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Computational methods and applications for quantitative systems pharmacology |
Fuda Xie1,2, Jiangyong Gu1,2( ) |
1. The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou 510006, China 2. Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou 510006, China |
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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/T evaluation, 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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Corresponding Author(s):
Jiangyong Gu
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Online First Date: 29 January 2019
Issue Date: 22 March 2019
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1 |
S. I.Berger, and R.Iyengar, (2009) Network analyses in systems pharmacology. Bioinformatics, 25, 2466–2472
https://doi.org/10.1093/bioinformatics/btp465
pmid: 19648136
|
2 |
S.Zhao, and R. Iyengar, (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
pmid: 22235860
|
3 |
A. D.Boran, and R.Iyengar, (2010) Systems pharmacology. Mt. Sinai J. Med., 77, 333–344
https://doi.org/10.1002/msj.20191
pmid: 20687178
|
4 |
W.Zhou, , Y. Wang, , A.Lu, and G.Zhang, (2016) Systems pharmacology in small molecular drug discovery. Int. J. Mol. Sci., 17, 246
https://doi.org/10.3390/ijms17020246
pmid: 26901192
|
5 |
J.Gu, , X. Zhang, , Y.Ma, , N.Li, , F. Luo, , L.Cao, , Z.Wang, , G.Yuan, , L.Chen, , W.Xiao, , et al. (2015) Quantitative modeling of dose-response and drug combination based on pathway network. J. Cheminform., 7, 19
https://doi.org/10.1186/s13321-015-0066-6
pmid: 26101547
|
6 |
A.Spiros, , P. Roberts, and H.Geerts, (2014) A computer-based quantitative systems pharmacology model of negative symptoms in schizophrenia: exploring glycine modulation of excitation-inhibition balance. Front. Pharmacol., 5, 229
https://doi.org/10.3389/fphar.2014.00229
pmid: 25374541
|
7 |
J.Fang, , Z. Wu, , C.Cai, , Q.Wang, , Y.Tang, and F.Cheng, (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, 2657–2671
https://doi.org/10.1021/acs.jcim.7b00216
pmid: 28956927
|
8 |
B.Fleisher, , A. N. Brown, and S.Ait-Oudhia, (2017) Application of pharmacometrics and quantitative systems pharmacology to cancer therapy: the example of luminal a breast cancer. Pharmacol. Res., 124, 20–33
https://doi.org/10.1016/j.phrs.2017.07.015
pmid: 28735000
|
9 |
H.Geerts, , A. Spiros, and P.Roberts, (2018) Impact of amyloid-beta changes on cognitive outcomes in Alzheimer’s disease: analysis of clinical trials using a quantitative systems pharma-cology model. Alzheimers Res. Ther., 10, 14
https://doi.org/10.1186/s13195-018-0343-5
pmid: 29394903
|
10 |
A. L.Barabási, , N.Gulbahce, and J.Loscalzo, (2011) Network medicine: a network-based approach to human disease. Nat. Rev. Genet., 12, 56–68
https://doi.org/10.1038/nrg2918
pmid: 21164525
|
11 |
V. I.Pérez-Nueno, (2015) Using quantitative systems pharmacology for novel drug discovery. Expert Opin. Drug Discov., 10, 1315–1331
https://doi.org/10.1517/17460441.2015.1082543
pmid: 26328768
|
12 |
J. L.Woodhead, , P. B.Watkins, , B. A.Howell, , S. Q.Siler, and L. K. M.Shoda, (2017) The role of quantitative systems pharmacology modeling in the prediction and explanation of idiosyncratic drug-induced liver injury. Drug Metab. Pharmacokinet., 32, 40–45
https://doi.org/10.1016/j.dmpk.2016.11.008
pmid: 28129975
|
13 |
I. P.Androulakis, (2016) Quantitative systems pharmacology: a framework for context. Curr. Pharmacol. Rep., 2, 152–160
https://doi.org/10.1007/s40495-016-0058-x
pmid: 27570730
|
14 |
P. H.van der Graaf, and N.Benson, (2011) Systems pharmacology: bridging systems biology and pharmacokinetics-pharmacodynamics (PKPD) in drug discovery and development. Pharm. Res., 28, 1460–1464
https://doi.org/10.1007/s11095-011-0467-9
pmid: 21560018
|
15 |
T. A.Leil, and R. Bertz, (2014) Quantitative systems pharma-cology can reduce attrition and improve productivity in pharmaceutical research and development. Front. Pharmacol., 5, 247
https://doi.org/10.3389/fphar.2014.00247
pmid: 25426074
|
16 |
R. T.Rao, , M. L. Scherholz, , C.Hartmanshenn, , S. A.Bae, and I. P.Androulakis, (2017) On the analysis of complex biological supply chains: from process systems engineering to quantitative systems pharmacology. Comput. Chem. Eng., 107, 100–110
https://doi.org/10.1016/j.compchemeng.2017.06.003
pmid: 29353945
|
17 |
J.Yu, , N. A. Cilfone, , E. M.Large, , U.Sarkar, , J. S.Wishnok, , S. R.Tannenbaum, , D. J.Hughes, , D. A.Lauffenburger, , L. G.Griffith, , C. L.Stokes, , et al. (2015) Quantitative systems pharmacology approaches applied to microphysiological systems (MPS): data interpretation and multi-MPS integration. CPT Pharmacometrics Syst. Pharmacol., 4, 585–594
https://doi.org/10.1002/psp4.12010
pmid: 26535159
|
18 |
C. J.Musante, , D. R.Abernethy, , S. R.Allerheiligen, , D. A.Lauffenburger, and M. G.Zager, (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, 449–451
https://doi.org/10.1002/psp4.12109
pmid: 27639191
|
19 |
B.Ribba, , H. P. Grimm, , B.Agoram, , M. R.Davies, , K.Gadkar, , S.Niederer, , N.van Riel, , J.Timmis, and P. H.van der Graaf, (2017) Methodologies for quantitative systems pharmacology (QSP) models: design and estimation. CPT Pharmacometrics Syst. Pharmacol., 6, 496–498
https://doi.org/10.1002/psp4.12206
pmid: 28585415
|
20 |
J.Timmis, , K. Alden, , P.Andrews, , E.Clark, , A.Nellis, , B.Naylor, , M.Coles, and P.Kaye, (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, 156–167
https://doi.org/10.1002/psp4.12157
pmid: 27863172
|
21 |
M. H.Cherkaoui-Rbati, , S. W.Paine, , P.Littlewood, and C.Rauch, (2017) A quantitative systems pharmacology approach, incorporating a novel liver model, for predicting pharmacokinetic drug-drug interactions. PLoS One, 12, e0183794
https://doi.org/10.1371/journal.pone.0183794
pmid: 28910306
|
22 |
M.Rogers, , P. Lyster, and R.Okita, (2013) NIH support for the emergence of quantitative and systems pharmacology. CPT Pharmacometrics Syst. Pharmacol., 2, e37
https://doi.org/10.1038/psp.2013.13
pmid: 23887687
|
23 |
A. D.Wist, , S. I. Berger, and R.Iyengar, (2009) Systems pharmacology and genome medicine: a future perspective. Genome Med., 1, 11
https://doi.org/10.1186/gm11
pmid: 19348698
|
24 |
Z.Wang, and T. S. Deisboeck, (2014) Mathematical modeling in cancer drug discovery. Drug Discov. Today, 19, 145–150
https://doi.org/10.1016/j.drudis.2013.06.015
pmid: 23831857
|
25 |
J. L.Medina-Franco, , M. A.Giulianotti, , G. S.Welmaker, and R. A.Houghten, (2013) Shifting from the single to the multitarget paradigm in drug discovery. Drug Discov. Today, 18, 495–501
https://doi.org/10.1016/j.drudis.2013.01.008
pmid: 23340113
|
26 |
A. L.Hopkins, (2007) Network pharmacology. Nat. Biotechnol., 25, 1110–1111
https://doi.org/10.1038/nbt1007-1110
pmid: 17921993
|
27 |
K. I.Goh, and I. G.Choi, (2012) Exploring the human diseasome: the human disease network. Brief. Funct. Genomics, 11, 533–542
https://doi.org/10.1093/bfgp/els032
pmid: 23063808
|
28 |
K. I.Goh, , M. E. Cusick, , D.Valle, , B.Childs, , M.Vidal, and A. L.Barabási, (2007) The human disease network. Proc. Natl. Acad. Sci. USA, 104, 8685–8690
https://doi.org/10.1073/pnas.0701361104
pmid: 17502601
|
29 |
W.Zhang, , J. Pei, and L.Lai, (2017) Computational multitarget drug design. J. Chem. Inf. Model., 57, 403–412
https://doi.org/10.1021/acs.jcim.6b00491
pmid: 28166637
|
30 |
M. A.Yildirim, , K. I.Goh, , M. E.Cusick, , A. L.Barabási, and M.Vidal, (2007) Drug-target network. Nat. Biotechnol., 25, 1119–1126
https://doi.org/10.1038/nbt1338
pmid: 17921997
|
31 |
F.Barneh, , M. Jafari, and M.Mirzaie, (2016) Updates on drug-target network; facilitating polypharmacology and data integration by growth of DrugBank database. Brief. Bioinformatics, 17, 1070–1080
pmid: 26490381
|
32 |
H.Geerts, , A. Spiros, , P.Roberts, and R.Carr, (2013) Quantitative systems pharmacology as an extension of PK/PD modeling in CNS research and development. J. Pharmacokinet. Pharmacodyn., 40, 257–265
https://doi.org/10.1007/s10928-013-9297-1
pmid: 23338980
|
33 |
N.Snelder, , B. A. Ploeger, , O.Luttringer, , D. F.Rigel, , F.Fu, , M. Beil, , D. R.Stanski, and M.Danhof, (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, 5076–5092
https://doi.org/10.1111/bph.12824
pmid: 24962208
|
34 |
E. K.Hansson, , M. A.Amantea, , P.Westwood, , P. A.Milligan, , B. E.Houk, , J.French, , M. O.Karlsson, and L. E.Friberg, (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, e84
https://doi.org/10.1038/psp.2013.61
pmid: 24257372
|
35 |
J. L.Reymond, and M.Awale, (2012) Exploring chemical space for drug discovery using the chemical universe database. ACS Chem. Neurosci., 3, 649–657
https://doi.org/10.1021/cn3000422
pmid: 23019491
|
36 |
S.Tian, , J. Wang, , Y.Li, , D.Li, , L. Xu, and T.Hou, (2015) The application of in silico drug-likeness predictions in pharmaceutical research. Adv. Drug Deliv. Rev., 86, 2–10
https://doi.org/10.1016/j.addr.2015.01.009
pmid: 25666163
|
37 |
E. R.May, (2014) Recent developments in molecular simulation approaches to study spherical virus capsids. Mol. Simul., 40, 878–888
https://doi.org/10.1080/08927022.2014.907899
pmid: 25197162
|
38 |
M. J.Field, (2015) Technical advances in molecular simulation since the 1980s. Arch. Biochem. Biophys., 582, 3–9
https://doi.org/10.1016/j.abb.2015.03.005
pmid: 25772387
|
39 |
L.Xie, , E. J. Draizen, and P. E.Bourne, (2017) Harnessing big data for systems pharmacology. Annu. Rev. Pharmacol. Toxicol., 57, 245–262
https://doi.org/10.1146/annurev-pharmtox-010716-104659
pmid: 27814027
|
40 |
X.Liu, , F. Zhu, , X. H.Ma, , Z.Shi, , S. Y.Yang, , Y. Q.Wei, and Y. Z.Chen, (2013) Predicting targeted polypharmacology for drug repositioning and multi- target drug discovery. Curr. Med. Chem., 20, 1646–1661
https://doi.org/10.2174/0929867311320130005
pmid: 23410165
|
41 |
S. H.Chiu, and L.Xie, (2016) Toward high-throughput predictive modeling of protein binding/unbinding kinetics. J. Chem. Inf. Model., 56, 1164–1174
https://doi.org/10.1021/acs.jcim.5b00632
pmid: 27159844
|
42 |
T.Hart, and L. Xie, (2016) Providing data science support for systems pharmacology and its implications to drug discovery. Expert Opin. Drug Discov., 11, 241–256
https://doi.org/10.1517/17460441.2016.1135126
pmid: 26689499
|
43 |
P.Bloomingdale, , V. A. Nguyen, , J.Niu, and D. E.Mager, (2018) Boolean network modeling in systems pharmacology. J. Pharmacokinet. Pharmacodyn., 45, 159–180
https://doi.org/10.1007/s10928-017-9567-4
pmid: 29307099
|
44 |
I.Irurzun-Arana, , J. M. Pastor, , I. F.Trocóniz, and J. D. Gómez-Mantilla, (2017) Advanced Boolean modeling of biological networks applied to systems pharmacology. Bioinformatics, 33, 1040–1048
https://doi.org/10.1093/bioinformatics/btw747
pmid: 28073755
|
45 |
M.Danhof, (2016) Systems pharmacology—towards the modeling of network interactions. Eur. J. Pharm. Sci., 94, 4–14
https://doi.org/10.1016/j.ejps.2016.04.027.
pmid: 27131606
|
46 |
Y.Tang, , Q. Tang, , C.Dong, , X.Li, , Z. Zhang, and F.An, (2015) Protein-protein interaction network and mechanism analysis of hepatitis C. Genet. Mol. Res., 14, 2069–2079
https://doi.org/10.4238/2015.March.20.17
pmid: 25867353
|
47 |
M. E.Schurdak, , F.Pei, , T. R.Lezon, , D.Carlisle, , R.Friedlander, , D. L.Taylor, and A. M.Stern, (2018) A quantitative systems pharmacology approach to infer pathways involved in complex disease phenotypes. Methods Mol. Biol., 1787, 207–222
https://doi.org/10.1007/978-1-4939-7847-2_16
pmid: 29736721
|
48 |
Q.Li, , X. Li, , C.Li, , L.Chen, , J.Song, , Y.Tang, and X.Xu, (2011) A network-based multi-target computational estimation scheme for anticoagulant activities of compounds. PLoS One, 6, e14774
https://doi.org/10.1371/journal.pone.0014774
pmid: 21445339
|
49 |
X.Zhang, , J. Gu, , L.Cao, , Y.Ma, , Z. Su, , F.Luo, , Z.Wang, , N.Li, , G. Yuan, , L.Chen, , 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, 1162–1169
https://doi.org/10.1039/C4IB00141A
pmid: 25228393
|
50 |
J.Gu, , Q. Li, , L.Chen, , Y.Li, , T. Hou, , G.Yuan, and X.Xu, (2013) Platelet aggregation pathway network-based approach for evaluating compounds efficacy. Evid. Based Complement. Alternat. Med., 2013, 425707
https://doi.org/10.1155/2013/425707
pmid: 23662134
|
51 |
P.Traynard, , L. Tobalina, , F.Eduati, , L.Calzone, and J. Saez-Rodriguez, (2017) Logic modeling in quantitative systems pharmacology. CPT Pharmacometrics Syst. Pharmacol., 6, 499–511
https://doi.org/10.1002/psp4.12225
pmid: 28681552
|
52 |
J.Ru, , P. Li, , J.Wang, , W.Zhou, , B.Li, , C. Huang, , P.Li, , Z.Guo, , W.Tao, , Y.Yang, , et al. (2014) TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J. Cheminform., 6, 13
https://doi.org/10.1186/1758-2946-6-13
pmid: 24735618
|
53 |
C.Chassagnole, , R. C. Jackson, , N.Hussain, , L.Bashir, , C.Derow, , J.Savin, and D. A.Fell, (2006) Using a mammalian cell cycle simulation to interpret differential kinase inhibition in anti-tumour pharmaceutical development. Biosystems, 83, 91–97
https://doi.org/10.1016/j.biosystems.2005.04.007
pmid: 16236428
|
54 |
W.Tao, , B. Li, , S.Gao, , Y.Bai, , P. A.Shar, , W.Zhang, , Z.Guo, , K.Sun, , Y.Fu, , C. Huang, , et al. (2015) CancerHSP: anticancer herbs database of systems pharmacology. Sci. Rep., 5, 11481
https://doi.org/10.1038/srep11481
pmid: 26074488
|
55 |
H.Huang, , X. Wu, , R.Pandey, , J.Li, , G. Zhao, , S.Ibrahim, and J. Y.Chen, (2012) C2Maps: a network pharmacology database with comprehensive disease-gene-drug connectivity relationships. BMC Genomics, 13, S17
https://doi.org/10.1186/1471-2164-13-S6-S17
pmid: 23134618
|
56 |
Z.Hu, , Y. C. Chang, , Y.Wang, , C. L.Huang, , Y.Liu, , F.Tian, , B.Granger, and C.Delisi, (2013) VisANT 4.0: integrative network platform to connect genes, drugs, diseases and therapies. Nucleic Acids Res., 41, W225–W 231
https://doi.org/10.1093/nar/gkt401
pmid: 23716640
|
57 |
D.Wang, , J. Gu, , W.Zhu, , F.Luo, , L.Chen, , X.Xu, and C.Lu, (2017) PDTCM: a systems pharmacology platform of traditional Chinese medicine for psoriasis. Ann. Med., 49, 652–660
https://doi.org/10.1080/07853890.2017.1364417
pmid: 28782992
|
58 |
J.Gu, , Y. Gui, , L.Chen, , G.Yuan, and X.Xu, (2013) CVDHD: a cardiovascular disease herbal database for drug discovery and network pharmacology. J. Cheminform., 5, 51
https://doi.org/10.1186/1758-2946-5-51
pmid: 24344970
|
59 |
C. J.Musante, , S.Ramanujan, , B. J.Schmidt, , O. G.Ghobrial, , J.Lu, and A. C.Heatherington, (2017) Quantitative systems pharmacology: a case for disease models. Clin. Pharmacol. Ther., 101, 24–27
https://doi.org/10.1002/cpt.528
pmid: 27709613
|
60 |
B. J.Schmidt, , F. P.Casey, , T.Paterson, and J. R.Chan, (2013) Alternate virtual populations elucidate the type I interferon signature predictive of the response to rituximab in rheumatoid arthritis. BMC Bioinformatics, 14, 221
https://doi.org/10.1186/1471-2105-14-221
pmid: 23841912
|
61 |
S.Ghosh, , Y. Matsuoka, , Y.Asai, , K. Y.Hsin, and H.Kitano, (2013) Toward an integrated software platform for systems pharmacology. Biopharm. Drug Dispos., 34, 508–526
https://doi.org/10.1002/bdd.1875
pmid: 24150748
|
62 |
A.Spiros, , P. Roberts, and H.Geerts, (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, 569–580
pmid: 24192755
|
63 |
J. E.Ming, , R. E. Abrams, , D. W.Bartlett, , M.Tao, , T.Nguyen, , H.Surks, , K.Kudrycki, , A.Kadambi, , C. M.Friedrich, , N.Djebli, , 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, 1177625017710941
https://doi.org/10.1177/1177625017710941
pmid: 28804243
|
64 |
C.Zheng, , T. Pei, , C.Huang, , X.Chen, , Y.Bai, , J.Xue, , Z.Wu, , J. Mu, , Y.Li, and Y.Wang, (2016) A novel systems pharmacology platform to dissect action mechanisms of traditional Chinese medicines for bovine viral diarrhea disease. Eur. J. Pharm. Sci., 94, 33–45
https://doi.org/10.1016/j.ejps.2016.05.018
pmid: 27208435
|
65 |
T. R.Rieger, , R. J.Allen, , L.Bystricky, , Y.Chen, , G. W.Colopy, , Y.Cui, , A.Gonzalez, , Y.Liu, , R. D.White, , R. A.Everett, , et al. (2018) Improving the generation and selection of virtual populations in quantitative systems pharmacology models. Prog. Biophys. Mol. Biol., 139, 15–22
https://doi.org/10.1016/j.pbiomolbio.2018.06.002
pmid: 29902482
|
66 |
H.Geerts, , A. Spiros, , P.Roberts, and R.Carr, (2017) Towards the virtual human patient. quantitative systems pharmacology in Alzheimer’s disease. Eur. J. Pharmacol., 817, 38–45
https://doi.org/10.1016/j.ejphar.2017.05.062
pmid: 28583429
|
67 |
B.Wiśniowska, and S.Polak, (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, 3415–3424
https://doi.org/10.1016/j.xphs.2016.08.002
pmid: 27640752
|
68 |
R. J.Allen, , T. R. Rieger, and C. J.Musante, (2016) Efficient generation and selection of virtual populations in quantitative systems pharmacology models. CPT Pharmacometrics Syst. Pharmacol., 5, 140–146
https://doi.org/10.1002/psp4.12063
pmid: 27069777
|
69 |
A.Rostami-Hodjegan, (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, 50–61
https://doi.org/10.1038/clpt.2012.65
pmid: 22644330
|
70 |
P.Bloomingdale, , C. Housand, , J. F.Apgar, , B. L.Millard, , D. E.Mager, , J. M.Burke, and D. K.Shah, (2017) Quantitative systems toxicology. Curr. Opin. Toxicol., 4, 79–87
https://doi.org/10.1016/j.cotox.2017.07.003
pmid: 29308440
|
71 |
C.Pichardo-Almarza, and V.Diaz-Zuccarini, (2017) From PK/PD to QSP: understanding the dynamic effect of cholesterol-lowering drugs on atherosclerosis progression and stratified medicine. Curr. Pharm. Des., 22, 6903–6910
https://doi.org/10.2174/1381612822666160905095402
pmid: 27592718
|
72 |
X. Y.Meng, , H. X. Zhang, , M.Mezei, and M.Cui, (2011) Molecular docking: a powerful approach for structure-based drug discovery. Curr. Comput. Aided Drug Des., 7, 146–157
https://doi.org/10.2174/157340911795677602
pmid: 21534921
|
73 |
A.Omer, and P. Singh, (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, 1547–1558
https://doi.org/10.1080/07391102.2016.1188417
pmid: 27484103
|
74 |
J.Gu, , L. Li, , D.Wang, , W.Zhu, , L.Han, , R.Zhao, , X.Xu, and C.Lu, (2018) Deciphering metabonomics biomarkers-targets interactions for psoriasis vulgaris by network pharmacology. Ann. Med., 50, 323–332
https://doi.org/10.1080/07853890.2018.1453169
pmid: 29537306
|
75 |
M.Yang, , J. Chen, , X.Shi, , L.Xu, , Z. Xi, , L.You, , R.An, and X.Wang, (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, 3691–3713
https://doi.org/10.1021/acs.molpharmaceut.5b00465
pmid: 26376206
|
76 |
M. K.Gilson, , T.Liu, , M.Baitaluk, , G.Nicola, , L.Hwang, and J.Chong, (2016) BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res., 44, D1045–D1053
https://doi.org/10.1093/nar/gkv1072
pmid: 26481362
|
77 |
Z.Liu, , F. Guo, , Y.Wang, , C.Li, , X. Zhang, , H.Li, , L.Diao, , J.Gu, , W. Wang, , D.Li, , et al. (2016) BATMAN-TCM: a bioinformatics analysis Tool for molecular mechanism of traditional Chinese medicine. Sci. Rep., 6, 21146
https://doi.org/10.1038/srep21146
pmid: 26879404
|
78 |
A. D.Boran, and R.Iyengar, (2010) Systems approaches to polypharmacology and drug discovery. Curr Opin Drug Discov Devel, 13, 297–309
pmid: 20443163
|
79 |
S. I.Berger, , A.Ma’ayan, and R.Iyengar, (2010) Systems pharmacology of arrhythmias. Sci. Signal., 3, ra30
https://doi.org/10.1126/scisignal.2000723
pmid: 20407125
|
80 |
M. R.Boland, , A.Jacunski, , T.Lorberbaum, , J. D.Romano, , R.Moskovitch, and N. P.Tatonetti, (2016) Systems biology approaches for identifying adverse drug reactions and elucidating their underlying biological mechanisms. Wiley Interdiscip. Rev. Syst. Biol. Med., 8, 104–122
https://doi.org/10.1002/wsbm.1323
pmid: 26559926
|
81 |
L. H.Goldstein, , M.Berlin, , W.Saliba, , M.Elias, and M.Berkovitch, (2013) Founding an adverse drug reaction (ADR) network: a method for improving doctors spontaneous ADR reporting in a general hospital. J. Clin. Pharmacol., 53, 1220–1225
https://doi.org/10.1002/jcph.149
pmid: 23852627
|
82 |
S.Zhao, , T. Nishimura, , Y.Chen, , E. U.Azeloglu, , O.Gottesman, , C.Giannarelli, , M. U.Zafar, , L.Benard, , J. J.Badimon, , R. J.Hajjar, , et al. (2013) Systems pharmacology of adverse event mitigation by drug combinations. Sci. Transl. Med., 5, 206ra140
https://doi.org/10.1126/scitranslmed.3006548
pmid: 24107779
|
83 |
Z.Wu, , F. Cheng, , J.Li, , W.Li, , G. Liu, and Y.Tang, (2017) SDTNBI: an integrated network and chemoinformatics tool for systematic prediction of drug-target interactions and drug repositioning. Brief. Bioinformatics, 18, 333–347
pmid: 26944082
|
84 |
Z.Wu, , W. Lu, , D.Wu, , A.Luo, , H.Bian, , J.Li, , W. Li, , G.Liu, , J.Huang, , F.Cheng, , et al. (2016) In silico prediction of chemical mechanism of action via an improved network-based inference method. Br. J. Pharmacol., 173, 3372–3385
https://doi.org/10.1111/bph.13629
pmid: 27646592
|
85 |
J.Wang, , Z. Guo, , Y.Fu, , Z.Wu, , C. Huang, , C.Zheng, , P. A.Shar, , Z.Wang, , W.Xiao, and Y.Wang, (2017) Weak-binding molecules are not drugs?—toward a systematic strategy for finding effective weak-binding drugs. Brief. Bioinformatics, 18, 321–332
pmid: 26962012
|
86 |
C.Huang, , C. Zheng, , Y.Li, , Y.Wang, , A.Lu, and L.Yang, (2014) Systems pharmacology in drug discovery and therapeutic insight for herbal medicines. Brief. Bioinform., 15, 710–733
https://doi.org/10.1093/bib/bbt035
pmid: 23736100
|
87 |
C.Mitrea, , Z. Taghavi, , B.Bokanizad, , S.Hanoudi, , R.Tagett, , M.Donato, , C.Voichiţa, and S.Drăghici, (2013) Methods and approaches in the topology-based analysis of biological pathways. Front. Physiol., 4, 278
https://doi.org/10.3389/fphys.2013.00278
pmid: 24133454
|
88 |
X. Z.Nie, , X. Du, , R. R.Zhang, , J.He, , R. Su, , H. Q.Ma, , J.Mu, , Y. Li, and F.Liu, (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 Chinese
pmid: 28952263
|
89 |
J.Gu, , P. S. Crosier, , C. J.Hall, , L.Chen, and X.Xu, (2016) Inflammatory pathway network-based drug repositioning and molecular phenomics. Mol. Biosyst., 12, 2777–2784
https://doi.org/10.1039/C6MB00222F
pmid: 27345454
|
90 |
R.Poltz, and M. Naumann, (2012) Dynamics of p53 and NF-κB regulation in response to DNA damage and identification of target proteins suitable for therapeutic intervention. BMC Syst. Biol., 6, 125
https://doi.org/10.1186/1752-0509-6-125
pmid: 22979979
|
91 |
N.Le Novère, (2015) Quantitative and logic modelling of molecular and gene networks. Nat. Rev. Genet., 16, 146–158
https://doi.org/10.1038/nrg3885
pmid: 25645874
|
92 |
C.Chaouiya, and E.Remy, (2013) Logical modelling of regulatory networks, methods and applications. Bull. Math. Biol., 75, 891–895
https://doi.org/10.1007/s11538-013-9863-0
pmid: 23722297
|
93 |
D. C.Kirouac, , J. Y.Du, , J.Lahdenranta, , R.Overland, , D.Yarar, , V.Paragas, , E.Pace, , C. F.McDonagh, , U. B.Nielsen, and M. D.Onsum, (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, ra68
https://doi.org/10.1126/scisignal.2004008
pmid: 23943608
|
94 |
L. K.Shoda, , J. L. Woodhead, , S. Q.Siler, , P. B.Watkins, and B. A.Howell, (2014) Linking physiology to toxicity using DILIsym®, a mechanistic mathematical model of drug-induced liver injury. Biopharm. Drug Dispos., 35, 33–49
https://doi.org/10.1002/bdd.1878
pmid: 24214486
|
95 |
J. L.Woodhead, , K.Yang, , S. Q.Siler, , P. B.Watkins, , K. L.Brouwer, , H. A.Barton, and B. A.Howell, (2014) Exploring BSEP inhibition-mediated toxicity with a mechanistic model of drug-induced liver injury. Front. Pharmacol., 5, 240
https://doi.org/10.3389/fphar.2014.00240
pmid: 25426072
|
96 |
J. L.Woodhead, , F.Paech, , M.Maurer, , M.Engelhardt, , A. H.Schmitt-Hoffmann, , J.Spickermann, , S.Messner, , M.Wind, , A. T.Witschi, , S.Krähenbühl, , 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, 498–505
https://doi.org/10.1111/cts.12560
pmid: 29877622
|
97 |
H.Geerts, , P. Roberts, and A.Spiros, (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, 198
https://doi.org/10.3389/fphar.2015.00198
pmid: 26441655
|
98 |
A. L.Hopkins, (2008) Network pharmacology: the next paradigm in drug discovery. Nat. Chem. Biol., 4, 682–690
https://doi.org/10.1038/nchembio.118
pmid: 18936753
|
99 |
S. R.Allerheiligen, (2010) Next-generation model-based drug discovery and development: quantitative and systems pharmacology. Clin. Pharmacol. Ther., 88, 135–137
https://doi.org/10.1038/clpt.2010.81
pmid: 20531467
|
100 |
B. M.Agoram, and O.Demin, (2011) Integration not isolation: arguing the case for quantitative and systems pharmacology in drug discovery and development. Drug Discov. Today, 16, 1031–1036
https://doi.org/10.1016/j.drudis.2011.10.001
pmid: 22020181
|
101 |
H.Geerts, and L. Kennis, (2014) Multitarget drug discovery projects in CNS diseases: quantitative systems pharmacology as a possible path forward. Future Med. Chem., 6, 1757–1769
https://doi.org/10.4155/fmc.14.97
pmid: 25574530
|
102 |
J.Fang, , L. Gao, , H.Ma, , Q.Wu, , T. Wu, , J.Wu, , Q.Wang, and F.Cheng, (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, 747
https://doi.org/10.3389/fphar.2017.00747
pmid: 29093681
|
103 |
S. C.Janga, and A.Tzakos, (2009) Structure and organization of drug-target networks: insights from genomic approaches for drug discovery. Mol. Biosyst., 5, 1536–1548
https://doi.org/10.1039/b908147j
pmid: 19763339
|
104 |
D. K.Arrell, and A.Terzic, (2010) Network systems biology for drug discovery. Clin. Pharmacol. Ther., 88, 120–125
https://doi.org/10.1038/clpt.2010.91
pmid: 20520604
|
105 |
V. R.Knight-Schrijver, , V.Chelliah, , L.Cucurull-Sanchez, and N.Le Novère, (2016) The promises of quantitative systems pharmacology modelling for drug development. Comput. Struct. Biotechnol. J., 14, 363–370.
https://doi.org/10.1016/j.csbj.2016.09.002
pmid: 27761201
|
106 |
S.Kim, , G. Lahu, , L. J.Lesko, and M. N.Trame, (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.
|
107 |
Y.Kariya, , M. Honma, and H.Suzuki, (2016) Mechanism analyses and mechanism-based prediction for adverse drug reactions using systems pharmacology. Nippon Yakurigaku Zasshi, 147, 89–94, in Japanese
https://doi.org/10.1254/fpj.147.89
pmid: 26860648
|
108 |
D. S.Cao, , N. Xiao, , Y. J.Li, , W. B.Zeng, , Y. Z.Liang, , A. P.Lu, , Q. S.Xu, and A. F.Chen, (2015) Integrating multiple evidence sources to predict adverse drug reactions based on a systems pharmacology model. CPT Pharmacometrics Syst. Pharmacol., 4, 498–506
https://doi.org/10.1002/psp4.12002
pmid: 26451329
|
109 |
S. I.Berger, and R.Iyengar, (2011) Role of systems pharmacology in understanding drug adverse events. Wiley Interdiscip. Rev. Syst. Biol. Med., 3, 129–135
https://doi.org/10.1002/wsbm.114
pmid: 20803507
|
110 |
V. I.Nueno, (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, 6881–6884
https://doi.org/10.2174/138161282246170124192058
pmid: 28236677
|
111 |
H.Liu, , J. Wang, , W.Zhou, , Y.Wang, and L.Yang, (2013) Systems approaches and polypharmacology for drug discovery from herbal medicines: an example using licorice. J. Ethnopharmacol., 146, 773–793
https://doi.org/10.1016/j.jep.2013.02.004
pmid: 23415946
|
112 |
F.Luo, , J. Gu, , L.Chen, and X.Xu, (2014) Systems pharmacology strategies for anticancer drug discovery based on natural products. Mol. Biosyst., 10, 1912–1917
https://doi.org/10.1039/c4mb00105b
pmid: 24802653
|
113 |
J.Dziuba, , P. Alperin, , J.Racketa, , U.Iloeje, , D.Goswami, , E.Hardy, , I.Perlstein, , H. L.Grossman, and M.Cohen, (2014) Modeling effects of SGLT-2 inhibitor dapagliflozin treatment versus standard diabetes therapy on cardiovascular and microvascular outcomes. Diabetes Obes. Metab., 16, 628–635
https://doi.org/10.1111/dom.12261
pmid: 24443793
|
114 |
B. R.Peskin, , A. V.Shcheprov, , K. S.Boye, , S.Bruce, , D. G.Maggs, and J. A.Gaebler, (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, 921–927
https://doi.org/10.1111/j.1463-1326.2011.01430.x
pmid: 21624032
|
115 |
K.Gadkar, , D. Kirouac, , N.Parrott, and S.Ramanujan, (2016) Quantitative systems pharmacology: a promising approach for translational pharmacology. Drug Discov. Today. Technol., 21-22, 57–65
https://doi.org/10.1016/j.ddtec.2016.11.001
pmid: 27978989
|
116 |
M.Cirit, and C. L. Stokes, (2018) Maximizing the impact of microphysiological systems with in vitro-in vivo translation. Lab Chip, 18, 1831–1837
https://doi.org/10.1039/C8LC00039E
pmid: 29863727
|
117 |
T.Yuraszeck, , S. Kasichayanula, and J. E.Benjamin, (2017) Translation and clinical development of bispecific T-cell engaging antibodies for cancer treatment. Clin. Pharmacol. Ther., 101, 634–645
https://doi.org/10.1002/cpt.651
pmid: 28182247
|
118 |
P.Schulthess, , T. M. Post, , J.Yates, and P. H.van der Graaf, (2018) Frequency-domain response analysis for quantitative systems pharmacology models. CPT Pharmacometrics Syst. Pharmacol.,7,111–123
pmid: PMID:29193852.
|
119 |
S. A.Visser, , D. P.de Alwis, , T.Kerbusch, , J. A.Stone, and S. R.Allerheiligen, (2014) Implementation of quantitative and systems pharmacology in large pharma. CPT Pharmacometrics Syst. Pharmacol., 3, e142
https://doi.org/10.1038/psp.2014.40
pmid: 25338195
|
120 |
H.Geerts, , P. Roberts, and A.Spiros, (2013) A quantitative system pharmacology computer model for cognitive deficits in schizophrenia. CPT Pharmacometrics Syst. Pharmacol., 2, e36
https://doi.org/10.1038/psp.2013.12
pmid: 23887686
|
121 |
J.Liu, , A. Ogden, , T. A.Comery, , A.Spiros, , P.Roberts, and H.Geerts, (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, e111
https://doi.org/10.1038/psp.2014.7
pmid: 24759548
|
122 |
H.Geerts, , P. Roberts, , A.Spiros, and S.Potkin, (2015) Understanding responder neurobiology in schizophrenia using a quantitative systems pharmacology model: application to iloperidone. J. Psychopharmacol. (Oxford), 29, 372–382
https://doi.org/10.1177/0269881114568042
pmid: 25691503
|
123 |
K.Vega-Villa, , R. Pluta, , R.Lonser, and S.Woo, (2013) Quantitative systems pharmacology model of NO metabolome and methemoglobin following long-term infusion of sodium nitrite in humans. CPT Pharmacometrics Syst. Pharmacol., 2, e60
https://doi.org/10.1038/psp.2013.35
pmid: 23903463
|
124 |
T.John, , T. Kiss, , C.Lever, and P.Érdi, (2014) Anxiolytic drugs and altered hippocampal theta rhythms: the quantitative systems pharmacological approach. Network, 25, 20–37
https://doi.org/10.3109/0954898X.2013.880003
pmid: 24571096
|
125 |
T. N.Johnson, and A.Rostami-Hodjegan, (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, 291–301
https://doi.org/10.1111/j.1460-9592.2010.03323.x
pmid: 20497354
|
126 |
C. D.Kaddi, , B. Niesner, , R.Baek, , P.Jasper, , J.Pappas, , J.Tolsma, , J.Li, , Z. van Rijn, , M.Tao, , C.Ortemann-Renon, , 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, 442–452
https://doi.org/10.1002/psp4.12304
pmid: 29920993
|
127 |
A. M.Stern, , M. E. Schurdak, , I.Bahar, , J. M.Berg, and D. L.Taylor, (2016) A perspective on implementing a quantitative systems pharmacology platform for drug discovery and the advancement of personalized medicine. J. Biomol. Screen., 21, 521–534
https://doi.org/10.1177/1087057116635818
pmid: 26962875
|
128 |
H.Geerts, , A. Spiros, , P.Roberts, and L.Alphs, (2018) A quantitative systems pharmacology study on optimal scenarios for switching to paliperidone palmitate once-monthly. Schizophr. Res., 197, 261–268
https://doi.org/10.1016/j.schres.2017.11.016
pmid: 29395607
|
129 |
A.Yin, , A. Yamada, , W. B.Stam, , J. G. C.van Hasselt, and P. H.van der Graaf, (2018) Quantitative systems pharmacology analysis of drug combination and scaling to humans: the interaction between noradrenaline and vasopressin in vasoconstriction. Br. J. Pharmacol., 175, 3394–3406
https://doi.org/10.1111/bph.14385
pmid: 29859008
|
130 |
Y.Chen, , Y. Sun, , W.Li, , H.Wei, , T.Long, , H.Li, , Q. Xu, and W.Liu, (2018) Systems pharmacology dissection of the anti-stroke mechanism for the Chinese traditional medicine Xing-Nao-Jing. J. Pharmacol. Sci., 136, 16–25
https://doi.org/10.1016/j.jphs.2017.11.005
pmid: 29336875
|
131 |
J.Li, , P. Zhao, , Y.Li, , Y.Tian, and Y.Wang, (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, 15290
https://doi.org/10.1038/srep15290
pmid: 26469778
|
132 |
P.Zhao, , L. Yang, , J.Li, , Y.Li, , Y. Tian, and S.Li, (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, 553–566
pmid: 27042044
|
133 |
P.Zhao, , J. Li, , L.Yang, , Y.Li, , Y. Tian, and S.Li, (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, 5247–5257
https://doi.org/10.3892/mmr.2018.8480
pmid: 29393428
|
134 |
W.Zhang, , Q. Tao, , Z.Guo, , Y.Fu, , X. Chen, , P. A.Shar, , M.Shahen, , J.Zhu, , J.Xue, , Y.Bai, , et al. (2016) Systems pharmacology dissection of the integrated treatment for cardiovascular and gastrointestinal disorders by traditional Chinese medicine. Sci. Rep., 6, 32400
https://doi.org/10.1038/srep32400
pmid: 27597117
|
135 |
M.Sun, , W. T. Chang, , E.Van Wijk, , M.He, , S.Koval, , M. K.Lin, , R.Van Wijk, , T.Hankemeier, , J.van der Greef, and M.Wang, (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, 1–11
https://doi.org/10.1016/j.jphotobiol.2017.01.018
pmid: 28147303
|
136 |
J.Wang, , Y. Li, , Y.Yang, , X.Chen, , J.Du, , Q. Zheng, , Z.Liang, and Y.Wang, (2017) A new strategy for deleting animal drugs from traditional Chinese medicines based on modified yimusake formula. Sci. Rep., 7, 1504
https://doi.org/10.1038/s41598-017-01613-7
pmid: 28473709
|
137 |
H.Ai, , X. Wu, , M.Qi, , L.Zhang, , H.Hu, , Q. Zhao, , J.Zhao, and H.Liu, (2018) Study on the mechanisms of active compounds in traditional Chinese medicine for the treatment of influenza virus by virtual screening. Interdiscip. Sci., 10, 320–328
https://doi.org/10.1007/s12539-018-0289-0
pmid: 29500549
|
138 |
Q. Y.Jiang, , M. S. Zheng, , X. J.Yang, and X. S.Sun, (2016) Analysis of molecular networks and targets mining of Chinese herbal medicines on anti-aging. BMC Complement. Altern. Med., 16, 520
https://doi.org/10.1186/s12906-016-1513-2
pmid: 28031022
|
139 |
J.Liu, , J. Liu, , F.Shen, , Z.Qin, , M.Jiang, , J.Zhu, , Z.Wang, , J.Zhou, , Y.Fu, , X. Chen, , et al. (2018) Systems pharmacology analysis of synergy of TCM: an example using saffron formula. Sci. Rep., 8, 380
https://doi.org/10.1038/s41598-017-18764-2
pmid: 29321678
|
140 |
W. T.Fan, and Q.Wang, (2018) Mechanism of Acori Tatarinowii Rhizoma-Curcumae Radix treating depression based on network pharmacology. Zhongguo Zhongyao Zazhi, 43, 2607–2611, in Chinese
pmid: 29950083
|
141 |
J.Wang, , L. Zhang, , B.Liu, , Q.Wang, , Y.Chen, , Z.Wang, , J.Zhou, , W.Xiao, , C.Zheng, and Y.Wang, (2018) Systematic investigation of the Erigeron breviscapus mechanism for treating cerebrovascular disease. J. Ethnopharmacol., 224, 429–440
https://doi.org/10.1016/j.jep.2018.05.022
pmid: 29783016
|
142 |
Y.Li, , C. Han, , J.Wang, , W.Xiao, , Z.Wang, , J.Zhang, , Y.Yang, , S.Zhang, and C.Ai, (2014) Investigation into the mechanism of Eucommia ulmoides Oliv. based on a systems pharmacology approach. J. Ethnopharmacol., 151, 452–460
https://doi.org/10.1016/j.jep.2013.10.067
pmid: 24239601
|
143 |
X.Liu, , J. Wu, , D.Zhang, , K.Wang, , X.Duan, and X.Zhang, (2018) A network pharmacology approach to uncover the multiple mechanisms of Hedyotis diffusa Willd. on colorectal cancer. Evid. Based Complement. Alternat. Med., 2018, 6517034
https://doi.org/10.1155/2018/7802639
pmid: 29619072
|
144 |
Y.Li, , J. Wang, , Y.Xiao, , Y.Wang, , S.Chen, , Y.Yang, , A.Lu, and S.Zhang, (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, 301–314
https://doi.org/10.1016/j.jep.2015.09.016
pmid: 26386382
|
145 |
Y. Y.Li, , G. Zheng, and L.Liu, (2018) Bioinformatics based therapeutic effects of Sinomenium Acutum. Chin. J. Integr. Med., 10.1007/s11655-018-2796-6
pmid: 29564801
|
146 |
F.Yi, , L. Sun, , L. J.Xu, , Y.Peng, , H. B.Liu, , C. N.He, and P. G.Xiao, (2017) In silico approach for anti-thrombosis drug discovery: P2Y1R structure-based TCMs screening. Front. Pharmacol., 7, 531
https://doi.org/10.3389/fphar.2016.00531
pmid: 28119608
|
147 |
J.Liu, , M. Pei, , C.Zheng, , Y.Li, , Y. Wang, , A.Lu, and L.Yang, (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, 938764
https://doi.org/10.1155/2013/938764
pmid: 24369484
|
148 |
L.Zhao, , Y. F. Wu, , Y.Gao, , H.Xiang, , X. M.Qin, and J. S.Tian, (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 Chinese
pmid: 29911798
|
149 |
P.Zhao, , J. Li, , Y.Li, , Y.Tian, , Y.Wang, and C.Zheng, (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, 2633–2656
pmid: 26674991
|
150 |
S. H.Shi, , Y. P. Cai, , X. J.Cai, , X. Y.Zheng, , D. S.Cao, , F. Q.Ye, and Z.Xiang, (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, e89123
https://doi.org/10.1371/journal.pone.0089123
pmid: 24598793
|
151 |
H.Cai, , Y. Luo, , X.Yan, , P.Ding, , Y.Huang, , S.Fang, , R.Zhang, , Y.Chen, , Z.Guo, , J.Fang, , et al. (2018) The mechanisms of Bushen-Yizhi formula as a therapeutic agent against alzheimer’s disease. Sci. Rep., 8, 3104
https://doi.org/10.1038/s41598-018-21468-w
pmid: 29449587
|
152 |
J.Huang, , H. Tang, , S.Cao, , Y.He, , Y. Feng, , K.Wang, and Q.Zheng, (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, 1930598
https://doi.org/10.1155/2017/1930598
pmid: 28620417
|
153 |
Y.Luo, , Q. Wang, and Y.Zhang, (2016) A systems pharmacology approach to decipher the mechanism of danggui-shaoyao-san decoction for the treatment of neurodegenerative diseases. J. Ethnopharmacol., 178, 66–81
https://doi.org/10.1016/j.jep.2015.12.011
pmid: 26680587
|
154 |
C. S.Zheng, , C. L. Fu, , C. B.Pan, , H. J.Bao, , X. Q.Chen, , H. Z.Ye, , J. X.Ye, , G. W.Wu, , X. H.Li, , H. F.Xu, , et al. (2015) Deciphering the underlying mechanisms of Diesun Miaofang in traumatic injury from a systems pharmacology perspective. Mol. Med. Rep., 12, 1769–1776
https://doi.org/10.3892/mmr.2015.3638
pmid: 25891262
|
155 |
H.Xu, , Y. Zhang, , Y.Lei, , X.Gao, , H.Zhai, , N.Lin, , S.Tang, , R.Liang, , Y.Ma, , D. Li, , 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, e101432
https://doi.org/10.1371/journal.pone.0101432
pmid: 25068885
|
156 |
H.Li, , L. Zhao, , B.Zhang, , Y.Jiang, , X.Wang, , Y.Guo, , H.Liu, , S.Li, and X.Tong, (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, 495840
https://doi.org/10.1155/2014/495840
pmid: 24527048
|
157 |
X.Liang, , H. Li, and S.Li, (2014) A novel network pharmacology approach to analyse traditional herbal formulae: the Liu-Wei-Di-Huang pill as a case study. Mol. Biosyst., 10, 1014–1022
https://doi.org/10.1039/C3MB70507B
pmid: 24492828
|
158 |
Y.Yao, , X. Zhang, , Z.Wang, , C.Zheng, , P.Li, , C. Huang, , W.Tao, , W.Xiao, , Y.Wang, , L.Huang, , et al. (2013) Deciphering the combination principles of traditional Chinese medicine from a systems pharmacology perspective based on Ma-huang decoction. J. Ethnopharmacol., 150, 619–638
https://doi.org/10.1016/j.jep.2013.09.018
pmid: 24064232
|
159 |
F.Tang, , Q. Tang, , Y.Tian, , Q.Fan, , Y.Huang, and X.Tan, (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, 402–412
https://doi.org/10.1016/j.jep.2015.10.040
pmid: 26545458
|
160 |
T.Huang, , Z. Ning, , D.Hu, , M.Zhang, , L.Zhao, , C.Lin, , L. L. D.Zhong, , Z.Yang, , H.Xu, and Z.Bian, (2018) Uncovering the mechanisms of Chinese herbal medicine (MaZiRenWan) for functional constipation by focused network pharmacology approach. Front. Pharmacol., 9, 270
https://doi.org/10.3389/fphar.2018.00270
pmid: 29632490
|
161 |
X.Wang, , S. Yu, , Q.Jia, , L.Chen, , J.Zhong, , Y.Pan, , P.Shen, , Y.Shen, , S.Wang, , Z.Wei, , et al. (2017) NiaoDuQing granules relieve chronic kidney disease symptoms by decreasing renal fibrosis and anemia. Oncotarget, 8, 55920–55937
https://doi.org/10.18632/oncotarget.18473
pmid: 28915563
|
162 |
Z. P.Ke, , X. Z. Zhang, , Y.Ding, , L.Cao, , N.Li, , G. Ding, , Z. Z.Wang, and W.Xiao, (2015) Study on effective substance basis and molecular mechanism of Qigui Tongfeng tablet using network pharmacology method. Zhongguo Zhongyao Zazhi, 40, 2837–2842, in Chinese
pmid: 26666036
|
163 |
W.Tao, , X. Xu, , X.Wang, , B.Li, , Y. Wang, , Y.Li, and L.Yang, (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, 1–10
https://doi.org/10.1016/j.jep.2012.09.051
pmid: 23142198
|
164 |
H.Yang, , W. Zhang, , C.Huang, , W.Zhou, , Y.Yao, , Z.Wang, , Y.Li, , W. Xiao, and Y.Wang, (2014) A novel systems pharmacology model for herbal medicine injection: a case using reduning injection. BMC Complement. Altern. Med., 14, 430
https://doi.org/10.1186/1472-6882-14-430
pmid: 25366653
|
165 |
F.Luo, , J. Gu, , X.Zhang, , L.Chen, , L.Cao, , N.Li, , Z. Wang, , W.Xiao, and X.Xu, (2015) Multiscale modeling of drug-induced effects of ReDuNing injection on human disease: from drug molecules to clinical symptoms of disease. Sci. Rep., 5, 10064
https://doi.org/10.1038/srep10064
pmid: 25973739
|
166 |
J.Liu, , K. Sun, , C.Zheng, , X.Chen, , W.Zhang, , Z.Wang, , P. A.Shar, , W.Xiao, and Y.Wang, (2015) Pathway as a pharmacological target for herbal medicines: an investigation from reduning injection. PLoS One, 10, e0123109
https://doi.org/10.1371/journal.pone.0123109
pmid: 25830385
|
167 |
L.Wu, , Y. Wang, , J.Nie, , X.Fan, and Y.Cheng, (2013) A network pharmacology approach to evaluating the efficacy of Chinese medicine using genome-wide transcriptional expression data. Evid. Based Complement. Alternat. Med., 2013, 915343
https://doi.org/10.1155/2013/915343
pmid: 23737854
|
168 |
X.Shen, , Z. Zhao, , X.Luo, , H.Wang, , B.Hu, and Z.Guo, (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, 9146378
https://doi.org/10.1155/2016/9146378
pmid: 28058059
|
169 |
H. H.Wang, , B. X. Zhang, , X. T.Ye, , S. B.He, , Y. L.Zhang, and Y.Wang, (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 Chinese
pmid: 26975092
|
170 |
C. S.Zheng, , X. J. Xu, , H. Z.Ye, , G. W.Wu, , X. H.Li, , H. F.Xu, and X. X.Liu, (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, 125–132
https://doi.org/10.3892/etm.2013.1106
pmid: 23935733
|
171 |
T.Wang, , Z. Wu, , L.Sun, , W.Li, , G. Liu, and Y.Tang, (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, 668
https://doi.org/10.3389/fphar.2018.00668
pmid: 29997503
|
172 |
Y.Li, , J. Zhang, , L.Zhang, , X.Chen, , Y.Pan, , S. S.Chen, , S.Zhang, , Z.Wang, , W.Xiao, , L.Yang, , et al. (2015) Systems pharmacology to decipher the combinational anti-migraine effects of Tianshu formula. J. Ethnopharmacol., 174, 45–56
https://doi.org/10.1016/j.jep.2015.07.043
pmid: 26231449
|
173 |
Y.Gao, , L. Gao, , X. X.Gao, , Y. Z.Zhou, , X. M.Qin, and J. S.Tian, (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 Chinese
pmid: 27169281
|
174 |
J.Liu, , T. Pei, , J.Mu, , C.Zheng, , X.Chen, , C.Huang, , Y.Fu, , Z. Liang, and Y.Wang, (2016) Systems pharmacology uncovers the multiple mechanisms of Xijiao Dihuang decoction for the treatment of viral hemorrhagic fever. Evid. Based Complement. Alternat. Med., 2016, 9025036
https://doi.org/10.1155/2016/9025036
pmid: 27239215
|
175 |
H. Q.Pang, , S. J. Yue, , Y. P.Tang, , Y. Y.Chen, , Y. J.Tan, , Y. J.Cao, , X. Q.Shi, , G. S.Zhou, , A.Kang, , S. L.Huang, , 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, 165
https://doi.org/10.3389/fphar.2018.00165
pmid: 29551975
|
176 |
L.Chen, , Y. Cao, , H.Zhang, , D.Lv, , Y. Zhao, , Y.Liu, , G.Ye, and Y.Chai, (2018) Network pharmacology-based strategy for predicting active ingredients and potential targets of Yangxinshi tablet for treating heart failure. J. Ethnopharmacol., 219, 359–368
https://doi.org/10.1016/j.jep.2017.12.011
pmid: 29366769
|
177 |
J.Huang, , F. Cheung, , H. Y.Tan, , M.Hong, , N.Wang, , J.Yang, , Y.Feng, and Q.Zheng, (2017) Identification of the active compounds and significant pathways of yinchenhao decoction based on network pharmacology. Mol. Med. Rep., 16, 4583–4592
https://doi.org/10.3892/mmr.2017.7149
pmid: 28791364
|
178 |
L.An, and F. Feng, (2015) Network pharmacology-based antioxidant effect study of Zhi-Zi-Da-Huang decoction for alcoholic liver disease. Evid. Based Complement. Alternat. Med., 2015, 492470
https://doi.org/10.1155/2015/492470
pmid: 25922610
|
179 |
F.Li, , Y. N. Lv, , Y. S.Tan, , K.Shen, , K. F.Zhai, , H. L.Chen, , J. P.Kou, and B. Y.Yu, (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, 1337–1348
https://doi.org/10.1038/aps.2015.70
pmid: 26456587
|
180 |
B.Li, , W. Tao, , C.Zheng, , P. A.Shar, , C.Huang, , Y.Fu, and Y.Wang, (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, 19–29
https://doi.org/10.1016/j.compbiomed.2014.05.007
pmid: 25105750
|
181 |
W.Zhou, and Y. Wang, (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, 66–77
https://doi.org/10.1016/j.jep.2013.11.007
pmid: 24269247
|
182 |
J.Wang, , R. Liu, , B.Liu, , Y.Yang, , J.Xie, and N.Zhu, (2017) Systems pharmacology-based strategy to screen new adjuvant for hepatitis B vaccine from traditional Chinese medicine ophiocordyceps sinensis. Sci. Rep., 7, 44788
https://doi.org/10.1038/srep44788
pmid: 28317886
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