Evaluation of the Effectiveness of Herbal Components Based on Their Regulatory Signature on Carcinogenic Cancer Cells

Predicting cancer cells’ response to a plant-derived agent is critical for the drug discovery process. Recently transcriptomes advancements have provided an opportunity to identify regulatory signatures to predict drug activity. Here in this study, a combination of meta-analysis and machine learning models have been used to determine regulatory signatures focusing on differentially expressed transcription factors (TFs) of herbal components on cancer cells. In order to increase the size of the dataset, six datasets were combined in a meta-analysis from studies that had evaluated the gene expression in cancer cell lines before and after herbal extract treatments. Then, categorical feature analysis based on the machine learning methods was applied to examine transcription factors in order to find the best signature/pattern capable of discriminating between control and treated groups. It was found that this integrative approach could recognize the combination of TFs as predictive biomarkers. It was observed that the random forest (RF) model produced the best combination rules, including AIP/TFE3/VGLL4/ID1 and AIP/ZNF7/DXO with the highest modulating capacity. As the RF algorithm combines the output of many trees to set up an ultimate model, its predictive rules are more accurate and reproducible than other trees. The discovered regulatory signature suggests an effective procedure to figure out the efficacy of investigational herbal compounds on particular cells in the drug discovery process.

Role of gut microbiota in identification of novel TCM-derived active metabolites

Traditional Chinese Medicine (TCM) has been extensively used to ameliorate diseases in Asia for over thousands of years. However, owing to a lack of formal scientific validation, the absence of information regarding the mechanisms underlying TCMs restricts their application. After oral administration, TCM herbal ingredients frequently are not directly absorbed by the host, but rather enter the intestine to be transformed by gut microbiota. The gut microbiota is a microbial community living in animal intestines, and functions to maintain host homeostasis and health. Increasing evidences indicate that TCM herbs closely affect gut microbiota composition, which is associated with the conversion of herbal components into active metabolites. These may significantly affect the therapeutic activity of TCMs. Microbiota analyses, in conjunction with modern multiomics platforms, can together identify novel functional metabolites and form the basis of future TCM research.

The use of chemometrics in combination with molecular spectroscopic and chromatographic methods for authentication of Curcuma species: a review

Currently, the awareness and public concern in the authenticity of herbal medicines has increased significantly, therefore, analytical methods capable of detecting the adulteration practice must be available. The rhizomes of Curcuma species such as Curcuma longa and Curcuma xanthorriza are the target of adulteration due to its popularity as components in herbal medicine formulation. For the sake of quality control of herbal medicines, a rapid and reliable method must be developed for authentication studies. Molecular spectroscopy including UV-Vis, infrared (near and mid) and 1H-NMR spectroscopy, as well as chromatographic-based methods especially liquid chromatography, can be an ideal method for herbal authentication due to its simplicity, however, the spectra and chromatogram obtained are usually complex which are difficult to interpret. To overcome this obstacle, a statistical approach known as chemometrics was used to treat spectra data to be easily used for authentication purposes including discrimination and classification between authentic and adulterated herbal components. This review highlighted molecular spectroscopic method in combination with multivariate data analysis (chemometrics) for authentication of herbal components.