Computational pharmacological comparison of Salvia miltiorrhiza and Panax notoginseng used in the therapy of cardiovascular diseases

Zheng,Chun‑Song; Xu,Xiao‑Jie; Ye,Hong‑Zhi; Wu,Guang‑Wen; Xu,Hui‑Feng; Li,Xi‑Hai; Huang,Su‑Ping; Liu,Xian‑Xiang

doi:10.3892/etm.2013.1291

Computational pharmacological comparison of Salvia miltiorrhiza and Panax notoginseng used in the therapy of cardiovascular diseases

Authors:
- Chun‑Song Zheng
- Xiao‑Jie Xu
- Hong‑Zhi Ye
- Guang‑Wen Wu
- Hui‑Feng Xu
- Xi‑Hai Li
- Su‑Ping Huang
- Xian‑Xiang Liu
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Affiliations: Fujian Academy of Integrative Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian 350122, P.R. China, Fujian Key Laboratory of Integrative Medicine on Geriatrics, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian 350122, P.R. China
Published online on: September 10, 2013 https://doi.org/10.3892/etm.2013.1291
Pages: 1163-1168

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Abstract

The herb pair comprising Salvia miltiorrhiza (SM) and Panax notoginseng (PN) has been used as a classical formula for cardiovascular diseases (CVDs) in China and in western countries. However, the pharmacology of SM and PN in this herb pair has not been fully elucidated. The aim of this study was to compare the mechanisms of SM and PN at the molecular level for the treatment of CVDs. We used a systems pharmacology approach, integrating ligand clustering, chemical space, docking simulation and network analysis, to investigate these two herbal medicines. The compounds in SM were attached to clusters 2, 3, 5, 6, 8 and 9, while the compounds in PN were attached to clusters 1, 2, 4, 5, 6, 7, 8 and 10. The distributions of chemical space between the compounds from SM and PN were discrete, with the existence of small portions of overlap, and the majority of the compounds did not violate ‘Lipinski's rule of five’. Docking indicated that the average number of targets correlated with each compound in SM and PN were 5.0 and 3.6, respectively. The minority nodes in the SM and PN drug‑target networks possessed common values of betweenness centrality, closeness centrality, topological coefficients and shortest path length. Furthermore, network analyses revealed that SM and PN exerted different modes of action between compounds and targets. These results suggest that the method of computational pharmacology is able to intuitively trace out the similarities and differences of two herbs and their interaction with targets from the molecular level, and that the combination of two herbs may extend their activities in different potential multidrug combination therapies for CVDs.

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