Drug stability analysis system

Abstract

This invention discloses a drug stability analysis system, belonging to the field of drug analysis technology. The system includes: a sample data acquisition module, a data fusion and normalization module, an intelligent analysis and state determination module, and a result output and trend prediction module. This invention achieves multi-dimensional and all-round scanning of drug samples during stability testing through the comprehensive application of optical imaging, fluorescence imaging, and infrared imaging technologies. This significantly improves the comprehensiveness and accuracy of data acquisition. Optical imaging technology captures macroscopic surface morphology information of the drug, while fluorescence imaging technology visualizes and tracks specific components in the drug to obtain component distribution information. Infrared imaging technology analyzes the chemical bond vibrations between drug molecules to obtain chemical composition information, providing material for subsequent data analysis and processing.

Description

Technical Field

[0001] This invention relates to the field of pharmaceutical analysis technology, specifically to a pharmaceutical stability analysis system. Background Technology

[0002] In drug research and development and manufacturing, drug stability is one of the key indicators for measuring its quality and efficacy. Drug stability not only relates to the effectiveness of drugs during storage and use, but also directly affects patient safety and treatment outcomes. With the continuous increase in the types of drugs and the continuous advancement of drug development technology, higher demands are placed on drug stability assessment methods. Traditional drug stability assessment methods mainly rely on chemical analysis and physical detection methods. While these methods can reflect the stability of drugs to a certain extent, they often suffer from problems such as complex operation, long time consumption, high cost, and inability to comprehensively reflect the stability changes of drugs under complex environmental conditions. Therefore, developing an efficient, accurate, and comprehensive drug stability analysis system has become an urgent technical challenge to be solved in the current drug development field.

[0003] Traditional drug stability assessment techniques primarily rely on single chemical analysis or physical detection methods, such as high-performance liquid chromatography (HPLC) and gas chromatography (GC). While these methods can accurately determine changes in the content of specific components in a drug, they often fail to comprehensively reflect changes in drug stability under complex environmental conditions. Furthermore, traditional techniques typically require large amounts of sample and time, are complex in operation, and have stringent experimental requirements, making them unsuitable for the rapid and efficient assessment needs of large-scale drug development and manufacturing. More importantly, traditional techniques often only provide stability information at a specific point in time or under specific conditions, failing to predict long-term stability trends under different environmental conditions, thus limiting their practical application in drug development and manufacturing.

[0004] Therefore, developing a drug stability analysis system not only improves the accuracy and efficiency of drug stability assessment, but also provides strong support for quality control in the drug research and development and production process. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a drug stability analysis system. Through optical, fluorescence and infrared imaging technologies, it can acquire multi-dimensional image information during drug stability testing in all aspects, and combine high-resolution fluorescence microscopy to perform in-situ imaging of key components. The system integrates multimodal data, adopts a hierarchical attention mechanism and stability state classification model to accurately determine the current stability state of the drug, and predicts the degradation rate and remaining shelf life under different environmental conditions in the future.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a drug stability analysis system, which includes: a sample data acquisition module, a data fusion and normalization module, an intelligent analysis and status determination module, and a result output and trend prediction module;

[0007] The sample data acquisition module employs optical imaging, fluorescence imaging, and infrared imaging technologies to perform a comprehensive scan of the drug sample, acquiring multi-dimensional image information of the drug during stability testing. Simultaneously, it utilizes a high-resolution fluorescence microscope to perform in-situ imaging of key drug components or degradation sites, acquiring microscopic dynamic evolution images under different time and environmental conditions, and preprocessing and storing the data.

[0008] The data fusion and normalization module fuses the image information acquired by multimodal imaging with microscopic dynamic images, removes redundant data and unifies the format, and collects the chemical structure, composition and historical stability data of the drug. It establishes the association between the data and the fused image data through an association matrix to form a joint dataset.

[0009] The intelligent analysis and state determination module employs a hierarchical attention mechanism to process image features, chemical structure features, component ratio features, and historical stability time series features in the joint dataset, extracts high-dimensional features, analyzes the high-dimensional feature vectors through a stability state classification model, and determines the current stability state of the drug based on a preset threshold.

[0010] The results output and trend prediction module uses a drug stability mathematical model to integrate historical data and real-time monitoring data to predict the degradation rate and remaining shelf life of the drug under different environmental conditions in the future, and presents the analysis results in a visual manner.

[0011] Furthermore, the multi-dimensional image information acquired by the sample data acquisition module is as follows:

[0012] Optical imaging technology acquires macroscopic surface morphology information of drugs;

[0013] Fluorescence imaging technology uses labels to visually track specific components in drugs and obtain information on component distribution;

[0014] Infrared imaging analysis technology obtains information about the chemical composition of drugs by observing the vibration of chemical bonds between molecules.

[0015] Furthermore, the specific steps in the sample data acquisition module for in-situ imaging of key drug components or degradation sites using a high-resolution fluorescence microscope are as follows:

[0016] (1) Identify key drug components and potential degradation sites, select appropriate fluorescent probes, label them by chemical coupling, and fix the samples on a special glass slide;

[0017] (2) Place the sample in an in-situ imaging chamber with integrated temperature, humidity, light and gas environment control, and set different simulated test conditions;

[0018] (3) An inverted high-resolution fluorescence microscope was used, equipped with an oil immersion objective and a high-speed camera. Excitation and emission light filter groups and an autofocus strategy were set according to the characteristics of the fluorescence probe.

[0019] (4) Collect images by scanning multiple fields of view according to the preset time series, use high-frequency sampling to cope with sudden environmental changes, and synchronously record metadata of collection time and environmental parameters;

[0020] (5) Perform background subtraction, motion correction and fluorescence bleaching compensation operations to eliminate noise and artifact interference;

[0021] (6) Store the original image, extract the fluorescence intensity time curve, and generate a dynamic pseudo-color image.

[0022] Furthermore, the data fusion and normalization module uses an image fusion algorithm to fuse multi-dimensional image information with microscopic dynamic image information from in-situ imaging. The calculation formula is as follows: ,in, These are the fused image features. and These are pre-set adjustment coefficients based on image type, used to balance the contributions of different modalities in the fusion process. It is the quantum superposition coefficient, and the formula is: , and They represent the first Image and the first Feature vectors of an image The norm of a vector This is an adjustment coefficient used to control the distribution range of the weights. The total number of images participating in the fusion.

[0023] Furthermore, the data fusion and normalization module establishes a data association between the fused image feature vector and the chemical structure, composition, and historical stability of the drug through a multimodal data association matrix. Let the multimodal data association matrix be... Its elements The formula representing the correlation strength between the fused image feature vector and the data associations of the drug's chemical structure, composition, and historical stability is: ,in, These are elements in the multimodal data association matrix, representing the correlation strength between fused image features and different data such as chemical structure, composition, and historical stability data. Representing vectors and The inner product, This indicates taking the absolute value. and Representing vectors respectively and The Euclidean norm, and These represent different feature vectors.

[0024] Furthermore, the intelligent analysis and state determination module employs a hierarchical attention mechanism to extract high-dimensional features, transforming the drug chemical structure into a molecular graph feature vector. The composition is represented as a proportional vector. Historical stability data were organized into a time series matrix. , and fused image feature vector Perform tensor concatenation to form a joint dataset. Calculate intramodal attention, and for each data modality, calculate self-attention weights and association weights. The formula is: ,in The features of each modality after internal attention processing, To generate a high-dimensional feature vector from the weight matrix and bias vector of the fusion layer, the formula is as follows: , Intermodal correlation weight vector.

[0025] Furthermore, the formula for constructing the stability state classification model in the intelligent analysis and state determination module is as follows: ,in, , For the weights and biases of the classic classification layer, Encoding quantum states for high-dimensional features, Using a predefined stable reference state, optimization is performed through training on historical data. For quantum-classical coupling coefficient, It is a fusion of image feature vectors. It is the output of the stability state classification model, used to determine the stable state of a drug.

[0026] Furthermore, the determination of the drug's stable state in the intelligent analysis and state determination module:

[0027] when When the drug is in a stable state, it is considered to be stable.

[0028] when At that time, the drug was judged to be in a stable state as slightly degraded;

[0029] when At that time, the drug was determined to be in a stable state of moderate degradation;

[0030] when At that time, the drug's stable state was determined to be severely degraded;

[0031] in, , , It is a threshold for judging drug status, determined by historical drug stability data, and .

[0032] Furthermore, the result output and trend prediction module predicts the degradation rate of the drug under different environmental conditions in the future using a stability state prediction model. Let the predicted drug degradation rate be... The calculation formula is: ,in, To predict the time step, For characteristic time, To simulate the number of scenes, As scene weight, In the scene The quantum Monte Carlo simulation results are given below, where GM(1,1)(HistData) is the grey prediction value based on historical data (HistData).

[0033] Furthermore, the result output and trend prediction module predicts the remaining shelf life of the drug under different environmental conditions in the future using a confidence interval prediction algorithm, with the following formula: ,in, Indicates the current moment The predicted remaining shelf life of the drug, i.e. from the current moment From the beginning until the point at which the drug no longer meets the criteria for effectiveness, This represents the minimum concentration threshold of the active ingredient in a drug. The posterior probability is calculated using the Markov chain Monte Carlo method. Represents the current point in time. It is the infimum symbol. Indicates from the current time Starting with the time increments, This represents the posterior probability, calculated using the Markov chain Monte Carlo method. Indicates the current time Add time increment The concentration of the active ingredient in the drug after that, It is the minimum concentration threshold of the active ingredient in a drug. This represents historical stability data and environmental condition data for the drug.

[0034] Compared with existing technologies, this drug stability analysis system has the following advantages:

[0035] I. This invention achieves multi-dimensional and all-round scanning of drug samples during stability testing through the integrated application of optical imaging, fluorescence imaging, and infrared imaging technologies. This significantly improves the comprehensiveness and accuracy of data acquisition. Optical imaging captures macroscopic surface morphology information of the drug, while fluorescence imaging visualizes and tracks specific components in the drug to obtain component distribution information. Infrared imaging analyzes the chemical bond vibrations between drug molecules to obtain chemical composition information, providing material for subsequent data analysis and processing. Through the data fusion and normalization module, the system can effectively correlate image information from different modalities with the chemical structure, composition, and historical stability data of the drug to form a joint dataset. On this basis, the intelligent analysis and state determination module adopts a hierarchical attention mechanism to extract and analyze high-dimensional features in the joint dataset. Through the stability state classification model, it accurately determines the current stability state of the drug, improving the accuracy and reliability of drug stability assessment and providing strong support for quality control in drug research and development and production processes.

[0036] Second, this invention, through its result output and trend prediction module, utilizes a drug stability mathematical model, integrating historical and real-time monitoring data, to predict the degradation rate and remaining shelf life of drugs under different environmental conditions in the future. This allows drug stability management to move beyond assessing the current state and instead predict drug trends under varying environmental conditions, providing a scientific basis for drug research and development, production, storage, and transportation. The analysis results are presented visually, allowing users to intuitively understand the drug's stability status and its changing trends, thus enabling more scientific and rational decision-making. Furthermore, this invention provides a confidence interval prediction algorithm for predicting the remaining shelf life of drugs, further enhancing the accuracy and reliability of the prediction results. This combination of trend prediction and visual output not only optimizes the decision-making process for drug stability management but also improves the efficiency of drug research and development and production.

[0037] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0039] Figure 1 A flowchart of a drug stability analysis system;

[0040] Figure 2 This is a framework diagram of a drug stability analysis system. Detailed Implementation

[0041] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0042] Example 1:

[0043] Stability analysis of solid dosage forms (tablets) of chemical drugs.

[0044] A pharmaceutical company is developing a new type of antihypertensive tablet and needs to assess its stability under high temperature and high humidity conditions, and determine its shelf life and storage conditions.

[0045] Sample data acquisition module:

[0046] Multi-dimensional image scanning: Optical imaging technology is used to photograph the tablet surface to detect macroscopic physical changes such as cracks and discoloration; fluorescent imaging is used to label the main components (hypertensive active substances) in the tablet and track their uniform distribution within the tablet to observe whether component migration is caused by humidity; infrared imaging is used to analyze the chemical bond vibrations between tablet excipients (such as starch, magnesium stearate) and the main components to determine whether chemical reactions (such as hydrolysis, oxidation) have occurred. Figure 2 As shown.

[0047] Microscopic dynamic imaging: The degradation sites of the main components in the tablets (such as the locations of easily oxidized functional groups) are located using a high-resolution fluorescence microscope. After being labeled with a fluorescent probe, the samples are fixed in an in-situ imaging chamber with adjustable temperature and humidity. Simulated conditions are set: 40℃, 75%RH (standard conditions for accelerated stability testing). Microscopic images are acquired every hour to record the dynamic process of component degradation (such as the decrease in fluorescence intensity and the formation of degradation products).

[0048] After data collection, all data is preprocessed and stored, such as... Figure 1 As shown.

[0049] Data fusion and normalization module:

[0050] Image fusion: This involves combining optical, fluorescence, and infrared image data with a microscopic dynamic image using an image fusion algorithm. The calculation formula is as follows: ,in, These are the fused image features. and These are pre-set adjustment coefficients based on image type, used to balance the contributions of different modalities in the fusion process. It is the quantum superposition coefficient, and the formula is: , and They represent the first Image and the first Feature vectors of an image The norm of a vector This is an adjustment coefficient used to control the distribution range of the weights. To determine the total number of images involved in the fusion, duplicate data (such as irrelevant background noise) is removed, and the images are standardized into a format such as TIFF image sequences.

[0051] Multimodal correlation: Collect chemical structure data of tablets (molecular structural formula of the main component), component ratio (mass ratio of main component to excipients), and historical accelerated testing data (such as stability reports from the previous 3 months). Establish a correlation between image features (such as crack area, fluorescence intensity decay rate) and chemical data (such as chemical bond breaking rate, component concentration change) through a multimodal data correlation matrix. Let the multimodal data correlation matrix be... Its elements The formula representing the correlation strength between the fused image feature vector and the data associations of the drug's chemical structure, composition, and historical stability is: ,in, These are elements in the multimodal data association matrix, representing the correlation strength between fused image features and different data such as chemical structure, composition, and historical stability data. Representing vectors and The inner product, This indicates taking the absolute value. and Representing vectors respectively and The Euclidean norm, and These represent different feature vectors, forming a comprehensive dataset.

[0052] Intelligent analysis and status determination module:

[0053] Layered attention mechanism: Self-attention weights are calculated for image features (such as surface defect degree), chemical structure features (molecular stability parameters), component ratios (interaction between excipients and main components), and historical time series data (early degradation rate), respectively, transforming the drug chemical structure into a molecular graph feature vector. The composition is represented as a proportional vector. Historical stability data were organized into a time series matrix. , and fused image feature vector Perform tensor concatenation to form a joint dataset. Calculate intra-modal attention, calculate self-attention weights for each data modality, and calculate inter-modal association weights. The formula is: ,in The features of each modality after internal attention processing are fused using an intermodal correlation weighting algorithm to generate a high-dimensional feature vector (such as a composite index of comprehensive humidity, component distribution, and chemical bond stability). The high-dimensional feature vector is as follows: , Intermodal correlation weight vector.

[0054] Stability classification: High-dimensional features are analyzed using a stability state classification model, with the following formula: ,in, , For the weights and biases of the classic classification layer, Encoding quantum states for high-dimensional features, Using a predefined stable reference state, optimization is performed through training on historical data. For quantum-classical coupling coefficient, It is a fusion of image feature vectors. This is the output of a stability state classification model, used to determine the drug's stable state by comparing it to a preset threshold. The stability of the tablets under the current conditions was determined to be slightly degraded.

[0055] Results Output and Trend Prediction Module:

[0056] Trend Forecast:

[0057] The degradation rate of tablets under different storage conditions (e.g., conventional conditions of 25℃ / 60%RH and extreme conditions of 30℃ / 80%RH) was predicted using a stability state prediction model. The predicted drug degradation rate was set at... The calculation formula is: ,in, To predict the time step, For characteristic time, To simulate the number of scenes, As scene weight, In the scene The quantum Monte Carlo simulation results are as follows: GM(1,1)(HistData) is the grey prediction value based on historical data (HistData). The remaining shelf life of the drug under different environmental conditions in the future is predicted using a confidence interval prediction algorithm, and the formula is: ,in, Indicates the current moment The predicted remaining shelf life of the drug, i.e. from the current moment From the beginning until the point at which the drug no longer meets the criteria for effectiveness, This represents the minimum concentration threshold of the active ingredient in a drug. The posterior probability is calculated using the Markov chain Monte Carlo method. Represents the current point in time. It is the infimum symbol. Indicates from the current time Starting with the time increments, This represents the posterior probability, calculated using the Markov chain Monte Carlo method. Indicates the current time Add time increment The concentration of the active ingredient in the drug after that, It is the minimum concentration threshold of the active ingredient in a drug. Historical stability data and environmental condition data of the drug (e.g., shelf life of 24 months under normal conditions and 6 months under extreme conditions).

[0058] Visual presentation: Based on the analysis results, a tablet stability report is generated, including macroscopic image comparison, dynamic graph of component distribution, and trend curve of chemical bond changes.

[0059] In summary, this embodiment achieves precise end-to-end evaluation of the stability of chemical drug tablets through multi-dimensional imaging and intelligent analysis technologies. The sample acquisition module combines optical, fluorescence, and infrared technologies to perform three-dimensional monitoring from macroscopic physical damage to microscopic chemical degradation, supplemented by in-situ dynamic imaging to capture real-time changes in environmentally sensitive components. The data fusion module uses image fusion algorithms and correlation matrices to establish the intrinsic connection between image features, chemical structures, and prescription data, constructing a multimodal joint dataset. The intelligent analysis module utilizes a hierarchical attention mechanism to achieve deep extraction and state classification of complex features, accurately identifying key factors affecting stability. The prediction module provides a scientific basis for prescription optimization and shelf-life determination. This solution not only meets ICH stability testing requirements but also improves analytical efficiency through digital means, providing an intelligent solution for quality control in chemical drug research and development and production.

[0060] Example 2:

[0061] Stability analysis of injectable biopharmaceuticals (monoclonal antibodies).

[0062] A biopharmaceutical company is developing an anti-tumor monoclonal antibody injection and needs to assess the impact of temperature fluctuations on its stability during transportation and optimize the cold chain storage solution.

[0063] Sample data acquisition module:

[0064] Multidimensional image scanning: Optical imaging to observe whether the injection solution is turbid or precipitated (indicating protein aggregation); fluorescence imaging to label specific structural domains of antibody proteins (such as the Fc segment) and monitor their spatial conformational changes (such as thermally induced structural unfolding); infrared imaging to analyze the interaction between the antibody and ions in the buffer (such as phosphate buffer) and to determine the effect of pH changes on protein stability.

[0065] Microscopic dynamic imaging: The aggregation behavior of antibody proteins at different temperatures (2℃, 8℃, 25℃) is tracked using a fluorescence microscope. Aggregation hotspots are displayed through dynamic pseudo-color images (e.g., red indicates high-concentration aggregation areas). The transportation scenario is simulated in an in-situ imaging chamber (e.g., the temperature fluctuates from 8℃ to 25℃ and then returns to normal within 48 hours). Images are acquired at high frequency (once every 10 minutes) to record changes in protein particle size and distribution.

[0066] Data fusion and normalization module:

[0067] Image fusion: Combining optical, fluorescence, and infrared images from different temperatures with changes in protein particle size and distribution. The calculation formula is as follows: It also removes artifacts caused by temperature changes (such as thermal noise) and generates a standardized dynamic image sequence.

[0068] Multimodal correlation:

[0069] The amino acid sequence data (aggregation sites), formulation components (such as the concentration of sucrose as a stabilizer), and accelerated degradation data (such as the aggregate content after 7 days of high-temperature incubation) of the associated antibodies were used to establish the correlation between image features (such as the number of aggregate particles and the entropy value of fluorescence signal distribution) and biochemical data (such as protein purity and charge heterogeneity) using an association matrix algorithm. The formula is as follows: This forms a joint dataset.

[0070] Intelligent analysis and status determination module:

[0071] Layered attention mechanism: Self-attention weights are calculated for image features (such as precipitation area ratio), chemical structural features (disulfide bond stability), component ratios (stabilizer concentration), and historical time series (aggregate growth curves at different temperatures), respectively. The formula is as follows: (For example, the aggregation rate increases significantly when temperature fluctuations are greater than 5°C). Data is fused using an intermodal correlation weighting algorithm to generate a high-dimensional feature vector (e.g., a stability index combining temperature, stabilizer concentration, and protein conformation). The formula is: .

[0072] Stability classification: Using high-dimensional features as input, a stability state classification model is used to determine the stability state of the antibody under simulated transport conditions. The results are calculated... It is at a moderate degradation level.

[0073] Results Output and Trend Prediction Module:

[0074] Trend prediction: The degradation rate of different cold chain solutions is predicted by combining the stability state prediction model. The formula is as follows: The remaining shelf life of a drug under different environmental conditions in the future is predicted using a confidence interval prediction algorithm. The formula is as follows: This provides a basis for optimizing cold chain strategies.

[0075] Visualization: Based on the output results, an injection stability report is generated, including dynamic videos of protein aggregation at different temperatures and trend graphs of key quality attributes (such as purity and activity).

[0076] In summary, this embodiment constructs a multi-level analysis system from molecular conformation to particle aggregation, targeting the stability characteristics of biopharmaceutical proteins. The sample acquisition module utilizes fluorescence polarization, infrared spectroscopy, and dynamic particle tracking technology to monitor the structural changes and aggregation behavior of antibodies under oxidation, light, and freeze-thaw stress conditions in real time. Combined with three-dimensional imaging, it reveals the spatial distribution pattern of degradation. The data fusion module establishes a correlation model between conformational features, structural data, and formulation parameters through sparse representation algorithms and mutual information analysis, clarifying the stabilizing mechanism of excipients. The intelligent analysis module achieves dynamic modeling and state classification of protein degradation pathways, accurately identifying high-risk stress conditions. The prediction module effectively reduces the risk of aggregate formation by simulating degradation chain reactions and optimizing cold chain monitoring strategies. This solution provides an innovative technical path for forced degradation testing and storage and transportation stability management of biopharmaceuticals, helping to improve the quality controllability and clinical safety of biopharmaceuticals.

[0077] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A drug stability analysis system, characterized in that, The system includes: a sample data acquisition module, a data fusion and normalization module, an intelligent analysis and status determination module, and a result output and trend prediction module; The sample data acquisition module employs optical imaging, fluorescence imaging, and infrared imaging technologies to perform a comprehensive scan of the drug sample, acquiring multi-dimensional image information of the drug during stability testing. Simultaneously, it utilizes a high-resolution fluorescence microscope to perform in-situ imaging of key drug components or degradation sites, acquiring microscopic dynamic evolution images under different time and environmental conditions, and preprocessing and storing the data. The data fusion and normalization module fuses the image information acquired by multimodal imaging with microscopic dynamic images, removes redundant data and unifies the format, and collects the chemical structure, composition and historical stability data of the drug. It establishes the association between the data and the fused image data through an association matrix to form a joint dataset. The intelligent analysis and state determination module employs a hierarchical attention mechanism to process image features, chemical structure features, component ratio features, and historical stability time series features in the joint dataset. It also extracts high-dimensional features using this mechanism, transforming the drug chemical structure into a molecular graph feature vector. The composition is represented as a proportional vector. Historical stability data were organized into a time series matrix. , and high-dimensional feature vectors Perform tensor concatenation to form a joint dataset. Calculate intramodal attention, and for each data modality, calculate self-attention weights and association weights. The formula is: ,in The features of each modality after internal attention processing, Generate high-dimensional feature vectors from the weight matrix and bias vector of the fusion layer. The formula is: , The intermodal correlation weight vector is used, and the high-dimensional feature vector is analyzed through a stability state classification model. The mathematical formula for the stability state classification model is: ,in, , For the weights and biases of the classic classification layer, Encoding quantum states for high-dimensional features, For a predefined stable reference state, For quantum-classical coupling coefficient, It is the output of the stability state classification model, used to determine the drug's stability state, and determines the current stability state of the drug based on a preset threshold. The results output and trend prediction module uses a drug stability mathematical model to integrate historical data and real-time monitoring data to predict the degradation rate and remaining shelf life of the drug under different environmental conditions in the future, and presents the analysis results in a visual manner.

2. The drug stability analysis system according to claim 1, characterized in that, The multi-dimensional image information acquired by the sample data acquisition module is as follows: Optical imaging technology acquires macroscopic surface morphology information of drugs; Fluorescence imaging technology uses labels to visually track specific components in drugs and obtain information on component distribution; Infrared imaging analysis technology obtains information about the chemical composition of drugs by observing the vibration of chemical bonds between molecules.

3. The drug stability analysis system according to claim 1, characterized in that, The specific steps for in-situ imaging of key drug components or degradation sites using a high-resolution fluorescence microscope in the sample data acquisition module are as follows: (1) Identify key drug components and potential degradation sites, select appropriate fluorescent probes, label them by chemical coupling, and fix the samples on a special glass slide; (2) Place the sample in an in-situ imaging chamber with integrated temperature, humidity, light and gas environment control, and set different simulated test conditions; (3) An inverted high-resolution fluorescence microscope was used, equipped with an oil immersion objective and a high-speed camera. Excitation and emission light filter groups and an autofocus strategy were set according to the characteristics of the fluorescence probe. (4) Collect images by scanning multiple fields of view according to the preset time sequence, use high-frequency sampling to cope with sudden environmental changes, and synchronously record metadata of collection time and environmental parameters; (5) Perform background subtraction, motion correction and fluorescence bleaching compensation operations to eliminate noise and artifact interference; (6) Store the original image, extract the fluorescence intensity time curve, and generate a dynamic pseudo-color image.

4. The drug stability analysis system according to claim 1, characterized in that, The data fusion and normalization module uses an image fusion algorithm to fuse multi-dimensional image information with microscopic dynamic image information from in-situ imaging. The calculation formula is as follows: ,in, These are the fused image features. and These are pre-set adjustment coefficients based on image type, used to balance the contributions of different modalities in the fusion process. It is the quantum superposition coefficient, and the formula is: , and They represent the first Image and the first Feature vectors of an image The norm of a vector This is an adjustment coefficient used to control the distribution range of the weights. The total number of images participating in the fusion.

5. The drug stability analysis system according to claim 1, characterized in that, The data fusion and normalization module establishes a data association between the fused image feature vector and the chemical structure, composition, and historical stability of the drug through a multimodal data association matrix. Let the multimodal data association matrix be... Its elements The formula representing the correlation strength between the fused image feature vector and the data associations of the drug's chemical structure, composition, and historical stability is: ,in, These are elements in the multimodal data association matrix, representing the correlation strength between fused image features and different data such as chemical structure, composition, and historical stability data. Representing vectors and The inner product, This indicates taking the absolute value. and Representing vectors respectively and The Euclidean norm, and These represent different feature vectors.

6. The drug stability analysis system according to claim 1, characterized in that, Determining the drug's stable state in the intelligent analysis and state determination module: when When the drug is in a stable state, it is considered to be stable. when At that time, the drug was judged to be in a stable state as slightly degraded; when At that time, the drug was determined to be in a stable state of moderate degradation; when At that time, the drug's stable state was determined to be severely degraded; in, , , It is a threshold for judging drug status, determined by historical drug stability data, and .

7. The drug stability analysis system according to claim 1, characterized in that, The results output and trend prediction module uses a stability state prediction model to predict the degradation rate of the drug under different environmental conditions in the future. Let the predicted drug degradation rate be... The calculation formula is: ,in, To predict the time step, For characteristic time, To simulate the number of scenes, As scene weight, In the scene The quantum Monte Carlo simulation results are given below, where GM(1,1)(HistData) is the grey prediction value based on the historical data HistData.

8. The drug stability analysis system according to claim 1, characterized in that, The result output and trend prediction module uses a confidence interval prediction algorithm to predict the remaining shelf life of the drug under different environmental conditions in the future. The formula is as follows: ,in, Indicates the current moment The predicted remaining shelf life of the drug, i.e. from the current moment From the beginning until the point at which the drug no longer meets the criteria for effectiveness, Represents the current point in time. It is the infimum symbol. Indicates from the current time Starting with the time increments, This represents the posterior probability, calculated using the Markov chain Monte Carlo method. Indicates the current time Add time increment The concentration of the active ingredient in the drug after that, It is the minimum concentration threshold of the active ingredient in a drug. This represents historical stability data and environmental condition data for the drug.

Images