Discovery of potential Q-marker of traditional Chinese medicine based on plant metabolomics and network pharmacology: Periplocae Cortex as an example

Abstract

BackgroundQuality control exerted great importance on the clinical application of drugs for ensuring effectiveness and safety. Due to chemical complexity, diversity among different producing areas and harvest seasons, as well as unintentionally mixed with non-medicinal parts, the current quality standards of traditional Chinese medicine (TCM) still faced challenges in evaluating the overall chemical consistency. PurposeWe aimed to develop a new strategy to discover potential quality marker (Q-marker) of TCM by integrating plant metabolomics and network pharmacology, using Periplocae Cortex (GP, the dried root bark of Periploca sepium Bge.) as an example. MethodsFirst, plant metabolomics analysis was performed by UPLC/Q-TOF MS in 89 batches of samples to discover chemical markers to distinguish medicinal parts (GP) and non-medicinal parts (the dried stem bark of Periploca sepium Bge. (JP)), harvest seasons and producing region of Periplocae Cortex. Second, network pharmacology was applied to explore the initial linkages among chemical constituents, targets and diseases. Last, potential Q-marker were selected by integrating analysis of plant metabolomics and network pharmacology, and the quantification method of Q-marker was developed by using UPLC-TQ-MS. ResultsThe chemical profiling of GP and JP was investigated. Fifteen distinguishing features were designated as core chemical markers to distinguish GP and JP. Besides, the content of 4-methoxybenzaldehyde-2-O-β-d-xylopyranosyl-(1→6)-β-d-glucopyranoside could be used to identify Periplocae Cortex harvested in spring-autumn or summer. Meanwhile, a total of 15 components targeted rheumatoid arthritis were screened out based on network pharmacology. Taking absorbed constituents into consideration, 23 constituents were selected as potential Q-marker. A simultaneous quantification method (together with 11 semi-quantitative analysis) was developed and applied to the analysis of 20 batches of commercial Periplocae Cortex on the market. The PLS-DA model was successfully developed to distinguish GP and JP samples. In addition, the artificially mixed GP sample, which contained no less than 10% of the adulterant (JP), could also be correctly identified. ConclusionOur results indicated that 9 ingredients could be considered as Q-marker of Periplocae Cortex. This study has also demonstrated that the plant metabolomics and network pharmacology could be used as an effective approach for discovering Q-marker of TCM to fulfill the evaluation of overall chemical consistency among samples from different producing areas, harvest seasons, and even those commercial crude drugs, which might be mixed with a small amount of non-medicinal parts.