Computational methods and applications for quantitative systems pharmacology

doi:10.1007/s40484-018-0161-6

Quant. Biol.

2019, Vol. 7

Issue (1) : 3-16 https://doi.org/10.1007/s40484-018-0161-6

REVIEW

Computational methods and applications for quantitative systems pharmacology

Fuda Xie^1,², Jiangyong Gu^1,²(

)

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.

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

Corresponding Author(s): Jiangyong Gu

Online First Date: 29 January 2019 Issue Date: 22 March 2019

Cite this article:

Fuda Xie,Jiangyong Gu. Computational methods and applications for quantitative systems pharmacology[J]. Quant. Biol., 2019, 7(1): 3-16.

URL:

https://academic.hep.com.cn/qb/EN/10.1007/s40484-018-0161-6
https://academic.hep.com.cn/qb/EN/Y2019/V7/I1/3

Tab.1 Computational methods for QSP

TCM	Computational method	Refs.
Acori Tatarinowii Rhizoma and Curcumae Radix	Data mining, pathway enrichment, network analysis	[¹⁴⁰]
Erigeron breviscapus	ADME pharmacokinetic screening, target fishing, protein-protein interaction network analysis and in vitro experiments verification	[¹⁴¹]
Eucommia ulmoides Oliv.	Drug-likeness evaluation, oral bioavailability prediction, multiple drug targets prediction and network pharmacology techniques	[¹⁴²]
Hedyotis diffusa Willd.	Active component gathering, target prediction, related gene collection, gene enrichment analysis and network analysis	[¹⁴³]
Licorice	Oral bioavailability screening, drug-likeness evaluation, blood-brain barrier permeation, target identification and network analysis	[¹¹¹]
Semen strychni and Tripterygium wilfordii Hook F.	Data mining, target prediction, network analysis	[¹⁴⁴]
Sinomenium acutum	Pathway, network and function analyses, data mining	[¹⁴⁵]
Anti-Thrombosis Drug from TCMs	Data mining, molecular docking, in silico screening	[¹⁴⁶]
Qi-enriching herbs and blood-tonifying herbs	ADME prediction, target fishing and network analysis	[¹⁴⁷]
Baihe Dihuang Tang	ADME/T calculation, target prediction, network analysis	[¹⁴⁸]
Bufei Jianpi formula	Systems pharmacology modeling based on absorption filtering, network targeting and systems analyses	[¹³²,¹⁴⁹]
Bushenhuoxue formula	Target screening, molecular docking, network analysis, literature mining	[¹⁵⁰]
Bushen-Yizhi prescription	ADME/T filter analysis, target prediction, network analysis	[¹⁵¹]
Danlu Capsules	Oral bioavailability and drug-likeness evaluation, gene enrichment analysis	[¹⁵²]
Danggui-shaoyao-san	Oral bioavailability screening, drug-likeness assessment, target identification and network analysis	[¹⁵³]
Diesun Miaofang	Cluster ligands, human intestinal absorption and aqueous solution prediction, chemical space mapping, molecular docking and network pharmacology techniques	[¹⁵⁴]
Dragon’s blood tablets	Chemical analysis, prediction of ADME, and network analysis	[¹⁵⁵]
Ge-Gen-Qin-Lian decoction	Target profile clustering, network target analysis	[¹⁵⁶]
Liu-Wei-Di-Huang pill	Chemical and therapeutic properties, network analysis	[¹⁵⁷]
Ma-huang Decoction	Pharmacokinetic analysis, drug targeting, and drug-target-disease network analysis	[¹⁵⁸]
Mahuang Fuzi Xixin decoction	Drug-likeness evaluation, oral bioavailability prediction, multiple drug target prediction, and network analysis	[¹⁵⁹]
MaZiRenWan	UPLC-QTOF-MS/MS identification, hierarchical clustering analysis, in vitro experiment verification, network analysis	[¹⁶⁰]
NiaoDuQing granules	ADME modelling and target prediction, topology analysis, pathway enrichment analysis, rat test	[¹⁶¹]
Qigui Tongfeng tablet	Molecular similarity analysis, network analysis	[¹⁶²]
Radix Curcumae formula	Chemical predictors based on chemical structure and chemogenomics data linking compounds, pharmacological information, a system biology functional data analysis and network reconstruction method	[¹⁶³]
Reduning injection	ADME filtering, network targeting, pathways integrating, target selection, reverse drug targeting and network analysis	[¹⁶⁴–¹⁶⁶]
Shenmai injection	Network construction, network recovery index evaluation	[¹⁶⁷]
SiNiSan formula	ADME screening, targets prediction, and DAVID enrichment analysis,	[¹⁶⁸,¹⁶⁹]
Taohong Siwu decoction	Chemical space analysis, virtual screening, chemical distribution and potential compound prediction	[¹⁷⁰]
Tian-Ma-Gou-Teng-Yin fomula	Network link prediction and statistical analysis	[¹⁷¹]
Tianshu formula	Pharmacokinetic filtering, target fishing and network analysis	[¹⁷²]
Xiaoyaosan	Reversed pharmacophore matching method, network analysis	[¹⁷³]
Xijiao Dihuang Decoction	ADME screening, drug targeting, network and pathway analysis	[¹⁷⁴]
Xin-Sheng-Hua Granule	Plasma metabolomics profiling with UHPLC-QTOF/MS and multivariate data method, network analysis	[¹⁷⁵]
Xing-Nao-Jing	Drug-likeness and brain-blood-barrier evaluation, biological process and pathway enrichment analyses	[¹³⁰]
Yangxinshi tablet	Molecular docking, network analysis	[¹⁷⁶]
Yinchenhao decoction	Oral bioavailability screening, drug-likeness and intestinal epithelial permeability evaluation, target prediction, pathway identification and network construction	[¹⁷⁷]
Zhi-Zi-Da-Huang decoction	Molecular docking and network analysis	[¹⁷⁸]
Ginsenoside Rb1, ginsenoside Rg1, schizandrin and DT-13 (effective compounds from ShengMai preparations)	Target-pathway network analysis	[¹⁷⁹]

Tab.2 Selected applications of QSP methods in TCM

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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