Abstract:
Traditional Chinese Mmedicine (TCM) has been applied in treating various complex diseases for thousands of years, and has been widely known for its distinguished holistic therapeutic strategy. How to ensure the safety and lower the risk of toxic effects of TCM herbal formulae are of critical importance during the process of the modernization of TCM industry. Due to the complex composition of TCM herbal formulae and its sophisticated interaction between the biomolecules inside the body, there is an urgent need for systematic methodology in the in-depth investigation on TCM herbal formulae regarding its mechanism of action (MoA), which is the basis for its toxicology assessment (Y. B. Li et al., 2019; S. Liu et al., 2017) and quality control (C. X. Liu et al., 2017).
As a new research paradigm for drug discovery and development, network pharmacology has aroused interests among researchers in the area of TCM since its emergence about 10 years ago (Hopkins, 2007; Li, 2007; Li et al., 2007). In particular, we proposed the new concept of “network target” (Li, 2011; Li, et al, 2011), an elementary entity for the development of TCM Network Pharmacology (TCM-NP), which offer the opportunity to describe and measure the emergence of an ON or OFF effect on the level of network modules, revealing the biological basis of both diseases and drugs by integrating multi-level omics data and temporal-spatial biomedical data with network analysis (Guo et al., 2017; Li, 2015). The network target may offer a powerful computable measurement for evaluating the complex MoA underlying the TCM compounds, herbs and formulae and thus help shift the classical “one target, one drug” research paradigm towards the new “network target, multicomponent therapeutics” strategy. It is suitable for exploring the mysteries of TCM from both the conceptual and the applicable perspective (Li & Zhang, 2013), including toxicology assessment and quality control of the TCM herbal formulae.
To answer the urgent needs in the field of TCM toxicology and quality control, new concepts are continuing to be put forwards, showing compatibility with the strategy of “network target, multicomponent therapeutics”, while forming the hot research field together with network pharmacology. In this issue, two reviews (X. Y. Li et al., 2019; Wang et al., 2019) on quality marker and network toxicology will be published, discussing the new progress on these two booming research fields, and their integration potential with network pharmacology.
The new concept of the quality marker (Q-marker) was first proposed by Liu et al. in 2016 (C. X. Liu et al., 2017; Liu, Guo, & Liu, 2018), establishing a valuable framework for the rational quality control of TCM. The framework emphasizes both the transitivity and traceability along the intact life-cycle of the TCM herbs, decoction-prieces and formulae. In the recent review (Wang et al., 2019), the role of network pharmacology is further demonstrated and integrated into the strategy for Q-marker prediction and application. The “effect-ingredient-target-fingerprint” is developed as the basis for network pharmacology analysis, and the predicted Q-markers based on the fingerprint are further validated both on the theoretical level of TCM compatibility rules, and also on the experimental level of pharmacokinetics, pharmacodynamics and toxicology.
Network toxicology is another important concept, characterizing the interaction and regulation of potential poisonous materials through network models (Liu et al., 2015), which may lead to precise prediction of the toxic components, indication of the biomarkers, and a deeper understanding of the MoA related to the side effect. As reviewed in the recent work (X. Y. Li et al., 2019), metabolomics with the help of liquid chromatography- – mass spectrometry (LC-MS), which provide big data on endogenous metabolites, serve as a powerful method facilitating the network building and analysis. The “toxicity-target-drugs” network is built for network pharmacology analysis, while metabolomics help withcan be useful in the in-depth interrogation of the network and more accurate infer of the toxic components.
There are still many theoretical, methodological, and practical challenges that need to be addressed for future developments of the current research area. With the integration of more cutting-edge assays and artificial intelligence, as well as the evolvement of the interdisciplinary research fields of TCM-NP, especially with network toxicology and Q-markers, we are expecting the fast extending of the research area, more and more successful application cases, and eventually innovative breakthroughs for both traditional and conventional medicines.
