To accurately, safely, and efficiently evaluate the bitterness of Traditional Chinese Medicines (TCMs), a robust predictor was developed using robust partial least squares (RPLS) regression method based on data obtained from an electronic tongue (e-tongue) system. The data quality was verified by the Grubb’s test. Moreover, potential outliers were detected based on both the standardized residual and score distance calculated for each sample. The performance of RPLS on the dataset before and after outlier detection was compared to other state-of-the-art methods including multivariate linear regression, least squares support vector machine, and the plain partial least squares regression. Both R² and root-mean-squares error (RMSE) of cross-validation (CV) were recorded for each model. With four latent variables, a robust RMSECV value of 0.3916 with bitterness values ranging from 0.63 to 4.78 were obtained for the RPLS model that was constructed based on the dataset including outliers. Meanwhile, the RMSECV, which was calculated using the models constructed by other methods, was larger than that of the RPLS model. After six outliers were excluded, the performance of all benchmark methods markedly improved, but the difference between the RPLS model constructed before and after outlier exclusion was negligible. In conclusion, the bitterness of TCM decoctions can be accurately evaluated with the RPLS model constructed using e-tongue data.

Panax ginseng is widely used in Asian countries and its active constituents—ginsenosides—need to be systematically studied. However, only a small part of ginsenosides have been characterized in the roots and rhizomes of panax ginseng (RRPG) up to date, mainly because of a lack of the fragmentation ions of many more ginsenosides. In order to comprehensively identify ginsenosides in RRPG, molecular features of ginsenosides orienting precursor ions selection and targeted tandem mass spectrometry (MS/MS) analysis strategy were proposed in our study, in which the precursor ions were selected according to the molecular features of ginsenosides irrespective of their peak abundances, and targeted MS/MS analysis was then performed to obtain their fragmentation ions for substance characterization. Using this strategy, a total of 620 ginsenosides were successfully characterized in RRPG, including 309 protopanaxadiol-type ginsenosides, 258 protopanaxatriol-type ginsenosides and 53 oleanane-type ginsenosides. It is worth noting that, except for the known aglycones mass-to-charge ratio (m/z) 459, 475 and 455, twelve other aglycones, including m/z 509, 507, 493, 491, 489, 487, 477, 473, 461, 457, 443 and 441, were first reported in our experiment and they were probably the derivatizations of the protopanaxatriol and protopanaxadiol. Our study will not only help people to improve the cognition of ginsenosides in RRPG, but will also play a guiding and reference role for the isolation and characterization of potentially new ginsenosides from RRPG.

Panax ginseng was a traditional Chinese medicine with various pharmacological activities and one of its important activities was hypoglycemic activity; therefore, panax ginseng has been used in China as an adjuvant in the treatment of diabetes mellitus. In vivo and in vitro tests have revealed that ginsenosides, which are derived from the roots and rhizomes of panax ginseng have anti-diabetic effects and produce different hypoglycemic mechanisms by acting on some specific molecular targets, such as SGLT1, GLP-1, GLUTs, AMPK, and FOXO1. α-Glucosidase is another important hypoglycemic molecular target, and its inhibitors can inhibit the activity of α-Glucosidase so as to delay the absorption of dietary carbohydrates and finally reduce postprandial blood sugar. However, whether ginsenosides have the hypoglycemic mechanism of inhibiting α-Glucosidase activity, and which ginsenosides exactly attribute to the inhibitory effect as well as the inhibition degree are not clear, which needs to be addressed and systematically studied. To solve this problem, affinity ultrafiltration screening coupled with UPLC-ESI-Orbitrap-MS technology was used to systematically select α-Glucosidase inhibitors from panax ginseng. The ligands were selected through our established effective data process workflow based on systematically analyzing all compounds in the sample and control specimens. As a result, a total of 24 α-Glucosidase inhibitors were selected from panax ginseng, and it was the first time that ginsenosides were systematically studied for the inhibition of α-Glucosidase. Meanwhile, our study revealed that inhibiting α-Glucosidase activity probably was another important mechanism for ginsenosides treating diabetes mellitus. In addition, our established data process workflow can be used to select the active ligands from other natural products using affinity ultrafiltration screening.

The fundamental principle of Quality by Design (QbD) is that the product quality should be designed into the process through an upstream approach, rather than be tested in the downstream. The keystone of QbD is process modeling, and thus, to develop a process control strategy based on the development of design space. Multivariate statistical analysis is a very useful tool to support the implementation of QbD in pharmaceutical process development and manufacturing. Nowadays, pharmaceutical process modeling is mainly focused on one-unit operations and system modeling for the development of design space across multi-unit operations is still limited. In this study, a general procedure that gives a holistic view for understanding and controlling the process settings for the entire manufacturing process was investigated. The proposed framework was tested on the Panax Notoginseng Saponins immediate release tablet (PNS IRT) production process. The critical variables and the critical units acting on the process were identified according to the importance of explaining the variability in the multi-block partial least squares path model. This improved understanding of the process by illustrating how the properties of the raw materials, the process parameters in the wet granulation and the compaction and the intermediate properties affect the tablet properties. Furthermore, the design space was developed to compensate for the variability source from the upstream. The results demonstrated that the proposed framework was an important tool to gain understanding and control the multi-unit operation process.

Temporal associations in longitudinal nontargeted metabolomics data are generally ignored by common pattern recognition methods such as partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA). To discover temporal patterns in longitudinal metabolomics, a multitask learning (MTL) method employing structural regularization was proposed. The group regularization term of the proposed MTL method enables the selection of a small number of tentative biomarkers while maintaining high prediction accuracy. Meanwhile, the nuclear norm imposed into the regression coefficient accounts for the interrelationship of the metabolomics data obtained on consecutive time points. The effectiveness of the proposed method was demonstrated by comparison study performed on a metabolomics dataset and a simulating dataset. The results showed that a compact set of tentative biomarkers charactering the whole antipyretic process of Qingkailing injection were selected with the proposed method. In addition, the nuclear norm introduced in the new method could help the group norm to improve the method’s recovery ability.

Magnolol, an essential bioactive natural lignans isolated from Magnolia Officinalis, has various pharmacological activities, such as antibacterial, anti-inflammatory, antioxidation and anti-tumor. In this study, a practical strategy based on ultra-high-performance liquid chromatography coupled with linear ion trap-Orbitrap mass spectrometry (UHPLC-LTQ-Orbitrap MS) was established to reveal the metabolic fate of magnolol in rat plasma and urine. First of all, in the data-dependent scanning (DDS) acquisition mode, the ESI-MSⁿ data sets of biological samples and reference standard were obtained by using a high-quality online data analysis method. Subsequently, off-line data-mining techniques including multiple mass defect filters (MMDFs) and high-resolution extracted ion chromatograms (HREICs) were applied to screen major-to-trace metabolites of magnolol at the full-scan ESI-MS¹ level. Finally, a total of 177 metabolites (prototype compound included) were preliminarily observed and characterized according to accurate mass measurement, the characteristic fragmentation patterns, chromato-graphic behaviors, and corresponding Clog P values. The results showed that magnolol experienced a variety of biotransformation reactions in rats, including hydroxylation, methoxylation, dehydrogenation, carboxylation, sulfonation, glucuronide conjugation, glucose conjugation, N-acetylcysteine (NAC) conjugation, glutathione conjugation and their composite reactions. In conclusion, this study not only greatly expanded the understanding of the therapeutic material basis and pharmacological mechanism of magnolol, but also provided new ideas for its toxicity evaluation, safety monitoring and drug delivery forms design.