Tropical Journal of Pharmaceutical Research

Original Research Article | OPEN ACCESS

Prediction of the anti-inflammatory effects of bioactive components of a Hippocampus species-based TCM formulation on chronic kidney disease using network pharmacology

Lingyu Zhang, Sitong Lu, Zhang Hu , Mingneng Liao, Chengpeng Li, Songzhi Kong

Faculty of Chemistry and Environmental Science, Guangdong Ocean University, Zhanjiang 524088, China;

For correspondence:- Zhang Hu Email: huzhangqyx@126.com Tel:+867592383300

Accepted: 18 October 2021 Published: 30 November 2021

Citation: Zhang L, Lu S, Hu Z, Liao M, Li C, Kong S. Prediction of the anti-inflammatory effects of bioactive components of a Hippocampus species-based TCM formulation on chronic kidney disease using network pharmacology. Trop J Pharm Res 2021; 20(11):2355-2362 doi: 10.4314/tjpr.v20i11.18

© 2021 The authors.
This is an Open Access article that uses a funding model which does not charge readers or their institutions for access and distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0) and the Budapest Open Access Initiative (http://www.budapestopenaccessinitiative.org/read), which permit unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited..

Abstract

Purpose: To systematically study and predict the therapeutic targets and signaling pathways of Hippocampus (HPC) against chronic kidney disease (CKD) using network pharmacology.

Methods: By combining database mining, literature searching, screening of disease targets, and network construction, the effects of various components of HPC on several proteins related to CKD were predicted and the active compounds were screened. Genes related to the selected compounds were linked using the SEA database. The correlation between CKD and genes was determined using OMIM, DisGenNet, and GeneCards databases. Pathway-enrichment analyses of overlapping genes were undertaken using online databases.

Results: A total of 144 compounds in HPC were identified. Analyses of clusters suggest that the active components of HPC and the target genes against the inflammation caused by CKD were due to 10 compounds and 25 genes. Metascape results showed that these HPC targets are related to CKD inflammation.

Conclusion: The active components of HPC and the target genes against CKD inflammation are involved in multiple signaling pathways, such as AGE-RAGE, TLR, TNF, and NF-κB. This work provides scientific evidence to support the clinical use of HPC against CKD.

Keywords: Hippocampus, Chronic kidney disease, Network pharmacology, Paeonol inflammation, AGE-RAGE signaling pathway