A method for detecting sildenafil citrate tablets

Abstract

The present application relates to the technical field of drug detection, and particularly relates to a detection method of sildenafil citrate tablets, which comprises the following steps: S1, placing contents with different water contents and a target sample in a plurality of containers; S2, determining the drug concentration of each region based on the image color development characteristics of the target sample after diffusion in the containers at different detection time points, and calculating the concentration gradient component and the diffusion concentration gradient of the target sample; S3, measuring the total amount or concentration of the remaining drug in the target sample in the containers within 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 present application not only improves the accuracy and depth of the research on the diffusion performance of drugs, but also further provides reliable data support for the research and quality control of drugs, and helps to improve the quality of drugs.

Description

Technical Field

[0001] This invention relates to the field of drug detection technology, and in particular to a method for detecting sildenafil citrate tablets. Background Technology

[0002] Sildenafil citrate tablets, also known as Viagra or Sildenafil, are a commonly used medication for treating erectile dysfunction (ED) in men. By inhibiting phosphodiesterase type 5 (PDE5), an enzyme that breaks down cyclic guanosine monophosphate (cGMP) within the corpora cavernosa, it enhances the action of nitric oxide (NO), thereby relaxing the smooth muscle of the corpora cavernosa, allowing arterial blood to flow in, and causing penile engorgement. Sildenafil citrate tablets can effectively improve erectile dysfunction in men.

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

[0004] Therefore, there is an urgent need for a method to detect the diffusion properties of sildenafil citrate tablets that can improve the accuracy of drug diffusion performance studies and provide reliable data support for drug development and quality control. Summary of the Invention

[0005] Therefore, the present invention provides a detection method for sildenafil citrate tablets to overcome the problem of low accuracy in the prior art for studying the diffusion performance of sildenafil citrate tablets.

[0006] To achieve the above objectives, in one aspect, the present invention provides a method for detecting sildenafil citrate tablets, comprising:

[0007] Step S1: Place a preset first amount of contents and a preset second amount of target sample into several containers respectively, wherein the water content of the contents in each container is different;

[0008] Step S2: Based on the color development characteristics of images of several regions after diffusion of the target sample in each container taken at several detection time points, determine the drug concentration in each region; calculate the concentration gradient components of the target sample in each direction in different contents based on the drug concentration in each region; and calculate 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, measure the total amount or concentration of the remaining drug in several containers of the target sample, and determine the residual level of the target sample in different containers based on the total amount or concentration of the remaining drug.

[0010] Step S4: Determine the diffusion difference of the target sample based on the diffusion concentration gradient and residual degree detected in different containers, and determine the diffusion adaptability of the target sample based on the diffusion difference.

[0011] The contents consist of PDE5 enzyme, chromogenic substance, and matrix. The matrix does not react with the PDE5 enzyme and is a different color from the chromogenic 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] Further, in step S2, determining the drug concentration in each region based on the image colorimetric features includes:

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

[0015] Step S22: Divide the image into several regions based on a preset segmentation method;

[0016] Step S23: Determine the drug concentration in the several regions based on the color intensity and concentration model of the several regions;

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

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

[0019] The first segmentation method divides the image into several rings with the center of the target sample as the origin and according to a preset dynamic radius;

[0020] The second segmentation method divides the image into several rectangles according to a preset length and a preset width.

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

[0022] Step S231: Prepare several sets of standard samples of the target sample at different concentrations, and take diffusion images of the target sample in the standard samples;

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

[0024] Step S233: Establish a concentration model between the color intensity and the concentration of the standard sample.

[0025] The analysis range of the target sample diffusion image is a preset target region, which is a specific region of the target sample diffusion image.

[0026] Further, step S233 includes:

[0027] Step S2331: Divide the several color intensities and corresponding standard sample concentrations into a training set and a validation set;

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

[0029] Step S2333: Determine the accuracy of the concentration model based on the validation set, and adjust the concentration model based on the accuracy and the comparison result of the preset accuracy.

[0030] Further, step S2333 includes: if the difference between the accuracy and the preset accuracy is lower than a preset difference, then the preset target area is adjusted.

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

[0032] Further, in step S4, the diffusion difference of the target sample is determined, including:

[0033] Step S41: Determine the diffusion concentration difference based on the diffusion concentration gradients detected in the different contents;

[0034] Step S42: Determine the degree of residue difference based on the degree of residue detected in the target samples in the different contents;

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

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

[0037] Compared with existing technologies, the beneficial effects of this invention are that it systematically evaluates the diffusion concentration gradient and residual degree of a target sample (sildenafil citrate tablets) in contents with different water contents. This includes: capturing and analyzing the colorimetric features of images of several regions after diffusion of the target sample at several detection time points to determine the concentration gradient components of the target sample in various directions, further visually displaying the diffusion concentration gradient of the target sample in contents with different water contents. Furthermore, it quantifies the residual degree of the target sample after diffusion within a preset time period by measuring the total remaining drug amount or the remaining drug concentration. Finally, by combining the diffusion concentration gradient and residual degree, the diffusion difference of the target sample is determined, and the diffusion adaptability of the target sample is evaluated. This not only improves the accuracy and depth of drug diffusion performance research but also provides reliable data support for drug development and quality control, contributing to improved drug quality.

[0038] Furthermore, this invention introduces fluorescent dyes to visualize the diffusion process of the target sample, facilitating the recording of the diffusion concentration. On the other hand, using a colorless gel matrix as the experimental environment avoids direct reaction between the matrix and the PDE5 enzyme, providing a stable and homogeneous diffusion medium for the target sample. This further enhances the accuracy and depth of drug diffusion performance studies, while also providing reliable data support for drug development and quality control, thus contributing to improved drug quality.

[0039] Furthermore, by combining image capture, region segmentation, and color intensity analysis, this invention achieves precise determination of drug concentration in each region after diffusion of the target sample. This not only enhances the intuitiveness and quantitative accuracy of drug diffusion performance research, but also provides reliable data support for drug formulation development and optimization, which helps to accelerate the new drug development process and improve drug quality.

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

[0041] Furthermore, this invention determines the initial region of target sample diffusion based on the spatial distribution of drug concentration at the initial detection time point, and derives the concentration gradient component by comparing the drug concentration difference between the initial region (the region with the highest drug concentration) and the remaining regions (other regions besides the initial region). This further enhances the intuitiveness and quantitative accuracy of drug diffusion performance research, and provides reliable data support for drug formulation development and optimization, which helps to accelerate the new drug development process and improve drug quality.

[0042] Furthermore, this invention comprehensively considers the differences in diffusion concentration gradients and residual levels of the target sample in different contents, fully evaluates the diffusion performance of the target sample, and determines the diffusion difference degree accordingly. It quantifies the impact of this diffusion difference degree on the diffusion distribution and residual status of the target sample, thereby further improving the intuitiveness and quantitative accuracy of drug diffusion performance research. At the same time, it provides reliable data support for drug formulation research and optimization, which helps to accelerate the new drug development process and improve drug quality. Attached Figure Description

[0043] Figure 1 This is a flowchart of the detection method for sildenafil citrate tablets of the present invention;

[0044] Figure 2 This is a flowchart illustrating how the drug concentration in each region is determined in this invention.

[0045] Figure 3 A flowchart for establishing the concentration model for this invention;

[0046] Figure 4 This is a flowchart for determining the diffusion difference of the target sample in this invention. Detailed Implementation

[0047] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0048] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0049] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0050] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0051] Please see Figure 1 The diagram shows a flowchart of the detection method for sildenafil citrate tablets according to the present invention. Specifically, the present invention provides a detection method for sildenafil citrate tablets, comprising:

[0052] Step S1: Place a preset first amount of contents and a preset second amount of target sample into several containers respectively, wherein the water content of the contents in each container is different;

[0053] Step S2: Based on the color development characteristics of images of several regions after diffusion of the target sample in each container taken at several detection time points, determine the drug concentration in each region; calculate the concentration gradient components of the target sample in each direction in different contents based on the drug concentration in each region; and calculate 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, measure the total amount or concentration of the remaining drug in several containers of the target sample, and determine the residual level of the target sample in different containers based on the total amount or concentration of the remaining drug.

[0055] Step S4: Determine the diffusion difference of the target sample based on the diffusion concentration gradient and residual degree detected in different containers, and determine the diffusion adaptability of the target sample based on the diffusion difference.

[0056] The contents consist of PDE5 enzyme, chromogenic substance, and matrix. The matrix does not react with the PDE5 enzyme and is a different color from the chromogenic substance. The target sample is sildenafil citrate tablets.

[0057] It is understood that this invention sets up a specific experimental environment in several containers, each containing a target sample (sildenafil citrate tablets) with different preset water contents (preset first dosage) and (preset second dosage). After stirring the contents and the target sample, the diffusion degree of the target sample is analyzed. The specific analysis process is as follows:

[0058] By using a chromogenic substance, the diffusion process of the target sample after being digested by PDE5 enzyme can be clearly observed. Images of the target sample after diffusion in several containers are captured at several detection time points. By analyzing the colorimetric characteristics of several regions, the drug concentration in each region is determined, thereby obtaining the concentration gradient components of the target sample in different contents in various directions. Based on the concentration gradient components, the diffusion concentration gradient of the target sample in different contents can be obtained.

[0059] Furthermore, within a preset time period, the target sample in the containers diffuses to a certain extent. At this point, the total amount or concentration of remaining drug in each container after the preset time period is measured. The residual level of the target sample in different containers is assessed based on the total amount or concentration of remaining drug, considering both the diffusion concentration gradient and the residual level of the target sample in different contents. Finally, by combining the diffusion concentration gradient and the residual level, the diffusion difference of the target sample in different contents is calculated, and this is used as a basis to evaluate the diffusion adaptability of the target sample.

[0060] In implementation, the preset first dosage is 5g to 10g, preferably 8g; the preset second dosage is one target sample (sildenafil citrate tablet) of standard specification, which is 50mg; the water content ranges from 70% to 98%, and the water content difference between each container ranges from 3% to 6%, preferably 80% to 95%, and the water content difference between each container ranges from 5%; the range of several detection time points is every 3min to 6min from the time the target sample is placed into the contents, preferably every 5min from the time the target sample is placed into the contents; the range of the preset time period is 25min to 35min, preferably 30min; the preset first dosage, the preset second dosage, the water content, the several detection time points, the preset time period range, and the preferred values ​​can be adjusted according to the actual situation, and will not be elaborated here.

[0061] This invention systematically evaluates the diffusion concentration gradient and residual level of a target sample (sildenafil citrate tablets) in contents with different water contents. This includes: capturing and analyzing the colorimetric features of images of several regions after diffusion of the target sample at several detection time points to determine the concentration gradient components of the target sample in various directions, further visually displaying the diffusion concentration gradient of the target sample in contents with different water contents. Furthermore, the residual level of the target sample after diffusion within a preset time period is quantified by measuring the total remaining drug amount or concentration. Finally, the diffusion difference of the target sample is determined by combining the diffusion concentration gradient and residual level, evaluating the diffusion adaptability of the target sample. This not only improves the accuracy and depth of drug diffusion performance research but also provides reliable data support for drug development and quality control, contributing to improved drug quality.

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

[0063] It is understandable that fluorescent dyes allow for direct observation of the diffusion of the target sample under different experimental conditions, and the gel matrix provides a stable and suitable experimental environment for this process.

[0064] In practice, preferably, the chromogenic substance is a fluorescent dye and the matrix is ​​a colorless gel matrix. The chromogenic substance and the matrix are not limited here, as long as they can represent the diffusion process of the target sample through the matrix and the matrix does not react with the PDE5 enzyme, and the matrix and the chromogenic substance are different colors in each state. Further details are not provided here.

[0065] This invention introduces fluorescent dyes to visualize the diffusion process of the target sample, facilitating the recording of the diffusion concentration. Furthermore, 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 homogeneous diffusion medium for the target sample. This further enhances the accuracy and depth of drug diffusion performance studies, while also providing reliable data support for drug development and quality control, ultimately contributing to improved drug quality.

[0066] Please see Figure 2 This is a flowchart illustrating the process of determining drug concentration in each region according to the present invention. Specifically, in step S2, determining the drug concentration in each region based on image color characteristics includes:

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

[0068] Step S22: Divide the image into several regions based on a preset segmentation method;

[0069] Step S23: Determine the drug concentration in the several regions based on the color intensity and concentration model of the several regions;

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

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

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

[0073] The first segmentation method divides the image into several rings with the center of the target sample as the origin and according to a preset dynamic radius;

[0074] The second segmentation method divides the image into several rectangles according to a preset length and a preset width.

[0075] Understandably, the preset dynamic radius is the difference between the radii of every two adjacent rings. The first segmentation method uses the center of the target sample as the origin and draws rings with the preset dynamic radius, dividing the image into multiple concentric ring regions. This helps to observe the diffusion process of the target sample from the center outwards. Furthermore, the first segmentation method has a certain degree of flexibility; different values ​​of the preset dynamic radius result in different numbers of regions. The several regions divided by the preset dynamic radius can adapt to different diffusion analysis accuracy requirements. Alternatively, the preset dynamic radius can be adjusted according to different diffusion stages of the target sample to determine the drug concentration of the target sample.

[0076] In implementation, the preset dynamic radius ranges from 0.3cm to 0.6cm, and preferably, it is 0.5cm. The range and preferred value of the preset dynamic radius can be determined based on actual conditions, and will not be elaborated here.

[0077] Understandably, the second segmentation method divides the image into several rectangular regions, allowing for detailed analysis of the drug concentration after diffusion in each region. Furthermore, when the diffusion of the target sample is not completely uniform, the rectangular grid allows for more precise capture of subtle changes in diffusion. The second segmentation method offers flexibility; by adjusting the preset length and width of the rectangular regions, different numbers of regions can be obtained to adapt to different analytical needs. Alternatively, different preset lengths and widths of grids can be used to observe the drug concentration of the target sample at different stages of diffusion.

[0078] In implementation, the preset length ranges from 0.5cm to 1cm, preferably 0.8cm, and the preset width ranges from 0.3cm to 0.5cm, preferably 0.4cm. The preset length and the preset width range and preferred values ​​can be determined according to the actual situation. The preset length and the preset width can be the same size, which will not be elaborated here.

[0079] In practice, preferably, the preset segmentation method is the second segmentation method. The preset segmentation method is not specifically limited, as long as it can achieve a reasonable division of the image after the target sample diffuses in each container, and the segmented areas can represent the drug diffusion characteristics. The preset segmentation method can be customized, and will not be elaborated here.

[0080] This invention, by combining image capture, region segmentation, and color intensity analysis, enables precise determination of drug concentration in each region after diffusion of a target sample. This not only enhances the intuitiveness and quantitative accuracy of drug diffusion performance research but also provides reliable data support for drug formulation development and optimization, helping to accelerate the new drug development process and improve drug quality.

[0081] Please see Figure 3 As shown, this is a flowchart of the concentration model establishment process of the present invention. Specifically, in step S23, the concentration model establishment step includes:

[0082] Step S231: Prepare several sets of standard samples of the target sample at different concentrations, and take diffusion images of the target sample in the standard samples;

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

[0084] Step S233: Establish a concentration model between the color intensity and the concentration of the standard sample.

[0085] It is understood that the standard sample also contains the contents and the target sample, the difference being that the concentration of the target sample is known and it is uniformly diffused in the standard sample.

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

[0087] The preset target region is a specific region of the diffusion image of the target sample.

[0088] Understandably, during the model building process, a specific region can be defined as the analysis range of the diffusion image of the target sample. Then, based on the specific color (fluorescent color produced by fluorescent dye) generated by the standard sample in the specific region, 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, or other color spaces for analysis). The color intensity corresponding to the standard sample at different concentrations can be determined within the specific region.

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

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

[0091] C=γ1+γ2×I+α (1)

[0092] Where C is the standard sample concentration, I is the color intensity, γ2 is the slope, representing the average change in color intensity for every unit change in concentration, γ1 is the intercept, and α is the error term.

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

[0094] Specifically, step S233 includes:

[0095] Step S2331: Divide the several color intensities and corresponding standard sample concentrations into a training set and a validation set;

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

[0097] Step S2333: Determine the accuracy of the concentration model based on the validation set, and adjust the concentration model based on the accuracy and the comparison result of the preset accuracy.

[0098] In practice, the preset accuracy ranges from 93% to 98%, and preferably, the preset accuracy is 96%. The range and preferred value of the preset accuracy can be adjusted according to the actual situation, which will not be elaborated here.

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

[0100] Adjusting the preset target area includes redefining the specific area.

[0101] In practice, after dividing the diffusion image of the target sample into several regions according to the second segmentation method, the color intensity corresponding to the several regions is determined, and the average or median value of the several color intensities is calculated. The average / median value of the several color intensities is then mapped to different concentrations of the standard sample.

[0102] In practice, the preset difference value ranges from 1% to 3%, and preferably, the preset difference value is 1.5%. The range and preferred value of the preset difference value can be adjusted according to the actual situation, which will not be elaborated here.

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

[0104] It is understood that the initial detection time point is the moment when the target sample is just placed into the containers, the initial region is the region with the highest drug concentration, and the remaining regions are the regions other than the initial region that are divided based on a preset segmentation method.

[0105] In implementation, the p-th container is divided into N regions, p = 1, 2, ..., M, and the initial drug concentration of each 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 denoted as . The formula for calculating the concentration gradient components between region m and region i is as follows:

[0106]

[0107] in, Let be the distance between region m and region i in the p-th container.

[0108] In practice, the distance between region m and region i can be represented 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 components between the two regions can be calculated from the distance. This will not be elaborated further here.

[0109] Understandably, when calculating the overall diffusion concentration gradient, it is necessary to consider the concentration gradient components in all directions. The formula for calculating the diffusion concentration gradient G is as follows:

[0110]

[0111] Where N is the total number of regions in the p-th container. Let N be the concentration gradient component between region m and region i in the p-th container. There are a total of N-1 concentration gradient components.

[0112] It is understandable that the above formula for calculating the diffusion concentration gradient ignores the direction information of the gradient and approximates the overall diffusion concentration gradient by calculating a diffusion concentration gradient.

[0113] This invention determines the initial region of target sample diffusion based on the spatial distribution of drug concentration at the initial detection time point, and derives the concentration gradient component by comparing the drug concentration difference between the initial region (the region with the highest drug concentration) and the remaining regions (other regions besides the initial region). This further improves the intuitiveness and quantitative accuracy of drug diffusion performance research, and provides reliable data support for drug formulation development and optimization, which helps to accelerate the new drug development process and improve drug quality.

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

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

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

[0117]

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

[0119] In another embodiment, the formula for calculating the residual level β of the target sample in the j-th container is as follows:

[0120]

[0121] Among them, C j C0 represents the remaining drug concentration of the target sample in the j-th container after a preset time period, where C0 is the drug concentration of the target sample after complete diffusion, and j = 1, 2, ..., M.

[0122] Please see Figure 4 The diagram shows a flowchart illustrating the process of determining the diffusion difference of a target sample according to the present invention. Specifically, in step S4, determining the diffusion difference of the target sample includes:

[0123] Step S41: Determine the diffusion concentration difference based on the diffusion concentration gradients detected in the different contents;

[0124] Step S42: Determine the degree of residue difference based on the degree of residue detected in the target samples in the different contents;

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

[0126] The formula for calculating the diffusion concentration difference σ1 is as follows:

[0127]

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

[0129] The formula for calculating the residual difference σ2 is as follows:

[0130]

[0131] Where, β c β represents the average residual level across all containers. u Let u represent the residual level.

[0132] The diffusion difference is calculated as follows:

[0133] τ=μ1×σ1+μ2×σ2 (8)

[0134] Where μ1 is the diffusion concentration difference weight and μ2 is the residual difference weight.

[0135] In practice, μ1 is preferably 0.7 and μ2 is preferably 0.3. The diffusion concentration difference weight μ1 and the residual difference weight μ2 can be adjusted according to the actual situation, which will not be elaborated here.

[0136] This invention comprehensively considers the differences in diffusion concentration gradients and residual levels of the target sample in different contents, fully evaluates the diffusion performance of the target sample, and determines the diffusion difference degree accordingly. It quantifies the impact of this diffusion difference degree on the diffusion distribution and residual status of the target sample, thereby further improving the intuitiveness and quantitative accuracy of drug diffusion performance research. At the same time, it provides reliable data support for drug formulation research and optimization, which helps to accelerate the new drug development process 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 based on the comparison result.

[0138] Understandably, predicting the time difference corresponding to the diffusion differences between different batches of target samples can provide a better understanding of the diffusion performance and trends of the target samples, helping to ensure the consistency and stability of the drug. For example, if the prediction results show that the time difference corresponding to the diffusion differences of several batches of the drug is large, it indicates that the drug diffusion rate is slow, and it may be necessary to adjust the production process or raw materials 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 drug diffusion concentration). The diffusion difference can be used as a constraint condition for the drug diffusion concentration and time model.

[0140]

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

[0142] The formula for calculating the time difference is as follows:

[0143] Δt=|t0-t| (10)

[0144] In practice, the preset diffusion difference value ranges from 0.1 to 0.3. Preferably, the preset diffusion difference value is 0.15. The range and preferred value of the preset diffusion difference value can be adjusted according to the actual situation, which will not be elaborated here.

[0145] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles 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 all fall within the scope of protection of the present invention.

Claims

1. A method for detecting sildenafil citrate tablets, characterized in that, include: Step S1: Place a preset first amount of contents and a preset second amount of target sample into several containers respectively, wherein the water content of the contents in each container is different; Step S2: Based on the color development characteristics of images of several regions after diffusion of the target sample in each container taken at several detection time points, determine the drug concentration in each region; calculate the concentration gradient components of the target sample in each direction in different contents based on the drug concentration in each region; and calculate 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, measure the total amount or concentration of the remaining drug in several containers of the target sample, and determine the residual level of the target sample in each container based on the total amount or concentration of the remaining drug. Step S4: Determine the diffusion difference of the target sample based on the diffusion concentration gradient and residual degree detected in each container, and determine the diffusion adaptability of the target sample based on the diffusion difference. The contents consist of PDE5 enzyme, chromogenic substance, and matrix. The matrix does not react with the PDE5 enzyme and is different in color from the chromogenic substance. The target sample is sildenafil citrate tablets.

2. The detection method for 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 detection method for sildenafil citrate tablets according to claim 1, characterized in that, In step S2, determining the drug concentration in each region based on the image colorimetric features includes: Step S21: At each detection time point, take images of the target sample after diffusion in each container; Step S22: Divide the image into several regions based on a preset segmentation method; Step S23: Determine the drug concentration in the several regions based on the color intensity and concentration model of the several regions; The color intensity is positively correlated with the drug concentration.

4. The detection method for sildenafil citrate tablets according to claim 3, characterized in that, In step S22, the preset segmentation method includes: The first segmentation method divides the image into several rings with the center of the target sample as the origin and according to a preset dynamic radius; The second segmentation method divides the image into several 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: Prepare several sets of standard samples of the target sample at different concentrations, and take diffusion images of the target sample in the standard samples; Step S232: Determine the color intensity of the standard sample based on the diffusion image of the target sample; Step S233: Establish a concentration model between the color intensity and the concentration of the standard sample; The analysis range of the target sample diffusion image is a preset target region, which is a specific region of the target sample diffusion image.

6. The method for detecting sildenafil citrate tablets according to claim 5, characterized in that, Step S233 includes: Step S2331: Divide the several color intensities and corresponding standard sample concentrations into a training set and a validation set; Step S2332: Construct a concentration model between the color intensity and the concentration of the standard sample based on the training set; Step S2333: Determine the accuracy of the concentration model based on the validation set, and adjust the concentration model based on the accuracy and the comparison result of the preset accuracy.

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

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 each direction in different contents are calculated based on the drug concentration in the several regions, including: determining the initial region of diffusion of the target sample based on the drug concentration in the several regions at the initial detection time point, and determining the concentration gradient components according to the drug concentration in the initial region and the drug concentration in the remaining region.

9. The method for detecting sildenafil citrate tablets according to claim 1, characterized in that, In step S4, the diffusion difference of the target sample is determined, including: Step S41: Determine the diffusion concentration difference based on the diffusion concentration gradients detected in the different contents; Step S42: Determine the degree of residue difference based on the degree of residue detected in the target samples in the different contents; Step S43: Determine the diffusion difference of the target sample based on the diffusion concentration difference and the residual 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 the time difference corresponding to the diffusion difference is predicted based on the comparison result.

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