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Network Pharmacology to Aid the Drug Discovery Process

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Abstract

The drug discovery process is expensive and time consuming. Hundreds of drugs enter the research and development pipeline only to be dismissed in late phase due to some risky side effect or lack of efficacy in human subjects. Using existing knowledge of approved drugs we can compare experimental drugs’ expression profiles and chemical structures to predict their mechanism of action, filtering out the drugs that will not survive the development process saving time and money. We can also use these approaches in combination with clinical data to repurpose and combine existing drugs for improved therapeutic use. Here we discuss many of the current approaches in network pharmacology which can aid in the drug discovery process. First, we describe the fundamental data that forms the basis of these approaches and investigate where we can find this data. Next, we present how to use different data types incorporating network approaches to model drug effects, including various tools and algorithms developed for this purpose. Finally, we present a global overview of how to apply all of these techniques for accurate side effect and new indication predictions.

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References

  1. Hopkins AL (2008) Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 4(11):682–690

    Article  PubMed  CAS  Google Scholar 

  2. Paul SM, Mytelka DS, Dunwiddie CT, Persinger CC, Munos BH, Lindborg SR, Schacht AL (2010) How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat Rev Drug Discov 9(3):203–214

    PubMed  CAS  Google Scholar 

  3. Swinney DC, Anthony J (2011) How were new medicines discovered? Nat Rev Drug Discov 10(7):507–519

    Article  PubMed  CAS  Google Scholar 

  4. Ma’ayan A, Jenkins SL, Goldfarb J, Iyengar R (2007) Network analysis of FDA approved drugs and their targets. Mt Sinai J Med J Transl Personal Med 74(1):27–32

    Article  Google Scholar 

  5. Ma’ayan A, Jenkins SL, Neves S, Hasseldine A, Grace E, Dubin-Thaler B, Eungdamrong NJ, Weng G, Ram PT, Rice JJ et al (2005) Formation of regulatory patterns during signal propagation in a mammalian cellular network. Science 309(5737):1078–1083

    Article  PubMed  Google Scholar 

  6. Wishart DS, Knox C, Guo AC, Cheng D, Shrivastava S, Tzur D, Gautam B, Hassanali M (2008) DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res 36(Suppl 1):D901–D906

    PubMed  CAS  Google Scholar 

  7. Chen X, Ji ZL, Chen YZ (2002) TTD: Therapeutic Target Database. Nucleic Acids Res 30(1):412–415

    Article  PubMed  CAS  Google Scholar 

  8. Klipp E, Wade RC, Kummer U (2010) Biochemical network-based drug-target prediction. Curr Opin Biotechnol 21(4):511–516

    Article  PubMed  CAS  Google Scholar 

  9. Liu Y, Hu B, Fu C, Chen X (2010) DCDB: drug combination database. Bioinformatics 26(4):587–588

    Article  PubMed  CAS  Google Scholar 

  10. Ganther S, Kuhn M, Dunkel M, Campillos M, Senger C, Petsalaki E, Ahmed J, Urdiales EG, Gewiess A, Jensen LJ et al (2008) SuperTarget and Matador: resources for exploring drug-target relationships. Nucleic Acids Res 36(Suppl 1):D919–D922

    Google Scholar 

  11. Aguero F, Al-Lazikani B, Aslett M, Berriman M, Buckner FS, Campbell RK, Carmona S, Carruthers IM, Chan AWE, Chen F et al (2008) Genomic-scale prioritization of drug targets: the TDR Targets database. Nat Rev Drug Discov 7(11):900–907

    Article  PubMed  Google Scholar 

  12. Chatr-aryamontri A, Ceol A, Palazzi LM, Nardelli G, Schneider MV, Castagnoli L, Cesareni G (2007) MINT: the Molecular INTeraction database. Nucleic Acids Res 35(Suppl 1):D572–D574

    Article  PubMed  CAS  Google Scholar 

  13. Stark C, Breitkreutz B-J, Reguly T, Boucher L, Breitkreutz A, Tyers M (2006) BioGRID: a general repository for interaction datasets. Nucleic Acids Res 34(Suppl 1):D535–D539

    Article  PubMed  CAS  Google Scholar 

  14. Prasad TSK, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S, Telikicherla D, Raju R, Shafreen B, Venugopal A et al (2009) Human protein reference database‐2009 update. Nucleic Acids Res 37(Suppl 1):D767–D772

    Article  CAS  Google Scholar 

  15. Aranda B, Achuthan P, Alam-Faruque Y, Armean I, Bridge A, Derow C, Feuermann M, Ghanbarian AT, Kerrien S, Khadake J et al (2010) The IntAct molecular interaction database in 2010. Nucleic Acids Res 38(Suppl 1):D525–D531

    Article  PubMed  CAS  Google Scholar 

  16. Lynn DJ, Winsor GL, Chan C, Richard N, Laird MR, Barsky A, Gardy JL, Roche FM, Chan THW, Shah N et al (2008) InnateDB: facilitating systems-level analyses of the mammalian innate immune response. Mol Syst Biol 4:218

    Article  PubMed  Google Scholar 

  17. Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P, Doerks T, Stark M, Muller J, Bork P et al (2011) The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res 39(Suppl 1):D561–D568

    Article  PubMed  Google Scholar 

  18. Chatr-aryamontri A, Ceol A, Peluso D, Nardozza A, Panni S, Sacco F, Tinti M, Smolyar A, Castagnoli L, Vidal M et al (2009) VirusMINT: a viral protein interaction database. Nucleic Acids Res 37(Suppl 1):D669–D673

    Article  PubMed  CAS  Google Scholar 

  19. Bader GD, Cary MP, Sander C (2006) Pathguide: a pathway resource list. Nucleic Acids Res 34(Suppl 1):D504–D506

    Article  PubMed  CAS  Google Scholar 

  20. Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T, Kawashima S, Okuda S, Tokimatsu T et al (2008) KEGG for linking genomes to life and the environment. Nucleic Acids Res 36(Suppl 1):D480–D484

    PubMed  CAS  Google Scholar 

  21. Newburger DE, Bulyk ML (2009) UniPROBE: an online database of protein binding microarray data on protein‐DNA interactions. Nucleic Acids Res 37(Suppl 1):D77–D82

    Article  PubMed  CAS  Google Scholar 

  22. Sandelin A, Alkema W, Engstrom P, Wasserman WW, Lenhard B (2004) JASPAR: an open-access database for eukaryotic transcription factor binding profiles. Nucleic Acids Res 32(Suppl 1):D91–D94. doi:D91

    Article  PubMed  CAS  Google Scholar 

  23. Matys V, Kel-Margoulis OV, Fricke E, Liebich I, Land S, Barre-Dirrie A, Reuter I, Chekmenev D, Krull M, Hornischer K et al (2006) TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes. Nucleic Acids Res 34(Suppl 1):D108–D110

    Article  PubMed  CAS  Google Scholar 

  24. Lachmann A, Xu H, Krishnan J, Berger SI, Mazloom AR, Ma’ayan A (2010) ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments. Bioinformatics 26(19):2438–2444

    Article  PubMed  CAS  Google Scholar 

  25. Lachmann A, Ma’ayan A (2009) KEA: kinase enrichment analysis. Bioinformatics 25(5):684–686

    Article  PubMed  CAS  Google Scholar 

  26. Gottlieb A, Stein GY, Ruppin E, Sharan R (2011) PREDICT: a method for inferring novel drug indications with application to personalized medicine. Mol Syst Biol 7:496

    Article  PubMed  Google Scholar 

  27. Keiser MJ, Setola V, Irwin JJ, Laggner C, Abbas AI, Hufeisen SJ, Jensen NH, Kuijer MB, Matos RC, Tran TB et al (2009) Predicting new molecular targets for known drugs. Nature 462(7270):175–181

    Article  PubMed  CAS  Google Scholar 

  28. Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH (2009) PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res 37(suppl 2):W623–W633

    Article  PubMed  CAS  Google Scholar 

  29. Schuffenhauer A, Floersheim P, Acklin P, Jacoby E (2002) Similarity metrics for ligands reflecting the similarity of the target proteins. J Chem Inf Comput Sci 43(2):391–405

    Google Scholar 

  30. Olah M, Mracec M, Ostopovici L, Rad R, Bora A, Hadaruga N, Olah I, Banda M, Simon Z, Mracec M et al (2005) WOMBAT: world of molecular bioactivity. In: Opera TI (ed) Chemoinformatics in drug discovery. Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, pp 221–239

    Chapter  Google Scholar 

  31. Hu G, Agarwal P (2009) Human disease-drug network based on genomic expression profiles. PLoS One 4(8):e6536

    Article  PubMed  Google Scholar 

  32. Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, Kim IF, Soboleva A, Tomashevsky M, Edgar R (2007) NCBI GEO: mining tens of millions of expression profiles‐database and tools update. Nucleic Acids Res 35(Suppl 1):D760–D765

    Article  PubMed  CAS  Google Scholar 

  33. Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, Lerner J, Brunet J-P, Subramanian A, Ross KN et al (2006) The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313(5795):1929–1935

    Article  PubMed  CAS  Google Scholar 

  34. Iskar M, Campillos M, Kuhn M, Jensen LJ, van Noort V, Bork P (2010) Drug-induced regulation of target expression. PLoS Comput Biol 6(9):e1000925

    Article  PubMed  Google Scholar 

  35. Peck D, Crawford E, Ross K, Stegmaier K, Golub T, Lamb J (2006) A method for high-throughput gene expression signature analysis. Genome Biol 7(7):R61

    Article  PubMed  Google Scholar 

  36. Wise RG, Tracey I (2006) The role of fMRI in drug discovery. J Magn Reson Imaging 23(6):862–876

    Article  PubMed  Google Scholar 

  37. Kuhn M, Campillos M, Letunic I, Jensen LJ, Bork P (2010) A side effect resource to capture phenotypic effects of drugs. Mol Syst Biol 6:343

    Article  PubMed  Google Scholar 

  38. Smith C, Goldsmith C-A, Eppig J (2004) The mammalian phenotype ontology as a tool for annotating, analyzing and comparing phenotypic information. Genome Biol 6(1):R7

    Article  PubMed  Google Scholar 

  39. Gravel S, Henn BM, Gutenkunst RN, Indap AR, Marth GT, Clark AG, Yu F, Gibbs RA, Project TG, Bustamante CD (2011) Demographic history and rare allele sharing among human populations. Proc Natl Acad Sci USA 108(29):11983–11988

    Article  PubMed  CAS  Google Scholar 

  40. Osborne J, Flatow J, Holko M, Lin S, Kibbe W, Zhu L, Danila M, Feng G, Chisholm R (2009) Annotating the human genome with Disease Ontology. BMC Genomics 10(Suppl 1):S6

    Article  PubMed  Google Scholar 

  41. Smith CL, Eppig JT (2009) The mammalian phenotype ontology: enabling robust annotation and comparative analysis. Wiley Interdiscip Rev Syst Biol Med 1(3):390–399

    Article  PubMed  CAS  Google Scholar 

  42. Gkoutos G, Green E, Mallon A-M, Hancock J, Davidson D (2004) Using ontologies to describe mouse phenotypes. Genome Biol 6(1):R8

    Article  PubMed  Google Scholar 

  43. Rzhetsky A, Wajngurt D, Park N, Zheng T (2007) Probing genetic overlap among complex human phenotypes. Proc Natl Acad Sci USA 104(28):11694–11699

    Article  PubMed  CAS  Google Scholar 

  44. Goh K-I, Cusick ME, Valle D, Childs B, Vidal M, Barabási A-L (2007) The human disease network. Proc Natl Acad Sci USA 104(21):8685–8690

    Article  PubMed  CAS  Google Scholar 

  45. Barabási A-L, Gulbahce N, Loscalzo J (2011) Network medicine: a network-based approach to human disease. Nat Rev Genet 12(1):56–68

    Article  PubMed  Google Scholar 

  46. Gandhi TKB, Zhong J, Mathivanan S, Karthick L, Chandrika KN, Mohan SS, Sharma S, Pinkert S, Nagaraju S, Periaswamy B et al (2006) Analysis of the human protein interactome and comparison with yeast, worm and fly interaction datasets. Nat Genet 38(3):285–293

    Article  PubMed  CAS  Google Scholar 

  47. Oti M, Snel B, Huynen MA, Brunner HG (2006) Predicting disease genes using protein‐protein interactions. J Med Genet 43(8):691–698

    Article  PubMed  CAS  Google Scholar 

  48. Cordeddu V, Di Schiavi E, Pennacchio LA, Ma’ayan A, Sarkozy A, Fodale V, Cecchetti S, Cardinale A, Martin J, Schackwitz W et al (2009) Mutation of SHOC2 promotes aberrant protein N-myristoylation and causes Noonan-like syndrome with loose anagen hair. Nat Genet 41(9):1022–1026

    Article  PubMed  CAS  Google Scholar 

  49. Berger SI, Ma’ayan A, Iyengar R (2010) Systems pharmacology of arrhythmias. Sci Signal 3(118):ra30

    Article  PubMed  Google Scholar 

  50. Krauthammer M, Kaufmann CA, Gilliam TC, Rzhetsky A (2004) Molecular triangulation: bridging linkage and molecular-network information for identifying candidate genes in Alzheimer’s disease. Proc Natl Acad Sci USA 101(42):15148–15153

    Article  PubMed  CAS  Google Scholar 

  51. Iossifov I, Zheng T, Baron M, Gilliam TC, Rzhetsky A (2008) Genetic-linkage mapping of complex hereditary disorders to a whole-genome molecular-interaction network. Genome Res 18(7):1150–1162

    Article  PubMed  CAS  Google Scholar 

  52. Köhler S, Bauer S, Horn D, Robinson PN (2008) Walking the interactome for prioritization of candidate disease genes. Am J Hum Genet 82(4):949–958

    Article  PubMed  Google Scholar 

  53. Vanunu O, Magger O, Ruppin E, Shlomi T, Sharan R (2010) Associating genes and protein complexes with disease via network propagation. PLoS Comput Biol 6(1):e1000641

    Article  PubMed  Google Scholar 

  54. Baggs JE, Hughes ME, Hogenesch JB (2010) The network as the target. Wiley Interdiscip Rev Syst Biol Med 2(2):127–133

    Article  PubMed  Google Scholar 

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Acknowledgements

This work was supported by NIH grants P50GM071558-03, R01DK088541-01A1, RC2LM010994-01, P01DK056492-10, RC4DK090860-01, and KL2RR029885-0109 to AM.

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Correspondence to Avi Ma’ayan .

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Dannenfelser, R., Xu, H., Raimond, C., Ma’ayan, A. (2012). Network Pharmacology to Aid the Drug Discovery Process. In: Ma'ayan, A., MacArthur, B. (eds) New Frontiers of Network Analysis in Systems Biology. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4330-4_9

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