Network Pharmacology Approach for Medicinal Plants: Review and Assessment
Abstract
:1. Introduction
2. Network Biology to Network Pharmacology
3. Network Pharmacology and Traditional Medicine
4. From Polypharmacology to Network Pharmacology: The Need to Reengineer Botanical Drugs
5. Methodology of Network Pharmacology Research
5.1. Data Mining
5.2. Network Construction and Analysis
5.3. Validation of Results
6. Research Approaches of Network Pharmacology
7. From Network Pharmacology to Integrated Multi-Omics Approaches
8. Merging the Molecular Disease Network with the Pharmacological Network of the Candidate Drugs
9. Implications of Network Pharmacology for Therapy
- If the potentially active compounds of herbs or herbal mixes are identified, they can be considered. This technique is mostly made based on their use in herbal medicine. Herbal formulations are similar to multi-drug targeted therapy [51].
- Proteins that are not required in normal cells could become therapeutically important if they’re linked together in a cancer network. Their simultaneous eradication or inhibition could result in more effective or even synergistic tumor cell eradication. What makes perfect sense in the human physiological process is to create significant therapies options. A potential answer to this difficulty could be to use polypharmacology to disrupt whole disease-causing networks using botanicals or sophisticated herbal mixes that target numerous targets, rather than knocking out specific proteins [150,151].
10. Databases and Data Analysis Tools Related to Network Pharmacology
11. Application of Network Pharmacology: From Understanding of Complex Interactomes to the Design of Multi-Target Specific Therapeutics from Nature
11.1. A Pneumonia Outbreak Associated with a New Coronavirus of Probable Bat Origin
11.2. Cancer
11.3. Cardio-Cerebrovascular Diseases (CCVDs)
11.4. Diabetes Mellitus
11.5. Neurodegenerative Diseases
12. Limitation and Solution
13. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
- Noor, F.; Ashfaq, U.A.; Javed, M.R.; Saleem, M.H.; Ahmad, A.; Aslam, M.F.; Aslam, S. Comprehensive computational analysis reveals human respiratory syncytial virus encoded microRNA and host specific target genes associated with antiviral immune responses and protein binding. J. King Saud Univ. Sci. 2021, 33, 101562. [Google Scholar] [CrossRef]
- Xin, W.; Zi-Yi, W.; Zheng, J.-H.; Shao, L. TCM network pharmacology: A new trend towards combining computational, experimental and clinical approaches. Chin. J. Nat. Med. 2021, 19, 1–11. [Google Scholar]
- Gertsch, J. Botanical drugs, synergy, and network pharmacology: Forth and back to intelligent mixtures. Planta Med. 2011, 77, 1086–1098. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zuo, H.-l.; Lin, Y.-C.-D.; Huang, H.-Y.; Wang, X.; Tang, Y.; Hu, Y.-j.; Kong, X.-j.; Chen, Q.-j.; Zhang, Y.-z.; Hong, H.-C. The challenges and opportunities of traditional Chinese medicines against COVID-19: A way out from a network perspective. Acta Pharmacol. Sin. 2021, 42, 845–847. [Google Scholar] [CrossRef]
- Noor, F.; Saleem, M.H.; Aslam, M.F.; Ahmad, A.; Aslam, S. Construction of miRNA-mRNA network for the identification of key biological markers and their associated pathways in IgA nephropathy by employing the integrated bioinformatics analysis. Saudi J. Biol. Sci. 2021, 28, 4938–4945. [Google Scholar] [CrossRef]
- Pal, S.K.; Shukla, Y. Herbal medicine: Current status and the future. Asian Pac J. Cancer Prev. 2003, 4, 281–288. [Google Scholar]
- Rehman, A.; Ashfaq, U.A.; Shahid, F.; Noor, F.; Aslam, S. The Screening of phytochemicals against NS5 Polymerase to treat Zika Virus infection: Integrated computational based approach. Comb. Chem. High Through. Screen. 2021, 25, 738–751. [Google Scholar] [CrossRef]
- Tan, N.; Gwee, K.A.; Tack, J.; Zhang, M.; Li, Y.; Chen, M.; Xiao, Y. Herbal medicine in the treatment of functional gastrointestinal disorders: A systematic review with meta-analysis. J. Gastroenterol Hepatol. 2020, 35, 544–556. [Google Scholar] [CrossRef]
- Shao, L.; Zhang, B. Traditional Chinese medicine network pharmacology: Theory, methodology and application. Chin. J. Nat. Med. 2013, 11, 110–120. [Google Scholar]
- Casas, A.I.; Hassan, A.A.; Larsen, S.J.; Gomez-Rangel, V.; Elbatreek, M.; Kleikers, P.W.; Guney, E.; Egea, J.; López, M.G.; Baumbach, J.J. From single drug targets to synergistic network pharmacology in ischemic stroke. Proc. Natl. Acad. Sci. USA 2019, 116, 7129–7136. [Google Scholar] [CrossRef] [Green Version]
- 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]
- Zhang, G.-B.; Li, Q.-Y.; Chen, Q.-I.; Su, S.-B. Network pharmacology: A new approach for Chinese herbal medicine research. Evid.-Based Complement. Altern. Med. 2013, 2013, 621423. [Google Scholar] [CrossRef] [Green Version]
- Hopkins, A.L. Network pharmacology: The next paradigm in drug discovery. Nature chemical biology 2008, 4, 682–690. [Google Scholar] [CrossRef] [PubMed]
- Dong, Y.; Hao, L.; Fang, K.; Han, X.-x.; Yu, H.; Zhang, J.-j.; Cai, L.-j.; Fan, T.; Zhang, W.-d.; Pang, K. A network pharmacology perspective for deciphering potential mechanisms of action of Solanum nigrum L. in bladder cancer. BMC Complement. Med. Ther. 2021, 21, 1–14. [Google Scholar] [CrossRef] [PubMed]
- Li, W.; Yuan, G.; Pan, Y.; Wang, C.; Chen, H. Network pharmacology studies on the bioactive compounds and action mechanisms of natural products for the treatment of diabetes mellitus: A review. Front. Pharmacol. 2017, 8, 74. [Google Scholar] [CrossRef] [Green Version]
- Li, J.-X.; Li, R.-Z.; Sun, A.; Zhou, H.; Neher, E.; Yang, J.-S.; Huang, J.-M.; Zhang, Y.-Z.; Jiang, Z.-B.; Liang, T.-L. Metabolomics and integrated network pharmacology analysis reveal Tricin as the active anti-cancer component of Weijing decoction by suppression of PRKCA and sphingolipid signaling. Pharmacol. Res. 2021, 171, 105574. [Google Scholar] [CrossRef]
- Qu, F.; Xu, Q.; Pelkonen, O. Chapter Network Pharmacology and Traditional Chinese Medicine. In Alternative Medicine; Sakagami, H., Ed.; IntechOpen: London, UK, 2012; Chapter 12. [Google Scholar] [CrossRef] [Green Version]
- Chandran, U.; Mehendale, N.; Patil, S.; Chaguturu, R.; Patwardhan, B. Network pharmacology. Innov. Approaches Drug Dis. 2017, 127–164. [Google Scholar]
- Pan, S.Y.; Pan, S.; Yu, Z.-L.; Ma, D.-L.; Chen, S.-B.; Fong, W.-F.; Han, Y.-F.; Ko, K.-M. New perspectives on innovative drug discovery: An overview. J. Pharm. Pharm. Sci. 2010, 13, 450–471. [Google Scholar] [CrossRef] [Green Version]
- Malas, T.B.; Kudrin, R.; Starikov, S.; ‘t Hoen, P.A.; Peters, D.J.; Roos, M.; Hettne, K.M. Drug repurposing using a semantic knowledge graph. Data Driven Knowl. Discov. Polycyst. Kidney 2021, 75. [Google Scholar]
- Noor, F.; Noor, A.; Ishaq, A.R.; Farzeen, I.; Saleem, M.H.; Ghaffar, K.; Aslam, M.F.; Aslam, S.; Chen, J.-T. Recent Advances in Diagnostic and Therapeutic Approaches for Breast Cancer: A Comprehensive Review. Cur. Pharm. Des. 2021, 27, 2344–2365. [Google Scholar] [CrossRef]
- Bergendahl, L.T.; Gerasimavicius, L.; Miles, J.; Macdonald, L.; Wells, J.N.; Welburn, J.P.; Marsh, J.A. The role of protein complexes in human genetic disease. Protein Sci. 2019, 28, 1400–1411. [Google Scholar] [CrossRef] [PubMed]
- Wang, X.; Gulbahce, N.; Yu, H. Network-based methods for human disease gene prediction. Brief. Funct. Genom. 2011, 10, 280–293. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Azmi, A.S.; Wang, Z.; Philip, P.A.; Mohammad, R.M.; Sarkar, F.H. Proof of concept: Network and systems biology approaches aid in the discovery of potent anticancer drug combinations. Mol. Cancer Ther. 2010, 9, 3137–3144. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Schrattenholz, A.; Soskic, V. What does systems biology mean for drug development? Cur. Med. Chem. 2008, 15, 1520–1528. [Google Scholar] [CrossRef]
- Anighoro, A.; Bajorath, J.; Rastelli, G. Polypharmacology: Challenges and opportunities in drug discovery: Miniperspective. J. Med. Chem. 2014, 57, 7874–7887. [Google Scholar] [CrossRef]
- Peters, J.-U. Polypharmacology–foe or friend? J. Med. Chem. 2013, 56, 8955–8971. [Google Scholar] [CrossRef]
- S Azmi, A. Adopting network pharmacology for cancer drug discovery. Cur. Drug Discov. Technol. 2013, 10, 95–105. [Google Scholar] [CrossRef]
- Stępnicki, P.; Kondej, M.; Koszła, O.; Żuk, J.; Kaczor, A.A. Multi-targeted drug design strategies for the treatment of schizophrenia. Expert Opin. Drug Discov. 2021, 16, 101–114. [Google Scholar] [CrossRef]
- Achenbach, J.; Tiikkainen, P.; Franke, L.; Proschak, E. Computational tools for polypharmacology and repurposing. Futur. Med. Chem 2011, 3, 961–968. [Google Scholar] [CrossRef]
- Schippmann, U.; Leaman, D.J.; Cunningham, A. Impact of cultivation and gathering of medicinal plants on biodiversity: Global trends and issues. In Biodiversity and the Ecosystem Approach in Agriculture, Forestry and Fisheries; Food and Agriculture Organization: Rome, Italy, 2002; pp. 142–167. [Google Scholar]
- Lambert, J.; Srivastava, J.; Vietmeyer, N. Medicinal Plants: Rescuing a Global Heritage; World Bank Publications: Washington, DC, USA, 1997; Volume 355. [Google Scholar]
- Verma, S.; Singh, S. Current and future status of herbal medicines. Vet. World 2008, 1, 347. [Google Scholar] [CrossRef]
- Sahil, K.; Sudeep, B.; Akanksha, M. Standardization of medicinal plant materials. Int. J. Res. Ayurveda Pharm. 2011, 2, 1100–1109. [Google Scholar]
- Gurib-Fakim, A. Medicinal plants: Traditions of yesterday and drugs of tomorrow. Mol. Asp. Med. 2006, 27, 1–93. [Google Scholar] [CrossRef] [PubMed]
- Bahmani, M.; Sarrafchi, A.; Shirzad, H.; Rafieian-Kopaei, M. Autism: Pathophysiology and promising herbal remedies. Cur. Pharm. Des. 2016, 22, 277–285. [Google Scholar] [CrossRef] [PubMed]
- Patwardhan, B.; Vaidya, A.D.; Chorghade, M. Ayurveda and natural products drug discovery. Curr. sci. 2004, 86, 789–799. [Google Scholar]
- Zhou, X.; Seto, S.W.; Chang, D.; Kiat, H.; Razmovski-Naumovski, V.; Chan, K.; Bensoussan, A. Synergistic effects of Chinese herbal medicine: A comprehensive review of methodology and current research. Front. Pharmacol. 2016, 7, 201. [Google Scholar] [CrossRef] [Green Version]
- Patwardhan, B. Ayurveda: The designer medicine. Indian drugs 2000, 37, 213–227. [Google Scholar]
- Yuan, H.; Ma, Q.; Ye, L.; Piao, G. The traditional medicine and modern medicine from natural products. Molecules 2016, 21, 559. [Google Scholar] [CrossRef] [Green Version]
- Huffman, B.J.; Shenvi, R.A. Natural products in the “marketplace”: Interfacing synthesis and biology. J. Am. Chem. Soc. 2019, 141, 3332–3346. [Google Scholar] [CrossRef]
- Newman, D.J.; Cragg, G.M.; Kingston, D.G. Natural products as pharmaceuticals and sources for lead structures. In The Practice of Medicinal Chemistry; Academic Press: Cambridge, MA, USA, 2008; Chapter 8; pp. 159–186. [Google Scholar]
- Du, H.; Zhao, X.; Zhang, A. Identifying potential therapeutic targets of a natural product Jujuboside B for insomnia through network pharmacology. Plant Sci. Today 2014, 1, 69–79. [Google Scholar] [CrossRef]
- Wu, L.; Wang, Y.; Nie, J.; Fan, X.; Cheng, Y. A network pharmacology approach to evaluating the efficacy of Chinese medicine using genome-wide transcriptional expression data. Evid. -Based Complement. Altern. Med. 2013, 2013, 915343. [Google Scholar] [CrossRef] [Green Version]
- Zuo, J.; Wang, X.; Liu, Y.; Ye, J.; Liu, Q.; Li, Y.; Li, S. Integrating network pharmacology and metabolomics study on anti-rheumatic mechanisms and antagonistic effects against methotrexate-induced toxicity of Qing-Luo-Yin. Front. Pharmacol. 2018, 9, 1472. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Yang, H.; Zhang, W.; Huang, C.; Zhou, W.; Yao, Y.; Wang, Z.; Li, Y.; Xiao, W.; Wang, Y. A novel systems pharmacology model for herbal medicine injection: A case using reduning injection. BMC Complement. Altern. Med. 2014, 14, 1–19. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hao, H.; Zheng, X.; Wang, G. Insights into drug discovery from natural medicines using reverse pharmacokinetics. Trends Pharmacol. Sci. 2014, 35, 168–177. [Google Scholar] [CrossRef] [PubMed]
- Emig, D.; Ivliev, A.; Pustovalova, O.; Lancashire, L.; Bureeva, S.; Nikolsky, Y.; Bessarabova, M. Drug target prediction and repositioning using an integrated network-based approach. PloS ONE 2013, 8, e60618. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lotfi Shahreza, M.; Ghadiri, N.; Mousavi, S.R.; Varshosaz, J.; Green, J.R. A review of network-based approaches to drug repositioning. Brief. Bioinform. 2018, 19, 878–892. [Google Scholar] [CrossRef]
- Kotlyar, M.; Fortney, K.; Jurisica, I. Network-based characterization of drug-regulated genes, drug targets, and toxicity. Methods 2012, 57, 499–507. [Google Scholar] [CrossRef] [PubMed]
- Hao, D.C.; Xiao, P.G. Network Pharmacology: A Rosetta Stone for Traditional C hinese Medicine. Drug Dev. Res. 2014, 75, 299–312. [Google Scholar] [CrossRef]
- Mao, Y.; Hao, J.; Jin, Z.-Q.; Niu, Y.-Y.; Yang, X.; Liu, D.; Cao, R.; Wu, X.-Z. Network pharmacology-based and clinically relevant prediction of the active ingredients and potential targets of Chinese herbs in metastatic breast cancer patients. Oncotarget 2017, 8, 27007. [Google Scholar] [CrossRef] [Green Version]
- Yu, G.; Zhang, Y.; Ren, W.; Dong, L.; Li, J.; Geng, Y.; Zhang, Y.; Li, D.; Xu, H.; Yang, H. Network pharmacology-based identification of key pharmacological pathways of Yin–Huang–Qing–Fei capsule acting on chronic bronchitis. Int. J. Chronic Obstr. Pulm. Dis. 2017, 12, 85. [Google Scholar] [CrossRef] [Green Version]
- Zhang, Y.-q.; Mao, X.; Guo, Q.-y.; Lin, N.; Li, S. Network pharmacology-based approaches capture essence of Chinese herbal medicines. Chin. Herb. Med. 2016, 8, 107–116. [Google Scholar] [CrossRef]
- Zuo, H.; Zhang, Q.; Su, S.; Chen, Q.; Yang, F.; Hu, Y. A network pharmacology-based approach to analyse potential targets of traditional herbal formulas: An example of Yu Ping Feng decoction. Sci. Rep. 2018, 8, 1–15. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Li, S. Exploring traditional chinese medicine by a novel therapeutic concept of network target. Chin. J. Integr. Med. 2016, 22, 647–652. [Google Scholar] [CrossRef] [PubMed]
- Zhang, W.; Chen, Y.; Jiang, H.; Yang, J.; Wang, Q.; Du, Y.; Xu, H. Integrated strategy for accurately screening biomarkers based on metabolomics coupled with network pharmacology. Talanta 2020, 211, 120710. [Google Scholar] [CrossRef] [PubMed]
- Patwardhan, B.; Vaidya, A.D.; Chorghade, M.; Joshi, S.P. Reverse pharmacology and systems approaches for drug discovery and development. Cur. Bioac. Compd. 2008, 4, 201–212. [Google Scholar] [CrossRef]
- Mann, D.L.; Barger, P.M.; Burkhoff, D. Myocardial recovery and the failing heart: Myth, magic, or molecular target? J. Control. Release 2012, 60, 2465–2472. [Google Scholar]
- Bae, Y.H.; Park, K. Targeted drug delivery to tumors: Myths, reality and possibility. J. Control. Release 2011, 153, 198. [Google Scholar] [CrossRef] [Green Version]
- Fitzgerald, J.B.; Schoeberl, B.; Nielsen, U.B.; Sorger, P.K. Systems biology and combination therapy in the quest for clinical efficacy. Nat. Chem. Biol. 2006, 2, 458–466. [Google Scholar] [CrossRef]
- Ji, H.F.; Li, X.J.; Zhang, H.Y. Natural products and drug discovery: Can thousands of years of ancient medical knowledge lead us to new and powerful drug combinations in the fight against cancer and dementia? EMBO Rep. 2009, 10, 194–200. [Google Scholar] [CrossRef] [Green Version]
- Hopkins, A.L. Network pharmacology. Nat. Biotechnol. 2007, 25, 1110–1111. [Google Scholar] [CrossRef]
- Medina-Franco, J.L.; Giulianotti, M.A.; Welmaker, G.S.; Houghten, R.A. Shifting from the single to the multitarget paradigm in drug discovery. Drug Discov. Today 2013, 18, 495–501. [Google Scholar] [CrossRef] [Green Version]
- Chaudhari, R.; Tan, Z.; Huang, B.; Zhang, S. Computational polypharmacology: A new paradigm for drug discovery. Expert Opin. Drug Discov. 2017, 12, 279–291. [Google Scholar] [CrossRef] [PubMed]
- Reddy, A.S.; Zhang, S. Polypharmacology: Drug discovery for the future. Expert Rev. Clin. Pharmacol. 2013, 6, 41–47. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Cichonska, A.; Rousu, J.; Aittokallio, T. Identification of drug candidates and repurposing opportunities through compound–target interaction networks. Expert Opin. Drug Discov. 2015, 10, 1333–1345. [Google Scholar] [CrossRef] [PubMed]
- Karuppasamy, R.; Veerappapillai, S.; Maiti, S.; Shin, W.-H.; Kihara, D. Current progress and future perspectives of polypharmacology: From the view of non-small cell lung cancer. In Seminars in Cancer Biology; Academic Press: Cambridge, MA, USA, 2021; pp. 84–91. [Google Scholar]
- Duarte, Y.; Márquez-Miranda, V.; Miossec, M.J.; González-Nilo, F. Integration of target discovery, drug discovery and drug delivery: A review on computational strategies. Wiley Interdiscip. Rev. Nanomed. Nanobiotechnol. 2019, 11, e1554. [Google Scholar] [CrossRef]
- Mishra, R.; Aneesh, T. Combination Vs. Multi-target drugs: The Clash of the titans in the arena of drug discovery; An overview and in silico evaluation. Res. J. Pharm. Technol. 2021, 14, 4455–4462. [Google Scholar] [CrossRef]
- Palve, V.; Liao, Y.; Rix, L.L.R.; Rix, U. Turning liabilities into opportunities: Off-target based drug repurposing in cancer. In Seminars in Cancer Biology; Academic Press: Cambridge, MA, USA, 2021; pp. 209–229. [Google Scholar]
- Ekins, S.; Mestres, J.; Testa, B. In silico pharmacology for drug discovery: Methods for virtual ligand screening and profiling. Br. J. Pharmacol. 2007, 152, 9–20. [Google Scholar] [CrossRef] [Green Version]
- Ekins, S.; Mestres, J.; Testa, B. In silico pharmacology for drug discovery: Applications to targets and beyond. Br. J. Pharmacol. 2007, 152, 21–37. [Google Scholar] [CrossRef] [Green Version]
- Niu, W.-h.; Wu, F.; Cao, W.-y.; Wu, Z.-g.; Chao, Y.-C.; Peng, F.; Liang, C. Network pharmacology for the identification of phytochemicals in traditional Chinese medicine for COVID-19 that may regulate interleukin-6. Biosci. Rep. 2021, 41, BSR20202583. [Google Scholar] [CrossRef]
- Gao, X.; Li, S.; Cong, C.; Wang, Y.; Xu, L. A Network Pharmacology Approach to Estimate Potential Targets of the Active Ingredients of Epimedium for Alleviating Mild Cognitive Impairment and Treating Alzheimer’s Disease. Evid. -Based Complement. Altern. Med. 2021, 2021. [Google Scholar] [CrossRef]
- Zhang, Y.; Yuan, T.; Li, Y.; Wu, N.; Dai, X. Network pharmacology analysis of the mechanisms of compound Herba Sarcandrae (Fufang Zhongjiefeng) aerosol in chronic pharyngitis treatment. Drug Des. Dev. Ther. 2021, 15, 2783. [Google Scholar] [CrossRef]
- Niemira, M.; Collin, F.; Szalkowska, A.; Bielska, A.; Chwialkowska, K.; Reszec, J.; Niklinski, J.; Kwasniewski, M.; Kretowski, A. Molecular signature of subtypes of non-small-cell lung cancer by large-scale transcriptional profiling: Identification of key modules and genes by weighted gene co-expression network analysis (WGCNA). Cancers 2020, 12, 37. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Liang, X.; Li, H.; Li, S. A novel network pharmacology approach to analyse traditional herbal formulae: The Liu-Wei-Di-Huang pill as a case study. Mol. BioSystems 2014, 10, 1014–1022. [Google Scholar] [CrossRef] [PubMed]
- Luo, T.-t.; Lu, Y.; Yan, S.-k.; Xiao, X.; Rong, X.-l.; Guo, J. Network pharmacology in research of Chinese medicine formula: Methodology, application and prospective. Chin. J. Integr. Med. 2020, 26, 72–80. [Google Scholar] [CrossRef] [PubMed]
- Li, J.; Ma, X.; Liu, C.; Li, H.; Zhuang, J.; Gao, C.; Zhou, C.; Liu, L.; Wang, K.; Sun, C. Exploring the mechanism of danshen against myelofibrosis by network pharmacology and molecular docking. Evid. Based Complement. Altern. Med. 2018, 2018. [Google Scholar] [CrossRef] [PubMed]
- Shi, S.-h.; Cai, Y.-p.; Cai, X.-j.; Zheng, X.-y.; Cao, D.-s.; Ye, F.-q.; Xiang, Z. A network pharmacology approach to understanding the mechanisms of action of traditional medicine: Bushenhuoxue formula for treatment of chronic kidney disease. PLoS ONE 2014, 9, e89123. [Google Scholar] [CrossRef]
- Ge, Q.; Chen, L.; Tang, M.; Zhang, S.; Liu, L.; Gao, L.; Ma, S.; Kong, M.; Yao, Q.; Feng, F. Analysis of mulberry leaf components in the treatment of diabetes using network pharmacology. Eur. J. Pharmacol. 2018, 833, 50–62. [Google Scholar] [CrossRef]
- Panossian, A.G.; Hamm, R.; Kadioglu, O.; Wikman, G.C.; Efferth, T. Synergy and antagonism of active constituents of ADAPT-232 on transcriptional level of metabolic regulation of isolated neuroglial cells. Front. Neurosci. 2013, 7, 16. [Google Scholar] [CrossRef] [Green Version]
- Panossian, A.; Hamm, R.; Wikman, G.; Efferth, T. Mechanism of action of Rhodiola, salidroside, tyrosol and triandrin in isolated neuroglial cells: An interactive pathway analysis of the downstream effects using RNA microarray data. Phytomedicine 2014, 21, 1325–1348. [Google Scholar] [CrossRef]
- Panossian, A.; Seo, E.-J.; Wikman, G.; Efferth, T. Synergy assessment of fixed combinations of Herba Andrographidis and Radix Eleutherococci extracts by transcriptome-wide microarray profiling. Phytomedicine 2015, 22, 981–992. [Google Scholar] [CrossRef] [Green Version]
- Panossian, A.; Seo, E.-J.; Efferth, T. Novel molecular mechanisms for the adaptogenic effects of herbal extracts on isolated brain cells using systems biology. Phytomedicine 2018, 50, 257–284. [Google Scholar] [CrossRef]
- Seo, E.-J.; Efferth, T.; Panossian, A. Curcumin downregulates expression of opioid-related nociceptin receptor gene (OPRL1) in isolated neuroglia cells. Phytomedicine 2018, 50, 285–299. [Google Scholar] [CrossRef] [PubMed]
- Seo, E.-J.; Klauck, S.M.; Efferth, T.; Panossian, A. Adaptogens in chemobrain (Part I): Plant extracts attenuate cancer chemotherapy-induced cognitive impairment–Transcriptome-wide microarray profiles of neuroglia cells. Phytomedicine 2019, 55, 80–91. [Google Scholar] [CrossRef] [PubMed]
- Seo, E.-J.; Klauck, S.M.; Efferth, T.; Panossian, A. Adaptogens in chemobrain (Part III): Antitoxic effects of plant extracts towards cancer chemotherapy-induced toxicity-transcriptome-wide microarray analysis of neuroglia cells. Phytomedicine 2019, 56, 246–260. [Google Scholar] [CrossRef]
- Panossian, A.; Seo, E.-J.; Efferth, T. Effects of anti-inflammatory and adaptogenic herbal extracts on gene expression of eicosanoids signaling pathways in isolated brain cells. Phytomedicine 2019, 60, 152881. [Google Scholar] [CrossRef] [PubMed]
- Panossian, A.; Seo, E.-J.; Klauck, S.M.; Efferth, T. Adaptogens in chemobrain (part IV): Adaptogenic plants prevent the chemotherapeutics-induced imbalance of redox homeostasis by modulation of expression of genes encoding Nrf2-mediated signaling proteins and antioxidant, metabolizing, detoxifying enzymes in neuroglia cells. Longhua Chin. Med. 2020, 3, 1–13. [Google Scholar]
- Panossian, A.; Abdelfatah, S.; Efferth, T. Network pharmacology of ginseng (Part II): The differential effects of red ginseng and ginsenoside Rg5 in cancer and heart diseases as determined by transcriptomics. Pharmaceuticals 2021, 14, 1010. [Google Scholar] [CrossRef]
- Panossian, A.; Abdelfatah, S.; Efferth, T. Network pharmacology of Red Ginseng (Part I): Effects of ginsenoside Rg5 at physiological and sub-physiological concentrations. Pharmaceuticals 2021, 14, 999. [Google Scholar] [CrossRef]
- Li, H.; Zhao, L.; Zhang, B.; Jiang, Y.; Wang, X.; Guo, Y.; Liu, H.; Li, S.; Tong, X. A network pharmacology approach to determine active compounds and action mechanisms of ge-gen-qin-lian decoction for treatment of type 2 diabetes. Evid. Based Complement. Altern. Med. 2014, 2014. [Google Scholar] [CrossRef] [Green Version]
- Zhao, F.; Guochun, L.; Yang, Y.; Shi, L.; Xu, L.; Yin, L. A network pharmacology approach to determine active ingredients and rationality of herb combinations of Modified-Simiaowan for treatment of gout. J. Ethnopharmacol. 2015, 168, 1–16. [Google Scholar] [CrossRef]
- Jiao, X.; Jin, X.; Ma, Y.; Yang, Y.; Li, J.; Liang, L.; Liu, R.; Li, Z. A comprehensive application: Molecular docking and network pharmacology for the prediction of bioactive constituents and elucidation of mechanisms of action in component-based Chinese medicine. Comput. Biol. Chem. 2021, 90, 107402. [Google Scholar] [CrossRef]
- Liu, J.; Lian, X.; Liu, F.; Yan, X.; Cheng, C.; Cheng, L.; Sun, X.; Shi, Z. Identification of novel key targets and candidate drugs in oral squamous cell carcinoma. Curr. Bioinform. 2020, 15, 328–337. [Google Scholar] [CrossRef]
- Alm, E.; Arkin, A.P. Biological networks. Curr. Opin. Struct. Biol. 2003, 13, 193–202. [Google Scholar] [CrossRef]
- Aslam, S.; Ahmad, S.; Noor, F.; Ashfaq, U.A.; Shahid, F.; Rehman, A.; Tahir ul Qamar, M.; Alatawi, E.A.; Alshabrmi, F.M.; Allemailem, K.S. Designing a Multi-Epitope Vaccine against Chlamydia trachomatis by Employing Integrated Core Proteomics, Immuno-Informatics and In Silico Approaches. Biology 2021, 10, 997. [Google Scholar] [CrossRef] [PubMed]
- Tao, Q.; Du, J.; Li, X.; Zeng, J.; Tan, B.; Xu, J.; Lin, W.; Chen, X.-l. Network pharmacology and molecular docking analysis on molecular targets and mechanisms of Huashi Baidu formula in the treatment of COVID-19. Drug Dev. Ind. Pharm. 2020, 46, 1345–1353. [Google Scholar] [CrossRef]
- Hsin, K.-Y.; Ghosh, S.; Kitano, H. Combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology. PloS ONE 2013, 8, e83922. [Google Scholar] [CrossRef] [Green Version]
- Yuan, C.; Wang, M.-H.; Wang, F.; Chen, P.-Y.; Ke, X.-G.; Yu, B.; Yang, Y.-F.; You, P.-T.; Wu, H.-Z. Network pharmacology and molecular docking reveal the mechanism of Scopoletin against non-small cell lung cancer. Life Sci. 2021, 270, 119105. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef] [Green Version]
- Zhang, X.; Shen, T.; Zhou, X.; Tang, X.; Gao, R.; Xu, L.; Wang, L.; Zhou, Z.; Lin, J.; Hu, Y. Network pharmacology based virtual screening of active constituents of Prunella vulgaris L. and the molecular mechanism against breast cancer. Sci. Rep. 2020, 10, 1–12. [Google Scholar] [CrossRef]
- Liu, W.; Fan, Y.; Tian, C.; Jin, Y.; Du, S.; Zeng, P.; Wang, A. Deciphering the molecular targets and mechanisms of HGWD in the treatment of rheumatoid arthritis via network pharmacology and molecular docking. Evid. Based Complement. Altern. Med. 2020, 2020. [Google Scholar] [CrossRef]
- Ruan, X.; Du, P.; Zhao, K.; Huang, J.; Xia, H.; Dai, D.; Huang, S.; Cui, X.; Liu, L.; Zhang, J. Mechanism of Dayuanyin in the treatment of coronavirus disease 2019 based on network pharmacology and molecular docking. Chin. Med. 2020, 15, 1–17. [Google Scholar] [CrossRef]
- Clough, E.; Barrett, T. The gene expression omnibus database. In Statistical Genomics; Springer: Berlin/Heidelberg, Germany, 2016; pp. 93–110. [Google Scholar]
- Hong, W.; Li, S.; Wu, L.; He, B.; Jiang, J.; Chen, Z. Prediction of VEGF-C as a key target of pure total flavonoids from citrus against NAFLD in mice via network pharmacology. Front. Pharmacol. 2019, 10, 582. [Google Scholar] [CrossRef] [PubMed]
- Zhang, G.-b.; Song, Y.-n.; Chen, Q.-l.; Dong, S.; Lu, Y.-y.; Su, M.-y.; Liu, P.; Su, S.-b. Actions of Huangqi decoction against rat liver fibrosis: A gene expression profiling analysis. Chin. Med. 2015, 10, 1–11. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Li, X.-M.; Li, M.-T.; Jiang, N.; Si, Y.-C.; Zhu, M.-M.; Wu, Q.-Y.; Shi, D.-C.; Shi, H.; Luo, Q.; Yu, B. Network Pharmacology-Based Approach to Investigate the Molecular Targets of Sinomenine for Treating Breast Cancer. Cancer Manag. Res. 2021, 13, 1189. [Google Scholar] [CrossRef] [PubMed]
- Cai, F.-F.; Bian, Y.-Q.; Wu, R.; Sun, Y.; Chen, X.-L.; Yang, M.-D.; Zhang, Q.-r.; Hu, Y.; Sun, M.-Y.; Su, S.-B. Yinchenhao decoction suppresses rat liver fibrosis involved in an apoptosis regulation mechanism based on network pharmacology and transcriptomic analysis. Biomed. Pharmacother. 2019, 114, 108863. [Google Scholar] [CrossRef]
- Guo, Q.; Zheng, K.; Fan, D.; Zhao, Y.; Li, L.; Bian, Y.; Qiu, X.; Liu, X.; Zhang, G.; Ma, C. Wu-Tou decoction in rheumatoid arthritis: Integrating network pharmacology and in vivo pharmacological evaluation. Front. Pharmacol. 2017, 8, 230. [Google Scholar] [CrossRef] [Green Version]
- Wang, W.; Liu, T.; Yang, L.; Ma, Y.; Dou, F.; Shi, L.; Wen, A.; Ding, Y. Study on the multi-targets mechanism of triphala on cardio-cerebral vascular diseases based on network pharmacology. Biomed. Pharmacother. 2019, 116, 108994. [Google Scholar] [CrossRef]
- Gustafsdottir, S.M.; Schlingemann, J.; Rada-Iglesias, A.; Schallmeiner, E.; Kamali-Moghaddam, M.; Wadelius, C.; Landegren, U. In vitro analysis of DNA–protein interactions by proximity ligation. Proc. Natl. Acad. Sci. USA 2017, 104, 3067–3072. [Google Scholar] [CrossRef] [Green Version]
- 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]
- Li, Q.; Huang, Z.; Liu, D.; Zheng, J.; Xie, J.; Chen, J.; Zeng, H.; Su, Z.; Li, Y. Effect of berberine on hyperuricemia and kidney injury: A network pharmacology analysis and experimental validation in a mouse model. Drug Des. Dev. Ther. 2021, 15, 3241. [Google Scholar] [CrossRef]
- Cheng, X.; Zhou, W.; Zhanag, Y. Experimental techniques in network pharmacology. Chin. J. Pharmacol. Toxicol. 2012, 26, 131–137. [Google Scholar]
- Edwards, B.S.; Oprea, T.; Prossnitz, E.R.; Sklar, L.A. Flow cytometry for high-throughput, high-content screening. Curr. Opin. Chem. Biol. 2004, 8, 392–398. [Google Scholar] [CrossRef] [PubMed]
- Miscevic, F.; Rotstein, O.; Wen, X.-Y. Advances in zebrafish high content and high throughput technologies. Comb. Chem. High Throughput Screen. 2012, 15, 515–521. [Google Scholar] [CrossRef] [PubMed]
- Fakhari, F.D.; Dittmer, D.P. Charting latency transcripts in Kaposi’s sarcoma-associated herpesvirus by whole-genome real-time quantitative PCR. J. Virol. 2002, 76, 6213–6223. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Guo, X. Surface plasmon resonance based biosensor technique: A review. J. Biophotonics 2012, 5, 483–501. [Google Scholar] [CrossRef] [PubMed]
- Wartchow, C.A.; Podlaski, F.; Li, S.; Rowan, K.; Zhang, X.; Mark, D.; Huang, K.-S. Biosensor-based small molecule fragment screening with biolayer interferometry. J. Comput. Aided Mol. Des. 2011, 25, 669–676. [Google Scholar] [CrossRef] [PubMed]
- Gu, J.; Zhang, H.; Chen, L.; Xu, S.; Yuan, G.; Xu, X. Drug–target network and polypharmacology studies of a Traditional Chinese Medicine for type II diabetes mellitus. Comput. Biol. Chem. 2011, 35, 293–297. [Google Scholar] [CrossRef]
- Li, S.; Zhang, B.; Jiang, D.; Wei, Y.; Zhang, N. Herb network construction and co-module analysis for uncovering the combination rule of traditional Chinese herbal formulae. BMC Bioinform. 2010, 11, 1–12. [Google Scholar] [CrossRef] [Green Version]
- Sharma, O.P.; Jadhav, A.; Hussain, A.; Kumar, M.S. VPDB: Viral protein structural database. Bioinformation 2011, 6, 324. [Google Scholar] [CrossRef] [Green Version]
- Ceze, L.; Nivala, J.; Strauss, K. Molecular digital data storage using DNA. Nat. Rev. Genet. 2019, 20, 456–466. [Google Scholar] [CrossRef]
- Graw, S.; Chappell, K.; Washam, C.L.; Gies, A.; Bird, J.; Robeson, M.S.; Byrum, S.D. Multi-omics data integration considerations and study design for biological systems and disease. Mol. Omics 2021, 17, 170–185. [Google Scholar] [CrossRef]
- Fisch, K.M.; Meißner, T.; Gioia, L.; Ducom, J.-C.; Carland, T.M.; Loguercio, S.; Su, A.I. Omics Pipe: A community-based framework for reproducible multi-omics data analysis. Bioinformatics 2015, 31, 1724–1728. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Biswas, N.; Chakrabarti, S. Artificial intelligence (AI)-based systems biology approaches in multi-omics data analysis of cancer. Front. Oncol. 2020, 10, 2224. [Google Scholar] [CrossRef]
- Sun, Y.V.; Hu, Y.-J. Integrative analysis of multi-omics data for discovery and functional studies of complex human diseases. Adv. Genet. 2016, 93, 147–190. [Google Scholar] [PubMed] [Green Version]
- Poornima, P.; Kumar, J.D.; Zhao, Q.; Blunder, M.; Efferth, T. Network pharmacology of cancer: From understanding of complex interactomes to the design of multi-target specific therapeutics from nature. Pharmacol. Res. 2016, 111, 290–302. [Google Scholar] [CrossRef] [PubMed]
- Buriani, A.; Fortinguerra, S.; Sorrenti, V.; Caudullo, G.; Carrara, M. Essential oil phytocomplex activity, a review with a focus on multivariate analysis for a network pharmacology-informed phytogenomic approach. Molecules 2020, 25, 1833. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Li, J.; Lu, C.; Jiang, M.; Niu, X.; Guo, H.; Li, L.; Bian, Z.; Lin, N.; Lu, A. Traditional chinese medicine-based network pharmacology could lead to new multicompound drug discovery. Evid. Based Complement. Altern. Med. 2012, 2012. [Google Scholar] [CrossRef] [PubMed]
- Zhang, W. Network pharmacology: A further description. Netw. Pharmacol. 2016, 1, 1–14. [Google Scholar]
- Gnad, F.; Doll, S.; Manning, G.; Arnott, D.; Zhang, Z. Bioinformatics analysis of thousands of TCGA tumors to determine the involvement of epigenetic regulators in human cancer. BMC Genom. 2015, 16, 1–15. [Google Scholar] [CrossRef] [Green Version]
- van der Greef, J.; McBurney, R.N. Rescuing drug discovery: In vivo systems pathology and systems pharmacology. Nat. Rev. Drug Discov. 2005, 4, 961–967. [Google Scholar] [CrossRef]
- Ideker, T.; Galitski, T.; Hood, L. A new approach to decoding life: Systems biology. Annu. Rev. Genom. Hum. Genet. 2001, 2, 343–372. [Google Scholar] [CrossRef]
- Kitano, H. Computational systems biology. Nature 2002, 420, 206–210. [Google Scholar] [CrossRef] [PubMed]
- Adams, C.P.; Brantner, V.V. Estimating the cost of new drug development: Is it really $802 million? Health Aff. 2006, 25, 420–428. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Dickson, M.; Gagnon, J.P. The cost of new drug discovery and development. Discov. Med. 2009, 4, 172–179. [Google Scholar]
- Kaitin, K. Obstacles and opportunities in new drug development. Clin. Pharmacol. Ther. 2008, 83, 210–212. [Google Scholar] [CrossRef]
- Azmi, A.S.; Mohammad, R.M. Rectifying cancer drug discovery through network pharmacology. Future Med. Chem. 2014, 6, 529–539. [Google Scholar] [CrossRef]
- Xu, X. New concepts and approaches for drug discovery based on traditional Chinese medicine. Drug Discov. Today Technol. 2006, 3, 247–253. [Google Scholar] [CrossRef]
- Tang, J.; Aittokallio, T. Network pharmacology strategies toward multi-target anticancer therapies: From computational models to experimental design principles. Curr. Pharm. Des. 2014, 20, 23–36. [Google Scholar] [CrossRef] [Green Version]
- Billur Engin, H.; Gursoy, A.; Nussinov, R.; Keskin, O. Network-based strategies can help mono-and poly-pharmacology drug discovery: A systems biology view. Curr. Pharm. Des. 2014, 20, 1201–1207. [Google Scholar] [CrossRef] [Green Version]
- Jeong, H.; Mason, S.P.; Barabási, A.-L.; Oltvai, Z.N. Lethality and centrality in protein networks. Nature 2001, 411, 41–42. [Google Scholar] [CrossRef] [Green Version]
- Korcsmáros, T.; Szalay, M.S.; Böde, C.; Kovács, I.A.; Csermely, P. How to design multi-target drugs: Target search options in cellular networks. Expert Opin. Drug Discov. 2007, 2, 799–808. [Google Scholar] [CrossRef]
- Morphy, R.; Kay, C.; Rankovic, Z. From magic bullets to designed multiple ligands. Drug discovery today 2004, 9, 641–651. [Google Scholar] [CrossRef]
- Hopkins, A.L.; Mason, J.S.; Overington, J.P. Can we rationally design promiscuous drugs? Curr. Opin. Struct. Biol. 2006, 16, 127–136. [Google Scholar] [CrossRef] [PubMed]
- Jackson, R.A.; Chen, E.S. Synthetic lethal approaches for assessing combinatorial efficacy of chemotherapeutic drugs. Pharmacol. Ther. 2016, 162, 69–85. [Google Scholar] [CrossRef]
- Kitano, H. Towards a theory of biological robustness. Mol. Syst. Bol. 2007, 3, 137. [Google Scholar] [CrossRef]
- Nishimura, D. BioCarta. Biotech Softw. Internet Rep. Comput. Softw. J. Sci. 2001, 2, 117–120. [Google Scholar] [CrossRef]
- Stark, C.; Breitkreutz, B.-J.; Reguly, T.; Boucher, L.; Breitkreutz, A.; Tyers, M. BioGRID: A general repository for interaction datasets. Nucleic Acids Res. 2006, 34, D535–D539. [Google Scholar] [CrossRef] [Green Version]
- Huang, H.; Wu, X.; Pandey, R.; Li, J.; Zhao, G.; Ibrahim, S.; Chen, J.Y. C 2 Maps: A network pharmacology database with comprehensive disease-gene-drug connectivity relationships. BMC Genom. 2012, 13, 1–14. [Google Scholar] [CrossRef] [Green Version]
- Xuemin, G. Chemical Book. 2008. Available online: http://www.chemicalbook.com/ProductIndex_EN.aspx (accessed on 14 August 2011).
- Gaulton, A.; Bellis, L.J.; Bento, A.P.; Chambers, J.; Davies, M.; Hersey, A.; Light, Y.; McGlinchey, S.; Michalovich, D.; Al-Lazikani, B. ChEMBL: A large-scale bioactivity database for drug discovery. Nucleic Acids Res. 2012, 40, D1100–D1107. [Google Scholar] [CrossRef] [Green Version]
- Kim Kjærulff, S.; Wich, L.; Kringelum, J.; Jacobsen, U.P.; Kouskoumvekaki, I.; Audouze, K.; Lund, O.; Brunak, S.; Oprea, T.I.; Taboureau, O. ChemProt-2.0: Visual navigation in a disease chemical biology database. Nucleic Acids Res. 2012, 41, D464–D469. [Google Scholar] [CrossRef]
- Ayers, M. ChemSpider: The free chemical database. Ref. Rev. 2012, 26, 45–46. [Google Scholar] [CrossRef]
- Fang, X.; Shao, L.; Zhang, H.; Wang, S. CHMIS-C: A comprehensive herbal medicine information system for cancer. J. Med. Chem. 2005, 48, 1481–1488. [Google Scholar] [CrossRef] [PubMed]
- Tatusov, R.L.; Fedorova, N.D.; Jackson, J.D.; Jacobs, A.R.; Kiryutin, B.; Koonin, E.V.; Krylov, D.M.; Mazumder, R.; Mekhedov, S.L.; Nikolskaya, A.N. The COG database: An updated version includes eukaryotes. BMC Bioinform. 2003, 4, 1–14. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kamburov, A.; Stelzl, U.; Lehrach, H.; Herwig, R. The ConsensusPathDB interaction database: 2013 update. Nucleic Acids Res. 2013, 41, D793–D800. [Google Scholar] [CrossRef] [PubMed]
- Doncheva, N.T.; Morris, J.H.; Gorodkin, J.; Jensen, L.J. Cytoscape StringApp: Network analysis and visualization of proteomics data. J. Proteome Res. 2018, 18, 623–632. [Google Scholar] [CrossRef]
- Huang, D.W.; Sherman, B.T.; Tan, Q.; Kir, J.; Liu, D.; Bryant, D.; Guo, Y.; Stephens, R.; Baseler, M.W.; Lane, H.C. DAVID Bioinformatics Resources: Expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res. 2007, 35, W169–W175. [Google Scholar] [CrossRef]
- Xenarios, I.; Rice, D.W.; Salwinski, L.; Baron, M.K.; Marcotte, E.M.; Eisenberg, D. DIP: The database of interacting proteins. Nucleic Acids Res. 2000, 28, 289–291. [Google Scholar] [CrossRef] [Green Version]
- 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. DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Res. 2018, 46, D1074–D1082. [Google Scholar] [CrossRef]
- Safran, M.; Dalah, I.; Alexander, J.; Rosen, N.; Iny Stein, T.; Shmoish, M.; Nativ, N.; Bahir, I.; Doniger, T.; Krug, H. GeneCards Version 3: The human gene integrator. Database 2010, 2010. [Google Scholar] [CrossRef]
- Adar, E. GUESS: A Language and Interface for Graph Exploration. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Montréal, QC, Canada, 22–27 April 2006; pp. 791–800. [Google Scholar]
- Chen, J.Y.; Mamidipalli, S.; Huan, T. HAPPI: An online database of comprehensive human annotated and predicted protein interactions. BMC Genom. 2009, 10, 1–11. [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. HIT: Linking herbal active ingredients to targets. Nucleic Acids Res. 2010, 39, D1055–D1059. [Google Scholar] [CrossRef]
- Keshava Prasad, T.; Goel, R.; Kandasamy, K.; Keerthikumar, S.; Kumar, S.; Mathivanan, S.; Telikicherla, D.; Raju, R.; Shafreen, B.; Venugopal, A. Human protein reference database—2009 update. Nucleic Acids Res. 2009, 37, D767–D772. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Mitchell, A.; Chang, H.-Y.; Daugherty, L.; Fraser, M.; Hunter, S.; Lopez, R.; McAnulla, C.; McMenamin, C.; Nuka, G.; Pesseat, S. The InterPro protein families database: The classification resource after 15 years. Nucleic Acids Res. 2015, 43, D213–D221. [Google Scholar] [CrossRef] [PubMed]
- Kanehisa, M.; Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000, 28, 27–30. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Guo, X.; Liu, K.; Ha, L.; Re, Y.; Wu, G. Analysis of potential plasma markers in Uyghur cervical cancer patients based on MetaCoreTM. Chin. J. Oncol. 2013, 40, 1020–1024. [Google Scholar]
- Masciocchi, J.; Frau, G.; Fanton, M.; Sturlese, M.; Floris, M.; Pireddu, L.; Palla, P.; Cedrati, F.; Rodriguez-Tomé, P.; Moro, S. MMsINC: A large-scale chemoinformatics database. Nucleic Acids Res. 2009, 37, D284–D290. [Google Scholar] [CrossRef] [Green Version]
- Huisman, M.; Van Duijn, M.A. Software for social network analysis. Models Methods Soc. Netw. Anal. 2005, 270, e316. [Google Scholar]
- Hagberg, A.; Conway, D. NetworkX: Network Analysis with Python. Available online: https://networkx.org/ (accessed on 29 April 2020).
- Brown, K.R.; Jurisica, I. Online predicted human interaction database. Bioinformatics 2005, 21, 2076–2082. [Google Scholar] [CrossRef] [Green Version]
- Batagelj, V.; Mrvar, A. Pajek—analysis and visualization of large networks. In Graph Drawing Software; Springer: Berlin/Heidelberg, Germany, 2004; pp. 77–103. [Google Scholar]
- Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The protein data bank. Nucleic Acids Res. 2000, 28, 235–242. [Google Scholar] [CrossRef] [Green Version]
- Gao, Z.; Li, H.; Zhang, H.; Liu, X.; Kang, L.; Luo, X.; Zhu, W.; Chen, K.; Wang, X.; Jiang, H. PDTD: A web-accessible protein database for drug target identification. BMC Bioinform. 2008, 9, 1–7. [Google Scholar] [CrossRef] [Green Version]
- Thorn, C.F.; Klein, T.E.; Altman, R.B. PharmGKB: The pharmacogenomics knowledge base. In Pharmacogenomics; Springer: Berlin/Heidelberg, Germany, 2013; pp. 311–320. [Google Scholar]
- Kim, S.; Thiessen, P.A.; Bolton, E.E.; Chen, J.; Fu, G.; Gindulyte, A.; Han, L.; He, J.; He, S.; Shoemaker, B.A. PubChem substance and compound databases. Nucleic Acids Res. 2016, 44, D1202–D1213. [Google Scholar] [CrossRef] [PubMed]
- Canese, K.; Weis, S. PubMed: The bibliographic database. NCBI Handb. 2013, 2, 1. [Google Scholar]
- Croft, D.; O’kelly, G.; Wu, G.; Haw, R.; Gillespie, M.; Matthews, L.; Caudy, M.; Garapati, P.; Gopinath, G.; Jassal, B. Reactome: A database of reactions, pathways and biological processes. Nucleic Acids Res. 2010, 39, D691–D697. [Google Scholar] [CrossRef]
- Fazekas, D.; Koltai, M.; Türei, D.; Módos, D.; Pálfy, M.; Dúl, Z.; Zsákai, L.; Szalay-Bekő, M.; Lenti, K.; Farkas, I.J. SignaLink 2–a signaling pathway resource with multi-layered regulatory networks. BMC Syst. Biol. 2013, 7, 1–15. [Google Scholar] [CrossRef] [Green Version]
- Chen, C.Y.-C.; Adams, J.D.; Hou, T.; Litscher, G. When modern technology meets ancient traditional chinese medicine. Evid. Based Complement. Altern. Med. 2015, 2015. [Google Scholar] [CrossRef] [PubMed]
- Kuhn, M.; von Mering, C.; Campillos, M.; Jensen, L.J.; Bork, P. STITCH: Interaction networks of chemicals and proteins. Nucleic Acids Res. 2007, 36, D684–D688. [Google Scholar] [CrossRef] [PubMed]
- Mering, C.v.; Huynen, M.; Jaeggi, D.; Schmidt, S.; Bork, P.; Snel, B. STRING: A database of predicted functional associations between proteins. Nucleic Acids Res. 2003, 31, 258–261. [Google Scholar] [CrossRef]
- Daina, A.; Michielin, O.; Zoete, V. SwissTargetPrediction: Updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Res. 2019, 47, W357–W364. [Google Scholar] [CrossRef] [Green Version]
- Fang, Y.-C.; Huang, H.-C.; Chen, H.-H.; Juan, H.-F. TCMGeneDIT: A database for associated traditional Chinese medicine, gene and disease information using text mining. BMC Complement. Altern. Med. 2008, 8, 1–11. [Google Scholar] [CrossRef] [Green Version]
- 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. 2012, 41, D1089–D1095. [Google Scholar] [CrossRef]
- Ru, J.; Li, P.; Wang, J.; Zhou, W.; Li, B.; Huang, C.; Li, P.; Guo, Z.; Tao, W.; Yang, Y. TCMSP: A database of systems pharmacology for drug discovery from herbal medicines. J. Cheminform. 2014, 6, 1–6. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Chen, X.; Ji, Z.L.; Chen, Y.Z. TTD: Therapeutic target database. Nucleic Acids Res. 2002, 30, 412–415. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Apostolato, I.-A. An overview of Software Applications for Social Network Analysis. International Review of Social Research 2013, 3. [Google Scholar] [CrossRef] [Green Version]
- UniProt Consortium. UniProt: The universal protein knowledgebase. Nucleic acids research 2017, 45, D158–D169. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Noor, F.; Rehman, A.; Ashfaq, U.A.; Saleem, M.H.; Okla, M.K.; Al-Hashimi, A.; AbdElgawad, H.; Aslam, S.J.P. Integrating Network Pharmacology and Molecular Docking Approaches to Decipher the Multi-Target Pharmacological Mechanism of Abrus precatorius L. Acting on Diabetes. Pharmaceuticals 2022, 15, 414. [Google Scholar] [CrossRef] [PubMed]
- Zou, X.; Chen, K.; Zou, J.; Han, P.; Hao, J.; Han, Z. Single-cell RNA-seq data analysis on the receptor ACE2 expression reveals the potential risk of different human organs vulnerable to 2019-nCoV infection. Front. Med. 2020, 14, 185–192. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Bouzid, M.; Hunter, P.R.; Chalmers, R.M.; Tyler, K.M. Cryptosporidium pathogenicity and virulence. Clin. Microbiol. Rev. 2013, 26, 115–134. [Google Scholar] [CrossRef] [Green Version]
- Rodriguez-Morales, A.J.; Cardona-Ospina, J.A.; Gutiérrez-Ocampo, E.; Villamizar-Peña, R.; Holguin-Rivera, Y.; Escalera-Antezana, J.P.; Alvarado-Arnez, L.E.; Bonilla-Aldana, D.K.; Franco-Paredes, C.; Henao-Martinez, A.F. Clinical, laboratory and imaging features of COVID-19: A systematic review and meta-analysis. Travel Med. Infect. Dis. 2020, 34, 101623. [Google Scholar] [CrossRef]
- Zhong, N.; Zheng, B.; Li, Y.; Poon, L.; Xie, Z.; Chan, K.; Li, P.; Tan, S.; Chang, Q.; Xie, J. Epidemiology and cause of severe acute respiratory syndrome (SARS) in Guangdong, People’s Republic of China, in February, 2003. Lancet 2003, 362, 1353–1358. [Google Scholar] [CrossRef] [Green Version]
- Nassar, M.; Bakhrebah, M.; Meo, S.; Alsuabeyl, M.; Zaher, W. Middle East respiratory syndrome coronavirus (MERS-CoV) infection: Epidemiology, pathogenesis and clinical characteristics. Eur. Rev. Med. Pharmacol. Sci. 2018, 22, 4956–4961. [Google Scholar]
- Jin, Y.-H.; Cai, L.; Cheng, Z.-S.; Cheng, H.; Deng, T.; Fan, Y.-P.; Fang, C.; Huang, D.; Huang, L.-Q.; Huang, Q. A rapid advice guideline for the diagnosis and treatment of 2019 novel coronavirus (2019-nCoV) infected pneumonia (standard version). Mil. Med Res. 2020, 7, 1–23. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wang, R.; Yang, S.; Xie, C.; Shen, Q.; Li, M.; Lei, X.; Li, J.; Huang, M. Clinical efficacy of Qingfei Paidu Decoction in the treatment of COVID-19. Pharmacol. Clin. Chin. Mater. Med. 2020, 36, 13–18. [Google Scholar]
- Li, B.; Nasser, M.; Masood, M.; Adlat, S.; Huang, Y.; Yang, B.; Luo, C.; Jiang, N. Efficiency of Traditional Chinese medicine targeting the Nrf2/HO-1 signaling pathway. Biomed. Pharmacother. 2020, 126, 110074. [Google Scholar] [CrossRef]
- Zhang, D.-h.; Zhang, X.; Peng, B.; Deng, S.-q.; Wang, Y.-f.; Yang, L.; Zhang, K.-z.; Ling, C.-q.; Wu, K.-l. Network pharmacology suggests biochemical rationale for treating COVID-19 symptoms with a Traditional Chinese Medicine. Commun. Biol. 2020, 3, 1–9. [Google Scholar] [CrossRef]
- Torre, L.A.; Siegel, R.L.; Ward, E.M.; Jemal, A. Global cancer incidence and mortality rates and trends—an update. Cancer Epidemiol. Prev. Biomark. 2016, 25, 16–27. [Google Scholar] [CrossRef] [Green Version]
- Duperret, E.K.; Liu, S.; Paik, M.; Trautz, A.; Stoltz, R.; Liu, X.; Ze, K.; Perales-Puchalt, A.; Reed, C.; Yan, J. A designer cross-reactive DNA immunotherapeutic vaccine that targets multiple MAGE-A family members simultaneously for cancer therapy. Clin. Cancer Res. 2018, 24, 6015–6027. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Suarez-Kurtz, G.; Vargens, D.D.; Santoro, A.B.; Hutz, M.H.; de Moraes, M.E.; Pena, S.D.; Ribeiro-dos-Santos, Â.; Romano-Silva, M.A.; Struchiner, C.J. Global pharmacogenomics: Distribution of CYP3A5 polymorphisms and phenotypes in the Brazilian population. PLoS ONE 2014, 9, e83472. [Google Scholar] [CrossRef] [Green Version]
- Radovich, M.; Clare, S.E.; Atale, R.; Pardo, I.; Hancock, B.A.; Solzak, J.P.; Kassem, N.; Mathieson, T.; Storniolo, A.M.V.; Rufenbarger, C. Characterizing the heterogeneity of triple-negative breast cancers using microdissected normal ductal epithelium and RNA-sequencing. Breast Cancer Res. Treat. 2014, 143, 57–68. [Google Scholar] [CrossRef] [Green Version]
- Zeng, L.; Yang, K. Exploring the pharmacological mechanism of Yanghe Decoction on HER2-positive breast cancer by a network pharmacology approach. J. Ethnopharmacol. 2017, 199, 68–85. [Google Scholar] [CrossRef]
- Zeng, S.; Yu, Z.; Xu, X.; Liu, Y.; Li, J.; Zhao, D.; Song, C.; Lu, H.; Zhao, Y.; Lu, W. Identification of the active constituents and significant pathways of shen-qi-yi-zhu decoction on antigastric cancer: A network pharmacology research and experimental validation. Evid. Based Complement. Altern. Med. 2021, 2021. [Google Scholar] [CrossRef]
- Liu, X.; Wu, J.; Zhang, D.; Wang, K.; Duan, X.; Zhang, X. A network pharmacology approach to uncover the multiple mechanisms of Hedyotis diffusa Willd. on colorectal cancer. Evid. Based Complement. Altern. 2018, 2018. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Song, Y.; Wang, H.; Pan, Y.; Liu, T. Investigating the multi-target pharmacological mechanism of hedyotis diffusa willd acting on prostate cancer: A network pharmacology approach. Biomolecules 2019, 9, 591. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Bing, Z.; Cheng, Z.; Shi, D.; Liu, X.; Tian, J.; Yao, X.; Zhang, J.; Wang, Y.; Yang, K. Investigate the mechanisms of Chinese medicine Fuzhengkangai towards EGFR mutation-positive lung adenocarcinomas by network pharmacology. BMC Complement. Altern. Med. 2018, 18, 1–17. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Meng, Z.; Liu, X.; Wu, J.; Zhou, W.; Wang, K.; Jing, Z.; Liu, S.; Ni, M.; Zhang, X. Mechanisms of compound kushen injection for the treatment of lung cancer based on network pharmacology. Evid. Based Complement. Altern. Med. 2019, 2019. [Google Scholar] [CrossRef] [Green Version]
- Virani, S.S.; Alonso, A.; Benjamin, E.J.; Bittencourt, M.S.; Callaway, C.W.; Carson, A.P.; Chamberlain, A.M.; Chang, A.R.; Cheng, S.; Delling, F.N. Heart disease and stroke statistics—2020 update: A report from the American Heart Association. Circulation 2020, 141, e139–e596. [Google Scholar] [CrossRef]
- Sun, K.; Fan, J.; Han, J. Ameliorating effects of traditional Chinese medicine preparation, Chinese materia medica and active compounds on ischemia/reperfusion-induced cerebral microcirculatory disturbances and neuron damage. Acta Pharm. Sin. B 2015, 5, 8–24. [Google Scholar] [CrossRef] [Green Version]
- Yang, Y.; Li, Y.; Wang, J.; Sun, K.; Tao, W.; Wang, Z.; Xiao, W.; Pan, Y.; Zhang, S.; Wang, Y. Systematic investigation of Ginkgo biloba leaves for treating cardio-cerebrovascular diseases in an animal model. ACS Chem. Biol. 2017, 12, 1363–1372. [Google Scholar] [CrossRef] [PubMed]
- Ren, L.; Zheng, X.; Liu, J.; Li, W.; Fu, W.; Tang, Q.; Wang, J.; Du, G. Network pharmacology study of traditional Chinese medicines for stroke treatment and effective constituents screening. J. Ethnopharmacol. 2019, 242, 112044. [Google Scholar] [CrossRef]
- Tao, W.; Xu, X.; Wang, X.; Li, B.; Wang, Y.; Li, Y.; Yang, L. Network pharmacology-based prediction of the active ingredients and potential targets of Chinese herbal Radix Curcumae formula for application to cardiovascular disease. J. Ethnopharmacol. 2013, 145, 1–10. [Google Scholar] [CrossRef]
- Wang, Y.; Shi, Y.; Zou, J.; Zhang, X.; Liang, Y.; Tai, J.; Cui, C.; Wang, M.; Guo, D. Network pharmacology exploration reveals a common mechanism in the treatment of cardio-cerebrovascular disease with Salvia miltiorrhiza Burge. and Carthamus tinctorius L. BMC Complement. Med. Ther. 2020, 20, 1–18. [Google Scholar] [CrossRef]
- Cui, Q.; Zhang, Y.-l.; Ma, Y.-h.; Yu, H.-y.; Zhao, X.-z.; Zhang, L.-h.; Ge, S.-q.; Zhang, G.-w. A network pharmacology approach to investigate the mechanism of Shuxuening injection in the treatment of ischemic stroke. J. Ethnopharmacol. 2020, 257, 112891. [Google Scholar] [CrossRef]
- Chen, L.; Cao, Y.; Zhang, H.; Lv, D.; Zhao, Y.; Liu, Y.; Ye, G.; Chai, Y. Network pharmacology-based strategy for predicting active ingredients and potential targets of Yangxinshi tablet for treating heart failure. J. Ethnopharmacol. 2018, 219, 359–368. [Google Scholar] [CrossRef] [PubMed]
- Tuei, V.C.; Maiyoh, G.K.; Ha, C.E. Type 2 diabetes mellitus and obesity in sub-Saharan Africa. Diabetes/Metab. Res. Rev. 2010, 26, 433–445. [Google Scholar] [CrossRef] [PubMed]
- Forouhi, N.G.; Wareham, N.J. The EPIC-InterAct Study: A study of the interplay between genetic and lifestyle behavioral factors on the risk of type 2 diabetes in European populations. Curr. Nutr. Rep. 2014, 3, 355–363. [Google Scholar] [CrossRef] [Green Version]
- Vermeire, E.I.; Wens, J.; Van Royen, P.; Biot, Y.; Hearnshaw, H.; Lindenmeyer, A. Interventions for improving adherence to treatment recommendations in people with type 2 diabetes mellitus. Cochrane Database Syst. Rev. 2005, 18, CD003638. [Google Scholar] [CrossRef] [PubMed]
- Wang, E.; Wang, L.; Ding, R.; Zhai, M.; Ge, R.; Zhou, P.; Wang, T.; Fang, H.; Wang, J.; Huang, J. Astragaloside IV acts through multi-scale mechanisms to effectively reduce diabetic nephropathy. Pharmacol. Res. 2020, 157, 104831. [Google Scholar] [CrossRef]
- Oh, K.K.; Adnan, M.; Cho, D.H. Network pharmacology of bioactives from Sorghum bicolor with targets related to diabetes mellitus. PLoS ONE 2020, 15, e0240873. [Google Scholar] [CrossRef]
- Zhou, J.; Wang, Q.; Xiang, Z.; Tong, Q.; Pan, J.; Wan, L.; Chen, J. Network pharmacology analysis of traditional Chinese medicine formula Xiao Ke Yin Shui treating type 2 diabetes mellitus. Evid.-Based Complement. Altern. Med. 2019, 2019, 4202563. [Google Scholar] [CrossRef] [Green Version]
- Zhou, F.; He, K.; Guan, Y.; Yang, X.; Chen, Y.; Sun, M.; Qiu, X.; Yan, F.; Huang, H.; Yao, L. Network pharmacology-based strategy to investigate pharmacological mechanisms of Tinospora sinensis for treatment of Alzheimer’s disease. Evid. Based Complement. Altern. Med. 2019, 2019. [Google Scholar] [CrossRef]
- Li, L.; Qiu, H.; Liu, M.; Cai, Y. A network pharmacology-based study of the molecular mechanisms of shaoyao-gancao decoction in treating Parkinson’s disease. Interdiscip. Sci. Comput. Life Sci. 2020, 12, 131–144. [Google Scholar] [CrossRef]
- Dai, W.; Chen, H.-Y.; Chen, C.Y.-C.; Medicine, A. A network pharmacology-based approach to investigate the novel TCM formula against huntington’s disease and validated by support vector machine model. J. Ethnopharmacol. 2020, 259, 112940. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Liu, X.G.; Lv, M.C.; Huang, M.Y.; Sun, Y.Q.; Gao, P.Y.; Li, D.Q. A network pharmacology study on the triterpene saponins from medicago sativa l. For the treatment of neurodegenerative diseases. Interdiscip. Sci. Comput. Life Sci. 2020, 12, 131–144. [Google Scholar]
- Cheng, B.-F.; Hou, Y.-Y.; Jiang, M.; Zhao, Z.-Y.; Dong, L.-Y.; Bai, G. Anti-inflammatory mechanism of Qingfei Xiaoyan Wan studied with network pharmacology. Yao Xue Xue Bao Acta Pharm. Sin. 2013, 48, 686–693. [Google Scholar]
- Yang, H.; Xing, L.; Zhou, M.; Liu, Y.; Guo, T.; Fu, J.; Dong, L.; Jiang, M. Network pharmacological research of volatile oil from Zhike Chuanbei Pipa Dropping Pills in treatment of airway inflammation. Chin. Tradit. Herb. Drugs 2012, 43, 1129–1135. [Google Scholar]
- Pei, L.; Bao, Y.; Liu, S.; Zheng, J.; Chen, X. Material basis of Chinese herbal formulas explored by combining pharmacokinetics with network pharmacology. PloS ONE 2013, 8, e57414. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lv, X.; Xu, Z.; Xu, G.; Li, H.; Wang, C.; Chen, J.; Sun, J. Investigation of the active components and mechanisms of Schisandra chinensis in the treatment of asthma based on a network pharmacology approach and experimental validation. Food Func. 2020, 11, 3032–3042. [Google Scholar] [CrossRef] [PubMed]
- Jiao, J.; Wu, J.; Wang, J.; Guo, Y.; Gao, L.; Liang, H.; Huang, J.; Wang, J. Ma Huang Tang ameliorates bronchial asthma symptoms through the TLR9 pathway. Pharm. Biol. 2018, 56, 580–593. [Google Scholar] [CrossRef] [Green Version]
- Li, C.; Niu, M.; Wang, R.; wei Zhou, X.; Dong, B.; Qi, S.; Chen, W.; Zhang, M.; Shi, Y.; Li, R. The modulatory properties of Si Jun Zi Tang enhancing anticancer of gefitinib by an integrating approach. Biomed. Pharmacother. 2019, 111, 1132–1140. [Google Scholar] [CrossRef]
- Chen, L.; Zhang, L.; Fang, Z.; Li, C.; Yang, Y.; You, X.; Song, M.; Coffie, J.; Zhang, L.; Gao, X. Naoxintong restores collateral blood flow in a murine model of hindlimb ischemia through PPARδ-dependent mechanism. J. Ethnopharmacol. 2018, 227, 121–130. [Google Scholar] [CrossRef]
- Chen, Y.; Li, M.; Zhang, Y.; Di, M.; Chen, W.; Liu, X.; Yu, F.; Wang, H.; Zhen, X.; Zhang, M. Traditional Chinese medication Tongxinluo attenuates apoptosis in ox-LDL-stimulated macrophages by enhancing Beclin-1-induced autophagy. Biochem. Biophys. Res. Commun. 2018, 501, 336–342. [Google Scholar] [CrossRef]
- Li, M.; Zhou, J.; Jin, W.; Li, X.; Zhang, Y. Danhong injection combined with t-PA improves thrombolytic therapy in focal embolic stroke. Front. Pharmacol. 2018, 9, 308. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Xie, G.; Peng, W.; Li, P.; Xia, Z.; Zhong, Y.; He, F.; Tulake, Y.; Feng, D.; Wang, Y.; Xing, Z. A network pharmacology analysis to explore the effect of astragali radix-radix angelica sinensis on traumatic brain injury. BioMed Res. Int. 2018, 2018. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lien, A.S.-Y.; Jiang, Y.-D.; Mou, C.-H.; Sun, M.-F.; Gau, B.-S.; Yen, H.-R. Integrative traditional Chinese medicine therapy reduces the risk of diabetic ketoacidosis in patients with type 1 diabetes mellitus. J. Ethnopharmacol. 2016, 191, 324–330. [Google Scholar] [CrossRef] [PubMed]
- Liu, H.-K.; Hung, T.-M.; Huang, H.-C.; Lee, I.; Chang, C.-C.; Cheng, J.-J.; Lin, L.-C.; Huang, C. Bai-Hu-Jia-Ren-Shen-Tang decoction reduces fatty liver by activating AMP-activated protein kinase in vitro and in vivo. Evid. Based Complement. Altern. Med. 2015, 2015. [Google Scholar] [CrossRef] [Green Version]
- Li, P.; Tang, T.; Liu, T.; Zhou, J.; Cui, H.; He, Z.; Zhong, Y.; Hu, E.; Yang, A.; Wei, G. Systematic analysis of tRNA-derived small RNAs reveals novel potential therapeutic targets of traditional Chinese medicine (Buyang-Huanwu-Decoction) on intracerebral hemorrhage. Int. J. Biol. Sci. 2019, 15, 895. [Google Scholar] [CrossRef] [PubMed]
- Liu, P.; Duan, J.-A.; Bai, G.; Su, S.-L. Network pharmacology study on major active compounds of siwu decoction analogous formulae for treating primary dysmenorrhea of gynecology blood stasis syndrome. Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China J. Chin. Mater. Med. 2014, 39, 113–120. [Google Scholar]
- Zheng, C.S.; Xu, X.J.; Ye, H.Z.; Wu, G.W.; Xu, H.F.; Li, X.H.; Huang, S.P.; Liu, X.X. Computational pharmacological comparison of Salvia miltiorrhiza and Panax notoginseng used in the therapy of cardiovascular diseases. Experimental and therapeutic medicine 2013, 6, 1163–1168. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Chen, D.; Lu, P.; Zhang, F.-B.; Tang, S.-H.; Yang, H.-J. Molecular mechanism research on simultaneous therapy of brain and heart based on data mining and network analysis. China J. Chin. Mater. Med. 2013, 38, 91–98. [Google Scholar]
- Li, X.; Wu, L.; Fan, X.; Zhang, B.; Gao, X.; Wang, Y.; Cheng, Y. Network pharmacology study on major active compounds of Fufang Danshen formula. China J. Chin. Mater. Med. 2011, 36, 2911–2915. [Google Scholar]
- Tao, J.; Hou, Y.; Ma, X.; Liu, D.; Tong, Y.; Zhou, H.; Gao, J.; Bai, G. An integrated global chemomics and system biology approach to analyze the mechanisms of the traditional Chinese medicinal preparation Eriobotrya japonica–Fritillaria usuriensis dropping pills for pulmonary diseases. BMC Complement. Altern. Med. 2015, 16, 1–10. [Google Scholar] [CrossRef]
- Wang, R.; Lin, J. Analysis of the mechanism of zhichuanling oral liquid in treating bronchial asthma based on network pharmacology. Evid. Based Complement. Altern. Med. 2020, 2020. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhao, P.; Yang, L.; Li, J.; Li, Y.; Tian, Y.; Li, S. Combining systems pharmacology, transcriptomics, proteomics, and metabolomics to dissect the therapeutic mechanism of Chinese herbal Bufei Jianpi formula for application to COPD. Int. J. Chronic Obstr. Pulm. Dis. 2016, 11, 553. [Google Scholar]
- Ruan, Z.; Niu, L.; Han, L.; Ren, R.; Xu, Z.; Dong, W.; Jiang, L. In silico comparative molecular docking analysis and analysis of the anti-inflammatory mechanisms of action of tanshinone from Salvia miltiorrhiza. Exp. Ther. Med. 2019, 18, 1131–1140. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhao, P.; Li, J.; Yang, L.; Li, Y.; Tian, Y.; Li, S. Integration of transcriptomics, proteomics, metabolomics and systems pharmacology data to reveal the therapeutic mechanism underlying Chinese herbal Bufei Yishen formula for the treatment of chronic obstructive pulmonary disease. Mol. Med. Rep. 2018, 17, 5247–5257. [Google Scholar] [CrossRef]
- Wang, J.; Zhang, L.; Liu, B.; Wang, Q.; Chen, Y.; Wang, Z.; Zhou, J.; Xiao, W.; Zheng, C.; Wang, Y. Systematic investigation of the Erigeron breviscapus mechanism for treating cerebrovascular disease. J. Ethnopharmacol 2018, 224, 429–440. [Google Scholar] [CrossRef] [PubMed]
- Li, B.; Tao, W.; Zheng, C.; Shar, P.A.; Huang, C.; Fu, Y.; Wang, Y. Systems pharmacology-based approach for dissecting the addition and subtraction theory of traditional Chinese medicine: An example using Xiao-Chaihu-Decoction and Da-Chaihu-Decoction. Comput. Biol. Med 2014, 53, 19–29. [Google Scholar] [CrossRef]
- Yang, S.; Zhang, J.; Yan, Y.; Yang, M.; Li, C.; Li, J.; Zhong, L.; Gong, Q.; Yu, H. Network pharmacology-based strategy to investigate the pharmacologic mechanisms of Atractylodes macrocephala Koidz. for the treatment of chronic gastritis. Front. Pharmacol 2020, 10, 1629. [Google Scholar] [CrossRef] [Green Version]
- Chen, X.; Lu, F.; Luo, G.; Ren, Y.; Ma, J.; Zhang, Y. Discovery of selective farnesoid X receptor agonists for the treatment of hyperlipidemia from traditional Chinese medicine based on virtual screening and in vitro validation. J. Biomol. Struct. Dyn. 2020, 38, 4461–4470. [Google Scholar] [CrossRef]
- Zhang, B.; Wang, X.; Li, S. An integrative platform of TCM network pharmacology and its application on a herbal formula, Qing-Luo-Yin. Evid. Based Complement. Altern. Med. 2013, 2013. [Google Scholar] [CrossRef] [Green Version]
- Wang, S.; Fu, L.; Zhang, S.; Tian, M.; Zhang, L.; Zheng, Y.; Wang, J.; Huang, J.; Ouyang, L. In silico analysis and experimental validation of active compounds from fructus Schisandrae chinensis in protection from hepatic injury. Cell prolif. 2015, 48, 86–94. [Google Scholar] [CrossRef]
- Chen, Y.; Chen, X.; Luo, G.; Zhang, X.; Lu, F.; Qiao, L.; He, W.; Li, G.; Zhang, Y. Discovery of potential inhibitors of squalene synthase from traditional Chinese medicine based on virtual screening and in vitro evaluation of lipid-lowering effect. Molecules 2018, 23, 1040. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Huo, X.; Lu, F.; Qiao, L.; Li, G.; Zhang, Y. A component formula of Chinese medicine for hypercholesterolemia based on virtual screening and biology network. Evid. Based Complement. Altern. Med. 2018, 2018. [Google Scholar] [CrossRef] [PubMed]
- Zhang, J.; Li, Y.; Chen, S.-S.; Zhang, L.; Wang, J.; Yang, Y.; Zhang, S.; Pan, Y.; Wang, Y.; Yang, L. Systems pharmacology dissection of the anti-inflammatory mechanism for the medicinal herb Folium eriobotryae. Int. J. Mol. Sci. 2015, 16, 2913–2941. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wang, L.; Zhou, G.-B.; Liu, P.; Song, J.-H.; Liang, Y.; Yan, X.-J.; Xu, F.; Wang, B.-S.; Mao, J.-H.; Shen, Z.-X. Dissection of mechanisms of Chinese medicinal formula Realgar-Indigo naturalis as an effective treatment for promyelocytic leukemia. Proc. Natl. Acad. Sci. USA 2008, 105, 4826–4831. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Li, S.; Wang, N.; Hong, M.; Tan, H.-Y.; Pan, G.; Feng, Y. Hepatoprotective effects of a functional formula of three Chinese medicinal herbs: Experimental evidence and network pharmacology-based identification of mechanism of action and potential bioactive components. Molecules 2018, 23, 352. [Google Scholar] [CrossRef] [Green Version]
- An, L.; Feng, F. Network pharmacology-based antioxidant effect study of Zhi-Zi-Da-Huang decoction for alcoholic liver disease. Evid. Based Complement. Altern. Med. 2015, 2015. [Google Scholar] [CrossRef]
- Zhang, S.; Wang, Y. Network pharmacology approach reveals the potential immune function activation and tumor cell apoptosis promotion of Xia Qi decoction in lung cancer. Med. Sci. 2020, 8, 1. [Google Scholar] [CrossRef] [Green Version]
- Li, W.; Tang, Y.; Shang, E.; Guo, J.; Huang, M.; Qian, D.; Duan, J. Analysis on correlation between general efficacy and chemical constituents of Danggui-Chuanxiong herb pair based on artificial neural network. China J. Chin. Mater. Med. 2012, 37, 2935–2942. [Google Scholar]
- Ding, F.; Zhang, Q.; Hu, Y.; Wang, Y. Mechanism study on preventive and curative effects of buyang huanwu decoction in Qi deficiency and blood stasis diseases based on network analysis. China J. Chin. Mater. Med. 2014, 39, 4418–4425. [Google Scholar]
- Wang, L.; Zhang, J.; Hong, Y.; Feng, Y.; Chen, M.; Wang, Y. Phytochemical and pharmacological review of da chuanxiong formula: A famous herb pair composed of chuanxiong rhizoma and gastrodiae rhizoma for headache. Evid. Based Complement. Altern. Med. 2013, 2013. [Google Scholar] [CrossRef] [Green Version]
- Wang, L.; Li, Z.; Zhao, X.; Liu, W.; Liu, Y.; Yang, J.; Li, X.; Fan, X.; Cheng, Y. A network study of chinese medicine xuesaitong injection to elucidate a complex mode of action with multicompound, multitarget, and multipathway. Evid. Based Complement. Altern. Med. 2013, 2013. [Google Scholar] [CrossRef] [PubMed]
- Deng, W.; Wang, Y.; Liu, Z.; Cheng, H.; Xue, Y. HemI: A toolkit for illustrating heatmaps. PloS ONE 2014, 9, e111988. [Google Scholar] [CrossRef] [PubMed]
- Zheng, C.S.; Xu, X.J.; Ye, H.Z.; Wu, G.W.; Li, X.H.; Xu, H.F.; Liu, X.X. Network pharmacology-based prediction of the multi-target capabilities of the compounds in Taohong Siwu decoction, and their application in osteoarthritis. Exp. Ther. Med. 2013, 6, 125–132. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hong, M.; Zhang, Y.; Li, S.; Tan, H.Y.; Wang, N.; Mu, S.; Hao, X.; Feng, Y. A network pharmacology-based study on the hepatoprotective effect of Fructus Schisandrae. Molecules 2017, 22, 1617. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sheng, S.; Wang, J.; Wang, L.; Liu, H.; Li, P.; Liu, M.; Long, C.; Xie, C.; Xie, X.; Su, W. Network pharmacology analyses of the antithrombotic pharmacological mechanism of Fufang Xueshuantong Capsule with experimental support using disseminated intravascular coagulation rats. J. Ethnopharmacol. 2014, 154, 735–744. [Google Scholar] [CrossRef] [PubMed]
- Zheng, C.S.; Fu, C.L.; Pan, C.B.; Bao, H.J.; Chen, X.Q.; Ye, H.Z.; Ye, J.X.; Wu, G.W.; Li, X.H.; Xu, H.F. Deciphering the underlying mechanisms of Diesun Miaofang in traumatic injury from a systems pharmacology perspective. Mol. Med. Rep. 2015, 12, 1769–1776. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Sr. No# | Resources | Brief Description | Usage | URL | Reference |
---|---|---|---|---|---|
1. | BioCarta | Online maps of metabolic and signalling pathways | Database of gene interaction models | https://maayanlab.cloud/Harmonizome/dataset/Biocarta+Pathways (accessed 29 April 2022) | [152] |
2. | BioGRID | Biological General Repository For Interaction Datasets | Retrieval of protein–protein interaction network | http://thebiogrid.org/ (accessed 29 April 2022) | [153] |
3. | C2Maps | Computational Connectivity Maps | Annotation of drug–protein pairs | http://bio.informatics.iupui.edu/ (accessed 29 April 2022) | [154] |
4. | CB | Chemical book | Retrieval of chemical structures | http://www.chemicalbook.com/ (accessed 29 April 2022) | [155] |
5. | ChEMBL | Database of bioactive compounds | Retrieval of functional as well as binding information of active compounds | https://www.ebi.ac.uk/chembl/ (accessed 29 April 2022) | [156] |
6. | ChemProt | Chemical–protein–disease annotation database | Analysis of interaction between chemical and protein | http://www.cbs.dtu.dk/services/ChemProt-2.0/ (accessed 29 April 2022) | [157] |
7. | ChemSpider | Database of chemical structures | Retrieval of chemical structures | http://www.chemspider.com/ (accessed 29 April 2022) | [158] |
8. | CHMIS-C | Comprehensive Herbal Medicine Information System for Cancer | Database of herbal medicine related cancer | http://sw16.im.med.umich.edu/chmis-c/ (accessed 29 April 2022) | [159] |
9. | COGs | Clusters of Orthologous Gene | Classification of proteins on phylogenetic basis | https://www.ncbi.nlm.nih.gov/COG/ (accessed 29 April 2022) | [160] |
10. | CPDB | Consensus Path DataBase | Molecular functional interaction database | http://cpdb.molgen.mpg.de/ (accessed 29 April 2022) | [161] |
11. | Cytoscape | Database for network construction and visualization | Network analysis | https://cytoscape.org/ (accessed 29 April 2022) | [162] |
12. | DAVID | Database for Annotation, Visualization & Integrated Discovery | Functional annotation | https://david.ncifcrf.gov/ (accessed 29 April 2022) | [163] |
13. | DIP | Database of Interacting proteins | Analysis of protein–protein interaction network | http://dip.doe-mbi.ucla.edu (accessed 29 April 2022) | [164] |
14. | DrugBank | Online database containing information on drugs | Analysis of detailed drug data | http://www.drugbank.ca/ (accessed 29 April 2022) | [165] |
15. | GeneCards | Database of human genes | For identification of disease-related genes | https://www.genecards.org/ (accessed 29 April 2022) | [166] |
16. | Guess | Computer program for the analysis and visualization of networks | Network analysis | http://www.levmuchnik.net/Content/Networks/ComplexNetworksPackage.html (accessed 29 April 2022) | [167] |
17. | HAPPI | Human Annotated & Predicted Protein | Retrieval of protein–protein interaction network | http://bio.informatics.iupui.edu/HAPPI/ (accessed 29 April 2022) | [168] |
18. | HIT | A comprehensive and fully curated database for linking herbal active ingredients to targets | Herbal ingredients’ targets identification | http://lifecenter.sgst.cn/hit/ (accessed 29 April 2022) | [169] |
19. | HPRD | Human Protein Reference Database | Retrieval of protein–protein interaction network | http://www.hprd.org/ (accessed 29 April 2022) | [170] |
20. | InterPro | Integrative database of protein families | Collection of protein families | http://www.ebi.ac.uk/interpro/ (accessed 29 April 2022) | [171] |
21. | KEGG | Kyoto Encyclopedia of Genes and Genomes | Pathway analysis | http://www.genome.jp/kegg/ (accessed 29 April 2022) | [172] |
22. | LookChem | Database of chemical structures | Retrieval of chemical structures | http://www.lookchem.com/ (accessed 29 April 2022) | [173] |
23. | MetaCoreTM | MetaCore (TM) | Pathway analysis | http://www.genego.com (accessed 29 April 2022) | [174] |
24. | MMsINC | Database of chemoinformatics | Retrieval of chemical structures | http://mms.dsfarm.unipd.it/MMsINC/search/ (accessed 29 April 2022) | [175] |
25. | NetMiner | Computer program for the analysis and visualization of networks | Network analysis | http://graphexploration.cond.org/ (accessed 29 April 2022) | [176] |
26. | NetPath | Network pathway analysis | Pathway analysis | http://www.netpath.org/ (accessed 29 April 2022) | [173] |
27. | NetworkX | Computer program for the analysis and visualization of networks | Network analysis | http://www.analytictech.com/ucinet/ (accessed 29 April 2022) | [177] |
28. | OPHID | Online predicted human interaction database | Retrieval of protein–protein interaction network | http://ophid.utoronto.ca (accessed 29 April 2022) | [178] |
29. | Pajek | Computer program for the analysis and visualization of network | Network analysis | http://pajek.imfm.si/doku.php (accessed 29 April 2022) | [179] |
30. | PDB | Protein Data bank | Retrieval of protein related information | http://www.rcsb.org/pdb/ (accessed 29 April 2022) | [180] |
31. | PDTD | Protein Database for Drug Target | Identification of drug target | http://www.dddc.ac.cn/pdtd/ (accessed 29 April 2022) | [181] |
32. | PharmGBK | Pharmacogenomics knowledge base | Analyze the genes response to drugs | http://www.pharmgkb.org/ (accessed 29 April 2022) | [182] |
33. | PubChem | Public repository for information on chemical substances | Analysis of chemical compounds | https://pubchem.ncbi.nlm.nih.gov/ (accessed 29 April 2022) | [183] |
34. | PubMed | Public/Publisher MEDLINE | Literature review | https://pubchem.ncbi.nlm.nih.gov/ (accessed 29 April 2022) | [184] |
35. | Reactome | Database of pathways, reactions, and biological processes | Pathway analysis | http://www.reactome.org (accessed 29 April 2022) | [185] |
36. | SignaLink | Signalling pathway analysis resource | Pathway analysis | http://signalink.org/ (accessed 29 April 2022) | [186] |
37. | SIRC-TCM | Shanghai Innovative Research Center of Traditional Chinese Medicine | Detailed analysis of traditional chinese medicine | http://www.tcm120.com/1w2k/tcm_species.asp (accessed 29 April 2022) | [187] |
38. | STITCH | Search Tool for Interactions of Chemicals | Analysis of target–drug relationship and biological pathways | http://stitch.embl.de/ (accessed 29 April 2022) | [188] |
39. | STRING | Search Tool for the Retrieval of Interacting Genes/Proteins | Retrieval of protein–protein interaction network | http://string-db.org/ (accessed 29 April 2022) | [189] |
SwissTargetPrediction | Estimate the macromolecular targets of a small molecule | Identification of compound related genes | http://www.swisstargetprediction.ch/ (accessed 29 April 2022) | [190] | |
40. | TCMGeneDIT | Database of traditional Chinese medicine, gene, and disease information using text mining | Detailed analysis of traditional chinese medicine | http://tcm.lifescience.ntu.edu.tw/ (accessed 29 April 2022) | [191] |
41. | TCMID | Traditional Chinese medicine integrated database | Detailed analysis of traditional chinese medicine | http://www.megabionet.org/tcmid/ (accessed 29 April 2022) | [192] |
42. | TcmSP | Traditional Chinese medicine systems pharmacology database | Detailed analysis of traditional chinese medicine | http://tcmspnw.com (accessed 29 April 2022) | [193] |
43. | TD@T | Database of traditional Chinese medicine @ Taiwan | Retrieval of traditional chinese medicine related information | http://tcm.cmu.edu.tw/ (accessed 29 April 2022) | [173] |
44. | TTD | Therapeutic Target database | Drug target identification | http://bidd.nus.edu.sg/group/cjttd/ (accessed 29 April 2022) | [194] |
45. | Ucinet | Computer program for the analysis and visualization of networks | Network analysis | http://www.netminer.com/ (accessed 29 April 2022) | [195] |
46. | UniProtKB | Universal protein knowledge database | Analysis of protein | http://www.uniprot.org/uniprot/ (accessed 29 April 2022) | [196] |
Diseases | Herb/Herbal Formula | Reference |
---|---|---|
Asthma | Qingfei Xiaoyan Wan | [235] |
Zhike Chuanbei Pipa Dropping Pill | [236] | |
Breast cancer | Bushen Zhuanggu formula | [237] |
Yanghe decoction | [211] | |
Bronchial Asthma | Schisandra chinensis | [238] |
Ma Huang Tang | [239] | |
Si Jun Zi Tang | [240] | |
Cardiovascular and cerebral vascular diseases | Nao Xin Tong | [241] |
Tong Xin Luo | [242] | |
Dan Hong injection | [243] | |
Astragali radix | [244] | |
Liu Wei Di Huang pill | [245] | |
Bai Hu Jia Ren Shen decoction | [246] | |
Bu Yang Huan Wu decoction | [247] | |
Cardiovascular disease | Panax notoginseng | [248] |
Salvia miltiorrhiza | [249] | |
Naoxintong | [250] | |
Fufang Danshen formula | [251] | |
Ginkgo biloba leaves | [219] | |
Radix Curcumae | [221] | |
Salvia miltiorrhiza Burge. and Carthamus tinctorius | [222] | |
Shuxuening injection | [223] | |
Chronic bronchitis | Eriobotrya japonica | [252] |
Zhi Chuan Ling | [253] | |
Chronic obstructive pulmonary lung disease | Bu Fei Jian Pi Formula | [254] |
Yin Huang Qing Fei | [53] | |
Tanshinone | [255] | |
Colorectal cancer | Hedyotis diffusa | [213] |
COVID-19 | Xuebijing injection | [203] |
Qingfeipaidu decoction | [204] | |
Lianhuaqingwen | [205] | |
Huashi Baidu formula | [100] | |
Jinhua Qinggan Granule, Lianhua Qingwen Capsule, Xuebijing Injection, Qingfei Paidu Decoction, HuaShi BaiDu Formula, and XuanFei BaiDu Granule | [74] | |
Diabetes mellitus | Bu-Fei-Yi-Shen formula | [256] |
Xiao Ke Yin Shui | [230] | |
Erigeron breviscapus | [257] | |
Astragaloside IV | [228] | |
Tangminling tablets | [123] | |
Sorghum bicolor | [229] | |
Xiao Ke Yin Shui | [230] | |
Dysmenorrhea of gynecology | Si Wu Tang | [248] |
Fever and chill | Da Chaihu Decoction and Xiao Chaihu Decoction | [258] |
Gastritis | Atractylodes macrocephala Koidz | [259] |
Arctigenin | [260] | |
Gastric cancer | Shen-qi-Yi-zhu decoction | [212] |
Gout | Modified Simiao wan | [95] |
Hepatocellular carcinoma, intestinal tuberculosis, and gastrointestinal inflammation | Gansui Banxia tang | [261] |
Hepatocyte injury | Fructus Schisandrae chinensis | [262] |
Hyperlipidemia | Cynarin | [263] |
Poncimarin, Hexahydrocurcumin, and Forsythoside C | [264] | |
Inflammation | Folium eriobotryae | [265] |
Kidney disease | Bushen Huoxue formula | [81] |
Leukemia | Realgar-Indigo naturalis formula | [266] |
Liver disease | Jian Gan Bao | [267] |
Zhi Zi Da Huang decoction | [268] | |
Lung cancer | Xia Qi Decoction | [269] |
Fuzheng Kangai | [215] | |
kushen injection | [216] | |
Maintain the stasis of blood | Danggui | [270] |
Buyang Huanwu decoction | [271] | |
Migraine | Da Chuanxiong formula | [272] |
Myocardial infarction | Xuesaitong injection | [273] |
QiShen YiQi | [274] | |
Shenmai injection | [44] | |
Neurodegenratve diseases | Tinospora sinensis | [231] |
Brucea javanica, Dichroa febrifuga, E. micranthum Harms, Erythrophleum guineense, Holarrhena antidysenterica, and Japanese ardisia | [233] | |
Shaoyao Gancao | [232] | |
Medicago sativa | [234] | |
Osteoarthritis | Taohong Siwu decoction | [275] |
Rheumatoid arthritis | Qing-Luo-Yin | [45] |
Fructus schisandrae | [276] | |
Thrombosis | Fufang Xueshuantong | [277] |
Traumatic injury | Diesun Miaofang | [278] |
Type 2 diabetes mellitus | Ge Gen Qin Lian decoction | [94] |
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Noor, F.; Tahir ul Qamar, M.; Ashfaq, U.A.; Albutti, A.; Alwashmi, A.S.S.; Aljasir, M.A. Network Pharmacology Approach for Medicinal Plants: Review and Assessment. Pharmaceuticals 2022, 15, 572. https://doi.org/10.3390/ph15050572
Noor F, Tahir ul Qamar M, Ashfaq UA, Albutti A, Alwashmi ASS, Aljasir MA. Network Pharmacology Approach for Medicinal Plants: Review and Assessment. Pharmaceuticals. 2022; 15(5):572. https://doi.org/10.3390/ph15050572
Chicago/Turabian StyleNoor, Fatima, Muhammad Tahir ul Qamar, Usman Ali Ashfaq, Aqel Albutti, Ameen S. S. Alwashmi, and Mohammad Abdullah Aljasir. 2022. "Network Pharmacology Approach for Medicinal Plants: Review and Assessment" Pharmaceuticals 15, no. 5: 572. https://doi.org/10.3390/ph15050572