Zhang Zhiwen, Li Xiaojing, Huang Zihui, Pan Zhenxing, Li Lingjie, Wang Yang, Wu Siwei, Xing Yan, Xiao Guanlin, He Yan, Cai Dake, Liu Xujie
School of Biomedical and Pharmaceutical Sciences, Guangdong University of Technology, Guangzhou, 510006, China.
Department of Pharmacy, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, Guangdong, 510090, China.
Heliyon. 2024 Oct 3;10(22):e38726. doi: 10.1016/j.heliyon.2024.e38726. eCollection 2024 Nov 30.
The multi-targets and multi-components of Traditional Chinese medicine (TCM) coincide with the complex pathogenesis of depression. Zhi-Zi-Hou-Pu Decoction (ZZHPD) has been approved in clinical medication with good antidepression effects for centuries, while the mechanisms under the iceberg haven't been addressed systematically. This study explored its inner active ingredients - potent pharmacological mechanism - DDI to explore more comprehensively and deeply understanding of the complicated TCM in treatment.
This research utilized network pharmacology combined with molecular docking to identify pharmacological targets and molecular interactions between ZZHPD and depression. Verification of major active compounds was conducted through UPLC-Q-TOF-MS/MS and assays on LPS-induced neuroblastoma cells. Additionally, the DDIMDL model, a deep learning-based approach, was used to predict DDIs, focusing on serum concentration, metabolism, effectiveness, and adverse reactions.
The antidepressant mechanisms of ZZHPD involve the serotonergic synapse, neuroactive ligand-receptor interaction, and dopaminergic synapse signaling pathways. Eighteen active compounds were identified, with honokiol and eriocitrin significantly modulating neuronal inflammation and promoting differentiation of neuroimmune cells through genes like COMT, PI3KCA, PTPN11, and MAPK1. DDI predictions indicated that eriocitrin's serum concentration increases when combined with hesperidin, while hesperetin's metabolism decreases with certain flavonoids. These findings provide crucial insights into the nervous system's effectiveness and potential cardiovascular or nervous system adverse reactions from core compound combinations.
This study provides insights into the TCM interpretation, drug compatibility or combined medication for further clinical application or potential drug pairs with a cost-effective method of integrated network pharmacology and deep learning.
中药的多靶点和多成分与抑郁症复杂的发病机制相契合。栀子厚朴汤（ZZHPD）在临床用药中已获认可，数百年来一直具有良好的抗抑郁效果，但其潜在机制尚未得到系统阐述。本研究探索其内在活性成分 - 强大的药理机制 - 药物 - 药物相互作用（DDI），以更全面、深入地理解中药治疗抑郁症的复杂性。
本研究利用网络药理学结合分子对接来确定ZZHPD与抑郁症之间的药理靶点和分子相互作用。通过超高效液相色谱 - 四极杆飞行时间串联质谱（UPLC - Q - TOF - MS/MS）以及对脂多糖（LPS）诱导的神经母细胞瘤细胞进行实验，对主要活性化合物进行验证。此外，基于深度学习的DDIMDL模型用于预测药物 - 药物相互作用，重点关注血清浓度、代谢、有效性和不良反应。
ZZHPD的抗抑郁机制涉及5 - 羟色胺能突触、神经活性配体 - 受体相互作用和多巴胺能突触信号通路。鉴定出18种活性化合物，厚朴酚和圣草枸橼苷通过儿茶酚 - O - 甲基转移酶（COMT）、磷脂酰肌醇 - 3 - 激酶催化亚基α（PI3KCA）、蛋白酪氨酸磷酸酶非受体型11（PTPN11）和丝裂原活化蛋白激酶1（MAPK1）等基因显著调节神经元炎症并促进神经免疫细胞分化。药物 - 药物相互作用预测表明，圣草枸橼苷与橙皮苷合用时血清浓度升高，而橙皮素与某些黄酮类化合物合用时代谢降低。这些发现为核心化合物组合对神经系统的有效性以及潜在的心血管或神经系统不良反应提供了关键见解。
本研究通过网络药理学与深度学习相结合的经济有效方法，为中药的阐释、药物配伍或联合用药提供了见解，以用于进一步的临床应用或潜在的药物组合。
