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

摘要

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. (C) 2019 Elsevier B.V. All rights reserved.