Analysis method for correlation between apparent crystal forms and impurities of mixed crystal polymorphic drugs

Abstract

The invention relates to a method for analyzing correlation between apparent crystal forms and impurities of mixed crystal polymorphic drugs. The method comprises the following steps: firstly, providing a method for obtaining the apparent crystal form of a drug through near infrared spectroscopy (NIR) so as to quickly obtain a large amount of data basis of numerous distributed drugs; the typical correlation between the macroscopic apparent crystal forms and the impurity spectrums is explored through a canonical correlation analysis method, the multi-dimensional complex correlation between the population apparent crystal forms and individual drug impurities in the population is mined, and then the transfer relation between individual crystal forms and individual impurities is established through individual correlation delivery information in the population. And searching the relevance between the impurity category and the crystal form category. According to the method, key point information of medicine quality and process control related to the crystal form is obtained, quality control of the type of medicine and collection of stability, safety and effectiveness data in medicine research and development can be achieved, and tracing and research of untoward effect reasons and mechanisms are facilitated.

Description

Technical Field

[0001] The present invention belongs to the technical field of data analysis, and in particular relates to a method for analyzing the correlation between the apparent crystal form and impurity formation of a mixed crystal polymorphic drug. 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] Many drugs have polymorphic forms. Polymorphic drugs not only differ in their solid physical and chemical properties, but also have significant effects on the stability, bioavailability and efficacy of the drugs. Polymorphic drugs refer to drugs with the same chemical composition that can exist in different crystal structures. Different crystal forms of drugs not only affect the solubility, stability and bioavailability of the drugs, but may also cause different impurities to be produced during the production and storage of the drugs. The presence of impurities will not only reduce the purity of the drugs, but may also cause adverse reactions, seriously affecting the safety and efficacy of the drugs.

[0004] There must be a certain correlation between the crystal form and impurities of polymorphic drugs, which has a profound impact on drug safety, efficacy and quality controllability. It has become a research direction that has attracted much attention. It not only has important theoretical value, but also has broad application prospects. Through in-depth research on the correlation between polymorphic drugs and impurities, the purity and stability of drugs can be effectively improved, the adverse reactions of drugs can be reduced, and the quality and safety of drugs can be improved. In addition, it will also provide strong support for the patent protection of drug crystal forms and help pharmaceutical companies gain advantages in the fierce market competition.

[0005] Polymorphic drugs include single crystals, co-crystals, mixed crystals, etc. Currently, commonly used methods for characterizing crystal forms include scanning electron microscopy, infrared spectroscopy, Raman spectroscopy, X-ray single crystal diffraction, X-ray powder diffraction, DSC, etc. In the case of a single crystal standard, XRD and molecular spectroscopy can determine the type and proportion of the crystal form.

[105] . For most drugs with polymorphic forms, it is often very difficult to obtain standard crystal forms. However, these methods can only characterize single crystals or drugs with known crystal composition. Usually, a single specific crystal must be obtained through cultivation, and then relevant research must be carried out to analyze and evaluate the correlation between different crystal forms (crystals) and impurities one by one.

[0006] Most drugs usually exist in the form of mixed crystals, and it is usually difficult to obtain single crystals. Although chemical imaging can be used for micro-area characterization, it is impossible to carry out analysis and evaluation of the correlation between mixed crystals and impurities because it is impossible to obtain single specific crystals. In many mixed crystal drugs, it is difficult to analyze which crystal is more stable and which crystal is related to which specific impurity, which poses risks to the safety, efficacy and quality control of such mixed crystal drugs.

[0007] Currently, there is still a lack of effective technical means and methods for evaluating the crystal form of mixed-crystal polymorphic drugs, resulting in a serious shortage of data on the stability, safety, and efficacy of the crystal form of such drugs in drug quality control and drug development, and the mechanism and cause of adverse reactions cannot be well traced. Summary of the invention

[0008] In order to solve the above technical problems, the present invention provides a method for analyzing the correlation between the apparent crystal form and impurity formation of mixed crystal polymorphic drugs.

[0009] The technical solution adopted by the present invention is: a method for analyzing the correlation between the apparent crystal form and impurity formation of mixed crystal polymorphic drugs, and the specific steps are as follows: S1: Use the apparent crystal form characterization model to detect the apparent crystal form of the target drug and obtain the apparent crystal form big data; screen the common impurity categories of the target drug and obtain the target drug impurity detection result big data; S2: Correlate the apparent crystal form information with the impurity information, and use the canonical correlation analysis method to analyze the correlation coefficient between the apparent crystal form and the impurity, including the canonical correlation coefficient between the canonical variable pairs and the correlation coefficient between the original variable and the canonical variable; S3: Determine the transfer relationship based on the correlation coefficient between impurities, typical variable pairs and crystal forms, and screen the correlation between specific categories of apparent crystal forms and specific categories of impurities.

[0010] Preferably, in step S1, the apparent crystal form characterization model is established as follows: S1.1: Screen typical samples, test each typical sample by MDSC, and obtain the apparent crystal model of each typical sample; establish the spectrum model of each typical sample by near infrared spectroscopy; establish the connection between the apparent crystal model and the spectrum model of the same typical sample; S1.2: Perform near-infrared spectroscopy characterization on the sample to be tested, and obtain the apparent crystal form big data information by combining the correspondence between the spectrum model and the apparent crystal form model.

[0011] Preferably, the typical variable pair includes an impurity typical variable and an apparent crystal form typical variable, which are in a paired relationship.

[0012] Preferably, the specific analysis steps in step S2 include: S2.1: Based on the data of various impurity contents and various apparent crystal forms, extract multiple typical variable pairs and calculate the correlation coefficients between each typical variable pair; S2.2: Calculate the conversion coefficients of various impurities and typical variables of impurities, various apparent crystal forms and typical variables of apparent crystal forms by using the canonical correlation method; generate the relationship formula for each typical variable; S2.3: Test the relationship formulas of the generated typical variable pairs and select the typical variable pairs with p-value less than 0.05; S2.4: Construct correlation coefficient matrices between the multiple typical variable pairs obtained by screening and the impurities and apparent crystal forms, respectively, to obtain the correlation coefficients between the impurities, each typical variable and the apparent crystal form.

[0013] Preferably, the canonical correlation coefficient is tested by the likelihood ratio method to screen the canonical variable pairs.

[0014] Preferably, in step S2.4, the impurity-impurity typical variable correlation coefficient matrix, the apparent crystal form-apparent crystal form typical variable correlation coefficient matrix, the apparent crystal form-impurity typical variable correlation coefficient matrix, and the impurity-apparent crystal form typical variable correlation coefficient matrix are calculated respectively.

[0015] Preferably, step S3 comprises, S3.1: Screen the correlation coefficient not less than 0.3 as the candidate transfer relationship; S3.2: It is determined that the apparent crystal form and impurities with the same positive correlation coefficient or the same negative correlation coefficient for the same group of typical impurity variables are correlated.

[0016] Preferably, in step S3.1, the correlation coefficient matrix of typical variables of apparent crystal form-impurity and the correlation coefficient matrix of typical variables of impurity-apparent crystal form with a correlation coefficient of not less than 0.3 are screened to screen the corresponding impurities or apparent crystal forms; In step S3.2, based on the correlation coefficient matrix between impurities and impurity typical variables and the correlation coefficient matrix between apparent crystal form and apparent crystal form typical variables, the correlation coefficients of impurities and apparent crystal forms for the same group of typical variable pairs are compared to see whether they are both positively correlated or negatively correlated.

[0017] Preferably, the correlation coefficient between the correlated impurities and the apparent crystalline form is equal to the canonical correlation coefficient between the corresponding canonical variable pairs.

[0018] Preferably, the apparent crystal form with no correlation or low correlation with impurities is screened as the most stable crystal form.

[0019] The advantages and positive effects of the present invention are: defining "apparent crystal form" from a macroscopic perspective, applying multiple crystal form analysis methods to jointly establish effective apparent crystal form response categories and crystal characterization methods, and then establishing a big data analysis method based on apparent crystal form and impurity spectrum; On the one hand, the multi-dimensional correlation correspondence between crystal forms and impurities is obtained, and then the relatively stable crystal form information in mixed crystal polymorphic drugs is analyzed; on the other hand, the key point information of drug quality and process control related to crystal forms is obtained, which can not only realize the quality control of such drugs and the collection of stability, safety and efficacy data in drug research and development, but also help to trace and study the causes and mechanisms of adverse reactions; the relatively most stable crystal form in the mixed crystal can be screened through the correlation between the apparent crystal form and the impurity spectrum. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Flow of analytical methods for the relationship between apparent crystal form and impurities; Figure 2 IR spectra of GBZ, FJFA and SDLX cefathiamidine for injection; Figure 3 XRD spectrum and characteristic peaks of cefathiamidine for injection; (a): GBZ; (b): FJFA; (c): SDLX; (d): GBTX; Figure 4 These are the TGA and MDSC spectra of cefathiamidine for injection in four types of apparent crystal response categories; (a): GBZ; (b): FJFA; (c): SDLX; (d): GBTX; Figure 5 The first canonical variable pair correlation diagram of crystal-impurity correlation; Figure 6 Crystal-impurity correlation second canonical variable pair correlation diagram; Figure 7 Transfer diagram of the correlation between crystal form-impurity variables and typical variables. DETAILED DESCRIPTION

[0021] The embodiments of the present invention are described below with reference to the accompanying drawings.

[0022] The present invention relates to a method for analyzing the correlation between the apparent crystal form and impurity formation of a mixed crystal polymorphic drug. First, a method for obtaining the "apparent crystal form" of a drug by near-infrared spectroscopy (NIR) is provided, so that the basic data of many distributed drugs can be obtained quickly and in large quantities. Using the thinking of big data analysis, firstly, the typical correlation between the macroscopic apparent crystal form and the impurity spectrum is used to explore the multi-dimensional complex correlation between the apparent crystal form of the group and the impurities of individual drugs in the group, and then the transmission relationship between the individual crystal form and the individual impurity is established by using the individual correlation delivery information in the group, and the correlation between the impurity category and the crystal form category is found. Furthermore, by analyzing the correlation between the apparent crystal form category and the impurity category, the relatively most stable crystal form can be reversely screened, breaking through the technical limitations of traditional methods.

[0023] In order to test a large number of drug samples and obtain crystal form data, the present invention proposes the concept of "apparent crystal form" of polymorphic drugs, which solves the problem that the crystal form characteristics of mixed-crystal polymorphic drugs are difficult to characterize. Effective information acquisition is the key to big data analysis. By establishing a NIR apparent crystal form classification and characterization method based on typical MDSC results, and promoting the acquisition of crystal form-related big data of different batches of drugs from different manufacturers, it is helpful to conduct typical correlation analysis of polymorphic characteristics and impurity spectra, and obtain the corresponding relationship between crystal form and impurities.

[0024] The apparent crystal form not only reflects the control level of the drug production process, but also has a close relationship with the stability of different raw material processes. According to the results of the apparent crystal form characterization, the drug impurity spectrum is used as a set of variables, and the apparent crystal form is used as another set of variables. The canonical correlation analysis method is used to explore the relationship between the two sets of variables, analyze the crystal form impurities, and explore the correlation between the apparent crystal form and impurities. Analyze the common impurity types of drug samples, associate the mixed crystal characteristic information of the group drugs (products of representative manufacturers) characterized by "apparent crystal form" with the specific impurity information of individual drugs (each batch of products) in the group drugs, and use the canonical correlation analysis method to explore the multi-dimensional complex correlation between the apparent crystal form of the group and the impurities of individual drugs in the group. Analysis methods such as Figure 1 The specific analysis method steps are as follows.

[0025] S1: Use near-infrared spectroscopy to establish an apparent crystal form characterization model, characterize the polymorphic phenomenon in drugs from multiple manufacturers, and obtain apparent crystal form big data.

[0026] First, typical samples are screened, and each typical sample is tested by modulated differential scanning calorimetry (MDSC) to obtain the apparent crystal form model of each typical sample; the spectrum model of each typical sample is established by near-infrared spectroscopy; the apparent crystal form model and the spectrum model of the same typical sample are linked; then the sample to be tested is characterized by infrared spectroscopy, and the apparent crystal form big data information is obtained by near-infrared spectroscopy based on the correspondence between the spectrum model and the apparent crystal form model; In big data analysis, the acquisition of big data information is the key. Although the MDSC method can effectively characterize the characteristics of mixed crystals, the experimental conditions are relatively harsh, and the single experimental method is time-consuming and cumbersome, and it is impossible to collect big data on crystal forms. Near-infrared spectroscopy is fast and non-destructive, and has good generalizability, but it cannot directly represent the crystal form composition of mixed crystals. By establishing an apparent crystal form model of near-infrared spectroscopy-MDSC, the crystal form category can be determined by near-infrared spectroscopy, thereby realizing the use of near-infrared spectroscopy results to determine which category of apparent crystal form response the drug sample belongs to. This detection method has accurate results and is suitable for the collection of big data related to crystal forms. The results can also be used for further big data analysis.

[0027] From a macroscopic perspective, we define "apparent crystal form (ACF)", which refers to the group mixed crystal characteristics such as the single crystal composition and its relative relationship in the mixed crystal of polymorphic drugs. We use MDSC to characterize the polycrystalline composition in the mixed crystal of typical manufacturers' products, establish a near-infrared spectroscopy classification model, classify based on macroscopic crystal characteristics at the molecular level, verify the model with the source of investigated APIs, and establish a near-infrared apparent crystal form characterization model. This model can be generalized to predict the apparent crystal form of all manufacturers, realize the classification and characterization of crystal information in mixed crystals in the absence of a single crystal form standard, and obtain basic big data information related to crystal form.

[0028] S2: Screening of common impurity categories in pharmaceuticals.

[0029] S3: Correlate the apparent crystal form information with the impurity information, and use the typical correlation analysis method to analyze the correlation coefficient between the apparent crystal form and the impurity. Based on the data of various impurity contents and various apparent crystal form contents, extract multiple typical variable pairs, and calculate the correlation coefficients between each typical variable pair; calculate the conversion coefficients of various impurities and typical impurity variables, various apparent crystal forms and typical apparent crystal form variables through the typical correlation method; generate the relationship formula for each typical variable.

[0030] A typical correlation analysis was performed on the apparent crystal form variable group and the impurity spectrum variable group. The apparent crystal form factor was used as the independent variable X and the impurity factor was used as the dependent variable Y. Based on the correlation between the two groups of variables, typical variables were obtained to replace the apparent crystal form and impurities. The relationship between the apparent crystal form and impurities was then projected onto the relationship between the typical variable pairs to obtain the conversion coefficients of the original variables and the typical variables. The calculation formulas for the typical apparent crystal form variable U and the typical 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.

[0031] The generated typical variable relationship formula is tested. Through the correlation analysis and hypothesis testing between typical variables, the correlation between apparent crystal form and impurities is studied. Assuming H0, the typical correlation coefficients of the current and subsequent typical variable pairs are all 0, and the likelihood ratio method is used to test the typical correlation coefficient. The required likelihood ratio statistic approximately follows the F distribution and Distribution. Screen for canonical variable pairs with p-values ​​less than 0.05.

[0032] The relationship matrix between the multiple typical variable pairs obtained by screening and the impurities and crystal forms was constructed respectively to obtain the correlation coefficients between impurities, typical variable pairs and apparent crystal forms. The typical correlation structure was analyzed to explore the transmission relationship between the original variable-typical variable pair-original variable, and the statistical significance of r1 and r2 was further evaluated. The typical correlation structure is a pairwise correlation coefficient matrix between each original variable and the typical variable. It is generally believed that when the absolute value of the correlation coefficient is greater than 0.30, there is a substantial correlation, when it is greater than 0.50, there is a significant correlation, and when it is greater than 0.80, there is a high correlation.

[0033] S4: Determine the transfer relationship based on the correlation coefficient between impurities, typical variable pairs and apparent crystal forms, and screen the correlation between specific categories of degradation impurities and specific categories of process impurities.

[0034] The correlation between the original variables and the typical variables is mainly transferred through r1, and the transfer diagram of the correlation between each variable and the typical variable obtained by the typical correlation analysis is drawn, including the correlation between the impurity typical variable and the apparent crystal form typical variable, the corresponding relationship between the impurity and the impurity typical variable, and the corresponding relationship between the apparent crystal form and the apparent crystal form typical variable. Among them, the correlation transfer route with consistent positive and negative correlation is the process control route that should be focused on. The transfer relationship of the significant correlation route is used to analyze the corresponding relationship between the apparent crystal form and the impurity.

[0035] In the model embodiment of the present invention, the transmission relationship between the apparent crystal form and the impurities can also be used to screen the most stable crystal form. The apparent crystal that has no correlation with all impurities is considered to be the most stable crystal form within the existing apparent crystal form model.

[0036] The present invention applies a variety of crystal form analysis methods to jointly establish an effective apparent crystal form response category and a crystal characterization method; then a big data analysis method based on the apparent crystal form and the impurity spectrum is established, on the one hand, a multi-dimensional correlation correspondence between crystal forms and impurities is obtained, and then relatively stable crystal form information in mixed crystal polymorphic drugs is analyzed; on the other hand, key point information on drug quality and process control related to crystal forms is obtained, which can not only realize the quality control of such drugs and the collection of stability, safety and effectiveness data in drug research and development, but also help to trace and study the causes and mechanisms of adverse reactions.

[0037] The method of the present invention is described by taking a plurality of distribution products with cefathiamidine for injection prepared by GBZ, FJFA, SDLX and GBTX manufacturers as the original source as examples.

[0038] Example 1: Apparent Crystalline Form Characterization Method Cefathiamidine for injection prepared by GBZ, FJFA and SDLX manufacturers were tested by infrared spectroscopy. The infrared characteristic results are as follows: Figure 2 As shown, 820~880 cm -1 The standard samples of GBZ, FJFA, SDLX and GBTX types of cefathiamidine for injection were tested by XRD. The XRD results are as follows: Figure 3 As shown in the figure, due to the mixed crystal, the XRD spectrum characteristics are homogenized, and the XRD characteristic peak information of different crystals cannot be obtained. Thermogravimetric analysis (TGA) and modulated differential scanning calorimetry (MDSC) analysis were performed on GBZ, FJFA, SDLX and GBTX models of Cefathiamidine for injection standard samples. The results are shown in the figure. Figure 4 As shown, it can be seen that MDSC can be used to distinguish four different types of crystal samples, and the collection of crystal big data can be achieved through MDSC detection of batch samples.

[0039] The MDSC method was used to characterize the polycrystalline composition of typical cefathiamidine mixed crystals for injection. The results are shown in Table 1 and Figure 4 As shown, each typical sample was tested by near infrared spectroscopy, and a near infrared apparent crystal form characterization model was established after one-to-one comparison. The apparent crystal forms of cefathiamidine for injection from different manufacturers were characterized by near infrared spectroscopy detection method, and the results are shown in Table 2 and Figure 2 As shown in Table 2, the raw material supply of each enterprise was investigated, and the results showed that the samples corresponding to the same raw material production enterprise belonged to the same apparent crystal form corresponding category. This shows that the apparent crystal form characterization method can accurately obtain the crystal form information of drugs from different manufacturers.

[0040] Table 1 Characterization results of MDSC crystals of four types of apparent crystal response categories (s-strong peak, w-weak peak)

[0041] Table 2 Sources of raw materials, near infrared characterization results and apparent crystal response categories of Cefathiamidine preparations for injection

[0042] The impurity content information in more than 100 batches of drugs was analyzed, and the apparent crystal form information was analyzed by MDSC. The data basis was established as shown in Table 3, and the relationship between the apparent crystal form and the impurity spectrum was analyzed.

[0043] Table 3 Impurity content and apparent crystal form information data of 117 batches of cefathiamidine preparations for injection

[0044]

[0045]

[0046] Comparative Example 1: Exploring the correlation between impurity content and apparent crystal form in the product through simple correlation analysis The results of simple correlation analysis of impurities are shown in Table 4. The correlation between variables in the impurity spectrum group is weak, among which impurity A and impurity B, impurity E and other largest single impurities have weak correlation. The results of simple correlation analysis of apparent crystal form are shown in Table 5. There is a certain correlation between variables in the apparent crystal form group, among which crystal II and crystal Ia are negatively correlated, with a correlation coefficient of -0.8295, and crystal III and crystal I are negatively correlated, with a correlation coefficient of -0.7846. Crystal II and crystal III are defined as high melting point crystal groups, and crystal Ia and crystal I are defined as low melting point crystal groups. The results of correlation analysis show that there may be crystal transformation between the two crystal groups. The apparent crystal forms in the two crystal groups are moderately positively correlated, with a correlation coefficient of 0.3270 between crystal Ia and crystal I, and a correlation coefficient of 0.5067 between crystal II and crystal III, indicating that the crystals in the group may be symbiotic.

[0047] However, it is obvious that the information obtained by the above simple correlation analysis is limited and cannot provide information on the stability of the crystal form.

[0048] Table 4 Simple correlation analysis results of impurity spectrum group

[0049] Table 5 Autocorrelation results of apparent crystal group

[0050] Example 2 Cefathiamidine for injection apparent crystal form-impurity spectrum big data analysis method 2.1 Big data collection and acquisition of Cefathiamidine for injection The impurity spectrum variable group of cefathiamidine for injection includes 5 variables, impurity A, impurity B, impurity D, impurity E and other largest single impurities; the apparent crystal form variable group includes 4 variables as shown in Table 1, specifically including crystal Ia, crystal I, crystal II and crystal III characterized by MDSC, with a total of 117 observed samples (N); the statistical data of the original variables are shown in Table 6, where the apparent crystal form mean represents the calculation result of the semi-quantitative assignment of MDSC peak ratio and standardized processing.

[0051] Table 6 Statistics of raw variables related to apparent crystal form and impurities of cefathiamidine

[0052] 2.2 Establishment of big data analysis methods The typical correlation coefficient, cumulative contribution rate and conversion coefficient of cefathiamidine for injection are shown in Table 7. A total of 4 pairs of common factors were extracted. The typical conversion coefficient of crystal factor X is A, and the typical conversion coefficient of impurity factor Y is B. Among them, the correlation coefficient r of the first typical variable pair is 0.5228, and the typical determination coefficient r 2 The correlation coefficient of the second typical variable pair is 0.3300, with a contribution rate of 22.13%; the correlation coefficient of the third typical variable pair is 0.2818, with a contribution rate of 9.9%; the correlation coefficient of the fourth typical variable pair is 0.1150, with a contribution rate of 7.84%. The cumulative contribution rate of the four groups of typical variable pairs is 98.68%, that is, the variation that can be explained accounts for 98.68% of the total variation, among which the first and second typical correlation coefficients play a major role, explaining 80.95% of the variation, and the third and fourth typical correlation coefficients play a relatively small role.

[0053] Table 7 Canonical correlation analysis results of ceftriaxone crystal-heterocorrelation

[0054] The relationship between the first and second pairs of typical variables is shown in Formula 4 to Formula 6.

[0055] (Formula 3); (Formula 4); (Formula 5); (Formula 6).

[0056] The correlation diagram of the first typical variable pair and the second typical variable pair is as follows Figure 5 , Figure 6 As shown, it can be seen that the two pairs of canonical correlation variables are significantly correlated.

[0057] 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 Distribution, the hypothesis test results of cefathiamidine for injection are shown in Table 8. The first column tests whether r1~r4 are 0, F test F=3.116, p=0.00001<0.01, the test is significantly different from the hypothesis, then H0 is not established, at least one r in r1~r4 is not 0; the second column tests whether r2~r4 are 0, F test F=2.001, p=0.0241<0.05, the test is significantly different from the hypothesis, then H0 is not established, at least one r in r2~r4 is not 0; the F test p values ​​in the third and fourth columns are both greater than 0.05, then H0 is established. The test results are consistent with the F test. Therefore, it can be confirmed that r3 and r4 are not statistically significant, while r1 and r2 are statistically significant to a certain extent.

[0058] Table 8 Canonical correlation likelihood ratio test statistics for Cefathiamidine crystals for injection

[0059] 2.4 Multidimensional Correlation Results of Big Data Analysis The typical related structures of cefathiamidine for injection are shown in Table 9.

[0060] Table 9 Typical related structures of cefathiamidine crystal form and impurities

[0061] According to the correlation coefficient matrix between apparent crystal form and impurity typical variables and the correlation coefficient matrix between impurity and apparent crystal form typical variables, crystal Ia, crystal II, and crystal III are substantially correlated with impurity typical variable 1; impurity B and impurity E are substantially correlated with apparent crystal form typical variable 1; and there is no substantially correlated variable in typical variable 2. Figure 7As shown, according to the impurity-impurity typical variable correlation coefficient matrix and the apparent crystal form-apparent crystal form typical variable correlation coefficient matrix data, crystal II and crystal typical variable 1 are positive correlation coefficients, impurity B and impurity typical variable 1 are also positive correlation coefficients, and the two are related in the same direction. Therefore, crystal II and impurity B are associated through the first pair of typical variables and have a significant correlation; crystal Ia, crystal III and apparent crystal form typical variable 1 are negative correlation coefficients, impurity E and impurity typical variable 1 are also negative correlation coefficients, and the two are related in the same direction. Therefore, crystal Ia, crystal III and impurity E are associated through the first pair of typical variables and have a significant correlation. Other correlations are weak and have little effect on the explanation of impurities. Therefore, in this example, only the first pair of typical variables and r1 are statistically significant for crystal form-impurity correlation.

[0062] Example 3: Screening of the most stable crystal form The impurity E of cefathiamidine is correlated with the crystal form Ia and the crystal form III through the first typical variable pair, and the impurity B is correlated with the crystal form II through the first typical variable pair. The typical correlation coefficient r1 is greater than 0.50 and has a significant correlation, that is, the crystal form II is more likely to produce impurity B; the crystal form Ia and the crystal form III are more likely to produce impurity E. Therefore, impurities B and impurity E can be used as key process indicators related to crystal forms. However, the crystal form I has no correlation with all impurities. Within the apparent crystal form range of existing samples, the crystal form I can be considered to be the most stable crystal form.

[0063] The results of the typical correlation analysis were verified by the impurity distribution of each manufacturer. The mean, batch probability and crystal distribution of impurity B and impurity E of each manufacturer are shown in Table 10. The results show that impurity B and crystal II exist at the same time, while impurity E exists at the same time as crystals Ia and III, which is consistent with the typical correlation analysis results and the correlation transmission route.

[0064] Table 10 Mean values, batch probability and crystal distribution of impurity B and impurity E of each manufacturer

[0065] 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 within the scope 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 correlation between the apparent crystal form and impurity formation of a mixed crystal polymorphic drug, characterized in that: The specific steps are as follows: S1: Use the apparent crystal form characterization model to detect the apparent crystal form of the target drug and obtain the apparent crystal form big data; screen the common impurity categories of the target drug and obtain the target drug impurity detection result big data; S2: Correlate the apparent crystal form information with the impurity information, and use the canonical correlation analysis method to analyze the correlation coefficient between the apparent crystal form and the impurity, including the canonical correlation coefficient between the canonical variable pairs and the correlation coefficient between the original variable and the canonical variable; S3: Determine the transfer relationship based on the correlation coefficient between impurities, typical variable pairs and crystal forms, and screen the correlation between specific categories of apparent crystal forms and specific categories of impurities.

2. The method for analyzing the correlation between the apparent crystal form and impurity formation of a mixed crystal polymorphic drug according to claim 1, characterized in that: In step S1, the apparent crystal form characterization model is established as follows: S1.1: Screen typical samples, test each typical sample by MDSC, and obtain the apparent crystal model of each typical sample; establish the spectrum model of each typical sample by near infrared spectroscopy; establish the connection between the apparent crystal model and the spectrum model of the same typical sample; S1.2: Perform near-infrared spectroscopy characterization on the sample to be tested, and obtain the apparent crystal form big data information by combining the correspondence between the spectrum model and the apparent crystal form model.

3. The method for analyzing the correlation between the apparent crystal form and impurity formation of a mixed crystal polymorphic drug according to claim 1, characterized in that: The typical variable pairs include impurity typical variables and apparent crystal form typical variables, which are in a paired relationship.

4. The method for analyzing the correlation between the apparent crystal form and impurity formation of a mixed crystal polymorphic drug according to claim 3, characterized in that: The specific analysis steps of step S2 include: S2.1: Based on the data of various impurity contents and various apparent crystal forms, extract multiple typical variable pairs and calculate the correlation coefficients between each typical variable pair; S2.2: The conversion coefficients of various impurities and typical variables of impurities, various apparent crystal forms and typical variables of apparent crystal forms are calculated by canonical correlation analysis method; Generate relationship formulas for each canonical variable; S2.3: Test the canonical correlation coefficients of the generated canonical variable pairs and select canonical variable pairs with p-values ​​less than 0.05; S2.4: Construct correlation coefficient matrices between the multiple typical variable pairs obtained by screening and the impurities and apparent crystal forms, respectively, to obtain the correlation coefficients between the impurities, each typical variable and the apparent crystal form.

5. The method for analyzing the correlation between the apparent crystal form and impurity formation of a mixed crystal polymorphic drug according to claim 4, characterized in that: The canonical correlation coefficient was tested by the likelihood ratio method to screen typical variable pairs.

6. The method for analyzing the correlation between the apparent crystal form and impurity formation of a mixed crystal polymorphic drug according to claim 5, characterized in that: In step S2.4, the impurity-impurity typical variable correlation coefficient matrix, the apparent crystal form-apparent crystal form typical variable correlation coefficient matrix, the apparent crystal form-impurity typical variable correlation coefficient matrix, and the impurity-apparent crystal form typical variable correlation coefficient matrix are calculated respectively.

7. The method for analyzing the correlation between the apparent crystal form and impurity formation of a mixed crystal polymorphic drug 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: It is determined that the apparent crystal form and impurities with the same positive correlation coefficient or the same negative correlation coefficient for the same group of typical impurity variables are correlated.

8. The method for analyzing the correlation between the apparent crystal form and impurity formation of a mixed crystal polymorphic drug according to claim 7, characterized in that: In step S3.1, the correlation coefficient matrix of typical variables of apparent crystal form-impurity and the correlation coefficient matrix of typical variables of impurity-apparent crystal form with a correlation coefficient of not less than 0.3 are screened to screen the corresponding impurities or apparent crystal forms; In step S3.2, based on the correlation coefficient matrix between impurities and impurity typical variables and the correlation coefficient matrix between apparent crystal form and apparent crystal form typical variables, the correlation coefficients of impurities and apparent crystal forms for the same group of typical variable pairs are compared to see whether they are both positively correlated or negatively correlated.

9. The method for analyzing the correlation between the apparent crystal form and impurity formation of a mixed crystal polymorphic drug according to claim 7, characterized in that: The correlation coefficients of the correlated impurities and apparent crystal forms are equivalent to the canonical correlation coefficients between the corresponding canonical variable pairs.

10. The method for analyzing the correlation between the apparent crystal form and impurity formation of a mixed crystal polymorphic drug according to any one of claims 1 to 9, characterized in that: The apparent crystal form with no correlation or low correlation with impurities is screened as the most stable crystal form.

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