A Network Pharmacology Approach to Reveal the Underlying Mechanisms of Paeonia lactiflora Pall. On the Treatment of Alzheimer's Disease

Qiang Zeng; Longfei Li; Yu Jin; Zongzheng Chen; Lihong Duan; Meiqun Cao; Min Ma; Zhengzhi Wu

doi:10.1155/2019/8706589

A Network Pharmacology Approach to Reveal the Underlying Mechanisms of Paeonia lactiflora Pall. On the Treatment of Alzheimer's Disease

Evid Based Complement Alternat Med. 2019 Nov 16:2019:8706589. doi: 10.1155/2019/8706589. eCollection 2019.

Authors

Qiang Zeng^{1

2

3}, Longfei Li⁴, Yu Jin^{2

3}, Zongzheng Chen^{1

2

3}, Lihong Duan^{2

3}, Meiqun Cao^{2

3}, Min Ma¹, Zhengzhi Wu^{2

3}

Affiliations

¹ Integrated Chinese and Western Medicine Postdoctoral Research Station, School of Traditional Chinese Medicine, Jinan University, Guangzhou 510632, China.
² The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen 518035, China.
³ Shenzhen Institute of Geriatrics, Shenzhen 518020, China.
⁴ Laboratory of Molecular Pharmacology, Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou 646000, China.

Abstract

Objective: To investigate the potential active compounds and underlying mechanisms of Paeonia lactiflora Pall. (PLP) on the treatment of Alzheimer's disease (AD) based on network pharmacology.

Methods: The active components of PLP were collected from Traditional Chinese Medicine System Pharmacology (TCMSP) database, and their possible target proteins were predicted using TCMSP, SwissTargetPrediction, and STITCH databases. The putative AD-related target proteins were identified from Therapeutic Target Database (TTD), GeneCards, and MalaCards database. The compound-target-disease network interactions were established to obtain the key targets about PLP acting on AD by network topology analysis. Then, the function annotation and signaling pathways of key targets were performed by GO and KEGG enrichment analysis using DAVID tools. Finally, the binding capacity between active ingredients and key targets was validated by molecular docking using SystemsDock tools.

Results: There were 7 active compounds involving in 151 predicted targets identified in PLP. Besides, a total of 160 AD-related targets were identified. Among these targets, 30 shared targets of PLP and AD were acquired. After topological analysis of the PLP potential target-AD target network, 33 key targets that were highly responsible for the therapeutic effects of PLP on AD were obtained. Further GO and KEGG enrichment analysis showed that these key targets were significantly involved in multiple biological processes and pathways which participated in cell apoptosis and inflammatory response and maintained the function of neurons to accomplish the anti-AD activity. The molecular docking analysis verified that the 7 active compounds had definite affinity with the key targets.

Conclusions: The ameliorative effects of PLP on AD were predicted to be associated with regulating neural cell apoptosis, inflammatory response, and neurotrophy via various pathways such as PI3K-Akt signaling pathway, MAPK signaling pathway, and neurotrophin signaling pathway.