Detection method of sildenafil citrate tablets

Abstract

The invention relates to the technical field of medicine detection, in particular to a detection method of sildenafil citrate tablets, which comprises the following steps: S1, placing contents and target samples with different water contents in a plurality of containers; s2, determining the drug concentration of each region based on the color development characteristics of the diffused image of the target sample in the container at different detection time points, and calculating the concentration gradient component and diffusion concentration gradient of the target sample; s3, measuring the total amount or the concentration of the residual medicine of the target sample in the plurality of containers in a preset time period so as to determine the residual degree of the target sample; and S4, determining the diffusion difference degree based on the diffusion concentration gradient and the residual degree, and evaluating the diffusion adaptability of the target sample based on the diffusion difference degree. The accuracy and depth of drug diffusion performance research are improved, reliable data support is further provided for drug research and development and quality control, and drug quality can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of drug detection, and in particular to a detection method for sildenafil citrate tablets. Background Art

[0002] Sildenafil citrate tablets, also known as Viagra or Viagra, are a common drug used to treat male erectile dysfunction (ED). Sildenafil citrate tablets can effectively improve male erectile dysfunction by inhibiting phosphodiesterase type 5 (PDE5) that decomposes cyclic guanosine monophosphate (cGMP) in the corpus cavernosum to enhance the effect of nitric oxide (NO), thereby relaxing the smooth muscle of the corpus cavernosum, allowing arterial blood to flow in and causing the penis to become congested.

[0003] In the existing technology, the diffusion performance of sildenafil citrate tablets is often evaluated to ensure the drug quality of sildenafil citrate tablets, but these technologies have some shortcomings, including: ignoring the characteristics and nature of drug diffusion itself, each model for evaluating drug diffusion performance has certain limitations, the degree of applicability of the model is different, and there are certain technical difficulties and technical costs.

[0004] Therefore, there is an urgent need for a method for detecting the diffusion performance of sildenafil citrate tablets that can improve the accuracy of drug diffusion performance research and further provide reliable data support for drug development and quality control. Summary of the invention

[0005] To this end, the present invention provides a method for detecting sildenafil citrate tablets, so as to overcome the problem of low accuracy in the study of diffusion performance of sildenafil citrate tablets in the prior art.

[0006] To achieve the above object, on the one hand, the present invention provides a method for detecting sildenafil citrate tablets, comprising:

[0007] Step S1, placing a preset first amount of content and a preset second amount of target sample into a plurality of containers, wherein the water content of the content in each container is different;

[0008] Step S2, based on the color development characteristics of the images of several regions after the diffusion of the target sample in each container at several detection time points, respectively photographing the image, determining the drug concentration of each region, calculating the concentration gradient components of the target sample in various directions in different contents based on the drug concentration of each region, and calculating the diffusion concentration gradient of the target sample in different contents based on the concentration gradient components;

[0009] Step S3, based on a preset time period, respectively measuring the total amount of remaining drug or the remaining drug concentration of the target sample in a plurality of containers, and determining the residual degree of the target sample in the different containers based on the total amount of remaining drug or the remaining drug concentration;

[0010] Step S4, determining the diffusion difference of the target sample based on the diffusion concentration gradients and residual levels detected in different containers, and determining the diffusion adaptability of the target sample based on the diffusion difference;

[0011] The content consists of PDE5 enzyme, color-developing substance and matrix, the matrix does not react with the PDE5 enzyme and is in a different color from the color-developing substance; the target sample is sildenafil citrate tablets.

[0012] Furthermore, the color-developing substance is a fluorescent dye, and the matrix is ​​a colorless gel matrix.

[0013] Furthermore, in step S2, the drug concentration of each area is determined according to the image color characteristics, including:

[0014] Step S21, at each detection time point, taking images of the target sample after diffusion in each container;

[0015] Step S22, dividing the image into a plurality of regions based on a preset segmentation method;

[0016] Step S23, determining the drug concentrations of the plurality of regions based on the color intensity and concentration model of the plurality of regions;

[0017] Wherein, the color intensity is positively correlated with the drug concentration.

[0018] Furthermore, in step S22, the preset segmentation method includes:

[0019] A first segmentation method is to divide the image into a plurality of circular rings according to a preset dynamic radius with the center of the target sample as the origin;

[0020] The second segmentation method is to divide the image into a plurality of rectangles according to a preset length and a preset width.

[0021] Furthermore, in step S23, the concentration model establishment step includes:

[0022] Step S231, preparing several groups of standard samples of target samples at different concentrations, and photographing diffusion images of the target samples in the standard samples;

[0023] Step S232, determining the color intensity of the standard sample based on the target sample diffusion image;

[0024] Step S233, establishing a concentration model between the color intensity and the standard sample concentration,

[0025] The analysis range of the target sample diffusion image is a preset target area, and the preset target area is a specific area of ​​the target sample diffusion image.

[0026] Furthermore, in step S233, it includes:

[0027] Step S2331, dividing a number of the color intensities and corresponding standard sample concentrations into a training set and a validation set;

[0028] Step S2332, constructing a concentration model between the color intensity and the standard sample concentration based on the training set;

[0029] Step S2333, determining the accuracy of the concentration model based on the validation set, and adjusting the concentration model based on the comparison result between the accuracy and the preset accuracy.

[0030] Further, in step S2333, it includes: if the difference between the accuracy and the preset accuracy is lower than the preset difference, adjusting the preset target area.

[0031] Furthermore, in step S2, the concentration gradient components of the target sample in various directions in different contents are calculated based on the drug concentrations in the several regions, including: determining the initial region of diffusion of the target sample based on the drug concentrations in the several regions at the initial detection time point, and determining the concentration gradient components according to the drug concentrations in the initial region and the drug concentrations in the remaining regions.

[0032] Furthermore, in step S4, determining the diffusion difference of the target sample includes:

[0033] Step S41, determining the degree of difference in diffusion concentration based on the diffusion concentration gradients detected in the different contents;

[0034] Step S42, determining the residual difference based on the residual degree of the target sample detected in the different contents;

[0035] Step S43: determining the diffusion difference of the target sample based on the diffusion concentration difference and the residue difference.

[0036] Furthermore, the diffusion difference is compared with a preset diffusion difference, and the time difference corresponding to the diffusion difference is predicted according to the comparison result.

[0037] Compared with the prior art, the beneficial effect of the present invention is that the diffusion concentration gradient and residual degree of the target sample (sildenafil citrate tablets) in different water content contents are systematically evaluated, including: by shooting and analyzing the image color characteristics of several areas after the target sample is diffused at several detection time points, the concentration gradient components of the target sample in all directions are determined, and the diffusion concentration gradient of the target sample in different water content contents is further intuitively displayed. Further based on the preset time period, the total amount of remaining drug or the remaining drug concentration is measured to quantify the residual degree of the target sample after diffusion in the preset time period. Finally, the diffusion difference of the target sample is determined in combination with the diffusion concentration gradient and the residual degree, and the diffusion adaptability of the target sample is evaluated, which not only improves the accuracy and depth of the research on drug diffusion performance, but also further provides reliable data support for drug research and development and quality control, which helps to improve drug quality.

[0038] Furthermore, the present invention introduces fluorescent dyes to visualize the diffusion process of the target sample, making it convenient to record the diffusion concentration of the target sample. On the other hand, the use of a colorless gel matrix as the experimental environment avoids direct reaction between the matrix and the PDE5 enzyme, providing a stable and uniform diffusion medium for the target sample, thereby further improving the accuracy and depth of the drug diffusion performance research, and also further providing reliable data support for drug research and development and quality control, which helps to improve drug quality.

[0039] Furthermore, the present invention combines image capture, area division and color intensity analysis to achieve accurate determination of drug concentration in each area after diffusion of the target sample, which not only improves the intuitiveness and quantitative accuracy of drug diffusion performance research, but also further provides reliable data support for drug preparation research and development and optimization, which helps to accelerate the new drug development process and improve drug quality.

[0040] Furthermore, the present invention prepares standard samples of relevant target samples at different concentrations, and takes diffusion images of the target samples, effectively utilizes image analysis technology to determine the color intensity of the standard samples at different concentrations, and then establishes a concentration model between the color intensity and the concentration of the standard samples. This not only improves the intuitiveness and quantitative accuracy of drug diffusion performance research, but also further provides reliable data support for the research and development and optimization of drug preparations, which helps to accelerate the new drug development process and improve drug quality.

[0041] Furthermore, the present invention determines the initial area of ​​diffusion of the target sample based on the spatial distribution of drug concentration at the initial detection time point, and derives the concentration gradient component by comparing the difference in drug concentration between the initial area (the area with the highest drug concentration) and the remaining area (other areas except the initial area), thereby further improving the intuitiveness and quantitative accuracy of drug diffusion performance research, and further providing reliable data support for drug preparation research and development and optimization, which helps to accelerate the new drug development process and improve drug quality.

[0042] Furthermore, the present invention comprehensively considers the differences in diffusion concentration gradients and residual levels of target samples in different contents, comprehensively evaluates the diffusion performance of target samples, and determines the diffusion difference degree accordingly, and quantifies the impact of this diffusion difference degree on the diffusion distribution and residual situation of the target samples, thereby further improving the intuitiveness and quantitative accuracy of drug diffusion performance research, and at the same time further provides reliable data support for the research and development and optimization of drug preparations, which helps to accelerate the new drug development process and improve drug quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 The present invention is a flow chart of the detection method of sildenafil citrate tablets;

[0044] Figure 2 A flow chart for determining drug concentration in each region for the present invention;

[0045] Figure 3 A flow chart for establishing a concentration model for the present invention;

[0046] Figure 4 A flow chart for determining the diffusion difference of a target sample according to the present invention. DETAILED DESCRIPTION

[0047] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; 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.

[0048] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0049] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0050] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0051] See also Figure 1 As shown, it is a flow chart of the detection method of sildenafil citrate tablets of the present invention. Specifically, the present invention provides a detection method of sildenafil citrate tablets, comprising:

[0052] Step S1, placing a preset first amount of content and a preset second amount of target sample into a plurality of containers, wherein the water content of the content in each container is different;

[0053] Step S2, based on the color development characteristics of the images of several regions after the diffusion of the target sample in each container at several detection time points, respectively photographing the image, determining the drug concentration of each region, calculating the concentration gradient components of the target sample in various directions in different contents based on the drug concentration of each region, and calculating the diffusion concentration gradient of the target sample in different contents based on the concentration gradient components;

[0054] Step S3, based on a preset time period, respectively measuring the total amount of remaining drug or the remaining drug concentration of the target sample in a plurality of containers, and determining the residual degree of the target sample in the different containers based on the total amount of remaining drug or the remaining drug concentration;

[0055] Step S4, determining the diffusion difference of the target sample based on the diffusion concentration gradients and residual levels detected in different containers, and determining the diffusion adaptability of the target sample based on the diffusion difference;

[0056] The content consists of PDE5 enzyme, color-developing substance and matrix, the matrix does not react with the PDE5 enzyme and is in a different color from the color-developing substance; the target sample is sildenafil citrate tablets.

[0057] It can be understood that the present invention sets a specific experimental environment in a plurality of containers, each container contains a target sample (sildenafil citrate tablets) of contents (preset first dosage) with preset different water contents (preset second dosage), and after the contents and the target sample are stirred, the diffusion degree of the target sample is analyzed. The specific analysis process is as follows:

[0058] The use of color-developing substances makes it possible to clearly see the diffusion process of the target sample after being hydrolyzed by the PDE5 enzyme. Images of the target samples after diffusion in several containers are taken at several detection time points. The image color characteristics of several areas are analyzed by image analysis to determine the drug concentration in each area, thereby obtaining the concentration gradient components of the target sample in various directions in different contents. Based on the concentration gradient components, the diffusion concentration gradient of the target sample in different contents can be derived.

[0059] Furthermore, within a preset time period, the target samples in the plurality of containers diffuse to a certain extent, and at this time, the total amount of remaining drugs or the remaining drug concentration in each container after the preset time period is measured, and the residual extent of the target samples in the different containers is evaluated by the total amount of remaining drugs or the remaining drug concentration, based on the diffusion concentration gradient and the residual extent of the target samples in different contents. Finally, the diffusion difference of the target samples in different contents is calculated based on the comprehensive diffusion concentration gradient and the residual extent, and the diffusion adaptability of the target samples is evaluated based on this.

[0060] In implementation, the preset first dosage is 5g to 10g, preferably, the preset first dosage is 8g; the preset second dosage is 1 tablet of target sample (sildenafil citrate tablets) under conventional specifications, which is 50mg; the water content range is 70% to 98%, and the water content difference between each container ranges from 3% to 6%, preferably, the water content range is 80% to 95%, and the water content difference between each container ranges from 5%; the range of the several detection time points is 3min to 6min from the time the target sample is put into the content as a detection point, preferably, the several detection points are 5min from the time the target sample is put into the content as a detection point; the range of the preset time period is 25min to 35min, preferably, the preset time period is 30min; the preset first dosage, the preset second dosage, the water content, the several detection time points, the value range and preferred value of the preset time period can be adjusted according to actual conditions, and will not be repeated here.

[0061] The present invention systematically evaluates the diffusion concentration gradient and residual degree of the target sample (sildenafil citrate tablets) in contents with different water contents, including: by shooting and analyzing the image color characteristics of several areas after the diffusion of the target sample at several detection time points, the concentration gradient components of the target sample in all directions are determined, and the diffusion concentration gradient of the target sample in contents with different water contents is further intuitively displayed. The residual degree of the target sample after diffusion in the preset time period is quantified by measuring the total amount of remaining drugs or the remaining drug concentration based on the preset time period. Finally, the diffusion difference of the target sample is determined in combination with the diffusion concentration gradient and the residual degree, and the diffusion adaptability of the target sample is evaluated, which not only improves the accuracy and depth of the research on drug diffusion performance, but also further provides reliable data support for drug research and development and quality control, which helps to improve drug quality.

[0062] Specifically, the color-developing substance is a fluorescent dye, and the matrix is ​​a colorless gel matrix.

[0063] It can be understood that the diffusion of target samples under different experimental conditions can be visually observed through fluorescent dyes, and the gel matrix provides a stable and suitable experimental environment for this process.

[0064] In implementation, preferably, the color-developing substance is a fluorescent dye, and the matrix is ​​a colorless gel matrix. The color-developing substance and the matrix are not limited here, as long as the diffusion process of the target sample can be indicated by the color-developing substance, passing through the matrix, and the matrix does not react with the PDE5 enzyme, and the matrix and the color-developing substance are different colors in each state, it will not be repeated here.

[0065] On the one hand, the present invention introduces fluorescent dyes to visualize the diffusion process of the target sample, which facilitates recording the diffusion concentration of the target sample. On the other hand, the use of a colorless gel matrix as an experimental environment avoids direct reaction between the matrix and the PDE5 enzyme, provides a stable and uniform diffusion medium for the target sample, thereby further improving the accuracy and depth of drug diffusion performance research, and also further provides reliable data support for drug research and development and quality control, which helps to improve drug quality.

[0066] See also Figure 2 , which is a flow chart of determining the drug concentration of each region of the present invention. Specifically, in step S2, determining the drug concentration of each region according to the image color characteristics includes:

[0067] Step S21, at each detection time point, taking images of the target sample after diffusion in each container;

[0068] Step S22, dividing the image into a plurality of regions based on a preset segmentation method;

[0069] Step S23, determining the drug concentrations of the plurality of regions based on the color intensity and concentration model of the plurality of regions;

[0070] Wherein, the color intensity is positively correlated with the drug concentration.

[0071] It can be understood that the images after the diffusion of the target sample in each container taken at each detection time point capture the diffusion state of the target sample at different times. Due to the different diffusion degrees and diffusion directions, the image is divided into several areas using a preset segmentation method. After the target sample diffuses to the several areas, it will show a specific color change. This color change is positively correlated with the drug concentration. By measuring the color intensity of each area (such as grayscale value, RGB value, etc.), the drug concentration of the target sample can be determined.

[0072] Specifically, in step S22, the preset segmentation method includes:

[0073] A first segmentation method is to divide the image into a plurality of circular rings according to a preset dynamic radius with the center of the target sample as the origin;

[0074] The second segmentation method is to divide the image into a plurality of rectangles according to a preset length and a preset width.

[0075] It can be understood that the preset dynamic radius is the difference between the radii of each adjacent two circular rings. The first segmentation method takes the center of the target sample as the origin, draws a circular ring with the preset dynamic radius, and divides the image into multiple concentric circular ring areas, which is helpful to observe the diffusion process of the target sample from the center to the outside. In addition, the first segmentation method has a certain flexibility. The preset dynamic radius has different values, and the number of divided areas is different. The several areas divided by the preset dynamic radius can adapt to different diffusion analysis accuracy requirements, or the preset dynamic radius can be adjusted according to the different diffusion stages of the target sample to determine the drug concentration of the target sample.

[0076] In implementation, the preset dynamic radius has a value range of 0.3 cm to 0.6 cm, preferably, the preset dynamic radius has a value of 0.5 cm. The value range and preferred value of the preset dynamic radius can be determined according to actual conditions, and will not be described in detail here.

[0077] It is understandable that the second segmentation method divides the image into several rectangular areas, and the drug concentration after diffusion in each area of ​​the image can be analyzed in detail, and when the diffusion of the target sample is not completely uniform, the rectangular grid can capture the subtle changes in diffusion more carefully. The second segmentation method has a certain flexibility. By adjusting the preset length and preset width of the rectangular area, different numbers of rectangular areas can be obtained to meet different analysis needs, or different preset lengths and preset widths of grids can be used to observe the drug concentration of the target sample according to the different diffusion stages of the target sample.

[0078] In implementation, the value range of the preset length is 0.5cm~1cm, preferably, the preset length is 0.8cm, the value range of the preset width is 0.3cm~0.5cm, preferably, the preset width is 0.4cm, the value range and preferred value of the preset length and the preset width can be determined according to actual conditions, the preset length and the preset width can be the same size, and will not be repeated here.

[0079] In implementation, preferably, the preset segmentation method is the second segmentation method, and the preset segmentation method is not specifically limited. As long as the preset segmentation method can be used to reasonably divide the image after the diffusion of the target sample in each container, and the several segmented areas can represent the drug diffusion characteristics, the preset segmentation method can be customized and will not be repeated here.

[0080] The present invention combines image capture, area division and color intensity analysis to achieve accurate determination of drug concentration in each area after diffusion of the target sample, which not only improves the intuitiveness and quantitative accuracy of drug diffusion performance research, but also further provides reliable data support for drug preparation research and development and optimization, which helps to accelerate the new drug development process and improve drug quality.

[0081] See also Figure 3 As shown, it is a flow chart of establishing a concentration model in the present invention. Specifically, in step S23, the concentration model establishment step includes:

[0082] Step S231, preparing several groups of standard samples of target samples at different concentrations, and photographing diffusion images of the target samples in the standard samples;

[0083] Step S232, determining the color intensity of the standard sample based on the target sample diffusion image;

[0084] Step S233, establishing a concentration model between the color intensity and the standard sample concentration.

[0085] It can be understood that the contents and the target sample are also present in the standard sample, the difference being that the concentration of the target sample is known and is evenly diffused in the standard sample.

[0086] Specifically, the analysis range of the target sample diffusion image is a preset target area;

[0087] Wherein, the preset target area is a specific area of ​​the diffusion image of the target sample.

[0088] It is understandable that, during the model building process, a specific area can be defined as the analysis range of the diffusion image of the target sample, and then the corresponding color channel can be selected for analysis (such as the red channel, green channel or blue channel in RGB, or converted to HSV, HSL and other color spaces for analysis) according to the specific color produced by the standard sample in the specific area (the fluorescent color produced by the fluorescent dye), and the color intensity corresponding to the standard sample at different concentrations can be determined in the specific area.

[0089] In a specific embodiment, the target sample diffusion image can be divided according to the second segmentation method, and the specific area is the middle area of ​​the target sample diffusion image. For example, assuming that the target sample diffusion image is divided into 3×3 square areas, the middle area is taken as the specific area for analysis. The side length of the square area ranges from 0.6 cm to 0.8 cm. Preferably, the side length is 0.7 cm. The range of the side length can be selected according to the actual situation, which will not be repeated here.

[0090] The calculation formula of the concentration model is as follows:

[0091] C=γ1+γ2×I+α (1)

[0092] Where C is the concentration of the standard sample, I is the color intensity, γ2 is the slope, which represents the average change in color intensity when the concentration changes by one unit, γ1 is the intercept, and α is the error term.

[0093] The present invention prepares standard samples of relevant target samples at different concentrations, captures diffusion images of the target samples, and effectively uses image analysis technology to determine the color intensity of the standard samples at different concentrations, thereby establishing a concentration model between the color intensity and the concentration of the standard samples. This not only improves the intuitiveness and quantitative accuracy of drug diffusion performance research, but also further provides reliable data support for the research and development and optimization of drug preparations, which helps to accelerate the process of new drug development and improve drug quality.

[0094] Specifically, in step S233, it includes:

[0095] Step S2331, dividing a number of the color intensities and corresponding standard sample concentrations into a training set and a validation set;

[0096] Step S2332, constructing a concentration model between the color intensity and the standard sample concentration based on the training set;

[0097] Step S2333, determining the accuracy of the concentration model based on the validation set, and adjusting the concentration model based on the comparison result between the accuracy and the preset accuracy.

[0098] In implementation, the preset accuracy ranges from 93% to 98%. Preferably, the preset accuracy is 96%. The preset accuracy range and preferred value can be adjusted according to actual conditions and will not be described in detail here.

[0099] Specifically, in step S2333, it includes: if the difference between the accuracy and the preset accuracy is lower than the preset difference, adjusting the preset target area.

[0100] The preset target area is adjusted, including redefining the specific area.

[0101] In implementation, after the target sample diffusion image is divided into several regions according to the second segmentation method, the corresponding color intensities in the several regions are determined, and the average or median of the several color intensities is calculated, and the average / median of the several color intensities is mapped to different concentrations of standard samples.

[0102] In implementation, the preset difference value ranges from 1% to 3%. Preferably, the preset difference value is 1.5%. The preset difference value range and preferred value can be adjusted according to actual conditions, which will not be described in detail here.

[0103] Specifically, in step S2, the concentration gradient components of the target sample in various directions in different contents are calculated based on the drug concentrations of the several regions, including: determining the initial region of diffusion of the target sample based on the drug concentrations of the several regions at the initial detection time point, and determining the concentration gradient components according to the drug concentrations of the initial region and the drug concentrations of the remaining regions.

[0104] It can be understood that the moment when the target sample is just placed in the several containers is the initial detection time point, the initial area is the area with the highest drug concentration, and the remaining area is the area other than the initial area divided based on the preset segmentation method.

[0105] In the implementation, the pth container is divided into N regions, p = 1, 2, ..., M, and the drug concentration in the initial region is set to The drug concentration in each region is set to Let the distance between region m and region i in the remaining N-1 regions be The concentration gradient component between region m and region i is calculated as follows:

[0106]

[0107] in, is the distance between region m and region i in the pth container.

[0108] In implementation, the distance between the region m and the region i can be expressed by the distance between the center points of the two regions. The method for determining the distance between the two regions is not specifically limited. As long as the concentration gradient component between the two regions can be calculated through the distance, it will not be repeated here.

[0109] It is understood that when calculating the overall diffusion concentration gradient, the concentration gradient components in each direction must be combined for calculation. The calculation formula for the diffusion concentration gradient G is as follows:

[0110]

[0111] Where N is the total number of regions in the pth container, is the concentration gradient component between region m and region i in the pth container, and there are N-1 concentration gradient components in total.

[0112] It can be understood that the above diffusion concentration gradient calculation formula ignores the direction information of the gradient and approximately calculates a diffusion concentration gradient to represent the overall diffusion concentration gradient.

[0113] The present invention determines the initial area of ​​diffusion of the target sample based on the spatial distribution of the drug concentration at the initial detection time point, and derives the concentration gradient component by comparing the difference in drug concentration between the initial area (the area with the highest drug concentration) and the remaining area (other areas except the initial area), thereby further improving the intuitiveness and quantitative accuracy of the study of drug diffusion performance, and further providing reliable data support for the research and development and optimization of drug preparations, which helps to accelerate the process of new drug development and improve drug quality.

[0114] Specifically, in step S3, a preset time period is set. During the preset time period, due to different experimental environments in different containers, the total amount of remaining drug or the remaining drug concentration of the target sample in the container is different. The residual degree of the target sample in the different containers can be determined based on the total amount of remaining drug or the remaining drug concentration.

[0115] It can be understood that the remaining drug concentration is the drug concentration in the initial area after a preset time period.

[0116] The calculation formula of the residual degree β of the target sample in the j-th container is as follows:

[0117]

[0118] Among them, P j is the total amount of remaining medicine of the target sample in the jth container after a preset time period, P0 is the preset second dosage of the target sample, and j=1, 2, ..., M.

[0119] In another embodiment, the calculation formula of the residual degree β of the target sample in the jth container is as follows:

[0120]

[0121] Among them, C j is the remaining drug concentration of the target sample in the jth container after a preset time period, C0 is the drug concentration after the target sample is completely diffused, j=1,2,...,M.

[0122] See also Figure 4 As shown, it is a flow chart of determining the diffusion difference of the target sample of the present invention. Specifically, in step S4, determining the diffusion difference of the target sample includes:

[0123] Step S41, determining the degree of difference in diffusion concentration based on the diffusion concentration gradients detected in the different contents;

[0124] Step S42, determining the residual difference based on the residual degree of the target sample detected in the different contents;

[0125] Step S43: determining the diffusion difference of the target sample based on the diffusion concentration difference and the residue difference.

[0126] The calculation formula of the diffusion concentration difference σ1 is as follows:

[0127]

[0128] Among them, G c is the average value of the diffusion concentration gradient of all containers, G u is the u-th diffusion concentration gradient.

[0129] The residual difference σ2 is calculated as follows:

[0130]

[0131] Among them, β c is the average residual degree of all containers, β u is the uth residual degree.

[0132] The diffusion difference is calculated as follows:

[0133] τ=μ1×σ1+μ2×σ2 (8)

[0134] Among them, μ1 is the diffusion concentration difference weight, and μ2 is the residual difference weight.

[0135] In implementation, it is preferred that μ1 is 0.7 and μ2 is 0.3. The diffusion concentration difference weight μ1 and the residual difference weight μ2 can be adjusted according to actual conditions, which will not be described in detail here.

[0136] The present invention comprehensively considers the differences in diffusion concentration gradients and residual levels of target samples in different contents, comprehensively evaluates the diffusion performance of target samples, and determines the diffusion difference degree accordingly, and quantifies the impact of this diffusion difference degree on the diffusion distribution and residual situation of the target samples, thereby further improving the intuitiveness and quantitative accuracy of drug diffusion performance research, and further providing reliable data support for the research and development and optimization of drug preparations, which helps to accelerate the process of new drug development and improve drug quality.

[0137] Specifically, the diffusion difference is compared with a preset diffusion difference, and the time difference corresponding to the diffusion difference is predicted according to the comparison result.

[0138] It is understandable that by predicting the time difference corresponding to the diffusion difference of target samples in different batches, we can better understand the diffusion performance of the target samples and their changing trends, which helps to ensure the consistency and stability of the drugs. For example, if the prediction results show that the time difference corresponding to the diffusion difference of several batches of drugs is large, it means that the drug diffuses slowly, then the production process or raw materials may need to be adjusted to ensure that the quality of the drug meets the standards.

[0139] In a specific embodiment, a drug diffusion concentration and time model of the target sample is constructed based on the diffusion concentration of the target sample at different time points (the independent variable is time, and the dependent variable is the drug diffusion concentration), and the diffusion difference can be used as a constraint condition of the drug diffusion concentration and time model.

[0140]

[0141] Wherein, C0 is the initial diffusion concentration, k is the diffusion rate constant, D(t) is the diffusion difference of target samples of different batches at time t after the preset time period, and D(t0) is the preset diffusion difference of target samples of different batches at time t0.

[0142] The time difference calculation formula is as follows:

[0143] Δt=|t0-t| (10)

[0144] In implementation, the preset diffusion difference has a value range of 0.1 to 0.3. Preferably, the preset diffusion difference has a value of 0.15. The preset diffusion difference has a value range and a preferred value that can be adjusted according to actual conditions and will not be described in detail here.

[0145] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A method for detecting sildenafil citrate tablets, characterized in that: include: Step S1, placing a preset first amount of content and a preset second amount of target sample into a plurality of containers, wherein the water content of the content in each container is different; Step S2, based on the color development characteristics of the images of several regions after the diffusion of the target sample in each container at several detection time points, respectively photographing the image, determining the drug concentration of each region, calculating the concentration gradient components of the target sample in various directions in different contents based on the drug concentration of each region, and calculating the diffusion concentration gradient of the target sample in different contents based on the concentration gradient components; Step S3, based on a preset time period, respectively measuring the total amount of remaining drug or the remaining drug concentration of the target sample in a plurality of containers, and determining the residual degree of the target sample in the different containers based on the total amount of remaining drug or the remaining drug concentration; Step S4, determining the diffusion difference of the target sample based on the diffusion concentration gradients and residual levels detected in different containers, and determining the diffusion adaptability of the target sample based on the diffusion difference; The content consists of PDE5 enzyme, color-developing substance and matrix, the matrix does not react with the PDE5 enzyme and is in a different color from the color-developing substance; the target sample is sildenafil citrate tablets.

2. The method for detecting sildenafil citrate tablets according to claim 1, characterized in that: The color-developing substance is a fluorescent dye, and the matrix is ​​a colorless gel matrix.

3. The method for detecting sildenafil citrate tablets according to claim 1, characterized in that: In step S2, the drug concentration of each area is determined according to the image color characteristics, including: Step S21, at each detection time point, taking images of the target sample after diffusion in each container; Step S22, dividing the image into a plurality of regions based on a preset segmentation method; Step S23, determining the drug concentrations of the plurality of regions based on the color intensity and concentration model of the plurality of regions; Wherein, the color intensity is positively correlated with the drug concentration.

4. The method for detecting sildenafil citrate tablets according to claim 3, characterized in that: In the step S22, the preset segmentation method includes: The first segmentation method is to divide the image into a plurality of circular rings according to a preset dynamic radius with the center of the target sample as the origin; The second segmentation method is to divide the image into a plurality of rectangles according to a preset length and a preset width.

5. The method for detecting sildenafil citrate tablets according to claim 3, characterized in that: In step S23, the concentration model establishment step includes: Step S231, preparing several groups of standard samples of target samples at different concentrations, and photographing diffusion images of the target samples in the standard samples; Step S232, determining the color intensity of the standard sample based on the target sample diffusion image; Step S233, establishing a concentration model between the color intensity and the standard sample concentration; The analysis range of the target sample diffusion image is a preset target area, and the preset target area is a specific area of ​​the target sample diffusion image.

6. The method for detecting sildenafil citrate tablets according to claim 5, characterized in that: In step S233, it includes: Step S2331, dividing a number of the color intensities and corresponding standard sample concentrations into a training set and a validation set; Step S2332, constructing a concentration model between the color intensity and the standard sample concentration based on the training set; Step S2333, determining the accuracy of the concentration model based on the validation set, and adjusting the concentration model based on the comparison result between the accuracy and the preset accuracy.

7. The method for detecting sildenafil citrate tablets according to claim 6, characterized in that: In step S2333, it includes: if the difference between the accuracy and the preset accuracy is lower than the preset difference, adjusting the preset target area.

8. The method for detecting sildenafil citrate tablets according to claim 1, characterized in that: In step S2, the concentration gradient components of the target sample in various directions in different contents are calculated based on the drug concentrations in the several regions, including: determining the initial region of diffusion of the target sample based on the drug concentrations in the several regions at the initial detection time point, and determining the concentration gradient components according to the drug concentrations in the initial region and the drug concentrations in the remaining regions.

9. The method for detecting sildenafil citrate tablets according to claim 1, characterized in that: In step S4, determining the diffusion difference of the target sample includes: Step S41, determining the degree of difference in diffusion concentration based on the diffusion concentration gradients detected in the different contents; Step S42, determining the residual difference based on the residual degree of the target sample detected in the different contents; Step S43: determining the diffusion difference of the target sample based on the diffusion concentration difference and the residue difference.

10. The method for detecting sildenafil citrate tablets according to claim 9, characterized in that: The diffusion difference is compared with a preset diffusion difference, and a time difference corresponding to the diffusion difference is predicted according to the comparison result.

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