Chemicalome and metabolome profiling of Chai-Gui Decoction using an integrated strategy based on UHPLC-Q-TOF-MS/MS analysis.

Traditional Chinese medicine prescriptions are widely believed to exert therapeutic benefits via a multiple-component and multiple-target mode. The systemic profiling of their in vitro chemicalome and in vivo metabolome is of great importance for further understanding their clinical value. Herein, an integrated strategy using ultra-performance liquid chromatography coupled with quadruple time-of-flight mass spectrometry was proposed to profile the chemicalome and metabolome of Chai-Gui Decoction. Particularly, an approach combined mass defect filter, characteristic product ion filter, and neutral loss filter was adopted to identify metabolites in plasma, urine, bile, and feces by MetabolitePilot. Consequently, a total of 174 constituents were identified or tentatively characterized and 70 metabolites that related to 21 representative structural components were matched in rat biofluids. Among them, 19 prototypes and 7 metabolites that contributed to flavonoids, monoterpenes, and phenylpropanoids were detected distribution in brain, heart, kidney, liver, lung or spleen. This study provided a generally applicable approach to comprehensive investigation on chemicalome and metabolome of traditional Chinese medicine prescriptions, and offered reasonable guidelines for further screening of quality control indicators of Chai-Gui Decoction.

Comparison of three officinal species of Callicarpa based on a biochemome profiling strategy with UHPLC-IT-MS and chemometrics analysis.

Traditional Chinese medicine (TCM) materials with closely related species are frequently fungible in clinical use. Therefore, holistic comparison of the composition in bioactive compounds is essential to evaluate whether they are equivalent in efficacy. Taking three officinal species of Callicarpa as a case, we proposed and validated a standardized strategy for the discrimination of closely related TCM materials, which focused on the extraction, profiling and multivariate statistical analysis of their biochemome. Firstly, serial liquid-liquid extractions were utilized to prepare different batches of Callicarpa biochemome, and the preparation yields were utilized for the normalization of sampling quantity prior to UHPLC-IT-MS analysis. Secondly, 34 compounds, including 19 phenylethanoid glycosides, 10 flavonoids and 5 terpenoids, were identified based on an untargeted UHPLC-IT-MS method. Thirdly, method validation of linearity, precision and stability showed that the UHPLC-IT-MS system was qualified (R2>0.995, RSD<15%) for subsequent biochemome profiling. After PCA and PLS-DA analysis, 30 marker compounds were screened and demonstrated to be of good predictability using genetic algorithm optimized support vector machines. Finally, a heatmap visualization was employed for clarifying the distribution of marker compounds, which could be helpful to determine whether the three Callicarpa species are, in fact, equivalent substitutes. This study provides a standardized biochemome profiling strategy for systemic comparison analysis of closely related TCM materials, which shows promising perspectives in tracking the supply chain of pharmaceutical suppliers.

A strategy to identify and quantify closely related adulterant herbal materials by mass spectrometry-based partial least squares regression.

In this study, a new strategy combining mass spectrometric (MS) techniques with partial least squares regression (PLSR) was proposed to identify and quantify closely related adulterant herbal materials. This strategy involved preparation of adulterated samples, data acquisition and establishment of PLSR model. The approach was accurate, sensitive, durable and universal, and validation of the model was done by detecting the presence of Fritillaria Ussuriensis Bulbus in the adulteration of the bulbs of Fritillaria unibracteata. Herein, three different MS techniques, namely wooden-tip electrospray ionization mass spectrometry (wooden-tip ESI/MS), ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) and UPLC-triple quadrupole tandem mass spectrometry (UPLC-TQ/MS), were applied to obtain MS profiles for establishing PLSR models. All three models afforded good linearity and good accuracy of prediction, with correlation coefficient of prediction (rp2) of 0.9072, 0.9922 and 0.9904, respectively, and root mean square error of prediction (RMSEP) of 0.1004, 0.0290 and 0.0323, respectively. Thus, this strategy is very promising in tracking the supply chain of herb-based pharmaceutical industry, especially for identifying adulteration of medicinal materials from their closely related herbal species.

Dose-response characteristics of Clematis triterpenoid saponins and clematichinenoside AR in rheumatoid arthritis rats by liquid chromatography/mass spectrometry-based serum and urine metabolomics.

Clematidis Radix et Rhizoma is a traditional Chinese medicine widely used for treating arthritic disease. Clematis triterpenoid saponins (TS) and clematichinenoside AR (C-AR) have been considered to be responsible for its antiarthritic effects. However, the underling mechanism is still unclear because of their low bioavailability. To address of this issue, metabolomics tools were performed to determine metabolic variations associated with rheumatoid arthritis (RA) and responses to Clematis TS, C-AR and positive drug (Triptolide, TP) treatments. This metabolomics investigation of RA was conducted in collagen-induced arthritis (CIA) rats. Liquid chromatography/mass spectrometry and multivariate statistical tools were used to identify the alteration of serum and urine metabolites associated with RA and responses to drug treatment. As a result, 45 potential metabolites associated with RA were identified. After treatment, a total of 24 biomarkers were regulated to normal like levels. Among these, PC(18:0/20:4), 9,11-octadecadienoic acid, arachidonic acid, 1-methyladenosine, valine, hippuric acid and pantothenic acid etc, were reversed in Clematis TS and C-AR groups. Tetrahydrocortisol was regulated to normal levels in Clematis TS and TP groups, while 3,7,12-trihydroxycholan-24-oic acid was regulated in C-AR and TP groups. Biomarkers like citric acid, p-cresol glucuronide, creatinine, cortolone were reversed in TP group.

An in vitro approach for lipolysis measurement using high-resolution mass spectrometry and partial least squares based analysis.

The elevation of free fatty acids (FFAs) has been regarded as a universal metabolic signature of excessive adipocyte lipolysis. Nowadays, in vitro lipolysis assay is generally essential for drug screening prior to the animal study. Here, we present a novel in vitro approach for lipolysis measurement combining UHPLC-Orbitrap and partial least squares (PLS) based analysis. Firstly, the calibration matrix was constructed by serial proportions of mixed samples (blended with control and model samples). Then, lipidome profiling was performed by UHPLC-Orbitrap, and 403 variables were extracted and aligned as dataset. Owing to the high resolution of Orbitrap analyzer and open source lipid identification software, 28 FFAs were further screened and identified. Based on the relative intensity of the screened FFAs, PLS regression model was constructed for lipolysis measurement. After leave-one-out cross-validation, ten principal components have been designated to build the final PLS model with excellent performances (RMSECV, 0.0268; RMSEC, 0.0173; R2, 0.9977). In addition, the high predictive accuracy (R2 = 0.9907 and RMSEP = 0.0345) of the trained PLS model was also demonstrated using test samples. Finally, taking curcumin as a model compound, its antilipolytic effect on palmitic acid-induced lipolysis was successfully predicted as 31.78% by the proposed approach. Besides, supplementary evidences of curcumin induced modification in FFAs compositions as well as lipidome were given by PLS extended methods. Different from general biological assays, high resolution MS-based method provide more sophisticated information included in biological events. Thus, the novel biological evaluation model proposed here showed promising perspectives for drug evaluation or disease diagnosis.

A direct ionization mass spectrometry-based approach for differentiation of medicinal Ephedra species.

Herein, rapid and efficient identification of species of medicinal Ephedrae Herba was performed using DI-MS (direct ionization-mass spectrometry)-based metabolomics analysis. As a direct ionization technique, DI-MS can provide rapid analysis of samples without sample preparation, so it has been advantageously applied to high-throughput metabolomics analysis. In this flow chart, the MS fingerprints of Ephedrae Herba samples obtained by DI-MS method were firstly pretreated by background subtracts, smooth and center procedures to extract MS features. Then, these MS features were aligned using in a house program written in MATLAB to produce MS dataset. After that, PCA and PLS-DA analysis were performed based on the obtained MS dataset. Finally, the parameter VIP (Variable importance in the Projection) was employed to screen the valuable MS features for discrimination. Using such an approach, three medicinal species of Ephedrae Herba, Ephedra sinica, Ephedra intermedia, and Ephedra equisetina., had been successfully differentiated. Additionally, this method has also been applied to identify the changes in components of Ephedrae Herba after honey treated. In present study, DI-MS in combination with metabolomics was shown to be an efficient and accurate way to identify the sources of herbs.