A multidimensional strategy for characterization, distinction, and quality control of two Clinopodium medicinal plants

ETHNOPHARMACOLOGICAL RELEVANCE

Clinopodium chinense Kuntze (CC) and Clinopodium polycephalum (Vaniot) C. Y. Wu & S. J. Hsuan (CP) are both included in the Pharmacopoeia of the People's Republic of China (edition 2020) as the legitimate source of "Duan Xue Liu" (DXL), which is a crucial traditional Chinese medicine used as a clinical remedy for bleeding diseases. However, the differences in plant endogenous metabolites and bioactivities between CC and CP are still unclear.

AIM OF THE STUDY

This study aims to provide a scientific basis to investigate the differences between CC and CP ensuring the efficient and safe use of DXL.

MATERIALS AND METHODS

A multidimensional strategy including plant metabolomics, digital reference standard (DRS) analyzer, and biological activities assay was creatively constructed for the characterization, distinction, and quality control of CC and CP.

RESULTS

There were apparent differences in the metabolites between CC and CP. 7 compounds contributing to the differences were successfully identified. On that basis, linear calibration using two reference substances (LCTRS) methods was proved as a more accurate and specific quality analysis method for CC and CP. In addition, bioactivity assays showed that both CC and CP exhibited obvious hemostatic activity, while CC showed greater potential to resist inflammation and free radicals.

CONCLUSION

In summary, it was the first time to investigate the chemical constituents and bioactivities differences between CC and CP with the help of plant metabolomics, DRS study, and biological activity assays. These two plants were significantly separated in the integrated analysis, suggesting that we should pay attention to the distinction to prevent unexpected risks caused by medicinal materials.

Structural characterization and discrimination of Paris polyphylla var. yunnanensis by a molecular networking strategy coupled with ultra-high-performance liquid chromatography with quadrupole time-of-flight mass spectrometry

RATIONALE

Paris polyphylla var. yunnanensis (Franch) Hand Mazz (PPY) is a traditional Chinese medicine with antitumor, antibacterial, hemostatic, and anthelmintic activities. Identification of the chemical composition in PPY is helpful to discover its active ingredients and can be used to establish its quality control protocols.

METHODS

The composition of PPY was identified using ultra-high-performance liquid chromatography combined with quadrupole time-of-flight mass spectrometry (UHPLC/QTOF-MS/MS) coupled with a molecular networking strategy. First, the UHPLC/QTOF-MS/MS approach was optimized for chemical compound profiling. Then, the MS data were processed using PeakView™ combined with an in-house database to quickly characterize the secondary metabolites. Finally, molecular networking excavated new molecular weights to discover unknown or trace natural products based on the characteristics of each cluster.

RESULTS

A total of 222 compounds, including 77 isospirostanols, 2 spirostanols, 19 furostanols, 10 pseudospirostanols, 6 cholesterols, 10 C₂₁ steroids, 5 insect metamorphosis hormones, 3 plant sterols, 6 five-ring triterpenoids, 4 flavonoids, 8 fatty acids, 2 phenylpropanoids, and 8 other compounds, were characterized in PPY by comparing their main fragmentation characteristics and pathways with the literature data, and 62 of them, 54 steroidals and 8 phenylpropanoids, were discovered or tentatively identified for the first time.

CONCLUSIONS

This study extended the application of a molecular networking strategy to traditional herbal medicines and developed a molecular networking based screening approach with a significant increase in efficiency for the discovery and identification of trace novel natural products.

Traceability of wild Paris polyphylla Smith var. yunnanensis based on data fusion strategy of FT-MIR and UV-Vis combined with SVM and random forest

Paris polyphylla Smith var. yunnanensis (Franch.) Hand.-Mazz (PPY) was a frequently used herbal medicine in pharmaceutical field and different provenances might affect the clinical efficacy. Tracing the geographical origin was an important portion for PPY authentication and quality assessment. Present study was compared low-, mid- and high-level data fusion methodology for geographical traceability of PPY samples (161 batches) combined with multivariate classification methods such as support vector machine gird search (SVM-GS) and random forest (RF) on the basis of Fourier transform mid-infrared (FT-MIR) and ultraviolet-visible (UV-Vis) spectra. Compared with the low- and mid-level data fusion strategy results basing on SVM-GS algorithm, result of high-level data fusion method (calculated by RF) was more satisfying. Result of RF basing on high-level data fusion strategy showed that merely two samples were misclassified and one sample was multiple assigned after voting with fuzzy set theory. Values of specificity, sensitivity, and accuracy rates were exceeded 0.91, 0.99 and 90.91%, for each class respectively, satisfying results of these were shown in training and test sets for high-level data fusion method. This feasible result indicated that the RF algorithm could establish a reliable and good performance model in geographical traceability on the basis of high-level data fusion strategy. Combination of high-level data fusion and RF algorithm could consider as a good choice for establishing a discrimination multivariate model for origins identification of PPY samples.