TCMFP: a novel herbal formula prediction method based on network target's score integrated with semi-supervised learning genetic algorithms

Traditional Chinese medicine (TCM) has accumulated thousands years of knowledge in herbal therapy, but the use of herbal formulas is still characterized by reliance on personal experience. Due to the complex mechanism of herbal actions, it is challenging to discover effective herbal formulas for diseases by integrating the traditional experiences and modern pharmacological mechanisms of multi-target interactions. In this study, we propose a herbal formula prediction approach (TCMFP) combined therapy experience of TCM, artificial intelligence and network science algorithms to screen optimal herbal formula for diseases efficiently, which integrates a herb score (Hscore) based on the importance of network targets, a pair score (Pscore) based on empirical learning and herbal formula predictive score (FmapScore) based on intelligent optimization and genetic algorithm. The validity of Hscore, Pscore and FmapScore was verified by functional similarity and network topological evaluation. Moreover, TCMFP was used successfully to generate herbal formulae for three diseases, i.e. the Alzheimer's disease, asthma and atherosclerosis. Functional enrichment and network analysis indicates the efficacy of targets for the predicted optimal herbal formula. The proposed TCMFP may provides a new strategy for the optimization of herbal formula, TCM herbs therapy and drug development.

Understanding the Mechanistic Contribution of Herbal Extracts in Compound Kushen Injection With Transcriptome Analysis

Herbal compatibility is the knowledge of which herbs to combine in traditional Chinese medicine (TCM) formulations. The lack of understanding of herbal compatibility is one of the key problems for the application and popularization of TCM in western society. Because of the chemical complexity of herbal medicines, it is simpler to begin to conduct compatibility research based on herbs rather than component plant secondary metabolites. We have used transcriptome analysis to explore the effects and interactions of two plant extracts (Kushen and Baituling) combined in Compound Kushen Injection (CKI). Based on shared chemical compounds and in vitro cytotoxicity comparisons, we found that both the major compounds in CKI, and the cytotoxicity effects of CKI were mainly derived from the extract of Kushen (Sophorae flavescentis). We generated and analyzed transcriptome data from MDA-MB-231 cells treated with single-herb extracts or CKI and results showed that Kushen contributed to the perturbation of the majority of cytotoxicity/cancer related pathways in CKI such as cell cycle and DNA replication. We also found that Baituling (Heterosmilax yunnanensis Gagnep) could not only enhance the cytotoxic effects of Kushen in CKI, but also activate immune-related pathways. Our analyses predicted that IL-1β gene expression was upregulated by Baituling in CKI and we confirmed that IL-1β protein expression was increased using an ELISA assay. Altogether, these findings help to explain the rationale for combining Kushen and Baituling in CKI, and show that transcriptome analysis using single herb extracts is an effective method for understanding herbal compatibility in TCM.

Chemical Profiling Combined with "Omics" Technologies (CP-Omics): a Strategy to Understand the Compatibility Mechanisms and Simplify Herb Formulas in Traditional Chinese Medicines

INTRODUCTION

The compatibility mechanisms of formulas in traditional Chinese medicine (TCM) are indistinct. In order to better understand the compatibility mechanisms and the quality control of the formulas, it is necessary to simplify formulas in TCM research.

OBJECTIVE

Developing a novel method by multi-analysing the contents of different compounds in formula and inferred simplified formula simultaneously.

METHODOLOGY

Chemical profiling combined with "omics" technologies (CP-omics) was employed in the present study. Wu-tou Tang (WTT) was taken as an example to elucidate the workflow. We used high definition mass spectrometry combined with pattern recognition methods to analyse WTT and eight herb combinations derived from it. By analysing the content variation of the compounds, the inter compatibility mechanisms of WTT was explained. Cluster analysis classified the herb combinations and inferred a simplified formula.

RESULTS

It was found that Glycyrrhiza Radix Preparata and Ephedrae Herba could reduce the contents of diester-diterpenoid alkaloids; Ephedrae Herba could increase the contents of triterpene saponins and monoterpene glycosides in WTT. Through the overall comparison, Aconiti Radix Preparata combined with Glycyrrhiza Radix Preparata, Ephedrae Herba combined with Glycyrrhiza Radix Preparata have a similar chemical profiling with WTT. We inferred that a new simplified prescription composed of Aconiti Radix Preparata, Ephedrae Herba and Glycyrrhiza Radix Preparata should also have a good clinical effect. At last, pharmacological results confirmed that the new herb combination possesses similar anti-inflammatory activities to WTT.

CONCLUSION

Our results demonstrated that the CP-omics has great advantages in pharmaceutical discovery and optimising complex formulas in TCM. Copyright © 2017 John Wiley & Sons, Ltd.