Deeper Chemical Perceptions for Better Traditional Chinese Medicine Standards

Abstract

Traditional Chinese medicines (TCMs), a complex system of natural resources with many diverse components, are widely used as approved medicinal agents in China. Quality control of TCMs is a huge challenge for the government and for testing institutes and is associated with numerous scientific issues. Among these considerations include the following questions: How many components are in TCMs? How can the multiple components in TCMs be comprehensively delineated and subsequently characterized? What is the level and range of these (active) metabolites within these multiple-component TCMs, in order to recommend standards? and What are the qualities required for a marker constituent to be selected, and from a practical perspective, how can these components be assessed with low cost and in a short time? All of these factors require significant and deep thinking in order to understand the individualistic chemistry of TCM in order to develop enhanced TCM quality standards for improved and consistent patient care. In this review, the latest exploratory research in TCM chemistry analytical techniques and methods is summarized in order to begin to develop responses to these scientific issues. Advances in these methods have included multidimensional separation for liquid chromatography–high-resolution mass spectrometry (LC–HRMS), smart triggering data-dependent acquisition of LC–HRMS, target analysis with liquid chromatography–mass spectrometry (LC–MS), supercritical fluid chromatography, and data mining of large mass spectrometry (MS) datasets. In addition, two quality strategies have been introduced in order to save reference standards and the analysis time for a TCM quality standard, including the application of the single standard to determine multi-components (SSDMC) and monomethod-heterotrait matrix methods. Finally, a series of future improvements for analytical methods for TCMs are proposed. Keywords: Traditional Chinese medicine, Liquid chromatography, Mass spectrometry, Data-dependent acquisition, Chemometrics, Untargeted, Data mining, Quality control