Anandamide Reuptake Inhibitor (VDM11) as a Possible Candidate for COVID-19 Associated Depression; a Combination of Network Pharmacology, Molecular Docking and In Vivo Experimental Analysis

Abstract

Objective: Post-COVID 19 depression has gained much attention due to the increasing percentage of depressive symptoms reported by COVID-19 survivors. Among many factors postulated to be responsible for this depression, neuroinflammation gained the most attention. Therefore, in current work, we selected an anandamide reuptake inhibitor, VDM11, as a possible candidate for managing post-COVID depression. Methods: The role of VDM11 in attenuating neuroinflammation was established by using network pharmacology, molecular docking, and an in vivo LPS-induced depression model. Results: The results of network pharmacology revealed that among all the genes that can be targeted by VDM11, 47 genes were directly linked to the pathophysiology of depression. Additionally, on the basis of protein–protein interaction (PPI) analysis, the top 10 hub genes probably responsible for VDM11 antidepressant attribute were screened. These genes include MAPK3, TNF-α, IL-1β, IL-6, PPARG, MAPK1, CNR1, MTOR, NR3C1, and IGF1R. These genes were also enriched in GO and KEGG analysis. Molecular docking was carried out with top five hub genes screened by PPI network and KEGG analysis which showed that VDM11 interacts well with these targets. The antidepressant potential of VDM11 was also assessed by employing a LPS-induced depression model. Animals provided with VDM11 demonstrated increased exploration time and spontaneous alterations in elevated plus and Y maze models. Additionally, the level of astrocyte marker GFAP, microglia marker CD11b, and proinflammatory cytokines, including TNFα, IL-1β, and IL-6, in the hippocampus were significantly reduced by VDM11, further strengthening its role in neuroinflammation. Conclusion: VDM11, an anandamide reuptake inhibitor, might serve as a possible candidate for post-COVID depression, probably by modulating neuroinflammation. However, detailed pharmacological studies are required to validate these outcomes.

Revealing the Underlying Mechanism of Acacia Nilotica against Asthma from a Systematic Perspective: A Network Pharmacology and Molecular Docking Study

“…KEGG pathway enrichment analysis was done to predict a possible molecular mechanisms of AN against asthma. GO KEGG pathway bar charts were made with SRPLOT ( , accessed on 29 November 2022) [ 27 ].…”

“…For binding pattern analysis, the compound’s lowest energy conformation was chosen. The minimized protein structure was used as the docking template [ 27 ].…”

Integrating Chemical Profiling, In Vivo Study, and Network Pharmacology to Explore the Anti-inflammatory Effect of Pterocarpus dalbergioides Fruits and Its Correlation with the Major Phytoconstituents

“…To predict the possible anti-inflammatory molecular mechanism of PEF, a KEGG pathway enrichment analysis was performed. SRPLOT (accessed on 20 November 2022, ) was used to create GO and KEGG pathway bar charts …”

“…To examine binding patterns, the compounds’ lowest energy conformation was chosen. The docking template was based on the minimized protein structure …”

Mechanism of baixiangdan capsules on anti-neuroinflammation: combining dry and wet experiments

“…In 2007, HOPKINS [ 26 ] first introduced the concept of “network pharmacology” and defined it as an emerging discipline that breaking through the traditional concept of “one gene, one drug, one disease” and building a bridge to study the interrelationship between new drug development and drug mechanism of action research. Molecular docking, which is commonly used to calculate the various possible binding conformations between biomolecules and small molecule ligands, construct a receptor-ligand conformation set and predict the strength and type of binding, scoring it according to the ranking of the conformation and energy match, has been used for decades so far [ 27 , 28 ] and is widely used in CNS disorders [ 29 ], restless legs syndrome [ 30 ] and depression [ 31 ]. Therefore, the study used techniques and methods such as neuroinflammation models and bioinformatics analysis to explore the anti-neuroinflammatory effects and mechanisms of BXD.…”