Network Pharmacology-Based Virtual Screening of Chemical Constituents from Vitexnegundo Linn’s Discovered Novel Molecular Targets for Breast Cancer Treatment
Authors: Basnet Rajesh , Basnet Buddha Bahadur , Amissah Obed Boadi , Huang Rongqi , Sun Yirong , Li Zhiyuan , Habimana Jean de Dieu 
Published: August 2024

Background:
The Chinese chaste tree Vitexnegundo (VN) is a popular herb in South
and Southeast Asia that has several health benefits, including the ability to inhibit tumor growth
and induce apoptosis in multiple tumors. Literature revealed scanty research on breast cancer,
with little focus on the molecular mechanism of the disease and an emphasis on targets, biological
networks, and active components. Exploring natural compounds as possible therapeutic options is
an old but still promising approach for drug discovery and development. This study used a thorough
computational and statistical method to screen potential drug candidates.

Methods:
The active ingredients and targets of VN were identified using SwissADME, SwissTargetPrediction,
STITCH, IMPPAT database, KNapSAcK database, and literature. The OMIM and
GeneCards databases were searched for possible targets related to breast cancer. The PASS online
server was used to check the probability of active metabolite (Pa) against breast cancer. To build
protein-protein interactions (PPI) networking, the intersection of disease and drug targets was
uploaded to the STITCH database. Cytoscape software was used to analyze the topology parameters
of networking to identify hub targets. Gene Ontology (GO) was analyzed using Metascape
and ShinyGO, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed
using the David database and SR plot, and the site of expression and protein domain were
studied using FunRich. We employed AutoDockvina, Discovery Studio, and UCSF ChimeraX
software and auxiliary tools for molecular docking and analysis. Zincpharmer was used for pharmacophore
mapping. ADMET analysis was conducted using ADMETsar, Swiss ADME, ADMETLab
servers, and mypresto using GROMACS for molecular dynamics simulation (MDS).

objective:
Even though there have been many studies on the pharmacological properties of the TCM-listed shrub &amp;amp;quot;VN,&amp;amp;quot; there hasn&amp;amp;#039;t been much research on the anticancer properties and target identification using network pharmacology and in silico analysis. Therefore, this study aimed to investigate the active compounds, possible targets, molecular docking, network pharmacology, and molecular simulations analysis used in the networking of the VN. This strategy could open the door for more research in this field and offer insightful information about VN&amp;amp;#039;s potential as a source of cutting-edge anticancer compounds.

Results:
A total of 65 targets and 21 active ingredients were identified. Further investigation was
conducted on 20 hub targets selected through PPI networking construction. The enrichment analysis
results indicated that the key factors were P, amyloid-beta response, cellular response to amyloid-
beta, Pos. reg. of G2/M transition of the mitotic cell cycle, and response to a toxic substance.
The molecular docking, pharmacophore mapping, and MD simulation results indicated that apigenin,
kaempferol, and luteolin positively interacted with CDK1 and CDK6 proteins.

Conclusion:
This study is the first to use network pharmacology, molecular docking, pharmacophore
mapping, and MD simulation to identify the active ingredients, molecular targets, and critical
biological pathways responsible for VN anti-breast cancer. The study provides a theoretical
basis for further research in this area.