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Computational methods and applications for quantitative systems pharmacology

  • Fuda Xie
  • Jiangyong GuEmail author
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Abstract

Background

Quantitative systems pharmacology (QSP) is an emerging discipline that integrates diverse data to quantitatively explore the interactions between drugs and multi-scale systems including small compounds, nucleic acids, proteins, pathways, cells, organs and disease processes.

Results

Various computational methods such as ADME/Tevaluation, molecular modeling, logical modeling, network modeling, pathway analysis, multi-scale systems pharmacology platforms and virtual patient for QSP have been developed. We reviewed the major progresses and broad applications in medical guidance, drug discovery and exploration of pharmacodynamic material basis and mechanism of traditional Chinese medicine.

Conclusion

QSP has significant achievements in recent years and is a promising approach for quantitative evaluation of drug efficacy and systematic exploration of mechanisms of action of drugs.

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Keywords

quantitative systems pharmacology network modeling multi-scale platforms traditional Chinese medicine 
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Notes

Acknowledgements

This work was supported by the start-up support for scientific research of Xinglin Young Scholar in Guangzhou University of Chinese Medicine (A1-AFD018161Z04).

References

  1. 1.
    Berger, S. I. and Iyengar, R. (2009) Network analyses in systems pharmacology. Bioinformatics, 25, 2466–2472CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Zhao, S. and Iyengar, R. (2012) Systems pharmacology: network analysis to identify multiscale mechanisms of drug action. Annu. Rev. Pharmacol. Toxicol., 52, 505–521CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Boran, A. D. and Iyengar, R. (2010) Systems pharmacology. Mt. Sinai J. Med., 77, 333–344CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Zhou, W., Wang, Y., Lu, A. and Zhang, G. (2016) Systems pharmacology in small molecular drug discovery. Int. J. Mol. Sci., 17, 246CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Gu, J., Zhang, X., Ma, Y., Li, N., Luo, F., Cao, L., Wang, Z., Yuan, G., Chen, L., Xiao, W., et al. (2015) Quantitative modeling of dose-response and drug combination based on pathway network. J. Cheminform., 7, 19CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Spiros, A., Roberts, P. and Geerts, H. (2014) A computer-based quantitative systems pharmacology model of negative symptoms in schizophrenia: exploring glycine modulation of excitationinhibition balance. Front. Pharmacol., 5, 229CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Fang, J., Wu, Z., Cai, C., Wang, Q., Tang, Y. and Cheng, F. (2017) Quantitative and systems pharmacology. 1. in silico prediction of drug-target interactions of natural products enables new targeted cancer therapy. J. Chem. Inf. Model., 57, 2657–2671CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Fleisher, B., Brown, A. N. and Ait-Oudhia, S. (2017) Application of pharmacometrics and quantitative systems pharmacology to cancer therapy: the example of luminal a breast cancer. Pharmacol. Res., 124, 20–33CrossRefPubMedGoogle Scholar
  9. 9.
    Geerts, H., Spiros, A. and Roberts, P. (2018) Impact of amyloidbeta changes on cognitive outcomes in Alzheimer’s disease: analysis of clinical trials using a quantitative systems pharmacology model. Alzheimers Res. Ther., 10, 14CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Barabási, A. L., Gulbahce, N. and Loscalzo, J. (2011) Network medicine: a network-based approach to human disease. Nat. Rev. Genet., 12, 56–68CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Pérez-Nueno, V. I. (2015) Using quantitative systems pharmacology for novel drug discovery. Expert Opin. Drug Discov., 10, 1315–1331CrossRefPubMedGoogle Scholar
  12. 12.
    Woodhead, J. L., Watkins, P. B., Howell, B. A., Siler, S. Q. and Shoda, L. K. M. (2017) The role of quantitative systems pharmacology modeling in the prediction and explanation of idiosyncratic drug-induced liver injury. Drug Metab. Pharmacokinet., 32, 40–45CrossRefPubMedGoogle Scholar
  13. 13.
    Androulakis, I. P. (2016) Quantitative systems pharmacology: a framework for context. Curr. Pharmacol. Rep., 2, 152–160CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    van der Graaf, P. H. and Benson, N. (2011) Systems pharmacology: bridging systems biology and pharmacokineticspharmacodynamics (PKPD) in drug discovery and development. Pharm. Res., 28, 1460–1464CrossRefPubMedGoogle Scholar
  15. 15.
    Leil, T. A. and Bertz, R. (2014) Quantitative systems pharmacology can reduce attrition and improve productivity in pharmaceutical research and development. Front. Pharmacol., 5, 247CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Rao, R. T., Scherholz, M. L., Hartmanshenn, C., Bae, S. A. and Androulakis, I. P. (2017) On the analysis of complex biological supply chains: from process systems engineering to quantitative systems pharmacology. Comput. Chem. Eng., 107, 100–110CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Yu, J., Cilfone, N. A., Large, E. M., Sarkar, U., Wishnok, J. S., Tannenbaum, S. R., Hughes, D. J., Lauffenburger, D. A., Griffith, L. G., Stokes, C. L., et al. (2015) Quantitative systems pharmacology approaches applied to microphysiological systems (MPS): data interpretation and multi-MPS integration. CPT Pharmacometrics Syst. Pharmacol., 4, 585–594CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Musante, C. J., Abernethy, D. R., Allerheiligen, S. R., Lauffenburger, D. A. and Zager, M. G. (2016) GPS for QSP: A summary of the ACoP6 symposium on quantitative systems pharmacology and a stage for near-term efforts in the field. CPT Pharmacometrics Syst. Pharmacol., 5, 449–451CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Ribba, B., Grimm, H. P., Agoram, B., Davies, M. R., Gadkar, K., Niederer, S., van Riel, N., Timmis, J. and van der Graaf, P. H. (2017) Methodologies for quantitative systems pharmacology (QSP) models: design and estimation. CPT Pharmacometrics Syst. Pharmacol., 6, 496–498CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Timmis, J., Alden, K., Andrews, P., Clark, E., Nellis, A., Naylor, B., Coles, M. and Kaye, P. (2017) Building confidence in quantitative systems pharmacology models: an engineer’s guide to exploring the rationale in model design and development. CPT Pharmacometrics Syst. Pharmacol., 6, 156–167CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Cherkaoui-Rbati, M. H., Paine, S. W., Littlewood, P. and Rauch, C. (2017) A quantitative systems pharmacology approach, incorporating a novel liver model, for predicting pharmacokinetic drug-drug interactions. PLoS One, 12, e0183794CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Rogers, M., Lyster, P. and Okita, R. (2013) NIH support for the emergence of quantitative and systems pharmacology. CPT Pharmacometrics Syst. Pharmacol., 2, e37CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Wist, A. D., Berger, S. I. and Iyengar, R. (2009) Systems pharmacology and genome medicine: a future perspective. Genome Med., 1, 11CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Wang, Z. and Deisboeck, T. S. (2014) Mathematical modeling in cancer drug discovery. Drug Discov. Today, 19, 145–150CrossRefPubMedGoogle Scholar
  25. 25.
    Medina-Franco, J. L., Giulianotti, M. A., Welmaker, G. S. and Houghten, R. A. (2013) Shifting from the single to the multitarget paradigm in drug discovery. Drug Discov. Today, 18, 495–501CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Hopkins, A. L. (2007) Network pharmacology. Nat. Biotechnol., 25, 1110–1111CrossRefPubMedGoogle Scholar
  27. 27.
    Goh, K. I. and Choi, I. G. (2012) Exploring the human diseasome: the human disease network. Brief. Funct. Genomics, 11, 533–542CrossRefPubMedGoogle Scholar
  28. 28.
    Goh, K. I., Cusick, M. E., Valle, D., Childs, B., Vidal, M. and Barabási, A. L. (2007) The human disease network. Proc. Natl. Acad. Sci. USA, 104, 8685–8690CrossRefPubMedGoogle Scholar
  29. 29.
    Zhang, W., Pei, J. and Lai, L. (2017) Computational multitarget drug design. J. Chem. Inf. Model., 57, 403–412CrossRefPubMedGoogle Scholar
  30. 30.
    Yildirim, M. A., Goh, K. I., Cusick, M. E., Barabási, A. L. and Vidal, M. (2007) Drug-target network. Nat. Biotechnol., 25, 1119–1126CrossRefPubMedGoogle Scholar
  31. 31.
    Barneh, F., Jafari, M. and Mirzaie, M. (2016) Updates on drugtarget network; facilitating polypharmacology and data integration by growth of DrugBank database. Brief. Bioinformatics, 17, 1070–1080PubMedGoogle Scholar
  32. 32.
    Geerts, H., Spiros, A., Roberts, P. and Carr, R. (2013) Quantitative systems pharmacology as an extension of PK/PD modeling in CNS research and development. J. Pharmacokinet. Pharmacodyn., 40, 257–265CrossRefPubMedGoogle Scholar
  33. 33.
    Snelder, N., Ploeger, B. A., Luttringer, O., Rigel, D. F., Fu, F., Beil, M., Stanski, D. R. and Danhof, M. (2014) Drug effects on the CVS in conscious rats: separating cardiac output into heart rate and stroke volume using PKPD modelling. Br. J. Pharmacol., 171, 5076–5092PubMedPubMedCentralGoogle Scholar
  34. 34.
    Hansson, E. K., Amantea, M. A., Westwood, P., Milligan, P. A., Houk, B. E., French, J., Karlsson, M. O. and Friberg, L. E. (2013) PKPD Modeling of VEGF, sVEGFR-2, sVEGFR-3, and sKIT as predictors of tumor dynamics and overall survival following sunitinib treatment in GIST. CPT Pharmacometrics Syst. Pharmacol., 2, e84CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Reymond, J. L. and Awale, M. (2012) Exploring chemical space for drug discovery using the chemical universe database. ACS Chem. Neurosci., 3, 649–657CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Tian, S., Wang, J., Li, Y., Li, D., Xu, L. and Hou, T. (2015) The application of in silico drug-likeness predictions in pharmaceutical research. Adv. Drug Deliv. Rev., 86, 2–10CrossRefPubMedGoogle Scholar
  37. 37.
    May, E. R. (2014) Recent developments in molecular simulation approaches to study spherical virus capsids. Mol. Simul., 40, 878–888CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Field, M. J. (2015) Technical advances in molecular simulation since the 1980s. Arch. Biochem. Biophys., 582, 3–9CrossRefPubMedGoogle Scholar
  39. 39.
    Xie, L., Draizen, E. J. and Bourne, P. E. (2017) Harnessing big data for systems pharmacology. Annu. Rev. Pharmacol. Toxicol., 57, 245–262CrossRefPubMedGoogle Scholar
  40. 40.
    Liu, X., Zhu, F., Ma, X. H., Shi, Z., Yang, S. Y., Wei, Y. Q. and Chen, Y. Z. (2013) Predicting targeted polypharmacology for drug repositioning and multi-target drug discovery. Curr. Med. Chem., 20, 1646–1661CrossRefPubMedGoogle Scholar
  41. 41.
    Chiu, S. H. and Xie, L. (2016) Toward high-throughput predictive modeling of protein binding/unbinding kinetics. J. Chem. Inf. Model., 56, 1164–1174CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Hart, T. and Xie, L. (2016) Providing data science support for systems pharmacology and its implications to drug discovery. Expert Opin. Drug Discov., 11, 241–256CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Bloomingdale, P., Nguyen, V. A., Niu, J. and Mager, D. E. (2018) Boolean network modeling in systems pharmacology. J. Pharmacokinet. Pharmacodyn., 45, 159–180CrossRefPubMedGoogle Scholar
  44. 44.
    Irurzun-Arana, I., Pastor, J. M., Trocóniz, I. F. and Gómez-Mantilla, J. D. (2017) Advanced Boolean modeling of biological networks applied to systems pharmacology. Bioinformatics, 33, 1040–1048PubMedGoogle Scholar
  45. 45.
    Danhof, M. (2016) Systems pharmacology—towards the modeling of network interactions. Eur. J. Pharm. Sci., 94, 4–14CrossRefPubMedGoogle Scholar
  46. 46.
    Tang, Y., Tang, Q., Dong, C., Li, X., Zhang, Z. and An, F. (2015) Protein-protein interaction network and mechanism analysis of hepatitis C. Genet. Mol. Res., 14, 2069–2079CrossRefPubMedGoogle Scholar
  47. 47.
    Schurdak, M. E., Pei, F., Lezon, T. R., Carlisle, D., Friedlander, R., Taylor, D. L. and Stern, A. M. (2018) A quantitative systems pharmacology approach to infer pathways involved in complex disease phenotypes. Methods Mol. Biol., 1787, 207–222CrossRefPubMedGoogle Scholar
  48. 48.
    Li, Q., Li, X., Li, C., Chen, L., Song, J., Tang, Y. and Xu, X. (2011) A network-based multi-target computational estimation scheme for anticoagulant activities of compounds. PLoS One, 6, e14774CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Zhang, X., Gu, J., Cao, L., Ma, Y., Su, Z., Luo, F., Wang, Z., Li, N., Yuan, G., Chen, L., et al. (2014) Insights into the inhibition and mechanism of compounds against LPS-induced PGE2 production: a pathway network-based approach and molecular dynamics simulations. Integr. Biol., 6, 1162–1169CrossRefGoogle Scholar
  50. 50.
    Gu, J., Li, Q., Chen, L., Li, Y., Hou, T., Yuan, G. and Xu, X. (2013) Platelet aggregation pathway network-based approach for evaluating compounds efficacy. Evid. Based Complement. Alternat. Med., 2013, 425707PubMedPubMedCentralGoogle Scholar
  51. 51.
    Traynard, P., Tobalina, L., Eduati, F., Calzone, L. and Saez-Rodriguez, J. (2017) Logic modeling in quantitative systems pharmacology. CPT Pharmacometrics Syst. Pharmacol., 6, 499–511CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Ru, J., Li, P., Wang, J., Zhou, W., Li, B., Huang, C., Li, P., Guo, Z., Tao, W., Yang, Y., et al. (2014) TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J. Cheminform., 6, 13CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Chassagnole, C., Jackson, R. C., Hussain, N., Bashir, L., Derow, C., Savin, J. and Fell, D. A. (2006) Using a mammalian cell cycle simulation to interpret differential kinase inhibition in anti-tumour pharmaceutical development. Biosystems, 83, 91–97CrossRefPubMedGoogle Scholar
  54. 54.
    Tao, W., Li, B., Gao, S., Bai, Y., Shar, P. A., Zhang, W., Guo, Z., Sun, K., Fu, Y., Huang, C., et al. (2015) CancerHSP: anticancer herbs database of systems pharmacology. Sci. Rep., 5, 11481CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Huang, H., Wu, X., Pandey, R., Li, J., Zhao, G., Ibrahim, S. and Chen, J. Y. (2012) C2Maps: a network pharmacology database with comprehensive disease-gene-drug connectivity relationships. BMC Genomics, 13, S17CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Hu, Z., Chang, Y. C., Wang, Y., Huang, C. L., Liu, Y., Tian, F., Granger, B. and Delisi, C. (2013) VisANT 4.0: integrative network platform to connect genes, drugs, diseases and therapies. Nucleic Acids Res., 41, W225–W231CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    Wang, D., Gu, J., Zhu, W., Luo, F., Chen, L., Xu, X. and Lu, C. (2017) PDTCM: a systems pharmacology platform of traditional Chinese medicine for psoriasis. Ann. Med., 49, 652–660CrossRefPubMedGoogle Scholar
  58. 58.
    Gu, J., Gui, Y., Chen, L., Yuan, G. and Xu, X. (2013) CVDHD: a cardiovascular disease herbal database for drug discovery and network pharmacology. J. Cheminform., 5, 51CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    Musante, C. J., Ramanujan, S., Schmidt, B. J., Ghobrial, O. G., Lu, J. and Heatherington, A. C. (2017) Quantitative systems pharmacology: a case for disease models. Clin. Pharmacol. Ther., 101, 24–27CrossRefPubMedGoogle Scholar
  60. 60.
    Schmidt, B. J., Casey, F. P., Paterson, T. and Chan, J. R. (2013) Alternate virtual populations elucidate the type I interferon signature predictive of the response to rituximab in rheumatoid arthritis. BMC Bioinformatics, 14, 221CrossRefPubMedPubMedCentralGoogle Scholar
  61. 61.
    Ghosh, S., Matsuoka, Y., Asai, Y., Hsin, K. Y. and Kitano, H. (2013) Toward an integrated software platform for systems pharmacology. Biopharm. Drug Dispos., 34, 508–526CrossRefPubMedPubMedCentralGoogle Scholar
  62. 62.
    Spiros, A., Roberts, P. and Geerts, H. (2013) Phenotypic screening of the Prestwick library for treatment of Parkinson’s tremor symptoms using a humanized quantitative systems pharmacology platform. J Parkinsons Dis, 3, 569–580PubMedGoogle Scholar
  63. 63.
    Ming, J. E., Abrams, R. E., Bartlett, D. W., Tao, M., Nguyen, T., Surks, H., Kudrycki, K., Kadambi, A., Friedrich, C. M., Djebli, N., et al. (2017) A quantitative systems pharmacology platform to investigate the impact of alirocumab and cholesterol-lowering therapies on lipid profiles and plaque characteristics. Gene Regul. Syst. Bio., 11, 1177625017710941PubMedPubMedCentralGoogle Scholar
  64. 64.
    Zheng, C., Pei, T., Huang, C., Chen, X., Bai, Y., Xue, J., Wu, Z., Mu, J., Li, Y. and Wang, Y. (2016) A novel systems pharmacology platform to dissect action mechanisms of traditional Chinese medicines for bovine viral diarrhea disease. Eur. J. Pharm. Sci., 94, 33–45CrossRefPubMedGoogle Scholar
  65. 65.
    Rieger, T. R., Allen, R. J., Bystricky, L., Chen, Y., Colopy, G.W., Cui, Y., Gonzalez, A., Liu, Y., White, R. D., Everett, R. A., et al. (2018) Improving the generation and selection of virtual populations in quantitative systems pharmacology models. Prog. Biophys. Mol. Biol., 139, 15–22CrossRefPubMedGoogle Scholar
  66. 66.
    Geerts, H., Spiros, A., Roberts, P. and Carr, R. (2017) Towards the virtual human patient. quantitative systems pharmacology in Alzheimer’s disease. Eur. J. Pharmacol., 817, 38–45CrossRefPubMedGoogle Scholar
  67. 67.
    Wisniowska, B. and Polak, S. (2016) Virtual clinical trial toward polytherapy safety assessment: combination of physiologically based pharmacokinetic/pharmacodynamic-based modeling and simulation approach with drug-drug interactions involving terfenadine as an example. J. Pharm. Sci., 105, 3415–3424CrossRefPubMedGoogle Scholar
  68. 68.
    Allen, R. J., Rieger, T. R. and Musante, C. J. (2016) Efficient generation and selection of virtual populations in quantitative systems pharmacology models. CPT Pharmacometrics Syst. Pharmacol., 5, 140–146CrossRefPubMedPubMedCentralGoogle Scholar
  69. 69.
    Rostami-Hodjegan, A. (2012) Physiologically based pharmacokinetics joined with in vitro?in vivo extrapolation of ADME: a marriage under the arch of systems pharmacology. Clin. Pharmacol. Ther., 92, 50–61CrossRefPubMedGoogle Scholar
  70. 70.
    Bloomingdale, P., Housand, C., Apgar, J. F., Millard, B. L., Mager, D. E., Burke, J. M. and Shah, D. K. (2017) Quantitative systems toxicology. Curr. Opin. Toxicol., 4, 79–87CrossRefPubMedPubMedCentralGoogle Scholar
  71. 71.
    Pichardo-Almarza, C. and Diaz-Zuccarini, V. (2017) From PK/ PD to QSP: understanding the dynamic effect of cholesterollowering drugs on atherosclerosis progression and stratified medicine. Curr. Pharm. Des., 22, 6903–6910CrossRefGoogle Scholar
  72. 72.
    Meng, X. Y., Zhang, H. X., Mezei, M. and Cui, M. (2011) Molecular docking: a powerful approach for structure-based drug discovery. Curr. Comput. Aided Drug Des., 7, 146–157CrossRefPubMedPubMedCentralGoogle Scholar
  73. 73.
    Omer, A. and Singh, P. (2017) An integrated approach of network-based systems biology, molecular docking, and molecular dynamics approach to unravel the role of existing antiviral molecules against AIDS-associated cancer. J. Biomol. Struct. Dyn., 35, 1547–1558CrossRefPubMedGoogle Scholar
  74. 74.
    Gu, J., Li, L., Wang, D., Zhu, W., Han, L., Zhao, R., Xu, X. and Lu, C. (2018) Deciphering metabonomics biomarkers-targets interactions for psoriasis vulgaris by network pharmacology. Ann. Med., 50, 323–332CrossRefPubMedGoogle Scholar
  75. 75.
    Yang, M., Chen, J., Shi, X., Xu, L., Xi, Z., You, L., An, R. and Wang, X. (2015) Development of in silico models for predicting p-glycoprotein inhibitors based on a two-step approach for feature selection and its application to Chinese herbal medicine screening. Mol. Pharm., 12, 3691–3713CrossRefPubMedGoogle Scholar
  76. 76.
    Gilson, M. K., Liu, T., Baitaluk, M., Nicola, G., Hwang, L. and Chong, J. (2016) BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res., 44, D1045–D1053CrossRefPubMedGoogle Scholar
  77. 77.
    Liu, Z., Guo, F., Wang, Y., Li, C., Zhang, X., Li, H., Diao, L., Gu, J., Wang, W., Li, D., et al. (2016) BATMAN-TCM: a bioinformatics analysis Tool for molecular mechanism of traditional Chinese medicine. Sci. Rep., 6, 21146CrossRefPubMedPubMedCentralGoogle Scholar
  78. 78.
    Boran, A. D. and Iyengar, R. (2010) Systems approaches to polypharmacology and drug discovery. Curr Opin Drug Discov Devel, 13, 297–309PubMedPubMedCentralGoogle Scholar
  79. 79.
    Berger, S. I., Ma’ayan, A. and Iyengar, R. (2010) Systems pharmacology of arrhythmias. Sci. Signal., 3, ra30PubMedPubMedCentralGoogle Scholar
  80. 80.
    Boland, M. R., Jacunski, A., Lorberbaum, T., Romano, J. D., Moskovitch, R. and Tatonetti, N. P. (2016) Systems biology approaches for identifying adverse drug reactions and elucidating their underlying biological mechanisms. Wiley Interdiscip. Rev. Syst. Biol. Med., 8, 104–122CrossRefPubMedGoogle Scholar
  81. 81.
    Goldstein, L. H., Berlin, M., Saliba, W., Elias, M. and Berkovitch, M. (2013) Founding an adverse drug reaction (ADR) network: a method for improving doctors spontaneous ADR reporting in a general hospital. J. Clin. Pharmacol., 53, 1220–1225PubMedGoogle Scholar
  82. 82.
    Zhao, S., Nishimura, T., Chen, Y., Azeloglu, E. U., Gottesman, O., Giannarelli, C., Zafar, M. U., Benard, L., Badimon, J. J., Hajjar, R. J., et al. (2013) Systems pharmacology of adverse event mitigation by drug combinations. Sci. Transl. Med., 5, 206ra140CrossRefGoogle Scholar
  83. 83.
    Wu, Z., Cheng, F., Li, J., Li, W., Liu, G. and Tang, Y. (2017) SDTNBI: an integrated network and chemoinformatics tool for systematic prediction of drug-target interactions and drug repositioning. Brief. Bioinformatics, 18, 333–347PubMedGoogle Scholar
  84. 84.
    Wu, Z., Lu, W., Wu, D., Luo, A., Bian, H., Li, J., Li, W., Liu, G., Huang, J., Cheng, F., et al. (2016) In silico prediction of chemical mechanism of action via an improved network-based inference method. Br. J. Pharmacol., 173, 3372–3385CrossRefPubMedPubMedCentralGoogle Scholar
  85. 85.
    Wang, J., Guo, Z., Fu, Y., Wu, Z., Huang, C., Zheng, C., Shar, P. A., Wang, Z., Xiao, W. and Wang, Y. (2017) Weak-binding molecules are not drugs?—toward a systematic strategy for finding effective weak-binding drugs. Brief. Bioinformatics, 18, 321–332PubMedGoogle Scholar
  86. 86.
    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, 710–733CrossRefPubMedGoogle Scholar
  87. 87.
    Mitrea, C., Taghavi, Z., Bokanizad, B., Hanoudi, S., Tagett, R., Donato, M., Voichita, C. and Draghici, S. (2013) Methods and approaches in the topology-based analysis of biological pathways. Front. Physiol., 4, 278CrossRefPubMedPubMedCentralGoogle Scholar
  88. 88.
    Nie, X. Z., Du, X., Zhang, R. R., He, J., Su, R., Ma, H. Q., Mu, J., Li, Y. and Liu, F. (2017) Study on regulation mechanism of Toutongning capsule through TNF signaling pathway in treatment of migraine based on systems pharmacology method. Zhongguo Zhongyao Zazhi, 42, 548–554, in ChinesePubMedGoogle Scholar
  89. 89.
    Gu, J., Crosier, P. S., Hall, C. J., Chen, L. and Xu, X. (2016) Inflammatory pathway network-based drug repositioning and molecular phenomics. Mol. Biosyst., 12, 2777–2784CrossRefPubMedGoogle Scholar
  90. 90.
    Poltz, R. and Naumann, M. (2012) Dynamics of p53 and NF-kB regulation in response to DNA damage and identification of target proteins suitable for therapeutic intervention. BMC Syst. Biol., 6, 125CrossRefPubMedPubMedCentralGoogle Scholar
  91. 91.
    Le Novère, N. (2015) Quantitative and logic modelling of molecular and gene networks. Nat. Rev. Genet., 16, 146–158CrossRefPubMedPubMedCentralGoogle Scholar
  92. 92.
    Chaouiya, C. and Remy, E. (2013) Logical modelling of regulatory networks, methods and applications. Bull. Math. Biol., 75, 891–895CrossRefPubMedGoogle Scholar
  93. 93.
    Kirouac, D. C., Du, J. Y., Lahdenranta, J., Overland, R., Yarar, D., Paragas, V., Pace, E., McDonagh, C. F., Nielsen, U. B. and Onsum, M. D. (2013) Computational modeling of ERBB2-amplified breast cancer identifies combined ErbB2/3 blockade as superior to the combination of MEK and AKT inhibitors. Sci. Signal., 6, ra68CrossRefPubMedGoogle Scholar
  94. 94.
    Shoda, L. K., Woodhead, J. L., Siler, S. Q., Watkins, P. B. and Howell, B. A. (2014) Linking physiology to toxicity using DILIsym®, a mechanistic mathematical model of drug-induced liver injury. Biopharm. Drug Dispos., 35, 33–49CrossRefPubMedGoogle Scholar
  95. 95.
    Woodhead, J. L., Yang, K., Siler, S. Q., Watkins, P. B., Brouwer, K. L., Barton, H. A. and Howell, B. A. (2014) Exploring BSEP inhibition-mediated toxicity with a mechanistic model of druginduced liver injury. Front. Pharmacol., 5, 240CrossRefPubMedPubMedCentralGoogle Scholar
  96. 96.
    Woodhead, J. L., Paech, F., Maurer, M., Engelhardt, M., Schmitt-Hoffmann, A. H., Spickermann, J., Messner, S., Wind, M., Witschi, A. T., Krähenbühl, S., et al. (2018) Prediction of safety margin and optimization of dosing protocol for a novel antibiotic using quantitative systems pharmacology modeling. Clin. Transl. Sci., 11, 498–505CrossRefPubMedPubMedCentralGoogle Scholar
  97. 97.
    Geerts, H., Roberts, P. and Spiros, A. (2015) Assessing the synergy between cholinomimetics and memantine as augmentation therapy in cognitive impairment in schizophrenia. A virtual human patient trial using quantitative systems pharmacology. Front. Pharmacol., 6, 198PubMedGoogle Scholar
  98. 98.
    Hopkins, A. L. (2008) Network pharmacology: the next paradigm in drug discovery. Nat. Chem. Biol., 4, 682–690CrossRefPubMedGoogle Scholar
  99. 99.
    Allerheiligen, S. R. (2010) Next-generation model-based drug discovery and development: quantitative and systems pharmacology. Clin. Pharmacol. Ther., 88, 135–137CrossRefPubMedGoogle Scholar
  100. 100.
    Agoram, B. M. and Demin, O. (2011) Integration not isolation: arguing the case for quantitative and systems pharmacology in drug discovery and development. Drug Discov. Today, 16, 1031–1036CrossRefPubMedGoogle Scholar
  101. 101.
    Geerts, H. and Kennis, L. (2014) Multitarget drug discovery projects in CNS diseases: quantitative systems pharmacology as a possible path forward. Future Med. Chem., 6, 1757–1769CrossRefPubMedGoogle Scholar
  102. 102.
    Fang, J., Gao, L., Ma, H., Wu, Q., Wu, T., Wu, J., Wang, Q. and Cheng, F. (2017) Quantitative and systems pharmacology 3. network-based identification of new targets for natural products enables potential uses in aging-associated disorders. Front. Pharmacol., 8, 747CrossRefPubMedPubMedCentralGoogle Scholar
  103. 103.
    Janga, S. C. and Tzakos, A. (2009) Structure and organization of drug-target networks: insights from genomic approaches for drug discovery. Mol. Biosyst., 5, 1536–1548CrossRefPubMedGoogle Scholar
  104. 104.
    Arrell, D. K. and Terzic, A. (2010) Network systems biology for drug discovery. Clin. Pharmacol. Ther., 88, 120–125CrossRefPubMedGoogle Scholar
  105. 105.
    Knight-Schrijver, V. R., Chelliah, V., Cucurull-Sanchez, L. and Le Novère, N. (2016) The promises of quantitative systems pharmacology modelling for drug development. Comput. Struct. Biotechnol. J., 14, 363–370.CrossRefPubMedPubMedCentralGoogle Scholar
  106. 106.
    Kim, S., Lahu, G., Lesko, L. J. and Trame, M. N. (2017) An exemplar of a systems pharmacology approach for a detailed investigation of an adverse drug event as a result of drug-drug interactions. Clin. Pharmacol. Ther., 101, S97–S97.Google Scholar
  107. 107.
    Kariya, Y., Honma, M. and Suzuki, H. (2016) Mechanism analyses and mechanism-based prediction for adverse drug reactions using systems pharmacology. Nippon Yakurigaku Zasshi, 147, 89–94, in JapaneseCrossRefPubMedGoogle Scholar
  108. 108.
    Cao, D. S., Xiao, N., Li, Y. J., Zeng, W. B., Liang, Y. Z., Lu, A. P., Xu, Q. S. and Chen, A. F. (2015) Integrating multiple evidence sources to predict adverse drug reactions based on a systems pharmacology model. CPT Pharmacometrics Syst. Pharmacol., 4, 498–506CrossRefPubMedPubMedCentralGoogle Scholar
  109. 109.
    Berger, S. I. and Iyengar, R. (2011) Role of systems pharmacology in understanding drug adverse events.Wiley Interdiscip. Rev. Syst. Biol. Med., 3, 129–135CrossRefGoogle Scholar
  110. 110.
    Nueno, V. I. (2016) Towards the integration of quantitative and systems pharmacology into drug discovery: a systems level understanding of therapeutic and toxic effects of drugs. Curr. Pharm. Des., 22, 6881–6884CrossRefPubMedGoogle Scholar
  111. 111.
    Liu, H., Wang, J., Zhou, W., Wang, Y. and Yang, L. (2013) Systems approaches and polypharmacology for drug discovery from herbal medicines: an example using licorice. J. Ethnopharmacol., 146, 773–793CrossRefPubMedGoogle Scholar
  112. 112.
    Luo, F., Gu, J., Chen, L. and Xu, X. (2014) Systems pharmacology strategies for anticancer drug discovery based on natural products. Mol. Biosyst., 10, 1912–1917CrossRefPubMedGoogle Scholar
  113. 113.
    Dziuba, J., Alperin, P., Racketa, J., Iloeje, U., Goswami, D., Hardy, E., Perlstein, I., Grossman, H. L. and Cohen, M. (2014) Modeling effects of SGLT-2 inhibitor dapagliflozin treatment versus standard diabetes therapy on cardiovascular and microvascular outcomes. Diabetes Obes. Metab., 16, 628–635CrossRefPubMedGoogle Scholar
  114. 114.
    Peskin, B. R., Shcheprov, A. V., Boye, K. S., Bruce, S., Maggs, D. G. and Gaebler, J. A. (2011) Cardiovascular outcomes associated with a new once-weekly GLP-1 receptor agonist vs. traditional therapies for type 2 diabetes: a simulation analysis. Diabetes Obes. Metab., 13, 921–927CrossRefPubMedGoogle Scholar
  115. 115.
    Gadkar, K., Kirouac, D., Parrott, N. and Ramanujan, S. (2016) Quantitative systems pharmacology: a promising approach for translational pharmacology. Drug Discov. Today. Technol., 21–22, 57–65CrossRefPubMedGoogle Scholar
  116. 116.
    Cirit, M. and Stokes, C. L. (2018) Maximizing the impact of microphysiological systems with in vitro-in vivo translation. Lab Chip, 18, 1831–1837CrossRefPubMedGoogle Scholar
  117. 117.
    Yuraszeck, T., Kasichayanula, S. and Benjamin, J. E. (2017) Translation and clinical development of bispecific T-cell engaging antibodies for cancer treatment. Clin. Pharmacol. Ther., 101, 634–645CrossRefPubMedPubMedCentralGoogle Scholar
  118. 118.
    Schulthess, P., Post, T. M., Yates, J. and van der Graaf, P. H. (2018) Frequency-domain response analysis for quantitative systems pharmacology models. CPT Pharmacometrics Syst. Pharmacol., 7, 111–123CrossRefGoogle Scholar
  119. 119.
    Visser, S. A., de Alwis, D. P., Kerbusch, T., Stone, J. A. and Allerheiligen, S. R. (2014) Implementation of quantitative and systems pharmacology in large pharma. CPT Pharmacometrics Syst. Pharmacol., 3, e142CrossRefPubMedPubMedCentralGoogle Scholar
  120. 120.
    Geerts, H., Roberts, P. and Spiros, A. (2013) A quantitative system pharmacology computer model for cognitive deficits in schizophrenia. CPT Pharmacometrics Syst. Pharmacol., 2, e36CrossRefPubMedPubMedCentralGoogle Scholar
  121. 121.
    Liu, J., Ogden, A., Comery, T. A., Spiros, A., Roberts, P. and Geerts, H. (2014) Prediction of efficacy of vabicaserin, a 5-HT2C agonist, for the treatment of schizophrenia using a quantitative systems pharmacology model. CPT Pharmacometrics Syst. Pharmacol., 3, e111CrossRefPubMedPubMedCentralGoogle Scholar
  122. 122.
    Geerts, H., Roberts, P., Spiros, A. and Potkin, S. (2015) Understanding responder neurobiology in schizophrenia using a quantitative systems pharmacology model: application to iloperidone. J. Psychopharmacol. (Oxford), 29, 372–382CrossRefGoogle Scholar
  123. 123.
    Vega-Villa, K., Pluta, R., Lonser, R. and Woo, S. (2013) Quantitative systems pharmacology model of NO metabolome and methemoglobin following long-term infusion of sodium nitrite in humans. CPT Pharmacometrics Syst. Pharmacol., 2, e60CrossRefPubMedPubMedCentralGoogle Scholar
  124. 124.
    John, T., Kiss, T., Lever, C. and Érdi, P. (2014) Anxiolytic drugs and altered hippocampal theta rhythms: the quantitative systems pharmacological approach. Network, 25, 20–37CrossRefPubMedGoogle Scholar
  125. 125.
    Johnson, T. N. and Rostami-Hodjegan, A. (2011) Resurgence in the use of physiologically based pharmacokinetic models in pediatric clinical pharmacology: parallel shift in incorporating the knowledge of biological elements and increased applicability to drug development and clinical practice. Paediatr. Anaesth., 21, 291–301CrossRefPubMedGoogle Scholar
  126. 126.
    Kaddi, C. D., Niesner, B., Baek, R., Jasper, P., Pappas, J., Tolsma, J., Li, J., van Rijn, Z., Tao, M., Ortemann-Renon, C., et al. (2018) Quantitative systems pharmacology modeling of acid sphingomyelinase deficiency and the enzyme replacement therapy olipudase alfa is an innovative tool for linking pathophysiology and pharmacology. CPT Pharmacometrics Syst. Pharmacol., 7, 442–452CrossRefPubMedPubMedCentralGoogle Scholar
  127. 127.
    Stern, A. M., Schurdak, M. E., Bahar, I., Berg, J. M. and Taylor, D. L. (2016) A perspective on implementing a quantitative systems pharmacology platform for drug discovery and the advancement of personalized medicine. J. Biomol. Screen., 21, 521–534CrossRefPubMedPubMedCentralGoogle Scholar
  128. 128.
    Geerts, H., Spiros, A., Roberts, P. and Alphs, L. (2018) A quantitative systems pharmacology study on optimal scenarios for switching to paliperidone palmitate once-monthly. Schizophr. Res., 197, 261–268CrossRefGoogle Scholar
  129. 129.
    Yin, A., Yamada, A., Stam, W. B., van Hasselt, J. G. C. and van der Graaf, P. H. (2018) Quantitative systems pharmacology analysis of drug combination and scaling to humans: the interaction between noradrenaline and vasopressin in vasoconstriction. Br. J. Pharmacol., 175, 3394–3406CrossRefGoogle Scholar
  130. 130.
    Chen, Y., Sun, Y., Li, W., Wei, H., Long, T., Li, H., Xu, Q. and Liu, W. (2018) Systems pharmacology dissection of the antistroke mechanism for the Chinese traditional medicine Xing-Nao-Jing. J. Pharmacol. Sci., 136, 16–25CrossRefPubMedGoogle Scholar
  131. 131.
    Li, J., Zhao, P., Li, Y., Tian, Y. and Wang, Y. (2015) Systems pharmacology-based dissection of mechanisms of Chinese medicinal formula Bufei Yishen as an effective treatment for chronic obstructive pulmonary disease. Sci. Rep., 5, 15290CrossRefPubMedPubMedCentralGoogle Scholar
  132. 132.
    Zhao, P., Yang, L., Li, J., Li, Y., Tian, Y. and Li, S. (2016) Combining systems pharmacology, transcriptomics, proteomics, and metabolomics to dissect the therapeutic mechanism of Chinese herbal Bufei Jianpi formula for application to COPD. Int. J. Chron. Obstruct. Pulmon. Dis., 11, 553–566PubMedPubMedCentralGoogle Scholar
  133. 133.
    Zhao, P., Li, J., Yang, L., Li, Y., Tian, Y. and Li, S. (2018) 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., 17, 5247–5257PubMedPubMedCentralGoogle Scholar
  134. 134.
    Zhang, W., Tao, Q., Guo, Z., Fu, Y., Chen, X., Shar, P. A., Shahen, M., Zhu, J., Xue, J., Bai, Y., et al. (2016) Systems pharmacology dissection of the integrated treatment for cardiovascular and gastrointestinal disorders by traditional Chinese medicine. Sci. Rep., 6, 32400CrossRefPubMedPubMedCentralGoogle Scholar
  135. 135.
    Sun, M., Chang, W. T., Van Wijk, E., He, M., Koval, S., Lin, M. K., VanWijk, R., Hankemeier, T., van der Greef, J. andWang, M. (2017) Characterization of the therapeutic properties of Chinese herbal materials by measuring delayed luminescence and dendritic cell-based immunomodulatory response. J. Photochem. Photobiol. B, 168, 1–11CrossRefPubMedGoogle Scholar
  136. 136.
    Wang, J., Li, Y., Yang, Y., Chen, X., Du, J., Zheng, Q., Liang, Z. and Wang, Y. (2017) A new strategy for deleting animal drugs from traditional Chinese medicines based on modified yimusake formula. Sci. Rep., 7, 1504CrossRefPubMedPubMedCentralGoogle Scholar
  137. 137.
    Ai, H., Wu, X., Qi, M., Zhang, L., Hu, H., Zhao, Q., Zhao, J. and Liu, H. (2018) Study on the mechanisms of active compounds in traditional Chinese medicine for the treatment of influenza virus by virtual screening. Interdiscip. Sci., 10, 320–328CrossRefPubMedGoogle Scholar
  138. 138.
    Jiang, Q. Y., Zheng, M. S., Yang, X. J. and Sun, X. S. (2016) Analysis of molecular networks and targets mining of Chinese herbal medicines on anti-aging. BMC Complement. Altern. Med., 16, 520CrossRefPubMedPubMedCentralGoogle Scholar
  139. 139.
    Liu, J., Liu, J., Shen, F., Qin, Z., Jiang, M., Zhu, J., Wang, Z., Zhou, J., Fu, Y., Chen, X., et al. (2018) Systems pharmacology analysis of synergy of TCM: an example using saffron formula. Sci. Rep., 8, 380CrossRefPubMedPubMedCentralGoogle Scholar
  140. 140.
    Fan, W. T. and Wang, Q. (2018) Mechanism of Acori Tatarinowii Rhizoma-Curcumae Radix treating depression based on network pharmacology. Zhongguo Zhongyao Zazhi, 43, 2607–2611, in ChinesePubMedGoogle Scholar
  141. 141.
    Wang, J., Zhang, L., Liu, B., Wang, Q., Chen, Y., Wang, Z., Zhou, J., Xiao, W., Zheng, C. and Wang, Y. (2018) Systematic investigation of the Erigeron breviscapus mechanism for treating cerebrovascular disease. J. Ethnopharmacol., 224, 429–440CrossRefPubMedGoogle Scholar
  142. 142.
    Li, Y., Han, C., Wang, J., Xiao, W., Wang, Z., Zhang, J., Yang, Y., Zhang, S. and Ai, C. (2014) Investigation into the mechanism of Eucommia ulmoides Oliv. based on a systems pharmacology approach. J. Ethnopharmacol., 151, 452–460CrossRefPubMedGoogle Scholar
  143. 143.
    Liu, X., Wu, J., Zhang, D., Wang, K., Duan, X. and Zhang, X. (2018) A network pharmacology approach to uncover the multiple mechanisms of Hedyotis diffusa Willd. on colorectal cancer. Evid. Based Complement. Alternat. Med., 2018, 6517034PubMedPubMedCentralGoogle Scholar
  144. 144.
    Li, Y., Wang, J., Xiao, Y., Wang, Y., Chen, S., Yang, Y., Lu, A. and Zhang, S. (2015) A systems pharmacology approach to investigate the mechanisms of action of semen strychni and Tripterygium wilfordii Hook F for treatment of rheumatoid arthritis. J. Ethnopharmacol., 175, 301–314CrossRefPubMedGoogle Scholar
  145. 145.
    Li, Y. Y., Zheng, G. and Liu, L. (2018) Bioinformatics based therapeutic effects of Sinomenium Acutum. Chin. J. Integr. Med., 10.1007/s11655-018-2796-6Google Scholar
  146. 146.
    Yi, F., Sun, L., Xu, L. J., Peng, Y., Liu, H. B., He, C. N. and Xiao, P. G. (2017) In silico approach for anti-thrombosis drug discovery: P2Y1R structure-based TCMs screening. Front. Pharmacol., 7, 531CrossRefPubMedPubMedCentralGoogle Scholar
  147. 147.
    Liu, J., Pei, M., Zheng, C., Li, Y., Wang, Y., Lu, A. and Yang, L. (2013) A systems-pharmacology analysis of herbal medicines used in health improvement treatment: predicting potential new drugs and targets. Evid. Based Complement. Alternat. Med., 2013, 938764PubMedPubMedCentralGoogle Scholar
  148. 148.
    Zhao, L., Wu, Y. F., Gao, Y., Xiang, H., Qin, X. M. and Tian, J. S. (2017) Intervention mechanism of psychological sub-health by Baihe Dihuang Tang based on network pharmacology. Acta Pharma. Sinica (Yao Xue Xue Bao ), 52, 99–105, in ChineseGoogle Scholar
  149. 149.
    Zhao, P., Li, J., Li, Y., Tian, Y., Wang, Y. and Zheng, C. (2015) Systems pharmacology-based approach for dissecting the active ingredients and potential targets of the Chinese herbal Bufei Jianpi formula for the treatment of COPD. Int. J. Chron. Obstruct. Pulmon. Dis., 10, 2633–2656PubMedPubMedCentralGoogle Scholar
  150. 150.
    Shi, S. H., Cai, Y. P., Cai, X. J., Zheng, X. Y., Cao, D. S., Ye, F. Q. and Xiang, Z. (2014) A network pharmacology approach to understanding the mechanisms of action of traditional medicine: Bushenhuoxue formula for treatment of chronic kidney disease. PLoS One, 9, e89123CrossRefPubMedPubMedCentralGoogle Scholar
  151. 151.
    Cai, H., Luo, Y., Yan, X., Ding, P., Huang, Y., Fang, S., Zhang, R., Chen, Y., Guo, Z., Fang, J., et al. (2018) The mechanisms of Bushen-Yizhi formula as a therapeutic agent against alzheimer’s disease. Sci. Rep., 8, 3104CrossRefPubMedPubMedCentralGoogle Scholar
  152. 152.
    Huang, J., Tang, H., Cao, S., He, Y., Feng, Y., Wang, K. and Zheng, Q. (2017) Molecular targets and associated potential pathways of danlu capsules in hyperplasia of mammary glands based on systems pharmacology. Evid. Based Complement. Alternat. Med., 2017, 1930598PubMedPubMedCentralGoogle Scholar
  153. 153.
    Luo, Y., Wang, Q. and Zhang, Y. (2016) A systems pharmacology approach to decipher the mechanism of danggui-shaoyao-san decoction for the treatment of neurodegenerative diseases. J. Ethnopharmacol., 178, 66–81CrossRefPubMedGoogle Scholar
  154. 154.
    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., et al. (2015) Deciphering the underlying mechanisms of Diesun Miaofang in traumatic injury from a systems pharmacology perspective. Mol. Med. Rep., 12, 1769–1776CrossRefPubMedPubMedCentralGoogle Scholar
  155. 155.
    Xu, H., Zhang, Y., Lei, Y., Gao, X., Zhai, H., Lin, N., Tang, S., Liang, R., Ma, Y., Li, D., et al. (2014) A systems biology-based approach to uncovering the molecular mechanisms underlying the effects of dragon’s blood tablet in colitis, involving the integration of chemical analysis, ADME prediction, and network pharmacology. PLoS One, 9, e101432CrossRefPubMedPubMedCentralGoogle Scholar
  156. 156.
    Li, H., Zhao, L., Zhang, B., Jiang, Y., Wang, X., Guo, Y., Liu, H., Li, S. and Tong, X. (2014) 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. Alternat. Med., 2014, 495840PubMedPubMedCentralGoogle Scholar
  157. 157.
    Liang, X., Li, H. and Li, S. (2014) A novel network pharmacology approach to analyse traditional herbal formulae: the Liu-Wei-Di-Huang pill as a case study. Mol. Biosyst., 10, 1014–1022CrossRefPubMedGoogle Scholar
  158. 158.
    Yao, Y., Zhang, X., Wang, Z., Zheng, C., Li, P., Huang, C., Tao, W., Xiao, W., Wang, Y., Huang, L., et al. (2013) Deciphering the combination principles of traditional Chinese medicine from a systems pharmacology perspective based on Ma-huang decoction. J. Ethnopharmacol., 150, 619–638CrossRefPubMedGoogle Scholar
  159. 159.
    Tang, F., Tang, Q., Tian, Y., Fan, Q., Huang, Y. and Tan, X. (2015) Network pharmacology-based prediction of the active ingredients and potential targets of Mahuang Fuzi Xixin decoction for application to allergic rhinitis. J. Ethnopharmacol., 176, 402–412CrossRefPubMedGoogle Scholar
  160. 160.
    Huang, T., Ning, Z., Hu, D., Zhang, M., Zhao, L., Lin, C., Zhong, L. L. D., Yang, Z., Xu, H. and Bian, Z. (2018) Uncovering the mechanisms of Chinese herbal medicine (MaZiRenWan) for functional constipation by focused network pharmacology approach. Front. Pharmacol., 9, 270CrossRefPubMedPubMedCentralGoogle Scholar
  161. 161.
    Wang, X., Yu, S., Jia, Q., Chen, L., Zhong, J., Pan, Y., Shen, P., Shen, Y., Wang, S., Wei, Z., et al. (2017) NiaoDuQing granules relieve chronic kidney disease symptoms by decreasing renal fibrosis and anemia. Oncotarget, 8, 55920–55937PubMedPubMedCentralGoogle Scholar
  162. 162.
    Ke, Z. P., Zhang, X. Z., Ding, Y., Cao, L., Li, N., Ding, G., Wang, Z. Z. and Xiao, W. (2015) Study on effective substance basis and molecular mechanism of Qigui Tongfeng tablet using network pharmacology method. Zhongguo Zhongyao Zazhi, 40, 2837–2842, in ChinesePubMedGoogle Scholar
  163. 163.
    Tao, W., Xu, X., Wang, X., Li, B., Wang, Y., Li, Y. and Yang, L. (2013) Network pharmacology-based prediction of the active ingredients and potential targets of Chinese herbal Radix Curcumae formula for application to cardiovascular disease. J. Ethnopharmacol., 145, 1–10CrossRefPubMedGoogle Scholar
  164. 164.
    Yang, H., Zhang, W., Huang, C., Zhou, W., Yao, Y., Wang, Z., Li, Y., Xiao, W. andWang, Y. (2014) A novel systems pharmacology model for herbal medicine injection: a case using reduning injection. BMC Complement. Altern. Med., 14, 430CrossRefPubMedPubMedCentralGoogle Scholar
  165. 165.
    Luo, F., Gu, J., Zhang, X., Chen, L., Cao, L., Li, N., Wang, Z., Xiao, W. and Xu, X. (2015) Multiscale modeling of drug-induced effects of ReDuNing injection on human disease: from drug molecules to clinical symptoms of disease. Sci. Rep., 5, 10064CrossRefPubMedPubMedCentralGoogle Scholar
  166. 166.
    Liu, J., Sun, K., Zheng, C., Chen, X., Zhang, W., Wang, Z., Shar, P. A., Xiao, W. and Wang, Y. (2015) Pathway as a pharmacological target for herbal medicines: an investigation from reduning injection. PLoS One, 10, e0123109CrossRefPubMedPubMedCentralGoogle Scholar
  167. 167.
    Wu, L., Wang, Y., Nie, J., Fan, X. and Cheng, Y. (2013) A network pharmacology approach to evaluating the efficacy of Chinese medicine using genome-wide transcriptional expression data. Evid. Based Complement. Alternat. Med., 2013, 915343PubMedPubMedCentralGoogle Scholar
  168. 168.
    Shen, X., Zhao, Z., Luo, X., Wang, H., Hu, B. and Guo, Z. (2016) Systems pharmacology based study of the molecular mechanism of SiNiSan formula for application in nervous and mental diseases. Evid. Based Complement. Alternat. Med., 2016, 9146378PubMedPubMedCentralGoogle Scholar
  169. 169.
    Wang, H. H., Zhang, B. X., Ye, X. T., He, S. B., Zhang, Y. L. and Wang, Y. (2015) Study on mechanism for anti-depression efficacy of Sini San through auxiliary mechanism elucidation system for Chinese medicine. Zhongguo Zhongyao Zazhi, 40, 3723–3728, in ChinesePubMedGoogle Scholar
  170. 170.
    Zheng, C. S., Xu, X. J., Ye, H. Z., Wu, G.W., Li, X. H., Xu, H. F. and Liu, X. X. (2013) Network pharmacology-based prediction of the multi-target capabilities of the compounds in Taohong Siwu decoction, and their application in osteoarthritis. Exp. Ther. Med., 6, 125–132CrossRefPubMedPubMedCentralGoogle Scholar
  171. 171.
    Wang, T., Wu, Z., Sun, L., Li, W., Liu, G. and Tang, Y. (2018) A computational systems pharmacology approach to investigate molecular mechanisms of herbal formula Tian-Ma-Gou-Teng-Yin for treatment of alzheimer’s disease. Front. Pharmacol., 9, 668CrossRefPubMedPubMedCentralGoogle Scholar
  172. 172.
    Li, Y., Zhang, J., Zhang, L., Chen, X., Pan, Y., Chen, S. S., Zhang, S., Wang, Z., Xiao, W., Yang, L., et al. (2015) Systems pharmacology to decipher the combinational anti-migraine effects of Tianshu formula. J. Ethnopharmacol., 174, 45–56CrossRefPubMedGoogle Scholar
  173. 173.
    Gao, Y., Gao, L., Gao, X. X., Zhou, Y. Z., Qin, X. M. and Tian, J. S. (2015) An exploration in the action targets for antidepressant bioactive components of Xiaoyaosan based on network pharmacology. Acta Pharma. Sinica (Yao Xue Xue Bao), 50, 1589–1595, in ChineseGoogle Scholar
  174. 174.
    Liu, J., Pei, T., Mu, J., Zheng, C., Chen, X., Huang, C., Fu, Y., Liang, Z. and Wang, Y. (2016) Systems pharmacology uncovers the multiple mechanisms of Xijiao Dihuang decoction for the treatment of viral hemorrhagic fever. Evid. Based Complement. Alternat. Med., 2016, 9025036PubMedPubMedCentralGoogle Scholar
  175. 175.
    Pang, H. Q., Yue, S. J., Tang, Y. P., Chen, Y. Y., Tan, Y. J., Cao, Y. J., Shi, X. Q., Zhou, G. S., Kang, A., Huang, S. L., et al. (2018) Integrated metabolomics and network pharmacology approach to explain possible action mechanisms of Xin-Sheng-Hua granule for treating Anemia. Front. Pharmacol., 9, 165CrossRefPubMedPubMedCentralGoogle Scholar
  176. 176.
    Chen, L., Cao, Y., Zhang, H., Lv, D., Zhao, Y., Liu, Y., Ye, G. and Chai, Y. (2018) Network pharmacology-based strategy for predicting active ingredients and potential targets of Yangxinshi tablet for treating heart failure. J. Ethnopharmacol., 219, 359–368CrossRefPubMedGoogle Scholar
  177. 177.
    Huang, J., Cheung, F., Tan, H. Y., Hong, M., Wang, N., Yang, J., Feng, Y. and Zheng, Q. (2017) Identification of the active compounds and significant pathways of yinchenhao decoction based on network pharmacology. Mol. Med. Rep., 16, 4583–4592CrossRefPubMedPubMedCentralGoogle Scholar
  178. 178.
    An, L. and Feng, F. (2015) Network pharmacology-based antioxidant effect study of Zhi-Zi-Da-Huang decoction for alcoholic liver disease. Evid. Based Complement. Alternat. Med., 2015, 492470CrossRefPubMedPubMedCentralGoogle Scholar
  179. 179.
    Li, F., Lv, Y. N., Tan, Y. S., Shen, K., Zhai, K. F., Chen, H. L., Kou, J. P. and Yu, B. Y. (2015) An integrated pathway interaction network for the combination of four effective compounds from ShengMai preparations in the treatment of cardio-cerebral ischemic diseases. Acta Pharmacol. Sin., 36, 1337–1348CrossRefPubMedPubMedCentralGoogle Scholar
  180. 180.
    Li, B., Tao, W., Zheng, C., Shar, P. A., Huang, C., Fu, Y. and Wang, Y. (2014) 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., 53, 19–29CrossRefPubMedGoogle Scholar
  181. 181.
    Zhou, W. and Wang, Y. (2014) A network-based analysis of the types of coronary artery disease from traditional Chinese medicine perspective: potential for therapeutics and drug discovery. J. Ethnopharmacol., 151, 66–77CrossRefPubMedGoogle Scholar
  182. 182.
    Wang, J., Liu, R., Liu, B., Yang, Y., Xie, J. and Zhu, N. (2017) Systems pharmacology-based strategy to screen new adjuvant for hepatitis B vaccine from traditional Chinese medicine ophiocordyceps sinensis. Sci. Rep., 7, 44788CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.The Second Clinical CollegeGuangzhou University of Chinese MedicineGuangzhouChina
  2. 2.Guangdong Provincial Academy of Chinese Medical SciencesGuangzhouChina

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