Online measurement method for content of dichloromethane in water phase in microsphere preparation process

Abstract

The invention discloses an on-line measurement method for the content of dichloromethane in a water phase in a microsphere preparation process, and belongs to the technical field of pharmaceutical analysis. The method comprises the following steps: designing a plurality of batches of microsphere preparation experiments, starting to prepare microspheres, collecting an original Raman spectrum on line, and analyzing the content of dichloromethane in a water phase in a curing process through headspace gas chromatography, the original Raman spectrum and the dichloromethane content in the water phase in the curing process are paired one by one, abnormal value removal and sample set division are carried out on data, the Raman spectrum is preprocessed, an optimal spectrum preprocessing method is selected, the spectrum interval is optimized, and the optimal spectrum preprocessing method is selected; and establishing an on-line quantitative analysis model for the content of the dichloromethane in the water phase in the microsphere preparation process by adopting a partial least square method under the optimal condition. The method has the beneficial effects that the accurate online measurement and quality control of the water phase DCM content in the microsphere preparation process are realized, the detection efficiency is effectively improved, and the influence of sample preparation on the measurement accuracy is avoided.

Description

Technical Field

[0001] The invention relates to a method for measuring dichloromethane content, in particular to an online method for measuring dichloromethane content in a water phase during a microsphere preparation process, and belongs to the technical field of drug analysis. Background Art

[0002] At present, the production of drugs usually adopts the traditional production method, that is, the offline analysis method is used to sample and test the raw materials, intermediates and final products in the drug production process, and then send the samples to the laboratory for analysis. The measurement data of this traditional method generally lags behind production, and cannot monitor product quality in real time, so it is impossible to adjust process parameters in time, which will consume a lot of manpower and material resources, and it is difficult to ensure the uniformity of the drug. In addition, the preparation process is often invisible, and the situation in the equipment cavity is not completely clear. The relevant information obtained after the process is limited, and the process control is difficult, which affects the final quality of the product. Therefore, it is of great value and significance to study process analytical technology (PAT) in the drug production process.

[0003] In order to introduce PAT into drug production and quality control, the U.S. Food and Drug Administration (FDA) officially released the PAT industry guide in September 2004, encouraging pharmaceutical companies to timely adjust the preparation process by real-time monitoring of key quality and performance parameters in the drug production process, prevent or reduce the risk of inferior product production, and ensure the final quality of the product. Compared with traditional offline analysis methods, PAT does not require sample preparation and transfer, improves production efficiency, can understand the key quality attributes of the drug preparation process in real time, master the production process, and improve drug production process control.

[0004] PAT provides key technologies such as near infrared spectroscopy and Raman spectroscopy for understanding, monitoring and controlling production. Although near infrared spectroscopy still dominates the pharmaceutical industry, Raman spectroscopy is becoming more and more widely used due to its characteristics of less or no sample preparation, fast analysis, less influence of moisture, high sensitivity and simple operation. Raman spectroscopy was invented in 1928 after Indian scientist CV Raman discovered the Raman effect. Raman spectroscopy is a scattering spectrum based on the Raman effect. Its principle is that when light irradiates a certain medium, energy exchange occurs between photons and material molecules, causing the frequency, energy and direction of photons to change, thereby forming inelastic scattering. This inelastic scattering phenomenon is Raman scattering. In practical applications, Raman spectroscopy has inherent advantages over near infrared spectroscopy and infrared spectroscopy due to its specificity, compatibility with aqueous systems and sampling flexibility.

[0005] Microsphere sustained-release preparations refer to tiny spherical entities formed by the adsorption or dispersion of drugs in macromolecules and polymer matrices, with a particle size usually between 1μm and 250μm. Microsphere preparations have certain advantages in terms of safety and effectiveness. They can not only delay the release of drugs in the body, reduce the frequency of administration, and improve patient compliance, but also mask the special irritating taste of drugs and improve the stability of drugs after administration. However, the unclear correlation between the key material properties, key process parameters and key quality attributes of microsphere preparations has seriously hindered the development of microspheres from laboratory to clinic. In the past 40 years, only a dozen PLGA (poly(lactide-glycolide)) microsphere products have been approved by the FDA for marketing. So far, the application of PAT in microsphere research is not common. In the preparation process of microspheres, dichloromethane (DCM) is the most studied and used organic solvent with the most stable process in microsphere preparations because of its strong dissolving ability, low boiling point, high volatility and low solubility in water. At the same time, during the preparation of microspheres, the removal of DCM will not only affect the internal structure of the microspheres, but also affect the final morphology and size of the microspheres, seriously affecting key quality attributes such as microsphere release. Therefore, the DCM content of the aqueous phase is an important indicator of concern in the preparation of microspheres and needs to be controlled. To date, most methods for measuring the DCM content of the aqueous phase use offline analysis methods, and the results are hysteretic and cannot timely grasp the dynamic information of the microsphere preparation process, resulting in quality problems in the final microsphere product. Summary of the invention

[0006] To address the deficiencies of the prior art, the present invention aims to provide an online measurement method for the DCM content of the aqueous phase during the preparation of microspheres. Compared with the previous offline analysis method, the online measurement method can effectively improve the detection efficiency and avoid the influence of sample preparation on the measurement accuracy.

[0007] In order to achieve the above object, the present invention adopts the following technical solution: The online measurement method of the dichloromethane content in the water phase during the microsphere preparation process comprises the following steps: (1) Design multiple batches of microsphere preparation experiments by changing a single factor within the range of normal and abnormal formulation process conditions; (2) Start preparing microspheres, use an online Raman spectrometer to collect original spectra during the entire curing process, and record the data collection time and Raman spectrum; (3) Analyze the dichloromethane content in the aqueous phase during the curing process by headspace gas chromatography; (4) Pair the original Raman spectra collected from each batch with the dichloromethane content in the water phase during the curing process determined by gas chromatography one by one, and filter the data to remove outliers; (5) The Kennard-Stone method is used to group each batch of data into a calibration set and a validation set; (6) Different spectral preprocessing methods were used to preprocess the Raman spectrum. The partial least squares method was selected as the modeling method. The best spectral preprocessing method was selected based on the evaluation parameters of the model results. (7) The variable importance projection algorithm is used to screen the Raman spectrum bands, compare them with the full spectrum modeling, and select the optimal modeling band; (8) Under the conditions of optimal spectral preprocessing method, 4 potential variables, and optimal modeling band, the partial least squares method was used to establish an online quantitative analysis model for the dichloromethane content in the aqueous phase during the microsphere preparation process.

[0008] Preferably, in step (1), the factors include: oil-water ratio, compressed air flow rate and curing temperature.

[0009] Preferably, in step (2), the parameters of the online Raman spectrometer are set as follows: laser intensity is 300 mW, exposure time is 3 s, scanning times is 10 times, spectrum is collected every 45 s, wavelength range is 100 cm -1 -3200cm -1 .

[0010] Preferably, in step (3), the method for analyzing the content of dichloromethane in the water phase during the curing process is: (i) Preparation of aqueous phase samples during the curing process: taking 0.1 mL of the aqueous phase during the curing process at different time points, accurately measuring and placing it in a 10 mL headspace bottle, adding 0.9 mL of N, N-dimethylacetamide, sealing and shaking, wherein the time point is consistent with the time point of collecting the Raman spectrum; (ii) preparing a reference solution: dissolving dichloromethane with N, N-dimethylacetamide to obtain a reference solution with a concentration of 0.5 mg / mL; (iii) using gas chromatograph and headspace gas chromatography to collect gas chromatograms of aqueous samples and reference solution during the solidification process; (iv) The dichloromethane content in the water phase during the curing process was calculated based on gas chromatography using the following formula:

[0011] Among them, A 供试品 is the peak area of ​​DCM in the test solution, A 对照品 is the average peak area of ​​DCM in the reference solution, C 对照品 is the concentration of DCM in the reference solution, in mg / mL.

[0012] Preferably, in step (4), the data are screened to remove outliers by using a combination of principal component analysis and influence diagram, and the confidence level is set to 95%.

[0013] Preferably, in step (5), the samples are divided into a calibration set and a validation set in a ratio of 3:1.

[0014] Preferably, in step (6), the spectral preprocessing method includes: Raw, MC, SNV, SNV+MC, MC+SNV, FD+SG+SNV+MC, SD+SG+SNV+MC, SNV+FD+SG+MC, SNV+SD+SG+MC, FD+SG+MC, SD+SG+MC, FD+SG+SNV, SD+SG+SNV, SNV+FD+SG ​​and SNV+SD+SG.

[0015] The present invention is beneficial in that: (1) The accurate online measurement and quality control of the DCM content in the aqueous phase during the microsphere preparation process was achieved. Compared with the previous offline analysis method, it effectively improved the detection efficiency and avoided the influence of sample preparation on the measurement accuracy. (2) Using Raman spectroscopy to introduce PAT into the monitoring of DCM content in the aqueous phase during the microsphere preparation process can obtain key information in real time and adjust parameters in a timely manner to ensure the final quality of the microsphere product. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a graph showing the change of DCM content in the water phase over time during the curing process; Figure 2 is the graph showing the change of DCM content in microspheres over time during the curing process; Figure 3 This is the original Raman spectrum of the rotigotine microsphere samples used for modeling (a total of 164 samples from batches 1 to 7, including microspheres in the curing process and final product microspheres); Figure 4 This is the abnormal sample elimination result diagram obtained by using the PCA method; Figure 5 It is the abnormal sample elimination result diagram obtained by using the influence diagram method; Figure 6 It is the PCA score scatter plot of the sample set partitioning during the microsphere preparation process; Figure 7 This is the result of using the VIP algorithm to screen the Raman spectrum bands; Figure 8 It is a comparison chart between the predicted value and the reference value of DCM content in the water phase during the curing process of the external validation set of the full-band PLS model; Fig. 9It is the relationship between the predicted value and the reference value of DCM content in the water phase during the curing process of the external validation set of the full-band PLS model; Fig.10 This is a graph showing the cumulative release calculation results of the final product, Rotigotine Microspheres, prepared in different batches over a period of 40 min to 6 h; Fig.11 This is the calculation result of the cumulative release of the final product Rotigotine Microspheres prepared in different batches from 40min to 8d. DETAILED DESCRIPTION

[0017] Rotigotine microspheres were used as model drugs, and the DCM content in the aqueous phase during the preparation of microspheres was used as the evaluation index. Raman spectrometer was used to collect sample spectra, and headspace gas chromatography was used to determine the DCM content in the aqueous phase. The partial least squares (PLS) method was used to correlate the spectral data with the gas phase data. After optimizing the spectral parameters, a quantitative analysis model for the DCM content in the aqueous phase was finally established.

[0018] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] 1. Design multiple batches of microsphere preparation experiments within normal and abnormal prescription process conditions Since changes in formulation composition and process parameters usually have a certain impact on the quality attributes of microspheres, in view of the factors that have a greater impact on the change of DCM content in the aqueous phase during the preparation process, including formulation composition (oil-water ratio) and process parameters (compressed air flow rate, curing temperature), within the normal and abnormal formulation process conditions, the method of changing a single factor was used to design multiple batches of microsphere preparation experiments, as shown in Table 1 for details.

[0020] Table 1 Parameter settings during microsphere preparation

[0021] The preparation of rotigotine microspheres adopts the single emulsion solvent evaporation method, specifically: (1) Add 8 L of 0.5% (w / v) polyvinyl alcohol (PVA) solution into the reactor and control the temperature to 8 °C (aqueous phase); (2) Take about 8.4 g of poly(lactic-co-glycolic acid) (PLGA) 5050 2A, about 8.4 g of PLGA 7525 4A, about 0.6 g of palmitic acid, and about 7.2 g of rotigotine, accurately weigh them, and dissolve them in 120 mL of DCM (oil phase); (3) At a homogenization speed of 700 rpm, the oil phase is added to the water phase through a peristaltic pump at a specified oil-water ratio (e.g., 0.5:100, 1:100, 1.5:100) for homogenization and emulsification. After the addition of the oil phase, the homogenization and emulsification are continued for 2 minutes. After the emulsification is completed, the compressed air flow rate is set (e.g., 20 m / s, 50 m / s, 80 m / s), and the microspheres are solidified at a specified curing temperature (e.g., 20°C, 30°C, 40°C) at 100 rpm for 5 hours to volatilize DCM; (4) After solidification, the microspheres with a particle size between 1200 mesh and 80 mesh are collected through 1200 mesh and 80 mesh sieves, washed with deionized water and placed in a -20°C refrigerator for pre-freezing. The obtained sample is then placed in a freeze dryer for freeze drying, and the freeze-dried product is passed through an 80 mesh sieve to obtain the corresponding final product.

[0022] 2. Preparation of microspheres and collection of Raman spectra The original spectrum was collected by an online Raman spectrometer. Before preparing the microspheres, the probe of the online Raman spectrometer was inserted into the reactor used to prepare the microspheres to prepare the original spectrum. The laser intensity was 300 mW, the exposure time was 3 s, the number of scans was 10 times, and the spectrum was collected every 45 s. The wavelength range was 100 cm -1 -3200cm -1 .

[0023] Begin to prepare microspheres, and record data collection time and Raman spectra during the entire curing process. In this specific embodiment, a total of 189 samples were collected from all batches, of which the data collected from batches 1 to 7 were used to establish the model, and the data collected from batch 8 were used for external verification of the model. The distribution of sample data collection is shown in Table 2.

[0024] Table 2 Distribution of sampling points during the curing process

[0025] The original Raman spectra of the rotigotine microsphere samples used for modeling (a total of 164 samples from batches 1 to 7, including microspheres during curing and final product microspheres) are shown in Figure 3 .

[0026] 3. Analysis of DCM content in samples by headspace gas chromatography 1. Prepare blank solution, test samples (microspheres during curing, aqueous phase during curing, and final microspheres) and reference sample (DCM) Blank solution: Accurately measure 1 mL of N, N-dimethylacetamide, place it in a 10 mL headspace bottle, and seal it.

[0027] Microspheres during the curing process: Take about 20 mg of microspheres during the curing process at different time points (consistent with the time point of collecting Raman spectra), accurately weigh, place in a vial, add 10 mL of N, N-dimethylacetamide to dissolve and shake well, take 1 mL and place in a 10 mL headspace bottle, seal, and prepare 2 copies in parallel.

[0028] Aqueous phase during curing: Take 0.1 mL of the aqueous phase during curing at different time points (consistent with the time point of collecting Raman spectra), place it in a 10 mL headspace bottle, add 0.9 mL of N, N-dimethylacetamide, seal and shake well, and prepare 2 portions in parallel.

[0029] Final product microspheres: Take about 20 mg of the final product rotigotine microspheres prepared in each batch, accurately weigh them, place them in 10 mL headspace bottles, add 1 mL of N, N-dimethylacetamide, seal them, and prepare 2 parts in parallel.

[0030] Reference solution: Take about 50 mg of DCM, accurately weigh it, place it in a 100 mL volumetric flask, dilute it to the scale with N, N-dimethylacetamide, shake well, and obtain a reference solution with a concentration of 0.5 mg / mL.

[0031] 2. Collect gas chromatograms of the test products (microspheres during curing, aqueous phase during curing, and final microspheres) and the reference product (DCM) The gas chromatogram was collected by a gas chromatograph using headspace gas chromatography. The chromatographic conditions were as follows: Chromatographic column: DB-624 quartz capillary column (30m×0.53mm×3µm); Temperature program: initial column temperature was 40 °C, maintained for 6 min, increased to 200 °C at 40 °C / min, maintained for 3 min; Injection chamber temperature: 200°C; Split ratio: 5:1; Detector: hydrogen flame ionization detector (FID), temperature 260°C, hydrogen flow rate 30 mL / min, air flow rate 300 mL / min; Carrier gas: nitrogen, flow rate 4.0 mL / min; Headspace sampler: equilibrium temperature 90°C, equilibrium time 30 min, quantitative loop temperature 105°C, transfer tube temperature 115°C, injection volume 1 mL; GC cycle time: 30 min.

[0032] 3. Calculate the DCM content in the test sample (microspheres during curing, water phase during curing, and final product microspheres) based on gas chromatography The calculation formulas for the DCM content in the microspheres during the curing process, the aqueous phase during the curing process, and the final product microspheres are as follows: ;

[0033] ;

[0034] ;

[0035] Where: C 对照品 is the concentration of DCM in the reference solution (mg / mL); C 供试品 is the concentration of microspheres in the test solution (mg / mL); A 供试品 is the peak area of ​​DCM in the test solution; A 对照品 is the average peak area of ​​DCM in the reference solution.

[0036] After calculation, the DCM content in the water phase changes with time during the curing process. Figure 1 The DCM content in the microspheres changes with time during the curing process. Figure 2 .

[0037] Depend on Figure 1 and Figure 2 It can be seen that: (1) Oil-water ratio: The oil-water ratio has a significant effect on the DCM content in the aqueous phase; (2) Curing temperature: At a lower curing temperature, the volatilization rate of DCM in the microspheres is slower, and the DCM content in the microspheres at the end of the reaction is relatively increased; as the curing temperature increases, the volatilization rate of DCM in the microspheres is significantly accelerated, and the DCM content in the microspheres at the end of the reaction is correspondingly reduced; (3) Flow rate of compressed air: The flow rate of compressed air has little effect on the DCM content in the aqueous phase.

[0038] 4. Build a quantitative model First, the original Raman spectra collected from batches 1 to 7 were paired one by one with the reference data measured by gas chromatography (DCM content in the water phase during the curing process), and the data were screened to remove outliers. Then, different combinations of preprocessing methods were studied to eliminate the interference of irrelevant information. In order to further simplify and improve the predictive ability of the PLS model, the variable importance projection (VIP) algorithm was used to select the bands of the Raman spectra and compare them with the full spectrum. The model evaluation parameters RMSEC, RMSECV, R 2 C , R 2 CV and R 2 PFinally, the eighth batch of samples was used as an external independent validation set to verify the predictive ability of the PLS model.

[0039] 1. Eliminate abnormal samples The principal components analysis (PCA) method is used to detect abnormal samples. According to the PCA score graph, samples outside the 95% confidence ellipse are identified as abnormal samples. The results of abnormal sample removal are shown in Figure 4 .

[0040] Furthermore, the influence plot method is introduced, using the F-residual and Hotelling T 2 Statistics are used to identify abnormal samples. The confidence level is set to 95%, which exceeds the F-residual and Hotelling T 2 Samples with a threshold value are classified as abnormal samples. The results of abnormal sample removal are shown in Figure 5 .

[0041] comprehensive Figure 4 and Figure 5 , 7 samples were identified as abnormal samples, which were samples with high DCM concentration (DCM content in the water phase during the curing process) with an oil-water ratio of 1.5:100 (the 7th batch of data). Finally, after removing the abnormal samples, 157 samples were retained.

[0042] 2. Divide the sample The Kennard-Stone method was used to group the data from batch 1 to batch 7 in Table 2, and the samples were divided into calibration set and validation set in a ratio of 3:1. The Kennard-Stone method of this study was performed in Matlab.

[0043] After the samples were grouped by the Kennard-Stone method, we finally obtained 118 samples in the calibration set and 39 samples in the validation set. The reference values ​​of the samples in the calibration set and validation set are shown in Table 3.

[0044] Table 3 Reference values ​​of samples in the calibration set and validation set

[0045] It can be seen from Table 3 that the DCM concentration range of the validation set falls within the DCM concentration range of the calibration set, indicating that the sample set division is reasonable.

[0046] During the microsphere preparation process, the PCA score scatter plot of the sample set is shown in Figure 6 .

[0047] Depend on Figure 6It can be seen that the validation set samples are evenly distributed in the calibration set samples, further indicating that the sample set division is reasonable.

[0048] 3. Screening of spectral preprocessing methods In order to eliminate the errors in Raman spectrum acquisition and establish an accurate online Raman spectrum model, this study adopted different spectral preprocessing methods (Raw, MC, SNV, SNV+MC, MC+SNV, FD+SG+SNV+MC, SD+SG+SNV+MC, SNV+FD+SG+MC, SNV+SD+SG+MC, FD+SG+MC, SD+SG+MC, FD+SG+SNV, SD+SG+SNV, SNV+FD+SG, SNV+SD+SG) to preprocess the Raman spectrum. The partial least squares method was selected as the modeling method, and the best spectral preprocessing method was selected according to the advantages and disadvantages of the model result evaluation parameters (R 2 C 、R 2 CV 、R 2 P 、RMSEC, RMSECV and RMSEP).

[0049] The model result evaluation parameters of each model are shown in Table 4.

[0050] Table 4 Model result evaluation parameters of each model

[0051] As can be seen from Table 4, for the rotigotine microsphere samples, when the spectral preprocessing method SNV+FD+SG+MC is selected, the model prediction effect is the best, and its RMSEC, RMSECV, RMSEP, R 2 C 、R 2 CV 、R 2 P values are 0.5857 mg / ml, 0.7436 mg / ml, 0.4223 mg / ml, 0.9878, 0.9804, 0.9918 respectively.

[0052] After calculation, the performance deviation ratio (Ratio of Performance Deviation, RPD) value of the optimal model is 10.23, and the model result is good.

[0053] 4. Optimization of spectral range The variable importance projection (VIP) algorithm is used to screen variables, and screen the variables that contribute most to the potential change of variable X. The Raman spectrum conversion method of this study uses the VIP algorithm to screen the Raman spectrum bands, compare them with the full spectrum modeling, and select the optimal modeling band.

[0054] The results of using VIP algorithm to screen the Raman spectrum bands are shown in Figure 7 .

[0055] The comparison results of the prediction accuracy of the models before and after band screening (the original spectra were preprocessed using the SNV+FD+SG+MC method) are shown in Table 5.

[0056] Table 5 Comparison of model evaluation parameters before and after band screening

[0057] It can be seen from Table 5 that the modeling effect before screening is better than the modeling effect after screening, that is, the effect of full-spectrum modeling is better than the effect of spectral interval modeling optimized by VIP algorithm.

[0058] Therefore, for the Rotigotine Microspheres sample, full spectrum modeling was chosen.

[0059] 5. Build and evaluate the model After eliminating abnormal samples, dividing the sample sets and examining the rationality of the optimization of the spectral interval, SNV+FD+SG+MC was finally selected as the spectral preprocessing method, the number of latent variables was selected as 4, and the partial least squares method was used in the full spectral range to establish an online quantitative analysis model for the DCM content in the aqueous phase during the preparation of rotigotine microspheres (full-band PLS model).

[0060] The 25 samples from the 8th batch (not involved in model building) were used as the external validation set to externally validate the full-band PLS model. The Raman spectrum was used as the X variable, and the DCM content (mg / ml) in the water phase during the curing process determined by headspace gas chromatography was used as the Y variable for regression.

[0061] The comparison between the predicted value of DCM content in the water phase and the reference value during the curing process of the full-band PLS model external validation set is shown in Figure 8 , the relationship between the predicted value and the reference value is shown in Fig. 9 .

[0062] Depend on Figure 8 and Fig. 9 It can be seen that the reference value of DCM content in the aqueous phase during the preparation of microspheres is basically consistent with the DCM content predicted by the full-band PLS model. A certain deviation may be caused by the small number of samples.

[0063] 5. Study on in vitro release In order to investigate the effect of DCM content in the aqueous phase on the release of microspheres and thus on the quality of microspheres, the in vitro release of the final product microspheres prepared from the 1st to 7th batches was studied.

[0064] 1. Prepare solution Blank solution: acetonitrile-purified water (volume ratio 55:45).

[0065] pH 9.5 ammonium bicarbonate solution: Take 0.79g of ammonium bicarbonate, accurately weigh it, dissolve it in 1000ml of water, adjust the pH value to 9.5 with ammonia water, and filter it with a 0.22μm aqueous microporous filter membrane.

[0066] Release medium: Take about 4.68 g of sodium dihydrogen phosphate dihydrate, about 7.02 g of sodium chloride, and about 0.33 g of Tween 80, accurately weigh, add water to dissolve and dilute to 3000 ml, adjust the pH value to 7.4 with 1.75 mol / L sodium hydroxide solution, add 6 g of sodium dodecyl sulfate and 0.6 g of sodium azide, dissolve, and stir evenly.

[0067] Reference solution: Take about 25 mg of Rotigotine reference substance, accurately weigh it, place it in a 100 ml volumetric flask, add an appropriate amount of blank solution, dissolve it by ultrasonication, dilute it to the scale, shake well, and obtain 0.25 mg / ml reference substance stock solution; accurately transfer 5 ml of Rotigotine reference substance stock solution to a 100 ml volumetric flask, dilute it to the scale with blank solution, shake well, and obtain 12.5 μg / ml Rotigotine reference substance solution I; accurately transfer 4 ml of Rotigotine reference substance solution I to a 100 ml volumetric flask, dilute it to the scale with blank solution, shake well, and obtain 0.5 μg / ml Rotigotine reference substance solution II.

[0068] Test solution: Take about 20 mg of the final product Rotigotine microspheres prepared from batches 1 to 7, weigh accurately, and place them in 250 ml dry stoppered glass bottles, in parallel 3 times. Preheat the release medium in an electric thermostatic water bath to 37°C in advance, accurately measure 200 ml of the release medium and add it to the sample bottle, cover the stopper, and place it in a 37°C electric thermostatic water bath. At 40 minutes, 2 hours, 6 hours, 1 day, 2 days, 3 days, 4 days, 5 days, 6 days, 7 days, and 8 days, remove 4 ml of the supernatant, add an equal volume of isothermal release medium, filter 3 ml of the solution through a 0.45 μm polytetrafluoroethylene filter membrane, and take 1 ml of the filtrate as the test solution.

[0069] 2. Chromatographic conditions and calculation methods of drug loading and encapsulation efficiency (1) Chromatographic conditions The chromatographic conditions are as follows: Chromatographic column: XSelect CSH C18 (4.6×50mm, 3.5μm); Mobile phase: acetonitrile-pH 9.5 ammonium bicarbonate solution (volume ratio 55:45); Column temperature: 40°C; Flow rate: 1.8 ml / min; Detection wavelength: 230 nm; Injection volume: 20 μl; Running time: 10 minutes.

[0070] (2) Calculation method of drug loading and encapsulation efficiency The drug loading and encapsulation efficiency were calculated according to the following formula: Drug loading (%) = (drug content in microspheres / microsphere sample weight) × 100%; Encapsulation efficiency (%) = (actual drug loading / theoretical drug loading) × 100%.

[0071] 3. Calculation of in vitro release 25 mg of rotigotine microspheres were accurately weighed in a glass bottle, and 200 ml of phosphate buffer with a pH value of 7.4 was added. The solution was placed in a 37°C electric thermostatic water bath. 4 ml of the supernatant was removed at 40 min, 2 h, 6 h, 1 d, 2 d, 3 d, 4 d, 5 d, 6 d, 7 d, and 8 d, and an equal volume of buffer was added. The drug concentration was determined by high performance liquid chromatography using an XSelect CSH C18 (4.6 × 50 mm × 3.5 μm) column, acetonitrile-pH 9.5 ammonium bicarbonate solution (volume ratio 55:45) as the mobile phase, a flow rate of 1.8 ml / min, a detection wavelength of 230 nm, and an injection volume of 20 μl. All release experiments were performed in triplicate, and all data were processed using origin.

[0072] The in vitro release of the final product rotigotine microspheres prepared from different batches 1 to 7 was determined according to the above method (n=3). Among them, the 0.5μg / ml rotigotine reference solution II was used as the reference solution for the test sample at 40min-6h, and the 12.5μg / ml rotigotine reference solution I was used as the reference solution for the test sample at other time points.

[0073] The cumulative release calculation results of the final product rotigotine microspheres prepared in different batches are shown in Fig.10 40min-8d, the cumulative release calculation results of the final product rotigotine microspheres prepared from different batches are shown in Fig.11 .

[0074] Depend on Fig.10 and Fig.11It can be seen that the final product rotigotine microspheres prepared in the 7th batch (oil-water ratio 1.5:100) showed a higher burst release and a faster overall release rate. The release rates in the early and middle stages of release were higher than the preset standards.

[0075] According to USP <1092> According to the guidelines for the development and validation of dissolution methods, there are at least three control points for the release of extended-release preparations, namely, early release, mid-release, and late release. Dose dumping should not occur in the early release period. The mid-release time point defines the in vivo release curve of the dosage form, and the final time point indicates that the drug is basically released completely. In order to better control the quality of the product, combined with the in vitro release curve, 2h is selected for controlling the burst release, 3d is selected for controlling the mid-release, and 8d is selected for controlling the release endpoint.

[0076] Based on the accumulation of multiple batches of data from clinical batches and stability batches, the preset standard for the release of rotigotine microspheres is tentatively set as follows: the 2-hour cumulative release shall not exceed 2%, the 3-day cumulative release should be between 37%-73%, and the 8-day cumulative release shall not be less than 85%, in order to evaluate the quality of the microspheres.

[0077] In summary, the present invention provides a new method for online real-time monitoring of the DCM content in the aqueous phase during the preparation of microspheres, which is beneficial to quality control during the preparation of microspheres, thereby improving the production efficiency of microspheres and reducing the loss of manpower and material resources.

[0078] It should be noted that the above embodiments are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the embodiments here. Any obvious changes or modifications derived from the technical solution of the present invention are still within the scope of protection of the present invention.

Claims

1. An online measurement method for the dichloromethane content in the aqueous phase during the microsphere preparation process, characterized in that: The following steps are involved: (1) Design multiple batches of microsphere preparation experiments by changing a single factor within the range of normal and abnormal formulation process conditions; (2) Start preparing microspheres, use an online Raman spectrometer to collect original spectra during the entire curing process, and record the data collection time and Raman spectrum; (3) Analyze the dichloromethane content in the aqueous phase during the curing process by headspace gas chromatography; (4) Pair the original Raman spectra collected from each batch with the dichloromethane content in the water phase during the curing process determined by gas chromatography one by one, and filter the data to remove outliers; (5) The Kennard-Stone method is used to group each batch of data into a calibration set and a validation set; (6) Different spectral preprocessing methods were used to preprocess the Raman spectrum. The partial least squares method was selected as the modeling method. The best spectral preprocessing method was selected based on the evaluation parameters of the model results. (7) The variable importance projection algorithm is used to screen the Raman spectrum bands, compare them with the full spectrum modeling, and select the optimal modeling band; (8) Under the conditions of optimal spectral preprocessing method, 4 potential variables, and optimal modeling band, the partial least squares method was used to establish an online quantitative analysis model for the dichloromethane content in the aqueous phase during the microsphere preparation process.

2. The method according to claim 1, characterized in that: In step (1), the factors include: oil-water ratio, compressed air flow rate and curing temperature.

3. The method according to claim 1, characterized in that In step (2), the parameters of the online Raman spectrometer were set as follows: laser intensity of 300 mW, exposure time of 3 s, number of scans of 10 times, spectrum acquisition every 45 s, wavelength range of 100 cm -1 -3200cm -1 .

4. The method according to claim 1, characterized in that In step (3), the method for analyzing the dichloromethane content in the water phase during the curing process is: (i) Preparation of aqueous phase samples during the curing process: taking 0.1 mL of the aqueous phase during the curing process at different time points, accurately measuring and placing it in a 10 mL headspace bottle, adding 0.9 mL of N, N-dimethylacetamide, sealing and shaking, wherein the time point is consistent with the time point of collecting the Raman spectrum; (ii) preparing a reference solution: dissolving dichloromethane with N, N-dimethylacetamide to obtain a reference solution with a concentration of 0.5 mg / mL; (iii) using gas chromatograph and headspace gas chromatography to collect gas chromatograms of aqueous samples and reference solution during the solidification process; (iv) The dichloromethane content in the water phase during the curing process was calculated based on gas chromatography using the following formula: ； Among them, A 供试品 is the peak area of ​​DCM in the test solution, A 对照品 is the average peak area of ​​DCM in the reference solution, C 对照品 is the concentration of DCM in the reference solution, in mg / mL.

5. The method according to claim 1, characterized in that In step (4), the data were screened to remove outliers using a combination of principal component analysis and influence diagrams, with the confidence level set at 95%.

6. The method according to claim 1, characterized in that In step (5), the samples are divided into a calibration set and a validation set in a ratio of 3:

1.

7. The method according to claim 1, characterized in that In step (6), the spectral preprocessing methods include: Raw, MC, SNV, SNV+MC, MC+SNV, FD+SG+SNV+MC, SD+SG+SNV+MC, SNV+FD+SG+MC, SNV+SD+SG+MC, FD+SG+MC, SD+SG+MC, FD+SG+SNV, SD+SG+SNV, SNV+FD+SG ​​and SNV+SD+SG.

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