A System Pharmacology Study for Deciphering Anti Depression Activity of Nardostachys jatamansi

BACKGROUND: The plant Nardostachys jatamansi from Valerianaceae family is a well known antidepressant plant and has historically been used in traditional medicine. As N. jatamansi contains many different compounds, to identify its mechanisms of action, we need a network-based study. Network-based studies are becoming an increasingly important tool in understanding the mechanisms of actions of drugs. Systems pharmacology (SP) and bioinformatics are two emerging tools that use computation to develop an understanding of drug actions in molecular and cellular levels. SP can provide mechanistic understanding of protein-protein (drug-target) interaction involved in a common biological pathway. The present study was undertaken to identify unknown targets and mechanisms of antidepressant activity of N. jatamansi according to a systems pharmacology approach.

METHOD: First of all a list of all the targets (receptors and metabolites) involved in depression process were provided based on KEGG database. The 3D structures of protein targets were collected as PDB files and their active sites coordinates were found. In the next step the structures of known compounds of N. jatamansi were collected. For identifying the protein-lagand interactions, a docking process was run in AutoDock and an output was received. To complete our study, the similarity between antidepressant conventional drugs and N. jatamansi compounds was analyzed. A SP map figured by Cytoscape Software, shows the relations between herbal compounds, molecular targets and depression.

RESULTS: According to the docking results, we can suggest several important targets that we have no drugs for, or several natural compounds that play an important role in depression process. According to the similarity results we can suggest several molecules for extraction or synthesis that need more researches for their therapeutic effects. This study shows that how N. jatamansi can effect on depression by multiple molecular targeting with multiple compounds.