BMB Reports

Korean Society for Biochemistry and Molecular Biology (생화학분자생물학회)
권호 표지
DOI QR코드

DOI QR Code

Systems pharmacology approaches in herbal medicine research: a brief review

  • Lee, Myunggyo (Department of Pharmacy, College of Pharmacy, Pusan National University) ;
  • Shin, Hyejin (Korean Medicine (KM) Convergence Research Division, Korea Institute of Oriental Medicine) ;
  • Park, Musun (Korean Medicine (KM) Data Division, Korea Institute of Oriental Medicine) ;
  • Kim, Aeyung (Korean Medicine (KM) Application Center, Korea Institute of Oriental Medicine) ;
  • Cha, Seongwon (Korean Medicine (KM) Data Division, Korea Institute of Oriental Medicine) ;
  • Lee, Haeseung (Department of Pharmacy, College of Pharmacy, Pusan National University)
  • Received : 2022.05.20
  • Accepted : 2022.07.21
  • Published : 2022.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

Herbal medicine, a multi-component treatment, has been extensively practiced for treating various symptoms and diseases. However, its molecular mechanism of action on the human body is unknown, which impedes the development and application of herbal medicine. To address this, recent studies are increasingly adopting systems pharmacology, which interprets pharmacological effects of drugs from consequences of the interaction networks that drugs might have. Most conventional network-based approaches collect associations of herb-compound, compound-target, and target-disease from individual databases, respectively, and construct an integrated network of herb-compound-target-disease to study the complex mechanisms underlying herbal treatment. More recently, rapid advances in high-throughput omics technology have led numerous studies to exploring gene expression profiles induced by herbal treatments to elicit information on direct associations between herbs and genes at the genome-wide scale. In this review, we summarize key databases and computational methods utilized in systems pharmacology for studying herbal medicine. We also highlight recent studies that identify modes of action or novel indications of herbal medicine by harnessing drug-induced transcriptome data.

Keywords

Acknowledgement

This work has been supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2021R1C1C1003988) and the research program of the Korea Institute of Oriental Medicine (KIOM) (KSN2023120 and KSN2022240).

References

  1. Zhao X, Zheng X, Fan TP, Li Z, Zhang Y and Zheng J (2015) A novel drug discovery strategy inspired by traditional medicine philosophies. Science 347, S38-S40
  2. Corson TW and Crews CM (2007) Molecular understanding and modern application of traditional medicines: triumphs and trials. Cell 130, 769-774 https://doi.org/10.1016/j.cell.2007.08.021
  3. Stancak B, Pella J, Resetar J, Dubikova M, Palinsky M and Bodnar J (1990) [Does the left atrial dimension predict the short-term effect of electric cardioversion?]. Vnitr Lek 36, 753-758
  4. Fernandes F, Barroso MF, De Simone A et al (2022) Multi-target neuroprotective effects of herbal medicines for Alzheimer's disease. J Ethnopharmacol 290, 115107
  5. Zhao S and Iyengar R (2012) Systems pharmacology: network analysis to identify multiscale mechanisms of drug action. Annu Rev Pharmacol Toxicol 52, 505-521 https://doi.org/10.1146/annurev-pharmtox-010611-134520
  6. Hopkins AL (2008) Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 4, 682-690 https://doi.org/10.1038/nchembio.118
  7. Kibble M, Saarinen N, Tang J, Wennerberg K, Makela S and Aittokallio T (2015) Network pharmacology applications to map the unexplored target space and therapeutic potential of natural products. Nat Prod Rep 32, 1249-1266 https://doi.org/10.1039/C5NP00005J
  8. Huang C, Zheng C, Li Y, Wang Y, Lu A and Yang L (2014) Systems pharmacology in drug discovery and therapeutic insight for herbal medicines. Brief Bioinform 15, 710-733 https://doi.org/10.1093/bib/bbt035
  9. Casas AI, Hassan AA, Larsen SJ et al (2019) From single drug targets to synergistic network pharmacology in ischemic stroke. Proc Natl Acad Sci U S A 116, 7129-7136 https://doi.org/10.1073/pnas.1820799116
  10. Wang X, Zhu H, Jiang Y et al (2022) PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein-protein interaction network. Brief Bioinform 23, bbab587
  11. Xu Z (2011) Modernization: one step at a time. Nature 480, S90-S92 https://doi.org/10.1038/480S90a
  12. Li P, Zhang H, Zhang W et al (2022) TMNP: a transcriptome-based multi-scale network pharmacology platform for herbal medicine. Brief Bioinform 23, bbab542
  13. Lamb J, Crawford ED, Peck D et al (2006) The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929-1935 https://doi.org/10.1126/science.1132939
  14. Subramanian A, Narayan R, Corsello SM et al (2017) A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell 171, 1437-1452 e1417
  15. Kwon OS, Kim W, Cha HJ and Lee H (2019) In silico drug repositioning: from large-scale transcriptome data to therapeutics. Arch Pharm Res 42, 879-889 https://doi.org/10.1007/s12272-019-01176-3
  16. Jiang H, Hu C and Chen M (2021) The advantages of connectivity map applied in traditional Chinese medicine. Front Pharmacol 12, 474267
  17. Lv C, Wu X, Wang X et al (2017) The gene expression profiles in response to 102 traditional Chinese medicine (TCM) components: a general template for research on TCMs. Sci Rep 7, 352
  18. Li P, Chen C, Zhang W et al (2019) Detection of vasodilators from herbal components by a transcriptome-based functional gene module reference approach. Front Pharmacol 10, 1144
  19. Wang L, Zhang W, Lu Z et al (2020) Functional gene module-based identification of phillyrin as an anticardiac fibrosis agent. Front Pharmacol 11, 1077
  20. Fang S, Dong L, Liu L et al (2021) HERB: a high-through-put experiment- and reference-guided database of traditional Chinese medicine. Nucleic Acids Res 49, D1197-D1206 https://doi.org/10.1093/nar/gkaa1063
  21. Huang L, Xie D, Yu Y et al (2018) TCMID 2.0: a comprehensive resource for TCM. Nucleic Acids Res 46, D1117-D1120 https://doi.org/10.1093/nar/gkx1028
  22. Ru J, Li P, Wang J et al (2014) TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform 6, 13
  23. Liu Z, Guo F, Wang Y et al (2016) BATMAN-TCM: a bioinformatics analysis tool for molecular mechANism of traditional Chinese medicine. Sci Rep 6, 21146
  24. Wu Y, Zhang F, Yang K et al (2019) SymMap: an integrative database of traditional Chinese medicine enhanced by symptom mapping. Nucleic Acids Res 47, D1110-D1117 https://doi.org/10.1093/nar/gky1021
  25. Kim S, Chen J, Cheng T et al (2021) PubChem in 2021: new data content and improved web interfaces. Nucleic Acids Res 49, D1388-D1395 https://doi.org/10.1093/nar/gkaa971
  26. Daina A, Michielin O and Zoete V (2017) SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 7, 42717
  27. Daina A, Michielin O and Zoete V (2019) SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Res 47, W357-W364 https://doi.org/10.1093/nar/gkz382
  28. Szklarczyk D, Santos A, von Mering C, Jensen LJ, Bork P and Kuhn M (2016) STITCH 5: augmenting protein-chemical interaction networks with tissue and affinity data. Nucleic Acids Res 44, D380-384 https://doi.org/10.1093/nar/gkv1277
  29. Chen X, Ji ZL and Chen YZ (2002) TTD: therapeutic target database. Nucleic Acids Res 30, 412-415 https://doi.org/10.1093/nar/30.1.412
  30. Yu H, Chen J, Xu X et al (2012) A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data. PLoS One 7, e37608
  31. Zhao S and Li S (2010) Network-based relating pharmacological and genomic spaces for drug target identification. PLoS One 5, e11764
  32. UniProt C (2021) UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res 49, D480-D489 https://doi.org/10.1093/nar/gkaa1100
  33. Stelzer G, Rosen N, Plaschkes I et al (2016) The genecards suite: from gene data mining to disease genome sequence analyses. Curr Protoc Bioinformatics 54, 1 30 31-31 30 33
  34. Kanehisa M, Furumichi M, Tanabe M, Sato Y and Morishima K (2017) KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 45, D353-D361 https://doi.org/10.1093/nar/gkw1092
  35. Jassal B, Matthews L, Viteri G et al (2020) The reactome pathway knowledgebase. Nucleic Acids Res 48, D498-D503
  36. The Gene Ontology Consortium (2019) The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Res 47, D330-D338 https://doi.org/10.1093/nar/gky1055
  37. Subramanian A, Tamayo P, Mootha VK et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102, 15545-15550 https://doi.org/10.1073/pnas.0506580102
  38. Szklarczyk D, Gable AL, Nastou KC et al (2021) The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res 49, D605-D612 https://doi.org/10.1093/nar/gkaa1074
  39. Keshava Prasad TS, Goel R, Kandasamy K et al (2009) Human Protein Reference Database--2009 update. Nucleic Acids Res 37, D767-772 https://doi.org/10.1093/nar/gkn892
  40. Athanasios A, Charalampos V, Vasileios T and Ashraf GM (2017) Protein-Protein Interaction (PPI) Network: recent advances in drug discovery. Curr Drug Metab 18, 5-10 https://doi.org/10.2174/138920021801170119204832
  41. Pinero J, Ramirez-Anguita JM, Sauch-Pitarch J et al (2020) The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res 48, D845-D855
  42. Amberger JS, Bocchini CA, Scott AF and Hamosh A (2019) OMIM.org: leveraging knowledge across phenotypegene relationships. Nucleic Acids Res 47, D1038-D1043 https://doi.org/10.1093/nar/gky1151
  43. Kohler S, Gargano M, Matentzoglu N et al (2021) The Human Phenotype Ontology in 2021. Nucleic Acids Res 49, D1207-D1217 https://doi.org/10.1093/nar/gkaa1043
  44. Dancey JE and Chen HX (2006) Strategies for optimizing combinations of molecularly targeted anticancer agents. Nat Rev Drug Discov 5, 649-659 https://doi.org/10.1038/nrd2089
  45. Reddy AS and Zhang S (2013) Polypharmacology: drug discovery for the future. Expert Rev Clin Pharmacol 6, 41-47 https://doi.org/10.1586/ecp.12.74
  46. Li S, Zhang B and Zhang N (2011) Network target for screening synergistic drug combinations with application to traditional Chinese medicine. BMC Syst Biol 5, S10
  47. Wang X, Xu X, Tao W, Li Y, Wang Y and Yang L (2012) A systems biology approach to uncovering pharmacological synergy in herbal medicines with applications to cardiovascular disease. Evid Based Complement Alternat Med 2012, 519031
  48. Li P, Chen J, Zhang W, Fu B and Wang W (2017) Transcriptome inference and systems approaches to polypharmacology and drug discovery in herbal medicine. J Ethnopharmacol 195, 127-136 https://doi.org/10.1016/j.jep.2016.10.020
  49. Wang N, Li P, Hu X et al (2019) Herb target prediction based on representation learning of symptom related heterogeneous network. Comput Struct Biotechnol J 17, 282-290 https://doi.org/10.1016/j.csbj.2019.02.002
  50. Zhao W, Wu H and He J (2021) HGNA-HTI: Heterogeneous graph neural network with attention mechanism for prediction of herb-target interactions. IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 3949-3956
  51. Keum J, Yoo S, Lee D and Nam H (2016) Prediction of compound-target interactions of natural products using large-scale drug and protein information. BMC Bioinformatics 17 Suppl 6, 219
  52. Grover A and Leskovec J (2016) node2vec: scalable feature learning for networks. KDD 2016, 855-864
  53. Bleakley K and Yamanishi Y (2009) Supervised prediction of drug-target interactions using bipartite local models. Bioinformatics 25, 2397-2403 https://doi.org/10.1093/bioinformatics/btp433
  54. Xu X (2006) New concepts and approaches for drug discovery based on traditional Chinese medicine. Drug Discov Today Technol 3, 247-253 https://doi.org/10.1016/j.ddtec.2006.09.008
  55. Yoo S, Nam H and Lee D (2018) Phenotype-oriented network analysis for discovering pharmacological effects of natural compounds. Sci Rep 8, 11667
  56. Yoo S, Yang HC, Lee S et al (2020) A deep learning-based approach for identifying the medicinal uses of plant-derived natural compounds. Front Pharmacol 11, 584875
  57. Yoo S, Kim K, Nam H and Lee D (2018) Discovering health benefits of phytochemicals with integrated analysis of the molecular network, chemical properties and ethnopharmacological evidence. Nutrients 10, 1042
  58. Kim E, Choi AS and Nam H (2019) Drug repositioning of herbal compounds via a machine-learning approach. BMC Bioinformatics 20, 247
  59. Gottlieb A, Stein GY, Ruppin E and Sharan R (2011) PREDICT: a method for inferring novel drug indications with application to personalized medicine. Mol Syst Biol 7, 496
  60. Li P, Chen J, Wang J et al (2014) Systems pharmacology strategies for drug discovery and combination with applications to cardiovascular diseases. J Ethnopharmacol 151, 93-107 https://doi.org/10.1016/j.jep.2013.07.001
  61. Cases M and Mestres J (2009) A chemogenomic approach to drug discovery: focus on cardiovascular diseases. Drug Discov Today 14, 479-485 https://doi.org/10.1016/j.drudis.2009.02.010
  62. Kong DX, Li XJ and Zhang HY (2009) Where is the hope for drug discovery? Let history tell the future. Drug Discov Today 14, 115-119 https://doi.org/10.1016/j.drudis.2008.07.002
  63. Wang Y, Yang H, Chen L, Jafari M and Tang J (2021) Network-based modeling of herb combinations in traditional Chinese medicine. Brief Bioinform 22, bbab106
  64. Wishart DS, Feunang YD, Guo AC et al (2018) DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res 46, D1074-D1082 https://doi.org/10.1093/nar/gkx1037
  65. Thorn CF, Klein TE and Altman RB (2005) PharmGKB: the pharmacogenetics and pharmacogenomics knowledge base. Methods Mol Biol 311, 179-191
  66. Ye H, Ye L, Kang H et al (2011) HIT: linking herbal active ingredients to targets. Nucleic Acids Res 39, D1055-D1059 https://doi.org/10.1093/nar/gkq1165
  67. Kuhn M, Letunic I, Jensen LJ and Bork P (2016) The SIDER database of drugs and side effects. Nucleic Acids Res 44, D1075-D1079 https://doi.org/10.1093/nar/gkv1075
  68. Rappaport N, Twik M, Plaschkes I et al (2017) MalaCards: an amalgamated human disease compendium with diverse clinical and genetic annotation and structured search. Nucleic Acids Res 45, D877-D887 https://doi.org/10.1093/nar/gkw1012
  69. Kilicoglu H, Shin D, Fiszman M, Rosemblat G and Rindflesch TC (2012) SemMedDB: a PubMed-scale repository of biomedical semantic predications. Bioinformatics 28, 3158-3160 https://doi.org/10.1093/bioinformatics/bts591
  70. Zhou X, Menche J, Barabasi AL and Sharma A (2014) Human symptoms-disease network. Nat Commun 5, 4212
  71. Hu Z, Dong Y, Wang K and Sun Y (2020) Heterogeneous graph transformer. Proceedings of The Web Conference 2020, 2704-2710
  72. Yang K, Liu G, Wang N et al (2018) Heterogeneous network propagation for herb target identification. BMC Med Inform Decis Mak 18, 17
  73. Li B, Ma C, Zhao X et al (2018) YaTCM: yet another traditional Chinese medicine database for drug discovery. Comput Struct Biotechnol J 16, 600-610 https://doi.org/10.1016/j.csbj.2018.11.002
  74. Xu HY, Liu ZM, Fu Y et al (2017) [Exploiture and application of an internet-based computation platform for integrative pharmacology of traditional chinese medicine]. Zhongguo Zhong Yao Za Zhi 42, 3633-3638
  75. Chen X, Zhou H, Liu YB et al (2006) Database of traditional Chinese medicine and its application to studies of mechanism and to prescription validation. Br J Pharmacol 149, 1092-1103 https://doi.org/10.1038/sj.bjp.0706945
  76. Chen CY (2011) TCM Database@Taiwan: the world's largest traditional Chinese medicine database for drug screening in silico. PLoS One 6, e15939
  77. Yoo S, Ha S, Shin M, Noh K, Nam H and Lee D (2018) A data-driven approach for identifying medicinal combinations of natural products. IEEE Access 6, 58106-58118 https://doi.org/10.1109/ACCESS.2018.2874089
  78. Davis AP, King BL, Mockus S et al (2011) The Comparative Toxicogenomics Database: update 2011. Nucleic Acids Res 39, D1067-1072 https://doi.org/10.1093/nar/gkq813
  79. Gunther S, Kuhn M, Dunkel M et al (2008) SuperTarget and Matador: resources for exploring drug-target relationships. Nucleic Acids Res 36, D919-922 https://doi.org/10.1093/nar/gkm862
  80. Chatr-Aryamontri A, Breitkreutz BJ, Oughtred R et al (2015) The BioGRID interaction database: 2015 update. Nucleic Acids Res 43, D470-478 https://doi.org/10.1093/nar/gku1204
  81. Tatonetti NP, Ye PP, Daneshjou R and Altman RB (2012) Data-driven prediction of drug effects and interactions. Sci Transl Med 4, 125ra131
  82. UniProt C (2015) UniProt: a hub for protein information. Nucleic Acids Res 43, D204-212 https://doi.org/10.1093/nar/gku989
  83. Griffith M, Griffith OL, Coffman AC et al (2013) DGIdb: mining the druggable genome. Nat Methods 10, 1209-1210 https://doi.org/10.1038/nmeth.2689
  84. Robinson PN, Kohler S, Bauer S, Seelow D, Horn D and Mundlos S (2008) The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease. Am J Hum Genet 83, 610-615 https://doi.org/10.1016/j.ajhg.2008.09.017
  85. Pinero J, Bravo A, Queralt-Rosinach N et al (2017) DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res 45, D833-D839 https://doi.org/10.1093/nar/gkw943
  86. Liu T, Lin Y, Wen X, Jorissen RN and Gilson MK (2007) BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res 35, D198-D201 https://doi.org/10.1093/nar/gkl999
  87. Cheng F, Kovacs IA and Barabasi AL (2019) Network-based prediction of drug combinations. Nat Commun 10, 1197
  88. Chavan P, Joshi K and Patwardhan B (2006) DNA microarrays in herbal drug research. Evid Based Complement Alternat Med 3, 447-457 https://doi.org/10.1093/ecam/nel075
  89. Hwang YH, Kim T, Cho WK, Jang D, Ha JH and Ma JY (2012) Food- and gender-dependent pharmacokinetics of paeoniflorin after oral administration with Samul-tang in rats. J Ethnopharmacol 142, 161-167 https://doi.org/10.1016/j.jep.2012.04.032
  90. Ryu JH and Yook CS (2001) The effects of Sa-Mul-Tang, a traditional Chinese medicine, on phenylhydrazineinduced anemic rats. J Appl Phamacol 9, 109-114
  91. Wen Z, Wang Z, Wang S et al (2011) Discovery of molecular mechanisms of traditional Chinese medicinal formula Si-Wu-Tang using gene expression microarray and connectivity map. PLoS One 6, e18278
  92. Liu M, Fan J, Wang S et al (2013) Transcriptional profiling of Chinese medicinal formula Si-Wu-Tang on breast cancer cells reveals phytoestrogenic activity. BMC Complement Altern Med 13, 11
  93. Fang Z, Lu B, Liu M et al (2013) Evaluating the pharmacological mechanism of Chinese medicine Si-Wu-Tang through multi-level data integration. PLoS One 8, e72334
  94. Huang GC, Tsai YZ, Lee CJ, Chang HY and Wang CC (2019) Elucidation of the Effects of Si-Wu Tang on menstrual disorder patterns through activation of aromatase and antioxidation. Evid Based Complement Alternat Med 2019, 4761651
  95. Kim J and You S (2020) Samul-tang ameliorates oocyte damage due to cyclophosphamide-induced chronic ovarian dysfunction in mice. Sci Rep 10, 21925
  96. Kim J and You S (2021) Effect of samul-tang on female fertility via RAS signaling pathway in ovaries of aged mice. Aging (Albany NY) 13, 14829-14842 https://doi.org/10.18632/aging.203150
  97. Kim J and You S (2022) Restoration of miR-223-3p expression in aged mouse uteri with Samul-tang administration. Integr Med Res 11, 100835
  98. Kim J, Fann DY, Seet RC, Jo DG, Mattson MP and Arumugam TV (2016) Phytochemicals in ischemic stroke. Neuromolecular Med 18, 283-305 https://doi.org/10.1007/s12017-016-8403-0
  99. Duan X, Han L, Peng D et al (2018) High throughput mRNA sequencing reveals potential therapeutic targets of Tao-Hong-Si-Wu decoction in experimental middle cerebral artery occlusion. Front Pharmacol 9, 1570
  100. Qu Z, Cui J, Harata-Lee Y et al (2016) Identification of candidate anti-cancer molecular mechanisms of compound Kushen injection using functional genomics. Oncotarget 7, 66003-66019 https://doi.org/10.18632/oncotarget.11788
  101. Shen H, Qu Z, Harata-Lee Y et al (2019) Understanding the mechanistic contribution of herbal extracts in compound Kushen injection with transcriptome analysis. Front Oncol 9, 632
  102. Nourmohammadi S, Aung TN, Cui J et al (2019) Effect of compound Kushen injection, a natural compound mixture, and its identified chemical components on migration and invasion of colon, brain, and breast cancer cell lines. Front Oncol 9, 314
  103. Cui J, Qu Z, Harata-Lee Y et al (2020) The effect of compound kushen injection on cancer cells: integrated identification of candidate molecular mechanisms. PLoS One 15, e0236395
  104. Zhang L, Ji YX, Jiang WL and Lv CJ (2015) Protective roles of pulmonary rehabilitation mixture in experimental pulmonary fibrosis in vitro and in vivo. Braz J Med Biol Res 48, 545-552 https://doi.org/10.1590/1414-431x20144301
  105. Li H, Wang Z, Zhang J et al (2018) Feifukang ameliorates pulmonary fibrosis by inhibiting JAK-STAT signaling pathway. BMC Complement Altern Med 18, 234
  106. Tan YQ, Chen HW, Li J and Wu QJ (2020) Efficacy, chemical constituents, and pharmacological actions of Radix Paeoniae Rubra and Radix Paeoniae Alba. Front Pharmacol 11, 1054
  107. Baek SJ, Lee H, Park SM et al (2022) Identification of a novel anticancer mechanism of Paeoniae Radix extracts based on systematic transcriptome analysis. Biomed Pharmacother 148, 112748
  108. Chen B, Garmire L, Calvisi DF, Chua MS, Kelley RK and Chen X (2020) Harnessing big 'omics' data and AI for drug discovery in hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol 17, 238-251 https://doi.org/10.1038/s41575-019-0240-9
  109. Luo S, Li H, Mo Z et al (2019) Connectivity map identifies luteolin as a treatment option of ischemic stroke by inhibiting MMP9 and activation of the PI3K/Akt signaling pathway. Exp Mol Med 51, 1-11
  110. Liu J, Lee J, Salazar Hernandez MA, Mazitschek R and Ozcan U (2015) Treatment of obesity with celastrol. Cell 161, 999-1011 https://doi.org/10.1016/j.cell.2015.05.011
  111. Wang TY, Li Q and Bi KS (2018) Bioactive flavonoids in medicinal plants: structure, activity and biological fate. Asian J Pharm Sci 13, 12-23 https://doi.org/10.1016/j.ajps.2017.08.004
  112. Zhu Q, Wong AK, Krishnan A et al (2015) Targeted exploration and analysis of large cross-platform human transcriptomic compendia. Nat Methods 12, 211-214, 213 p following 214
  113. Lee JJ, Kim KH, Kim EJ et al (2018) Anti-inflammatory activity of the decoction of Forsythia suspensa (Thunb.) Vahl is related to Nrf2 and A20. J Ethnopharmacol 227, 97-104 https://doi.org/10.1016/j.jep.2018.08.027
  114. Belyaeva A, Cammarata L, Radhakrishnan A et al (2021) Causal network models of SARS-CoV-2 expression and aging to identify candidates for drug repurposing. Nat Commun 12, 1024
  115. Budhraja A, Basu A, Gheware A et al (2022) Molecular signature of postmortem lung tissue from COVID-19 patients suggests distinct trajectories driving mortality. Dis Model Mech 15, dmm049572