Real time release testing of disintegration time of uncoated Tianshu Tablets / 中国中药杂志

In this paper, a real time release testing(RTRT) model for predicting the disintegration time of Tianshu tablets was established on the basis of the concept of quality by design(QbD), in order to improve the quality controllability of the production process. First, 49 batches of raw materials and intermediates were collected. Afterwards, the physical quality attributes of all materials were comprehensively characterized. The partial least square(PLS) regression model was established with the 72 physical quality attributes of raw materials and intermediates as input and the disintegration time(DT) of uncoated tablets as output. Then, the variable screening was carried out based on the variable importance in the projection(VIP) indexes. Moisture content of raw materials(%HR), tapped density of wet masses(D_c), hygroscopicity of dry granules(%H), moisture content of milling granules(%HR) and Carr's index of mixed granules(IC) were determined as the potential critical material attributes(pCMAs). According to the effects of interactions of pCMAs on the performance of the prediction model, it was finally determined that the wet masses' D_c and the dry granules'%H were critical material attributes(CMAs). A RTRT model of the disintegration time prediction was established as DT=34.09+2×D_c+3.59×%H-5.29×%H×D_c,with R~2 equaling to 0.901 7 and the adjusted R~2 equaling to 0.893 3. The average relative prediction error of validation set for the RTRT model was 3.69%. The control limits of the CMAs were determined as 0.55 g·cm~(-3)<D_c<0.63 g·cm~(-3) and 4.77<%H<7.59 according to the design space. The RTRT model of the disintegration time reflects the understanding of the process system, and lays a foundation for the implementation of intelligent control strategy of the key process of Tianshu Tablets.

Rapid Determination of Coating Film Thickness of Tianshu Tablets by Near Infrared Spectroscopy / 中国实验方剂学杂志

<b>Objective::To investigate the feasibility of near-infrared spectroscopy for detecting the coating film thickness of Tianshu tablets. <b>Method::Nine batches of Tianshu tablets were taken during the coating process. Then, their near-infrared diffuse reflection spectra were collected. The sample set was divided into calibration set and validation set by Kennard-Stone algorithm. The preprocessing method was selected. The synergy interval partial least squares (siPLS) and moving window partial least squares (mwPLS) were employed to screen the optimal spectral interval. And the corresponding quantitative calibration model of partial least squares (PLS) were established. Some evaluation parameters were adopted to assess the performance of the model. <b>Result::The method of first derivative and Norris Derivative smoothing combined with standard normal variate transformation was suitable for processing the spectra. The optimal PLS model was established in the preferred band interval of siPLS. The correlation coefficient between the predicted value and the measured value of calibration set was 0.966, and the correlation coefficient between the predicted value and the measured value of validation set was 0.991.Both root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) values were small, which showed the fitness and predictive performance of the model were favorable. <b>Conclusion::The near-infrared spectroscopy technique can be used to determine coating film thickness of Tianshu tablets with high accuracy, which provides technical supports for the in-line determination of coating thickness in the production process of traditional Chinese medicine tablets.

Study on fast prediction of film coating terminal point of Tianshu Tablets by near- infrared spectroscopy / 中草药

Objective: To investigate how to rapidly predict coating terminal point in the thin film coating of Tianshu Tablets by near-infrared spectroscopy (NIRS). Methods: Firstly, synergy interval partial least square (siPLS) was used to optimize the modeling intervals and the pretreatment methods were screened. Secondly, according to the optimal modeling parameters, the conformity test and similarity matching were used to establish calibration models based on the established reference spectra library. Subsequently, by comparing the similarities between the test spectra and the reference spectra, the threshold value was set. The relation among the similarities, threshold value and the coating end point was obtained. Finally, the performance of the calibration model was verified by the validation set. Results: During the coating process, the similarities between the test spectra and the reference spectra were gradually increasing until the similarities of continuous test spectra exceeded the threshold, thus, indicating the end point. Conclusion: Conformity test and similarity matching can sensitively monitor the change of similarities among spectra and the corresponding trend. Both models have high performance, which can accurately predict the coating terminal point. It’s of great significance to reduce batch-to-batch variation in film coating and the loss of coating materials, and improve the coating efficiency and the quality uniformity of solid preparation.