CONTEXT: Chinese herbal medicine (CHM) is a complex multicomponent complex system that interacts with multiple targets and functions via multiple pathways based on the whole human system. Therefore, identification of key constituents of Chinese herbals (CH) not only plays a critical role in the quality control of CHM, but also paves a basis for redevelopment of them. OBJECTIVE: Identification of key constituents in volatile oil (VO) of Ligusticum chuanxiong Hort (Umbelliferae) (LCH), which is a CHM clinically used in China thousands of years ago. MATERIALS AND METHODS: The VO of LCH was pharmacologically demonstrated to have blood vessel activity (BVA) in vitro and chemically investigated by gas chromatography-mass spectrometry (GC-MS) analysis. Data mining approaches were used to bridge the gap between chemical constituents (CCS) and bioactivities as well as contribute to select key constituents of LCH automatically. RESULTS: Thirteen effective constituents of LCH with significant association with BVA were identified. CONCLUSION: The combination of 13 key constituents would accurately predict the bioactivities of blood vessel of LCH. Furthermore, the strategy presented here paves a strong basis for identification of key constituents of CH and elucidation of material basis of CHM.
CONTEXT: Chinese herbal medicine (CHM) is a complex multicomponent complex system that interacts with multiple targets and functions via multiple pathways based on the whole human system. Therefore, identification of key constituents of Chinese herbals (CH) not only plays a critical role in the quality control of CHM, but also paves a basis for redevelopment of them. OBJECTIVE: Identification of key constituents in volatile oil (VO) of Ligusticum chuanxiong Hort (Umbelliferae) (LCH), which is a CHM clinically used in China thousands of years ago. MATERIALS AND METHODS: The VO of LCH was pharmacologically demonstrated to have blood vessel activity (BVA) in vitro and chemically investigated by gas chromatography-mass spectrometry (GC-MS) analysis. Data mining approaches were used to bridge the gap between chemical constituents (CCS) and bioactivities as well as contribute to select key constituents of LCH automatically. RESULTS: Thirteen effective constituents of LCH with significant association with BVA were identified. CONCLUSION: The combination of 13 key constituents would accurately predict the bioactivities of blood vessel of LCH. Furthermore, the strategy presented here paves a strong basis for identification of key constituents of CH and elucidation of material basis of CHM.