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Network Pharmacology and Modern Drug R&D Cases

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Network Pharmacology

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Abstract

Drug discovery has predominantly followed the concept of “one drug, one target, one disease” for an extended period, case in point, to design chemical entities that can specifically bind to one key target related to a specific disease [1]. Collateral pharmacology aims to develop drugs that earmark multiple proteins or networks connected to diseases. It also demonstrates the possibility of finding multi-target and multi-component drugs that earmark disease-related networks at the system level [2]. Network pharmacology research integrates the data of various public databases, high-throughput screening (HTS), genome-wide association studies (GWAS), and large-scale omics (such as genomics, transcriptomics, metabonomics, and proteomics) to construct a network prediction or inference model. The analysis of the complex biological pathways influenced by drug therapy at different biological levels (molecules, cells, tissues, organs, and phenotypes) has given a boost to cognition of the biological mechanisms of complex diseases, the systemic mechanism of the impact of drugs, and the development of multi-target, multi-component drugs. This chapter selects some exceptional results of network pharmacology in the R&D and application of modern drugs in recent years, and analyzes the results from the dimensions of research purpose, data source, analysis index and algorithm, analysis results, experimental verification, and main conclusion, as a means to introduce the principal research contents, ideas, and procedures of frontier research in network pharmacology, for readers.

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References

  1. Kaufmann SH. Paul Ehrlich: founder of chemotherapy. Nat Rev Drug Discov. 2008;7(5):373.

    Article  CAS  PubMed  Google Scholar 

  2. Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol. 2008;4(11):682.

    Article  CAS  PubMed  Google Scholar 

  3. Oliver S. Guilt-by-association goes global. Nature. 2000;403(6770):601–2.

    Article  CAS  PubMed  Google Scholar 

  4. Wu X, Jiang R, Zhang MQ, et al. Network-based global inference of human disease genes. Mol Syst Biol. 2008;4:1.

    Article  CAS  Google Scholar 

  5. Lin L, Yang T, Fang L, et al. Gene gravity-like algorithm for disease gene prediction based on phenotype-specific network. BMC Syst Biol. 2017;11(1):121.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Zhang B, Gaiteri C, Bodea L-G, et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer’s disease. Cell. 2013;153(3):707–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Mani KM, Lefebvre C, Wang K, et al. A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas. Mol Syst Biol. 2008;4:1.

    Article  Google Scholar 

  8. Menche J, Sharma A, Kitsak M, et al. Uncovering disease-disease relationships through the incomplete interactome. Science. 2015;347(6224):1257601.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Chen C, Meng Q, **a Y, et al. The transcription factor POU3F2 regulates a gene coexpression network in brain tissue from patients with psychiatric disorders. Sci Transl Med. 2018;10(472):eaat8178.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Torrey EF, Webster M, Knable M, et al. The stanley foundation brain collection and neuropathology consortium. Schizophr Res. 2000;44(2):151–5.

    Article  CAS  PubMed  Google Scholar 

  11. Gibbs JR, van der Brug MP, Hernandez DG, et al. Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain. PLoS Genet. 2010;6:5.

    Article  CAS  Google Scholar 

  12. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinf. 2008;9(1):559.

    Article  CAS  Google Scholar 

  13. Aten JE, Fuller TF, Lusis AJ, et al. Using genetic markers to orient the edges in quantitative trait networks: the NEO software. BMC Syst Biol. 2008;2(1):34.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. de Leeuw CA, Mooij JM, Heskes T, et al. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol. 2015;11:4.

    Article  CAS  Google Scholar 

  15. Lee PH, O'dushlaine C, Thomas B, et al. INRICH: interval-based enrichment analysis for genome-wide association studies. Bioinformatics. 2012;28(13):1797–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Purcell SM, Moran JL, Fromer M, et al. A polygenic burden of rare disruptive mutations in schizophrenia. Nature. 2014;506(7487):185–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Genovese G, Fromer M, Stahl EA, et al. Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia. Nat Neurosci. 2016;19(11):1433.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Li J, Cai T, Jiang Y, et al. Genes with de novo mutations are shared by four neuropsychiatric disorders discovered from NPdenovo database. Mol Psychiatry. 2016;21(2):290–7.

    Article  PubMed  CAS  Google Scholar 

  19. Bass JIF, Sahni N, Shrestha S, et al. Human gene-centered transcription factor networks for enhancers and disease variants. Cell. 2015;161(3):661–73.

    Article  PubMed Central  CAS  Google Scholar 

  20. Kheradpour P, Kellis M. Systematic discovery and characterization of regulatory motifs in ENCODE TF binding experiments. Nucleic Acids Res. 2014;42(5):2976–87.

    Article  CAS  PubMed  Google Scholar 

  21. Guo Y, Bao C, Ma D, et al. Network-based combinatorial crispr-cas9 screens identify synergistic modules in human cells. ACS Synth Biol. 2019;8(3):482–90.

    Article  CAS  PubMed  Google Scholar 

  22. Schäfer H, Struck B, Feldmann E, et al. TGF-β1-dependent L1CAM expression has an essential role in macrophage-induced apoptosis resistance and cell migration of human intestinal epithelial cells. Oncogene. 2013;32(2):180–9.

    Article  PubMed  CAS  Google Scholar 

  23. Bassik MC, Kampmann M, Lebbink RJ, et al. A systematic mammalian genetic interaction map reveals pathways underlying ricin susceptibility. Cell. 2013;152(4):909–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Kampmann M, Bassik MC, Weissman JS. Integrated platform for genome-wide screening and construction of high-density genetic interaction maps in mammalian cells. Proc Natl Acad Sci. 2013;110(25):E2317–26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Bandyopadhyay S, Mehta M, Kuo D, et al. Rewiring of genetic networks in response to DNA daMage. Science. 2010;330(6009):1385–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Lamb J, Crawford ED, Peck D, et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science. 2006;313(5795):1929–35.

    Article  CAS  PubMed  Google Scholar 

  27. Subramanian A, Narayan R, Corsello SM, et al. A Next generation connectivity map: l1000 platform and the first 1,000,000 profiles. Cell. 2017;171(6):1437–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Iwata M, Sawada R, Iwata H, et al. Elucidating the modes of action for bioactive compounds in a cell-specific manner by large-scale chemically-induced transcriptomics. Sci Rep. 2017;7:40164.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Guney E, Menche J, Vidal M, et al. Network-based in silico drug efficacy screening. Nat Commun. 2016;7:10331.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Wei W-Q, Cronin RM, Xu H, et al. Development and evaluation of an ensemble resource linking medications to their indications. J Am Med Inform Assoc. 2013;20(5):954–61.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Sartor MA, Ade A, Wright Z, et al. Metab2MeSH: annotating compounds with medical subject headings. Bioinformatics. 2012;28(10):1408–10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Iorio F, Bosotti R, Scacheri E, et al. Discovery of drug mode of action and drug repositioning from transcriptional responses. Proc Natl Acad Sci. 2010;107(33):14621–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Kruskal JB. On the shortest spanning subtree of a graph and the traveling salesman problem. Proc Am Math Soc. 1956;7(1):48–50.

    Article  Google Scholar 

  34. Diaconis P, Graham RL. Spearman’s footrule as a measure of disarray. J R Stat Soc. 1977;39(2):262–8.

    Google Scholar 

  35. Lin S. Space oriented rank-based data integration. Stat Appl Genet Mol Biol. 2010;9:1.

    Article  CAS  Google Scholar 

  36. Frey BJ, Dueck D. Clustering by passing messages between data points. Science. 2007;315(5814):972–6.

    Article  CAS  PubMed  Google Scholar 

  37. Csermely P, Agoston V, Pongor S. The efficiency of multi-target drugs: the network approach might help drug design. Trends Pharmacol Sci. 2005;26(4):178–82.

    Article  CAS  PubMed  Google Scholar 

  38. Keith CT, Borisy AA, Stockwell BR. Multicomponent therapeutics for networked systems. Nat Rev Drug Discov. 2005;4(1):71–8.

    Article  CAS  PubMed  Google Scholar 

  39. Korcsmáros T, Szalay MS, Böde C, et al. How to design multi-target drugs: target search options in cellular networks. Expert Opin Drug Discovery. 2007;2(6):799–808.

    Article  Google Scholar 

  40. Ramsay RR, Popovic-Nikolic MR, Nikolic K, et al. A perspective on multi-target drug discovery and design for complex diseases. Clin Transl Med. 2018;7(1):3.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Meng H, Liu Y, Lai L. Diverse ways of perturbing the human arachidonic acid metabolic network to control inflammation. Acc Chem Res. 2015;48(8):2242–50.

    Article  CAS  PubMed  Google Scholar 

  42. Keane H, Ryan BJ, Jackson B, et al. Protein-protein interaction networks identify targets which rescue the MPP+ cellular model of Parkinson’s disease. Sci Rep. 2015;5(1):1–12.

    Article  CAS  Google Scholar 

  43. Casas AI, Hassan AA, Larsen SJ, et al. From single drug targets to synergistic network pharmacology in ischemic stroke. Proc Natl Acad Sci. 2019;116(14):7129–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Kotlyar M, Pastrello C, Sheahan N, et al. Integrated interactions database: tissue-specific view of the human and model organism interactomes. Nucleic Acids Res. 2016;44(D1):D536–41.

    Article  CAS  PubMed  Google Scholar 

  45. Wishart DS, Feunang YD, Marcu A, et al. HMDB 4.0: the human metabolome database for. Nucleic Acids Res. 2018;46(D1):D608–17.

    Article  CAS  PubMed  Google Scholar 

  46. Li YH, Yu CY, Li XX, et al. Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics. Nucleic Acids Res. 2018;46(D1):D1121–7.

    Article  CAS  PubMed  Google Scholar 

  47. Wishart DS, Tzur D, Knox C, et al. HMDB: the human metabolome database. Nucleic Acids Res. 2007;35(suppl_1):D521–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Sidders B, Karlsson A, Kitching L, et al. Network-based drug discovery: coupling network pharmacology with phenotypic screening for neuronal excitability. J Mol Biol. 2018;430(18):3005–15.

    Article  CAS  PubMed  Google Scholar 

  49. Jamieson DG, Moss A, Kennedy M, et al. The pain interactome: connecting pain-specific protein interactions. Pain. 2014;155(11):2243–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Razick S, Magklaras G, Donaldson IM. iRefIndex: a consolidated protein interaction database with provenance. BMC Bioinf. 2008;9(1):405.

    Article  CAS  Google Scholar 

  51. Pushpakom S, Iorio F, Eyers PA, et al. Drug repurposing: progress, challenges and recommendations. Nat Rev Drug Discov. 2019;18(1):41–58.

    Article  CAS  PubMed  Google Scholar 

  52. Zhang Y, Cheng X, Zhou W. Drug reorientation: an important application field of network pharmacology. Chin J Pharmacol Toxicol. 2012;26(6):779–86.

    Google Scholar 

  53. Cheng F, Liu C, Jiang J, et al. Prediction of drug-target interactions and drug repositioning via network-based inference. PLoS Comput Biol. 2012;8(5):e1002503.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Iorio F, Isacchi A, di Bernardo D, et al. Identification of small molecules enhancing autophagic function from drug network analysis. Autophagy. 2010;6(8):1204–5.

    Article  CAS  PubMed  Google Scholar 

  55. Luo H, Wang J, Li M, et al. Drug repositioning based on comprehensive similarity measures and bi-random walk algorithm. Bioinformatics. 2016;32(17):2664–71.

    Article  CAS  PubMed  Google Scholar 

  56. Cheng F, Desai RJ, Handy DE, et al. Network-based approach to prediction and population-based validation of in silico drug repurposing. Nat Commun. 2018;9(1):1–12.

    Article  CAS  Google Scholar 

  57. Rolland T, Taşan M, Charloteaux B, et al. A proteome-scale map of the human interactome network. Cell. 2014;159(5):1212–26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Rual J-F, Venkatesan K, Hao T, et al. Towards a proteome-scale map of the human protein–protein interaction network. Nature. 2005;437(7062):1173–8.

    Article  CAS  PubMed  Google Scholar 

  59. Cheng F, Jia P, Wang Q, et al. Quantitative network map** of the human kinome interactome reveals new clues for rational kinase inhibitor discovery and individualized cancer therapy. Oncotarget. 2014;5(11):3697.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Keshava Prasad T, Goel R, Kandasamy K, et al. Human protein reference database—2009 update. Nucleic Acids Res. 2009;37(suppl_1):D767–72.

    Article  CAS  PubMed  Google Scholar 

  61. Hu J, Rho H-S, Newman RH, et al. PhosphoNetworks: a database for human phosphorylation networks. Bioinformatics. 2014;30(1):141–2.

    Article  CAS  PubMed  Google Scholar 

  62. Hornbeck PV, Zhang B, Murray B, et al. PhosphoSitePlus, 2014: mutations, PTMs and recalibrations. Nucleic Acids Res. 2015;43(D1):D512–20.

    Article  CAS  PubMed  Google Scholar 

  63. Lu C-T, Huang K-Y, Su M-G, et al. DbPTM 3.0: an informative resource for investigating substrate site specificity and functional association of protein post-translational modifications. Nucleic Acids Res. 2013;41(D1):D295–305.

    Article  CAS  PubMed  Google Scholar 

  64. Dinkel H, Chica C, Via A, et al. Phospho. ELM: a database of phosphorylation sites—update 2011. Nucleic Acids Res. 2010;39(suppl_1):D261–7.

    PubMed  PubMed Central  Google Scholar 

  65. Oughtred R, Stark C, Breitkreutz B-J, et al. The BioGRID interaction database: 2019 update. Nucleic Acids Res. 2019;47(D1):D529–41.

    Article  CAS  PubMed  Google Scholar 

  66. Cowley MJ, Pinese M, Kassahn KS, et al. PINA v2. 0: mining interactome modules. Nucleic Acids Res. 2012;40(D1):D862–5.

    Article  CAS  PubMed  Google Scholar 

  67. Chatr-Aryamontri A, Ceol A, Palazzi LM, et al. MINT: the Molecular INTeraction database. Nucleic Acids Res. 2007;35(suppl_1):D572–4.

    Article  CAS  PubMed  Google Scholar 

  68. Orchard S, Ammari M, Aranda B, et al. The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res. 2014;42(D1):D358–63.

    Article  CAS  PubMed  Google Scholar 

  69. Breuer K, Foroushani AK, Laird MR, et al. InnateDB: systems biology of innate immunity and beyond—recent updates and continuing curation. Nucleic Acids Res. 2013;41(D1):D1228–33.

    Article  CAS  PubMed  Google Scholar 

  70. Meyer MJ, Das J, Wang X, et al. INstruct: a database of high-quality 3D structurally resolved protein interactome networks. Bioinformatics. 2013;29(12):1577–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Fazekas D, Koltai M, Türei D, et al. SignaLink 2–a signaling pathway resource with multi-layered regulatory networks. BMC Syst Biol. 2013;7(1):7.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Bodenreider O. The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res. 2004;32(suppl_1):D267–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Hamosh A, Scott AF, Amberger JS, et al. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 2005;33(suppl_1):D514–7.

    CAS  PubMed  Google Scholar 

  74. Davis AP, Grondin CJ, Lennon-Hopkins K, et al. The comparative toxicogenomics database’s 10th year anniversary: update 2015. Nucleic Acids Res. 2015;43(D1):D914–20.

    Article  CAS  PubMed  Google Scholar 

  75. Yu W, Gwinn M, Clyne M, et al. A navigator for human genome epidemiology. Nat Genet. 2008;40(2):124–5.

    Article  CAS  PubMed  Google Scholar 

  76. Piñero J, Bravo À, Queralt-Rosinach N, et al. DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res. 2016;2016:gkw943.

    Google Scholar 

  77. Landrum MJ, Lee JM, Riley GR, et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 2014;42(D1):D980–5.

    Article  CAS  PubMed  Google Scholar 

  78. Welter D, Macarthur J, Morales J, et al. The NHGRI GWAS catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 2014;42(D1):D1001–6.

    Article  CAS  PubMed  Google Scholar 

  79. Li MJ, Liu Z, Wang P, et al. GWASdb v2: an update database for human genetic variants identified by genome-wide association studies. Nucleic Acids Res. 2016;44(D1):D869–76.

    Article  CAS  PubMed  Google Scholar 

  80. Denny JC, Bastarache L, Ritchie MD, et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat Biotechnol. 2013;31(12):1102.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Wishart DS, Feunang YD, Guo AC, et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018;46(D1):D1074–82.

    Article  CAS  PubMed  Google Scholar 

  82. Hernandez-Boussard T, Whirl-Carrillo M, Hebert JM, et al. The pharmacogenetics and pharmacogenomics knowledge base: accentuating the knowledge. Nucleic Acids Res. 2007;36(suppl_1):D913–8.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  83. Bento AP, Gaulton A, Hersey A, et al. The ChEMBL bioactivity database: an update. Nucleic Acids Res. 2014;42(D1):D1083–90.

    Article  CAS  PubMed  Google Scholar 

  84. Gilson MK, Liu T, Baitaluk M, et al. BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res. 2016;44(D1):D1045–53.

    Article  CAS  PubMed  Google Scholar 

  85. Pawson AJ, Sharman JL, Benson HE, et al. The IUPHAR/BPS Guide to pharmacology: an expert-driven knowledgebase of drug targets and their ligands. Nucleic Acids Res. 2014;42(D1):D1098–106.

    Article  CAS  PubMed  Google Scholar 

  86. Consortium G. The genotype-tissue expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science. 2015;348(6235):648–60.

    Article  CAS  Google Scholar 

  87. Wang S, Verpillat P, Rassen J, et al. Transparency and reproducibility of observational cohort studies using large healthcare databases. Clin Pharmacol Therap. 2016;99(3):325–32.

    Article  CAS  Google Scholar 

  88. Zhang T-T, Xue R, Wang X, et al. Network-based drug repositioning: a novel strategy for discovering potential antidepressants and their mode of action. Eur Neuropsychopharmacol. 2018;28(10):1137–50.

    Article  CAS  PubMed  Google Scholar 

  89. Zhao S, Li S. Network-based relating pharmacological and genomic spaces for drug target identification. PLoS One. 2010;5:7.

    Article  Google Scholar 

  90. Cheng F, Kovács IA, Barabási A-L. Network-based prediction of drug combinations. Nat Commun. 2019;10(1):1–11.

    Article  CAS  Google Scholar 

  91. Li W, Mao X, Guo Q, et al. Application of network pharmacology research strategy in combinatorial drug research. E-j Transl Med. 2018;5(3):3–16.

    Google Scholar 

  92. Wang Y-Y, Xu K-J, Song J, et al. Exploring drug combinations in genetic interaction network. BMC Bioinf. 2012;2017:S7.

    Article  CAS  Google Scholar 

  93. Cheng F, Zhao Z. Machine learning-based prediction of drug–drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. J Am Med Inform Assoc. 2014;21(e2):e278–86.

    Article  PubMed  PubMed Central  Google Scholar 

  94. Chandrasekaran S, Cokol-Cakmak M, Sahin N, et al. Chemogenomics and orthology-based design of antibiotic combination therapies. Mol Syst Biol. 2016;12:5.

    Article  CAS  Google Scholar 

  95. Li S, Zhang B, Zhang N. Network target for screening synergistic drug combinations with application to traditional Chinese medicine. BMC Syst Biol. 2011;5(S1):S10.

    Article  PubMed  PubMed Central  Google Scholar 

  96. He L, Wennerberg K, Aittokallio T, et al. TIMMA-R: an R package for predicting synergistic multi-targeted drug combinations in cancer cell lines or patient-derived samples. Bioinformatics. 2015;31(11):1866–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Huang L, Li F, Sheng J, et al. DrugComboRanker: drug combination discovery based on target network analysis. Bioinformatics. 2014;30(12):i228–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Gaulton A, Bellis LJ, BENTO AP, et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 2012;40(D1):D1100–7.

    Article  CAS  PubMed  Google Scholar 

  99. LIU T, LIN Y, WEN X, et al. BindingDB: a web-accessible database of experimentally determined protein–ligand binding affinities. Nucleic Acids Res. 2007;35(suppl_1):D198–201.

    Article  CAS  PubMed  Google Scholar 

  100. Tatonetti NP, Patrick PY, Daneshjou R, et al. Data-driven prediction of drug effects and interactions. Sci Transl Med. 2012;4(125):125ra131.

    Article  Google Scholar 

  101. SUN Y, SHENG Z, MA C, et al. Combining genomic and network characteristics for extended capability in predicting synergistic drugs for cancer. Nat Commun. 2015;6(1):1–10.

    Article  Google Scholar 

  102. Liu Y, Wei Q, Yu G, et al. DCDB 2.0: a major update of the drug combination database. Database. 2014;2014:124.

    Article  CAS  Google Scholar 

  103. Sun Y, Sheng Z, Ma C, et al. Combining genomic and network characteristics for extended capability in predicting synergistic drugs for cancer. Nat Commun. 2015;28(6):8481.

    Article  CAS  Google Scholar 

  104. Liu Y, Wei Q, Yu G, Gai W, Li Y, Chen X. DCDB 2.0: a major update of the drug combination database. Database (Oxford). 2014;2014:bau124.

    Article  CAS  Google Scholar 

  105. Bansal M, Yang J, Karan C, et al. A community computational challenge to predict the activity of pairs of compounds. Nat Biotechnol. 2014;32(12):1213–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Barretina J, Caponigro G, Stransky N, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483(7391):603–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Zhao J, Zhang XS, Zhang S. Predicting cooperative drug effects through the quantitative cellular profiling of response to individual drugs. CPT Pharmacometrics Syst Pharmacol. 2014;3(2):e102.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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  1. The Second Military Medical University, Shanghai, China

    Weidong Zhang

  2. Shanghai University of Traditional Chinese Medicine, Shanghai, China

    **g Zhao

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  1. Weidong Zhang
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  1. Institute of TCM-X / Department of Automation, Tsinghua University, Bei**g, China

    Shao Li

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Zhang, W., Zhao, J. (2021). Network Pharmacology and Modern Drug R&D Cases. In: Li, S. (eds) Network Pharmacology. Springer, Singapore. https://doi.org/10.1007/978-981-16-0753-0_6

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