The Methodological Trends of Traditional Herbal Medicine Employing Network Pharmacology
Abstract
:1. Introduction
2. Materials and Methods
2.1. Search Strategy
2.2. Inclusion Criteria
2.3. Study Selection and Data Extraction
2.4. Categorizing Drug-Target Interaction Methods
2.5. Construction of the Co-Author Network and Affiliation Network
3. Results
3.1. Description of the Search
3.2. Methodological Trends in Constructing the Herb-Compound Network
3.3. Methodological Trends for Constructing Compound-Target Networks
3.4. Methodological Trends for Target Interpretation
3.5. Combinatorial Patterns in Methodologies of THM-NP Studies
3.6. Co-Author Network and Affiliation Network
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
- Kong, D.X.; Li, X.J.; Zhang, H.Y. Where is the hope for drug discovery? Let history tell the future. Drug Discov. Today 2009, 14, 115–119. [Google Scholar] [CrossRef] [PubMed]
- Verpoorte, R.; Crommelin, D.; Danhof, M.; Gilissen, L.J.; Schuitmaker, H.; van der Greef, J.; Witkamp, R.F. Commentary: “A systems view on the future of medicine: Inspiration from Chinese medicine?”. J. Ethnopharmacol. 2009, 121, 479–481. [Google Scholar] [CrossRef] [PubMed]
- Zheng, J.; Wu, M.; Wang, H.; Li, S.; Wang, X.; Li, Y.; Wang, D.; Li, S. Network Pharmacology to Unveil the Biological Basis of Health-Strengthening Herbal Medicine in Cancer Treatment. Cancers (Basel) 2018, 10, 461. [Google Scholar] [CrossRef] [PubMed]
- Ma, X.H.; Zheng, C.J.; Han, L.Y.; Xie, B.; Jia, J.; Cao, Z.W.; Li, Y.X.; Chen, Y.Z. Synergistic therapeutic actions of herbal ingredients and their mechanisms from molecular interaction and network perspectives. Drug Discov. Today 2009, 14, 579–588. [Google Scholar] [CrossRef] [PubMed]
- Berg, E.L. Systems biology in drug discovery and development. Drug Discov. Today 2014, 19, 113–125. [Google Scholar] [CrossRef] [PubMed]
- Barabási, A.-L.; Gulbahce, N.; Loscalzo, J. Network medicine: A network-based approach to human disease. Nat. Rev. Genet. 2011, 12, 56–68. [Google Scholar] [CrossRef] [PubMed]
- Hopkins, A.L. Network pharmacology: The next paradigm in drug discovery. Nat. Chem. Biol. 2008, 4, 682–690. [Google Scholar] [CrossRef]
- Van der Greef, J. Perspective: All systems go. Nature 2011, 480, S87. [Google Scholar] [CrossRef]
- Li, S.; Fan, T.-P.; Jia, W.; Lu, A.; Zhang, W. Network Pharmacology in Traditional Chinese Medicine. Evid. Based Complement. Altern. Med. 2014, 2014, 138460. [Google Scholar] [CrossRef]
- Li, S.; Zhang, B. Traditional Chinese medicine network pharmacology: Theory, methodology and application. Chin. J. Nat. Med. 2013, 11, 110–120. [Google Scholar] [CrossRef]
- Zhang, R.; Zhu, X.; Bai, H.; Ning, K. Network Pharmacology Databases for Traditional Chinese Medicine: Review and Assessment. Front. Pharmacol. 2019, 10, 123. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Yang, M.; Chen, J.-L.; Xu, L.-W.; Ji, G. Navigating Traditional Chinese Medicine Network Pharmacology and Computational Tools. Evid. Based Complement. Altern. Med. 2013, 2013, 731969. [Google Scholar] [CrossRef] [PubMed]
- Kibble, M.; Saarinen, N.; Tang, J.; Wennerberg, K.; Mäkelä, S.; Aittokallio, T. Network pharmacology applications to map the unexplored target space and therapeutic potential of natural products. Nat. Prod. Rep. 2015, 32, 1249–1266. [Google Scholar] [CrossRef] [PubMed]
- Hao, D.C.; Xiao, P.G. Network pharmacology: A rosetta stone for traditional chinese medicine. Drug Dev. Res. 2014, 75, 299–312. [Google Scholar] [CrossRef] [PubMed]
- Liu, Y.; Ai, N.; Keys, A.; Fan, X.; Chen, M. Network Pharmacology for Traditional Chinese Medicine Research: Methodologies and Applications. Chin. Herb. Med. 2015, 7, 18–26. [Google Scholar] [CrossRef]
- Yu, H.; Chen, J.; Xu, X.; Li, Y.; Zhao, H.; Fang, Y.; Li, X.; Zhou, W.; Wang, W.; Wang, Y. A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data. PLoS ONE 2012, 7, e37608. [Google Scholar] [CrossRef] [PubMed]
- Ru, J.; Li, P.; Wang, J.; Zhou, W.; Li, B.; Huang, C.; Li, P.; Guo, Z.; Tao, W.; Yang, Y.; et al. TCMSP: A database of systems pharmacology for drug discovery from herbal medicines. J. Cheminform. 2014, 6, 13. [Google Scholar] [CrossRef]
- Katsila, T.; Spyroulias, G.A.; Patrinos, G.P.; Matsoukas, M.T. Computational approaches in target identification and drug discovery. Comput. Struct. Biotechnol. J. 2016, 14, 177–184. [Google Scholar] [CrossRef] [Green Version]
- Macalino, S.J.Y.; Gosu, V.; Hong, S.; Choi, S. Role of computer-aided drug design in modern drug discovery. Arch. Pharm. Res. 2015, 38, 1686–1701. [Google Scholar] [CrossRef]
- Yang, S.Y. Pharmacophore modeling and applications in drug discovery: Challenges and recent advances. Drug Discov. Today 2010, 15, 444–450. [Google Scholar] [CrossRef]
- Ding, H.; Takigawa, I.; Mamitsuka, H.; Zhu, S. Similarity-basedmachine learning methods for predicting drug-target interactions: A brief review. Brief. Bioinform. 2013, 15, 734–747. [Google Scholar] [CrossRef] [PubMed]
- Lengauer, T.; Rarey, M. Computational methods for biomolecular docking. Curr. Opin. Struct. Biol. 1996, 6, 402–406. [Google Scholar] [CrossRef]
- Cherfils, J.; Duquerroy, S.; Janin, J. Protein-protein recognition analyzed by docking simulation. Proteins Struct. Funct. Bioinform. 1991, 11, 271–280. [Google Scholar] [CrossRef] [PubMed]
- Vidal, D.; Garcia-Serna, R.; Mestres, J. Ligand-Based Approaches to In Silico Pharmacology. Methods Mol. Biol. 2010, 672, 489–502. [Google Scholar]
- Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13, 2498–2504. [Google Scholar] [CrossRef]
- Xue, R.; Fang, Z.; Zhang, M.; Yi, Z.; Wen, C.; Shi, T. TCMID: Traditional Chinese medicine integrative database for herb molecular mechanism analysis. Nucleic Acids Res. 2013, 41, 1089–1095. [Google Scholar] [CrossRef]
- Chen, C.Y.C. TCM Database@Taiwan: The world’s largest traditional Chinese medicine database for drug screening In Silico. PLoS ONE 2011, 6, e15939. [Google Scholar] [CrossRef]
- Liu, X.; Ouyang, S.; Yu, B.; Liu, Y.; Huang, K.; Gong, J.; Zheng, S.; Li, Z.; Li, H.; Jiang, H. PharmMapper server: A web server for potential drug target identification using pharmacophore mapping approach. Nucleic Acids Res. 2010, 38, 5–7. [Google Scholar] [CrossRef]
- Kuhn, M.; von Mering, C.; Campillos, M.; Jensen, L.J.; Bork, P. STITCH: Interaction networks of chemicals and proteins. Nucleic Acids Res. 2008, 36, 684–688. [Google Scholar] [CrossRef]
- Chen, X.; Ji, Z.L.; Chen, Y.Z. TTD: Therapeutic Target Database. Nucleic Acids Res. 2002, 30, 412–415. [Google Scholar] [CrossRef] [Green Version]
- Keiser, M.J.; Roth, B.L.; Armbruster, B.N.; Ernsberger, P.; Irwin, J.J.; Shoichet, B.K. Relating protein pharmacology by ligand chemistry. Nat. Biotechnol. 2007, 25, 197–206. [Google Scholar] [CrossRef] [Green Version]
- Ye, H.; Ye, L.; Kang, H.; Zhang, D.; Tao, L.; Tang, K.; Liu, X.; Zhu, R.; Liu, Q.; Chen, Y.Z.; et al. HIT: Linking herbal active ingredients to targets. Nucleic Acids Res. 2011, 39, 1055–1059. [Google Scholar] [CrossRef]
- Wishart, D.S. DrugBank: A comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 2005, 34, D668–D672. [Google Scholar] [CrossRef]
- Ogata, H.; Goto, S.; Sato, K.; Fujibuchi, W.; Bono, H.; Kanehisa, M. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 1999, 28, 27–30. [Google Scholar] [CrossRef]
- Harris, M.A.; Clark, J.; Ireland, A.; Lomax, J.; Ashburner, M.; Foulger, R.; Eilbeck, K.; Lewis, S.; Marshall, B.; Mungall, C.; et al. The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res. 2004, 32, D258–D261. [Google Scholar]
- Hamosh, A.; Scott, A.F.; Amberger, J.S.; Bocchini, C.A.; McKusick, V.A. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 2005, 33, D514–D517. [Google Scholar] [CrossRef]
- Hewett, M.; Oliver, D.E.; Rubin, D.L.; Easton, K.L.; Stuart, J.M.; Altman, R.B.; Klein, T.E. PharmGKB: The pharmacogenetics knowledge base. Nucleic Acids Res. 2002, 30, 163–165. [Google Scholar] [CrossRef]
- Safran, M.; Solomon, I.; Shmueli, O.; Lapidot, M.; Shen-Orr, S.; Adato, A.; Ben-Dor, U.; Esterman, N.; Rosen, N.; Peter, I.; et al. GeneCardsTM 2002: Towards a complete, object-oriented, human gene compendium. Bioinformatics 2002, 18, 1542–1543. [Google Scholar] [CrossRef]
- Wang, X.; Xu, X.; Tao, W.; Li, Y.; Wang, Y.; Yang, L. A Systems Biology Approach to Uncovering Pharmacological Synergy in Herbal Medicines with Applications to Cardiovascular Disease. Evid. Based Complement. Altern. Med. 2012, 2012, 519031. [Google Scholar] [CrossRef]
- Vistoli, G.; Pedretti, A.; Testa, B. Assessing drug-likeness—What are we missing? Drug Discov. Today 2008, 13, 285–294. [Google Scholar] [CrossRef]
- Kanehisa, M.; Furumichi, M.; Tanabe, M.; Sato, Y.; Morishima, K. KEGG: New perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017, 45, D353–D361. [Google Scholar] [CrossRef]
- Wishart, D.S.; Feunang, Y.D.; Guo, A.C.; Lo, E.J.; Marcu, A.; Grant, J.R.; Sajed, T.; Johnson, D.; Li, C.; Sayeeda, Z.; et al. DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Res. 2018, 46, D1074–D1082. [Google Scholar] [CrossRef]
- Soundararajan, K.; Ho, H.K.; Su, B. Sankey diagram framework for energy and exergy flows. Appl. Energy 2014, 136, 1035–1042. [Google Scholar] [CrossRef]
- Liu, Z.; Guo, F.; Wang, Y.; Li, C.; Zhang, X.; Li, H.; Diao, L.; Gu, J.; Wang, W.; Li, D.; et al. BATMAN-TCM: A Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine. Sci. Rep. 2016, 6, 21146. [Google Scholar] [CrossRef]
- Zhang, R.Z.; Yu, S.J.; Bai, H.; Ning, K. TCM-Mesh: The database and analytical system for network pharmacology analysis for TCM preparations. Sci. Rep. 2017, 7, 2821. [Google Scholar] [CrossRef]
- Zhao, S.; Li, S. Network-based relating pharmacological and genomic spaces for drug target identification. PLoS ONE 2010, 5, e11764. [Google Scholar] [CrossRef]
- Wu, Z.; Lu, W.; Wu, D.; Luo, A.; Bian, H.; Li, J.; Li, W.; Liu, G.; Huang, J.; Cheng, F.; et al. In silico prediction of chemical mechanism of action via an improved network-based inference method. Br. J. Pharmacol. 2016, 173, 3372–3385. [Google Scholar] [CrossRef]
- Wang, X.; Shen, Y.; Wang, S.; Li, S.; Zhang, W.; Liu, X.; Lai, L.; Pei, J.; Li, H. PharmMapper 2017 update: A web server for potential drug target identification with a comprehensive target pharmacophore database. Nucleic Acids Res. 2017, 45, W356–W360. [Google Scholar] [CrossRef]
- Szklarczyk, D.; Santos, A.; Von Mering, C.; Jensen, L.J.; Bork, P.; Kuhn, M. STITCH 5: Augmenting protein-chemical interaction networks with tissue and affinity data. Nucleic Acids Res. 2016, 44, D380–D384. [Google Scholar] [CrossRef]
- Bahi, M.; Batouche, M. Deep semi-supervised learning for DTI prediction using large datasets and H2O-spark platform. In Proceedings of the 2018 International Conference on Intelligent Systems and Computer Vision (ISCV), Fez, Morocco, 2–4 April 2018; pp. 1–7. [Google Scholar]
- Wen, M.; Zhang, Z.; Niu, S.; Sha, H.; Yang, R.; Yun, Y.; Lu, H. Deep-Learning-Based Drug-Target Interaction Prediction. J. Proteome Res. 2017, 16, 1401–1409. [Google Scholar] [CrossRef]
Name | Providing Information | Description | Website | PMID (Reference) | ||
---|---|---|---|---|---|---|
H-C | C-T | TI | ||||
TCMSP | ○ | ○ | ○ | A system of pharmacology platforms that provide information about ingredients, ADME-related properties, targets, and diseases of herbal medicines. | http://lsp.nwu.edu.cn/tcmsp.php | 24735618 [26] |
TCMID | ○ | ○ | ○ | An integrative database which stores the information of herbs, herbal compounds, targets, and their related information from different resources and through text-mining method | http://www.megabionet.org/tcmid/ | 23203875 [17] |
TCM Databasetaiwan | ○ | A database that includes the information of molecular properties and substructures, TCM ingredients with their 2D and 3D structures. | http://tcm.cmu.edu.tw/ | 21253603 [27] | ||
PharmMapper | ○ | A web server for potential drug target identification by reversed pharmacophore matching the query compound against an in-house pharmacophore model database | http://lilab.ecust.edu.cn/pharmmapper/ | 20430828 [28] | ||
STITCH | ○ | A database that integrates disparate data sources of interactions between proteins and small molecules | http://stitch.embl.de/ | 18084021 [29] | ||
TTD | ○ | ○ | A database that provides information about the therapeutic targets in the literature, targeted disease condition, and the corresponding drugs/ligands directed at each of these targets. | http://xin.cz3.nus.edu.sg/group/ttd/ttd.asp | 11752352 [30] | |
SEA | ○ | A computational tool that relates proteins and chemicals based on the set-wise chemical similarity among their ligands. | http://sea.bkslab.org/ | 17287757 [31] | ||
HIT | ○ | ○ | A comprehensive and fully curated database for herbal ingredients with protein target information | http://lifecenter.sgst.cn/hit/ | 21097881 [32] | |
Drugbank | ○ | ○ | A unique bioinformatics and cheminformatics resource that combines detailed drug data with comprehensive drug target information | https://www.drugbank.ca/ | 16381955 [33] | |
KEGG | ○ | A database resource for understanding high-level functions and utilities of the biological system from molecular-level information | https://www.genome.jp/kegg/ | 9847135 [34] | ||
Gene ontology | ○ | The world’s largest source of information on the functions of genes | http://geneontology.org/ | 18792943 [35] | ||
OMIM | ○ | A comprehensive and authoritative compendium of human genes and genetic phenotypes | https://www.omim.org/ | 11752252 [36] | ||
PharmGkb | ○ | A database for the aggregation, curation, integration, and dissemination of knowledge regarding the impact of human genetic variation on drug response | https://www.pharmgkb.org/ | 11752281 [37] | ||
Genecards | ○ | A searchable and integrated database of human genes that provides concise genomic related information, on all known and predicted human genes. | https://www.genecards.org/ | 12424129 [38] |
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Lee, W.-Y.; Lee, C.-Y.; Kim, Y.-S.; Kim, C.-E. The Methodological Trends of Traditional Herbal Medicine Employing Network Pharmacology. Biomolecules 2019, 9, 362. https://doi.org/10.3390/biom9080362
Lee W-Y, Lee C-Y, Kim Y-S, Kim C-E. The Methodological Trends of Traditional Herbal Medicine Employing Network Pharmacology. Biomolecules. 2019; 9(8):362. https://doi.org/10.3390/biom9080362
Chicago/Turabian StyleLee, Won-Yung, Choong-Yeol Lee, Youn-Sub Kim, and Chang-Eop Kim. 2019. "The Methodological Trends of Traditional Herbal Medicine Employing Network Pharmacology" Biomolecules 9, no. 8: 362. https://doi.org/10.3390/biom9080362