A method and system for optimizing the production of Poria cocos vitamin C drugs based on spectral technology

Abstract

The present invention relates to the field of drug analysis technology, and specifically discloses a method and system for optimizing the production of Poria cocos vitamin C drugs based on spectral technology, the method comprising: collecting excipient data, production process data and drug preparation data of the production process of Poria cocos vitamin C drugs; processing the excipient data to obtain the excipient quality evaluation index of each candidate excipient, screening to obtain each qualified excipient; processing the production process data to obtain the production abnormality evaluation index, processing the drug preparation data to obtain the drug preparation stability evaluation index, and comprehensively analyzing to obtain the vitamin C drug production quality evaluation value; evaluating the production process of Poria cocos vitamin C drugs. The present invention provides a method and system for optimizing the production of Poria cocos vitamin C drugs based on spectral technology, which can find bottlenecks and problems in the production process, and then optimize and improve the process, which is helpful to understand the efficacy and stability performance of drugs under different conditions, and further improve the overall quality of drugs.

Description

Technical Field

[0001] The invention relates to the technical field of drug analysis, and in particular to a method and system for optimizing the production of Poria cocos vitamin C drugs based on spectroscopy technology. Background Art

[0002] At present, Poria cocos has multiple functions such as regulating the body, nourishing the spleen and stomach, and improving sleep, while vitamin C has a significant effect on enhancing immunity and preventing colds. With the advancement of science and technology, people have begun to explore the combination of Poria cocos and vitamin C. By optimizing the production process, such as using advanced extraction, concentration and drying technology, as well as reasonable formula design, drug particles that retain the medicinal efficacy of Poria cocos and are rich in vitamin C are prepared. This optimization not only improves the quality and stability of the drug, but also broadens the application field of Poria cocos, injecting new vitality into traditional Chinese medicine.

[0003] For example, the invention patent with announcement number CN102998392B is a method for detecting residual solvents in the macroporous adsorption resin of Kanggan capsules. Through the optimization of chromatographic conditions and the testing of the preparation methods of test samples and reference samples, a gas chromatography analysis method for residual benzene, n-hexane, toluene, xylene, styrene, and divinylbenzene that may exist in the preparation was established; through the optimization of the chromatographic separation system, a detection method for organic residues in the macroporous adsorption resin in the preparation was established. By controlling the organic residues in the macroporous adsorption resin in the preparation, the safety of the preparation is ensured, which is beneficial to the quality control of the production process.

[0004] For example, the invention patent with announcement number CN118962042A is a drug production quality identification method and platform based on big data, which involves the field of data processing technology. The method includes: obtaining the target raw materials and target solvents for producing the target nano-drugs, constructing production control optimization constraints, optimizing the production control indicators, and obtaining the optimal control decision; activating the chromatographic probe, performing chromatographic detection on the pre-finished nano-drugs produced according to the optimal control decision, and comparing the chromatogram of the benchmark product to analyze and generate the pre-finished impurity index; if the pre-finished impurity index is within the predetermined impurity index threshold, the pre-finished nano-drug is dried and post-processed.

[0005] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems: the current optimization method for the production of Poria cocos and Vitamin C drugs focuses more on fluorescence detection and quality control in the production process, but the overall optimization of the drug production process is lacking, and the production efficiency and quality stability of Poria cocos and Vitamin C drugs are difficult to guarantee. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention provides a method and system for optimizing the production of Poria cocos vitamin C drugs based on spectral technology, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: In the first aspect, the present invention provides a method for optimizing the production of Poria cocos and Vitamin C drugs based on spectral technology, including: collecting auxiliary material data, production process data and drug preparation data in the production process of Poria cocos and Vitamin C drugs.

[0008] The excipient data is processed to obtain the excipient quality evaluation index of each candidate excipient, and each qualified excipient is screened according to the excipient quality evaluation index of each candidate excipient.

[0009] The production process data is processed to obtain the production abnormality assessment index, the drug preparation data is processed to obtain the drug preparation stability assessment index, and the excipient quality assessment index of each qualified excipient is obtained. Comprehensive analysis is performed to obtain the vitamin C drug production quality assessment value.

[0010] Based on the vitamin C drug production quality assessment value, the production process of Poria vitamin C drug was evaluated and fed back.

[0011] As a further method, the excipient data is processed to obtain the excipient quality evaluation index of each candidate excipient. The specific processing process is: the excipient data includes the purity, average particle size, microbial count and bulk density of each candidate excipient.

[0012] The critical purity of each excipient, the average particle size of each excipient reference standard, the average particle size with allowable deviation, the critical microbial count of each excipient, the bulk density of each excipient reference standard and the bulk density with allowable deviation were extracted from the Poria cocos vitamin C database, and the excipient quality evaluation index of each candidate excipient was obtained through comprehensive analysis.

[0013] As a further method, the qualified excipients are screened according to the excipient quality evaluation index of each candidate excipient. The specific screening process is: extracting each excipient quality evaluation threshold from the Poria vitamin C database, comparing the excipient quality evaluation index of each candidate excipient with each excipient quality evaluation threshold, if the excipient quality evaluation index of a candidate excipient is greater than or equal to the excipient quality evaluation threshold corresponding to the candidate excipient, then the candidate excipient is marked as a qualified excipient, if the excipient quality evaluation index of a candidate excipient is less than the excipient quality evaluation threshold corresponding to the candidate excipient, then the candidate excipient is marked as an unqualified excipient.

[0014] The unqualified auxiliary materials are statistically obtained, auxiliary material replacement operations are performed on each unqualified auxiliary material, and the auxiliary material quality of each unqualified auxiliary material is re-evaluated.

[0015] As a further method, the production process data is processed to obtain the production abnormality assessment index, and the specific processing process is: the production process data includes the granulation temperature, light intensity and solvent ratio at each time node in the monitoring period.

[0016] The reference standard granulation temperature, allowable deviation granulation temperature, reference standard light intensity, allowable deviation light intensity, reference standard solvent ratio and allowable deviation solvent ratio were extracted from the Poria cocos vitamin C database, and the production abnormality assessment index was obtained through comprehensive analysis.

[0017] As a further method, the drug preparation data is processed to obtain a drug preparation stability evaluation index, and the specific analysis process is: the drug preparation data includes the content of each key component, water content and impurity content at each time node.

[0018] The critical content of each key component, the reference standard moisture content at each time point, the allowable deviation moisture content and the critical impurity content were extracted from the Poria cocos vitamin C database, and a comprehensive analysis was performed to obtain the drug preparation stability evaluation index. The need to add a stabilizer was determined based on the drug preparation stability evaluation index.

[0019] As a further method, the comprehensive analysis obtains the vitamin C drug production quality assessment value, and the specific analysis process is: the excipient quality assessment index of each qualified excipient is summed and averaged to obtain the excipient comprehensive quality assessment index.

[0020] Based on the production abnormality assessment index, drug preparation stability assessment index and excipient comprehensive quality assessment index, a comprehensive analysis was conducted to obtain the vitamin C drug production quality assessment value.

[0021] As a further method, the production process of Poria cocos vitamin C drug is evaluated and fed back according to the vitamin C drug production quality evaluation value. The specific evaluation process is: extracting the vitamin C drug production quality evaluation threshold from the Poria cocos vitamin C database, comparing the vitamin C drug production quality evaluation value with the vitamin C drug production quality evaluation threshold, if the vitamin C drug production quality evaluation value is greater than or equal to the vitamin C drug production quality evaluation threshold, then the production process is evaluated as qualified, if the vitamin C drug production quality evaluation value is less than the vitamin C drug production quality evaluation threshold, then the production process is evaluated as unqualified and the drug production is immediately terminated, and an early warning operation is performed at the same time.

[0022] As a further method, the determination of whether a stabilizer needs to be added is made based on the drug preparation stability evaluation index. The specific determination process is as follows: extracting a drug preparation stability evaluation threshold from a Poria cocos vitamin C database, comparing the drug preparation stability evaluation index with the drug preparation stability evaluation threshold; if the drug preparation stability evaluation index is greater than or equal to the drug preparation stability evaluation threshold, no additional operation is performed; if the drug preparation stability evaluation index is less than the drug preparation stability evaluation threshold, inputting the drug preparation stability evaluation index into the Poria cocos vitamin C database to match and obtain a stabilizer addition amount, and adding a stabilizer according to the stabilizer addition amount.

[0023] As a further method, the vitamin C drug production quality assessment value, the specific numerical expression is: in, It represents the production quality assessment value of vitamin C drug. Represents the comprehensive quality evaluation index of excipients, represents the production abnormality assessment index, Represents the stability assessment index of drug preparations. It indicates the impact factor of vitamin C drug production quality assessment corresponding to the set excipient comprehensive quality assessment index. It indicates the impact factor of vitamin C drug production quality assessment corresponding to the set production abnormality assessment index. It represents the impact factor of vitamin C drug production quality assessment corresponding to the set drug preparation stability assessment index.

[0024] The second aspect of the present invention provides a spectral technology-based Poria cocos vitamin C drug production optimization system, including: a drug data acquisition module for collecting auxiliary material data, production process data and drug preparation data in the production process of Poria cocos vitamin C drug.

[0025] The qualified excipient screening module is used to process the excipient data to obtain the excipient quality evaluation index of each candidate excipient, and screen each qualified excipient according to the excipient quality evaluation index of each candidate excipient.

[0026] The Poria cocos vitamin C production quality analysis module is used to process the production process data to obtain the production abnormality assessment index, process the drug preparation data to obtain the drug preparation stability assessment index, and obtain the excipient quality assessment index of each qualified excipient, and comprehensively analyze to obtain the vitamin C drug production quality assessment value.

[0027] The Poria cocos vitamin C drug production process evaluation module is used to evaluate and provide feedback on the Poria cocos vitamin C drug production process based on the vitamin C drug production quality evaluation value.

[0028] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0029] (1) The present invention provides a method and system for optimizing the production of Poria cocos vitamin C drugs based on spectral technology, which can identify bottlenecks and problems in the production process, and then optimize and improve the process, which helps to understand the efficacy and stability of the drug under different conditions, thereby ensuring that the drug can maintain a stable efficacy within the validity period and further improving the overall quality of the drug.

[0030] (2) By evaluating the stability evaluation index of drug preparations, the present invention can timely discover potential safety hazards in drug preparations and take measures to eliminate them, thereby improving the safety of drugs. It can also understand the impact of various factors on drug stability, thereby optimizing the formulation and process, improving the stability and efficacy of drugs, and further ensuring the safety of patients' medication.

[0031] (3) The present invention can select excipients with better stability by evaluating the excipient quality evaluation index of each candidate excipient, thereby improving the shelf life of the drug and optimizing the efficacy of the drug, and can also reduce the content of impurities in the drug and improve the purity and safety of the drug. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0033] Figure 1 It is a schematic diagram of the method flow of the present invention.

[0034] Figure 2 It is a schematic diagram of system module connection of the present invention.

[0035] Figure 3 It is a schematic diagram of the functional relationship between the vitamin C drug production quality assessment value and the drug preparation stability assessment index of the present invention. DETAILED DESCRIPTION

[0036] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0037] Reference Figure 1 As shown, the first aspect of the present invention provides a method for optimizing the production of Poria cocos and vitamin C drugs based on spectral technology, including: collecting auxiliary material data, production process data and drug preparation data in the production process of Poria cocos and vitamin C drugs.

[0038] The excipient data is processed to obtain the excipient quality evaluation index of each candidate excipient, and each qualified excipient is screened according to the excipient quality evaluation index of each candidate excipient.

[0039] The production process data is processed to obtain the production abnormality assessment index, the drug preparation data is processed to obtain the drug preparation stability assessment index, and the excipient quality assessment index of each qualified excipient is obtained. Comprehensive analysis is performed to obtain the vitamin C drug production quality assessment value.

[0040] Based on the vitamin C drug production quality assessment value, the production process of Poria vitamin C drug was evaluated and fed back.

[0041] Specifically, the excipient data are processed to obtain the excipient quality evaluation index of each candidate excipient, and the specific processing process is: the excipient data include the purity, average particle size, microbial count and bulk density of each candidate excipient.

[0042] The critical purity of each excipient, the average particle size of each excipient reference standard, the average particle size with allowable deviation, the critical microbial count of each excipient, the bulk density of each excipient reference standard and the bulk density with allowable deviation were extracted from the Poria cocos vitamin C database, and the excipient quality evaluation index of each candidate excipient was obtained through comprehensive analysis.

[0043] In a specific embodiment, purity refers to the content of the target ingredient in the excipient, which reflects the purity and impurity content of the excipient, and can be obtained by high performance liquid chromatography; average particle size refers to the coarseness and distribution of the excipient particles, which affects the solubility, bioavailability and stability of the drug, and can be measured by a laser particle size analyzer; the number of microorganisms refers to the number of bacteria, molds, and yeast microorganisms that may exist in the excipient, which reflects the sanitary condition of the excipient and can be measured by a microbial counter; bulk density refers to the ratio of the volume to mass of the excipient particles in a naturally stacked state, which reflects the filling and fluidity of the excipient, and can be measured by a powder density tester.

[0044] Furthermore, the excipient quality evaluation index of each candidate excipient is specifically expressed as: in, represents the excipient quality evaluation index of the i-th candidate excipient, represents the purity of the i-th candidate excipient, represents the critical purity of the i-th candidate excipient, represents the average particle size of the i-th candidate excipient, represents the average particle size of the reference standard of the i-th candidate excipient, Indicates the allowable deviation of the average particle size, represents the number of microorganisms in the i-th candidate excipient, represents the critical microbial count of the i-th candidate excipient, represents the bulk density of the i-th candidate auxiliary material, represents the reference standard bulk density of the i-th candidate excipient, Indicates the allowable deviation bulk density, Indicates the influencing factor of auxiliary material quality assessment corresponding to the set purity. Indicates the influencing factor of auxiliary material quality assessment corresponding to the set average particle size, Indicates the influencing factor of auxiliary material quality assessment corresponding to the set number of microorganisms, It represents the auxiliary material quality assessment influencing factor corresponding to the set bulk density, i represents the number of each candidate auxiliary material, i=1,2,3,...,m, and m represents the total number of candidate auxiliary materials.

[0045] The algorithm of this embodiment combines the purity, average particle size, number of microorganisms and bulk density of each candidate excipient, and comprehensively analyzes to obtain the excipient quality evaluation index of each candidate excipient. Excipients with higher purity usually mean fewer impurities and contaminants, including microorganisms, and more stable physical properties, including bulk density; excipients with uniform particle size distribution contribute to uniform release and absorption of drugs in the body, and purity is one of the important factors affecting the uniformity of particle size distribution; smaller particle size may increase the specific surface area of ​​the excipient, thereby increasing the risk of microbial contamination; excipients with smaller average particle size may have higher bulk density, because smaller particles are easier to pack tightly; excipients with a high number of microorganisms may mean worse sanitary conditions and storage environment, which may cause changes in the bulk density of the excipient and make it more susceptible to microbial contamination. Comprehensive analysis can obtain a more comprehensive excipient quality evaluation index.

[0046] It should be explained that four key factors are considered in this embodiment, namely, the purity, average particle size, microbial count and bulk density of each candidate excipient, which helps to reduce potential sources of contamination in the drug production process, improve the purity and quality of the drug, improve the bioavailability and efficacy of the drug, reduce the risk of drug deterioration or failure due to microbial contamination, help the filling, compression and fluidity of the drug, and thus affect the preparation process and final quality of the drug, help enhance the stability of the drug, and improve the accuracy and efficiency of the preparation process. By standardizing the purity, average particle size, microbial count and bulk density of each candidate excipient, ensuring that they are compared at the same level, the fairness and comparability of the evaluation are improved, and at the same time , , and The setting can avoid excipient quality problems caused by low purity, unreasonable average particle size, excessive number of microorganisms and unreasonable bulk density. By weighting the influence of purity, average particle size, number of microorganisms and bulk density of each candidate excipient, it reflects their relative importance in the evaluation index. The weights of different factors can be adjusted according to different needs, making the formula highly adaptable. It is not difficult to see that the greater the purity or the smaller the average particle size deviation or the smaller the number of microorganisms or the smaller the bulk density deviation, the greater the excipient quality evaluation index. By evaluating the excipient quality evaluation index of each candidate excipient, excipients with better stability can be selected, thereby increasing the shelf life of the drug, helping to select excipients suitable for drug release requirements, thereby optimizing the efficacy of the drug and patient experience, and also reducing the content of impurities in the drug, improving the purity and safety of the drug, and further improving the bioavailability and efficacy of the drug. In a specific embodiment, the value range of the auxiliary material quality assessment influencing factor corresponding to purity, average particle size, number of microorganisms and bulk density is between 0 and 1, which represents the numerical value of the influence degree of purity, average particle size, number of microorganisms and bulk density on the auxiliary material quality assessment index. Each auxiliary material quality assessment influencing factor can be obtained from the Poria cocos vitamin C database. By adjusting the value of the influencing factor, the influence degree of different factors on the final auxiliary material quality assessment index can be flexibly adjusted. The corresponding relationship can be a pre-set mapping relationship. For example, purity, average particle size, number of microorganisms and bulk density form a mapping set with the weight factors corresponding to the purity, average particle size, number of microorganisms and bulk density preset in the Poria cocos vitamin C database, and the real-time purity, average particle size, number of microorganisms and bulk density are brought into the mapping set to obtain the weight factors corresponding to the purity, average particle size, number of microorganisms and bulk density, wherein the mapping relationship can be a one-to-one correspondence or a many-to-one relationship.

[0047] Furthermore, qualified excipients are screened according to the excipient quality evaluation index of each candidate excipient. The specific screening process is: extracting each excipient quality evaluation threshold from the Poria vitamin C database, comparing the excipient quality evaluation index of each candidate excipient with each excipient quality evaluation threshold respectively, if the excipient quality evaluation index of a candidate excipient is greater than or equal to the excipient quality evaluation threshold corresponding to the candidate excipient, then the candidate excipient is marked as a qualified excipient, if the excipient quality evaluation index of a candidate excipient is less than the excipient quality evaluation threshold corresponding to the candidate excipient, then the candidate excipient is marked as an unqualified excipient.

[0048] Obtain statistics of each unqualified auxiliary material, and perform auxiliary material replacement operations on each unqualified auxiliary material.

[0049] Specifically, the production process data are processed to obtain the production anomaly assessment index, and the specific processing process is as follows: the production process data include the granulation temperature, light intensity and solvent ratio at each time node within the monitoring period.

[0050] The reference standard granulation temperature, allowable deviation granulation temperature, reference standard light intensity, allowable deviation light intensity, reference standard solvent ratio and allowable deviation solvent ratio were extracted from the Poria cocos vitamin C database, and the production abnormality assessment index was obtained through comprehensive analysis.

[0051] In a specific embodiment, the granulation temperature refers to the temperature reached by the material during the drug granulation process, which is an important factor in controlling the quality of the particles and the performance of the drug, and can be measured using an infrared thermometer; reasonable control of light intensity can ensure the quality stability of the drug during production and storage, and the light intensity can be monitored by a light sensor; the solvent ratio refers to the proportion of the solvent, i.e., ethanol, in the solution. A reasonable solvent ratio can ensure that the active ingredients in Poria are fully extracted, thereby improving the extraction efficiency and product quality, and can be measured by a high performance liquid chromatograph.

[0052] Furthermore, the specific numerical expression of the production abnormality evaluation index is: in, represents the production abnormality assessment index, e represents the natural constant, represents the granulation temperature at the jth time node, Indicates reference standard granulation temperature, Indicates the allowable deviation of granulation temperature, represents the light intensity at the jth time node, Indicates the reference standard light intensity, Indicates the allowable deviation of light intensity, represents the solvent ratio at the jth time node, Indicates the reference standard solvent ratio, Indicates the allowable deviation solvent ratio, Indicates the production abnormality assessment impact factor corresponding to the set granulation temperature, Indicates the production abnormality assessment impact factor corresponding to the set light intensity, It represents the production abnormality assessment impact factor corresponding to the set solvent ratio, j represents the number of each time node, j=1,2,3,...,n, and n represents the total number of time nodes.

[0053] The algorithm of this embodiment combines the granulation temperature, light intensity and solvent ratio at each time node, and comprehensively analyzes to obtain the production abnormality assessment index. The granulation temperature will affect the volatility of the solvent. The higher the temperature, the stronger the volatility of the solvent, which may lead to a decrease in the solvent ratio; the light intensity may also affect the extraction efficiency of the solvent for the active ingredients in Poria cocos. During the extraction process, the closer the light conditions are to the reference standard, the more likely it is to promote the interaction between the solvent and the active ingredients in Poria cocos and improve the extraction efficiency; the closer the temperature is to the reference standard, the faster the volatilization of the solvent can be, so that the particles can be dried and solidified quickly, thereby improving production efficiency. Comprehensive analysis can obtain a more comprehensive production abnormality assessment index.

[0054] Table 1 Example of production abnormality assessment index data

[0055]

[0056] As shown in Table 1, the production abnormality assessment index is jointly determined by the granulation temperature, light intensity and solvent ratio at each time node. In a specific embodiment, n=1, the reference standard granulation temperature is 100°C, the allowable deviation granulation temperature is 10°C, the reference standard light intensity is 50lx, the allowable deviation light intensity is 5lx, the reference standard solvent ratio is 60%, the allowable deviation solvent ratio is 10%, the set granulation temperature corresponds to a production abnormality assessment impact factor of 0.4, the set light intensity corresponds to a production abnormality assessment impact factor of 0.3, and the set solvent ratio corresponds to a production abnormality assessment impact factor of 0.4. This formula takes into account three key factors, namely, granulation temperature, light intensity, and solvent ratio at each time point. It can speed up granulation, shorten production cycle, improve production efficiency and product quality, optimize production process, reduce unnecessary waste and loss, and help prevent the degradation of drug ingredients or the occurrence of photosensitivity reactions, thereby reducing errors and mistakes in the production process, improving product qualification rate, and ensuring that there are no excessive solvent residues in the drug, thereby ensuring the safety of the drug and further ensuring the quality stability of the drug during production and storage. By standardizing the granulation temperature, light intensity, and solvent ratio at each time point and ensuring that they are compared at the same magnitude, the fairness and comparability of the evaluation are improved, and at the same time , and The setting can avoid production anomalies and drug quality problems caused by unreasonable granulation temperature, light intensity and solvent ratio. By weighting the influence of granulation temperature, light intensity and solvent ratio at each time node, it reflects their relative importance in the evaluation index. The weights of different factors can be adjusted according to different needs, making the model very adaptable. It is not difficult to see that the greater the deviation of granulation temperature, light intensity or solvent ratio, the greater the production anomaly evaluation index. By evaluating the production anomaly evaluation index, abnormal situations in the production process can be discovered in time, which helps companies to take quick measures to correct them and prevent the problem from further expanding, thereby ensuring the stability of the final product quality, reducing the incidence of product quality problems, and improving the product qualification rate. The key factors affecting production efficiency can be found, further ensuring the stability of the production process, reducing fluctuations and uncertainties in the production process, thereby improving product stability and consistency, and meeting the market demand for high-quality products.

[0057] In a specific embodiment, the value range of the auxiliary material quality assessment influencing factor corresponding to the granulation temperature, light intensity and solvent ratio is between 0 and 1, which represents the numerical value of the influence degree of the granulation temperature, light intensity and solvent ratio on the auxiliary material quality assessment index. Each auxiliary material quality assessment influencing factor can be obtained from the Poria cocos vitamin C database. By adjusting the value of the influencing factor, the influence degree of different factors on the final auxiliary material quality assessment index can be flexibly adjusted. The corresponding relationship can be a pre-set mapping relationship. For example, the granulation temperature, light intensity and solvent ratio and the weight factors corresponding to the granulation temperature, light intensity and solvent ratio preset in the Poria cocos vitamin C database form a mapping set, and the real-time granulation temperature, light intensity and solvent ratio are brought into the mapping set to obtain the weight factors corresponding to the granulation temperature, light intensity and solvent ratio, wherein the mapping relationship can be a one-to-one correspondence or a many-to-one relationship.

[0058] Specifically, the drug preparation data is processed to obtain a drug preparation stability evaluation index, and the specific analysis process is as follows: the drug preparation data includes the content of each key component, water content and impurity content at each time node.

[0059] The critical content of each key component, the reference standard moisture content at each time point, the allowable deviation moisture content and the critical impurity content were extracted from the Poria cocos vitamin C database, and a comprehensive analysis was performed to obtain the drug preparation stability evaluation index. The need to add a stabilizer was determined based on the drug preparation stability evaluation index.

[0060] In a specific embodiment, the key ingredients include polysaccharides, pachymic acid, ergosterol and vitamin C. Ensuring that the content of these key ingredients meets the specified standards is the primary task in drug production. The closer the moisture content is to the reference standard, the more stable the drug can be during production and storage, and the drug can be prevented from deteriorating or degrading due to excessive moisture. At the same time, the impurity content is one of the important indicators for measuring drug purity, which is crucial to ensuring the efficacy and safety of the drug. The content of each key ingredient, moisture content and impurity content can be monitored in real time by a spectrometer.

[0061] Furthermore, the specific numerical expression of the drug preparation stability evaluation index is: in, represents the stability evaluation index of drug preparations, e represents the natural constant, represents the content of the xth key component at the jth time node, represents the critical content of the xth key component, represents the moisture content at the jth time node, represents the reference standard moisture content at the jth time node, Indicates the allowable deviation moisture content, represents the impurity content at the jth time node, Indicates the critical impurity content, Indicates the impact factor of drug preparation stability assessment corresponding to the set key component content, Indicates the influencing factor of drug preparation stability assessment corresponding to the set moisture content, It represents the influencing factor of drug preparation stability assessment corresponding to the set impurity content, x represents the number of each key component, x=1,2,3,...,y, y represents the total number of key components, j represents the number of each time node, j=1,2,3,...,n, n represents the total number of time nodes.

[0062] The algorithm of this embodiment combines the content of each key component, moisture content and impurity content at each time node, and comprehensively analyzes to obtain the drug preparation stability evaluation index. The content of the main active ingredient in Poria cocos directly affects the efficacy of Poria cocos, and the moisture content is an important factor affecting the quality and storage stability of Poria cocos. The higher the moisture content, the more likely it is that Poria cocos will be moldy and worm-eaten, thereby affecting its efficacy; the content of key components in Poria cocos is closely related to the removal effect of impurities. For example, if the polysaccharide content in Poria cocos is higher, more attention should be paid to removing impurities bound to polysaccharides during extraction and purification; similarly, if the content of components such as pachymic acid in Poria cocos is higher, it is also necessary to pay attention to whether these components will be affected by impurities during extraction and purification; there is also a certain correlation between the moisture content and impurity content of Poria cocos. The higher the moisture content, the more likely it is that the impurities in Poria cocos will increase, because moisture can promote the dissolution and diffusion of impurities. Comprehensive analysis can obtain a more comprehensive drug preparation stability evaluation index.

[0063] It should be explained that the three key factors, namely the content of each key component, the water content and the impurity content, are taken into consideration in this embodiment, which can ensure that the drug exerts its pharmacological effect, thereby enhancing the efficacy of the drug, and can further enhance the immunomodulatory effect of the drug, promote normal cell growth and differentiation, help extend the shelf life of the drug, ensure that the drug maintains its efficacy and safety during the validity period, and improve the purity of the drug, thereby ensuring that the drug exerts its best efficacy, further ensuring the safety of the drug, and avoiding adverse reactions in patients during use. By standardizing the content of each key component, water content and impurity content, ensuring that they are compared at the same magnitude, the fairness and comparability of the evaluation are improved, and at the same time , and The setting can avoid efficacy and drug quality problems caused by too low content of key ingredients, unreasonable moisture content and too high impurity content. By weighting the impact of the content of each key ingredient, moisture content and impurity content, it reflects their relative importance in the evaluation index. The weights of different factors can be adjusted according to different needs, making the formula very adaptable. It is not difficult to see that the smaller the content of each key ingredient, moisture content or impurity content, the greater the stability evaluation index of the drug preparation. By evaluating the stability evaluation index of drug preparations, it helps companies to take timely measures, such as adjusting production processes, improving storage conditions, etc., to ensure the stability of drug quality, timely discover potential safety hazards in drug preparations, and take measures to eliminate them, thereby improving the safety of drugs. It can also understand the impact of various factors on drug stability, thereby optimizing the formula and process, improving the stability and efficacy of drugs, and further ensuring the safety of patients' medication.

[0064] In a specific embodiment, the value range of the drug preparation stability assessment influencing factor corresponding to the key component content, moisture content and impurity content is between 0 and 1, which represents the numerical value of the influence degree of the key component content, moisture content and impurity content on the drug preparation stability assessment index. Each drug preparation stability assessment influencing factor can be obtained from the Poria cocos vitamin C database. By adjusting the value of the influencing factor, the influence degree of different factors on the final drug preparation stability assessment index can be flexibly adjusted. The corresponding relationship can be a pre-set mapping relationship. For example, the key component content, moisture content and impurity content and the weight factors corresponding to the key component content, moisture content and impurity content preset in the Poria cocos vitamin C database form a mapping set, and the real-time key component content, moisture content and impurity content are brought into the mapping set to obtain the weight factors corresponding to the key component content, moisture content and impurity content, wherein the mapping relationship can be a one-to-one correspondence or a many-to-one relationship.

[0065] Furthermore, whether it is necessary to add a stabilizer is determined according to the drug preparation stability evaluation index. The specific judgment process is: extracting a drug preparation stability evaluation threshold from the Poria cocos vitamin C database, comparing the drug preparation stability evaluation index with the drug preparation stability evaluation threshold, if the drug preparation stability evaluation index is greater than or equal to the drug preparation stability evaluation threshold, no additional operation is performed, if the drug preparation stability evaluation index is less than the drug preparation stability evaluation threshold, then inputting the drug preparation stability evaluation index into the Poria cocos vitamin C database to match the stabilizer addition amount, and adding the stabilizer according to the stabilizer addition amount.

[0066] Specifically, a comprehensive analysis is performed to obtain a vitamin C drug production quality assessment value, and the specific analysis process is as follows: the excipient quality assessment indexes of each qualified excipient are summed and averaged to obtain an excipient comprehensive quality assessment index.

[0067] Based on the production abnormality assessment index, drug preparation stability assessment index and excipient comprehensive quality assessment index, a comprehensive analysis was conducted to obtain the vitamin C drug production quality assessment value.

[0068] Furthermore, the specific numerical expression of the vitamin C drug production quality assessment value is: in, It represents the production quality assessment value of vitamin C drug. Represents the comprehensive quality evaluation index of excipients, represents the production abnormality assessment index, Represents the stability assessment index of drug preparations. It indicates the impact factor of vitamin C drug production quality assessment corresponding to the set excipient comprehensive quality assessment index. It indicates the impact factor of vitamin C drug production quality assessment corresponding to the set production abnormality assessment index. It represents the impact factor of vitamin C drug production quality assessment corresponding to the set drug preparation stability assessment index.

[0069] like Figure 3 As shown, in a specific embodiment, =0.4, =0.3, =0.4, =1.0. =0.1, the functional relationship between the vitamin C drug production quality assessment value and the drug preparation stability assessment index is shown in curve a; when =0.5, the functional relationship between the vitamin C drug production quality assessment value and the drug preparation stability assessment index is shown in curve b; when =1, the functional relationship between the vitamin C drug production quality assessment value and the drug preparation stability assessment index is shown in curve c.

[0070] The algorithm of this embodiment combines the production anomaly assessment index, the drug preparation stability assessment index and the excipient comprehensive quality assessment index, and comprehensively analyzes to obtain the vitamin C drug production quality assessment value. The level of the production anomaly assessment index can directly reflect the risk level of the drug preparation stability assessment index. When the production anomaly assessment index is higher, it means that there are more potential problems in the production process, and the stability of the drug preparation may also be greatly affected; when the excipient comprehensive quality assessment index is higher, it means that the quality of the excipient is stable and meets the production requirements, which helps to ensure the stability of the drug preparation. Comprehensive analysis can obtain a more comprehensive vitamin C drug production quality assessment value.

[0071] It should be explained that three key factors, namely, production abnormality evaluation index, drug preparation stability evaluation index and excipient comprehensive quality evaluation index, are considered in this embodiment, which can optimize the production process, reduce waste and loss in the production process, thereby improving production efficiency, and timely discover and deal with safety hazards in the production process, avoid accidents, ensure the safety of employees and products, and ensure that the drug maintains a stable efficacy during the validity period, thereby avoiding toxic reactions caused by drug degradation or deterioration, and can help R&D personnel understand the stability and quality changes of drugs under different conditions, thereby optimizing drug formulations and production processes, and further more accurately evaluating the quality and safety of new drugs, thereby accelerating the development of new drugs and providing patients with more treatment options. By weighting the impact of the production abnormality evaluation index, the drug preparation stability evaluation index and the excipient comprehensive quality evaluation index, it reflects their relative importance in the evaluation index, and the weights of different factors can be adjusted according to different needs, so that the formula has good adaptability. It is not difficult to see that when the production abnormality evaluation index is smaller or the drug preparation stability evaluation index is larger or the excipient comprehensive quality evaluation index is larger, the vitamin C drug production quality evaluation value is larger. By evaluating the production quality assessment value of vitamin C drugs, we can discover bottlenecks and problems in the production process, and then optimize and improve the process. This will help us understand the efficacy and stability of the drug under different conditions, thereby ensuring that the drug can maintain stable efficacy during its shelf life. It can more accurately evaluate the quality and safety of new drugs, thereby accelerating the research and development process of new drugs and further improving the overall quality of drugs.

[0072] In a specific embodiment, the value range of the vitamin C drug production quality assessment influencing factor corresponding to the production abnormality assessment index, the drug preparation stability assessment index and the excipient comprehensive quality assessment index is between 0 and 1, indicating the numerical value of the influence of the production abnormality assessment index, the drug preparation stability assessment index and the excipient comprehensive quality assessment index on the vitamin C drug production quality assessment value. Each vitamin C drug production quality assessment influencing factor can be obtained from the Poria vitamin C database. By adjusting the value of the influencing factor, the influence of different factors on the final vitamin C drug production quality assessment value can be flexibly adjusted. The corresponding relationship can be a pre-set mapping relationship. For example, the production abnormality assessment index, the drug preparation stability assessment index and the excipient comprehensive quality assessment index are preset in the Poria vitamin C database. The weight factors corresponding to the production abnormality assessment index, the drug preparation stability assessment index and the excipient comprehensive quality assessment index form a mapping set, and the real-time production abnormality assessment index, the drug preparation stability assessment index and the excipient comprehensive quality assessment index are brought into the mapping set to obtain the weight factors corresponding to the production abnormality assessment index, the drug preparation stability assessment index and the excipient comprehensive quality assessment index, wherein the mapping relationship can be a one-to-one correspondence or a many-to-one relationship.

[0073] Furthermore, the production process of Poria cocos vitamin C drug is evaluated and fed back according to the vitamin C drug production quality assessment value. The specific evaluation process is: the vitamin C drug production quality assessment threshold is extracted from the Poria cocos vitamin C database, and the vitamin C drug production quality assessment value is compared with the vitamin C drug production quality assessment threshold. If the vitamin C drug production quality assessment value is greater than or equal to the vitamin C drug production quality assessment threshold, the production process is evaluated as qualified; if the vitamin C drug production quality assessment value is less than the vitamin C drug production quality assessment threshold, the production process is evaluated as unqualified and drug production is immediately terminated. At the same time, an early warning operation is performed, that is, a warning signal is sent to relevant staff.

[0074] Reference Figure 2 As shown, the second aspect of the present invention provides a Poria cocos vitamin C drug production optimization system based on spectral technology, including: a drug data acquisition module for collecting auxiliary material data, production process data and drug preparation data in the production process of Poria cocos vitamin C drug.

[0075] The qualified excipient screening module is used to process the excipient data to obtain the excipient quality evaluation index of each candidate excipient, and screen each qualified excipient according to the excipient quality evaluation index of each candidate excipient.

[0076] The Poria cocos vitamin C production quality analysis module is used to process the production process data to obtain the production abnormality assessment index, process the drug preparation data to obtain the drug preparation stability assessment index, and obtain the excipient quality assessment index of each qualified excipient, and comprehensively analyze to obtain the vitamin C drug production quality assessment value.

[0077] The Poria cocos vitamin C drug production process evaluation module is used to evaluate and provide feedback on the Poria cocos vitamin C drug production process based on the vitamin C drug production quality evaluation value.

[0078] The Poria cocos vitamin C database is used to store Poria cocos vitamin C related data, including: critical purity of each excipient, average particle size of each excipient reference standard, average particle size with allowable deviation, critical number of microorganisms of each excipient, bulk density of each excipient reference standard, bulk density with allowable deviation, granulation temperature of reference standard, granulation temperature with allowable deviation, light intensity of reference standard, light intensity with allowable deviation, solvent ratio of reference standard, solvent ratio with allowable deviation, critical content of each key ingredient, moisture content of reference standard at each time node, moisture content with allowable deviation, critical impurity content, vitamin C drug production quality assessment influencing factor corresponding to the set excipient comprehensive quality assessment index, vitamin C drug production quality assessment influencing factor corresponding to the set production abnormality assessment index, vitamin C drug production quality assessment influencing factor corresponding to the set drug preparation stability assessment index, quality assessment thresholds of each excipient, stability assessment thresholds of drug preparations and vitamin C drug production quality assessment thresholds.

[0079] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A method for optimizing the production of Poria cocos vitamin C drugs based on spectral technology, characterized in that: include: Collect excipient data, production process data and drug preparation data in the production process of Poria cocos vitamin C drugs; Processing the excipient data to obtain the excipient quality evaluation index of each candidate excipient, and screening each qualified excipient according to the excipient quality evaluation index of each candidate excipient; The production process data is processed to obtain the production abnormality assessment index, the drug preparation data is processed to obtain the drug preparation stability assessment index, and the excipient quality assessment index of each qualified excipient is obtained, and the vitamin C drug production quality assessment value is obtained through comprehensive analysis; According to the vitamin C drug production quality assessment value, the production process of Poria vitamin C drug was evaluated and fed back; The excipient data is processed to obtain the excipient quality evaluation index of each candidate excipient, and the specific processing process is: the excipient data includes the purity, average particle size, microbial count and bulk density of each candidate excipient; The critical purity of each excipient, the average particle size of each excipient reference standard, the average particle size with allowable deviation, the critical microbial count of each excipient, the bulk density of each excipient reference standard and the bulk density with allowable deviation were extracted from the Poria cocos vitamin C database, and the excipient quality evaluation index of each candidate excipient was obtained through comprehensive analysis. The qualified excipients are obtained by screening according to the excipient quality evaluation index of each candidate excipient, and the specific screening process is: extracting each excipient quality evaluation threshold from the Poria cocos vitamin C database, comparing the excipient quality evaluation index of each candidate excipient with each excipient quality evaluation threshold, if the excipient quality evaluation index of a candidate excipient is greater than or equal to the excipient quality evaluation threshold corresponding to the candidate excipient, then marking the candidate excipient as a qualified excipient, if the excipient quality evaluation index of a candidate excipient is less than the excipient quality evaluation threshold corresponding to the candidate excipient, then marking the candidate excipient as an unqualified excipient; Obtain statistics of each unqualified auxiliary material, and perform auxiliary material replacement operations on each unqualified auxiliary material.

2. The method for optimizing the production of Poria cocos vitamin C drugs based on spectroscopy technology according to claim 1, characterized in that: The production process data is processed to obtain the production abnormality assessment index, and the specific processing process is as follows: The production process data include granulation temperature, light intensity and solvent ratio at each time point in the monitoring cycle; The reference standard granulation temperature, allowable deviation granulation temperature, reference standard light intensity, allowable deviation light intensity, reference standard solvent ratio and allowable deviation solvent ratio were extracted from the Poria cocos vitamin C database, and the production abnormality assessment index was obtained through comprehensive analysis.

3. The method for optimizing the production of Poria cocos vitamin C drugs based on spectroscopy according to claim 1, characterized in that: The pharmaceutical preparation data is processed to obtain the pharmaceutical preparation stability evaluation index, and the specific analysis process is as follows: The drug preparation data includes the content of each key component, moisture content and impurity content at each time point; The critical content of each key component, the reference standard moisture content at each time point, the allowable deviation moisture content and the critical impurity content were extracted from the Poria cocos vitamin C database, and a comprehensive analysis was performed to obtain the drug preparation stability evaluation index. The need to add a stabilizer was determined based on the drug preparation stability evaluation index.

4. A method for optimizing the production of Poria cocos vitamin C drugs based on spectroscopy technology according to claim 3, characterized in that: The comprehensive analysis obtains the production quality assessment value of vitamin C medicine, and the specific analysis process is as follows: The excipient quality assessment index of each qualified excipient is summed and averaged to obtain the excipient comprehensive quality assessment index; Based on the production abnormality assessment index, drug preparation stability assessment index and excipient comprehensive quality assessment index, a comprehensive analysis was conducted to obtain the vitamin C drug production quality assessment value.

5. A method for optimizing the production of Poria cocos vitamin C drugs based on spectroscopy technology according to claim 4, characterized in that: The production process of Poria cocos vitamin C drug is evaluated and fed back according to the vitamin C drug production quality evaluation value. The specific evaluation process is as follows: The vitamin C drug production quality assessment threshold is extracted from the Poria vitamin C database, and the vitamin C drug production quality assessment value is compared with the vitamin C drug production quality assessment threshold. If the vitamin C drug production quality assessment value is greater than or equal to the vitamin C drug production quality assessment threshold, the production process is assessed as qualified; if the vitamin C drug production quality assessment value is less than the vitamin C drug production quality assessment threshold, the production process is assessed as unqualified and drug production is immediately terminated, and an early warning operation is performed at the same time.

6. The method for optimizing the production of Poria cocos vitamin C medicine based on spectroscopy technology according to claim 3, characterized in that: The specific process of judging whether to add a stabilizer according to the stability evaluation index of the drug preparation is as follows: The drug preparation stability assessment threshold is extracted from the Poria cocos vitamin C database, and the drug preparation stability assessment index is compared with the drug preparation stability assessment threshold. If the drug preparation stability assessment index is greater than or equal to the drug preparation stability assessment threshold, no additional operation is performed. If the drug preparation stability assessment index is less than the drug preparation stability assessment threshold, the drug preparation stability assessment index is input into the Poria cocos vitamin C database to match the stabilizer addition amount, and the stabilizer is added according to the stabilizer addition amount.

7. The method for optimizing the production of Poria cocos vitamin C drugs based on spectroscopy according to claim 4, characterized in that: The specific numerical expression of the vitamin C drug production quality assessment value is: in, It represents the production quality assessment value of vitamin C drug. Represents the comprehensive quality evaluation index of excipients, represents the production abnormality assessment index, Represents the stability assessment index of drug preparations. It indicates the impact factor of vitamin C drug production quality assessment corresponding to the set excipient comprehensive quality assessment index. It indicates the impact factor of vitamin C drug production quality assessment corresponding to the set production abnormality assessment index. It represents the impact factor of vitamin C drug production quality assessment corresponding to the set drug preparation stability assessment index.

8. A system for optimizing the production of Poria cocos vitamin C drugs based on spectroscopy technology as described in any one of claims 1 to 7, characterized in that: include: The drug data collection module is used to collect auxiliary material data, production process data and drug preparation data in the production process of Poria cocos vitamin C drugs; A qualified excipient screening module is used to process excipient data to obtain an excipient quality evaluation index of each candidate excipient, and to screen each qualified excipient according to the excipient quality evaluation index of each candidate excipient; The Poria cocos vitamin C production quality analysis module is used to process the production process data to obtain the production abnormality assessment index, process the drug preparation data to obtain the drug preparation stability assessment index, and obtain the excipient quality assessment index of each qualified excipient, and comprehensively analyze to obtain the vitamin C drug production quality assessment value; The Poria cocos vitamin C drug production process evaluation module is used to evaluate and provide feedback on the Poria cocos vitamin C drug production process based on the vitamin C drug production quality evaluation value.

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