Device and method for rapidly detecting quality attribute of transdermal patch intermediate glue solution

Abstract

The invention provides a device and method for rapidly detecting the quality attribute of a transdermal patch intermediate glue solution. The device comprises a sample cup and a reflecting plate, and the reflecting plate is placed in the sample cup and pressed to the bottom of the sample cup; the sample cup is made of a material containing few hydrogen groups, and the reflecting plate is made of a material with low absorption and high reflectivity in a near-infrared spectrum band; an intermediate glue solution of a transdermal patch is contained in the sample cup, and an intermediate glue solution sample with fixed capacity is contained between the cup bottom of the sample cup and the bottom surface of the reflecting plate. The near infrared spectrum of the transdermal patch intermediate glue solution is rapidly obtained through the specially-made sample cup and the reflecting plate, it is guaranteed that each sample amount is fixed, interference of factors such as bubbles is eliminated, the sample treatment time is shortened, and the scanning process is greatly shortened; the component content and the solid content of the intermediate glue solution are rapidly detected through a Fourier transform near infrared spectrum instrument, a high-precision and high-stability quantitative detection model is established through spectrum pretreatment and characteristic wavelength screening, and the detection efficiency and accuracy are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of continuous drug manufacturing, in particular, to the technical field of near-infrared rapid detection of a transdermal patch intermediate adhesive solution; specifically, to a device and a detection method for rapidly detecting the quality attributes of a transdermal patch intermediate adhesive solution. Background Art

[0002] In the continuous manufacturing of transdermal patches, monitoring the quality attributes of the intermediate gel is an important step to ensure the stable quality of transdermal patch products.

[0003] Conventional offline detection technology for the properties of intermediate glue solutions is cumbersome and time-consuming. Content determination involves complex chromatographic sample processing and injection procedures. Determination of the solid content of the glue solution requires the solvent to be completely evaporated. All these factors result in the inability to provide timely and effective feedback on the properties of the intermediate glue solution for continuous production.

[0004] Near-infrared spectroscopy is time-efficient and highly efficient, allowing for rapid characterization of the properties of intermediate adhesives. However, there are also challenges associated with near-infrared spectroscopy of transdermal patch intermediate adhesives, primarily due to the following factors: On the one hand, the intermediate glue is a mixture system formed by multiple polymers dissolved in an organic solvent. It has a complex chemical composition, which poses a great challenge to the use of near-infrared spectroscopy to detect the properties of its components.

[0005] On the other hand, the physical properties of the intermediate glue make obtaining its near-infrared spectrum much more difficult than with other liquid samples. Due to its high viscosity, bubbles are present within the glue after sampling, significantly impacting the accuracy of the near-infrared spectrum. If the sample is allowed to stand for the bubbles to disappear before scanning the near-infrared spectrum, the rapid detection advantage is lost.

[0006] At the same time, in order to prevent spectral detection from being interfered with by other factors, the sample amount scanned each time must be basically the same. However, the viscosity of the intermediate colloid makes it very troublesome and difficult to transfer between containers. In addition, the organic solvent in the intermediate colloid also limits the choice of container, and certain pipetting tools cannot be used.

[0007] In addition, since the organic solvent used in the intermediate glue solution is volatile, the component content and solid content of the glue solution will change rapidly after sampling, and the temperature of the near-infrared spectrum scanning window is high, which will accelerate the volatilization of the organic solvent and affect the accuracy of the property detection of the intermediate glue solution. Summary of the Invention

[0008] In view of this, the purpose of the present invention is to design a device and detection method for rapidly detecting the quality attributes of a transdermal patch intermediate glue solution by near-infrared spectroscopy scanning based on the characteristics of the intermediate glue solution. Fourier transform near-infrared spectroscopy is used to perform offline or near-line rapid detection of the component content and solid content of the transdermal patch intermediate glue solution. The near-infrared spectrum of the intermediate glue solution is quickly obtained using a special sample cup and reflective plate, ensuring that the sample amount of the intermediate glue solution scanned for each spectral sample is fixed, and the near-infrared spectrum of the intermediate glue solution can be rapidly obtained while eliminating interference from factors such as bubbles. After spectral preprocessing and characteristic wavelength screening, a high-precision and highly stable quantitative detection model is established to improve the detection efficiency and accuracy of the quality attributes of the transdermal patch intermediate glue solution.

[0009] The present invention provides a device for rapidly detecting the quality attributes of an intermediate adhesive solution of a transdermal patch, comprising: a sample cup and a reflective plate, wherein the reflective plate is placed in the sample cup and pressed to the bottom of the cup; the sample cup is made of a material with few hydrogen groups, and the reflective plate is made of a material with low absorption and high reflectivity in the near-infrared spectrum; the sample cup contains the intermediate adhesive solution of the transdermal patch, and a fixed volume of the intermediate adhesive solution sample is accommodated between the bottom of the sample cup and the bottom surface of the reflective plate.

[0010] Because the near-infrared spectrum mainly includes the absorption spectrum information of hydrogen-containing groups, the sample cup is made of a material with fewer hydrogen groups (preferably low-hydroxyl quartz material), which can greatly reduce the impact of the sample cup on the near-infrared spectrum.

[0011] Preferably, the surface of the sample cup is as smooth as possible, and the bottom of the sample cup is slightly larger than the scanning window of the near-infrared spectrometer, so that the scanning window can be completely covered by the intermediate glue sample in the sample cup.

[0012] The reflector is made of a material with high reflectivity and low absorption in the near-infrared spectrum.

[0013] Preferably, the reflective plate is made of frosted stainless steel, which has high reflectivity, stable chemical properties, high mechanical strength, heat resistance, and is easy to clean.

[0014] Preferably, the bottom surface of the reflective plate is slightly smaller than the interior of the sample cup, so that the reflective plate can slide smoothly into the bottom of the sample cup.

[0015] The present invention utilizes a sample cup and reflector for detecting a transdermal patch intermediate adhesive solution. When scanning a near-infrared spectrum, a predetermined amount of the intermediate adhesive solution is sampled and poured into the sample cup. The reflector is then placed into the sample cup and pressed to the bottom of the cup to begin scanning. The volume of the intermediate adhesive solution sample between the bottom of the sample cup and the bottom surface of the reflector remains constant, ensuring a constant sample volume for each scan and improving detection accuracy.

[0016] Furthermore, a circle of protrusions is provided on the periphery of the bottom surface of the reflector, and the circle of protrusions and the bottom surface of the reflector together form a groove with a depth of several millimeters. A notch is provided on the circle of protrusions, and the notch provides overflow for the intermediate glue in the groove.

[0017] Quantification is performed through the grooves on the reflective plate, eliminating the sample amount differences between each sample caused by manual sampling, while also simplifying the scanning steps and shortening the process time.

[0018] Because the reflector is slightly smaller than the sample cup and has a notch in the bottom groove, the intermediate glue overflows from the bottom of the reflector as it is pressed down, leaving only a layer of intermediate glue in the groove. The depth of the reflector groove is fixed, so the amount of sample scanned remains constant. The groove is extremely shallow, and after the reflector is pressed to the bottom of the sample cup, most of the bubbles in the groove are discharged through the notch on the side of the groove. This ensures that the amount of intermediate glue sample scanned is fixed, and there is no time-consuming bubble removal, making the entire process from sampling to scanning convenient and fast.

[0019] In the bubble removal process of one embodiment of the present invention, the traditional static bubble elimination time that requires tens of minutes can be shortened to about ten seconds. Through the sample cup and reflective plate designed by the present invention, the intermediate glue sample can be quickly transferred and the bubbles can be quickly removed, which simplifies the scanning steps and reduces the impact of solvent volatilization on the sample.

[0020] The present invention also provides a method for rapidly detecting the quality attributes of a transdermal patch intermediate adhesive solution, using the device for rapidly detecting the quality attributes of a transdermal patch intermediate adhesive solution as described above, comprising the following steps: S1. Scanning an intermediate glue sample of a transdermal patch using a Fourier transform near-infrared spectrometer to obtain a near-infrared spectrum and reference data of the intermediate glue sample; The invention uses the specially made sample cup and reflective plate of the invention and a Fourier transform near infrared spectrometer to collect the near infrared spectrum of the transdermal patch intermediate glue.

[0021] Preferably, the scanning range of the near infrared spectrum of the transdermal patch intermediate glue is 12000-4000 cm -1 , the acquisition parameters are resolution 16cm -1 , the number of scans is 20 times.

[0022] The entire near-infrared spectrum sampling and scanning process takes less than 2 minutes.

[0023] S2. Processing the near-infrared spectra using a variety of spectral preprocessing and characteristic wavelength screening methods, and establishing chemometric quantitative detection models for the quality attributes of the intermediate gel using an improved kernel partial least squares method; The method for establishing a quantitative detection model for the quality attributes of the intermediate colloid includes: correlating spectra with the quality attributes of the intermediate colloid of corresponding samples (such as rivastigmine content and solid content), establishing a database, eliminating abnormal samples through principal component analysis, and using a KS algorithm to divide the samples in the database into a calibration set and a test set for the quantitative detection model, selecting samples with large spectral differences into the calibration set to ensure the representativeness of the calibration samples; and after spectral preprocessing and characteristic wavelength screening, establishing a quantitative model correlating near-infrared spectra and quality attributes through an improved kernel partial least squares algorithm.

[0024] The KS algorithm is a uniform design method. The calculation steps of the KS algorithm are as follows: (1) Calculate the distance between two samples and select the two samples with the largest distance; (2) Calculate the distance between the remaining samples and the two selected samples respectively; (3) For each remaining sample, the shortest distance between it and the selected sample is selected, and then the sample corresponding to the longest distance among these shortest distances is selected as the third sample; repeat step (3) until the number of selected samples is equal to the predetermined number.

[0025] Specifically, the quality attributes (properties) of the intermediate adhesive solution of the transdermal patch include the following indicators: the content of the ingredients in the intermediate adhesive solution, and the solid content of the adhesive solution.

[0026] When modeling large matrices, the classic partial least squares method (PLS) suffers from long iteration times and high memory usage. The improved kernel partial least squares (IKPLS) method uses a recursive approach to avoid the computational complexity of the classic PLS method, offering significant advantages in computational efficiency.

[0027] S3. Compare the accuracies of the various quantitative detection models established in step S2, select the optimal quantitative detection model, and use the optimal quantitative detection model to quickly detect the quality attributes of the intermediate glue solution of the transdermal patch product to be tested, thereby realizing monitoring of the transdermal patch coating process.

[0028] By establishing a quantitative model, the key properties of the intermediate gel, including component content and solid content, can be quickly detected, which greatly saves the time of offline testing and helps ensure the stable and controllable quality of transdermal patch products.

[0029] In one embodiment of the present invention, the rivastigmine drug content and solid content of the transdermal patch intermediate glue containing rivastigmine are subjected to near-infrared rapid detection. Specifically, acrylic resin pressure-sensitive adhesives with different solid contents are used, and the amount of rivastigmine added is adjusted to prepare a solid content of 80%, 90%, 100%, 105%, and 110% of the standard solid content, and a modeling intermediate glue sample having a rivastigmine content of 80%, 90%, 100%, 110%, and 120% of the standard content.

[0030] Furthermore, the method of processing the near-infrared spectrum reference data using spectral preprocessing in step S2 includes: Preprocessing the spectra corresponding to the component content and solid content of the transdermal patch intermediate glue solution by using any one or more of the following methods: mean centering (MC), Z-score standardization (Z-score), vector normalization (VN), substract straightline (SSL), eliminate constant offset (ECO), smoothing and derivative, standard normal variate transformation (SNV), detrending algorithm (detrending), multivariate scatter correction (MSC), and max-min normalization; The vector normalization is to process each sample of the intermediate glue solution separately, and divide the original spectrum of each sample by the spectrum modulus to obtain the vector normalization. : (1) In formula (1), x is the original spectrum, is the spectral mode length; In spectral processing calculations, large values ​​can lead to numerical instability. After vector normalization preprocessing, all values ​​are within a similar range, helping to enhance the numerical stability of the spectral processing algorithm. Vector normalization can also eliminate the effects of different dimensions and value ranges between data features, thereby improving model accuracy.

[0031] The mean centering is to subtract the average spectrum of all samples from the original spectrum of each sample, reduce the spectral offset caused by the fixed deviation, associate the spectral changes with the material changes of each intermediate glue sample, increase the difference between the sample spectra, and make the characteristic of the changes clearer; mean centering The calculation expression is: (2) In formula (2), For the raw spectrum of each sample, is the average spectrum of all samples; After mean centering, the coefficient of variation of each indicator included in the quality attributes of the intermediate glue can be retained, and the coefficient of variation of each indicator remains unchanged. That is, the degree of variation of the data after averaging remains the same as before averaging.

[0032] Mean centering can transform dimensional expressions into dimensionless expressions, facilitating the comparison and weighting of indicators of different units or magnitudes. It simplifies calculations and enables different evaluation indicators to be compared and evaluated under the same standard.

[0033] By centering the data, the data can be made to follow a standard normal distribution, which helps to improve the accuracy and stability of the model.

[0034] The constant offset elimination is to subtract the minimum value of the absorbance of each sample from the original spectrum of each sample. The calculation expression is: (3) In formula (3), For the raw spectrum of each sample, is the minimum absorbance value for each sample.

[0035] Eliminating the constant offset can eliminate deviations caused by instrument drift, background noise or other factors, thereby improving data accuracy.

[0036] The above-mentioned spectral preprocessing methods can be used alone or in combination for spectral processing. Different spectral preprocessing methods can be used to process the spectrum for modeling, and the method with the best preprocessing effect can be screened out based on the modeling results.

[0037] Furthermore, the method of processing the reference data of the near-infrared spectrum using the characteristic wavelength screening method in step S2 includes: Preprocessing the spectra corresponding to the component content and solid content of the transdermal patch intermediate glue solution using any one of the methods selected from the group consisting of inverse interval partial least squares (biPLS), uninformation variable elimination (UVE), Monte Carlo uninformation variable elimination (MCUVE), and competitive adaptive reweighted sampling (CARS); The competitive adaptive reweighted sampling method is: Assume that the spectral matrix is ​​x (m × n), m is the number of samples, n is the number of variables, y (m × 1) represents the target property vector, e is the correction error, and the correction model expression is: y=xb+e(4) In formula (4), b is the regression coefficient vector under any number of latent variables, b=[b1,b2,…b n ], the absolute value of the i-th element in b | b i |(1≤i≤n) represents the contribution of the i-th wavelength point to y, |b i The larger the value of |bi|, the more important the variable corresponding to the value of |bi| is; to define the importance of each wavelength, define the weight : (5) The weights of variables removed by Competitive Adaptive Reweighted Sampling (CARS) All are set to 0.

[0038] The competitive adaptive reweighted sampling (CARS) imitates the principle of survival of the fittest in Darwin's theory of evolution. Each time, the reweighted sampling (ARS) technology is used to screen out wavelength points with large absolute values ​​of regression coefficients in the model, and wavelength points with small weights are removed. Cross-validation is used to select the subset with the lowest cross-validation mean square error value of the model, so as to effectively select the optimal wavelength combination related to the measured property.

[0039] Furthermore, the method of the reverse interval partial least squares method is: dividing the near-infrared spectrum into several equally spaced sub-regions, starting from the full spectrum region, removing one sub-region at a time, and finally finding the optimal combination until the RMSECV no longer decreases.

[0040] Furthermore, the method for comparing the accuracy of multiple different quantitative detection models established in step S2 in step S3 includes: using a k-fold cross-validation method to obtain the cross-validation correlation coefficient and cross-validation root mean square error of the quantitative detection model, using a test set for external verification to obtain a prediction correlation coefficient and a prediction root mean square error, and judging the accuracy of each of the established quantitative detection models by comparing the cross-validation correlation coefficient, prediction correlation coefficient, cross-validation root mean square error and prediction root mean square error of each different quantitative detection model.

[0041] Cross-validation is primarily used in modeling applications, such as PLS regression. K-fold cross-validation involves splitting the sample into k subsamples. A single subsample is retained as validation data, while the remaining k-1 samples are used for training. Cross-validation is repeated k times, with each subsample validated once. The k results are averaged to produce a single validation result. Finally, the actual prediction error of the final calibration model is evaluated using test data (which remain exclusive of the modeling and internal cross-validation processes) to determine its generalization ability.

[0042] The present invention uses a cross-validation method to test the accuracy of the model, and can obtain a reliable and stable quantitative detection model.

[0043] After cross-validation and comparison of model parameters, one embodiment of the present invention uses vector normalization + mean centering to preprocess the spectrum when modeling the rivastigmine content, and uses competitive adaptive reweighted sampling to screen the characteristic wavelengths of the spectrum; when modeling the solid content, the elimination of constant offset + mean centering is used to preprocess the spectrum, and the reverse interval partial least squares method is used to screen the characteristic wavelengths of the spectrum.

[0044] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for rapidly detecting the quality attributes of a transdermal patch intermediate glue solution as described above.

[0045] The present invention also provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the method for rapidly detecting the quality attributes of the transdermal patch intermediate glue solution as described above are implemented.

[0046] Compared with the prior art, the present invention has the following beneficial effects: The method for rapidly detecting the quality attributes of an intermediate glue solution of a transdermal patch provided by the present invention rapidly obtains the near-infrared spectrum of the intermediate glue solution through a specially made sample cup and a reflective plate, ensures that the sample amount of the intermediate glue solution scanned for each spectral sample is fixed, can rapidly obtain the near-infrared spectrum of the intermediate glue solution, and eliminates interference from factors such as bubbles, thereby solving the problems existing in the traditional near-infrared scanning of the intermediate glue solution of a transdermal patch, reducing the time for processing the intermediate glue solution sample, significantly shortening the process for scanning the intermediate glue solution, improving the convenience of near-infrared spectral scanning, and maximizing the advantages of rapid near-infrared spectral detection; The Leaf Transform Near Infrared Spectrometer performs offline or near-line rapid detection of the component content and solid content of the intermediate gel solution of the transdermal patch. After spectral preprocessing and characteristic wavelength screening, a calibration model for the component content and solid content of the intermediate gel solution is established. The model is verified using test set samples to obtain a high-precision and stable chemometric quantitative detection model. This model can quickly detect key properties of the intermediate gel solution, including component content and solid content, significantly saving the time for offline or near-line detection, which is conducive to ensuring the stable and controllable quality of transdermal patch products and improving the detection efficiency and accuracy of the quality attributes of the intermediate gel solution of the transdermal patch. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Various other advantages and benefits will become apparent to those skilled in the art by reading the following detailed description of the preferred embodiment.The accompanying drawings are only for the purpose of illustrating the preferred embodiment and are not to be considered as limiting the present invention.

[0048] In the attached figure: Figure 1 This is a flow chart of the method for rapidly detecting the quality attributes of the transdermal patch intermediate glue of the present invention; Figure 2 This is a flow chart of a method for rapidly detecting the quality attributes of a transdermal patch intermediate glue solution according to an embodiment of the present invention; Figure 3 A schematic diagram of the structure and shape of a sample cup and a reflector specially made for an embodiment of the present invention; Figure 4 This is a basic operation flow chart of near-infrared spectrum sampling and scanning according to an embodiment of the present invention; Figure 5 This is a graph showing the results of screening the wavelength variables of the spectrum for the drug (rivastigmine) content of the intermediate gel solution according to an embodiment of the present invention; Figure 6 This is a graph showing the screening results of the solid content spectrum wavelength variables of the intermediate glue solution according to an embodiment of the present invention; Figure 7 This is a model diagram for quantitative detection of the drug (rivastigmine) content in the intermediate gel solution of an embodiment of the present invention; Figure 8This is a diagram of a quantitative detection model for the solid content of the intermediate glue solution according to an embodiment of the present invention; Figure 9 This is a graph showing the prediction results of the test set using the quantitative detection model for the drug (rivastigmine) content in the intermediate gel solution according to an embodiment of the present invention; Figure 10 This is a graph showing prediction results of a test set using a quantitative detection model for the solid content of an intermediate glue solution according to an embodiment of the present invention; Figure 11 Schematic diagram of the structure of a computer device according to an embodiment of the present invention.

[0049] The symbols in the accompanying drawings are: 1. Sample cup, 2. Reflector. DETAILED DESCRIPTION

[0050] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, like numbers in different figures represent like or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present disclosure. Rather, they are merely examples of devices and products consistent with certain aspects of the present disclosure, as detailed in the appended claims.

[0051] The terms used in this disclosure are for the purpose of describing specific embodiments only and are not intended to limit the disclosure. As used in this disclosure and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0052] It should be understood that although the terms first, second, third, etc. may be used in this disclosure to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of this disclosure, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining."

[0053] The embodiments of the present invention are further described in detail below with reference to the accompanying drawings.

[0054] The embodiment of the present invention provides a device for rapidly detecting the quality attributes of the transdermal patch intermediate glue solution, see Figure 3As shown, the apparatus comprises a sample cup 1 and a reflector 2, which is placed into the sample cup 1 and pressed to the bottom. The sample cup 1 is made of a material with few hydrogen groups. Because the near-infrared spectrum primarily contains absorption spectral information from hydrogen groups, the sample cup 1 is constructed of low-hydroxyl quartz, a material with few hydrogen groups. This significantly minimizes the impact of the sample cup 1 on the near-infrared spectrum. The sample cup 1 has a smooth surface, and its bottom is slightly larger than the scanning window of the near-infrared spectrometer, allowing the scanning window to be completely covered by the intermediate glue sample in the sample cup 1. The reflector 2 is made of frosted stainless steel with low absorption and high reflectivity in the near-infrared spectrum. This material offers high reflectivity, chemical stability, high mechanical strength, heat resistance, and ease of cleaning. The bottom of the reflector 2 is slightly smaller than the interior of the sample cup 1, allowing the reflector 2 to slide smoothly into the bottom of the sample cup 1.

[0055] Sample cup 1 contains the intermediate adhesive solution for the transdermal patch. A fixed volume of intermediate adhesive solution sample can be accommodated between the bottom of sample cup 1 and the underside of reflector plate 2. During near-infrared spectral scanning, a predetermined amount of intermediate adhesive solution is sampled and poured into sample cup 1. Then, reflector plate 2 is placed into sample cup 1 and pressed to the bottom, allowing for scanning. The fixed volume of intermediate adhesive solution sample between the bottom of sample cup 1 and the underside of reflector plate 2 ensures a constant sample volume for each scan, improving detection accuracy.

[0056] The bottom surface of reflector 2 is surrounded by a ring of protrusions. Together, these protrusions and the bottom surface of reflector 2 form a groove several millimeters deep. A notch is provided in the ring of protrusions, allowing the intermediate glue solution in the groove to escape. Using the grooves in reflector 2 for quantification eliminates sample size variations between samples caused by manual sampling, simplifies the scanning process, and shortens the process time. Because reflector 2 is slightly smaller than sample cup 1 and the notch in the bottom groove of reflector 2 allows the intermediate glue solution to overflow from the bottom of reflector 2 as reflector 2 is pressed downward, until it reaches the bottom of sample cup 1, leaving only a thin layer of intermediate glue solution in the groove at the bottom of reflector 2. The depth of the groove in reflector 2 is fixed, so the sample volume scanned remains constant. Furthermore, the groove is extremely shallow. Once reflector 2 reaches the bottom of sample cup 1, most bubbles in the groove are expelled through the notch on the side of the groove. This ensures a consistent sample volume for each spectral sample scan and eliminates the need for time-consuming bubble removal, making the entire process from sampling to scanning quick and easy. During the bubble removal process of the embodiment of the present invention, the traditional standing still time for eliminating bubbles, which takes tens of minutes, is shortened to about ten seconds. Through the specially prepared sample cup and reflective plate of this embodiment, the intermediate glue sample can be quickly transferred and the bubbles can be quickly removed, which simplifies the scanning steps and reduces the impact of solvent volatilization on the sample.

[0057] The embodiment of the present invention also provides a method for rapidly detecting the quality attributes of the intermediate glue solution of the transdermal patch, using the device for rapidly detecting the quality attributes of the intermediate glue solution of the transdermal patch as described above, such as Figure 1 As shown, the following steps are included: S1. Scanning an intermediate glue sample of the transdermal patch using a Fourier transform near-infrared spectrometer to obtain reference data of a near-infrared spectrum of the intermediate glue sample; In an embodiment of the present invention, the rivastigmine drug content and solid content of the transdermal patch intermediate glue containing rivastigmine are subjected to near infrared rapid detection. Using acrylic resin pressure-sensitive adhesives with different solid contents, and adjusting the addition amount of rivastigmine, a solid content of 80%, 90%, 100%, 105%, 110% of a standard solid content and a modeling intermediate glue sample of 80%, 90%, 100%, 110% of a standard content of rivastigmine are prepared.

[0058] This embodiment uses a special sample cup and reflector such as Figure 3 As shown ( Figure 3 The bottom of the reflective plate in the sample cup is upward and has not yet been pressed into the sample cup). A Fourier transform near-infrared spectrometer is used to collect the near-infrared spectrum of the transdermal patch intermediate glue solution (such as Figure 2 As shown in the figure, the transmittance and reflectance spectra of the samples were collected using the process set by NirNetLocal, the software provided with the Fourier transform near-infrared spectrometer. The acquisition method is as follows: a certain amount of the intermediate glue sample for modeling is poured into a sample cup, the reflective plate is pressed to the bottom of the sample cup, and the sample cup is placed on the scanning window for scanning. While ensuring spectral quality, the spectral scanning time is minimized. The near-infrared spectrum scanning range is 12000-4000cm -1 , the acquisition parameters are resolution 16cm -1 The number of scans is 20. The basic operation process of the entire near-infrared spectrum sampling scan is as follows: Figure 4 As shown, the total time is less than 2 minutes.

[0059] S2. Processing the near-infrared spectra using a variety of spectral preprocessing and characteristic wavelength screening methods, and establishing chemometric quantitative detection models for the quality attributes of the intermediate gel using an improved kernel partial least squares method; The quality attributes of the intermediate gel solution of transdermal patches include the following indicators: the content of the ingredient (rivastigmine) in the intermediate gel solution and the solid content of the gel solution.

[0060] When establishing the model, SpecMC spectral analysis chemometrics software was used to preprocess the near-infrared spectrum by selecting appropriate spectral preprocessing and characteristic wavelength screening methods.

[0061] The method for establishing a quantitative detection model for the quality attributes of an intermediate colloid includes: correlating the spectrum with the quality attributes of the intermediate colloid of the corresponding sample (including the rivastigmine content and the solid content), establishing a database, eliminating abnormal samples through principal component analysis, and using the KS algorithm to divide the samples in the database into a calibration set and a test set of the quantitative detection model, and selecting samples with large spectral differences into the calibration set to ensure the representativeness of the calibration samples; after spectral preprocessing and characteristic wavelength screening, a quantitative model correlating near-infrared spectra and quality attributes is established through an improved kernel partial least squares algorithm.

[0062] The calculation steps of the KS algorithm are as follows: (1) Calculate the distance between two samples and select the two samples with the largest distance; (2) Calculate the distance between the remaining samples and the two selected samples respectively; (3) For each remaining sample, the shortest distance between it and the selected sample is selected, and then the sample corresponding to the longest distance among these shortest distances is selected as the third sample; repeat step (3) until the number of selected samples is equal to the predetermined number.

[0063] The method for processing the reference data of the near-infrared spectrum by using spectral preprocessing is as follows: Preprocessing the spectra corresponding to the component content and solid content of the transdermal patch intermediate glue solution by using any one or more methods selected from the group consisting of mean centering, standardization, vector normalization, subtracting a straight line, eliminating constant offset, smoothing and derivation, standard normal variable transformation, detrending algorithm, multivariate scattering correction, and maximum and minimum normalization; The vector normalization method is used to process each sample of the intermediate glue solution separately. The original spectrum of each sample is divided by the spectrum modulus to obtain the vector normalization. : (1) In formula (1), x is the original spectrum, is the spectral mode length; After vector normalization preprocessing, all values ​​are within a similar range, which helps enhance the numerical stability of the spectral processing algorithm. Vector normalization also eliminates the effects of different dimensions and value ranges between data features, improving model accuracy.

[0064] The mean centering method is used to subtract the average spectrum of all samples from the original spectrum of each sample, reducing the spectral shift caused by the fixed deviation, and correlating the spectral changes with the material changes of each intermediate gel sample, thereby increasing the differences between sample spectra and making the characteristics of the changes clearer. The calculation expression is: (2) In formula (2), For the raw spectrum of each sample, is the average spectrum of all samples; Mean centering preserves the coefficient of variation of each indicator in the intermediate glue's quality attributes, maintaining the same coefficient of variation. This means that the degree of variation in the data after averaging remains consistent with the pre-averaging data. Mean centering converts dimensional expressions into dimensionless ones, facilitating the comparison and weighting of indicators of different units or magnitudes. This simplifies calculations and allows different evaluation indicators to be compared and evaluated under the same standards. Mean centering can also conform data to a standard normal distribution, improving model accuracy and stability.

[0065] The constant offset is eliminated by subtracting the minimum absorbance of each sample from the original spectrum to eliminate the constant offset. The calculation expression is: (3) In formula (3), For the raw spectrum of each sample, is the minimum absorbance value for each sample.

[0066] Eliminating the constant offset can eliminate deviations caused by instrument drift, background noise or other factors, thereby improving data accuracy.

[0067] The spectra were processed using the above different spectral preprocessing methods for modeling, and the method with the best pretreatment effect was screened out based on the modeling results. The optimal spectral preprocessing method for the content of the component (rivastigmine) of the intermediate gel solution screened out in this example was vector normalization + mean centering, and the optimal spectral preprocessing method for solid content was elimination of constant offset + mean centering.

[0068] The method for processing the reference data of the near-infrared spectrum using the characteristic wavelength screening method includes: Preprocessing the spectra corresponding to the component content and solid content of the transdermal patch intermediate glue solution by using any one of the methods selected from reverse interval partial least squares, uninformative variable elimination, Monte Carlo-uninformative variable elimination, and competitive adaptive reweighted sampling; Among them, the method of competitive adaptive reweighted sampling is as follows: Assume that the spectral matrix is ​​x (m × n), m is the number of samples, n is the number of variables, y (m × 1) represents the target property vector, e is the correction error, and the correction model expression is: y=xb+e(4) In formula (4), b is the regression coefficient vector under any number of latent variables, b=[b1,b2,…b n ], the absolute value of the i-th element in b | b i |(1≤i≤n) represents the contribution of the i-th wavelength point to y, |b i The larger the value of |bi|, the more important the variable corresponding to the value of |bi| is; to define the importance of each wavelength, define the weight : (5) The weights of variables removed by Competitive Adaptive Reweighted Sampling (CARS) All are set to 0.

[0069] Among them, the method of reverse interval partial least squares method is as follows: The near-infrared spectrum is divided into several equally spaced sub-regions. Starting from the full spectrum, one sub-region is removed in turn, and the optimal combination is finally found until the RMSECV no longer decreases.

[0070] The spectrum was processed using the above-mentioned different characteristic wavelength screening methods for modeling, and the method with the best wavelength screening effect was screened out based on the modeling results. The optimal characteristic wavelength screening method for the content of the component (rivastigmine) of the intermediate glue screened out in this embodiment was competitive adaptive reweighted sampling, and the optimal characteristic wavelength screening method for solid content was reverse interval partial least squares method.

[0071] Improved kernel partial least squares (IKPLS) is used for modeling. IKPLS uses a recursive approach to avoid the computationally intensive nature of the classic partial least squares method, offering significant advantages in computational efficiency.

[0072] Figure 5 The results of rivastigmine content spectrum wavelength variable screening are shown. Figure 6 The solid content spectrum wavelength variable screening results are shown. Figure 5 、 Figure 6 The horizontal axis represents the wavelength range, the vertical axis represents the absorbance, and the vertical line represents the selected wavelength variable.

[0073] Figure 7 A quantitative detection model of rivastigmine content is shown. Figure 8 The quantitative detection model of solid content is shown. Figure 7 、 Figure 8The horizontal axis represents the measured concentration value, and the vertical axis represents the predicted value assigned to the sample by the model during internal cross-validation. Figure 7 、 Figure 8 The circles in the figure represent the samples involved in modeling. Figure 7 、 Figure 8 The oblique line in the figure represents the regression line. The darker line represents the actual regression line of the quantitative detection model, and the lighter line represents the ideal regression line of the model. The closer the sample is to the regression line, the better the quantitative detection model is.

[0074] S3. Compare the accuracies of the various quantitative detection models established in step S2, select the optimal quantitative detection model, and use the optimal quantitative detection model to quickly detect the quality attributes of the intermediate glue solution of the transdermal patch product to be tested, thereby realizing monitoring of the transdermal patch coating process.

[0075] A k-fold cross-validation method is used to obtain the cross-validation correlation coefficient and cross-validation root mean square error of the quantitative detection model. A test set is used for external verification to obtain the prediction correlation coefficient and prediction root mean square error. By comparing the cross-validation correlation coefficient, prediction correlation coefficient, cross-validation root mean square error, and prediction root mean square error of each different quantitative detection model, the accuracy of each established quantitative detection model is determined. The embodiment of the present invention uses a cross-validation method to test the accuracy of the model and obtain a reliable and stable quantitative detection model.

[0076] Cross-validation correlation coefficient R in the quantitative detection model of rivastigmine content 2 =0.98493, cross validation root mean square error RMSECV = 1.68926, cross validation correlation coefficient R in the quantitative detection model of solid content 2 =0.99663, cross validation root mean square error RMECV = 0.61705. Figure 9 The figure shows the result of the quantitative detection model prediction of the test set samples of rivastigmine content, Figure 10 The figure shows the results of the quantitative detection model predicting the solid content of the test set samples. Figure 9 、 Figure 10 The horizontal axis represents the measured value of the test set, the vertical axis represents the model predicted value, the oblique line represents the regression line, and the circle represents the predicted sample. The closer the sample is to the regression line, the better the model prediction effect is, and the closer the predicted value is to the true value. The correlation coefficient R of the quantitative detection model of rivastigmine content to the prediction result of the test set is 2 =0.98268, root mean square error of prediction RMSEP = 1.79469; the correlation coefficient R of the prediction results of the quantitative detection model of solid content to the test set 2 =0.99564, the root mean square error of prediction RMSEP = 0.65444, and the prediction is relatively accurate.

[0077] By selecting the optimal quantitative detection model with the best performance, the key properties of the intermediate gel, including component content and solid content, can be quickly detected, which greatly saves the time of offline testing and is conducive to ensuring the stable and controllable quality of transdermal patch products.

[0078] An embodiment of the present invention further provides a computer device, Figure 11 This is a schematic diagram of the structure of a computer device provided by an embodiment of the present invention; see the accompanying drawings Figure 11 As shown, the computer device includes: an input device 23, an output device 24, a memory 22 and a processor 21; the memory 22 is used to store one or more programs; when the one or more programs are executed by the one or more processors 21, the one or more processors 21 implement the detection method for rapidly detecting the quality attributes of the transdermal patch intermediate glue solution as provided in the above embodiment; wherein the input device 23, the output device 24, the memory 22 and the processor 21 can be connected by a bus or other means, Figure 11 The bus connection is taken as an example.

[0079] The memory 22 is a readable and writable storage medium of a computing device and can be used to store software programs and computer executable programs, such as program instructions corresponding to the method for rapidly detecting the quality attributes of the transdermal patch intermediate glue solution described in an embodiment of the present invention. The memory 22 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system and at least one application required for a function; the data storage area can store data created based on the use of the device, etc. In addition, the memory 22 can include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some examples, the memory 22 can further include a memory remotely located relative to the processor 21, and these remote memories can be connected to the device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0080] The input device 23 may be used to receive input digital or character information, and generate key signal input related to user settings and function control of the device; the output device 24 may include a display device such as a display screen.

[0081] The processor 21 executes various functional applications and data processing of the device by running the software programs, instructions and modules stored in the memory 22, that is, realizes the above-mentioned method for rapidly detecting the quality attributes of the transdermal patch intermediate glue solution.

[0082] The computer device provided above can be used to execute the detection method for rapidly detecting the quality attributes of the transdermal patch intermediate glue provided in the above embodiment, and has corresponding functions and beneficial effects.

[0083] The embodiment of the present invention also provides a storage medium comprising computer executable instructions, which are used to perform the detection method for rapidly detecting the quality attributes of the transdermal patch intermediate glue solution provided by the above embodiment when executed by a computer processor. The storage medium is any of various types of memory devices or storage devices. The storage medium includes: installation media, such as CD-ROM, floppy disk or tape device; computer system memory or random access memory, such as DRAM, DDRRAM, SRAM, EDORAM, Rambus RAM, etc.; non-volatile memory, such as flash memory, magnetic media (such as hard disk or optical storage); registers or other similar types of memory components, etc.; the storage medium may also include other types of memory or a combination thereof; in addition, the storage medium may be located in the first computer system in which the program is executed, or may be located in a different second computer system, the second computer system being connected to the first computer system via a network (such as the Internet); the second computer system may provide program instructions to the first computer for execution. The storage medium includes two or more storage media that can reside in different locations (e.g., in different computer systems connected via a network). The storage medium can store program instructions (e.g., embodied as a computer program) that can be executed by one or more processors.

[0084] Of course, the computer-executable instructions of the storage medium provided in the embodiment of the present invention are not limited to the method for rapidly detecting the quality attributes of the transdermal patch intermediate adhesive solution as described in the above embodiment, and can also execute the relevant operations in the method for rapidly detecting the quality attributes of the transdermal patch intermediate adhesive solution provided in any embodiment of the present invention.

[0085] Thus far, the technical solutions of the present invention have been described in conjunction with 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 may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0086] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A device for rapidly detecting the quality attributes of a transdermal patch intermediate adhesive, characterized in that: include: A sample cup and a reflective plate, wherein the reflective plate is placed in the sample cup and pressed to the bottom of the cup; The sample cup is made of a material with few hydrogen groups, and the reflective plate is made of a material with low absorption and high reflectivity in the near-infrared spectrum; the interior of the sample cup contains an intermediate glue solution of the transdermal patch, and a fixed volume of the intermediate glue solution sample is accommodated between the bottom of the sample cup and the bottom surface of the reflective plate.

2. The device for rapidly detecting the quality attributes of a transdermal patch intermediate glue according to claim 1, wherein: The outer periphery of the bottom surface of the reflector is provided with a circle of protrusions, which together with the bottom surface of the reflector form a groove with a depth of several millimeters. The circle of protrusions is provided with a notch, which provides overflow for the intermediate glue in the groove.

3. A method for rapidly detecting the quality attributes of a transdermal patch intermediate adhesive solution, comprising: The following steps are involved: S1. Scanning an intermediate glue sample of a transdermal patch using a Fourier transform near-infrared spectrometer to obtain a near-infrared spectrum and reference data of the intermediate glue sample; S2. Processing the near-infrared spectra using a variety of spectral preprocessing and characteristic wavelength screening methods, and establishing chemometric quantitative detection models for the quality attributes of the intermediate gel using an improved kernel partial least squares method; S3. Compare the accuracies of the various quantitative detection models established in step S2, select the optimal quantitative detection model, and use the optimal quantitative detection model to quickly detect the quality attributes of the intermediate glue solution of the transdermal patch product to be tested, thereby realizing monitoring of the transdermal patch coating process.

4. The method for rapidly detecting the quality attributes of a transdermal patch intermediate glue according to claim 3, wherein: The method of processing the reference data of the near-infrared spectrum by using spectral preprocessing in step S2 includes: Preprocessing the spectra corresponding to the component content and solid content of the transdermal patch intermediate glue solution by using any one or more methods selected from the group consisting of mean centering, standardization, vector normalization, subtracting a straight line, eliminating constant offset, smoothing and derivation, standard normal variable transformation, detrending algorithm, multivariate scattering correction, and maximum-minimum normalization; The vector normalization is to process each sample of the intermediate glue solution separately, and divide the original spectrum of each sample by the spectrum modulus to obtain the vector normalization. : （1） In formula (1), x is the original spectrum, is the spectral mode length; The mean centering is to subtract the average spectrum of all samples from the original spectrum of each sample, reduce the spectral offset caused by the fixed deviation, associate the spectral changes with the material changes of each intermediate glue sample, increase the difference between the sample spectra, and make the characteristic of the changes clearer; mean centering The calculation expression is: （2） In formula (2), For the raw spectrum of each sample, is the average spectrum of all samples; The constant offset elimination is to subtract the minimum value of the absorbance of each sample from the original spectrum of each sample. The calculation expression is: （3） In formula (3), For the raw spectrum of each sample, is the minimum absorbance value for each sample.

5. The method for rapidly detecting the quality attributes of a transdermal patch intermediate glue according to claim 3, wherein: The method of processing the reference data of the near-infrared spectrum using the characteristic wavelength screening method in step S2 includes: Preprocessing the spectra corresponding to the component content and solid content of the transdermal patch intermediate glue solution by using any one of the methods selected from reverse interval partial least squares, uninformative variable elimination, Monte Carlo-uninformative variable elimination, and competitive adaptive reweighted sampling; The competitive adaptive reweighted sampling method is: Assume that the spectral matrix is ​​x (m × n), m is the number of samples, n is the number of variables, y (m × 1) represents the target property vector, e is the correction error, and the correction model expression is: y=xb+e(4) In formula (4), b is the regression coefficient vector under any number of latent variables, b=[b1,b2,…b n ], the absolute value of the i-th element in b | b i |(1≤i≤n) represents the contribution of the i-th wavelength point to y, |b i The larger the value of |bi|, the more important the variable corresponding to the value of |bi| is; to define the importance of each wavelength, define the weight : （5） The weights of the variables removed by competitive adaptive reweighted sampling All are set to 0.

6. The method for rapidly detecting the quality attributes of a transdermal patch intermediate glue according to claim 5, wherein: The method of the reverse interval partial least squares method is: The near-infrared spectrum is divided into several equally spaced sub-regions. Starting from the full spectrum, one sub-region is removed in turn, and the optimal combination is finally found until the RMSECV no longer decreases.

7. The method for rapidly detecting the quality attributes of a transdermal patch intermediate glue according to claim 3, wherein: The method for comparing the accuracy of the various different quantitative detection models established in step S2 in step S3 includes: using a k-fold cross-validation method to obtain the cross-validation correlation coefficient and cross-validation root mean square error of the quantitative detection model, using a test set for external verification to obtain a prediction correlation coefficient and a prediction root mean square error, and judging the accuracy of each of the established quantitative detection models by comparing the cross-validation correlation coefficient, prediction correlation coefficient, cross-validation root mean square error and prediction root mean square error of each different quantitative detection model.

8. The method for rapidly detecting the quality attributes of a transdermal patch intermediate glue according to claim 3, wherein: The method for establishing a quantitative detection model for the quality attributes of the intermediate glue in step S2 includes: associating the spectrum with the quality attributes of the intermediate glue of the corresponding sample, establishing a database, eliminating abnormal samples through principal component analysis, and using the KS algorithm to divide the samples in the database into a calibration set and a test set of the quantitative detection model, selecting samples with large spectral differences into the calibration set to ensure the representativeness of the calibration samples; after spectral preprocessing and characteristic wavelength screening, establishing a quantitative model associating near-infrared spectra with quality attributes through an improved kernel partial least squares algorithm.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method for rapidly detecting the quality attributes of a transdermal patch intermediate glue solution according to any one of claims 3 to 8 are implemented.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method for rapidly detecting the quality attributes of the transdermal patch intermediate glue solution as described in any one of claims 3 to 8 are implemented.

Images