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Liu M, Du Y, Gao D. Licochalcone A: a review of its pharmacology activities and molecular mechanisms. Front Pharmacol 2024; 15:1453426. [PMID: 39188947 PMCID: PMC11345200 DOI: 10.3389/fphar.2024.1453426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2024] [Accepted: 08/02/2024] [Indexed: 08/28/2024] Open
Abstract
Licorice, derived from the root of Glycyrrhiza uralensis Fisch, is a key Traditional Chinese Medicine known for its detoxifying, spleen-nourishing, and qi-replenishing properties. Licochalcone A (Lico A), a significant component of licorice, has garnered interest due to its molecular versatility and receptor-binding affinity. This review explores the specific roles of Lico A in various diseases, providing new insights into its characteristics and guiding the rational use of licorice. Comprehensive literature searches using terms such as "licorice application" and "pharmacological activity of Lico A" were conducted across databases including CNKI, PubMed, and Google Scholar to gather relevant studies on Lico A's pharmacological activities and mechanisms. Lico A, a representative chalcone in licorice, targets specific mechanisms in anti-cancer and anti-inflammatory activities. It also plays a role in post-transcriptional regulation. This review delineates the similarities and differences in the anti-cancer and anti-inflammatory mechanisms of Lico A, concluding that its effects on non-coding RNA through post-transcriptional mechanisms deserve further exploration.
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Affiliation(s)
- Meihua Liu
- Research Center of Emotional Diseases, Shenyang Anning Hospital, Shenyang, China
- Shenyang Key Laboratory for Causes and Drug Discovery of Chronic, Shenyang, China
| | - Yang Du
- The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Dejiang Gao
- Research Center of Emotional Diseases, Shenyang Anning Hospital, Shenyang, China
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Fan M, Jin C, Li D, Deng Y, Yao L, Chen Y, Ma YL, Wang T. Multi-level advances in databases related to systems pharmacology in traditional Chinese medicine: a 60-year review. Front Pharmacol 2023; 14:1289901. [PMID: 38035021 PMCID: PMC10682728 DOI: 10.3389/fphar.2023.1289901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 11/03/2023] [Indexed: 12/02/2023] Open
Abstract
The therapeutic effects of traditional Chinese medicine (TCM) involve intricate interactions among multiple components and targets. Currently, computational approaches play a pivotal role in simulating various pharmacological processes of TCM. The application of network analysis in TCM research has provided an effective means to explain the pharmacological mechanisms underlying the actions of herbs or formulas through the lens of biological network analysis. Along with the advances of network analysis, computational science has coalesced around the core chain of TCM research: formula-herb-component-target-phenotype-ZHENG, facilitating the accumulation and organization of the extensive TCM-related data and the establishment of relevant databases. Nonetheless, recent years have witnessed a tendency toward homogeneity in the development and application of these databases. Advancements in computational technologies, including deep learning and foundation model, have propelled the exploration and modeling of intricate systems into a new phase, potentially heralding a new era. This review aims to delves into the progress made in databases related to six key entities: formula, herb, component, target, phenotype, and ZHENG. Systematically discussions on the commonalities and disparities among various database types were presented. In addition, the review raised the issue of research bottleneck in TCM computational pharmacology and envisions the forthcoming directions of computational research within the realm of TCM.
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Affiliation(s)
- Mengyue Fan
- Innovation Research Institute of Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Ching Jin
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL, United States
| | - Daping Li
- Innovation Research Institute of Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yingshan Deng
- College of Acupuncture and Massage, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Lin Yao
- Innovation Research Institute of Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yongjun Chen
- Innovation Research Institute of Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yu-Ling Ma
- Oxford Chinese Medicine Research Centre, University of Oxford, Oxford, United Kingdom
| | - Taiyi Wang
- Innovation Research Institute of Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
- Oxford Chinese Medicine Research Centre, University of Oxford, Oxford, United Kingdom
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Wang T, Liang L, Zhao C, Sun J, Wang H, Wang W, Lin J, Hu Y. Elucidating direct kinase targets of compound Danshen dropping pills employing archived data and prediction models. Sci Rep 2021; 11:9541. [PMID: 33953309 PMCID: PMC8100098 DOI: 10.1038/s41598-021-89035-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 04/19/2021] [Indexed: 12/17/2022] Open
Abstract
Research on direct targets of traditional Chinese medicine (TCM) is the key to study the mechanism and material basis of it, but there is still no effective methods at present. We took Compound Danshen dropping pills (CDDP) as a study case to establish a strategy to identify significant direct targets of TCM. As a result, thirty potential active kinase targets of CDDP were identified. Nine of them had potential dose-dependent effects. In addition, the direct inhibitory effect of CDDP on three kinases, AURKB, MET and PIM1 were observed both on biochemical level and cellular level, which could not only shed light on the mechanisms of action involved in CDDP, but also suggesting the potency of drug repositioning of CDDP. Our results indicated that the research strategy including both in silico models and experimental validation that we built, were relatively efficient and reliable for direct targets identification for TCM prescription, which will help elucidating the mechanisms of TCM and promoting the modernization of TCM.
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Affiliation(s)
- Tongxing Wang
- GeneNet Pharmaceuticals Co. Ltd., No. 1, Tingjiang West Road, Beichen District, Tianjin, 300410, China
| | - Lu Liang
- College of Pharmacy, Nankai University, Haihe Education Park, 38 Tongyan Road, Jinnan District, Tianjin, 300353, China
| | - Chunlai Zhao
- GeneNet Pharmaceuticals Co. Ltd., No. 1, Tingjiang West Road, Beichen District, Tianjin, 300410, China
| | - Jia Sun
- GeneNet Pharmaceuticals Co. Ltd., No. 1, Tingjiang West Road, Beichen District, Tianjin, 300410, China
| | - Hairong Wang
- GeneNet Pharmaceuticals Co. Ltd., No. 1, Tingjiang West Road, Beichen District, Tianjin, 300410, China
| | - Wenjia Wang
- GeneNet Pharmaceuticals Co. Ltd., No. 1, Tingjiang West Road, Beichen District, Tianjin, 300410, China
| | - Jianping Lin
- College of Pharmacy, Nankai University, Haihe Education Park, 38 Tongyan Road, Jinnan District, Tianjin, 300353, China
| | - Yunhui Hu
- GeneNet Pharmaceuticals Co. Ltd., No. 1, Tingjiang West Road, Beichen District, Tianjin, 300410, China.
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Jiang H, Hu C, Chen M. The Advantages of Connectivity Map Applied in Traditional Chinese Medicine. Front Pharmacol 2021; 12:474267. [PMID: 33776757 PMCID: PMC7991830 DOI: 10.3389/fphar.2021.474267] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Accepted: 01/11/2021] [Indexed: 01/11/2023] Open
Abstract
Amid the establishment and optimization of Connectivity Map (CMAP), the functional relationships among drugs, genes, and diseases are further explored. This biological database has been widely used to identify drugs with common mechanisms, repurpose existing drugs, discover the molecular mechanisms of unknown drugs, and find potential drugs for some diseases. Research on traditional Chinese medicine (TCM) has entered a new era in the wake of the development of bioinformatics and other subjects including network pharmacology, proteomics, metabolomics, herbgenomics, and so on. TCM gradually conforms to modern science, but there is still a torrent of limitations. In recent years, CMAP has shown its distinct advantages in the study of the components of TCM and the synergetic mechanism of TCM formulas; hence, the combination of them is inevitable.
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Affiliation(s)
- Huimin Jiang
- School of Medicine and Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing, China.,CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Cheng Hu
- School of Medicine and Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing, China.,The First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, China
| | - Meijuan Chen
- School of Medicine and Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing, China
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Zheng W, Wu J, Gu J, Weng H, Wang J, Wang T, Liang X, Cao L. Modular Characteristics and Mechanism of Action of Herbs for Endometriosis Treatment in Chinese Medicine: A Data Mining and Network Pharmacology-Based Identification. Front Pharmacol 2020; 11:147. [PMID: 32210799 PMCID: PMC7069061 DOI: 10.3389/fphar.2020.00147] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Accepted: 02/04/2020] [Indexed: 12/13/2022] Open
Abstract
Endometriosis is a common benign disease in women of reproductive age. It has been defined as a disorder characterized by inflammation, compromised immunity, hormone dependence, and neuroangiogenesis. Unfortunately, the mechanisms of endometriosis have not yet been fully elucidated, and available treatment methods are currently limited. The discovery of new therapeutic drugs and improvements in existing treatment schemes remain the focus of research initiatives. Chinese medicine can improve the symptoms associated with endometriosis. Many Chinese herbal medicines could exert antiendometriosis effects via comprehensive interactions with multiple targets. However, these interactions have not been defined. This study used association rule mining and systems pharmacology to discover a method by which potential antiendometriosis herbs can be investigated. We analyzed various combinations and mechanisms of action of medicinal herbs to establish molecular networks showing interactions with multiple targets. The results showed that endometriosis treatment in Chinese medicine is mainly based on methods of supplementation with blood-activating herbs and strengthening qi. Furthermore, we used network pharmacology to analyze the main herbs that facilitate the decoding of multiscale mechanisms of the herbal compounds. We found that Chinese medicine could affect the development of endometriosis by regulating inflammation, immunity, angiogenesis, and other clusters of processes identified by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The antiendometriosis effect of Chinese medicine occurs mainly through nervous system–associated pathways, such as the serotonergic synapse, the neurotrophin signaling pathway, and dopaminergic synapse, among others, to reduce pain. Chinese medicine could also regulate VEGF signaling, toll-like reporter signaling, NF-κB signaling, MAPK signaling, PI3K-Akt signaling, and the HIF-1 signaling pathway, among others. Synergies often exist in herb pairs and herbal prescriptions. In conclusion, we identified some important targets, target pairs, and regulatory networks, using bioinformatics and data mining. The combination of data mining and network pharmacology may offer an efficient method for drug discovery and development from herbal medicines.
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Affiliation(s)
- Weilin Zheng
- The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Jiayi Wu
- The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Jiangyong Gu
- Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Heng Weng
- Department of Big Medical Data, Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Jie Wang
- The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Tao Wang
- The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xuefang Liang
- Department of Gynecology, Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Lixing Cao
- Team of Application of Chinese Medicine in Perioperative Period, Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
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