ABSTRACT
Objective:This paper constructs a generalized regression neural network （GRNN） model to predict the disintegration time of traditional Chinese medicine （TCM） tablets. Method:Taking Astragali Radix as a model drug， the mixed Astragali Radix powders with different powder properties were prepared by mixing Astragali Radix extract powders with microcrystalline cellulose and lactose， which were made to Astragali Radix tablets by direct compression method. The powder properties of mixed Astragali Radix powders and the disintegration time of Astragali Radix tablets were determined， respectively. The correlation between the original data was eliminated by principal component analysis （PCA）. The principal component factors were used as the input layer of the GRNN model， and the disintegration time was used as the output layer for network training. Finally， the verification group data was used to predict the disintegration time， and the network prediction accuracy was calculated by comparing with the actual value. Result:Three principal component factors were obtained through PCA by analyzing the original nine variables that were correlated with each other （Hausner ratio， true density， tap density， compression degree， angle of repose， bulk density， porosity， water content and total dissolved solids）， which reduced the complexity of the network. The prediction value of the disintegration time based on this prediction method was in good agreement with the actual value， the error of disintegration time was 0.01-1.34 min and the average relative error was 3.16%. Conclusion:Based on the GRNN mathematical model， the physical properties of Astragali Radix extract powders can be used to accurately predict the disintegration time of Astragali Radix tablets， which provides a reference for studying the disintegration time of TCM tablets.
ABSTRACT
Objective:To investigate the compatible stability of Xingnaojing injection in combination with 9 common medicines, and to provide a reference for clinical application of this injection. Method:According to the clinical application, Xingnaojing injection was mixed with 9 common medicines and placed in the room under dark and light conditions for 6 h. The appearance of compatible solutions was observed, and the HPLC fingerprint was analyzed by similarity evaluation and principal component analysis(PCA). Result:There were no significant changes in the appearance of compatibility of Xingnaojing injection and 9 common medicines, including piracetam and sodium chloride injection, sodium chloride injection and others. The similarities of fingerprint among compatibility of Xingnaojing injection and 9 common medicines were >0.98 at 0 h of compatibility, 6 h of placement and 6 h of illumination. The results of PCA showed that 9 groups of compatible solutions were clustered into 2 categories, the compatibility of Xingnaojing injection and 8 groups including piracetam and sodium chloride injection clustered into one category, and the relative peak areas of the characteristic components of Xingnaojing injection did not change significantly after compatibility, the compatibility of Xingnaojing injection and Danshen Chuanxiongqin injection clustered into another category, the relative peak areas of some characteristic components of Xingnaojing injection increased after compatibility of 0 h and 6 h,and it was more obvious after 6 h of illumination. Conclusion:The compatibility of Xingnaojing injection and 8 common medicines including piracetam and sodium chloride injection has good stability, while the compatibility has stability problems after Xingnaojing injection mixed with Danshen Chuanxiongqin injection. It is suggested that clinical attention should be paid to their compatibility and rational combination of medicines.
ABSTRACT
Objective:To carry out the risk assessment on the factors in the process of granulation fluidized bed of traditional Chinese medicine(TCM) by using failure model and effect analysis(FMEA) and Bayesian network(BN), in order to effectively control risk factors and improve product quality. Method:The risk analysis of the fluidized bed granulation process was carried out by FMEA and the selected medium risk and high risk factors were taken as the main control points, the corresponding BN was established. The sensitivity analysis was used to screen out the main risk factors affecting particle fluidity, particle size uniformity, solubility and product cleanliness, the occurrence probability of each risk factor was determined by the evidence of unqualified particle quality, finally, taking fluidized bed granulation process of Sanye tablets as an example, the FMEA and BN were combined into the risk assessment process to verify the effectiveness and reliability of the method. Result:Based on the middle and high risk points of fluidized bed process, particle size of raw materials, moisture content and hygroscopicity of raw materials, dosage, concentration and addition amount of binder, cleaning degree and integrity of collection bag, and nozzle position, which were selected by FMEA, a fluidized bed granulation risk network with causality was constructed. Among them, hygroscopicity of raw materials, concentration and addition amount of binder, inlet temperature and atomization pressure were high probability risk factors, and the probability of occurrence were 55%, 63%, 59%and 58%, respectively. According to the Bayesian risk relationship network which controlled Sanye tablets fluidized bed granulation analysis results showed that the P values of inlet temperature, atomization pressure and concentration of binder were 0.003 4, 0.032 6 and 0.041 8, respectively in the regression model of influencing factors and particle size uniformity, indicating that there was a significant correlation between the three factors and the particle quality, which was basically consistent with the conclusion obtained by FMEA-BN method. Conclusion:The combination of FMEA and BN for visualized risk assessment of fluidized bed granulation helps to effectively control the risk factors in the granulation process, reduce product quality risks and provide strong support for the improvement of granulation process of TCM.