A strategy for intelligent chemical profiling-guided precise quantitation of multi-components in traditional Chinese medicine formulae-QiangHuoShengShi decoction

Due to the tremendous clinical value, more and more Traditional Chinese Medicines (TCMs) and their formulae are attracted by world's attention. QiangHuoShengShi (QHSS) decoction is one of classic TCM formulae, which is clinically used for treating various rheumatic diseases. However, the phytochemical constituents of QHSS have rarely been reported. A simple, intelligent, and comprehensive strategy was developed to characterize the phytochemical-fingerprint and quantify the chemical-markers for precise quality evaluation of QHSS. Firstly, a new deep-learning assisted mass defect filter (MDF) method was built for rapid and accurate classification of mass spectrum (MS) ions acquired by ultra-high performance liquid chromatography quadrupole time of flight tandem mass spectrometry (UHPLC-Q-TOF/MS). Subsequently, herb species-specific chemical-category and characteristic identification were used for further characterization of multi-components. As the result, seven major types of compounds in QHSS were intelligently differentiated and 183 phytochemical compounds were tentatively identified. Finally, a sensitive scheduled multiple reaction monitoring (sMRM) detection method was applied to precisely quantify 37 target analytes in QHSS decoction. This integrated strategy would provide an alternative method for chemical-material basis study of more herbal medicine or natural products.

Systematic analysis of the metabolites of Angelicae Pubescentis Radix by UPLC-Q-TOF-MS combined with metabonomics approaches after oral administration to rats

Angelicae Pubescentis Radix (APR) is a typical Traditional Chinese Medicine (TCM) and has been widely used to treat rheumatism and headache diseases in China. This research aimed to illustrate the metabolites of APR in vivo to lay a foundation for the clinics application. A UPLC-Q-TOF-MS method combined with metabonomics approaches is used to address this objective. The separation was achieved on an Agilent SB-C18 column (1.8 μm, 2.1 × 50 mm) with a gradient elution system (ACN and 0.1 % formic acid-water). An electrospray ionization (ESI) was used for mass spectrometer and operated in a full-scan mode at m/z 100 - 800. The data were collected in the positive ion mode and analyzed by the Masslynx 4.1 and SIMCA 13.0 software. Furthermore, an orthogonal partial least-squares discriminant analysis (OPLS-DA) using SIMCA 13.0 software was applied to investigate the differences between the blank and drug groups in bio-samples of rats (plasma, urine, feces). Totally 213 compounds including 41 prototype ingredients, 107 phase I and 65 phase II metabolites were detected, according to the MS and MS/MS data. Among them, 134 metabolites are potential new compounds.

A classification of liquid chromatography mass spectrometry techniques for evaluation of chemical composition and quality control of traditional medicines

Natural products (NPs) and traditional medicines (TMs) are used for treatment of various diseases and also to develop new drugs. However, identification of drug leads within the immense biodiversity of living organisms is a challenging task that requires considerable time, labor, and computational resources as well as the application of modern analytical instruments. LC-MS platforms are widely used for both drug discovery and quality control of TMs and food supplements. Moreover, a large dataset generated during LC-MS analysis contains valuable information that could be extracted and handled by means of various data mining and statistical tools. Novel sophisticated LC-MS based approaches are being introduced every year. Therefore, this review is prepared for the scientists specialized in pharmacognosy and analytical chemistry of NPs as well as working in related areas, in order to navigate them in the world of diverse LC-MS based techniques and strategies currently employed for NP discovery and dereplication, quality control, pattern recognition and sample comparison, and also in targeted and untargeted metabolomic studies. The suggested classification system includes the following LC-MS based procedures: elemental composition determination, isotopic fine structure analysis, mass defect filtering, de novo identification, clustering of the compounds in Molecular Networking (MN), diagnostic fragment ion (or neutral loss) filtering, manual dereplication using MS/MS data, database-assisted peak annotation, annotation of spectral trees, MS fingerprinting, feature extraction, bucketing of LC-MS data, peak profiling, predicted metabolite screening, targeted quantification of biomarkers, quantitative analysis of multi-component system, construction of chemical fingerprints, multi-targeted and untargeted metabolite profiling.

Defence mechanisms of Ficus: pyramiding strategies to cope with pests and pathogens

Ficus species have adapted to diverse environments and pests by developing physical or chemical protection strategies. Physical defences are based on the accumulation of minerals such as calcium oxalate crystals, amorphous calcium carbonates and silica that lead to tougher plants. Additional cellular structures such as non-glandular trichomes or laticifer cells make the leaves rougher or sticky upon injury. Ficus have also established structures that are able to produce specialized metabolites (alkaloids, terpenoids, and phenolics) or proteins (proteases, protease inhibitors, oxidases, and chitinases) that are toxic to predators. All these defence mechanisms are distributed throughout the plant and can differ depending on the genotype, the stage of development or the environment. In this review, we present an overview of these strategies and discuss how these complementary mechanisms enable effective and flexible adaptation to numerous hostile environments.