Systems Pharmacology and Pharmacogenomics for Drug Discovery and Development

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Systems and Synthetic Biology

Abstract

Systems pharmacology involves the application of systems biology approaches, integrating high throughput experimental data from different experimental techniques such as genomics and proteomics involving computational analytical approaches, to understand the mechanism of action of drugs, identify potential drug targets, use existing drugs for other disease indications and study adverse drug reactions. The significance of using integrated approach is that it allows drug action and drug response to be studied in the context of whole genome or proteome. Basically, a strong and simplified platform for the development of systems pharmacology is provided by information from genetic studies, disease pathophysiology, pharmacology, protein-protein and protein-drug interactions. Network analyses of interactions involved in disease pathophysiology and drug response will allow the integration of the systems-level understanding of drug action with genetic information enabling personalized medicine. Developments and insights from merging systems pharmacology and pharmacogenomics studies will provide new information on the complexities of disease associated with the identification of multiple targets for drug treatment and understanding adverse events caused by off-targets of drugs. In this chapter, we explored the current and future application of systems biology approaches in integrating large scale data from high-throughput genomic technologies with complex disease phenotypes, drug disposition pathways which might lead to not only newer and more effective therapies, but safer medications with fewer side effects.

“Variability is the law of life, and as no two faces are the same, so no two bodies are alike, and no two individuals react alike and behave alike under abnormal conditions which we know as disease.” Sir William Osler (1849–1919)

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References

  • Agapito G, Guzzi PH, CannataroM(2013) Visualization of protein interaction networks: problems and solutions. BMC Bioinform 14(Suppl 1):S1

    Google Scholar 

  • Antonelli A, Ferrari SM, Fallahi P, Piaggi S, Paolicchi A et al (2011) Cytokines (interferon-gamma and tumor necrosis factor-alpha)-induced nuclear factor-kappaB activation and chemokine (C-X-C motif) ligand 10 release in Graves disease and ophthalmopathy are modulated by pioglitazone. Metabolism 60:277–283

    Google Scholar 

  • Aranda B, Achuthan P, Alam-Faruque Y, Armean I, Bridge A et al (2010) The IntAct molecular interaction database in 2010. Nucleic Acids Res 38:D525–531

    Google Scholar 

  • Arrowsmith J (2011a) Trial watch: phase II failures: 2008–2010. Nat Rev Drug Discov 10:328–329

    Google Scholar 

  • Arrowsmith J (2011b) Trial watch: phase III and submission failures: 2007–2010. Nat Rev Drug Discov 10:87

    Google Scholar 

  • Audouze K, Juncker AS, Roque FJ, Krysiak-Baltyn K, Weinhold N et al (2010) Deciphering diseases and biological targets for environmental chemicals using toxicogenomics networks. PLoS Comput Biol 6:e1000788

    Google Scholar 

  • Azuaje FJ, Dewey FE, Brutsaert DL, Devaux Y, Ashley EA et al (2012) Systems-based approaches to cardiovascular biomarker discovery. Circ Cardiovasc Genet 5:360–367

    Google Scholar 

  • Barabasi AL, Gulbahce N, Loscalzo J (2011) Network medicine: a network-based approach to human disease. Nat Rev Genet 12:56–68

    Google Scholar 

  • Bauer-Mehren A, Furlong LI, Rautschka M, Sanz F (2009) From SNPs to pathways: integration of functional effect of sequence variations on models of cell signalling pathways. BMC Bioinform 10(Suppl 8):S6

    Google Scholar 

  • Berger SI, Iyengar R (2009) Network analyses in systems pharmacology. Bioinformatics 25:2466–2472

    Google Scholar 

  • Bhogal N, Balls M (2008) Translation of new technologies: from basic research to drug discovery and development. Curr Drug Discov Technol 5:250–262

    Google Scholar 

  • Boran AD, Iyengar R (2010) Systems pharmacology. Mt Sinai J Med 77:333–344

    Google Scholar 

  • Brouwers L, Iskar M, Zeller G, van Noort V, Bork P (2011) Network neighbors of drug targets contribute to drug side-effect similarity. PLoS One 6:e22187

    Google Scholar 

  • Butcher EC, Berg EL, Kunkel EJ (2004) Systems biology in drug discovery. Nat Biotechnol 22:1253–1259

    Google Scholar 

  • Campillos M, Kuhn M, Gavin AC, Jensen LJ, Bork P (2008) Drug target identification using side-effect similarity. Science 321:263–266

    Google Scholar 

  • Capon F, Allen MH, Ameen M, Burden AD, Tillman D et al (2004) A synonymous SNP of the corneodesmosin gene leads to increased mRNA stability and demonstrates association with psoriasis across diverse ethnic groups. Hum Mol Genet 13:2361–2368

    Google Scholar 

  • Cartegni L, Chew SL, Krainer AR (2002) Listening to silence and understanding nonsense: exonic mutations that affect splicing. Nat Rev Genet 3:285–298

    Google Scholar 

  • Caskey CT (2007) The drug development crisis: efficiency and safety. Annu Rev Med 58:1–16

    Google Scholar 

  • Cavallo A, MartinAC (2005) Map** SNPs to protein sequence and structure data. Bioinformatics 21:1443–1450

    Google Scholar 

  • Csermely P, Korcsmaros T, Kiss HJ, London G, Nussinov R (2013) Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review. Pharmacol Ther 138:333–408

    Google Scholar 

  • Csete ME, Doyle JC (2002) Reverse engineering of biological complexity. Science 295:1664–1669

    Google Scholar 

  • Cucurull-Sanchez L, Spink KG, Moschos SA (2012) Relevance of systems pharmacology in drug discovery. Drug Discov Today 17:665–670

    Google Scholar 

  • De Gobbi M, Viprakasit V, Hughes JR, Fisher C, Buckle VJ et al (2006) A regulatory SNP causes a human genetic disease by creating a new transcriptional promoter. Science 312:1215–1217

    Google Scholar 

  • Drazen JM, Silverman EK, Lee TH (2000) Heterogeneity of therapeutic responses in asthma. Br Med Bull 56:1054–1070

    Google Scholar 

  • Ekins S, NikolskyY, Nikolskaya T (2005) Techniques: application of systems biology to absorption, distribution, metabolism, excretion and toxicity. Trends Pharmacol Sci 26:202–209

    Google Scholar 

  • Ekins S, Williams AJ, Krasowski MD, Freundlich JS (2011) In silico repositioning of approved drugs for rare and neglected diseases. Drug Discov Today 16:298–310

    Google Scholar 

  • Evans WE, McLeod HL (2003) Pharmacogenomics–drug disposition, drug targets, and side effects. N Engl J Med 348:538–549

    Google Scholar 

  • Fernald GH, Capriotti E, Daneshjou R, Karczewski KJ, Altman RB (2011) Bioinformatics challenges for personalized medicine. Bioinformatics 27:1741–1748

    Google Scholar 

  • Franke L, van Bakel H, Fokkens L, de Jong ED, Egmont-Petersen M et al (2006) Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes. Am J Hum Genet 78:1011–1025

    Google Scholar 

  • Fredman D, Siegfried M, YuanYP, Bork P, Lehvaslaiho H et al (2002) HGVbase: a human sequence variation database emphasizing data quality and a broad spectrum of data sources. Nucleic Acids Res 30:387–391

    Google Scholar 

  • Giacomini KM, Yee SW, Ratain MJ, Weinshilboum RM, Kamatani N et al (2012) Pharmacogenomics and patient care: one size does not fit all. Sci Transl Med 4:153ps118

    Google Scholar 

  • Goh KI, Cusick ME, Valle D, Childs B, Vidal M et al (2007) The human disease network. Proc Natl Acad Sci U S A 104:8685–8690

    Google Scholar 

  • Hannum G, Srivas R, Guenole A, van Attikum H, Krogan NJ et al (2009) Genome-wide association data reveal a global map of genetic interactions among protein complexes. PLoS Genet 5:e1000782

    Google Scholar 

  • Hood L (2002) A personal view of molecular technology and how it has changed biology. J Proteome Res 1:399–409

    Google Scholar 

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

    Google Scholar 

  • Huang LC, Wu X, Chen JY (2011) Predicting adverse side effects of drugs. BMC Genomics 12(Suppl 5):S11

    Google Scholar 

  • Hughes JP, Rees S, Kalindjian SB, Philpott KL (2011) Principles of early drug discovery. Br J Pharmacol 162:1239–1249

    Google Scholar 

  • International HapMap Consortium (2005) A haplotype map of the human genome. Nature 437:1299–1320

    Google Scholar 

  • Jegga AG, Gowrisankar S, Chen J, Aronow BJ (2007) PolyDoms: a whole genome database for the identification of non-synonymous coding SNPs with the potential to impact disease. Nucleic Acids Res 35:D700–706

    Google Scholar 

  • Jia P, Zheng S, Long J, Zheng W, Zhao Z (2011) dmGWAS: dense module searching for genome-wide association studies in protein-protein interaction networks. Bioinformatics 27:95–102

    Google Scholar 

  • Judson R, Richard A, Dix D, Houck K, Elloumi F et al (2008) ACToR–aggregated computational toxicology resource. Toxicol Appl Pharmacol 233:7–13

    Google Scholar 

  • Keshava Prasad TS, Goel R, Kandasamy K, Keerthikumar S, Kumar S et al (2009) Human protein reference database–2009 update. Nucleic Acids Res 37:D767–772

    Google Scholar 

  • Kim BC, Kim WY, Park D, Chung WH, Shin KS et al (2008) SNP@Promoter: a database of human SNPs (single nucleotide polymorphisms) within the putative promoter regions. BMC Bioinform 9(Suppl 1):S2

    Google Scholar 

  • Kimchi-Sarfaty C, Oh JM, Kim IW, Sauna ZE, Calcagno AM et al (2007) A “silent” polymorphism in the MDR1 gene changes substrate specificity. Science 315:525–528

    Google Scholar 

  • Kinnings SL, Liu N, Buchmeier N, Tonge PJ, **e L et al (2009) Drug discovery using chemical systems biology: repositioning the safe medicine Comtan to treat multi-drug and extensively drug resistant tuberculosis. PLoS Comput Biol 5:e1000423

    Google Scholar 

  • Kitano H (2002) Systems biology: a brief overview. Science 295:1662–1664

    Google Scholar 

  • Klipp E, Herwig R, Kowald A, Wierling C, Lehrach H (2008) Systems biology in practice: concepts, implementation and application. Wiley, Weinheim

    Google Scholar 

  • Kola I, Landis J (2004) Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 3:711–715

    Google Scholar 

  • 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

    Google Scholar 

  • Lage K, Karlberg EO, Storling ZM, Olason PI, Pedersen AG et al (2007) A human phenome-interactome network of protein complexes implicated in genetic disorders. Nat Biotechnol 25:309–316

    Google Scholar 

  • Levy S, Sutton G, Ng PC, Feuk L, Halpern AL et al (2007) The diploid genome sequence of an individual human. PLoS Biol 5:e254

    Google Scholar 

  • Li J, Zhu X, Chen JY (2009) Building disease-specific drug-protein connectivity maps from molecular interaction networks and PubMed abstracts. PLoS Comput Biol 5:e1000450

    Google Scholar 

  • Ma Q, Lu AY (2011) Pharmacogenetics, pharmacogenomics, and individualized medicine. Pharmacol Rev 63:437–459

    Google Scholar 

  • Ma’ayan A, Iyengar R (2006) From components to regulatory motifs in signalling networks. Brief Funct Genomic Proteomic 5:57–61

    Google Scholar 

  • Ma’ayan A, Blitzer RD, Iyengar R (2005) Toward predictive models of mammalian cells. Annu Rev Biophys Biomol Struct 34:319–349

    Google Scholar 

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

    Google Scholar 

  • Mah JT, Low ES, Lee E (2011) In silico SNP analysis and bioinformatics tools: a review of the state of the art to aid drug discovery. Drug Discov Today 16:800–809

    Google Scholar 

  • Mendrick DL (2011) Transcriptional profiling to identify biomarkers of disease and drug response. Pharmacogenomics 12:235–249

    Google Scholar 

  • Fukuzaki M, Seki M, Kashima H, Sese J (2009) Side Effect Prediction Using Cooperative Pathways. IEEE International Conference on Bioinformatics and Biomedicine, pp 142–147

    Google Scholar 

  • Naidoo N, Pawitan Y, Soong R, Cooper DN, Ku CS (2011) Human genetics and genomics a decade after the release of the draft sequence of the human genome. Hum Genomics 5:577–622

    Google Scholar 

  • Naylor S, Cavanagh J (2004) Status of systems biology-does it have a future? Drug Discov Today: BIOSILICO 2:171–174

    Google Scholar 

  • Naylor S, Chen JY (2010) Unraveling human complexity and disease with systems biology and personalized medicine. Per Med 7:275–289

    Google Scholar 

  • Nebert DW (1999) Pharmacogenetics and pharmacogenomics: why is this relevant to the clinical geneticist? Clin Genet 56:247–258

    Google Scholar 

  • Noorbakhsh F, Overall CM, Power C (2009) Deciphering complex mechanisms in neurodegenerative diseases: the advent of systems biology. Trends Neurosci 32:88–100

    Google Scholar 

  • Oltvai ZN, Barabasi AL (2002) Systems biology. Life’s complexity pyramid. Science 298:763–764

    Google Scholar 

  • Oprea TI, Nielsen SK, Ursu O, Yang JJ, Taboureau O et al (2011) Associating drugs, targets and clinical outcomes into an integrated network affords a new platform for computer-aided drug repurposing. Mol Inform 30:100–111

    Google Scholar 

  • Pagel P, Kovac S, Oesterheld M, Brauner B, Dunger-Kaltenbach I et al (2005) The MIPS mammalian protein-protein interaction database. Bioinformatics 21:832–834

    Google Scholar 

  • Penrod NM, Cowper-Sal-lari R, Moore JH (2011) Systems genetics for drug target discovery. Trends Pharmacol Sci 32:623–630

    Google Scholar 

  • Pierri CL, Parisi G, Porcelli V (2010) Computational approaches for protein function prediction: a combined strategy from multiple sequence alignment to molecular docking-based virtual screening. Biochim Biophys Acta 1804:1695–1712

    Google Scholar 

  • Pouliot Y, Chiang AP, Butte AJ (2011) Predicting adverse drug reactions using publicly available PubChem BioAssay data. Clin Pharmacol Ther 90:90–99

    Google Scholar 

  • Reumers J, Maurer-Stroh S, Schymkowitz J, Rousseau F (2006) SNPeffect v2.0: a new step in investigating the molecular phenotypic effects of human non-synonymous SNPs. Bioinformatics 22:2183–2185

    Google Scholar 

  • Rossin EJ, Lage K, Raychaudhuri S, Xavier RJ, Tatar D et al (2011) Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology. PLoS Genet 7:e1001273

    Google Scholar 

  • Ryan M, Diekhans M, Lien S, Liu Y, Karchin R (2009) LS-SNP/PDB: annotated non-synonymous SNPs mapped to Protein Data Bank structures. Bioinformatics 25:1431–1432

    Google Scholar 

  • Sayers EW, Barrett T, Benson DA (2013) Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 41:D8–D20

    Google Scholar 

  • Schoeberl B, Pace EA, Fitzgerald JB, Harms BD, Xu L et al (2009) Therapeutically targeting ErbB3: a key node in ligand-induced activation of the ErbB receptor-PI3K axis. Sci Signal 2:ra31

    Google Scholar 

  • Sherry ST, Ward MH, Kholodov M, Baker J, Phan L et al (2001) dbSNP: the NCBI database of genetic variation. Nucleic Acids Res 29:308–311

    Google Scholar 

  • Song YC, Kawas E, Good BM, Wilkinson MD, Tebbutt SJ (2007) DataBiNS: a BioMoby-based data-mining workflow for biological pathways and non-synonymous SNPs. Bioinformatics 23:780–782

    Google Scholar 

  • Sorger PK, Allerheiligen SR, Abernethy DR, Altman RB, Brouwer KL et al (2011) Quantitative and systems pharmacology in the post-genomic era: new approaches to discovering drugs and understanding therapeutic mechanisms. In An NIH white paper by the QSP workshop group (pp 1–48). Bethesda: NIH

    Google Scholar 

  • Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A et al (2006) BioGRID: a general repository for interaction datasets. Nucleic Acids Res 34:D535–539

    Google Scholar 

  • Sun YV (2012) Integration of biological networks and pathways with genetic association studies. Hum Genet 131:1677–1686

    Google Scholar 

  • Thusberg J, Olatubosun A, Vihinen M (2011) Performance of mutation pathogenicity prediction methods on missense variants. Hum Mutat 32:358–368.

    Google Scholar 

  • Tsai CJ, Ma B, Nussinov R (2009) Protein-protein interaction networks: how can a hub protein bind so many different partners? Trends Biochem Sci 34:594–600

    Google Scholar 

  • Tuncbag N, Gursoy A, Keskin O (2011) Prediction of protein-protein interactions: unifying evolution and structure at protein interfaces. Phys Biol 8:035006

    Google Scholar 

  • Tyers M, Mann M (2003) From genomics to proteomics. Nature 422:193–197

    Google Scholar 

  • Venter JC, Adams MD, Myers EW, Li PW, Mural RJ et al (2001) The sequence of the human genome. Science 291:1304–1351

    Google Scholar 

  • Villoutreix BO (2002) Structural bioinformatics: methods, concepts and applications to blood coagulation proteins. Curr Protein Pept Sci 3:341–364

    Google Scholar 

  • Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R et al (2010) The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res 38:W214–220

    Google Scholar 

  • Weiss ST, McLeod HL, Flockhart DA, Dolan ME, Benowitz NL et al (2008) Creating and evaluating genetic tests predictive of drug response. Nat Rev Drug Discov 7:568–574

    Google Scholar 

  • Weston AD, Hood L (2004) Systems biology, proteomics, and the future of health care: toward predictive, preventative, and personalized medicine. J Proteome Res 3:179–196

    Google Scholar 

  • Whirl-Carrillo M, McDonagh EM, Hebert JM, Gong L, Sangkuhl K et al (2012) Pharmacogenomics knowledge for personalized medicine. Clin Pharmacol Ther 92:414–417

    Google Scholar 

  • Woollard PM (2010) Asking complex questions of the genome without programming. Methods Mol Biol 628:39–52

    Google Scholar 

  • Woollard PM, Mehta NA, Vamathevan JJ, Van Horn S, Bonde BK et al (2011) The application of next-generation sequencing technologies to drug discovery and development. Drug Discov Today 16:512–519

    Google Scholar 

  • Xenarios I, Salwinski L, Duan XJ, Higney P, Kim SM et al (2002) DIP, the database of interacting proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Res 30:303–305

    Google Scholar 

  • **e L, Bourne PE (2011) Structure-based systems biology for analyzing off-target binding. Curr Opin Struct Biol 21:189–199

    Google Scholar 

  • Xu J, Li Y (2006) Discovering disease-genes by topological features in human protein-protein interaction network. Bioinformatics 22:2800–2805

    Google Scholar 

  • Yan Q (2003) Pharmacogenomics of membrane transporters: an overview. In: Yan Q (ed) Membrane transporters: methods and protocols, Methods in molecular biology. Humana, Totowa, pp 1–20

    Google Scholar 

  • Yan Q (2008) The integration of personalized and systems medicine: bioinformatics support for pharmacogenomics and drug discovery. Methods Mol Biol 448:1–19

    Google Scholar 

  • Yan Q (2010) Bioinformatics for transporter pharmacogenomics and systems biology: data integration and modeling with UML. Methods Mol Biol 637:23–45

    Google Scholar 

  • Yang R, Niepel M, Mitchison TK, Sorger PK (2010) Dissecting variability in responses to cancer chemotherapy through systems pharmacology. Clin Pharmacol Ther 88:34–38

    Google Scholar 

  • Yang L, Wang KJ, Wang LS, Jegga AG, Qin SY et al (2011) Chemical-protein interactome and its application in off-target identification. Interdiscip Sci 3:22–30

    Google Scholar 

  • Yildirim MA, Goh KI, Cusick ME, Barabasi AL, Vidal M (2007) Drug-target network. Nat Biotechnol 25:1119–1126

    Google Scholar 

  • Zhao S, Iyengar R (2012) Systems pharmacology: network analysis to identify multiscale mechanisms of drug action. Annu Rev Pharmacol Toxicol 52:505–521

    Google Scholar 

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Acknowledgements

We thank the Director, Institute of Genomics and Integrative Biology (CSIR) for the support. We appreciate Prof. Pawan Dhar for critical evaluation of the manuscript. Financial support from Council of Scientific and Industrial Research (CSIR) (BSC0123) is duly acknowledged. The authors are grateful to Prof. Samir K Brahmachari for his vision and intellectual inputs. PT, YS and SG acknowledge CSIR, Govt. of India and GKG acknowledge DBT, Govt. of India for providing their fellowships.

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Talwar, P., Silla, Y., Grover, S., Gupta, M., Grewal, G., Kukreti, R. (2015). Systems Pharmacology and Pharmacogenomics for Drug Discovery and Development. In: Singh, V., Dhar, P. (eds) Systems and Synthetic Biology. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9514-2_9

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