In Silico Oncology Drug Repositioning and Polypharmacology

  • Feixiong ChengEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1878)


Network-aided in silico approaches have been widely used for prediction of drug-target interactions and evaluation of drug safety to increase the clinical efficiency and productivity during drug discovery and development. Here we review the advances and new progress in this field and summarize the translational applications of several new network-aided in silico approaches we developed recently. In addition, we describe the detailed protocols for a network-aided drug repositioning infrastructure for identification of new targets for old drugs, failed drugs in clinical trials, and new chemical entities. These state-of-the-art network-aided in silico approaches have been used for the discovery and development of broad-acting and targeted clinical therapies for various complex diseases, in particular for oncology drug repositioning. In this chapter, the described network-aided in silico protocols are appropriate for target-centric drug repositioning to various complex diseases, but expertise is still necessary to perform the specific oncology projects based on the cancer targets of interest.

Key words

Drug-target interactions Drug repositioning Polypharmacology Network-based inference Systems biology Systems pharmacology Panomics Cancer genomics Precision oncology Targeted therapy 


  1. 1.
    Lavecchia A, Cerchia C (2016) In silico methods to address polypharmacology: current status, applications and future perspectives. Drug Discov Today 21:288–298CrossRefGoogle Scholar
  2. 2.
    Xie L, Xie L, Kinnings SL et al (2012) Novel computational approaches to polypharmacology as a means to define responses to individual drugs. Annu Rev Pharmacol Toxicol 52:361–379CrossRefGoogle Scholar
  3. 3.
    Wang J, Hu K, Guo J et al (2016) Suppression of KRas-mutant cancer through the combined inhibition of KRAS with PLK1 and ROCK. Nat Commun 7:11363CrossRefGoogle Scholar
  4. 4.
    Zhao Y, Hu Q, Cheng F et al (2015) SoNar, a highly responsive NAD+/NADH sensor, allows high-throughput metabolic screening of anti-tumor agents. Cell Metab 21:777–789CrossRefGoogle Scholar
  5. 5.
    Cheng F, Zhao J, Zhao Z (2015) Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes. Brief Bioinform 17:642–656CrossRefGoogle Scholar
  6. 6.
    Cheng F, Liu C, Lin CC et al (2015) A gene gravity model for the evolution of cancer genomes: a study of 3,000 cancer genomes across 9 cancer types. PLoS Comput Biol 11:e1004497CrossRefGoogle Scholar
  7. 7.
    Hudson TJ, Anderson W, Artez A et al (2010) International network of cancer genome projects. Nature 464:993–998CrossRefGoogle Scholar
  8. 8.
    Chin L, Andersen JN, Futreal PA (2011) Cancer genomics: from discovery science to personalized medicine. Nat Med 17:297–303CrossRefGoogle Scholar
  9. 9.
    Moses H 3rd, Matheson DH, Cairns-Smith S et al (2015) The anatomy of medical research: US and international comparisons. JAMA 313:174–189CrossRefGoogle Scholar
  10. 10.
    DiMasi JA, Grabowski HG, Hansen RW (2015) The cost of drug development. N Engl J Med 372:1972CrossRefGoogle Scholar
  11. 11.
    Cheng F, Murray JL, Zhao J et al (2016) Systems biology-based investigation of cellular antiviral drug targets identified by gene-trap insertional mutagenesis. PLoS Comput Biol 12:e1005074CrossRefGoogle Scholar
  12. 12.
    Bertolini F, Sukhatme VP, Bouche G (2015) Drug repurposing in oncology--patient and health systems opportunities. Nat Rev Clin Oncol 12:732–742CrossRefGoogle Scholar
  13. 13.
    Cheng F, Murray JL, Rubin DH (2016) Drug repurposing: new treatments for Zika virus infection? Trends Mol Med 22:919–921CrossRefGoogle Scholar
  14. 14.
    Lu W, Yao X, Ouyang P et al (2017) Drug repurposing of histone deacetylase inhibitors that alleviate neutrophilic inflammation in acute lung injury and idiopathic pulmonary fibrosis via inhibiting leukotriene A4 hydrolase and blocking LTB4 biosynthesis. J Med Chem 60:1817–1828CrossRefGoogle Scholar
  15. 15.
    Cheng F, Hong H, Yang S et al (2016) Individualized network-based drug repositioning infrastructure for precision oncology in the panomics era. Brief Bioinform 18:682–697Google Scholar
  16. 16.
    Cheng F, Zhou Y, Li J et al (2012) Prediction of chemical-protein interactions: multitarget-QSAR versus computational chemogenomic methods. Mol BioSyst 8:2373–2384CrossRefGoogle Scholar
  17. 17.
    Cheng F, Xu Z, Liu G et al (2010) Insights into binding modes of adenosine A(2B) antagonists with ligand-based and receptor-based methods. Eur J Med Chem 45:3459–3471CrossRefGoogle Scholar
  18. 18.
    Lu W, Cheng F, Jiang J et al (2015) FXR antagonism of NSAIDs contributes to drug-induced liver injury identified by systems pharmacology approach. Sci Rep 5:8114CrossRefGoogle Scholar
  19. 19.
    Cheng F, Li W, Zhou Y et al (2013) Prediction of human genes and diseases targeted by xenobiotics using predictive toxicogenomic-derived models (PTDMs). Mol BioSyst 9:1316–1325CrossRefGoogle Scholar
  20. 20.
    Cheng F, Liu C, Jiang J et al (2012) Prediction of drug-target interactions and drug repositioning via network-based inference. PLoS Comput Biol 8:e1002503CrossRefGoogle Scholar
  21. 21.
    Cheng F, Zhou Y, Li W et al (2012) Prediction of chemical-protein interactions network with weighted network-based inference method. PLoS One 7:e41064CrossRefGoogle Scholar
  22. 22.
    Li J, Wu Z, Cheng F et al (2014) Computational prediction of microRNA networks incorporating environmental toxicity and disease etiology. Sci Rep 4:5576CrossRefGoogle Scholar
  23. 23.
    Cheng F, Li W, Liu G et al (2013) In silico ADMET prediction: recent advances, current challenges and future trends. Curr Top Med Chem 13:1273–1289CrossRefGoogle Scholar
  24. 24.
    Cheng F, Zhao Z (2014) Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. J Am Med Inform Assoc 21:e278–e286CrossRefGoogle Scholar
  25. 25.
    Zheng MW, Zhang CH, Chen K et al (2016) Preclinical evaluation of a novel orally available SRC/Raf/VEGFR2 inhibitor, SKLB646, in the treatment of triple-negative breast cancer. Mol Cancer Ther 15:366–378CrossRefGoogle Scholar
  26. 26.
    Pan Y, Zheng M, Zhong L et al (2015) A preclinical evaluation of SKLB261, a multikinase inhibitor of EGFR/Src/VEGFR2, as a therapeutic agent against pancreatic cancer. Mol Cancer Ther 14:407–418CrossRefGoogle Scholar
  27. 27.
    Wang Y, Cheng F, Yuan X et al (2016) Dihydropyrazole derivatives as telomerase inhibitors: structure-based design, synthesis, SAR and anticancer evaluation in vitro and in vivo. Eur J Med Chem 112:231–251CrossRefGoogle Scholar
  28. 28.
    Zhao J, Cheng F, Wang Y et al (2016) Systematic prioritization of druggable mutations in approximately 5000 genomes across 16 cancer types using a structural genomics-based approach. Mol Cell Proteomics 15:642–656CrossRefGoogle Scholar
  29. 29.
    Vuong H, Cheng F, Lin CC et al (2014) Functional consequences of somatic mutations in cancer using protein pocket-based prioritization approach. Genome Med 6:81CrossRefGoogle Scholar
  30. 30.
    Wu Z, Lu W, Wu D et al (2016) In silico prediction of chemical mechanism-of-action via an improved network-based inference method. Br J Pharmacol 173:3372–3385CrossRefGoogle Scholar
  31. 31.
    Cheng F, Jia P, Wang Q et al (2014) Quantitative network mapping of the human kinome interactome reveals new clues for rational kinase inhibitor discovery and individualized cancer therapy. Oncotarget 5:3697–3710PubMedPubMedCentralGoogle Scholar
  32. 32.
    Cheng F, Zhao J, Fooksa M et al (2016) A network-based drug repositioning infrastructure for precision cancer medicine through targeting significantly mutated genes in the human cancer genomes. J Am Med Inform Assoc 23:681–691CrossRefGoogle Scholar
  33. 33.
    Cheng F, Liu C, Shen B et al (2016) Investigating cellular network heterogeneity and modularity in cancer: a network entropy and unbalanced motif approach. BMC Syst Biol 10(Suppl 3):65CrossRefGoogle Scholar
  34. 34.
    Li J, Lei K, Wu Z et al (2016) Network-based identification of microRNAs as potential pharmacogenomic biomarkers for anticancer drugs. Oncotarget 7:45584–45596PubMedPubMedCentralGoogle Scholar
  35. 35.
    Cheng F, Li W, Wu Z et al (2013) Prediction of polypharmacological profiles of drugs by the integration of chemical, side effect, and therapeutic space. J Chem Inf Model 53:753–762CrossRefGoogle Scholar
  36. 36.
    Yap CW (2011) PaDEL-Descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem 32:1466–1474CrossRefGoogle Scholar
  37. 37.
    O'Boyle NM, Banck M, James CA et al (2011) Open babel: an open chemical toolbox. J Cheminform 3:33CrossRefGoogle Scholar
  38. 38.
    Law V, Knox C, Djoumbou Y et al (2014) DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res 42:D1091–D1097CrossRefGoogle Scholar
  39. 39.
    Yang H, Qin C, Li YH et al (2016) Therapeutic target database update 2016: enriched resource for bench to clinical drug target and targeted pathway information. Nucleic Acids Res 44:D1069–D1074CrossRefGoogle Scholar
  40. 40.
    Gaulton A, Bellis LJ, Bento AP et al (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40:D1100–D1107CrossRefGoogle Scholar
  41. 41.
    Liu T, Lin Y, Wen X et al (2007) BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res 35:D198–D201CrossRefGoogle Scholar
  42. 42.
    Gunther S, Kuhn M, Dunkel M et al (2008) SuperTarget and Matador: resources for exploring drug-target relationships. Nucleic Acids Res 36:D919–D922CrossRefGoogle Scholar
  43. 43.
    Kanehisa M, Goto S, Sato Y et al (2014) Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res 42:D199–D205CrossRefGoogle Scholar
  44. 44.
    Hewett M, Oliver DE, Rubin DL et al (2002) PharmGKB: the pharmacogenetics knowledge base. Nucleic Acids Res 30:163–165CrossRefGoogle Scholar
  45. 45.
    Szklarczyk D, Santos A, von Mering C et al (2016) STITCH 5: augmenting protein-chemical interaction networks with tissue and affinity data. Nucleic Acids Res 44:D380–D384CrossRefGoogle Scholar
  46. 46.
    Wang Y, Xiao J, Suzek TO et al (2009) PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res 37:W623–W633CrossRefGoogle Scholar
  47. 47.
    Kim Kjaerulff S, Wich L, Kringelum J et al (2013) ChemProt-2.0: visual navigation in a disease chemical biology database. Nucleic Acids Res 41:D464–D469CrossRefGoogle Scholar
  48. 48.
    Seiler KP, George GA, Happ MP et al (2008) ChemBank: a small-molecule screening and cheminformatics resource database. Nucleic Acids Res 36:D351–D359CrossRefGoogle Scholar
  49. 49.
    von Eichborn J, Murgueitio MS, Dunkel M et al (2011) PROMISCUOUS: a database for network-based drug-repositioning. Nucleic Acids Res 39:D1060–D1066CrossRefGoogle Scholar
  50. 50.
    Bulusu KC, Tym JE, Coker EA et al (2014) canSAR: updated cancer research and drug discovery knowledgebase. Nucleic Acids Res 42:D1040–D1047CrossRefGoogle Scholar
  51. 51.
    Wagner AH, Coffman AC, Ainscough BJ et al (2016) DGIdb 2.0: mining clinically relevant drug-gene interactions. Nucleic Acids Res 44:D1036–D1044CrossRefGoogle Scholar
  52. 52.
    Lamb J, Crawford ED, Peck D et al (2006) The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313:1929–1935CrossRefGoogle Scholar
  53. 53.
    Duan Q, Flynn C, Niepel M et al (2014) LINCS canvas browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures. Nucleic Acids Res 42:W449–W460CrossRefGoogle Scholar
  54. 54.
    Keshava Prasad TS, Goel R, Kandasamy K et al (2009) Human protein reference database – 2009 update. Nucleic Acids Res 37:D767–D772CrossRefGoogle Scholar
  55. 55.
    Gene Ontology C (2015) Gene ontology consortium: going forward. Nucleic Acids Res 43:D1049–D1056CrossRefGoogle Scholar
  56. 56.
    Coordinators NR (2016) Database resources of the national center for biotechnology information. Nucleic Acids Res 44:D7–D19CrossRefGoogle Scholar
  57. 57.
    UniProt C (2015) UniProt: a hub for protein information. Nucleic Acids Res 43:D204–D212CrossRefGoogle Scholar
  58. 58.
    Altschul SF, Madden TL, Schaffer AA et al (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25:3389–3402CrossRefGoogle Scholar
  59. 59.
    Shannon P, Markiel A, Ozier O et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504CrossRefGoogle Scholar
  60. 60.
    Shen J, Cheng F, Xu Y et al (2010) Estimation of ADME properties with substructure pattern recognition. J Chem Inf Model 50:1034–1041CrossRefGoogle Scholar
  61. 61.
    Willett P (2006) Similarity-based virtual screening using 2D fingerprints. Drug Discov Today 11:1046–1053CrossRefGoogle Scholar
  62. 62.
    Cheng F, Li W, Wang X et al (2013) Adverse drug events: database construction and in silico prediction. J Chem Inf Model 53:744–752CrossRefGoogle Scholar
  63. 63.
    Gene Ontology C, Blake JA, Dolan M et al (2013) Gene ontology annotations and resources. Nucleic Acids Res 41:D530–D535CrossRefGoogle Scholar
  64. 64.
    Yamanishi Y, Araki M, Gutteridge A et al (2008) Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics 24:i232–i240CrossRefGoogle Scholar
  65. 65.
    Perlman L, Gottlieb A, Atias N et al (2011) Combining drug and gene similarity measures for drug-target elucidation. J Comput Biol 18:133–145CrossRefGoogle Scholar
  66. 66.
    Wu Z, Cheng F, Li J et al (2016) SDTNBI: an integrated network and chemoinformatics tool for systematic prediction of drug-target interactions and drug repositioning. Brief Bioinform 18:333–347Google Scholar
  67. 67.
    Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50:742–754CrossRefGoogle Scholar
  68. 68.
    Collins GS, de Groot JA, Dutton S et al (2014) External validation of multivariable prediction models: a systematic review of methodological conduct and reporting. BMC Med Res Methodol 14:40CrossRefGoogle Scholar
  69. 69.
    Fang J, Wu Z, Cai C et al (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. [Epub ahead of print]CrossRefGoogle Scholar
  70. 70.
    Shen Q, Cheng F, Song H et al (2017) Proteome-scale investigation of protein allosteric regulation perturbed by somatic mutations in 7,000 cancer genomes. Am J Hum Genet 100:5–20CrossRefGoogle Scholar
  71. 71.
    Fang J, Liu C, Wang Q et al (2017) In silico polypharmacology of natural products. Brief Bioinform. [Epub ahead of print]
  72. 72.
    Cheng F, Jia P, Wang Q et al (2014) Studying tumorigenesis through network evolution and somatic mutational perturbations in the cancer interactome. Mol Biol Evol 31:2156–2169CrossRefGoogle Scholar
  73. 73.
    Zhang C, Hong H, Mendrick DL et al (2015) Biomarker-based drug safety assessment in the age of systems pharmacology: from foundational to regulatory science. Biomark Med 9:1241–1252CrossRefGoogle Scholar
  74. 74.
    Baryshnikova A (2016) Systematic functional annotation and visualization of biological networks. Cell Syst 2:412–421CrossRefGoogle Scholar
  75. 75.
    Fang J, Cai C, Wang Q et al (2017) Systems pharmacology-based discovery of natural products for precision oncology through targeting cancer mutated genes. CPT Pharmacometrics Syst Pharmacol 6:177–187CrossRefGoogle Scholar
  76. 76.
    Fang JS, Gao L, Ma HL et al (2017) Quantitative and systems pharmacology 3. Network-based identification of new targets for natural products enables potential uses in aging-associated disorders. Front Pharmacol 8:747CrossRefGoogle Scholar
  77. 77.
    Zhao J, Cheng F, Zhao Z (2017) Tissue-specific signaling networks rewired by major somatic mutations in human cancer revealed by proteome-wide discovery. Cancer Res 77:2810–2821CrossRefGoogle Scholar
  78. 78.
    Lu W, Cheng F, Yan W et al (2017) Selective targeting p53WT lung cancer cells harboring homozygous p53 Arg72 by an inhibitor of CypA. Oncogene 36:4719–4731CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Center for Complex Networks ResearchNortheastern UniversityBostonUSA

Personalised recommendations