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

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.

Common phyto-remedies used against cardiovascular diseases and their potential to induce adverse events in cardiovascular patients

“…Cardiovascular diseases (CVDs) are the leading cause of death globally [93]. A recent bulletin from the World Health Organization (WHO) in 2013 estimated that 17.3 million people died from CVDs and it is forecasted that by 2030 mortality figures will reach 23.3 million [83].…”

“…The underlying pathology of CVD is atheromatous vascular disease, resulting in a plethora of other disorders such as coronary artery disease (CAD), cerebrovascular disease, peripheral vascular disease, and the subsequent development of heart failure and cardiac arrhythmias [80]. Obviously, the most opted treatment for CVDs remains conventional drug therapies for example diuretics, vasodilators, anticoagulants, antiplatelet agents and β-blockers [93].…”

Systems pharmacology exploration of botanic drug pairs reveals the mechanism for treating different diseases

“…In this case, several computational methods are becoming more and more powerful to predict and analysis such complex relationships, which have always been the major objective of bioinformatics technology1315161718. For instance, a method of NRWRH (the method of Network-based Random Walk with Restart on the Heterogeneous network) has been developed which takes full advantage of the tool of the network for data integration to predict potential drug-target associations on a large scale19.…”

Effect of ayurvedic preparation Mrityunjay in digoxin-induced arrhythmic rats

“…Cardiovascular diseases (CVDs) are one of the major causes of mortality in the world (Zheng et al, 2013). As proclaimed by World Health Organization (WHO), CVDs were the leading non-communicable disease (NCD) in 2012 which caused 17.5 million, or 46% of NCD deaths (Mendis et al, 2015).…”