CDCS-TCM: A framework based on complex network theory to analyze the causality and dynamic correlation of substances in the metabolic process of traditional Chinese medicine

Abstract

Ethnopharmacological relevanceTraditional Chinese medicine, with the feature of synergistic effects of multi-component, multi-pathway and multi-target, plays an important role in the treatment of cancer, cardiovascular and cerebrovascular diseases, etc. However, chemical components in traditional Chinese medicine are complex and most of the pharmacological mechanisms remain unclear, especially the relationships of chemical components change during the metabolic process. Aim of studyOur aim is to provide a method based on complex network theory to analyze the causality and dynamic correlation of substances in the metabolic process of traditional Chinese medicine. Materials and methodsWe proposed a framework named CDCS-TCM to analyze the causality and dynamic correlation between substances in the metabolic process of traditional Chinese medicine. Our method mainly consists two parts. The first part is to discover the local and global causality by the causality network. The second part is to investigate the dynamic correlations and identify the essential substance by dynamic substance correlation network. ResultsWe developed a CDCS-TCM method to analyze the causality and dynamic correlation of substances. Using the XiangDan Injection for ischemic stroke as an example, we have identified the important substances in the metabolic process including substance pairs with strong causality and the dynamic changes of the core effector substance clusters. ConclusionThe proposed framework will be useful for exploring the correlations of active ingredients in traditional Chinese medicine more effectively and will provide a new perspective for the elucidation of drug action mechanisms and the new drug discovery.