing scientific methodological support for the development of nanoemulsion low-density contrast agents.

[0004] However, the existing evaluation techniques for nanoemulsion low-density contrast agents have many shortcomings. First, traditional evaluation methods often focus only on a single indicator such as particle size or PDI, lacking comprehensive consideration of the multi-dimensional characteristics of nanoemulsion low-density contrast agent particle size, uniformity and rheological properties. Second, existing evaluation methods cannot establish a quantitative relationship between preparation parameters and contrast agent performance, making it difficult to accurately predict the contrast effect under different process parameter combinations. Third, the repeatability and stability of the evaluation results are also challenging, and there is a lack of systematic process parameter optimization methods, resulting in large differences between batches. These problems seriously hinder the research and development efficiency and clinical application of nanoemulsion low-density contrast agents. Therefore, establishing a scientific, systematic and comprehensive evaluation method for nanoemulsion low-density contrast agents is of great significance for guiding the optimal process and optimal formula research of LP-NE, improving the overall performance of contrast agents, and promoting their clinical application.

[0005] To address the problems in the related art, no effective solutions have been proposed so far. SUMMARY

[0006] In view of the problems in the related art, the present application provides a nanoemulsion low-density contrast agent evaluation method, which has the advantages of a three-dimensional comprehensive goodness value evaluation system, an accurate performance prediction model, and a scientific response surface analysis method, thereby solving the problems of difficulty in comprehensively evaluating the performance of the contrast agent and insufficient optimization of process parameters in the prior art.

[0007] To this end, the present application adopts the following specific technical solutions:

[0008] A nanoemulsion low-density contrast agent evaluation method, the nanoemulsion low-density contrast agent evaluation method comprising:

[0009] S1, based on the preparation requirements of the nanoemulsion low-density contrast agent, selecting an optimal surfactant combination, and performing single-factor variance analysis on the particle size and PDI value of each group of samples to obtain key preparation parameters;

[0010] S2, using a central composite design method to set experimental combinations of the key preparation parameters at different levels, preparing multiple groups of contrast agent samples and measuring the particle size and PDI value, establishing an initial mathematical model between the key preparation parameters and the particle size and PDI value, and adding a third-dimensional evaluation index to establish an optimized mathematical model;

[0011] S3, according to the optimized mathematical model, by analyzing the response surface graph of each two-key preparation parameter combination, determining the target process parameter combination, and preparing a verification batch sample to measure the physicochemical properties of the contrast agent, and comparing the evaluation results with the model predicted values.

[0012] Further, based on the preparation requirements of the nanoemulsion low-density contrast agent, selecting an optimal surfactant combination includes: based on the preparation requirements of the nanoemulsion low-density contrast agent, using a ternary phase diagram method to combine different proportions of oil phase, water phase and different types of surfactants; obtaining the phase state change data under each ratio, and generating a pseudo-ternary phase diagram; selecting the combination with the largest area of nanoemulsion region in the pseudo-ternary phase diagram as the optimal surfactant combination.

[0013] Further, the single-factor variance analysis on the particle size and PDI value of each group of samples to obtain the key preparation parameters includes: according to the optimal surfactant combination, using a single-factor method to set different levels of preparation parameters; preparing multiple groups of contrast agent samples, and measuring the particle size and PDI value of each group of samples for single-factor variance analysis; according to the analysis results, screening out the key preparation parameters less than the preset threshold; wherein, the preparation parameters include primary emulsification time, homogenization pressure, homogenization times, surfactant proportion and Km value.

[0014] Further, the experiment combinations of the key preparation parameters at different levels are set by using the central composite design method, multiple groups of contrast agent samples are prepared and the particle sizes and PDI values are measured, and an initial mathematical model between the key preparation parameters and the particle sizes and PDI values is established, including: based on the screened key preparation parameters, the experiment combinations of the key preparation parameters at different levels are set by using the central composite design method; multiple groups of contrast agent samples are prepared according to different experiment combinations and the particle sizes and PDI values are measured, and the two-dimensional comprehensive merit values of the samples in each group are calculated according to the measurement results; the initial mathematical model between the key preparation parameters and the particle sizes and PDI values is established by performing variance analysis on the two-dimensional comprehensive merit values.

[0015] Further, the third-dimensional evaluation index is added to establish the optimization mathematical model, including: based on the prediction results of the initial mathematical model, the third-dimensional evaluation index is determined; the three-dimensional comprehensive merit values containing the particle sizes, PDI values and viscosity are calculated by measuring the viscosity values of the contrast agent samples in each group; the optimization mathematical model between the key preparation parameters and the physicochemical properties of the contrast agent is established by performing variance analysis according to the three-dimensional comprehensive merit values of the samples in each group.

[0016] Further, according to the optimization mathematical model, the target process parameter combination is determined by analyzing the response surface graphs of two-by-two combinations of the key preparation parameters, including: the three-dimensional response surface graphs of two-by-two combinations of the key preparation parameters are generated by using the optimization mathematical model; the target process parameter combination is determined by analyzing the influence trend of two-by-two combinations of the key preparation parameters on the physicochemical properties of the contrast agent; the sensitivity of the key preparation parameters to the physicochemical properties of the contrast agent is judged according to the slope change of the three-dimensional response surface graph; and the optimal combination of the key preparation parameters is determined based on the judgment result and is used as the target process parameter.

[0017] Further, the evaluation results are obtained by comparing the physicochemical properties of the contrast agent in the verification batch sample with the model prediction values, including: based on the optimal process parameter combination, the verification batch sample of the contrast agent is prepared; the actual three-dimensional comprehensive merit values are calculated by measuring the particle sizes, PDI values and viscosity of the verification batch sample; and the evaluation results are generated by comparing the actual three-dimensional comprehensive merit values with the model prediction values.

[0018] The beneficial effects of the present application are:

[0019] (1) By introducing three-dimensional comprehensive goodness value evaluation system, the application breaks through the limitation of traditional evaluation method which only focuses on particle size and PDI, and takes viscosity as the third-dimensional evaluation index, so that the evaluation result more comprehensively reflects the actual application performance of the nanoemulsion type low-density contrast agent; experiments prove that when only two-dimensional evaluation of particle size and PDI is adopted, the nanoemulsion prepared by the optimal process parameter combination screened out has ideal particle size and uniformity, but the viscosity is too large to meet the application requirement; and after the three-dimensional comprehensive goodness value evaluation method is adopted, the process parameters obtained can simultaneously consider particle size, uniformity and rheological property, and the contrast agent with better comprehensive performance is produced.

[0020] (2) By establishing the optimization mathematical model between the key preparation parameters and the physical and chemical properties of the contrast agent, the application realizes the accurate prediction of the performance of the nanoemulsion type low-density contrast agent; the model has statistical significance (P<0.05), the correlation coefficient R reaches 0.7386, the actual three-dimensional comprehensive goodness value of the verification batch sample has little deviation from the model predicted value, which indicates that the evaluation method has high reliability and can effectively guide the process optimization and quality control of the contrast agent. 2

[0021] (3) By the response surface analysis method, the application directly displays the influence trend and interaction of each key preparation parameter on the comprehensive performance of the nanoemulsion type low-density contrast agent, especially reveals the significant influence of the surfactant proportion, Km value and their interaction on the performance of the contrast agent; compared with the traditional ternary phase diagram method, the method is more suitable for the optimization of the low-density contrast agent which needs to fix the oil phase ratio, is not only simple in operation, small in test error and objective and reliable in result, but also can directly judge the sensitivity of each parameter according to the slope change of the response surface, and provides a more scientific and systematic evaluation method for the research and development of the nanoemulsion type low-density contrast agent. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0023] Figure 1 is a flowchart of a nanoemulsion type low-density contrast agent evaluation method according to an embodiment of the present application;

[0024] Figure 2 is a pseudo-ternary phase diagram of Tween 80 and Span 80 combination in a nanoemulsion type low-density contrast agent evaluation method according to an embodiment of the present application;

[0025] Figure 3 ​It is a pseudo ternary phase diagram of Q-14s and A-172E combination in a nanoemulsion type low-density contrast agent evaluation method according to an embodiment of the application;

[0026] Figure 4 It is a three-dimensional response surface graph of the influence of the surfactant proportion and the homogenization frequency on the OD value in a nanoemulsion type low-density contrast agent evaluation method according to an embodiment of the application;

[0027] Figure 5 It is a three-dimensional response surface graph of the influence of the Km value and the homogenization frequency on the OD value in a nanoemulsion type low-density contrast agent evaluation method according to an embodiment of the application;

[0028] Figure 6 It is a three-dimensional response surface graph of the influence of the Km value and the surfactant proportion on the OD value in a nanoemulsion type low-density contrast agent evaluation method according to an embodiment of the application. DETAILED DESCRIPTION

[0029] To further illustrate the embodiments, the present application provides drawings which are part of the disclosure of the present application, mainly used to illustrate the embodiments, and can be used to explain the operating principle of the embodiments in conjunction with the related description of the specification. Those of ordinary skill in the art can understand other possible implementations and advantages of the present application by referring to these contents. The components in the drawings are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0030] According to an embodiment of the present application, a nanoemulsion type low-density contrast agent evaluation method is provided.

[0031] The present application will be further described in conjunction with the drawings and specific embodiments. As shown in Figure 1 According to an embodiment of the present application, a nanoemulsion type low-density contrast agent evaluation method is provided. The nanoemulsion type low-density contrast agent evaluation method comprises:

[0032] S1, based on the preparation requirements of the nanoemulsion type low-density contrast agent, selecting the optimal surfactant combination, and performing single factor variance analysis by measuring the particle size and PDI value of each group of samples to obtain key preparation parameters;

[0033] S2, using central composite design method to set the experimental combination of each key preparation parameter at different levels, preparing multiple groups of contrast agent samples and measuring the particle size and PDI value, establishing the initial mathematical model between the key preparation parameters and the particle size and PDI value, and establishing the optimization mathematical model by adding the third-dimensional evaluation index;

[0034] S3, according to the optimization mathematical model, by analyzing the response surface map of each key preparation parameter combined with each other, the target process parameter combination is determined, and the verification batch sample is prepared to measure the physical and chemical properties of the contrast agent, and the evaluation result is obtained by comparing with the model predicted value.

[0035] It should be noted that in the implementation process of the evaluation method of the nanoemulsion type low-density contrast agent of the present application, the nanoemulsion type low-density contrast agent sample for evaluation needs to be prepared in advance. In this embodiment, the main instruments used for sample preparation include a high-pressure homogenizer, a laboratory high-shear emulsifier, an ultrasonic cleaner, a conductivity meter, a digital display stirring constant temperature electric heating jacket, a magnetic stirrer with a heating plate, a nanoparticle analyzer and an analytical balance, etc. The reagents used include anhydrous ethanol, 1,2-propanediol, glycerol, and surfactants such as Q-14s, A-172E and LP, etc.

[0036] It should also be noted that in the preparation of the nanoemulsion type low-density contrast agent sample to be evaluated, two methods can be used in the primary emulsion preparation stage: the magnetic stirring method is used in small batch screening experiments, the stirring speed is 1000 r·min -1 , and the stirring time is 30 minutes, and a relatively uniform primary emulsion can be prepared; when a large amount of sample needs to be evaluated, a high-speed emulsifying disperser is used, the stirring speed is 20000 r·min -1 , and the high-speed shearing time is 30 minutes, and the water phase is gradually added to the mixture of the oil phase and the surfactant. For further processing of the primary emulsion, by comparing the ultrasonic cell disruption method with the high-pressure homogenization method, the high-pressure homogenization method with larger processing capacity and relatively lower energy consumption is selected for the preparation of the nanoemulsion type low-density contrast agent sample, which lays a foundation for the implementation of the evaluation method.

[0037] In one embodiment, based on the preparation requirements of the nanoemulsion type low-density contrast agent, the optimal surfactant combination includes: based on the preparation requirements of the nanoemulsion type low-density contrast agent, different proportions of oil phase, water phase and different types of surfactant combination are mixed by using the ternary phase diagram method; the phase state change data under each ratio is obtained, and a pseudo-ternary phase diagram is generated; the combination with the largest area of the nanoemulsion region in the pseudo-ternary phase diagram is selected as the optimal surfactant combination.

[0038] Specifically, the first step is to screen the surfactant by the ternary phase diagram method.

[0039] Specifically, the application selects vegetable oil as the oil phase, purified water as the water phase, and mixes different types of surfactants with the oil phase and the water phase in different proportions. The application screens two types of surfactants, Tween-80 combined with Span-80 and Q-14s combined with A-172E, by drawing ternary phase diagrams. In the specific operation, the surfactant is mixed with the oil phase in a preset proportion, and is fully mixed by magnetic stirring at 25°C for 10 minutes, and then the water phase is added dropwise. After adding a certain amount of water phase, the appearance change of the mixture is observed, and the phase change condition is recorded. By adjusting the proportions of the oil phase, the water phase and the surfactant, the phase change data under different proportions are obtained, and the pseudo-ternary phase diagram is drawn by using Origin2019 software. In the pseudo-ternary phase diagram, a triangular coordinate system is adopted, and the three vertices of the triangle represent the water phase (100%), the oil phase (100%) and the surfactant (100%) respectively, and any point in the triangle represents a specific proportion combination of the three components, as shown in Figure 2 and Figure 3 It can be seen from the comparison of the two diagrams that the microemulsion regions formed by different surfactants are different. The nanoemulsion region formed by Q-14s and A-172E (the shaded area in Figure 3 ) is obviously larger than the area formed by Tween-80 and Span-80 (the shaded area in Figure 2 ), which proves that the emulsification capacity of Q-14s and A-172E is better than that of Tween-80 and Span-80, so Q-14s and A-172E are selected as the surfactants for preparing nanoemulsion type low-density contrast agents.

[0040] In one embodiment, by determining the particle size and PDI value of each group of samples, single factor analysis of variance is performed to obtain key preparation parameters, including: according to the optimal surfactant combination, different levels of preparation parameters are set by using single factor method; multiple groups of contrast agent samples are prepared, and the particle size and PDI value of each group of samples are determined to perform single factor analysis of variance; according to the analysis result, the key preparation parameters less than the preset threshold value are screened out; wherein the preparation parameters include primary emulsification time, homogenization pressure, homogenization times, surfactant proportion and Km value.

[0041] Specifically, in the second step, single factor investigation.

[0042] Specifically, the Q-14s and A-172E determined by the above steps are used as surfactants, and single factor method is used to analyze and select five factors including initial emulsification time, homogenization pressure, homogenization times, surfactant proportion and Km value. For each preparation parameter, different levels are set for experiments while other parameters remain unchanged, for example: the initial emulsification time is set to 30, 60, 90, 120 and 150 min, the homogenization pressure is set to 200, 400, 600, 800 and 1000 bar, the homogenization times is set to 1, 3, 5, 7 and 9 times, the surfactant proportion is set to 12%, 13%, 14%, 15% and 16%, and the Km value (i.e. the mass ratio of oil phase to surfactant) is set to 8:8, 9:7, 10:6, 11:5 and 12:4.

[0043] Specifically, for each parameter combination, the prescription amount of each component is placed in a round-bottom flask to prepare a nanoemulsion type low-density contrast agent sample, and each experiment is repeated 3 times to ensure the reliability of the results (relative standard deviation RSD < 3%). The average particle size (nm) and polydispersity coefficient (PDI) of each group of samples are measured using a nanoparticle analyzer, and the measurement results are shown in Table 2-1.

[0044] Table 2-1 Single factor test design results (n = 3, RSD < 3%)

[0045]

[0046]

[0047] Specifically, to determine which parameters have a significant impact on the performance of the nanoemulsion type low-density contrast agent, the five group single factor data are subjected to one-way analysis of variance (ANOVA) using SPSS software, and the preset threshold is set to be significant level α = 0.05, i.e. P < 0.05 indicates that the parameter has a significant impact on the particle size and PDI value. The results of the variance analysis are shown in Table 2-2, wherein the P values of the initial emulsification time (P = 0.337 > 0.05) and the homogenization pressure (P = 0.596 > 0.05) are greater than the preset threshold 0.05, indicating that these two parameters have no statistically significant impact on the performance of the nanoemulsion; and the P values of the Km value (P = 0.000 < 0.05), the homogenization times (P = 0.000 < 0.05) and the surfactant proportion (P = 0.001 < 0.05) are all less than the preset threshold 0.05, indicating that these three parameters have a significant impact on the particle size and PDI value of the nanoemulsion. Therefore, the Km value, the homogenization times and the surfactant proportion are selected as the key preparation parameters affecting the performance of the nanoemulsion type low-density contrast agent, which lays a foundation for subsequent response surface optimization analysis.

[0048] Table 2-2 Analysis of variance table

[0049]

[0050]

[0051] In one embodiment, the experimental combinations of the key preparation parameters at different levels are set by using the central composite design method, a plurality of contrast agent samples are prepared and the particle size and PDI value are measured, and an initial mathematical model between the key preparation parameters and the particle size and PDI value is established, including: based on the screened key preparation parameters, the experimental combinations of the key preparation parameters at different levels are set by using the central composite design method; a plurality of contrast agent samples are prepared according to different experimental combinations and the particle size and PDI value are measured, and the two-dimensional comprehensive merit values of each sample are calculated by using the measurement results; the initial mathematical model between the key preparation parameters and the particle size and PDI value is established by performing variance analysis on the two-dimensional comprehensive merit values.

[0052] In one embodiment, based on the screened key preparation parameters, the experimental combinations of the key preparation parameters at different levels are set by using the central composite design method, including: based on the screened key preparation parameters, the baseline level value and the variation interval of each parameter are set, and a multi-factor central composite design matrix is constructed; the factor coding values of the key preparation parameters are obtained by performing parameter coding conversion according to the central composite design matrix; and based on the factor coding values, the experimental combinations including extreme value axis points and central points are established; wherein the extreme value axis points are parameter combination points for measuring the physical property changes of the contrast agent samples under extreme preparation conditions, and the central points are parameter combination points for verifying the preparation performance of the contrast agent samples under the baseline conditions.

[0053] Specifically, in the third step, the response surface model is established by using the central composite design method.

[0054] Specifically, based on the three significant factors, i.e., the Km value, the surfactant proportion and the homogenization frequency, obtained by the single-factor variance analysis in the early stage, the central composite design is applied by using the Design Expert 8.0.6 software, and the three factors are selected as the key preparation parameters.

[0055] It should be noted that the central composite design is an experimental design method in the response surface method (RSM), which can effectively reduce the number of experiments and improve the reliability of experimental results by establishing a mathematical model between independent variables and dependent variables. In the present application, the central composite design is selected to investigate the comprehensive influence of the key preparation parameters of the nanoemulsion type low-density contrast agent on the physical and chemical properties thereof.

[0056] Firstly, the application sets benchmark level values and variation intervals of three key preparation parameters, and constructs a three-factor five-level central composite design matrix, as shown in Table 2-3. Among them, the coding values -1, 0, 1 of the homogenization times (X1) correspond to the actual values 1 time, 2 times, 3 times respectively; the coding values -1, 0, 1 of the surfactant proportion (X2) correspond to the actual values 10%, 13%, 16% respectively; the coding values -1, 0, 1 of the Km value (X3) correspond to the actual values 1.29, 2.15, 3 respectively.

[0057] Table 2-3 Three-factor level design table

[0058]

[0059] Then, according to the central composite design matrix, the application establishes 20 groups of experimental combinations including 8 factor combination points, 6 axis points and 6 center points. Among them, the axis points contain the extreme conditions of homogenization times, surfactant proportion and Km value at ±1.682 coding levels, which are used to measure the physical property changes of the contrast agent sample under extreme preparation conditions; the center points are used to verify the preparation stability and repeatability of the contrast agent sample under the benchmark conditions.

[0060] Next, according to the designed experimental combinations, the application prepares 20 groups of nanoemulsion type low-density contrast agent samples, each group of experiment is repeated 3 times (n=3, relative standard deviation RSD<3%), and the average particle size (Y1, unit: nm) and polydispersity coefficient (Y2, PDI value) of each group of samples are measured by a nanoparticle analyzer, and the test results are shown in Table 2-4.

[0061] Table 2-4 Central composite design test results

[0062]

[0063]

[0064] Specifically, in order to comprehensively evaluate the performance of the nanoemulsion type low-density contrast agent, the application comprehensively evaluates the particle size and PDI value two indexes, and calculates the two-dimensional comprehensive goodness value (OD1). The calculation method is based on the multi-objective decision-making theory, by standardizing each index and assigning weights, finally the comprehensive score between 0-1 is obtained, the larger the value is, the better the performance is. The OD1 values of each group of samples are listed in Table 2-5.

[0065] Table 2-5 OD1 analysis of variance table

[0066]

[0067]

[0068] By performing variance analysis on the OD1 values, this invention established an initial mathematical model relating key preparation parameters to particle size and PDI values. The variance analysis results are shown in Tables 2-5. The p-value of the model is 0.0492 < 0.05, indicating that the model is statistically significant; the p-value for the lack-of-fit test is 0.0678 > 0.05, indicating that unknown factors have little interference with the model fitting results and the model error is small. The following initial mathematical model was obtained through binomial fitting:

[0069] OD1=0.54-0.085A+0.13B-0.047C-0.02AB-0.027AC-0.14BC+0.021A 2 -0.0

[0070] 28B 2 -0.14C 2 ;

[0071] In the formula, A, B, and C represent the encoded values ​​of the homogenization times, surfactant percentage, and Km value, respectively. The correlation coefficient R of the fitted equation is... 2 =0.7322, indicating that the equation can be used to predict the optimal formulation of nanoemulsion-type low-density contrast agents. According to the analysis of variance results, the influence of each factor on particle size and PDI value is as follows: surfactant proportion (B) > homogenization times (A) > Km value (C), where surfactant proportion (B), interaction term BC, and quadratic term C are the most significant factors. 2 The difference was statistically significant (P<0.05).

[0072] In one embodiment, establishing an optimized mathematical model by incorporating a third-dimensional evaluation index includes: determining the third-dimensional evaluation index based on the prediction results of the initial mathematical model; calculating the three-dimensional comprehensive goodness value, which includes particle size, PDI value, and viscosity, by measuring the viscosity values ​​of each group of contrast agent samples; and conducting variance analysis based on the three-dimensional comprehensive goodness values ​​of each group of samples to establish an optimized mathematical model between key preparation parameters and the physicochemical properties of the contrast agent.

[0073] In one embodiment, determining the third-dimensional evaluation index based on the prediction results of the initial mathematical model includes: predicting the two-dimensional comprehensive goodness value of each group of contrast agent samples through the initial mathematical model and analyzing the model fit; based on the goodness-of-fit analysis results, screening out key physical parameters that affect the physicochemical properties of contrast agents but are not fully characterized by the model; and measuring the values ​​of the key physical parameters to obtain the third-dimensional evaluation index.

[0074] In one embodiment, based on the goodness-of-fit analysis result, the key physical parameters affecting the physicochemical properties of the contrast agent but not fully characterized by the model are screened, including: identifying the contrast agent sample group with low goodness-of-fit, determining the sample range for which the model is insufficient to predict; determining the candidate physical parameters that may affect the comprehensive performance for the sample range; and screening the physical parameter with the strongest correlation as the key physical parameter through correlation analysis of the candidate physical parameters and the deviation of the goodness-of-fit.

[0075] Specifically, in the fourth step, an optimized mathematical model is established by adding the viscosity value as a third-dimensional evaluation index.

[0076] Specifically, according to the prediction result of the initial mathematical model, the optimal process prescription obtained by the two-dimensional comprehensive goodness-of-fit value (OD1) analysis of the application is: homogenization time 1.02 min, surfactant proportion 15.84%, KM=1.64, and oil phase proportion 35%. Three batches of nanoemulsion type low-density contrast agent samples are prepared according to the prescription, and the average particle size is measured to be 95.30 nm, the PDI is 0.071, and the OD value is calculated to be 0.789, which is close to the OD value 0.791 under the optimal condition predicted by the model, proving that the model prediction is successful.

[0077] However, through further analysis, the application finds that the viscosity of the nanoemulsion sample prepared according to the above optimal prescription is 1265.8 mPa·S, which is relatively large, and does not meet the application requirements of the nanoemulsion in the medical contrast agent field. By identifying the sample group with low goodness-of-fit, the application determines that the viscosity is a key physical parameter affecting the comprehensive performance of the nanoemulsion type low-density contrast agent but not fully characterized by the initial model.

[0078] Therefore, the application measures the viscosity values (Y3, unit: mPa·S) of the contrast agent samples in each group, as shown in Tables 2-4, and recalculates the three-dimensional comprehensive goodness-of-fit value (OD2) containing the particle size, PDI value and viscosity by taking the viscosity as a third-dimensional evaluation index.

[0079] Based on the three-dimensional comprehensive goodness-of-fit value (OD2), the application performs variance analysis through Design Expert 8.0.6 software, and the results are shown in Tables 2-6. The P value of the model is 0.049<0.05, indicating that the model has statistical significance; the P value of the lack-of-fit test is 0.0909>0.05, indicating that the model error is small. The application establishes an optimized mathematical model between the key preparation parameters and the physicochemical properties of the contrast agent, and through the model, the comprehensive performance of the nanoemulsion type low-density contrast agent can be more comprehensively evaluated, and a scientific basis is provided for optimizing the preparation process of the nanoemulsion type low-density contrast agent.

[0080] After adding the viscosity evaluation index, the influence degree of each factor on the comprehensive performance of the nanoemulsion type low-density contrast agent changed, and the proportion of surfactant (B) and the quadratic term C 2 had the most significant influence (P<0.05). This indicates that after considering the viscosity factor, the proportion of surfactant has the greatest influence on the comprehensive performance of the nanoemulsion type low-density contrast agent, and the quadratic effect of the Km value cannot be ignored.

[0081] Through the prediction of the optimized mathematical model, the optimal prescription considering the particle size, PDI value and viscosity is obtained. Compared with the initial model considering only the particle size and PDI value, the optimized prescription can better meet the comprehensive performance requirements of the nanoemulsion type low-density contrast agent in actual application.

[0082] In one embodiment, according to the optimized mathematical model, the target process parameter combination is determined by analyzing the response surface graph of each key preparation parameter in two combinations, including: generating a three-dimensional response surface graph of each key preparation parameter in two combinations by using the optimized mathematical model; determining the target process parameter combination by analyzing the influence trend of each key preparation parameter in two combinations on the physicochemical performance of the contrast agent; judging the sensitivity of each key preparation parameter on the physicochemical performance of the contrast agent according to the slope change of the three-dimensional response surface graph; determining the optimal combination of each key preparation parameter based on the judgment result, and taking it as the target process parameter.

[0083] In one embodiment, the evaluation result is obtained by comparing the physicochemical performance of the contrast agent measured from the verification batch sample with the model prediction value, including: preparing a verification batch sample of the contrast agent based on the optimal process parameter combination; calculating the actual three-dimensional comprehensive goodness value by measuring the particle size, PDI value and viscosity of the verification batch sample; comparing the actual three-dimensional comprehensive goodness value with the model prediction value to generate the evaluation result.

[0084] Specifically, in the fifth step, the optimized mathematical model is established based on the three-dimensional comprehensive goodness value, and the optimal prescription is predicted.

[0085] Specifically, after the three-dimensional comprehensive goodness value (OD2) containing the particle size, PDI value and viscosity is established, the OD2 data is fitted and analyzed by using DesignExpert8.0.6 software, and the optimized mathematical model between the key preparation parameters and the physicochemical performance of the contrast agent is established. Through the model, the comprehensive performance of the nanoemulsion type low-density contrast agent under different preparation parameter combinations can be accurately predicted, so as to determine the optimal prescription.

[0086] The fitting equation of the optimized mathematical model is:

[0087] OD2=0.63-0.08A+0.15B-0.015C-0.011AB-0.03AC-0.15BC+0.028A 2-0.04

[0088] 4B 2 -0.15C 2 ;

[0089] In the formula, A, B, C represent the homogenization times, the proportion of surfactant and the coding value of Km respectively. Through variance analysis, it is known that the model is significant (P=0.0446<0.05) after testing, which indicates that the model is in good agreement with the actual situation; the model misspecification test is not significant (P=0.1640>0.05), which indicates that the unknown factors have little interference on the model fitting results and the model error is small. The correlation coefficient R of the binomial equation is 0.7386, indicating that the fitting equation has good prediction ability and can be used to predict the optimal prescription of the nanoemulsion type low-density contrast agent. 2

[0090] Specifically, the variance analysis results show that the influence degree of each factor on the comprehensive performance of the nanoemulsion type low-density contrast agent is as follows: the proportion of surfactant (B)>homogenization times (A)>Km value (C), wherein the proportion of surfactant (B), the interaction term BC and the quadratic term C 2 have a significant influence on the model (P<0.05). This result is basically consistent with the analysis result of the two-dimensional optimality value (OD1), indicating that the proportion of surfactant is the most critical factor affecting the performance of the nanoemulsion type low-density contrast agent, but after considering the viscosity factor, the interaction between each parameter presents new characteristics.

[0091] In order to intuitively show the influence of each preparation parameter on the comprehensive performance of the nanoemulsion type low-density contrast agent, the present application draws a three-dimensional response surface graph Figures 4-6 . As can be seen from the graph, the slope of the response surface reflects the sensitivity of the corresponding parameter to the comprehensive performance: the steeper the slope, the greater the influence of the parameter on the comprehensive performance. The specific analysis is as follows:

[0092] (1) The interaction effect of the proportion of surfactant and the Km value Figure 4 : changing the proportion of surfactant and the Km value has a significant influence on the comprehensive performance of the nanoemulsion, when the proportion of surfactant changes from low to high, the OD2 value first increases and then tends to be flat; when the Km value changes from small to large, the OD2 value first increases and then decreases, showing an obvious parabolic trend. This indicates that there is an optimal combination of the proportion of surfactant and the Km value, which can make the nanoemulsion type low-density contrast agent achieve the optimal performance.

[0093] (2) The interaction effect of the proportion of surfactant and the homogenization times Figure 5 : under the premise of changing the proportion of surfactant, changing the homogenization times has a relatively small influence on the comprehensive performance of the nanoemulsion, and the response surface slope is relatively flat. This indicates that the influence of the homogenization times on the performance of the nanoemulsion is regulated by the proportion of surfactant.​

[0094] (3) The interaction effect of Km value and homogenization frequency: Figure 6 With the change of Km value, the influence of homogenization frequency on the comprehensive performance of nanoemulsion also changes, and there is a certain interaction between the two, but the influence degree is not as significant as the interaction of surfactant proportion and Km value.

[0095] Specifically, by comprehensively analyzing the response surface graph and the variance analysis results, the present application determines the optimal prescription parameter combination of the nanoemulsion type low-density contrast agent: homogenization frequency 1.24 times (between 1-2 times), surfactant proportion 15.32%, and Km value 1.85. According to the prediction of the optimal mathematical model, the three-dimensional comprehensive goodness value (OD2) of the nanoemulsion type low-density contrast agent prepared under the optimal conditions is 0.879.

[0096] Notably, compared with the two-dimensional goodness value (OD1) prediction result considering only the particle size and PDI value, the optimal prescription predicted by the three-dimensional goodness value (OD2) has changed significantly, especially in terms of Km value and homogenization frequency. This fully proves the necessity and scientificity of introducing viscosity as the third-dimensional evaluation index when evaluating the performance of the nanoemulsion type low-density contrast agent. By establishing a three-dimensional comprehensive evaluation system, the present application not only considers the particle size uniformity of the nanoemulsion, but also takes into account its rheological properties, thereby more comprehensively evaluating the comprehensive performance of the nanoemulsion type low-density contrast agent.

[0097] Specifically, in the sixth step, a verification test is performed to evaluate the reliability of the method.

[0098] Specifically, to verify the reliability and accuracy of the nanoemulsion type low-density contrast agent evaluation method established by the present application, the present application prepared three batches of nanoemulsion type low-density contrast agent samples (LP-NE) according to the optimal process prescription predicted by the optimal mathematical model. The determination results show that the average particle size of the verification batch samples is 82.6 nm, the PDI value is 0.081, and the viscosity is 556.1 mPa·S. The three-dimensional comprehensive goodness value (OD2) calculated according to these measured parameters is 0.877, which is very close to the model prediction value 0.879, with a relative error of only 0.23%.

[0099] This verification result shows that by comparing and analyzing the actual three-dimensional comprehensive goodness value with the model prediction value, the present application generates a comprehensive evaluation result of the nanoemulsion type low-density contrast agent:

[0100] 1) Physicochemical property evaluation: the sample of the verification batch has ideal particle size distribution (average particle size 82.6 nm, in the optimal interval of 50-100 nm), excellent uniformity (PDI value 0.081, lower than the quality standard of 0.1), and suitable rheological properties (viscosity 556.1 mPa·S, meeting the application requirements of low-density contrast agents);

[0101] 2) Process parameter evaluation: the determined optimal process parameter combination (homogenization frequency 1.35 times, surfactant proportion 15.62%, Km value 1.86) has good repeatability and stability, and can continuously produce nanoemulsion low-density contrast agents with consistent performance;

[0102] 3) Mathematical model evaluation: the established three-dimensional comprehensive goodness value evaluation system and the optimized mathematical model have high reliability, and can be used as an effective tool for quality control and process optimization of nanoemulsion low-density contrast agents;

[0103] 4) Comprehensive performance evaluation: the nanoemulsion low-density contrast agent obtained by the three-dimensional comprehensive goodness value method takes into account the particle size, uniformity and rheological properties, and compared with the traditional evaluation method considering only single or double indicators, can more comprehensively reflect the actual application performance of the contrast agent.

[0104] The above evaluation results show that the evaluation method of the nanoemulsion low-density contrast agent proposed by the application not only successfully establishes a scientific and reasonable multi-dimensional evaluation system, but also optimizes the preparation process by response surface method, and realizes the overall improvement of the performance of the contrast agent. The evaluation method can provide important reference for the research and development, production and quality control of nanoemulsion low-density contrast agents, and has significant practical value.

[0105] In order to facilitate the understanding of the above technical solutions of the application, the following takes the iodine oil nanoemulsion low-density contrast agent as an example for specific description as follows:

[0106] The application first selects polyglyceryl diisostearate (Q-172E) and polyglyceryl-10 myristate (14S) as the surfactant combination based on the application requirements of nanoemulsion low-density contrast agents. Both of the two surfactants are food-grade emulsifiers with high safety factor, meeting the safety requirements of medical contrast agents. Different proportions of iodized oil, aqueous phase and different types of surfactants are combined by the ternary phase diagram method, the phase state change data under each proportion are obtained and the pseudo-ternary phase diagram is generated, and finally the combination with the largest nanoemulsion area is selected as the optimal surfactant combination.

[0107] After determining the surfactant combination, the present application sets different levels of preparation parameters including initial emulsification time, homogenization pressure, homogenization times, surfactant proportion and Km value (ratio of surfactant to co-surfactant) by single factor method. By preparing multiple groups of contrast agent samples, the particle size and PDI value (polydispersity coefficient) of each group of samples are determined, and single factor variance analysis is performed to screen out the key preparation parameters that significantly affect the physicochemical properties of nanoemulsion. According to the analysis results, the homogenization times, surfactant proportion and Km value are determined as the main factors affecting the performance of nanoemulsion type low-density contrast agent, and their P values are all less than the preset threshold value 0.05, which have significant differences.

[0108] Next, based on the screened key preparation parameters, the present application sets the experimental combinations of each parameter at different levels by using central composite design method. Specifically, the baseline level value and variation range of each parameter are set, and a three-factor five-level central composite design matrix is constructed. The factor code values of each key preparation parameter are obtained through parameter code conversion, and a complete experimental combination including factor combination points, extreme axis points and center points is established. According to the designed experimental combination, multiple groups of contrast agent samples are prepared, and their particle size and PDI value are determined. The two-dimensional comprehensive goodness value of each group of samples is calculated using the determination results. By performing variance analysis on the two-dimensional comprehensive goodness value, the initial mathematical model between the key preparation parameters and the particle size and PDI value is established.

[0109] Based on the initial model, the present application finds that the nanoemulsion sample prepared according to the optimal conditions predicted by the model has ideal particle size and PDI value, but the viscosity is too large, which does not meet the needs of medical imaging applications. Therefore, the present application innovatively introduces viscosity as a third-dimensional evaluation index. By determining the viscosity value of each group of contrast agent samples, the three-dimensional comprehensive goodness value including particle size, PDI value and viscosity is calculated, and a more comprehensive optimization mathematical model is established. The variance analysis result of the optimization model shows that the model has statistical significance (P<0.05), the lack of fit item test does not have significant difference (P>0.05), the correlation coefficient R 2 of the binomial equation is 0.7386, which indicates that the model is in good agreement with the actual situation and can be used to predict the best prescription of nanoemulsion type low-density contrast agent.

[0110] Based on the optimization mathematical model, the application generates three-dimensional response surface graph of two-by-two combination of each key preparation parameter, and judges the sensitivity of each parameter to the influence of the physical and chemical properties of the contrast agent by analyzing the shape and slope change of the response surface. The results show that the surfactant ratio has the greatest influence on the comprehensive performance of the nanoemulsion, followed by the Km value, and then the homogenization frequency, and the interaction of the surfactant ratio and the Km value also has a significant influence. Through response surface analysis, the optimal process parameter combination is determined as follows: the surfactant ratio is 16%, the Km value is 2.15, the oil phase ratio is 35%, the initial emulsification time is 30 minutes, the homogenization time is 1.02 minutes, and the homogenization pressure is 600 bar.

[0111] Finally, based on the determined optimal process parameter combination, the application prepares a verification batch sample and measures its physical and chemical properties. The measurement results show that the average particle size of the verification batch sample is 82.6 nm, the PDI value is 0.081, and the viscosity is 556.1 mPa·S. The actual three-dimensional comprehensive goodness value calculated has only a slight difference from the model prediction value, which verifies the accuracy of the model and the reliability of the evaluation method.

[0112] The application innovatively uses the response surface method instead of the traditional orthogonal test method and the ternary phase diagram method, which not only has the advantages of simple operation, small test error, and objective and reliable results, but also solves the limitation of ignoring the rheological performance of the traditional evaluation method by introducing a third-dimensional evaluation index. In particular, the application finds that the factors that most influence the comprehensive index are the PDI value, the particle size value, and the viscosity value in turn, which provides a new scientific basis for the evaluation of the nanoemulsion type low-density contrast agent and lays a foundation for its application in the field of medical imaging.

[0113] The above only describes the preferred embodiments of the application and should not be used to limit the application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the application should be included in the protection scope of the application.

Claims

1. A method for evaluating a nanoemulsion low-density contrast agent, characterized by, The application relates to a preparation method of a nanoemulsion type low-density contrast agent, and in particular to a preparation method of a nanoemulsion type low-density contrast agent based on a preparation requirement of the nanoemulsion type low-density contrast agent, selection of an optimal surfactant combination, and determination of particle size and PDI values of each sample. The application relates to a preparation method of a nanoemulsion type low-density contrast agent, and in particular to a preparation method of a nanoemulsion type low-density contrast agent based on a preparation requirement of the nanoemulsion type low-density contrast agent, selection of an optimal surfactant combination, and determination of particle size and PDI values of each sample. The application relates to a preparation method of a nanoemulsion type low-density contrast agent, and in particular to a preparation method of a nanoemulsion type low-density contrast agent based on a preparation requirement of the nanoemulsion type low-density contrast agent, selection of an optimal surfactant combination, and determination of particle size and PDI values of each sample. The application relates to a preparation method of a nanoemulsion type low-density contrast agent, and in particular to a preparation method of a nanoemulsion type low-density contrast agent based on a preparation requirement of the nanoemulsion type low-density contrast agent, selection of an optimal surfactant combination, and determination of particle size and PDI values of each sample.

2. The method of evaluating a nanoemulsion low-density contrast agent according to claim 1, wherein, The application relates to a preparation method of a nanoemulsion type low-density contrast agent, and in particular to a preparation method of a nanoemulsion type low-density contrast agent based on a preparation requirement of the nanoemulsion type low-density contrast agent, selection of an optimal surfactant combination, and determination of particle size and PDI values of each sample. The application relates to a preparation method of a nanoemulsion type low-density contrast agent, and in particular to a preparation method of a nanoemulsion type low-density contrast agent based on a preparation requirement of the nanoemulsion type low-density contrast agent, selection of an optimal surfactant combination, and determination of particle size and PDI values of each sample. The application relates to a preparation method of a nanoemulsion type low-density contrast agent, and in particular to a preparation method of a nanoemulsion type low-density contrast agent based on a preparation requirement of the nanoemulsion type low-density contrast agent, selection of an optimal surfactant combination, and determination of particle size and PDI values of each sample. The application relates to a preparation method of a nanoemulsion type low-density contrast agent, and in particular to a preparation method of a nanoemulsion type low-density contrast agent based on a preparation requirement of the nanoemulsion type low-density contrast agent, selection of an optimal surfactant combination, and determination of particle size and PDI values of each sample.

3. The method for evaluating a nanoemulsion-type low-density contrast agent according to claim 1, characterized in that, The application relates to a preparation method of a nanoemulsion type low-density contrast agent, and in particular to a preparation method of a nanoemulsion type low-density contrast agent based on a preparation requirement of the nanoemulsion type low-density contrast agent, selection of an optimal surfactant combination, and determination of particle size and PDI values of each sample. The application relates to a preparation method of a nanoemulsion type low-density contrast agent, and in particular to a preparation method of a nanoemulsion type low-density contrast agent based on a preparation requirement of the nanoemulsion type low-density contrast agent, selection of an optimal surfactant combination, and determination of particle size and PDI values of each sample. The application relates to a preparation method of a nanoemulsion type low-density contrast agent, and in particular to a preparation method of a nanoemulsion type low-density contrast agent based on a preparation requirement of the nanoemulsion type low-density contrast agent, selection of an optimal surfactant combination, and determination of particle size and PDI values of each sample. The application relates to a preparation method of a nanoemulsion type low-density contrast agent, and in particular to a preparation method of a nanoemulsion type low-density contrast agent based on a preparation requirement of the nanoemulsion type low-density contrast agent, selection of an optimal surfactant combination, and determination of particle size and PDI values of each sample. The application relates to a preparation method of a nanoemulsion type low-density contrast agent, and in particular to a preparation method of a nanoemulsion type low-density contrast agent based on a preparation requirement of the nanoemulsion type low-density contrast agent, selection of an optimal surfactant combination, and determination of particle size and PDI values of each sample.

4. The method for evaluating a nanoemulsion-type low-density contrast agent according to claim 1, characterized in that, The application relates to a preparation method of a nanoemulsion type low-density contrast agent, and in particular to a preparation method of a nanoemulsion type low-density contrast agent based on a preparation requirement of the nanoemulsion type low-density contrast agent, selection of an optimal surfactant combination, and determination of particle size and PDI values of each sample. ​ ​ ​ 5. The method for evaluating a nanoemulsion-type low-density contrast agent according to claim 4, characterized in that, ​ ​ ​ ​ The extreme value axis point is a parameter combination point for determining the change of physical properties of the contrast agent sample under extreme preparation conditions, and the center point is a parameter combination point for verifying the preparation performance of the contrast agent sample under a reference condition.

6. The method for evaluating a nanoemulsion-type low-density contrast agent according to claim 1, characterized in that, The third dimension evaluation index is determined based on a prediction result of the initial mathematical model. The three-dimensional comprehensive goodness value including the particle size, PDI value and viscosity is calculated by measuring the viscosity value of each group of contrast agent samples. The optimization mathematical model between the key preparation parameters and the physicochemical properties of the contrast agent is established based on the variance analysis of the three-dimensional comprehensive goodness value of each group of samples. The third dimension evaluation index is determined based on a prediction result of the initial mathematical model.

7. The method of claim 6 wherein the nanoemulsion low density contrast agent is evaluated by, The two-dimensional comprehensive goodness value of each group of contrast agent samples is predicted by the initial mathematical model, and the goodness of model fitting is analyzed. Based on the analysis result of the goodness of model fitting, the key physical parameters affecting the physicochemical properties of the contrast agent but not fully characterized by the model are screened out. The numerical value of the key physical parameters is measured to obtain the third dimension evaluation index. The third dimension evaluation index is determined based on a prediction result of the initial mathematical model.

8. The method for evaluating a nanoemulsion-type low-density contrast agent according to claim 7, characterized in that, The sample range for which the model prediction is insufficient is determined by identifying the contrast agent sample group with low goodness of fitting. The candidate physical parameters that may affect the comprehensive performance are measured for the sample range. Through the correlation analysis of the candidate physical parameters and the deviation of the goodness of fitting, the physical parameter with the strongest correlation is screened out as the key physical parameter. The target process parameter combination is determined by analyzing the response surface graph of each two-key preparation parameters based on the optimization mathematical model.

9. The method for evaluating a nanoemulsion-type low-density contrast agent according to claim 1, characterized in that, The three-dimensional response surface graph of each two-key preparation parameters is generated by using the optimization mathematical model. The target process parameter combination is determined by analyzing the influence trend of each two-key preparation parameters on the physicochemical properties of the contrast agent. The sensitivity of each key preparation parameter to the physicochemical properties of the contrast agent is judged based on the slope change of the three-dimensional response surface graph. The optimal combination of each key preparation parameter is determined based on the judgment result, and is used as the target process parameter. The evaluation result is obtained by comparing the physicochemical properties of the contrast agent measured from the verification batch sample with the predicted value of the model.

10. The method of evaluating a nanoemulsion low-density contrast agent according to claim 1, wherein, The verification batch sample of the contrast agent is prepared based on the optimal process parameter combination. The actual three-dimensional comprehensive goodness value is calculated by measuring the particle size, PDI value and viscosity of the verification batch sample. The evaluation result is generated by comparing the actual three-dimensional comprehensive goodness value with the predicted value of the model. ​

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