Rapid characterization of physical properties for the pharmaceutical pellet cores based on NIR spectroscopy and ensemble learning.

During the development of sustained-release pellets, the physical characteristics of the pellet cores can affect drug release in the preparation. The method based on near-infrared (NIR) spectroscopy and ensemble learning was proposed to swiftly assess the physical properties of the pellet cores. In the research, the potential of three algorithms, direct standardization (DS), partial least squares regression (PLSR) and generalized regression neural network (GRNN), was investigated and compared. The performance of the DS, PLSR and GRNN models were improved after applying bootstrap aggregating (Bagging) ensemble learning. And the Bagging-GRNN model showed the best predictive capacity. Except for inter-particle porosity, the mean absolute deviations of other 11 physical parameters were less than 1.0. Furthermore, the cosine coefficient values between the actual and predicted physical fingerprints was higher than 0.98 for 15 out of the 16 validation samples when using the Bagging-GRNN model. To reduce the model complexity, the 60 variables significantly correlated with angle of repose, particle size (D50) and roundness were utilized to develop the simplified Bagging-GRNN model. And the simplified model showed satisfactory predictive capacity. In summary, the developed ensemble modelling strategy based NIR spectra is a promising approach to rapidly characterize the physical properties of the pellet cores.

Identification and quality control strategy of impurities in Zhengqing Fengtongning injection.

Zhengqing Fengtongning injection is the sterile aqueous solution of Sinomenine Hydrochloride extracted from the root and stem of Sinomenium acutum, and is widely used to treat rheumatoid arthritis. Due to the processes of extraction, separation, purification, preparation and storage, some related impurities might be formed, which may cause side effects on patients. It is important to rapidly separate and identify the related impurities to ensure the safe use of Zhengqing Fengtongning injection. However, there are few literatures about the impurity in Zhengqing Fengtongning injection. In this work, ultra-high performance liquid chromatography- quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) was developed to analyze impurities in both Zhengqing Fengtongning injection and its drug substance, with Sinomenine Hydrochloride as its active pharmaceutical ingredient (API). Six impurities of the Zhengqing Fengtongning injection were found. Structures of impurities 1 and 6 were confirmed by NMR and other impurities were identified from the fragmentation pattern of Sinomenine, the similarity of molecular weight and fragment ions in references. Finally, the HPLC analytical technique was developed to achieve the quantification of impurities 1 and 6. In addition, some reasonable suggestions are put forward on the quality control of Zhengqing Fengtongning injection and its drug substance based on the processes and structural characteristics of the related substances. The technical system established in this paper is helpful to strengthen the quality control of Zhengqing Fengtongning injection and improve production, and can also provide references for the production and quality control of similar drugs.