Method for analyzing relation between process impurities and degraded impurities based on big data analysis technology

Abstract

The invention relates to a big data analysis technology-based process impurity and degraded impurity relationship analysis method, which comprises the following steps of: calculating correlation coefficients among process impurities, typical impurity variables and degraded impurities through a canonical correlation analysis method; and constructing a transfer relationship among various impurities, typical impurity variables and various degraded impurities through correlation coefficient screening, so as to judge the correlation degree between the process impurities and the degraded impurities. The analysis method provided by the invention comprehensively analyzes the impurity formation rule of the medicine in the production, storage and use processes, deeply excavates the correlation between process impurities and degraded impurities, reveals the internal relevance of different impurities, not only helps to guide the direction of impurity spectrum research, but also helps to improve the quality of the medicine. Scientific basis is provided for control of key impurities of drugs and process optimization, the safety and effectiveness of the drugs are improved, and further development of pharmaceutical science and pharmaceutical industry is promoted.

Description

Technical Field

[0001] The present invention belongs to the field of data analysis technology, and in particular relates to an analysis method for the relationship between process impurities and degradation impurities based on big data analysis technology. Background Art

[0002] In the process of modern drug development and production, drug quality control has always been a crucial link. With the advancement of science and technology and the development of data analysis technology, drug quality research guided by big data has gradually become a research hotspot and an important means for drug development, drug safety and effectiveness evaluation. However, drug quality big data research is a cutting-edge cross-disciplinary field and is still in its infancy. There is still a lack of effective big data analysis methods that can solve specific problems, so big data cannot play the expected role in the actual work of drug research.

[0003] Drug impurities are any substances that affect the purity of drugs during the production, storage and use of drugs. The presence of impurities will not only reduce the purity of drugs, but also may cause adverse reactions, seriously affecting the safety and efficacy of drugs. According to the source of impurities, they are mainly divided into degradation impurities and process impurities. They have significant differences in source, formation mechanism, control measures and monitoring focus.

[0004] Process impurities are mainly introduced during the production process and may come from raw materials, solvents, reagents, intermediates, by-products, containers, etc. Degradation impurities are mainly introduced during the storage process and may come from external conditions, such as improper packaging, transportation, and storage, and are affected by environmental factors such as light temperature, air, and microorganisms. Although process impurities and degradation impurities differ in their sources and characteristics, they are both indispensable components of drug treatment control. Therefore, a comprehensive understanding and effective control of these two types of impurities are crucial to ensuring the safety, effectiveness, and quality controllability of drugs.

[0005] The currently recognized basic idea of ​​impurity research and control is: starting from the analysis of the source of impurities, combining the actual production process and structural characteristics of the product to analyze the various potential impurities such as reaction materials, intermediates, by-products, degradation products, etc. that may exist in the product, and through a comprehensive analysis of the impurity spectrum to grasp the impurity profile of the product, according to the risk level of various potential impurities, to establish appropriate analysis methods in a targeted manner to ensure the effective detection and confirmation of each potential impurity. At the same time, through the analysis of the impurity spectrum, the possible sources and destinations of impurities can be clarified, and targeted control measures can be set for the corresponding impurities in the preparation process. Through the study of the storage conditions of the product packaging box, the degradation of the drug can be effectively inhibited, and the impurities in the drug can be systematically and comprehensively controlled from the source of impurity generation to ensure the quality and safety of the drug.

[0006] The study of impurity sources is a systematic project and is very complex. At present, the study of impurity spectra is basically a case-by-case study. In most cases, it is like a blind man touching an elephant. There is no clear research direction, and the research results often cannot obtain key impurities and key process control points. Impurities are important pharmaceutical parameters that affect the quality of drugs. Controlling the key impurities of products and associating them with the key process control points in the production process is an important part of quality control and a focus of process evaluation. At present, there is a lack of effective technical means and methods for the study of the correlation between general impurities and processes, resulting in unclear key process control points in drug quality control and drug production, and domestic drugs have not known how to improve their processes; and the stability data in drug quality research has not been fully utilized, so that the key problems and causes in the process cannot be well traced and improved. Summary of the invention

[0007] In order to solve the problems of inability to obtain key process control points of drugs, underutilization of relevant stability data analysis and prediction of related safety and effectiveness risks, the present invention provides an analysis method for the relationship between process impurities and degradation impurities based on big data analysis technology.

[0008] The technical solution adopted by the present invention is: an analysis method for the relationship between process impurities and degradation impurities based on big data analysis technology, and the specific steps are as follows: S1: Summarize the types of process impurities and degradation impurities according to the product synthesis process; S2: Correlate the process impurity information with the degradation impurity information, and obtain the correlation coefficients among process impurities, typical impurity variables and degradation impurities by canonical correlation analysis method; S3: Determine the transfer relationship based on the correlation coefficients among process impurities, typical impurity variables and degradation impurities, and screen the correlation between specific categories of degradation impurities and specific categories of process impurities.

[0009] Preferably, step S2 comprises, S2.1: Based on the content of process impurities and degradation impurities in drugs, extract multiple typical impurity variables and calculate the correlation coefficient of each typical impurity variable; S2.2: Calculate the conversion coefficients of each process impurity and each degradation impurity by canonical correlation analysis method; generate multiple canonical variable relationship formulas; S2.3: Test the generated typical variable relationship formula and screen the typical impurity variables corresponding to the typical variables with p-value less than 0.05; S2.4: For the typical impurity variables obtained by screening, the correlation coefficient matrices of the typical impurity variables and the process impurities and degradation impurities are constructed respectively to obtain the correlation coefficients among the process impurities, typical impurity variables and degradation impurities.

[0010] Preferably, the typical correlation coefficient is tested by the likelihood ratio method to screen typical impurity variables.

[0011] Preferably, the typical impurity variables include degradation impurity typical variables and process impurity typical variables.

[0012] Preferably, in step S2.4, the correlation coefficient matrix between degradation impurities and typical variables of degradation impurities, the correlation coefficient matrix between process impurities and typical variables of process impurities, the correlation coefficient matrix between degradation impurities and typical variables of process impurities, and the correlation coefficient matrix between process impurities and typical variables of degradation impurities are calculated respectively.

[0013] Preferably, step S3 comprises, S3.1: Screen the correlation coefficient not less than 0.3 as the candidate transfer relationship; S3.2: Degradation impurities and process impurities that have the same positive correlation coefficient or the same negative correlation coefficient with the same group of typical impurity variables are considered to be correlated.

[0014] Preferably, in step S3.1, the correlation coefficient matrix of typical variables of degradation impurities and process impurities, and the correlation coefficient matrix of typical variables of process impurities and degradation impurities, which have a correlation coefficient of not less than 0.3, are screened to screen the corresponding degradation impurities or process impurities; In step S3.2, based on the correlation coefficients in the correlation coefficient matrix between degradation impurities and typical variables of degradation impurities, and the correlation coefficient matrix between process impurities and typical variables of process impurities, it is compared whether the degradation impurities and process impurities for the same group of typical impurity variables are both positively correlated or both negatively correlated.

[0015] Preferably, the typical impurity variables include typical variables of degradation impurities and typical variables of process impurities, and the typical correlation coefficients of correlated process impurities and degradation impurities are equivalent to the typical correlation coefficients between the typical variables of degradation impurities and the typical variables of process impurities in the corresponding typical impurity variables.

[0016] The advantages and positive effects of the present invention are as follows: on the basis of big data analysis, impurities are associated with process characteristics, correlation analysis technology is applied to perform group correlation analysis from a general perspective, an impurity and group process correlation analysis method is established, and the key process control points, key impurities and other information obtained have guiding significance and application value for drug process improvement, impurity spectrum research, and drug quality evaluation, and provide an objective basis and reference direction for how to improve product quality and process level, thereby promoting continuous process improvement. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Analytical method flow for the relationship between process impurities and degradation impurities; Figure 2The first canonical variable relationship diagram of degradation impurities and process impurities of aztreonam for injection; Figure 3 The second canonical variable relationship diagram of degradation impurities and process impurities of aztreonam for injection; Figure 4 The third canonical variable relationship diagram of degradation impurities and process impurities of aztreonam for injection; Figure 5 Transfer diagram of the correlation between degradation impurities, process impurities and typical variables. DETAILED DESCRIPTION

[0018] The embodiments of the present invention are described below with reference to the accompanying drawings.

[0019] The present invention discloses an analysis method for the relationship between process impurities and degradation impurities based on big data analysis technology. The impurity formation rules of drugs during production, storage and use are comprehensively analyzed, the correlation between process impurities and degradation impurities is deeply explored, and the intrinsic correlation and possible formation mechanism of different impurities are revealed, which not only helps to guide the direction of impurity spectrum research, but also provides a scientific basis for the control of key drug impurities and process optimization, and also helps to improve the safety and effectiveness of drugs, and promote the further development of drug science and pharmaceutical industry.

[0020] First, process impurities and degradation impurities are classified and named, and the correlation coefficients among process impurities, typical impurity variables and degradation impurities are calculated by canonical correlation analysis. Then, the transfer relationship among various process impurities, typical impurity variables and various degradation impurities is constructed by correlation coefficient screening, so as to determine the correlation between process impurities and degradation impurities. Figure 1 The specific analysis method is as follows.

[0021] S1: Summarize the types of process impurities and degradation impurities according to the product synthesis process and name them.

[0022] First, publicly available data is used to collect drug synthesis processes and all relevant impurity information, and the impurity data is cleaned and classified. The relevant information is messy, complex, and has strong professional knowledge. The sources of data currently available include impurity spectrum research data, synthesis process information, and related mechanism research information, etc. The main sources are: stability research data of new drug approvals at home and abroad, impurities and synthesis process related research literature, quality review data, national evaluation sampling data, CAS information, etc. In addition to multiple data sources, the key to the present invention is the need to obtain group drug data, that is, it cannot be data from a single manufacturer or a few manufacturers, but should be from the perspective of variety, as much as possible to collect information on all products on the market for research, so as to avoid the one-sidedness of the data causing the reliability of big data analysis results.

[0023] In order to ensure that the data obtained is authentic and reliable, the acquired public data must be cleaned and the correspondence between different impurity data naming systems and structures must be checked. Impurity naming forms include "Impurity A, B, C...", "Impurity 1, 2, 3...", "Impurity I, II, III...", but it is worth noting that the impurity naming systems in different studies correspond to different impurities. Therefore, the correspondence between impurities from different sources should be ensured based on the structural information of the impurities to obtain basic big data information related to impurities.

[0024] S2: Correlate the process impurity information with the degradation impurity information, and obtain the correlation coefficients among process impurities, typical impurity variables and degradation impurities through canonical correlation analysis method.

[0025] The impurity content data obtained are classified into degradation impurities and process impurities. Process impurities not only reflect the control level of the drug production process, but are also closely related to the stability of different raw material processes and have an intrinsic connection with degradation impurities. Taking the process impurities of the drug as one set of variables and the degradation impurities as another set of variables, the canonical correlation analysis method is used to explore the relationship between the two sets of variables, explore the correlation between different types of process impurities and degradation impurities, study the intrinsic connection between the two, and then reveal the correlation between impurities and processes.

[0026] S2.1: Based on the content of process impurities and degradation impurities in drugs, multiple typical impurity variables are extracted and the correlation coefficients of each typical impurity variable are calculated.

[0027] S2.2: Calculate the conversion coefficients of various process impurities and various degradation impurities through canonical correlation analysis method; generate canonical variable relationship formulas.

[0028] Taking the degradation impurity factor as the independent variable X and the process impurity factor as the dependent variable Y, according to the correlation between the two groups of variables, obtain the typical variable to replace the original variables of process impurities and degradation impurities, and then project the relationship between process impurities and degradation impurities onto the relationship between typical variables to obtain the conversion coefficients of original variables and typical variables. The calculation formulas for the typical degradation impurity variable U and the typical process impurity variable V are: (Formula 1); (Formula 2); Where i=1, 2, …, M, M is the number of common factors, j=1, 2, …, N, N is the observed sample size.

[0029] S2.3: Test the generated typical variable relationship formula; specifically test the typical correlation coefficient by the likelihood ratio method; screen the typical impurity variables corresponding to the typical variables with a p value less than 0.05. Through correlation analysis and hypothesis testing between typical variables, study the correlation between process impurities and degradation impurities. Assume H0, the typical correlation coefficients corresponding to the current and subsequent typical variables are all 0; use the likelihood ratio method to test the typical correlation coefficient, and the required likelihood ratio statistic approximately follows the F distribution and Distribution, typical impurity variables with p-value less than 0.05 were retained for subsequent analysis of the relationship between process impurities and degradation impurities.

[0030] S2.4 Construct and screen the correlation coefficient matrix between typical impurity variables and process impurities and degradation impurities, and obtain the correlation coefficients between process impurities, typical impurity variables and degradation impurities; analyze the typical correlation structure, explore the transfer relationship between original variables-typical variables-original variables, and further evaluate the statistical significance of r1 and r2. Among them, typical impurity variables include typical variables of degradation impurities and typical variables of process impurities. The typical correlation structure is the correlation coefficient matrix between each original variable and the typical variable.

[0031] S3: Determine the transfer relationship based on the correlation coefficients among process impurities, typical impurity variables and degradation impurities, and screen the correlation between specific categories of degradation impurities and specific categories of process impurities.

[0032] The correlation between the original variable and the typical variable may need to be transferred through multiple typical correlation coefficients (r1, r2, r3...). The transfer diagram of the correlation relationship between each variable and the typical variable obtained by the typical correlation analysis includes the correlation between the typical variables of degradation impurities and the typical variables of process impurities, the corresponding relationship between the typical variables of degradation impurities and degradation impurities, and the corresponding relationship between the typical variables of process impurities and process impurities. The correlation coefficient with a correlation coefficient of not less than 0.3 is screened as an alternative transfer relationship. It is generally believed that the absolute value of the correlation coefficient is greater than 0.30, there is a substantial correlation, greater than 0.50, there is a significant correlation, and greater than 0.80, there is a high correlation. Typical impurity variables include typical variables of degradation impurities and typical variables of process impurities; the correlation transfer routes with consistent positive and negative correlations are the process control routes that should be focused on. Pay attention to the significant correlation transfer routes with typical correlation coefficients r greater than 0.5, and analyze the corresponding relationship between degradation impurities and process impurities. Degradation impurities and process impurities with the same positive correlation coefficient or the same negative correlation coefficient with the same group of typical impurity variables are correlated. Its typical correlation coefficient is equivalent to the typical correlation coefficient between the typical variables of degradation impurities and the typical variables of process impurities in the corresponding typical impurity variables.

[0033] Specifically, the correlation coefficient matrix of typical variables of degradation impurities and process impurities, as well as the correlation coefficient of typical variables of process impurities and degradation impurities with a correlation coefficient of not less than 0.3, are screened to screen the corresponding degradation impurities or process impurities; based on the correlation coefficient matrix of typical variables of degradation impurities and degradation impurities, as well as the correlation coefficient matrix of typical variables of process impurities and process impurities, compare whether the degradation impurities and process impurities for the same group of typical impurity variables have the same positive correlation coefficient or the same negative correlation coefficient. The degradation impurities and process impurities screened for the same typical variables are interrelated process impurities and degradation impurities. According to these corresponding relationships, the key points of process control and process concerns are determined to clarify the direction of impurity spectrum research.

[0034] The analysis method of the relationship between process impurities and degradation impurities based on big data analysis technology associates the process characteristics of group drugs (process impurities) with the stability of individual drugs (degradation impurities), establishes a group correlation analysis method from a general perspective, and obtains the key points of process control through the typical correlation analysis method based on process impurities and degradation impurities, achieving the expected big data analysis effect. The group correlation analysis method requires that the process group has a high representativeness and is less affected by the size of the individual process sample; the correlation analysis method is not affected by the limitation of data resources, and only seeks internal correlation within the selected range, and the result is reliable; the correlation relationship replaces the causal relationship, and the result reveals the possible objective phenomenon and provides further research and evaluation directions. Using the thinking of big data analysis, firstly, the typical correlation relationship between degradation impurity variables and process impurity variables is used to mine the multidimensional and complex correlation between the process characteristics of group drugs (process impurities) and the stability of individual drugs (degradation impurities) in group drug data, and then the correlation information between group process characteristics (process impurities) and individual drug stability (degradation impurities) is used to establish the transmission relationship between process impurities and degradation impurities, screen the key points of process control, and break through the technical limitations of traditional methods.

[0035] The industrially produced aztreonam is taken as an example below, and the scheme of the present invention is specifically described in conjunction with the accompanying drawings.

[0036] Example 1: Acquisition of impurity-related big data The key to the industrial production of aztreonam lies in the synthesis of the main ring of aztreonam. Currently, there are six relatively mature process routes, namely: sulfur trioxide cyclization method (sulfuric acid monoester), aminosulfonic acid process, O-benzylhydroxylamine process (ring-opening side reaction), [2+2] reaction preparation process, tert-butyloxycarbonyl protection process, and chloromethyl benzyl ester protection process. The above six process routes are the main sources of process impurities. Among them, process impurities include reaction by-products (desulfonated aztreonam, aztreonam methyl ester, aztreonam ethyl ester) and reaction intermediates (aztreonam tert-butyl ester). Degradation impurities are produced by strong degradation or accelerated test conditions, and are obtained through structural confirmation and verification, such as polymers. Including process impurities and degradation impurities, the 11 impurities confirmed in aztreonam for injection are shown in Table 1; 188 samples were tested, and the content of each impurity is shown in Table 2.

[0037] Table 1 Detected impurities and structures of aztreonam for injection

[0038]

[0039]

[0040] Table 2 Impurity content in 188 samples

[0041]

[0042]

[0043]

[0044]

[0045] Comparative example: Simple correlation analysis The data of various impurity contents in drug samples were analyzed by simple correlation analysis, and the data were shown in Table 3. From the content of Table 3, it can be inferred that the direct correlation between various impurities exists. For example, process impurities IMP10 and IMP11 are correlated with degradation impurities IMP9, and process impurity IMP5 is correlated with degradation impurities IMP2 and IMP3. In addition, the known degradation relationship between IMP1 and IMP8 reported in the literature is insufficient, and it is impossible to obtain the key process control points and achieve the expected purpose of guiding and improving the process.

[0046] Table 3 Simple correlation analysis results of impurities of aztreonam for injection

[0047] Example 2 Analysis of the relationship between process impurities and degradation mass spectrometry of aztreonam for injection based on big data analysis technology 2.1 Big data collection of aztreonam for injection The impurity spectrum of aztreonam for injection is divided into a process impurity variable group and a degradation impurity variable group. The process impurity variable group contains 4 variables: IMP5, IMP7, IMP10, and IMP11; the degradation impurity variable group contains 7 variables: IMP1, IMP2, IMP3, IMP4, IMP6, IMP8, and IMP9.

[0048] 2.2 Establishment of group correlation analysis method Based on the data in Table 2, typical variables were extracted using the method of canonical correlation analysis to explore the intrinsic correlation between process impurities and degradation impurities. The conversion coefficients of the original impurity variables and the typical variables are shown in Table 4. A total of 4 pairs of typical variables were extracted. The first and second typical correlation coefficients were 0.7737 and 0.6980, respectively, with significant correlation; the third typical correlation coefficient was 0.4502, with substantial correlation; the fourth typical correlation coefficient was 0.1206, with no substantial correlation. The cumulative contribution rate of the first, second, and third typical correlation coefficients reached 78.57%, and the first, second, and third typical variables were selected for subsequent analysis.

[0049] Table 4 Typical correlation analysis results of process impurities and degradation impurities of aztreonam for injection

[0050] The relationship between the first, second, and third pairs of typical variables is shown in Formula 3 to Formula 8.

[0051] (Formula 3); (Formula 4); (Formula 5); (Formula 6); (Formula 7); (Formula 8); The relationship diagram of three pairs of typical variables is as follows Figure 2-Figure 4 As shown, it can be seen that the three pairs of canonical correlation variables have obvious correlation.

[0052] 2.3 Hypothesis Testing for Big Data Analysis Perform hypothesis testing on canonical correlation analysis. Assuming that the current and subsequent canonical correlation coefficients are 0, this paper uses the likelihood ratio method to test the canonical correlation coefficient. The required likelihood ratio statistic approximately follows the F distribution and The distribution is shown in Table 5. The F test and The test results are consistent, and p is 0.0000, indicating that the test and the hypothesis are extremely significantly different (generally p < 0.05 is significantly different, p < 0.01 is extremely significantly different), and the hypothesis is not established; the F test of r4 and The test results, p, were 0.6174 and 0.6029, both greater than 0.05, indicating that there was no significant difference between the test and the hypothesis, and the hypothesis was established. Therefore, the typical correlation coefficients r1, r2, and r3 of degradation impurities and process impurities were statistically significant, while r4 was not statistically significant.

[0053] Table 5 Likelihood ratio test statistics for degradation impurities and process impurities of aztreonam for injection

[0054] 2.4 Multidimensional correlation results of big data analysis The canonical correlation structures of the first, second, and third pairs of canonical variables are calculated and shown in Table 6.

[0055] Table 6 Typical related structures of process impurities and degradation impurities of aztreonam for injection

[0056] From the process impurity-degradation impurity typical variable correlation coefficient matrix, and the degradation impurity-process impurity typical variable correlation coefficient matrix analysis, degradation impurities IMP3, IMP4 and process impurity typical variable 1 have a substantial correlation; degradation impurities IMP1, IMP2 and process impurity typical variable 2 have a substantial correlation; degradation impurity IMP2 and process impurity typical variable 3 have a substantial correlation; process impurities IMP5, IMP10, IMP11 and degradation impurity typical variable quality typical variable 1 have a substantial correlation; process impurity IMP7 and degradation impurity 2 have a substantial correlation; process impurities IMP5, IMP11 and degradation impurity typical variable 3 have a substantial correlation. Draw the degradation impurity-process impurity variable and typical variable correlation transfer diagram as shown Figure 5 shown.

[0057] Data analysis was performed based on the correlation coefficient matrix of degradation impurities-typical variables of degradation impurities and the correlation coefficient matrix of process impurities-typical variables of process impurities. Among them, the correlation coefficient of degradation impurity IMP4 with typical variable 1 of degradation impurities is negative, and the correlation coefficients of process impurities IMP5, IMP10, and IMP11 with typical variable 1 of process impurities are all negative, and the correlation directions are consistent. It can be seen that degradation impurity IMP4 is correlated with process impurities IMP5, IMP10, and IMP11, and the typical correlation coefficient is 0.7737, which is significantly correlated. The correlation coefficients of degradation impurities IMP1 and IMP2 with typical variable 2 of degradation impurities are both positive, and the correlation coefficients of process impurity IMP7 with typical variable 2 of process impurities are both positive, and the correlation directions are consistent. It can be seen that degradation impurities IMP1 and IMP2 are correlated with process impurity IMP7, and the typical correlation coefficient is 0.6980, which is significantly correlated. The correlation coefficient between degradation impurity IMP2 and typical variable 3 of degradation impurity is negative, and the correlation coefficient between process impurity IMP5 and typical variable of process impurity is negative. It can be seen that degradation impurity IMP2 and process impurity IMP5 are related, and the typical correlation coefficient is 0.4502, which has substantial correlation.

[0058] It is worth noting that the pairwise correlation analysis results of IMP4, IMP5 and IMP10 (Table 3) did not show substantial correlation. Therefore, we speculate that IMP5 and IMP10 may come from the same process, and the combined action of the two may be a necessary condition for the formation of the open-ring degradation product IMP4, and further research and discussion on the relevant mechanism is needed.

[0059] Example 3: Analysis of key impurities, key process control points and impurity spectrum research directions The canonical correlation determination coefficient was calculated, and the results are shown in Table 7. The degradation impurities IMP3 and IMP4 are associated with the process impurities IMP5 and IMP10 through the first pair of canonical variables, and the correlation is strong; the degradation impurities IMP1 and IMP2 are associated with the process impurity IMP7 through the second pair of canonical variables, and the correlation is strong; the degradation impurities IMP2 and IMP4 are associated with the process impurity IMP11 through the third pair of canonical variables, and the correlation is weak; the effect of other impurities is very small. The analysis of the research direction of the impurity spectrum can be obtained by analyzing the key impurities and key process control points.

[0060] Table 7 Typical correlation determination coefficients of process impurities and degradation impurities of aztreonam for injection

[0061] The embodiments of the present invention are described in detail above, but the contents described are only preferred embodiments of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of application of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A method for analyzing the relationship between process impurities and degradation impurities based on big data analysis technology, characterized in that: The specific steps are as follows: S1: Summarize the types of process impurities and degradation impurities according to the product synthesis process; S2: Correlate the process impurity information with the degradation impurity information, and obtain the correlation coefficients among process impurities, typical impurity variables and degradation impurities by canonical correlation analysis method; S3: Determine the transfer relationship based on the correlation coefficients among process impurities, typical impurity variables and degradation impurities, and screen the correlation between specific categories of degradation impurities and specific categories of process impurities.

2. The method for analyzing the relationship between process impurities and degradation impurities based on big data analysis technology according to claim 1, characterized in that: Step S2 comprises, S2.1: Based on the content of process impurities and degradation impurities in drugs, extract multiple typical impurity variables and calculate the correlation coefficient of each typical impurity variable; S2.2: The conversion coefficients of each process impurity and each degradation impurity are calculated by canonical correlation analysis method; Generate multiple canonical variable relationship formulas; S2.3: Test the generated typical variable relationship formula and screen the typical impurity variables corresponding to the typical variables with p-value less than 0.05; S2.4: For the typical impurity variables obtained by screening, the correlation coefficient matrices of the typical impurity variables and the process impurities and degradation impurities are constructed respectively to obtain the correlation coefficients among the process impurities, typical impurity variables and degradation impurities.

3. The method for analyzing the relationship between process impurities and degradation impurities based on big data analysis technology according to claim 2, characterized in that: The typical correlation coefficient was tested by the likelihood ratio method to screen typical impurity variables.

4. The method for analyzing the relationship between process impurities and degradation impurities based on big data analysis technology according to claim 2, characterized in that: Typical impurity variables include typical variables of degradation impurities and typical variables of process impurities.

5. The method for analyzing the relationship between process impurities and degradation impurities based on big data analysis technology according to claim 4, characterized in that: In step S2.4, the degradation impurity-degradation impurity typical variable correlation coefficient matrix, the process impurity-process impurity typical variable correlation coefficient matrix, the degradation impurity-process impurity typical variable correlation coefficient matrix, and the process impurity-degradation impurity typical variable correlation coefficient matrix are calculated respectively.

6. The method for analyzing the relationship between process impurities and degradation impurities based on big data analysis technology according to claim 1, characterized in that: Step S3 comprises, S3.1: Screen the correlation coefficient not less than 0.3 as the candidate transfer relationship; S3.2: Determine that degradation impurities and process impurities that have the same positive correlation coefficient or the same negative correlation coefficient with the same group of typical impurity variables have a transfer relationship.

7. The method for analyzing the relationship between process impurities and degradation impurities based on big data analysis technology according to claim 6, characterized in that: In step S3.1, the correlation coefficient matrix of typical variables of degradation impurities and process impurities, and the correlation coefficient matrix of typical variables of process impurities and degradation impurities, which have a correlation coefficient of not less than 0.3, are screened to screen the corresponding degradation impurities or process impurities; In step S3.2, based on the correlation coefficients in the correlation coefficient matrix between degradation impurities and typical variables of degradation impurities, and the correlation coefficients in the correlation coefficient matrix between process impurities and typical variables of process impurities, it is compared whether the degradation impurities and process impurities for the same group of typical impurity variables are both positively correlated or both negatively correlated.

8. The method for analyzing the relationship between process impurities and degradation impurities based on big data analysis technology according to claim 6 or 7, characterized in that: Typical impurity variables include typical variables of degradation impurities and typical variables of process impurities. The typical correlation coefficients of correlated process impurities and degradation impurities are equivalent to the typical correlation coefficients between the typical variables of degradation impurities and the typical variables of process impurities in the corresponding typical impurity variables.

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