High-throughput analysis method for solid preparation drug component distribution imaging

Abstract

The invention provides a high-throughput analysis method for solid preparation drug component distribution imaging, which comprises the following steps of: observing morphology by an electron microscope and selecting a to-be-detected area for energy spectrum splicing through an electron microscope SE / BSE dual-contrast imaging system in combination with energy spectrum imaging and Raman spectrum, performing element distribution analysis on the spliced image, and determining the distribution of the solid preparation drug component. Materials containing characteristic elements are distinguished and identified by adopting different colors, coordinates of an electron microscope are associated with a Raman spectrum analysis technology, substances without characteristic elements are subjected to Raman spectrum qualitative determination by selecting a proper amount of collection points, substances with characteristic elements can be subjected to Raman spectrum secondary identification by selecting a proper amount of collection points, and the identification accuracy is improved. The prescription composition of the solid preparation is confirmed according to the distribution condition after different compound colors are endowed in the component distribution diagram, the component distribution condition of a larger test sample can be obtained in a shorter time, rapid high-throughput analysis is achieved, and the method has important practical significance and application value for analysis, processing and quality control of medicine tablets.

Description

Technical Field

[0001] The invention belongs to the field of solid preparation drug analysis, and in particular relates to a high-throughput analysis method for solid preparation drug component distribution imaging. Background Art

[0002] Solid preparations, such as tablets and granules, play an important role in pharmaceutical preparations. They have a considerable share in the pharmaceutical market due to their advantages of low production cost, convenient transportation, good stability, and high patient acceptance. With the continuous advancement of medical technology and the diversification of patient needs, the process analysis methods of solid preparations are also constantly innovating and developing.

[0003] Traditional solid dosage form process analysis methods often focus on quality inspection of solid dosage form finished products, such as appearance, hardness, disintegration time, etc., but lack in-depth research and control of key parameters and influencing factors in the process. With the rapid development of the solid dosage form pharmaceutical industry, higher requirements are placed on the analysis and quality control methods of solid dosage forms. Although traditional solid dosage form analysis methods, such as gravimetric method, titration method, spectroscopy, etc., can meet the quality control needs of drugs to a certain extent, they still need to be improved in terms of sensitivity, accuracy, rapidity and degree of automation. The inspection cycle is long and the evaluation of the drug dissolution process is not intuitive enough.

[0004] As a fast and non-destructive analytical method, confocal Raman spectroscopy has the advantage of obtaining real-time component analysis results without complicated sample processing. Since the imaging process requires point-by-point scanning, generating a Raman image of a solid preparation often requires thousands of Raman spectra, which has low throughput and is time-consuming. Even with the latest generation of confocal microscopic Raman imaging systems, it still takes a long time to detect. Although Raman imaging can perform simple imaging distribution characterization of the microstructure of the preparation, the imaging speed is still slow when the resolution is not high (1μm resolution) (1μm resolution, imaging at an area of ​​10 square millimeters requires 2000+ hours), which does not meet the test time requirements of daily research and development.

[0005] Energy dispersive X-ray spectroscopy (EDS) combined with scanning electron microscopy (SEM) is an important technique for analyzing the elemental composition of samples. However, when applied to high-throughput analysis of solid dosage drug components, electron microscope energy spectrum also has certain limitations, such as sample preparation and the inability to identify CHO compounds.

[0006] Therefore, developing a high-throughput analytical method for imaging the distribution of drug components in solid dosage forms has important practical significance and application value for drug tablet analysis, processing, and quality control. Summary of the invention

[0007] The technical problem solved by the present invention is to overcome the deficiencies of the prior art and provide a new high-throughput analysis method for imaging the distribution of solid dosage drug components, which can accurately obtain the components and component distribution in the solid dosage drug in a shorter time.

[0008] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0009] The present invention provides a high-throughput analysis method for imaging the distribution of drug components in solid preparations, the high-throughput analysis method comprising the following steps:

[0010] (1) Cryopolishing a solid dosage form drug to obtain a cross section to be tested;

[0011] (2) Obtain a complete electron microscope image of the cross section to be tested under the SE / BSE dual contrast imaging system of the electron microscope, and randomly select a 10 mm 2 ~100mm 2 The area is taken as the test area;

[0012] (3) Under the SE / BSE dual-contrast imaging system of the electron microscope, an energy spectrometer is used to scan the area to be tested to obtain an energy spectrum mosaic image, and according to the distribution results of different elements in the energy spectrum mosaic image combined with the appearance and / or grayscale level of the substances in the area to be tested in the complete electron microscope image, the same substance in the energy spectrum mosaic image is colored with the same color, and different substances are colored with different colors;

[0013] (4) In the test area, collection points can be selectively selected for different substances, and Raman spectra of the collection points can be obtained. The names of the substances represented by different colors in the colored energy spectrum splicing diagram are determined according to the characteristic elements of the substances and / or the Raman spectra of the collection points. The distribution of different colors represents the distribution of different substances in the solid dosage form.

[0014] In the present invention, a "characteristic element" is a unique element that distinguishes a substance from other substances.

[0015] The cross section to be tested is smooth, with a roughness less than 20 nm. In the embodiment of the present invention, the roughness of the cross section to be tested is about 10 nm.

[0016] Preferably, the solid preparation drug is a solid preparation drug with uniformly mixed components.

[0017] Preferably, the solid preparation drug is solid particles in tablets, granules, capsules, etc.

[0018] Preferably, the step (2) further comprises an operation of ensuring that the components in the solid dosage form are uniformly mixed, the operation comprising:

[0019] The cross section to be tested is divided into several areas, the distribution uniformity of a certain substance in each area is counted, the area percentage of a certain substance in each area is calculated, the percentage of each area is multiplied by 100 and the variance between them is counted. When the variance is ≤1, it is considered that the internal components of the solid dosage form are evenly distributed, and a region to be tested can be randomly selected to represent the entire solid dosage form.

[0020] Preferably, the certain substance is a substance having the same morphology and the same gray level in the complete electron microscope image.

[0021] Preferably, the plurality of regions are 4 to 10 regions.

[0022] Preferably, the cross section to be tested in step (1) is the maximum cross section of the solid dosage drug.

[0023] Preferably, the temperature of the freeze polishing in step (1) is -40°C to 0°C, and the time of the freeze polishing is 1 to 5 hours.

[0024] Preferably, in step (4), a confocal microscopic Raman imaging system is used to detect the Raman spectrum of the collection point.

[0025] Further preferably, under the SE / BSE dual contrast imaging system of the electron microscope, collection points are selected for different substances in the area to be tested, and the horizontal and vertical coordinates of these collection points are recorded. A confocal microscopic Raman imaging system is used to detect the Raman spectrum of the substance at the same coordinate position as that under the electron microscope under the same coordinates as the electron microscope, which is the Raman spectrum of the collection point.

[0026] Further preferably, the substances marked with the same color are detected at at least three dispersed collection points.

[0027] In some embodiments of the present invention, substances marked with the same color are tested at 3 to 5 dispersed collection points.

[0028] Preferably, in step (4), the substance with characteristic elements can be directly labeled with the substance name according to the characteristic elements, and then it can be selected whether to perform secondary identification or redetermine the correct substance name through Raman spectroscopy according to the situation. The correct substance name of the substance without characteristic elements is determined through Raman spectroscopy.

[0029] Preferably, in step (3), the material without characteristic elements can be identified and distinguished by recognizing the morphology through a deep learning neural network.

[0030] Preferably, steps (1) to (4) are used to analyze substances added to the solid preparation by a dry method, and the high-concentration analysis method may further selectively include step (5), wherein step (5) is: randomly selecting a small area in the area to be tested, performing Raman imaging on the small area, and confirming whether the solid preparation contains substances added by a wet method based on the Raman fingerprint peak.

[0031] Further preferably, the wet added substances include but are not limited to binders and / or lubricants, such as povidone.

[0032] Preferably, the test resolution of the high-throughput analysis method reaches above 300 nm. In some specific embodiments, the test resolution reaches 300 nm to 200 μm.

[0033] In some specific embodiments of the present invention, the solid dosage drug comprises enteric-coated adenosine methionine butanedisulfonate tablets, rivaroxaban tablets or quetiapine fumarate.

[0034] Compared with the prior art, the present invention has the following advantages:

[0035] The present invention combines energy spectrum imaging and Raman spectroscopy through an electron microscope SE / BSE dual contrast imaging system, observes the morphology through an electron microscope, performs energy spectrum splicing in a selected area to be detected, performs element distribution analysis on the spliced ​​image, distinguishes and identifies substances containing characteristic elements by using different colors, associates the coordinates of the electron microscope with confocal Raman, selects an appropriate amount of collection points for substances without characteristic elements to perform Raman spectrum qualitative analysis, and selects an appropriate amount of collection points for substances with characteristic peaks to perform Raman spectrum secondary identification, and finally confirms the prescription composition of the solid preparation through the distribution of different colors in the energy spectrum diagram. The present invention can obtain the component distribution of a larger test sample in a shorter time, realizes rapid high-throughput analysis, and has important practical significance and application value for drug tablet analysis, processing and quality control. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is an electron microscope image of the largest cross section of the test sample of adenosylmethionine butanedisulfonate enteric-coated tablets in Example 1 after freeze polishing;

[0037] Figure 2 For the general Figure 1 The electron microscope diagram after the black material in the middle is recolored and the area division lines are attached;

[0038] Figure 3 This is a schematic diagram of recoloring the substance containing the S element in the selected area in Example 1;

[0039] Figure 4This is a schematic diagram of recoloring the substance containing the Na element in the selected area in Example 1;

[0040] Figure 5 This is a schematic diagram of recoloring the material containing the Mg element in the selected area in Example 1;

[0041] Figure 6 This is a schematic diagram of recoloring the substance containing Si element in the selected area in Example 1;

[0042] Figure 7 This is a schematic diagram of recoloring the material without characteristic elements in the selected area in Example 1;

[0043] Figure 8 The energy spectrum mosaic image of the selected area of ​​Example 1 before coloring;

[0044] Fig. 9 The colored energy spectrum mosaic image of the selected area of ​​Example 1;

[0045] Fig.10 for Figure 3 Raman spectrum of the material collection point containing S element;

[0046] Fig.11 for Figure 4 Raman spectrum of the material collection point containing Na element;

[0047] Fig.12 for Figure 5 Raman spectrum of the material collection point containing Mg element;

[0048] Fig.13 for Figure 7 Raman spectra of material collection points without characteristic elements;

[0049] Fig.14 This is a schematic diagram of component distribution in a selected area in Example 1;

[0050] Fig.15 This is an electron microscope image of the largest cross section of the rivaroxaban tablet test sample after cryopolishing in Example 2;

[0051] Fig.16 For the general Fig.15 The electron microscope diagram after the white material in the middle is recolored and the area division lines are attached;

[0052] Fig.17 This is a schematic diagram of recoloring the substance containing the S element in the selected area in Example 2;

[0053] Fig.18 This is a schematic diagram of recoloring the substance containing the Na element in the selected area in Example 2;

[0054] Fig.19 This is a schematic diagram of recoloring the material containing the Mg element in the selected area in Example 2;

[0055] Fig. 20 This is a schematic diagram of identifying featureless elements in a selected area according to the morphology of the auxiliary material (lactose) through a deep learning neural network in Example 2;

[0056] Fig.21 This is a schematic diagram of recoloring the material in the selected area without feature elements and in the area that has not been recolored in Example 2;

[0057] Fig. 22 The energy spectrum mosaic image of the selected area of ​​Example 2 before coloring;

[0058] Fig.23 The colored energy spectrum mosaic image of the selected area of ​​Example 2;

[0059] Fig.24 for Fig.17 Raman spectrum of the material collection point containing S element;

[0060] Fig.25 for Fig.18 Raman spectrum of the material collection point containing Na element;

[0061] Fig.26 for Fig.19 Raman spectrum of the material collection point containing Mg element;

[0062] Fig. 27 for Fig. 20 Raman spectra of the compound collection points without characteristic elements;

[0063] Fig.28 for Fig.21 Raman spectrum of the material collection point without feature elements recolored in the middle;

[0064] Fig.29 Schematic diagram of component distribution in the selected area in Example 2.

[0065] Fig.30 This is an electron microscope image of the largest cross section of the quetiapine fumarate tablet test sample after freeze polishing in Example 3;

[0066] Fig.31 For the general Fig.30 The electron microscope diagram after the white material in the middle is recolored and the area division lines are attached;

[0067] Fig.32 This is a schematic diagram of recoloring the substance containing the S element in the selected area in Example 3;

[0068] Fig.33 This is a schematic diagram of recoloring the material containing the Ca element in the selected area in Example 3;

[0069] Fig.34 This is a schematic diagram of recoloring the substance containing the Na element in the selected area in Example 3;

[0070] Fig.35 This is a schematic diagram of recoloring the material containing the Mg element in the selected area in Example 3;

[0071] Fig.36 This is a schematic diagram of the substance without characteristic elements in the selected area after being recolored by identifying it as lactose based on the morphology of the auxiliary material through a deep learning neural network in Example 3;

[0072] Fig.37 This is a schematic diagram of the substance without characteristic elements in the selected area after being identified as microcrystalline cellulose by a deep learning neural network according to the morphology of the auxiliary material and then recolored in Example 3;

[0073] Fig.38 The energy spectrum mosaic image of the selected area of ​​Example 3 before coloring;

[0074] Fig.39 for Figure 32 to Figure 37 The energy spectrum mosaic obtained by superimposing them one by one;

[0075] Fig.40 for Fig.32 Raman spectrum of the material collection point containing S element;

[0076] Fig.41 for Fig.34 Raman spectrum of the material collection point containing Ca element;

[0077] Fig.42 for Fig.35 Raman spectrum of the material collection point containing Na element;

[0078] Fig.43 for Fig.36 Raman spectrum of the material collection point containing Mg element;

[0079] Fig.44 for Fig.37 Raman spectrum of the material (lactose) with no characteristic elements in the sample;

[0080] Fig.45 for Fig.38 Raman spectrum of the material (microcrystalline cellulose) with no characteristic elements in the sample;

[0081] Fig.46Schematic diagram of component distribution in a selected area in Example 3 (showing substances added by dry method);

[0082] Fig.47 This is the imaging result of a small area in Example 3. DETAILED DESCRIPTION

[0083] The present invention is further described below in conjunction with the examples. However, the present invention is not limited to the following examples. The implementation conditions used in the examples can be further adjusted according to the different requirements of specific use, and the implementation conditions not specified are conventional conditions in the industry. The technical features involved in each embodiment of the present invention can be combined with each other as long as they do not conflict with each other.

[0084] Example 1

[0085] This embodiment provides a high-throughput method for rapidly analyzing the composition and distribution of the core components of enteric-coated adenosylmethionine butanedisulfonate tablets, which specifically includes the following steps:

[0086] (1) Take a sample of enteric-coated adenosylmethionine butanedisulfonate tablets and perform cryo-polishing on the sample along the maximum cross section using a soft ion beam cryo-polishing instrument (JIT-SCP-100). The cryo-polishing temperature is set to -20°C and the cryo-polishing time is set to 1 hour. The cryo-polished sample is observed under the SE / BSE dual contrast imaging system of an electron microscope (JIT Phamar 5000). The maximum cross section of the cryo-polished sample is smooth and the fine structure is clear, such as Figure 1 shown.

[0087] (2) Under the SE / BSE dual contrast imaging system of the electron microscope, select Figure 1 The black material in the sample (later analyzed to be microcrystalline cellulose) was used to analyze the uniformity of the internal component distribution of the test sample. Figure 1 The selected black material is recolored and output on the electron microscope image shown (recolored yellow in this embodiment), as shown in FIG. Figure 2 As shown, the maximum cross-section of the test sample is divided into four roughly equal areas, and the uniformity of the distribution of the black material is used to evaluate whether the components inside the test sample are evenly mixed. Figure 2 The upper left area is marked as area 1, the upper right area is marked as area 2, the lower left area is marked as area 3, and the lower right area is marked as area 4. The uniformity analysis of the internal component distribution of the test sample is based on the JIT uniformity calculation method: the percentage of the yellow area in the selected area to the area of ​​the selected area, and then the percentage of each area is multiplied by 100 and the variance between them is calculated. When the variance is ≤1, it is considered that the internal component distribution of the test sample is uniform. The test sample with uniform internal component distribution is selected for high-throughput analysis. The test data of the uniformity of internal component distribution of the test sample implemented in this embodiment is shown in Table 1.

[0088] Table 1

[0089] name Area 1 (%) Area 2 (%) Area 3 (%) Region 4 (%) variance Uniformity 3.84 5.55 5.34 5.52 0.51

[0090] according to Figure 2 As shown in Table 1, the selected test samples are well mixed internally and can be analyzed in the next step.

[0091] (3) According to the conventional components of enteric-coated adenosylmethionine butane disulfonate tablets, it is inferred that the tablet core components must include the API adenosylmethionine butane disulfonate, the auxiliary material magnesium stearate, the auxiliary material sodium carboxymethyl starch, and the auxiliary material silicon dioxide. The element S is selected as the characteristic element of adenosylmethionine butane disulfonate, the element Mg is selected as the characteristic element of magnesium stearate, the element Na is selected as the characteristic element of sodium carboxymethyl starch, and the element Si is selected as the characteristic element of silicon dioxide. Under the SE / BSE dual contrast imaging system of the electron microscope, select one of the above-mentioned maximum cross sections (10 mm 2 ) area, an EDS spectrometer (Oxford Ultimately MAX100) used in conjunction with an electron microscope was used to scan the area and the energy spectrum splicing map of the area was obtained by energy spectrum splicing ( Figure 8 ), use the same color for substances with the same characteristic element, and use different colors for substances with different characteristic elements to recolor and output, and obtain different substance coloring maps ( Figure 3 The substances containing S element in the selected area are colored orange, and the rest are displayed in black; Figure 4 The substances containing Na in the selected area are colored yellow, and the rest are displayed in black; Figure 5 The substances containing Mg in the selected area are colored purple, and the rest are displayed in black; Figure 6 The substances containing Si element in the selected area are colored blue, and the rest are displayed in black; Figure 7 To color the material without the above characteristic elements in the selected area green (the energy spectrum scan shows that it contains C element), the rest are first displayed in black). Figure 3 to Figure 7 The energy spectrum mosaics superimposed on the selected area in sequence ( Figure 8 ) Fig. 9 The colored spectrum mosaic is shown.

[0092] (4) For substances with characteristic elements, the name of the substance can be directly marked according to the characteristic elements, and then it can be selected whether to perform secondary identification or re-determine the correct substance name through Raman spectroscopy according to the situation. For substances without characteristic elements, the correct substance name is determined by Raman spectroscopy. For example, in this embodiment, the substance containing the Mg element is first marked as magnesium stearate; the substance containing the S element is marked as API (adenosylmethionine disulfonate); the substance containing the Si element is marked as silicon dioxide, and according to the shape of the Si element coloring, it can be inferred to be colloidal silicon dioxide; the substance containing the Na element is marked as sodium carboxymethyl starch. Then, in the area intercepted in step (3), collection points are selected for different color areas, and the horizontal and vertical coordinates of these collection points under the SE / BSE double contrast imaging system of the electron microscope are recorded. Under the same coordinates, the Raman spectrum of the substance at the collection point is detected using a confocal microscopic Raman imaging system (Witec-300R). For the same color, 3 to 5 points are randomly selected for detection, and the corresponding substance name is determined according to the Raman spectrum, thereby determining the name of the substance without characteristic elements or performing secondary verification on the substance with characteristic elements. For example, in this embodiment, Fig.10 The Raman spectrum of the material containing the S element is automatically compared with the Raman database to determine that the material is API (adenosylmethionine butanedisulfonate); Fig.11 This is the Raman spectrum of the sample point of the substance containing the Na element. After automatic comparison with the Raman database, it is determined that the substance is sodium carboxymethyl starch; Fig.12 The Raman spectrum of the sample point of the substance containing Mg element is automatically compared with the Raman database to determine that the substance is magnesium stearate; Fig.13 The Raman spectrum of the material point without characteristic elements is automatically compared with the Raman database to determine that the material is microcrystalline cellulose. Different colors are marked to obtain Fig.14 The results shown can represent the composition and distribution of the test sample core.

[0093] Example 2

[0094] This embodiment provides a high-throughput method for rapidly analyzing the composition and distribution of the core components of rivaroxaban tablets, and the steps are the same as those in Example 1.

[0095] (1) According to step (1) in Example 1, the rivaroxaban tablet test sample was cryo-polished. The maximum cross-section of the test sample after cryo-polishing had a smooth surface and clear microstructure, such as Fig.15 shown.

[0096] (2) Confirm whether the internal mixing of the test sample is uniform by referring to the method of step (2) in Example 1. Under the SE / BSE dual contrast imaging system of the electron microscope, select Fig.15The gray material inside the test sample (later analyzed to be lactose) was recolored and output on the electron microscope image, and then the Fig.16 After the area division is performed as shown, the uniformity analysis of the internal component distribution of the test sample is performed. The test data of the uniformity of the internal component distribution of the test sample in this implementation is shown in Table 2.

[0097] Table 2

[0098]

[0099] according to Fig.16 As shown in Table 2, the selected test samples are evenly mixed internally.

[0100] (3) Referring to step (3) in Example 1, based on the conventional components of rivaroxaban tablets, it is inferred that the tablet core components must include API rivaroxaban, auxiliary material magnesium stearate, and auxiliary material cross-linked carboxymethyl cellulose sodium. S element is selected as the characteristic element of rivaroxaban, Mg element as the characteristic element of magnesium stearate, and Na element as the characteristic element of cross-linked carboxymethyl cellulose sodium. Under the SE / BSE dual contrast imaging system of an electron microscope, select any one (10 mm 2 ) area, an EDS spectrometer (Oxford Ultimately MAX100) used in conjunction with an electron microscope was used to scan the area and the energy spectrum splicing map of the area was obtained by using energy spectrum splicing ( Fig. 22 ), use the same color for substances with the same characteristic element, and use different colors for substances with different characteristic elements to recolor and output, and obtain different substance coloring maps ( Fig.17 Color the S elements in the selected area blue, and the rest are displayed in black; Fig.18 The Na element in the selected area is colored green, and the rest are displayed in black; Fig.19 The Mg element in the selected area is colored purple, and the rest are displayed in black. The substance without characteristic elements (lactose) in the electron microscope image is colored yellow after the auxiliary material morphology is recognized by the deep learning neural network, and the rest are displayed in black. Fig. 20 As shown; the uncolored area in the electron microscope image is colored orange, and the rest is first displayed in black, as shown Fig.21 As shown. Figures 17 to 21 Superimposed on the energy spectrum mosaic ( Fig. 22 ) Fig.23 The colored spectrum mosaic is shown.

[0101] (4) Referring to step (4) in Example 1, substances with characteristic elements can be directly marked with the name of the substance according to the characteristic elements, and then it can be chosen whether to perform secondary identification or redetermine the correct substance name through Raman spectroscopy according to the situation. For substances without characteristic elements, the correct substance name is determined through Raman spectroscopy. For example, in this embodiment, the substance containing the S element is first marked as API (rivaroxaban), the substance containing the Na element is marked as cross-linked sodium carboxymethyl cellulose, and the substance containing the Mg element is marked as magnesium stearate. Then, in the area intercepted in step (3), collection points are selected for different color areas, and the horizontal and vertical coordinates of these collection points under the SE / BSE dual contrast imaging system of the electron microscope are recorded. Under the same coordinates, the Raman spectrum of the substance at the collection point is detected using a confocal microscopy Raman imaging system (Witec-300R). 3-5 points of the same color are randomly selected for detection, and the corresponding substance name is determined according to the Raman spectrum, thereby determining the name of the substance without characteristic elements or performing secondary verification on the substance with characteristic elements. For example, in this embodiment, Fig.24 The Raman spectrum of the material collection point containing the S element is automatically compared with the Raman spectrum database by the system, and the material is determined to be API; Fig.25 This is the Raman spectrum of the sample point of the substance containing the Na element. After the system automatically compares the Raman spectrum database, it is determined that the substance is cross-linked sodium carboxymethyl cellulose; Fig.26 The Raman spectrum of the collection point of the substance containing Mg element is automatically compared with the Raman spectrum database by the system, and it is determined that the substance is magnesium stearate; Fig. 27 The Raman spectrum of the substance without characteristic elements that was recolored yellow in the previous step is determined to be lactose after the system automatically compares the Raman spectrum library; Fig.28 This is the Raman spectrum of the substance without characteristic elements that was recolored orange in the previous step. After the system automatically compares the Raman spectrum library, it is determined that the substance is microcrystalline cellulose. Fig.29 The results shown can represent the composition and distribution of the test sample core.

[0102] Example 3

[0103] This embodiment provides a high-throughput method for rapidly analyzing the composition and distribution of the components of quetiapine fumarate tablet cores, and the steps are the same as those in Example 1.

[0104] (1) According to step (1) in Example 1, the quetiapine fumarate test sample is cryo-polished. The maximum cross-section surface of the test sample after cryo-polishing is flat and the fine structure is clear, such as Fig.30 shown.

[0105] (2) Confirm whether the internal mixing of the test sample is uniform by referring to the method of step (2) in Example 1. Under the SE / BSE dual contrast imaging system of the electron microscope, select Fig.15 The white material inside the test sample (later analyzed to be calcium hydrogen phosphate) was recolored and output on the electron microscope image, and then Fig.31 After the regional division is performed as shown, the uniformity analysis of the internal component distribution of the test sample is performed. The test data of the uniformity of the internal component distribution of the test sample in this implementation is shown in Table 3.

[0106] Table 3

[0107]

[0108] according to Fig.31 As shown in Table 3, the selected test samples are evenly mixed internally.

[0109] (3) Referring to step (3) in Example 1, based on the conventional components of quetiapine fumarate tablets, it is inferred that the tablet core components must include API quetiapine fumarate, auxiliary materials magnesium stearate, auxiliary materials calcium hydrogen phosphate, and auxiliary materials sodium carboxymethyl starch. The element S is selected as the characteristic element of quetiapine fumarate, the element Mg is selected as the characteristic element of magnesium stearate, the element Ca is selected as the characteristic element of calcium hydrogen phosphate, and the element Na is selected as the characteristic element of sodium carboxymethyl starch. Under the SE / BSE dual contrast imaging system of an electron microscope, select one (10 mm 2 ) area, an EDS spectrometer (Oxford Ultimately MAX100) used in conjunction with an electron microscope was used to scan the area and the energy spectrum splicing map of the area was obtained by using energy spectrum splicing ( Fig.38 ), use the same color for substances with the same characteristic element, and use different colors for substances with different characteristic elements to recolor and output, and obtain different substance coloring maps ( Fig.32 Color the S element in the selected area orange, and the rest are displayed in black; Fig.33 The Ca element in the selected area is colored green, and the rest are displayed in black; Fig.34 The Na element in the selected area is colored yellow, and the rest are displayed in black; Fig.35 The Mg element in the selected area is colored purple, and the rest are displayed in black. The material without characteristic elements in the electron microscope image is identified by deep learning neural network. The material with morphology identified as lactose is colored dark blue, such as Fig.36 As shown; the material morphologically identified as microcrystalline fibers is colored cyan, such as Fig.37 As shown; Figure 32 to Figure 37 Superimposed on the energy spectrum mosaic ( Fig.38 ) Fig.39 The colored spectrum mosaic is shown.

[0110] (4) Referring to step (4) in Example 1, substances with characteristic elements can be directly marked with the name of the substance according to the characteristic elements, and then it can be selected whether to perform secondary identification or re-determine the correct substance name through Raman spectroscopy according to the situation. For substances without characteristic elements, the correct substance name is determined through Raman spectroscopy. For example, in this embodiment, the substance containing the S element is first marked as API (quetiapine fumarate), the substance containing the Na element is marked as sodium carboxymethyl starch, the substance containing the Ca element is marked as calcium hydrogen phosphate, and the substance containing the Mg element is marked as magnesium stearate. Then, in the area intercepted in step (3), collection points are selected for different color areas, and the horizontal and vertical coordinates of these collection points under the SE / BSE dual contrast imaging system of the electron microscope are recorded. Under the same coordinates, the Raman spectrum of the substance at the collection point is detected using a confocal microscopy Raman imaging system (Witec-300R). 3 to 5 points of the same color are randomly selected for detection, and the corresponding substance name is determined according to the Raman spectrum, thereby determining the name of the substance without characteristic elements or performing secondary verification on the substance with characteristic elements. For example, in this embodiment, Fig.40 The Raman spectrum of the material collection point containing the S element is automatically compared with the Raman spectrum database by the system, and the material is determined to be API; Fig.41 The Raman spectrum of the material containing Ca element is collected at the point. After the system automatically compares the Raman spectrum database, it is determined that the material is calcium hydrogen phosphate; Fig.42 This is the Raman spectrum of the sample point of the substance containing the Na element. After the system automatically compares it with the Raman spectrum database, it is determined that the substance is sodium carboxymethyl starch; Fig.43 The Raman spectrum of the collection point of the substance containing Mg element is automatically compared with the Raman spectrum database by the system, and it is determined that the substance is magnesium stearate; Fig.44 The Raman spectrum of the substance without characteristic elements that was recolored yellow in the previous step is determined to be lactose after the system automatically compares the Raman spectrum library; Fig.45 This is the Raman spectrum of the substance without characteristic elements that was recolored orange in the previous step. After the system automatically compares the Raman spectrum library, it is determined that the substance is microcrystalline cellulose. Fig.46 Results shown.

[0111] (5) The above compounds are mainly dry-added compounds (with particle morphology). If there are wet-added compounds (mainly adhesives used to bond API and excipients into particles), Raman imaging can be performed on a small area to confirm its composition. Specifically, a small area (50 μm×50 μm) is randomly selected within the above selected granulation particle area, and Raman imaging is performed on the selected small area to determine the substance in the small area through the Raman fingerprint peak. Fig.47Schematic diagram of small area Raman imaging in this embodiment (the red area is API, the blue area is calcium hydrogen phosphate, and the green area is povidone).

[0112] Fig.46 Combination Fig.47 It can represent the composition and distribution of the test sample core.

[0113] The test range of the above embodiments 1, 2 and 3 is 10 mm. 2 When the test resolution is 300nm, the test time of Example 1 and Example 2 is about 2h, and the test time of Example 3 is about 2.5h; when the test resolution is 10μm, the test time of Example 1, Example 2 and Example 3 is about 45min. The component analysis results of Example 1, Example 2 and Example 3 are consistent with the corresponding pharmaceutical prescriptions, and the proportions of each component converted according to the area proportion based on the component distribution are basically consistent with the corresponding pharmaceutical prescriptions, and the test results are accurate.

[0114] Electron microscope energy spectrum can only distinguish substances containing characteristic elements, while Raman imaging can distinguish compounds without elemental differences, but the efficiency is low and time-consuming. Confocal microscopic Raman imaging system is more suitable for multimodal imaging analysis of target molecules or smaller areas. The 10mm selected in this embodiment is also tested. 2 If the confocal microscopic Raman imaging system is used for Raman imaging, the test resolution is 300nm, and the test time is about 643 days. If the test resolution is 10μm, the test time is about 13.8h. The method of the present invention greatly improves the efficiency of analyzing the drug components and component distribution of solid preparations. At the same time, it is non-destructive to the drug and has a large sample size (the energy spectrum splicing area can be further expanded, for example, 100mm 2 Or directly detect the cross-section of the entire test sample), the test results are accurate, which is of great significance to the subsequent generic drug development, quality control and solid preparation improvement.

[0115] The above detailed description of the present invention is intended to enable persons familiar with the art to understand the contents of the present invention and implement them. It does not limit the protection scope of the present invention. Any equivalent changes or modifications made according to the spirit of the present invention should be included in the protection scope of the present invention.

Claims

1. A high-throughput analytical method for imaging the distribution of drug components in solid preparations, characterized in that: The high-throughput analysis method comprises the following steps: (1) Cryopolishing a solid dosage form drug to obtain a cross section to be tested; (2) Obtain a complete electron microscope image of the cross section to be tested under the SE / BSE dual contrast imaging system of the electron microscope, and randomly select a 10 mm 2 ~100mm 2 The area is taken as the test area; (3) Under the SE / BSE dual-contrast imaging system of the electron microscope, an energy spectrometer is used to scan the area to be tested to obtain an energy spectrum mosaic image, and according to the distribution results of different elements in the energy spectrum mosaic image combined with the appearance and / or grayscale level of the substances in the area to be tested in the complete electron microscope image, the same substance in the energy spectrum mosaic image is colored with the same color, and different substances are colored with different colors; (4) In the test area, collection points can be selectively selected for different substances, and Raman spectra of the collection points can be obtained. The names of the substances represented by different colors in the colored energy spectrum splicing diagram are determined according to the characteristic elements of the substances and / or the Raman spectra of the collection points. The distribution of different colors represents the distribution of different substances in the solid dosage form.

2. The high-throughput analysis method according to claim 1, characterized in that: The solid preparation drug is a solid preparation drug with components mixed evenly; and / or, the solid dosage drug is a solid particle or colloid in a tablet, granule, or capsule; And / or, the test resolution of the high-throughput analysis method is 300nm to 200μm.

3. The high-throughput analysis method according to claim 1 or 2, characterized in that: The step (2) further includes a step of ensuring that the components in the solid dosage form are uniformly mixed, the step comprising: The cross section to be tested is divided into several areas, the distribution uniformity of a certain substance in each area is counted, the area percentage of a certain substance in each area is calculated, the percentage of each area is multiplied by 100 and the variance between them is counted. When the variance is ≤1, it is considered that the internal components of the solid dosage form are evenly distributed, and a region to be tested can be randomly selected to represent the entire solid dosage form.

4. The high-throughput analysis method according to claim 3, characterized in that: The certain substance is a substance having the same morphology and the same grayscale level in the complete electron microscope image; And / or, the plurality of regions are 4 to 10 regions.

5. The high-throughput analysis method according to claim 1, characterized in that: The cross section to be tested in step (1) is the maximum cross section of the solid dosage form drug; And / or, the temperature of the freeze polishing in step (1) is -40°C to 0°C, and the time of the freeze polishing is 1 to 5 hours.

6. The high-throughput analysis method according to claim 1, characterized in that: In the step (4), a confocal microscopic Raman imaging system is used to detect the Raman spectrum of the collection point.

7. The high-throughput analysis method according to claim 6, characterized in that: Under the SE / BSE dual contrast imaging system of the electron microscope, collection points are selected for different substances in the area to be tested, the horizontal and vertical coordinates of these collection points are recorded, and the Raman spectrum of the substance at the same coordinate position as that under the electron microscope is detected using a confocal microscopic Raman imaging system, which is the Raman spectrum of the collection point; And / or, the substances marked with the same color are tested at at least three dispersed collection points.

8. The high-throughput analysis method according to claim 1, characterized in that: In step (4), substances with characteristic elements can be directly labeled with the substance name according to the characteristic elements, and then it can be selected whether to perform secondary identification or redetermine the correct substance name through Raman spectroscopy according to the situation. For substances without characteristic elements, the correct substance name is determined through Raman spectroscopy.

9. The high-throughput analysis method according to claim 1, characterized in that: In the step (3), the material without characteristic elements can be identified and distinguished by recognizing the morphology through a deep learning neural network.

10. The high-throughput analysis method according to claim 1, characterized in that: The steps (1) to (4) are used to analyze the substances added by the dry method in the solid preparation. The high-throughput analysis method may also selectively include step (5), which is: randomly selecting a small area in the area to be tested, performing Raman imaging on the small area, and confirming whether the solid preparation contains the substances added by the wet method based on the Raman fingerprint peak.

Images