Evidence map›Paper›PMID 39730024›Full record

ArticleJournal of advanced research2025

Evaluating current status of network pharmacology for herbal medicine focusing on identifying mechanisms and therapeutic effects.

Won-Yung Lee, Kwang-Il Park, Seon-Been Bak, Seungho Lee, Su-Jin Bae, Min-Jin Kim, Sun-Dong Park, Choon Ok Kim, Ji-Hwan Kim, Young Woo Kim and 1 more

Abstract read
In one paragraph

Article in Journal of advanced research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
–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

20 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  14. Network-Based Identification ofCurrent pharmaceutical design · 2026
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  17. Special Issue: New Research on Bioactive Natural Products.International journal of molecular sciences · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Won-Yung LeeSchool of Korean Medicine, Wonkwang University, Iksan 54538, Republic of Korea; Research Center of Traditional Korean Medicine, Wonkwang University, Iksan 54538, Republic of Korea; School of Korean Medicine, Woosuk University, Jeonju 54986, Republic of Korea.
Kwang-Il ParkDepartment of Veterinary Medicine, Research Institute of Life Science, Gyeongsang National University, Jinju 52828, Republic of Korea.
Seon-Been BakSchool of Korean Medicine, Dongguk University, Gyeongju 38066, Republic of Korea; Department of Nutritional Science and Food Management, Ewha Womans University, Seoul 03760, Republic of Korea.
Seungho LeeSchool of Korean Medicine, Woosuk University, Jeonju 54986, Republic of Korea.
Su-Jin BaeSchool of Korean Medicine, Wonkwang University, Iksan 54538, Republic of Korea.
Min-Jin KimSchool of Korean Medicine, Dongguk University, Gyeongju 38066, Republic of Korea.
Sun-Dong ParkSchool of Korean Medicine, Dongguk University, Gyeongju 38066, Republic of Korea.
Choon Ok KimDepartment of Clinical Pharmacology, Severance Hospital, Yonsei University Health System, Seoul 03722, Republic of Korea.
Ji-Hwan KimSchool of Korean Medicine, Pusan National University, Yangsan-si 50612, Republic of Korea.
Young Woo KimSchool of Korean Medicine, Dongguk University, Gyeongju 38066, Republic of Korea. Electronic address: ywk@dongguk.ac.kr.
Chang-Eop KimSchool of Korean Medicine, Gachon University, Seongnam 13110, Republic of Korea. Electronic address: eopchang@gachon.ac.kr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionNetwork pharmacology has gained significant traction as a tool for identifying the mechanisms and therapeutic effects of herbal medicines. However, despite the usefulness of these approaches, their diversity underscores the critical need for a systematic evaluation to ensure consistency and reliability.

objectivesWe aimed to evaluate the network pharmacological analyses, focusing on identifying the mechanisms and therapeutic effects of herbal medicines.

methodsWe employed a comprehensive approach involving systematic data retrieval, network construction, and analysis. Herbal compounds and their targets were meticulously extracted from five distinct network pharmacology databases to ensure extensive coverage and high data reliability. Advanced network-based methods were used to identify key herbal targets and predict therapeutic effects, thereby enriching the depth and breadth of the analysis. Experimental validation was performed on prostate cancer models to substantiate the computational predictions.

resultsThe results of the recapitulating task for known herbal ingredient targets revealed distinct patterns in performance and coverage based on network construction and aggregation methods. We performed the same analysis to identify herbal targets and found that network centrality, path counts, and downweighted path counts had their own pros and cons. By comparing network-based methods, we found that considering the impact on the multiscale interactome yielded the highest accuracy in discriminating known therapeutic effects. Using optimal conditions, we successfully identified new indications for herbal medicines and validated these findings through follow-up in vitro and in vivo experiments.

conclusionThis study presents the first comprehensive and critical evaluation of the current network pharmacology analyses in the field of herbal medicine and provides valuable guidance for continued advances in the elucidation of the mechanisms and therapeutic effects.

Indexed as

Herbal MedicineNetwork PharmacologyPhytotherapyProstatic NeoplasmsAnimalsHumansMaleMiceComprehensive evaluationHerbal medicineNetwork pharmacologyTherapeutic mechanisms

Identifiers

PMID39730024
PMCPMC12793800

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.