CN111540417A

CN111540417A - Crystal form quantification method of canagliflozin

Info

Publication number: CN111540417A
Application number: CN201911140601.1A
Authority: CN
Inventors: 白海波; 樊梦霖
Original assignee: Hangzhou Huadong Medicine Group Biopharmaceutical Co ltd
Current assignee: Hangzhou Huadong Medicine Group Biopharmaceutical Co ltd
Priority date: 2019-11-20
Filing date: 2019-11-20
Publication date: 2020-08-14

Abstract

The invention discloses a quantitative method for a crystal form of canagliflozin. Firstly, the near-infrared spectrogram of a series of canagliflozin standard samples with different crystal form ratios is measured, and the crystal form ratios are known. Screening proper characteristic wave number, and establishing a linear regression model of the ratio of the spectral data and the crystal form at the characteristic wave number. And finally, measuring the canagliflozin sample to be measured, and substituting the spectral data of the sample to be measured into the linear regression model to obtain a measured value. The method has the advantages of low quantitative limit, small relative error, high recovery rate of added standard and good specificity.

Description

Crystal form quantification method of canagliflozin

Technical Field

The invention relates to a method for analyzing a drug crystal form, in particular to a method for quantifying a crystal form of canagliflozin.

Background

The crystal form of the medicine is one of the important factors influencing the quality of the medicine. Polymorphism of chemical raw material drugs can generate differences of solubility and solubility, and directly influences bioavailability and bioequivalence of solid oral drugs. Therefore, the qualitative analysis of the crystal form in the existing raw material medicine or preparation can not meet the requirement, and the accurate quantitative and control analysis method of the polycrystalline form is needed to ensure the curative effect of the medicine. The method also becomes one of the technical bottlenecks restricting the international connection of the Chinese medicines and is also an important factor influencing the consistency evaluation of the quality and the curative effect of the domestic and original research. The existing research methods for drug crystal forms mainly comprise an X-ray diffraction method, an infrared and near-infrared spectroscopy method, a solid-state nuclear magnetic resonance method, a differential scanning calorimetry method and a Raman spectroscopy method.

The method is difficult to meet the quantitative detection requirement of the crystal form of the canagliflozin bulk drug. If the content of the crystal form III is more than 1%, the dissolution rate of the tablet and the bioavailability of the preparation are obviously reduced. However, the existing crystal form quantification methods have certain defects.

Most commonly used powder X-ray diffraction methods are complex to operate, expensive in equipment, expensive in analysis cost, and do not provide immediate results. Therefore, the method is not suitable for real-time detection of production of bulk drugs in industrial production, and the solid-state nuclear magnetic resonance method has similar problems. And the PXRD patterns of the two crystal forms of canagliflozin have low distinguishing degree, so that certain difficulty exists in modeling quantitative analysis. The raman spectroscopy method does not meet the requirements of precision and reproducibility in quantitative analysis.

The spectrum region of the near infrared spectroscopy is a vibration spectrum of molecular frequency doubling and frequency combination, and the near infrared spectroscopy is characterized in that signals are weak, and spectral peaks are overlapped seriously, so that the quantitative limit is high, and the requirement of 1% of the quantitative limit is difficult to meet. Especially for solid samples, the particle size and uniformity of the solid sample have a large influence on the near infrared spectrum. As can be seen from the prior literature, the limit of quantitation of the solid crystal form by the near infrared spectroscopy is generally difficult to break through 2% due to the inherent defects. For example:

(1) a triple crystal system was explored and a Quantitative crystal analysis model was established in the book Quantitative analysis of chemical analysis in tertiary mixtures, near-isolated and Raman spectroscopy, published by Patrick et al in 2010. The lowest limit of quantitation of the near infrared spectroscopy on the crystal form I is 10.9%, and the limits of quantitation on the other two crystal forms are 17.6% and 19.0%, respectively.

(2) Nalini et al, in a paper of Quantification of nitro amide polymorphism morphologies-a compatible study by Raman, NIR and MIR using chemometric techniques published in 2019, explored the problem of quantifying the three crystal forms of niclosamide, which are two monohydrate and one anhydrous compound, respectively. In binary mixtures, the lowest limit of quantitation for each form is 7.52%.

(3) Yangyongjia et al established a quantitative model of furosemide crystal form in near infrared spectroscopy analysis of furosemide crystal form and establishment of quantitative model thereof published in 2018. Wherein the lowest quantitative limit of the crystal form is 2.01 percent respectively.

In conclusion, the existing quantitative analysis methods for the crystal form are difficult to meet the detection requirements of low quantitative limit, simplicity, rapidness and real-time detection of canagliflozin bulk drug. Therefore, a canagliflozin crystal form determination method which is low in quantitative limit, simple and rapid and can be used for real-time detection is urgently needed, and the method also needs to meet strict requirements on precision and reproducibility in quantitative analysis of medicines.

Disclosure of Invention

The invention aims to overcome the defects of high quantitative limit, complex method and incapability of real-time detection in the conventional crystal form quantitative method, and provides a crystal form quantitative method of canagliflozin, which has low quantitative limit, is simple and quick and can be detected in real time.

The specific implementation scheme adopted for achieving the purpose is as follows:

the invention discloses a quantitative method for a crystal form of canagliflozin, which comprises the following steps:

1) preparing a plurality of canagliflozin standard samples with different crystal form proportions, wherein the crystal form proportion in each standard sample is known; measuring the near infrared spectrogram of each standard sample;

2) processing the near-infrared spectrogram obtained in the step 1) to obtain spectral data of each standard sample at a characteristic wave number;

3) establishing a linear regression model of the ratio of the spectral data at the characteristic wave number to the crystal form;

4) measuring near infrared spectrum data of a canagliflozin sample to be detected with unknown crystal form proportion at a characteristic wave number; and obtaining the crystal form proportion of the sample to be detected according to the linear regression model in the step 3).

Preferably, the canagliflozin comprises form I and/or form III.

Further preferably, the canagliflozin is a binary mixture of the crystal form I and the crystal form III.

The method can also be used for quantitative detection of other canagliflozin crystal forms, such as the crystal form II, the crystal form IV, the crystal form V and the like. For each crystal form, the processing method of the near infrared spectrum and the selected characteristic wave number of the crystal form are also changed in a targeted manner, so that the use requirement of low quantitative limit can be met. The low quantitative limit is that the quantitative limit is less than 2%. Wherein the canagliflozin crystal form III is a canagliflozin monohydrate, and the canagliflozin crystal form I is a canagliflozin hemihydrate.

The method for processing the near-infrared spectrogram in the step 2) comprises one or more of derivative, smoothing, standard normal variable transformation, orthogonal signal correction and multivariate scattering correction; the characteristic wave number refers to the wave number of the corresponding spectral data which linearly changes along with the crystal form proportion.

For the quantitative detection of different crystal forms, the map processing method can be selected from the processing methods in a combined way. However, this combination is not an arbitrary combination, and in order to meet the requirement of low quantitative limit, it is necessary to eliminate the influence of solid particle size, surface scattering and optical path change on the diffuse reflection spectrum, and also have the requirement of leveling the baseline. Only a method satisfying the above conditions can be used. Similarly, the characteristic wave number varies according to the crystal type. For the selection of the wave number, methods such as non-information variable elimination (UVE), competitive adaptive weighting (CARS), continuous projection (SPA), and Interval Partial Least Squares (iPLS) can be used. The spectral data at the selected characteristic wavenumbers should be in a significant linear relationship with the content of the measured crystal form.

The processing method in the step 2) specifically includes standard normal variable transformation, second derivative and smoothing.

Wherein the characteristic wave number of the step 2) is 5095-5087cm^-1。

When the content of the crystal form III is detected, the quantitative limit of the crystal form III can be reduced to 1 percent or even lower than 1 percent by using the processing method and the characteristic wave number.

Preferably, the number of canagliflozin standard samples in the step 1) is greater than or equal to 5.

In addition, when the standard samples are prepared, the crystal form proportion of each standard sample should be evenly distributed. The crystal form proportion of the sample to be detected is preferably within the crystal form proportion range of the series of standard samples. The greater the number of standard samples, the greater the accuracy of the established linear regression model.

Preferably, the method for establishing the linear regression model in the step 3) is a partial least squares method.

Other regression algorithms, such as linear correction methods such as Multiple Linear Regression (MLR) and Principal Component Regression (PCR), and nonlinear correction methods such as Artificial Neural Network (ANN) and Support Vector Machine (SVM), may also be used.

Preferably, the standard sample and the sample to be measured are ground, mixed uniformly and sieved before measurement.

If necessary, the sample to be tested needs to be subjected to the previous grinding and sieving treatment, and the treatment cannot cause the crystal form transformation of the sample to be tested. The particle size and uniformity of the solid sample can significantly affect the near infrared spectrum, so the sample needs to be ground and mixed before measurement. The mesh size used for screening may be 40, 60, 80 or other mesh sizes.

Preferably, the near infrared spectrum obtained by measuring the standard sample or the sample to be measured in the steps 1) and 4) is measured by using a rotary integrating sphere method.

The rotating integrating sphere method can perform integral integration on the near infrared spectrum of diffuse reflection of the sample so as to obtain more uniform and reliable spectrum information, and other near infrared spectrum measuring methods capable of performing integral integration can also be used.

The method has the following beneficial effects: 1. the method has low quantitative limit, higher accuracy and precision, and can meet the requirement of 1% low quantitative limit of the canagliflozin crystal form; 2. the method is simple and convenient to operate, can provide a measurement result in real time, and meets the real-time detection requirement in the production process of the canagliflozin bulk drug.

Drawings

FIG. 1 is a near infrared spectrum of each standard sample in example 1;

FIG. 2 is a graph of FIG. 1 after SNV +1 order derivative + smoothing in example 1;

FIG. 3 is a graph of FIG. 1 after SNV +2 order derivative + smoothing in example 1;

FIG. 4 is a map of FIG. 2 in example 1 after treatment with an iPLS model;

FIG. 5 is a map of FIG. 3 in example 1 after treatment with an iPLS model;

FIG. 6 is a preferred near infrared linear regression model of example 1;

FIG. 7 is a Raman spectrum of each standard sample in example 4;

FIG. 8 is a preferred Raman linear regression model of example 4.

Detailed Description

The technical solution of the present invention will be specifically described below by way of examples.

The information on the near infrared spectrometer used in example 1 is as follows:

instrument model Antaris II Fourier transform near infrared spectrometer

Test type diffuse reflectance

Principle of monochromatic transmitter fourier transform

Detector InGaAs 2.6um

Wavelength range 10001-4000cm^-1

Optical resolution 8.0cm^-1

The wave number precision is better than 0.05cm^-1

Wave number accuracy of +/-0.03 cm^-1

Number of spectrum acquisitions 64

Spectral resolution 8.0cm^-1

Example 1:

1. establishing a linear regression model

Weighing pure crystal forms I and III of canagliflozin, and preparing 7 binary mixed standard samples of canagliflozin, wherein the mass fractions of the crystal forms III are 0.125,0.25,0.5,1,2,4 and 8 percent respectively.

Each standard sample was mixed well, ground, sieved, placed in a sample cup, and compacted. The sample cup was placed in a near infrared spectrometer and the near infrared spectrum of each standard sample was obtained by the spin integrating sphere method, the results are shown in fig. 1.

And (3) carrying out 'standard normal variable transformation (SNV) + first derivative + smoothing' and 'SNV + second derivative + smoothing' treatment on the near-infrared spectrogram obtained in the step (1) by using Matlab, and obtaining a first-order treatment graph and a second-order treatment graph. The first order processing diagram and the second order processing diagram are respectively shown in fig. 2 and fig. 3.

The first order processing graph and the second order processing graph are processed by using an iPLS model and used for screening the characteristic wave number, and the results are respectively shown in FIG. 4 and FIG. 5. The iPLS model divides the spectrum into a plurality of equidistant wave numbers, and calculates the Root Mean Square Error (RMSECV) of a left cross validation method of the spectral data corresponding to each wave number. The smaller the RMSECV value, the more linear the spectral data corresponding to the selected wavenumber. In fig. 4 and 5, the values below the respective equidistant wave numbers are the corresponding influence factors, and the smaller the influence factor, the smaller the interference factor. Therefore, the wave number with a small RMSECV value and a small influence factor is selected. Screening 4 wave numbers from the first-order processing chart, wherein the wave numbers are respectively as follows: 6043-^-1(ii) a Four wave numbers, 5095-5087,5014-5006,4682-4674,6012-6005cm, can be screened from the second-order treatment chart^-1。

And respectively establishing 8 different linear regression models by using the PLS method by taking the spectral data at each group of wave numbers as the ordinate and the content of the crystal form III as the abscissa. The 8 sets of data were corrected for Root Mean Square Error (RMSE) and correlation coefficient value (R)²) As shown in table 1. When evaluating a linear regression model created by PLS method, the smaller the RMSE, the smaller the R²Closer to 1 indicates better modeling.

TABLE 1

R for these 8 linear regression models according to the data in Table 1²All of which are 0.995 or more, but the RMSE values are widely different. This indicates that the precision and accuracy of the prediction results of the linear regression model are very different, wherein "SNV + second derivative" is usedWave number of 5095-5087cm treated by the + smoothing "method^-1The model of (2) has the best prediction capability and is a preferred model; characteristic wave number of 5095-5087cm^-1. L1 in FIG. 6 is shown as a calibration curve of the preferred model, L2 is a calibration curve of the results of the interactive test set, and L1 is less different from L2, which indicates that the preferred model has better prediction capability.

2. The characteristic wave number is 5095-5087cm by using a 'SNV + second derivative + smoothing' method by utilizing a plurality of groups of known sample pairs^-1The accuracy of the preferred model of (a) was further verified and its quantitative limit was determined.

Weighing pure crystal forms I and III of canagliflozin, and preparing 6 parts of each sample with the mass fraction of the crystal form III being 0.5 and 1 percent. The measurement method of the standard sample was adopted to obtain a sample at 5095-5087cm^-1And processing the near infrared spectrum data by SNV, second derivative and smoothing. The processed spectral data were substituted into the preferred model in step 1, and the resulting measured contents are shown in table 2. Further analysis showed that the Relative Standard Deviation (RSD) of this model was 4.6% for the 0.5% content sample of form III and 2.0% (less than 3%) for the 1% content sample.

In near infrared analysis, the RSD of the sample is determined to be less than 3.0%, and the concentration or amount is the limit of quantitation, wherein the amount of the sample should not be less than 6 groups. Thus, the quantification limit for this preferred model is 1%.

In addition, 7 other models were also verified, wherein the measured content of each model is shown in table 2 and the measured mean value and RSD of each model are shown in table 3.

The comparison shows that the optimal model has great advantages, and only the quantitative limit of the optimal model meets the quantitative limit requirement of the canagliflozin bulk drug.

TABLE 2

TABLE 3

3. Sample recovery rate measurement

Weighing 500mg of canagliflozin crystal powder sample A, adding 5mg of crystal form III, and uniformly mixing to obtain a standard addition sample B. According to the near infrared spectrum measuring method in the step 1, the content of the canagliflozin crystal form III in the A is calculated to be 1.91 percent and the content of the canagliflozin crystal form III in the B is calculated to be 2.86 percent according to an optimal model.

The sample recovery rate (505 × 2.86% -500 × 1.91%)/5 ═ 98% can be calculated.

Therefore, the method has good sample recovery rate.

4. Specificity evaluation

Weighing 500mg of canagliflozin crystal form I powder sample A, then adding 50mg of canagliflozin crystal form IV to obtain a sample C, and predicting that the content of the crystal form III in the sample C is-0.02% according to the near infrared spectrum of the sample C and the model, namely that no inter-crystal measurement interference exists.

And (4) conclusion: the method has good accuracy and specificity on the prediction model of the crystal form III content in the canagliflozin crystal form I.

Example 2:

sample D of canagliflozin was prepared according to the method of patent WO 2009035969. Sample D was ground and sieved, placed in a sample cup, and compacted. And (3) placing the sample cup in a near-infrared spectrometer, and obtaining a near-infrared spectrogram of the sample D by adopting a rotating integrating sphere method. The near infrared spectrogram is subjected to 'SNV + second derivative + smoothing', and 5095-5087cm^-1The spectral data of (a) were substituted into the preferred model described in example 1, and the content of form III therein was calculated to be 2.13%.

Example 3:

7.20g (15.88mmol) of the pure canagliflozin crystal form III product and 0.46mL (7.94mmol, 0.5eq) of glacial acetic acid are weighed, 90mL of ethyl acetate dried by a molecular sieve in advance is added, and a rubber plug is sealed. When the solid was completely dissolved, the water content of the solids was determined to be 0.33% Karl. 0.85mL of water (i.e., 4 molar equivalents of water) was added and stirred well. And (3) dropwise adding 75mL of n-heptane at room temperature, standing for crystallization for one hour, cooling, filtering under reduced pressure, and heating and vacuum-drying the obtained solid for two hours to obtain a sample E.

Sample E was ground and sieved, placed in a sample cup, and compacted. And (3) placing the sample cup in a near-infrared spectrometer, and obtaining a near-infrared spectrogram of the sample E by adopting a rotating integrating sphere method. The near infrared spectrogram is subjected to 'SNV + second derivative + smoothing', and 5095-5087cm^-1The spectral data of (a) were substituted into the preferred model described in example 1, and the content of form III therein was calculated to be 0.235%. This value is below the limit of quantitation and although the actual amount cannot be accurately measured, it can be determined that form III is below 1% in this sample.

Example 4:

1. 7 binary mixed canagliflozin standards (0.125,0.25,0.5,1,2,4, 8%) were prepared according to the method in step 1 of example 1, each standard was added to a sealed jar, tapped and the raman spectrum of the sample was measured under the following conditions (fig. 7).

2. According to the method of step 2 in the embodiment 1, the optimal model processing method obtained by using the iPLS model is 'SNV + second derivative + smoothing', and the Raman shift range is 1700-^-1。

Using each standard sample at 1700-700cm^-1And (4) establishing a Raman linear regression model through PLS by taking the spectral data of the crystal form III as an ordinate and the mass fraction of the crystal form III as an abscissa. In fig. 8, L3 refers to the calibration curve of the model, and L4 is the calibration curve of its cross-check set. Wherein R of L3²R of L4 ═ 0.994, RMSEC ═ 0.205²0.757, RMSECV 1.528. Only RMSEC<When RMSECV, both small and comparable values, the model is under-fit balanced with over-fit. Therefore, the raman model has a poor fit compared to the near-infrared linear regression model.

The present invention has been described in detail with reference to the above examples using specific embodiments and experiments, but it will be apparent to those skilled in the art that modifications or improvements can be made thereto without departing from the spirit of the present invention. Accordingly, such modifications and improvements do not depart from the spirit of the invention and are intended to be included within the scope of the invention.

Claims

1. A method for quantifying a crystal form of canagliflozin is characterized by comprising the following steps:

2. The method of claim 1, wherein the canagliflozin comprises form I and/or form III.

3. The method of claim 2, wherein the canagliflozin is a binary mixture of form I and form III.

4. The method according to claim 3, wherein the method of processing the near-infrared spectrogram in step 2) comprises one or more of derivative, smoothing, normal-to-normal variate transformation, orthogonal signal correction, and multivariate scatter correction; the characteristic wave number refers to the wave number of the corresponding spectral data which linearly changes along with the crystal form proportion.

5. The method of claim 4, wherein the methods processed in step 2) include standard normal variable transformation, second derivative, and smoothing.

6. The method as claimed in claim 4, wherein the characteristic wavenumber of step 2) is 5095-5087cm^-1。

7. The method of claim 1, wherein the number of canagliflozin standard samples in the step 1) is greater than or equal to 5.

8. The method of claim 1, wherein the linear regression model established in step 3) is partial least squares.

9. The method of claim 1, wherein the standard sample and the sample to be tested are ground, mixed and sieved before measurement.

10. The method as claimed in claim 1, wherein the near infrared spectrum obtained by measuring the standard sample or the sample to be measured in step 1) and step 4) is measured by a rotating integrating sphere method.