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Contributions from emerging transcriptomics technologies and computational strategies for drug discovery

Investigational New Drugs volume 32, pages 1316–1319 (2014)Cite this article

Summary

Drug discovery involves various steps and is a long process being even more demanding for complex diseases such as cancer. Tumors are ensembles of subpopulations with different mutations, require very specific and effective strategies. Conventional drug screening technologies may not be adequate and efficient anymore. Drug repositioning is a useful strategy to identify new uses for previously failed drugs. High throughput and deep sequencing technologies provide valuable support by yielding enormous amounts of “-omics” data and contribute to understanding the molecular mechanisms responsible for drug action. Computational methods coupled with systems biology represent a promising step to interpret pharmacogenomic data and establish strong connections with drug discovery. Genomic variations have been found to be linked with differential drug response among individuals. Large genome wide association studies are necessary to identify reliable connections between genomic variations and drug response since personalized medicine has been accepted as an important phenomenon in the drug discovery and development process post approval.

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Fig. 1

Abbreviations

ENCODE:

Encyclopedia of DNA elements

GWAS:

Genome wide association studies

OMIM:

Online mendelian ınheritance ın men

R&D:

Research and development

SNP:

Single nucleotide polymorphism

WES:

Whole exome sequencing

References

  1. Mullard A (2012) 2011 FDA drug approvals. Nat Rev Drug Discov 11:91–94

    CAS  Article  PubMed  Google Scholar 

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

    CAS  Article  PubMed  Google Scholar 

  3. Dopazo J (2013) Genomics and transcriptomics in drug discovery. Drug Discov Today pii:S1359–6446(13):00166–9

    Google Scholar 

  4. Stratton MR (2011) Exploring the genomes of cancer cells: progress and promise. Science 331:1553–1558

    CAS  Article  PubMed  Google Scholar 

  5. Hawkins RD, Hon GC, Ren B (2010) Next-generation genomics: an integrative approach. Nat Rev Genet 11:476–486

    PubMed Central  CAS  PubMed  Google Scholar 

  6. Auffray C, Chen Z, Hood L (2009) Systems medicine: the future of medical genomics and healthcare. Genome Med 1:2

    PubMed Central  Article  PubMed  Google Scholar 

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

    PubMed Central  Article  PubMed  Google Scholar 

  8. Moreau Y, Tranchevent LC (2012) Computational tools for prioritizing candidate genes: boosting disease gene discovery. Nat Rev Genet 13:523–536

    CAS  Article  PubMed  Google Scholar 

  9. Ashburn TT, Thor KB (2004) Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov 3:673–683

    CAS  Article  PubMed  Google Scholar 

  10. Ekins S, Mestres J, Testa B (2007) In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Br J Pharmacol 152:9–20

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  11. Dudley JT, Deshpande T, Butte AJ (2011) Exploiting drug–disease relationships for computational drug repositioning. Brief Bioinform 12:303–311

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  12. Aronson JK (2007) Old drugs–new uses. J Clin Pharmacol 64:563–565

    CAS  Article  Google Scholar 

  13. Sirota M, Dudley JT, Kim J, Chiang AP, Morgan AA, Sweet-Cordero A, Sage J, Butte AJ (2011) Discovery and preclinical validation of drug indications using compendia of public gene expression data. Sci Transl Med 3:96ra77

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  14. Wu Z, Wang Y, Chen L (2013) Network-based drug repositioning. Mol BioSyst 9:1268

    CAS  Article  PubMed  Google Scholar 

  15. Feero WG, Guttmacher AE, Collins FS (2010) Genomic medicine—an updated primer. N Engl J Med 362:2001–2011

    CAS  Article  PubMed  Google Scholar 

  16. ENCODE Project Consortium, Bernstein BE, Birney E, Dunham I, Green ED, Gunter C, Snyder M (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489:57–74

    Article  Google Scholar 

  17. Xue Y, Chen Y, Ayub Q, Huang N, Ball EV, Mort M, Phillips AD, Shaw K, Stenson PD, Cooper DN, Tyler-Smith C, 1000 Genomes Project Consortium (2012) Deleterious- and disease-allele prevalence in healthy individuals: insights from current predictions, mutation databases, and population-scale resequencing. Am J Hum Genet 91:1022–1032

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  18. Carbonell J, Alloza E, Arce P, Borrego S, Santoyo J, Ruiz-Ferrer M, Medina I, Jiménez-Almazán J, Méndez-Vidal C, González-Del Pozo M, Vela A, Bhattacharya SS, Antiñolo G, Dopazo J (2012) A map of human microRNA variation uncovers unexpectedly high levels of variability. Genome Med 4:62

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  19. Barabasi AL, Oltvai ZN (2004) Network biology: understanding the cell’s functional organization. Nat Rev Genet 5:101–113

    CAS  Article  PubMed  Google Scholar 

  20. Luo H, Chen J, Shi L, Mikailov M, Zhu H, Wang K, He L, Yang L (2011) DRAR-CPI: a server for identifying drug repositioning potential and adverse drug reactions via the chemical-protein interactome. Nucleic Acids Res 39:W492–W498

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  21. Zhao S, Li S (2012) A co-module approach for elucidating drug-disease associations and revealing their molecular basis. Bioinformatics 28:955–961

    CAS  Article  PubMed  Google Scholar 

  22. Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E, Martinez P, Matthews N, Stewart A, Tarpey P, Varela I, Phillimore B, Begum S, McDonald NQ, Butler A, Jones D, Raine K, Latimer C, Santos CR, Nohadani M, Eklund AC, Spencer-Dene B, Clark G, Pickering L, Stamp G, Gore M, Szallasi Z, Downward J, Futreal PA, Swanton C (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 366:883–892

    CAS  Article  PubMed  Google Scholar 

  23. Creixell P, Schoof EM, Erler JT, Linding R (2012) Navigating cancer network attractors for tumor-specific therapy. Nat Biotechnol 30:842–848

    CAS  Article  PubMed  Google Scholar 

  24. Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel M, Lerner J, Brunet JP, Subramanian A, Ross KN, Reich M, Hieronymus H, Wei G, Armstrong SA, Haggarty SJ, Clemons PA, Wei R, Carr SA, Lander E, Golub TR (2006) The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313:1929–1935

    CAS  Article  PubMed  Google Scholar 

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Conflict of interest

The authors declare that they have no conflict of interest whatsoever.

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Affiliations

  1. Department of Pharmaceutical Biology, Institute of Pharmacy and Biochemistry, University of Mainz, Staudinger Weg 5, 55128, Mainz, Germany

    Onat Kadioglu & Thomas Efferth

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  1. Onat Kadioglu
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  2. Thomas Efferth
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Correspondence to Thomas Efferth.

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Kadioglu, O., Efferth, T. Contributions from emerging transcriptomics technologies and computational strategies for drug discovery. Invest New Drugs 32, 1316–1319 (2014). https://doi.org/10.1007/s10637-014-0081-x

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  • DOI: https://doi.org/10.1007/s10637-014-0081-x

Keywords

  • Computational biology
  • Connectivity map
  • Deep sequencing
  • Drug discovery
  • Drug repositioning
  • Genome-wide association studies
  • -omics technologies
  • Systems biology
  • Whole exon sequencing