Evidence map›Paper›PMID 39991188›Full record

ArticleAnalytical science advances2025

Exploring of Antidepressant Components and Mechanisms of Zhizichi Decoction: Integration of Serum Pharmacochemistry, Network Pharmacology and Anti-inflammatory Analysis Verification.

Chuan Chai, Bo Jin, Jinghan Bi, Yuhan Cui, Xiaobing Cui, Chenxiao Shan, Sheng Yu, Hongmei Wen

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Article in Analytical science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Chuan ChaiSchool of Pharmacy Nanjing University of Chinese Medicine Nanjing Jiangsu China.
Bo JinChina Mobile Group Jiangsu Co. Ltd. Nanjing Jiangsu China.
Jinghan BiNanjing Zenkom Pharmaceutical Co. Ltd. Nanjing Jiangsu China.
Yuhan CuiSchool of Pharmacy Nanjing University of Chinese Medicine Nanjing Jiangsu China.
Xiaobing CuiSchool of Pharmacy Nanjing University of Chinese Medicine Nanjing Jiangsu China.
Chenxiao ShanSchool of Pharmacy Nanjing University of Chinese Medicine Nanjing Jiangsu China.
Sheng YuSchool of Pharmacy Nanjing University of Chinese Medicine Nanjing Jiangsu China.
Hongmei WenSchool of Pharmacy Nanjing University of Chinese Medicine Nanjing Jiangsu China.ORCID https://orcid.org/0000-0002-5345-604X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Using LC-MS to screen and analyse the characteristics of components and biological systems is a new approach to study the pharmacological substance basis of traditional Chinese medicine, which has strong novelty in analytical science. This study analyses the antidepressant material basis of Zhizichi decoction through the integration strategy of serum pharmacochemistry, network pharmacology and immunoreactivity verification, which helps in overcoming the limitations of TCM research and provided a new perspective and approach for studying the components of ZZCD. First of all, blood from SD rats was collected before and after Zhizichi decoction administration initially. The migration constituents in the serum were then analysed using ultra-high performance liquid chromatography-Q-TOF-MS/MS. By integrating the TCMSP databases with the serum pharmacochemistry results, we constructed the 'ingredients-targets-pathways' network and the protein-protein interaction network for Zhizichi decoction's depression-relieving. Finally, the inhibitory effects of Zhizichi decoction and active ingredient groups comprised of pharmacodynamic components identified in prior network pharmacology study on IL-1β, IL-6 and TNF-α were measured through an inflammatory cytokines experiments. From the serum pharmacochemistry study, 146 migration constituents in serum and their attribution were hypothesized and characterized. They were identified as 18 prototype components and 128 metabolites, of which 121 were Phase I and 7 were Phase II metabolites. The Zhizichi decoction pharmacology network illustrated the relationships of the 20 definitive ingredients, 85 potential targets and 21 signalling pathways in connection with the depression. The targets predicted by pharmacology and protein-protein interaction network were reported to be associated with neuroinflammation, which suggested that further anti-inflammatory experiment was required. For the anti-inflammatory effect of AIGs 1 composed of 14 pharmacodynamic components was basically equivalent to that of whole ZZCD recipe, AIG 1 was hypothesized to be the critical pharmacodynamic components to inhibit inflammatory factors and defined as the antidepressant components' of Zhizichi decoction, providing a scientific foundation for the pursuit of potential new drugs for depression treatments.

Indexed as

anti‐depressionanti‐inflammatory analysisnetwork pharmacologyserum pharmacochemistryultra‐high performance liquid chromatography‐Q‐TOF‐MS/MSZhizichi decoction

Identifiers

PMID39991188
PMCPMC11845310

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