Exploring the potential pharmacological mechanism of aripiprazole against hyperprolactinemia based on network pharmacology and molecular docking.

IF 3 Q2 PSYCHIATRY Schizophrenia (Heidelberg, Germany) Pub Date : 2024-11-07 DOI:10.1038/s41537-024-00523-8
Lei Yang, Qiuyu Zhang, Chao Li, Hongjun Tian, Chuanjun Zhuo
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Abstract

The current primary therapeutic approach for schizophrenia is antipsychotic medication, and antipsychotic-induced hyperprolactinemia occurs in 40-80% of patients with schizophrenia. Aripiprazole, an atypical antipsychotic belonging to the quinolinone derivative class, can reduce the likelihood of developing hyperprolactinemia, but the pharmacological mechanisms of this reduction are unknown. This study aimed to explore the molecular mechanism of action of aripiprazole in treating hyperprolactinemia based on network pharmacology and molecular docking techniques. This study identified a total of 151 potential targets for aripiprazole from the DrugBank, TCMSP, BATMAN-TCM, TargetNet, and SwissTargetPrediction databases. Additionally, 71 hyperprolactinemia targets were obtained from the PharmGKB, DrugBank, TTD, GeneCards, OMIM, and DisGENET databases. Utilizing Venny 2.1.0 software, an intersection of 27 genes was identified between aripiprazole and hyperprolactinemia. To construct a common target protein-protein interaction (PPI) network, the common targets obtained from both sources were input into the STRING database. The resulting PPI network was then imported into Cytoscape 3.7.2 software, which identified eight core targets associated with aripiprazole's treatment of hyperprolactinemia. Subsequently, a PPI network was established for these targets. Enrichment analysis of the key targets was conducted using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes in the DAVID database. Additionally, molecular docking verification of the interaction between aripiprazole and the core targets was performed using AutoDock Vina software. Aripiprazole's intervention in hyperprolactinemia primarily targets the following core proteins: Solute Carrier Family 6 Member 3 (SLC6A3), monoamine oxidase (MAO-B), Dopamine D2 receptor (DRD2), 5-hydroxytryptamine (serotonin) receptor 2A (HTR2A), 5-hydroxytryptamine (serotonin) receptor 2C (HTR2C), cytochrome P450 2D6 (CYP2D6), Dopamine D1 receptor (DRD1), Dopamine D4 receptor (DRD4). These targets are predominantly involved in biological processes such as the adenylate cyclase-activating adrenergic receptor signaling pathway, G-protein coupled receptor signaling pathway coupled to cyclic nucleotide second messenger, phospholipase C-activating G-protein coupled receptor signaling pathway, chemical synaptic transmission, and response to xenobiotic stimulus. Primary enrichment occurs in signaling pathways such as the neuroactive ligand-receptor interaction and serotonergic synapse pathways. Molecular docking results demonstrate a favorable affinity between aripiprazole and the core target proteins MAO-B, DRD2, SLC6A3, HTR2C, HTR2A, CYP2D6, DRD4, and DRD1. Network pharmacology predicted potential targets and signaling pathways for aripiprazole's intervention in hyperprolactinemia, offering theoretical support and a reference basis for optimizing clinical strategies and drug development involving aripiprazole.

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基于网络药理学和分子对接,探索阿立哌唑抗高催乳素血症的潜在药理机制。
目前治疗精神分裂症的主要方法是服用抗精神病药物,40%-80%的精神分裂症患者会出现抗精神病药物诱发的高泌乳素血症。阿立哌唑是一种属于喹啉酮衍生物类的非典型抗精神病药物,可以降低高泌乳素血症的发病几率,但降低的药理机制尚不清楚。本研究旨在基于网络药理学和分子对接技术,探索阿立哌唑治疗高催乳素血症的分子作用机制。本研究从 DrugBank、TCMSP、BATMAN-TCM、TargetNet 和 SwissTargetPrediction 数据库中发现了阿立哌唑的 151 个潜在靶点。此外,还从 PharmGKB、DrugBank、TTD、GeneCards、OMIM 和 DisGENET 数据库中获得了 71 个高催乳素血症靶点。利用 Venny 2.1.0 软件,确定了阿立哌唑与高催乳素血症之间有 27 个基因的交集。为了构建共同靶标蛋白质-蛋白质相互作用(PPI)网络,将从这两个来源获得的共同靶标输入 STRING 数据库。然后将得到的 PPI 网络导入 Cytoscape 3.7.2 软件,该软件确定了与阿立哌唑治疗高泌乳素血症相关的八个核心靶点。随后,为这些靶点建立了一个 PPI 网络。利用 DAVID 数据库中的基因本体和京都基因和基因组百科全书对关键靶点进行了富集分析。此外,还使用 AutoDock Vina 软件对阿立哌唑与核心靶点之间的相互作用进行了分子对接验证。阿立哌唑对高催乳素血症的干预主要针对以下核心蛋白:溶质运载家族 6 成员 3 (SLC6A3)、单胺氧化酶 (MAO-B)、多巴胺 D2 受体 (DRD2)、5-羟色胺(血清素)受体 2A (HTR2A)、5-羟色胺(血清素)受体 2C (HTR2C)、细胞色素 P450 2D6 (CYP2D6)、多巴胺 D1 受体 (DRD1)、多巴胺 D4 受体 (DRD4)。这些靶点主要参与生物过程,如腺苷酸环化酶激活肾上腺素能受体信号通路、与环核苷酸第二信使相耦合的 G 蛋白偶联受体信号通路、磷脂酶 C 激活 G 蛋白偶联受体信号通路、化学突触传递以及对异生物刺激的反应。神经活性配体-受体相互作用和血清素能突触通路等信号通路的主要富集发生。分子对接结果表明,阿立哌唑与核心靶蛋白 MAO-B、DRD2、SLC6A3、HTR2C、HTR2A、CYP2D6、DRD4 和 DRD1 之间具有良好的亲和力。网络药理学预测了阿立哌唑干预高催乳素血症的潜在靶点和信号通路,为优化阿立哌唑的临床策略和药物开发提供了理论支持和参考依据。
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