[Predictive model for hygroscopicity of contents in Guizhi Fuling Capsules]

To control the risks of powder caking and capsule shell embrittlement of Guizhi Fuling Capsules, a predictive model for hygroscopicity of contents in Guizhi Fuling Capsules was built. A total of 90 batches of samples, including raw materials, intermediate powders and capsules, were collected during the manufacturing of Guizhi Fuling Capsules. According to the production sequence, 47 batches were used as the calibration set, and the properties of raw materials and the four intermediate powders were comprehensively characterized by the physical fingerprint. Then, the partial least squares(PLS) model was developed with the content hygroscopicity as the response variable. The variable importance in projection(VIP), variance inflation factor(VIF) and regression coefficients were used to screen out potential critical material attributes(pCMAs). As a result, five pCMAs from 54 physical parameters were screened out. Furthermore, different models were built by different combinations of pCMAs, and their predictive robustness of 43 batches was evaluated on the basis of the validation set. Finally, the tap density(D_c) of wet granules obtained from wet granulation and the angle of repose(α) of raw materials were identified as the critical material attributes(CMAs) affecting the hygroscopicity of the contents of Guizhi Fuling Capsules. The prediction model established with the two CMAs as independent variables had an average relative prediction error of 2.68% for samples in the validation set, indicating a good accuracy of prediction. This paper proved the feasibility of predictive modeling toward the control of critical quality attributes of Chinese medicine oral solid dosage(OSD). The combination of the continuous quality improvement, the industrial big data and the process modeling technique paved the way for the intelligent manufacturing of Chinese medicine oral solid preparations.

[Identification of critical process parameters of Jinqing alcohol precipitation of Reduning Injection by big data]

Lonicerae Japonicae Flos and Artemisiae Annuae Herba(LA or Jinqing) alcohol precipitation has various process parameters and complex process mechanism, and is one of the key units for manufacturing Reduning Injection. In order to identify the critical process parameters(CPPs) affecting the weight of the extract produced from the alcohol precipitation process, 259 batches of historical production data from 2017 to 2018 were collected, with a total of 829 318 data points. These data showed characteristics of large data, such as a large data volume, a low value density, and diverse sources. The data cleaning and feature extraction were first performed, and 48 feature variables were selected. The original data points were reduced to 9 936. Then, a combination of Pearson correlation analysis and grey correlation analysis were used to screen out 15 potential critical process parameters(pCPPs). After that, the partial least squares(PLS) was used in prediction of the weight of the extract, proving that the performance of predictive model based on 15 pCMAs is equivalent to that of predictive model based on 48 feature variables. The variable importance in projection(VIP) index was used to identify 9 CPPs, including 2 alcohol precipitation supernatant volume parameters, 4 initial extract weight parameters and 3 added alcohol volume parameters. As a result, the number of data points was 1 863, accounting for 0.28% of the original data. The big data analysis approach from a holistic point of view can effectively increase the value density of the original data. The critical process parameters obtained can help to accurately describe the quality transfer mechanism of the Jinqing alcohol precipitation process.

[Real time release testing of disintegration time of uncoated Tianshu Tablets]

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.

"Smart" Nanoparticles Enhance the Cytoplasmic Delivery of Anti-RhoC Silencing RNA and Inhibit the Migration and Invasion of Aggressive Breast Cancer Cells

Rho-GTPases are small GTP-binding proteins that contribute to the epithelial-to-mesenchymal transition by regulating several cellular processes including organization of the actin cytoskeleton, cell motility, transcription, and cell proliferation. Overexpression of RhoC-GTPases (RhoC) in breast cancer has been implicated in poor disease prognosis due to increased cancer cells invasion, migration, and motility, which warranted its consideration as a therapeutic target for inhibiting breast cancer metastasis. Using silencing RNA (siRNA) molecules to knockdown RhoC expression is a promising approach to inhibit breast cancer metastases. However, transforming anti-RhoC siRNA molecules into a viable therapy remains a challenge due to the lack of a biocompatible carrier that can selectively deliver the RNA cargo into breast cancer cells. We report the use of a degradable, pH-sensitive, β-cyclodextrin (βCD)-based polymeric carrier that condenses anti-RhoC siRNA forming "smart" particles. These smart anti-RhoC particles were efficiently internalized, successfully escaped the endosome, and delivered the RNA cargo into the cytoplasm of SUM149 and MDA-MB-231 breast cancer cells. Our results show that anti-RhoC particles used at a low N/P ratio of 2.5/1 suppressed RhoC protein levels by 100% and 90% in SUM149 and MDA-MB-231 cells, respectively. Further, anti-RhoC particles inhibited the invasion, motility, and migration of SUM149 and MDA-MB-231 cells by 40-47%, 57-60%, and 61.5-73%, respectively. Smart particles encapsulating the scrambled siRNA sequence did not affect RhoC protein expression or the invasion, motility, and migration of SUM149 and MDA-MB-231 cells, which indicate the biocompatibility of the polymeric carrier and selectivity of the observed RhoC knockdown. These results collectively indicate the therapeutic potential of smart anti-RhoC particles in arresting the metastatic spread of breast cancer cells.

RhoC GTPase, a novel transforming oncogene for human mammary epithelial cells that partially recapitulates the inflammatory breast cancer phenotype

Inflammatory breast cancer (IBC) is the most aggressive form of breast cancer and is phenotypically distinct from other forms of locally advanced breast cancer. In a previous study, we identified specific genetic alterations of IBC that could account for a highly invasive phenotype. RhoC GTPase was overexpressed in 90% of IBC archival tumor samples, but not in stage-matched, non-IBC tumors. To study the role of RhoC GTPase in contributing to an IBC-like phenotype, we generated stable transfectants of human mammary epithelial cells overexpressing the RhoC gene. The HME-RhoC transfectants formed large colonies under anchorage-independent growth conditions, were more motile, and were invasive. In conjunction with an increase in motility, overexpression of RhoC led to an increase in actin stress fiber and focal adhesion contact formation. Furthermore, orthotopic injection into immunocompromised mice led to tumor formation. Taken together, these data indicate that RhoC GTPase is a transforming oncogene in human mammary epithelial cells and can lead to a highly invasive phenotype, akin to that seen in IBC.