Evidence map›Paper›PMID 39356317›Full record

ArticleNaunyn-Schmiedeberg's archives of pharmacology2025

Exploring the bioactive ingredients of three traditional Chinese medicine formulas against age-related hearing loss through network pharmacology and experimental validation.

Wenying Shi, Qi Zhao, Hongwei Gao, Yaxin Yang, Zhiyong Tan, Na Li, Hongjie Wang, Yonghua Ji, You Zhou

Erratum issuedAbstract read
In one paragraph

Article in Naunyn-Schmiedeberg's archives of pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Wenying Shi *School of Basic Medical Sciences, Hebei University, Baoding, 071030, China.
Qi Zhao *School of Basic Medical Sciences, Hebei University, Baoding, 071030, China.
Hongwei Gao *School of Basic Medical Sciences, Hebei University, Baoding, 071030, China.
Yaxin YangSchool of Basic Medical Sciences, Hebei University, Baoding, 071030, China.
Zhiyong TanSchool of Basic Medical Sciences, Hebei University, Baoding, 071030, China.
Na LiSchool of Basic Medical Sciences, Hebei University, Baoding, 071030, China.
Hongjie WangSchool of Basic Medical Sciences, Hebei University, Baoding, 071030, China.
Yonghua JiSchool of Basic Medical Sciences, Hebei University, Baoding, 071030, China.
You ZhouSchool of Basic Medical Sciences, Hebei University, Baoding, 071030, China. youzhou@hbu.edu.cn.

Funding

Medical Science Foundation of Hebei University 2022A04Natural Science Foundation of Hebei Province H2022201050Natural Science Foundation of Hebei Province H2022201059Science and Technology Research Project of Universities in Hebei Province QN2024198
6 · The paper itself

Abstract

Traditional Chinese medicine (TCM) formulas, including the Er-Long-Zuo-Ci pill, Tong-Qiao-Er-Long pill, and Er-Long pill, have long been utilized in China for managing age-related hearing loss (ARHL). However, the specific bioactive compounds, pharmacological targets, and underlying mechanisms remain elusive. This study aims to find the shared bioactive ingredients among these three formulas, uncover the molecular pathways they regulate, and identify potential therapeutic targets for ARHL. Furthermore, it seeks to validate the efficacy of these major components through both in vivo and in vitro experiments. Common bioactive ingredients were extracted from the TCMSP database, and their putative target proteins were predicted using the Swiss Target Prediction database. ARHL-related target proteins were collected from GeneCards and OMIM databases. Our approach involved constructing drug-target networks and drug-disease-specific protein-protein interaction networks and conducting clustering, topological property analyses, and functional annotation through GO and KEGG enrichment analysis. Molecular docking analysis was utilized to delineate interaction mechanisms between major bioactive ingredients and key target proteins. Finally, in vivo and in vitro experiments involving ABR recording, immunofluorescent staining, HE staining, and quantitative PCR were conducted to validate the treatment effects of flavonoids on the declining auditory function in DBA/2 J mice. We identified 11 common chemical compounds across the three formulas and their associated 276 putative targets. Additionally, 3350 ARHL-related targets were compiled. As an intersection of the putative targets of the common compounds and ARHL-related proteins, 145 shared targets were determined. Functional enrichment analysis indicated that these compounds may modulate various biological processes, including cell proliferation, apoptosis, inflammatory response, and synaptic connections. Notably, potential targets such as TNFα, MAPK1, SRC, AKT, EGFR, ESR1, and AR were implicated. Flavonoids emerged as major bioactive components against ARHL based on target numbers, with molecular docking demonstrating diverse interaction models between these flavonoids and protein targets. Furthermore, baicalin could mitigate the age-related cochlear damage and hearing loss of DBA/2 J mice through its multi-target and multi-pathway mechanism, involving anti-inflammation, modulation of sex hormone-related pathways, and activation of potassium channels. This study offers an integrated network pharmacology approach, validated by in vivo and in vitro experiments, shedding light on the potential mechanisms, major active components, and therapeutic targets of TCM formulas for treating ARHL.

Indexed as

Drugs, Chinese HerbalPresbycusisAnimalsMaleMedicine, Chinese TraditionalMiceMolecular Docking SimulationNetwork PharmacologyProtein Interaction MapsDrugs, Chinese HerbalAge-related hearing lossFlavonoidMolecular dockingNetwork pharmacologyTraditional Chinese medicine

Identifiers

PMID39356317
PMCPMC11978554

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.