A method for analyzing particle size distribution of fluticasone propionate raw material

Abstract

The present invention relates to the technical field of drug analysis, and provides an analysis method for the particle size distribution of fluticasone propionate bulk drug, the method comprising: performing dispersion treatment on a screened drug sample to obtain a dispersed drug sample; analyzing the bubble secretion state of the dispersed drug sample, and obtaining a static drug sample when the bubble secretion state meets a preset state; collecting a near-infrared spectrum of the drug sample when it is stirred, analyzing the particle dispersion state of the static drug sample, and obtaining a stirred drug sample when the particle dispersion state meets a preset state; collecting multiple groups of particle size distribution parameters of the stirred drug sample under multiple preset groups of experiments, and collecting working environment parameters, constructing a regression model of the drug sample to be analyzed, analyzing the optimal parameters using the regression model, and analyzing the drug particle size distribution state of the drug sample to be analyzed using the optimal parameters. The present invention can improve the accuracy of the particle size distribution of the fluticasone propionate bulk drug.

Description

Technical Field

[0001] The invention relates to the technical field of drug analysis, and in particular to an analysis method for particle size distribution of a fluticasone propionate raw material drug. Background Art

[0002] As an important drug API, the particle size distribution of fluticasone propionate has a key impact on the performance and quality of the drug. Accurate analysis of the particle size distribution of fluticasone propionate API plays an important role in drug research and development and production. Constructing an effective particle size distribution analysis method for fluticasone propionate API can accurately measure the particle size characteristics of the API, provide key data support for drug formulation development, quality control and production process optimization, thereby ensuring the stability, effectiveness and safety of the drug. At the same time, through in-depth analysis of the particle size distribution, R&D personnel can better understand the physical properties of the drug, predict its absorption, distribution and metabolism in the body, so as to provide a more reliable basis for clinical application.

[0003] At present, the analysis of the particle size distribution of fluticasone propionate APIs usually uses traditional analysis techniques, such as laser diffraction and screening methods. However, these methods have certain limitations. When dealing with complex particle size distributions, the laser diffraction method may be interfered by factors such as particle shape and refractive index, resulting in inaccurate analysis results. The screening method is cumbersome and time-consuming, and has limited analytical capabilities for fine particles. Moreover, in practical applications, since the particle size distribution of fluticasone propionate APIs may present a multi-peak distribution or irregular shape, it is difficult for traditional methods to accurately capture subtle particle size changes when processing large amounts of data, resulting in inaccurate analysis results and failure to provide sufficient and reliable guidance for drug research and development and production. Summary of the invention

[0004] In order to solve the above problems, the present invention provides an analysis method for the particle size distribution of fluticasone propionate bulk drug, which can improve the accuracy of the particle size distribution of fluticasone propionate bulk drug.

[0005] To achieve the above object, the present invention provides a method for analyzing the particle size distribution of fluticasone propionate bulk drug, comprising:

[0006] Obtaining a drug sample to be analyzed, performing sample screening on the drug sample to be analyzed to obtain a screened drug sample, and configuring an active agent for the screened drug sample to disperse the screened drug sample using the active agent to obtain a dispersed drug sample, wherein the drug sample to be analyzed is a fluticasone propionate raw material;

[0007] Performing a static treatment on the dispersed drug sample, collecting a static picture of the dispersed drug sample during the static treatment, analyzing the bubble secretion state of the dispersed drug sample by using the static picture, and completing the static treatment when the bubble secretion state meets a preset state to obtain a static drug sample;

[0008] collecting a near infrared spectrum of the static drug sample when the static drug sample is stirred using a laser particle size analyzer, correlating the particle state and particle dispersion level of the static drug sample based on the near infrared spectrum to obtain correlation data, analyzing the particle dispersion state of the static drug sample using the correlation data, and obtaining a stirred drug sample when the particle dispersion state meets a preset state;

[0009] Collect multiple groups of particle size distribution parameters of the stirred drug sample under multiple preset groups of experiments, and collect instrument parameters and working environment parameters of the corresponding laser particle size analyzer under the multiple groups of experiments, use the multiple groups of particle size distribution parameters, the instrument parameters and the working environment parameters to build a regression model of the drug sample to be analyzed, use the regression model to analyze the preferred parameters in the instrument parameters that are convenient for analyzing the drug particle size of the drug sample to be analyzed, use the preferred parameters to set the parameters of the laser particle size analyzer to obtain a target laser particle size analyzer, and use the target laser particle size analyzer to analyze the drug particle size distribution state of the drug sample to be analyzed.

[0010] Furthermore, the active agent for configuring the drug sample for screening is used to disperse the drug sample for screening using the active agent to obtain a dispersed drug sample, including:

[0011] preparing a dispersant for screening drug samples;

[0012] After adding the pre-configured active material to the dispersant, the dispersant is stirred to obtain an active agent;

[0013] After the screening drug sample is added to the active agent according to a preset mass, the active agent is stirred to obtain a dispersed drug sample.

[0014] Furthermore, the static image is used to analyze the bubble secretion state of the dispersed drug sample to determine whether the static state is completed.

[0015] As an embodiment of the present invention, the step of analyzing the bubble secretion state of the dispersed drug sample by using the static picture includes:

[0016] identifying the number of bubbles of the secreted bubbles of the dispersed drug sample according to the static picture;

[0017] detecting a bubble edge of the secreted bubble, and calculating a bubble area of ​​the secreted bubble based on the bubble edge;

[0018] The bubble secretion state of the dispersed drug sample is analyzed based on the bubble number and the bubble area.

[0019] Further, the calculating the bubble size of the secretion bubble based on the bubble edge includes:

[0020] Constructing vertex coordinates of the secretion bubble based on the bubble edge;

[0021] Based on the vertex coordinates, the bubble area of ​​the secretion bubble is calculated.

[0022] Furthermore, based on the near infrared spectrum, the particle state and particle dispersion level of the static drug sample are data-correlated to obtain the associated data, including:

[0023] identifying subtle spectral features of the near infrared spectrum;

[0024] Calculating a subtle characteristic value of the near infrared spectrum based on the subtle spectral characteristics;

[0025] Performing continuous wavelet transform on the near infrared spectrum to obtain wavelet transform coefficients;

[0026] Identify multi-scale features and multi-position features of the near infrared spectrum based on the wavelet transform coefficients;

[0027] Based on the subtle feature values, the multi-scale features and the multi-position features, correlation data of the particle state and particle dispersion level of the stationary drug sample are constructed.

[0028] Further, the identifying of the subtle spectral features of the near infrared spectrum includes:

[0029] Calculating the second-order derivative spectrum of the near-infrared spectrum to obtain the second-order derivative value,

[0030] Based on the second derivative values, subtle spectral features of the near infrared spectrum are identified.

[0031] Further, the identifying of the subtle spectral features of the near infrared spectrum includes:

[0032] Performing continuous wavelet transform on the near infrared spectrum to obtain wavelet transform coefficients,

[0033] Based on the wavelet transform coefficients, multi-scale features and multi-position features of the near infrared spectrum are identified.

[0034] Furthermore, the using the associated data to analyze the particle dispersion state of the statically placed drug sample includes:

[0035] Performing vector conversion on the associated data to obtain vector data;

[0036] Based on the vector data, construct a sample label of the associated data;

[0037] Performing particle dispersion state classification analysis on the statically placed drug sample based on the sample label to obtain a dispersion classification;

[0038] Based on the dispersion category, the particle dispersion state of the stationary drug sample is determined.

[0039] Furthermore, the use of the multiple groups of particle size distribution parameters, the instrument parameters and the working environment parameters to construct a regression model of the drug sample to be analyzed includes:

[0040] Arranging the data of the plurality of groups of particle size distribution parameters, the instrument parameters and the working environment parameters to obtain arranged data;

[0041] analyzing data relationships of the collated data;

[0042] Based on the data relationship, configuring an initial model of the sorted data;

[0043] Using the collated data, after performing model training on the initial model, perform model evaluation on the initial model;

[0044] When the evaluation result of the model evaluation meets the preset result, a regression model is obtained.

[0045] In order to solve the above problems, the present invention also provides an analysis system for the particle size distribution of fluticasone propionate raw material, the system comprising:

[0046] A drug dispersion processing module is used to obtain a drug sample to be analyzed, perform sample screening on the drug sample to be analyzed to obtain a screened drug sample, and configure an active pharmaceutical agent for the screened drug sample to perform dispersion processing on the screened drug sample using the active pharmaceutical agent to obtain a dispersed drug sample, wherein the drug sample to be analyzed is a fluticasone propionate raw material;

[0047] A sample static module is used to perform static treatment on the dispersed drug sample, collect static pictures of the dispersed drug sample during the static treatment, analyze the bubble secretion state of the dispersed drug sample by using the static pictures, and complete the static treatment when the bubble secretion state meets the preset state to obtain the static drug sample;

[0048] a stirring analysis module, for collecting a near-infrared spectrum when the static drug sample is stirred by using a laser particle size analyzer, and based on the near-infrared spectrum, performing data correlation between the particle state and the particle dispersion level of the static drug sample to obtain correlation data, and using the correlation data to analyze the particle dispersion state of the static drug sample, and obtaining a stirred drug sample when the particle dispersion state meets a preset state;

[0049] The drug particle size distribution analysis module is used to collect multiple groups of particle size distribution parameters of the stirred drug sample under multiple preset groups of experiments, and collect the instrument parameters and working environment parameters of the corresponding laser particle size analyzer under the multiple groups of experiments, and use the multiple groups of particle size distribution parameters, the instrument parameters and the working environment parameters to construct a regression model of the drug sample to be analyzed, use the regression model to analyze the preferred parameters in the instrument parameters that are convenient for analyzing the drug particle size of the drug sample to be analyzed, use the preferred parameters to set the parameters of the laser particle size analyzer to obtain a target laser particle size analyzer, and use the target laser particle size analyzer to analyze the drug particle size distribution state of the drug sample to be analyzed.

[0050] Compared with the prior art, the technical principle and beneficial effects of this solution are:

[0051] The embodiment of the present invention obtains the drug sample to be analyzed, performs sample screening on the drug sample to be analyzed, and obtains the screened drug sample, which can remove samples that do not meet specific standards or have obvious defects, leaving only the representative and research-worthy parts. Further, the embodiment of the present invention can make the bubbles generated by the drug sample during the dispersion process naturally rise to the liquid surface and escape by performing static treatment on the dispersed drug sample and collecting the static picture of the dispersed drug sample during the static treatment, thereby reducing the interference of bubbles on subsequent analysis, and by collecting the static picture, it is helpful to observe the generation and disappearance of bubbles in the sample more clearly, and by collecting the near-infrared spectrum when the static drug sample is stirred by using a laser particle size analyzer, it can be determined by analyzing the changes in the spectral data whether the dispersion degree of the drug particles during the stirring process is uniform. Further, the embodiment of the present invention can determine the most suitable instrument parameter settings for the analysis of the drug sample by collecting multiple groups of particle size distribution parameters of the stirred drug sample under multiple preset groups of experiments, and collecting the instrument parameters and working environment parameters of the corresponding laser particle size analyzer under the multiple groups of experiments. By analyzing the relationship between the instrument parameters and the particle size distribution results, the instrument parameter settings can be determined. The method for analyzing the particle size distribution of a fluticasone propionate bulk drug provided in an embodiment of the present invention can improve the accuracy of the particle size distribution of the fluticasone propionate bulk drug. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1A schematic flow chart of a method for analyzing particle size distribution of a fluticasone propionate bulk drug provided by one embodiment of the present invention;

[0053] Figure 2 A functional module diagram of a fluticasone propionate bulk drug particle size distribution analysis system provided by one embodiment of the present invention;

[0054] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0055] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0056] The embodiment of the present application provides an analysis method for the particle size distribution of fluticasone propionate bulk drug. The execution subject of the analysis method for the particle size distribution of fluticasone propionate bulk drug includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided in the embodiment of the present application. In other words, the analysis method for the particle size distribution of fluticasone propionate bulk drug can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms.

[0057] Reference Figure 1 FIG. 1 is a flow chart of a method for analyzing the particle size distribution of a fluticasone propionate API provided by one embodiment of the present invention. In this embodiment, the method for analyzing the particle size distribution of a fluticasone propionate API includes:

[0058] S1. Obtain a drug sample to be analyzed, perform sample screening on the drug sample to be analyzed to obtain a screened drug sample, and configure an active agent for the screened drug sample to disperse the screened drug sample using the active agent to obtain a dispersed drug sample, wherein the drug sample to be analyzed is a fluticasone propionate raw material.

[0059] The embodiment of the present invention obtains the drug sample to be analyzed, performs sample screening on the drug sample to be analyzed, and obtains the screened drug sample, which can remove samples that do not meet specific standards or have obvious defects, leaving only representative and research-worthy parts. For example, if the drug under study is a drug with a specific purity requirement, screening can exclude samples that do not meet the purity standard, ensuring that subsequent analysis targets samples that meet the purity requirements. Among them, the drug sample to be analyzed refers to fluticasone propionate raw material.

[0060] Furthermore, the embodiment of the present invention configures the active agent of the screened drug sample to disperse the screened drug sample using the active agent to obtain a dispersed drug sample, which can improve the dispersion performance of the raw material in water and provide a good basis for subsequent particle size analysis.

[0061] As an embodiment of the present invention, the active agent for configuring the screening drug sample to disperse the screening drug sample using the active agent to obtain a dispersed drug sample includes: configuring a dispersant for the screening drug sample, adding a pre-configured active material to the dispersant, stirring the dispersant to obtain the active agent, adding the screening drug sample according to a preset mass to the active agent, and stirring the active agent to obtain a dispersed drug sample.

[0062] The dispersant refers to purified water, the pre-configured active material refers to Tween 20, 10-12 drops of which can be added, and the dispersant is stirred to obtain the active agent which can be stirred by a magnetic stirring device.

[0063] S2. Perform a static treatment on the dispersed drug sample, and collect a static picture of the dispersed drug sample during the static treatment. Use the static picture to analyze the bubble secretion state of the dispersed drug sample. When the bubble secretion state meets a preset state, complete the static treatment to obtain a static drug sample.

[0064] The embodiment of the present invention allows the bubbles generated by the drug sample during the dispersion process to naturally rise to the liquid surface and escape by allowing the dispersed drug sample to stand still and collecting the still picture of the dispersed drug sample during the standing still process, thereby reducing the interference of the bubbles on subsequent analysis. The collection of the still picture helps to more clearly observe the generation and disappearance of bubbles in the sample.

[0065] Furthermore, the embodiment of the present invention can determine whether the static state is completed by analyzing the bubble secretion state of the dispersed drug sample by utilizing the static state screen.

[0066] As an embodiment of the present invention, the use of the static screen to analyze the bubble secretion state of the dispersed drug sample includes: identifying the number of bubbles of secreted bubbles of the dispersed drug sample according to the static screen, detecting the bubble edges of the secreted bubbles, calculating the bubble area of ​​the secreted bubbles based on the bubble edges, and analyzing the bubble secretion state of the dispersed drug sample based on the bubble number and the bubble area.

[0067] Optionally, the identification of the number of bubbles of the secreted bubbles of the dispersed drug sample based on the static screen extracts the characteristic points of the secreted bubbles, such as the center of a circle, a vertex, etc., through the static screen, and then determines the number of bubbles through matching and statistics of the characteristic points. The detection of the bubble edge of the secreted bubbles can find the clear edge of the bubble through the Canny edge detection algorithm, and the analysis of the bubble secretion state of the dispersed drug sample based on the bubble number and the bubble area can be performed by setting a bubble number threshold and a bubble area threshold range. For example, when the bubble number exceeds a certain value and the sum of the bubble areas exceeds a specific range, it is determined to be an active secretion state; conversely, if it is lower than the set value and range, it is considered that the secretion is inactive.

[0068] As an optional embodiment of the present invention, the calculating the bubble size of the secretion bubble based on the bubble edge includes: constructing the vertex coordinates of the secretion bubble based on the bubble edge, and calculating the bubble area of ​​the secretion bubble based on the vertex coordinates using the following formula:

[0069]

[0070] Among them, S represents the bubble area, n represents the number of vertex coordinates, and x i represents the x coordinate value of the i-th vertex, y i+1 represents the y coordinate value of the i+1th vertex, x i+1 Indicates the x coordinate value of the i+1th vertex, y i Represents the y-coordinate value of the i-th vertex.

[0071] Furthermore, the embodiment of the present invention can obtain a drug sample that meets the requirements and has been left still by completing the standing treatment when the bubble secretion state meets the preset state, and obtaining a drug sample that has been left still. The preset state refers to a certain specific state standard for bubble secretion that is set in advance, such as a preset state that the number of bubbles does not exceed 5 per minute, and the area of ​​a single bubble does not exceed 2 square centimeters. During the standing treatment, the bubble secretion of the drug sample is continuously observed. If, starting from a certain moment, the number of bubbles per minute is less than or equal to 5 for 5 consecutive minutes, and the area of ​​all bubbles does not exceed 2 square centimeters, then it is determined that the bubble secretion state meets the preset state, the standing treatment is completed, and the drug sample that has been left still is obtained. The specific setting can be based on the actual application scenario.

[0072] S3. Collect a near-infrared spectrum of the stationary drug sample when it is stirred using a laser particle size analyzer, and based on the near-infrared spectrum, perform data correlation between the particle state and the particle dispersion level of the stationary drug sample to obtain correlation data, and use the correlation data to analyze the particle dispersion state of the stationary drug sample, and when the particle dispersion state meets a preset state, obtain a stirred drug sample.

[0073] In the embodiment of the present invention, by collecting the near-infrared spectrum when the static drug sample is stirred using a laser particle size analyzer, it is possible to determine whether the dispersion degree of the drug particles during the stirring process is uniform by analyzing the changes in spectral data. The near-infrared spectrum can be collected by a near-infrared spectrometer.

[0074] Furthermore, the embodiment of the present invention associates the particle state and particle dispersion level of the stationary drug sample based on the near infrared spectrum to obtain the associated data. By analyzing the associated data, the influence of different formulation process parameters (such as stirring speed, time, temperature, etc.) on the particle state and dispersion level can be understood, thereby optimizing the process and improving the quality of the drug preparation. Among them, the particle state refers to some physical characteristics and performances of drug particles, such as the size and shape of particles, and the particle dispersion level refers to the uniformity of distribution of drug particles in the sample and the degree of mutual separation, such as the degree of agglomeration and separation.

[0075] As an embodiment of the present invention, based on the near-infrared spectrum, the particle state and particle dispersion level of the stationary drug sample are data associated to obtain associated data, including: identifying subtle spectral features of the near-infrared spectrum, calculating subtle characteristic values ​​of the near-infrared spectrum based on the subtle spectral features, performing continuous wavelet transform on the near-infrared spectrum to obtain wavelet transform coefficients, identifying multi-scale features and multi-position features of the near-infrared spectrum based on the wavelet transform coefficients, and constructing associated data of the particle state and particle dispersion level of the stationary drug sample based on the subtle characteristic values, the multi-scale features and the multi-position features.

[0076] Exemplarily, the identifying the subtle spectral features of the near infrared spectrum includes: calculating the second-order derivative spectrum of the near infrared spectrum using the following formula to obtain a second-order derivative value:

[0077]

[0078] in, Represents the wavelength α in the near-infrared spectrum i The second-order derivative value at α i+1 , α i-1 Indicates the wavelength of the near-infrared spectrum α i adjacent points, ∪ represents the wavelength interval of the near-infrared spectrum,

[0079] Based on the second derivative values, subtle spectral features of the near infrared spectrum are identified.

[0080] Optionally, the subtle spectral features of the near-infrared spectrum based on the second-order derivative value can be identified by reading the peak and valley values, zero crossing points, relative intensity changes and band shapes of the near-infrared spectrum through the second-order derivative value to determine the subtle spectral features of the near-infrared spectrum.

[0081] Further, as an optional embodiment of the present invention, the identifying the subtle spectral features of the near-infrared spectrum includes: performing a continuous wavelet transform on the near-infrared spectrum using the following formula to obtain a wavelet transform coefficient:

[0082]

[0083] Among them, P(a, b) represents the wavelet transform coefficient, a represents the scale factor, b represents the translation factor, ω represents the original spectral signal of the near-infrared spectrum, and F represents the wavelet function.

[0084] Based on the wavelet transform coefficients, identifying multi-scale features and multi-position features of the near infrared spectrum;

[0085] The association data of the particle state and particle dispersion level of the static drug sample based on the subtle feature value, the multi-scale feature and the multi-position feature can be obtained by integrating the subtle feature value, the multi-scale feature and the multi-position feature to form a comprehensive feature vector, taking the feature vector as the output vector, taking the particle state and particle dispersion level of the drug sample as the output vector, and performing association analysis using a machine learning algorithm. For example, assuming that the peak values ​​at three wavelengths (subtle features), the average absorbance at two different scales (multi-scale features), and the absorbance of specific wavelengths from three different positions of the sample (multi-position features) are obtained through second-order derivative processing. These feature values ​​are quantified into values ​​x1, x2, ..., x8, respectively, and the particle state of the known drug sample is represented by a value y1 (such as 1 for good dispersion and 0 for poor dispersion), and the particle dispersion level is represented by a value y2 (such as a value between 0 and 1). The linear regression model y1 = a1*x1 + a2*x2 + ... + a8*x8 + b1 and y2 = c1*x1 + c2*x2 + ... + c8*x8 + b2 can be used to determine the coefficients a1 to a8, b1 and c1 to c8, b2 through the training data. Then, for new samples, the same feature values ​​are extracted and substituted into the model to obtain the predicted y1 and y2 values, and the associated data is constructed together with the feature values.

[0086] Furthermore, the embodiments of the present invention can help users find out possible problems in the preparation process by analyzing the particle dispersion state of the static drug sample using the associated data, thereby adjusting and optimizing the process parameters.

[0087] The particle dispersion state refers to the comprehensive performance of the uniformity of distribution of drug particles in the entire system, the degree of separation between them, and the agglomeration state.

[0088] As an embodiment of the present invention, the use of the associated data to analyze the particle dispersion state of the stationary drug sample includes: performing vector conversion on the associated data to obtain vector data, constructing a sample label of the associated data based on the vector data, and performing particle dispersion state category analysis on the stationary drug sample using the following formula based on the sample label to obtain a dispersion category:

[0089]

[0090] Where L represents the dispersion category, sign represents the sign function, M represents the number of dispersion categories, β represents the Lagrange multiplier, and H i represents the i-th label category, γ represents the kernel function, and D i represents the i-th sample label, e represents the bias term, and D represents the vector data.

[0091] Based on the dispersion category, the particle dispersion state of the stationary drug sample is determined.

[0092] Furthermore, in the embodiment of the present invention, when the particle dispersion state meets the preset state, the stirred drug sample can be subjected to various quality tests, such as chemical analysis, purity test, etc., to ensure that the drug meets the quality standard. For example, the stirred sample is subjected to component analysis to confirm whether the content of the active ingredient therein meets the standard.

[0093] The preset state may be that the particle dispersion state satisfies that the RSD% of D50 is less than 6%, and the RSD% of D10 and D90 are less than 10%.

[0094] S4, collecting multiple groups of particle size distribution parameters of the stirred drug sample under multiple preset groups of experiments, and collecting instrument parameters and working environment parameters of the corresponding laser particle size analyzer under the multiple groups of experiments, using the multiple groups of particle size distribution parameters, the instrument parameters and the working environment parameters to construct a regression model of the drug sample to be analyzed, using the regression model to analyze the preferred parameters in the instrument parameters that are convenient for analyzing the drug particle size of the drug sample to be analyzed, using the preferred parameters to set parameters of the laser particle size analyzer to obtain a target laser particle size analyzer, and using the target laser particle size analyzer to analyze the drug particle size distribution state of the drug sample to be analyzed.

[0095] The embodiment of the present invention collects multiple sets of particle size distribution parameters of the stirred drug sample under multiple preset experiments, and collects the instrument parameters and working environment parameters of the corresponding laser particle size analyzer under the multiple experiments. By analyzing the relationship between the instrument parameters and the particle size distribution results, the instrument parameter settings most suitable for the analysis of the drug sample can be determined. For example, it is found that a specific combination of laser intensity and measurement time can obtain more accurate particle size distribution data, so that this optimized setting can be used in future analysis.

[0096] The multiple groups of particle size distribution parameters and instrument parameters under the preset multiple groups of experiments can be obtained by real-time recording of each group of experimental data, and the working environment parameters can be collected by temperature and humidity sensors.

[0097] Furthermore, the embodiment of this aspect can predict the particle size distribution of the drug sample under different instrument parameters and working environment conditions by constructing the regression model of the drug sample to be analyzed using the multiple groups of particle size distribution parameters, the instrument parameters and the working environment parameters. This helps to determine the best experimental conditions before actual operation to obtain the desired particle size results. Among them, the regression model refers to a statistical model for studying the relationship between variables, including linear regression, nonlinear regression (such as polynomial regression, logarithmic regression, etc.), multiple regression (involving multiple independent variables), logistic regression (used to deal with binary classification problems), etc.

[0098] As an embodiment of the present invention, the use of the multiple groups of particle size distribution parameters, the instrument parameters and the working environment parameters to construct the regression model of the drug sample to be analyzed includes: data sorting of the multiple groups of particle size distribution parameters, the instrument parameters and the working environment parameters to obtain sorted data, analyzing the data relationship of the sorted data, configuring an initial model of the sorted data based on the data relationship, using the sorted data, after model training the initial model, performing model evaluation on the initial model, and obtaining a regression model when the evaluation result of the model evaluation meets the preset result.

[0099] Optionally, the data sorting of the multiple groups of particle size distribution parameters, the instrument parameters and the working environment parameters to obtain sorted data refers to cleaning and preprocessing the data, checking whether there are missing values, outliers, etc., and performing appropriate processing, such as deleting outliers or filling missing values ​​with reasonable methods. The analysis of the data relationship of the sorted data refers to analyzing the relationship between data variables in the sorted data. For example, if the relationship between the variables is expected to be linear, linear regression can be considered first. If there is a complex nonlinear relationship, it may be necessary to select a decision tree or random forest model. The final solution can be selected by taking both solutions into consideration and testing with subsequent models.

[0100] The initial model for configuring the sorted data can be configured by linear regression, polynomial regression, ridge regression, l asso regression, support vector regression, decision tree regression, random forest regression, etc., and the specific selection needs to be combined with the data type to be analyzed. The model training of the initial model refers to dividing the sorted data into a training set and a test set to train the initial model. The model evaluation of the initial model can be performed by mean square error (MSE), mean absolute error (MAE), determination coefficient (R 2 ) and other methods for evaluation.

[0101] Further, the embodiment of the present invention can improve the accuracy of data analysis by using the regression model to analyze the preferred parameters in the instrument parameters that are convenient for analyzing the drug particle size of the drug sample to be analyzed to determine the instrument parameters that are most suitable for the drug sample to be analyzed, and can more accurately measure the drug particle size and reduce measurement errors, thereby providing more accurate particle size distribution data.

[0102] Furthermore, the embodiment of the present invention sets the parameters of the laser particle size analyzer by using the preferred parameters to obtain the target laser particle size analyzer, and uses the optimal parameters to perform drug particle size analysis to ensure the accuracy of data analysis. The optimal parameters such as dispersion conditions, ultrasonic intensity of 40-50%, ultrasonic time of 180-240 seconds, rotation speed of 2200-2800rpm, shading degree of 7-12%, and measurement time of 10-20 seconds can be obtained by combining actual application analysis.

[0103] Finally, the embodiment of the present invention can evaluate the influence of different steps in the production process on the drug particle size by analyzing the drug particle size distribution state of the drug sample to be analyzed using the target laser particle size analyzer, thereby optimizing process parameters and improving production efficiency and product quality stability.

[0104] like Figure 2 1 is a functional module diagram of a fluticasone propionate bulk drug particle size distribution analysis system provided by one embodiment of the present invention.

[0105] The fluticasone propionate bulk drug particle size distribution analysis system 200 of the present invention can be installed in an electronic device. According to the functions to be implemented, the fluticasone propionate bulk drug particle size distribution analysis system 200 can include a drug dispersion processing module 201, a sample standing module 202, a stirring analysis module 203, and a drug particle size distribution analysis module 204. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0106] In this embodiment, the functions of each module / unit are as follows:

[0107] The drug dispersion processing module 201 is used to obtain a drug sample to be analyzed, perform sample screening on the drug sample to be analyzed to obtain a screened drug sample, and configure an active agent for the screened drug sample to perform dispersion processing on the screened drug sample using the active agent to obtain a dispersed drug sample, wherein the drug sample to be analyzed is a fluticasone propionate raw material;

[0108] The sample static module 202 is used to perform static processing on the dispersed drug sample, collect static pictures of the dispersed drug sample during the static processing, analyze the bubble secretion state of the dispersed drug sample by using the static pictures, and complete the static processing when the bubble secretion state meets the preset state to obtain the static drug sample;

[0109] The stirring analysis module 203 is used to collect a near-infrared spectrum when the static drug sample is stirred using a laser particle size analyzer, and based on the near-infrared spectrum, the particle state and particle dispersion level of the static drug sample are data-correlated to obtain correlation data, and the particle dispersion state of the static drug sample is analyzed using the correlation data, and when the particle dispersion state meets a preset state, the stirred drug sample is obtained;

[0110] The drug particle size distribution analysis module 204 is used to collect multiple groups of particle size distribution parameters of the stirred drug sample under multiple preset groups of experiments, and collect instrument parameters and working environment parameters of the corresponding laser particle size analyzer under the multiple groups of experiments, and use the multiple groups of particle size distribution parameters, the instrument parameters and the working environment parameters to construct a regression model of the drug sample to be analyzed, use the regression model to analyze the preferred parameters in the instrument parameters that are convenient for analyzing the drug particle size of the drug sample to be analyzed, use the preferred parameters to set the parameters of the laser particle size analyzer to obtain a target laser particle size analyzer, and use the target laser particle size analyzer to analyze the drug particle size distribution state of the drug sample to be analyzed.

[0111] In detail, each module described in the fluticasone propionate bulk drug particle size distribution analysis system 200 described in the embodiment of the present invention adopts the same technical means as the fluticasone propionate bulk drug particle size distribution analysis method described in the accompanying drawings when used, and can produce the same technical effects, which will not be repeated here.

[0112] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0113] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0114] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for analyzing the particle size distribution of fluticasone propionate bulk drug, characterized in that: The method comprises: Obtaining a drug sample to be analyzed, performing sample screening on the drug sample to be analyzed to obtain a screened drug sample, and configuring an active agent for the screened drug sample to disperse the screened drug sample using the active agent to obtain a dispersed drug sample, wherein the drug sample to be analyzed is a fluticasone propionate raw material; Performing a static treatment on the dispersed drug sample, collecting a static picture of the dispersed drug sample during the static treatment, analyzing the bubble secretion state of the dispersed drug sample by using the static picture, and completing the static treatment when the bubble secretion state meets a preset state to obtain a static drug sample; collecting a near infrared spectrum of the static drug sample when the static drug sample is stirred using a laser particle size analyzer, correlating the particle state and particle dispersion level of the static drug sample based on the near infrared spectrum to obtain correlation data, analyzing the particle dispersion state of the static drug sample using the correlation data, and obtaining a stirred drug sample when the particle dispersion state meets a preset state; Collecting multiple groups of particle size distribution parameters of the stirred drug sample under multiple preset groups of experiments, and collecting instrument parameters and working environment parameters of the corresponding laser particle size analyzer under the multiple groups of experiments, using the multiple groups of particle size distribution parameters, the instrument parameters and the working environment parameters to construct a regression model of the drug sample to be analyzed, using the regression model to analyze the preferred parameters in the instrument parameters that are convenient for analyzing the drug particle size of the drug sample to be analyzed, using the preferred parameters to set parameters of the laser particle size analyzer to obtain a target laser particle size analyzer, and using the target laser particle size analyzer to analyze the drug particle size distribution state of the drug sample to be analyzed; Wherein, based on the near infrared spectrum, the particle state and particle dispersion level of the static drug sample are data-correlated to obtain the associated data, including: identifying subtle spectral features of the near infrared spectrum; Calculating a subtle characteristic value of the near infrared spectrum based on the subtle spectral characteristics; Performing continuous wavelet transform on the near infrared spectrum to obtain wavelet transform coefficients; Identify multi-scale features and multi-position features of the near infrared spectrum based on the wavelet transform coefficients; Constructing the correlation data of the particle state and particle dispersion level of the static drug sample based on the subtle feature values, the multi-scale features and the multi-position features; constructing the correlation data of the particle state and particle dispersion level of the static drug sample based on the subtle feature values, the multi-scale features and the multi-position features, by integrating the subtle feature values, the multi-scale features and the multi-position features to form a comprehensive feature vector, taking the feature vector as an input vector, taking the particle state and particle dispersion level of the drug sample as an output vector, and performing correlation analysis using a machine learning algorithm to obtain the result; Wherein, the identifying of the subtle spectral features of the near infrared spectrum includes: Calculating the second-order derivative spectrum of the near-infrared spectrum to obtain the second-order derivative value, Based on the second-order derivative value, identifying subtle spectral features of the near-infrared spectrum; The step of analyzing the particle dispersion state of the statically placed drug sample using the associated data includes: Performing vector conversion on the associated data to obtain vector data; Based on the vector data, construct a sample label of the associated data; Performing particle dispersion state classification analysis on the statically placed drug sample based on the sample label to obtain a dispersion classification; Based on the dispersion category, the particle dispersion state of the stationary drug sample is determined.

2. The method for analyzing the particle size distribution of fluticasone propionate bulk drug according to claim 1, characterized in that: The active agent for preparing the screening drug sample is used to disperse the screening drug sample using the active agent to obtain a dispersed drug sample, including: preparing a dispersant for screening drug samples; After adding the pre-configured active material to the dispersant, the dispersant is stirred to obtain an active agent; After the screening drug sample is added to the active agent according to a preset mass, the active agent is stirred to obtain a dispersed drug sample.

3. The method for analyzing particle size distribution of fluticasone propionate bulk drug according to claim 1, characterized in that: The step of analyzing the bubble secretion state of the dispersed drug sample by using the static picture includes: identifying the number of bubbles of the secreted bubbles of the dispersed drug sample according to the static picture; detecting a bubble edge of the secreted bubble, and calculating a bubble area of ​​the secreted bubble based on the bubble edge; The bubble secretion state of the dispersed drug sample is analyzed based on the bubble number and the bubble area.

4. The method for analyzing the particle size distribution of fluticasone propionate bulk drug according to claim 3, characterized in that: The calculating the bubble size of the secretion bubble based on the bubble edge comprises: Constructing vertex coordinates of the secretion bubble based on the bubble edge; Based on the vertex coordinates, the bubble area of ​​the secretion bubble is calculated.

5. The method for analyzing particle size distribution of fluticasone propionate bulk drug according to claim 1, characterized in that: The method of constructing a regression model of the drug sample to be analyzed by using the multiple groups of particle size distribution parameters, the instrument parameters and the working environment parameters includes: Arranging data of the plurality of groups of particle size distribution parameters, the instrument parameters and the working environment parameters to obtain arranged data; analyzing data relationships of the collated data; Based on the data relationship, configuring an initial model of the sorted data; Using the collated data, after performing model training on the initial model, perform model evaluation on the initial model; When the evaluation result of the model evaluation meets the preset result, a regression model is obtained.

6. An analysis system for particle size distribution of fluticasone propionate bulk drug, characterized in that: A method for analyzing the particle size distribution of a fluticasone propionate bulk drug according to any one of claims 1 to 5, the system comprising: A drug dispersion processing module is used to obtain a drug sample to be analyzed, perform sample screening on the drug sample to be analyzed to obtain a screened drug sample, and configure an active pharmaceutical agent for the screened drug sample to perform dispersion processing on the screened drug sample using the active pharmaceutical agent to obtain a dispersed drug sample, wherein the drug sample to be analyzed is a fluticasone propionate raw material; A sample static module is used to perform static treatment on the dispersed drug sample, collect static pictures of the dispersed drug sample during the static treatment, analyze the bubble secretion state of the dispersed drug sample by using the static pictures, and complete the static treatment when the bubble secretion state meets the preset state to obtain the static drug sample; a stirring analysis module, for collecting a near-infrared spectrum when the static drug sample is stirred by using a laser particle size analyzer, and based on the near-infrared spectrum, performing data correlation between the particle state and the particle dispersion level of the static drug sample to obtain correlation data, and using the correlation data to analyze the particle dispersion state of the static drug sample, and obtaining a stirred drug sample when the particle dispersion state meets a preset state; The drug particle size distribution analysis module is used to collect multiple groups of particle size distribution parameters of the stirred drug sample under multiple preset groups of experiments, and collect the instrument parameters and working environment parameters of the corresponding laser particle size analyzer under the multiple groups of experiments, and use the multiple groups of particle size distribution parameters, the instrument parameters and the working environment parameters to construct a regression model of the drug sample to be analyzed, use the regression model to analyze the preferred parameters in the instrument parameters that are convenient for analyzing the drug particle size of the drug sample to be analyzed, use the preferred parameters to set the parameters of the laser particle size analyzer to obtain a target laser particle size analyzer, and use the target laser particle size analyzer to analyze the drug particle size distribution state of the drug sample to be analyzed.

Images