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The use of animal models in expression pharmacogenomic analyses

Abstract

Expression pharmacogenomics applies genome/proteome scale differential expression technologies to both in vivo and in vitro models of drug response to identify candidate markers correlative with and predictive of drug toxicity and efficacy. It is anticipated to streamline drug development by triaging towards lead compounds and clinical candidates that maximize efficacy while minimizing safety risks. As the majority of expression pharmacogenomics will be performed on preclinical therapeutic candidates, compatibility with favored preclinical animal model systems will be essential. This review will address expression pharmacogenomics in the context of those animal model systems commonly used for pharmacokinetic, pharmacodynamic and toxicologic analyses. Specific discussions will cover: (A) relative robustness of genomic and proteomic technology platforms used to generate drug response data in critical model systems; (B) animal handling, treatment and other experimental design optimizations; (C) data analysis strategies for extracting and validating candidate pharmacogenomic markers; and (D) overarching limitations in applying expression pharmacogenomics to animal model systems.

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Abbreviations

DGE:

differential gene expression

UTHS:

ultra-high-throughput screening

IND:

investigational new drug

RYQ-PCR:

real-time quantitative polymerase chain reaction

EST:

expressed sequence tag

DD:

differential display

SAGE:

serial analysis of gene expression

RDA:

representational difference analysis

TOGA:

total gene expression analysis

2DE:

two-dimensional gene electrophoresis

SSRI:

selective serotonin uptake inhibitor

ANOVA:

analysis of variance

PCA:

principle components analysis

GO:

gene ontology

SHR:

spontaneously hypertensive

WKY:

Wistar-Kyoto

References

  1. Evans WE, Relling MV . Pharmacogenomics: translating functional genomics into rational therapeutics Science 1999 286: 487–491

    Article  CAS  Google Scholar 

  2. Bailey DS, Bondar A, Furness LM . Pharmacogenomics—it’s not just pharmacogenetics Curr Opin Biotechnol 1998 9: 595–601

    Article  CAS  Google Scholar 

  3. R & D Directions Staff . Beyond genomics R & D Directions 1999 5: 40–44

    Google Scholar 

  4. Farr S . Dunn II RT. Concise review: gene expression applied to toxicology Toxicol Sci 1999 50: 1–9

    Article  CAS  Google Scholar 

  5. Davies H, Lomas L, Austen B . Profiling of amyloid beta peptide variants using SELDI ProteinChip arrays Biotechniques 1999 27: 1258–1261

    CAS  PubMed  Google Scholar 

  6. Rininger JA, DiPippo VA, Gould Rothberg BE . Differential gene expression technologies for identifying surrogate markers of drug efficacy and toxicity Drug Discov Today 2000 5: 560–568

    Article  CAS  Google Scholar 

  7. Green CD, Simons JF, Taillon BE, Lewin DA . Open systems: panoramic views of gene expression J Immunol Meth 2001 250: 67–79

    Article  CAS  Google Scholar 

  8. Roberts CA, Dietzgen RG, Heelan LA, Maclean DJ . Real-time RT-PCR fluorecent detection of tomato spotted wilt virus J Virol Meth 2000 88: 1–8.

    Article  CAS  Google Scholar 

  9. Heid CA, Stevens J, Livak KJ, Williams PM . Real time quantitative PCR Genome Res 1996 6: 986–994

    Article  CAS  Google Scholar 

  10. Schena M, Shalon D, Davis RW, Brown PO . Quantitative monitoring of gene expression patterns with a complimentary DNA microarray Science 1995 270: 467–470

    Article  CAS  Google Scholar 

  11. Gerlach JH et al . Expression monitoring by hybridization to high density oligonucleotide arrays Nat Biotechnol 1996 14: 1675–1680

    Article  Google Scholar 

  12. Harrington CA, Rosenow C, Retief J . Monitoring gene expression using DNA microarrays Curr Opin Microbiol 2000 3: 285–291

    Article  CAS  Google Scholar 

  13. Lipshutz RJ, Fodor SP, Gingeras TR, Lockhart DJ . High density synthetic nucleotide arrays Nat Genet 1999 21 (1 Suppl): 20–24

    Article  Google Scholar 

  14. Lovett RA . Toxicologists brace for genomics revolution Science 2000 289: 536–537

    Article  CAS  Google Scholar 

  15. Pennisi E . Rat genome off to an early start Science 2000 289: 1267–1269

    Article  CAS  Google Scholar 

  16. Liang P, Pardee AB . Differential display of eukaryotic messenger RNA by means of the polymerase chain reaction Science 1992 257: 967–971.

    Article  CAS  Google Scholar 

  17. Velculescu VE, Zhang L, Vogelstein B, Kinzler KW . Serial analysis of gene expression Science 1995 270: 484–487

    Article  CAS  Google Scholar 

  18. Hubank M, Schatz DG . Identifying differences in mRNA expression by representational difference analysis of cDNA Nucleic Acids Res 1994 22: 5640–5648

    Article  CAS  Google Scholar 

  19. Lee S, Tomasetto C, Sager R . Positive selection of candidate tumor suppressor genes by subtractive hybridization Proc Natl Acad Sci USA 1991 88: 2825–2829

    Article  CAS  Google Scholar 

  20. Gerlach JH et al . TOGA: an automated parsing technology for analyzing expression of nearly all genes Proc Natl Acad Sci USA 2000 97: 1976–1981

    Article  Google Scholar 

  21. Gerlach JH et al . Gene expression analysis by transcript profiling coupled to a gene database query Nat Biotechnol 1999 17: 798–803

    Article  Google Scholar 

  22. Bertelsen AH, Velculescu VE . High throughput gene expression analysis using SAGE Drug Discov Today 1998 3: 152–159

    Article  CAS  Google Scholar 

  23. Hubank M, Schatz DG . cDNA representational difference analysis: a sensitive and flexible method for identification of differentially expressed genes Meth Enzymol 1999 303: 325–349

    Article  CAS  Google Scholar 

  24. Bowler LD, Hubank M, Spratt BG . Representational difference analysis of cDNA for the detection of differential gene expression in bacteria: development using a model of iron-regulated gene expression in Neisseria meningitidis Microbiology 1999 145: 3529–3537

    Article  CAS  Google Scholar 

  25. Chalmers MJ, Gaskell SJ . Advances in mass spectrometry for proteome analysis Curr Opin Biotechnol 2000 11: 384–390

    Article  CAS  Google Scholar 

  26. Gygi SP, Rist B, Aebersold R . Measuring gene expression by quantitative proteome analysis Curr Opin Biotechnol 2000 11: 396–401

    Article  CAS  Google Scholar 

  27. Anderson NL, Matheson AD, Steiner S . Proteomics: applications in basic and applied biology Curr Opin Biotechnol 2000 11: 408–412

    Article  CAS  Google Scholar 

  28. Gerlach JH et al . The effects of peroxisome proliferators on protein abundances in mouse liver Toxicol Appl Pharmacol 1996 137: 75–89

    Article  Google Scholar 

  29. Giometti CS, Tollaksen SL, Liang X . Cunningham ML. A comparison of liver protein changes in mice and hamsters treated with the peroxisome proliferator WY-14643 Electrophoresis 1998 19: 2498–2505

    Article  CAS  Google Scholar 

  30. Gerlach JH et al . Alterations in rabbit kidney protein expression following lead exposure as analyzed by two-dimensional gel electrophoresis Electrophoresis 1999 20: 2977–2985

    Article  Google Scholar 

  31. Celis JE, Gromov P . 2D protein electrophoresis: can it be perfected? Curr Opin Biotechnol 1999 10: 16–21

    Article  CAS  Google Scholar 

  32. Gerlach JH et al . Valvular heart disease associated with fenfluramine-phentermine N Engl J Med 1997 337: 581–588

    Article  Google Scholar 

  33. Cookson J, Duffett R . Fluoxetine: therapeutic and undesirable effects Hosp Med 1998 96: 622–626

    Google Scholar 

  34. McNeely W, Goa KL . Sibutramine: a review of its contribution to the management of obesity Drugs 1998 56: 1093–1124

    Article  CAS  Google Scholar 

  35. Redfield MM, Nicholson WJ, Edwards WD, Tajik AJ . Valve disease associated with ergot alkaloid use: echocardiographic and pathologic correlations Ann Intern Med 1992 117: 50–52

    Article  CAS  Google Scholar 

  36. Roy A, Brand NJ, Yacoub MH . Expression of 5-hydroxytryptamine receptor subtype messenger RNA in interstitial cells from human heart valves J Heart Valve Dis 2000 9: 256–260

    CAS  PubMed  Google Scholar 

  37. Pollack A . DNA chip may help usher in a new era of product testing New York Times 28 November 2000

  38. Brazma A, Vilo J . Gene expression data analysis FEBS Lett 2000 480: 17–24

    Article  CAS  Google Scholar 

  39. Eisen MB, Spellman PT, Brown PO, Botstein D . Cluster analysis and display of genome-wide expression patterns Proc Natl Acad Sci USA 1998 95: 14863–14868

    Article  CAS  Google Scholar 

  40. Gerlach JH et al . Gene ontology: tool for the unification of biology. The gene ontology consortium Nat Genet 2000 25: 25–29

    Article  Google Scholar 

  41. Johnson MD, Campbell LK, Campbell RK . Troglitazone: review and assessment of its role in the treatment of patients with impaired glucose tolerance and diabetes mellitus Ann Pharmacother 1998 32: 337–348

    Article  CAS  Google Scholar 

  42. Schoonjans K, Staels B, Auwerx J . The peroxiosme proliferator activated receptors [PPARs] and their effects on lipid metabolism and adipocyte differentiation Biochim Biophys Acta 1996 1302: 93–109

    Article  CAS  Google Scholar 

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Correspondence to B E Gould Rothberg.

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Rothberg, B. The use of animal models in expression pharmacogenomic analyses. Pharmacogenomics J 1, 48–58 (2001). https://doi.org/10.1038/sj.tpj.6500008

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