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Rescuing drug discovery: in vivo systems pathology and systems pharmacology

Abstract

The pharmaceutical industry is currently beleaguered by close scrutiny from the financial community, regulators and the general public. Productivity, in terms of new drug approvals, has generally been falling for almost a decade and the safety of a number of highly successful drugs has recently been brought into question. Here, we discuss whether taking an in vivo systems approach to drug discovery and development could be the paradigm shift that rescues the industry.

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Figure 1: Flowchart for systems pathology and systems pharmacology.
Figure 2: Potential use of systems pathology and systems pharmacology to identify potential drug combinations for treating a disease.
Figure 3: Assessing the properties of a system in response to combination drug therapy.

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References

  1. Butcher, E. C. Can systems biology rescue drug discovery? Nature Rev. Drug Discov. 4, 461–467 (2005).

    Article  CAS  Google Scholar 

  2. Grabowski, H. Are the economics of pharmaceutical research and development changing?: productivity, patents and political pressures. Pharmacoeconomics 22 (Suppl. 2), 15–24 (2004).

    Article  Google Scholar 

  3. Booth, B. & Zemmel, R. Prospects for productivity. Nature Rev. Drug Discov. 3, 451–456 (2004).

    Article  CAS  Google Scholar 

  4. CenterWatch. Drugs approved by the FDA (2005) [online], <http://www.centerwatch.com/patient/drugs/druglist.html> (2005).

  5. Pharmaceutical Research and Manufacturers of America. Pharmaceutical Industry Profile 2005 [online] <http://www.phrma.org/publications/publications//2005-3-17.1143.pdf> (PhRMA, Washington DC, 2005).

  6. Nature Biotechnol. [whole issue] 22 (October, 2004).

  7. von Bertalanffy, L. General Systems Theory: Foundations, Development, Application (George Braziller, New York, 1968).

    Google Scholar 

  8. Sheldrake, R. The Rebirth of Nature (Bantam Books, New Jersey, 1991).

    Google Scholar 

  9. Capra, F. The Web of Life (Anchor Books, New York, 1996).

    Google Scholar 

  10. Laszlo, E. The Systems View of the World: A Holistic View of Our Time (Hampton, New Jersey, 1996).

    Google Scholar 

  11. Rosen, R. Life Itself (Columbia Univ. Press, New York, 1991).

    Google Scholar 

  12. Ideker, T., Galitski, T. & Hood, L. A new approach to decoding life: systems biology. Annu. Rev. Genomics Hum. Genet. 2, 343–372 (2001).

    Article  CAS  Google Scholar 

  13. Kitano, H. Computational systems biology. Nature 421, 573 (2002).

    Google Scholar 

  14. Oltvai, Z. N. & Barabasi, A. L. Systems biology: life's complexity pyramid. Science 298, 763–764 (2002).

    Article  CAS  Google Scholar 

  15. Glass, L. & Mackey, M. C. From Clocks to Chaos: The Rhythms of Life (Princeton, New Jersey, 1998).

    Google Scholar 

  16. Tas, A. C. et al. Pyrolysis-direct chemical ionization mass spectrometry of the dimorphic fungus C andida albicans and the polymorphic fungus Ophiostoma ulmi. J. Anal. Appl. Pyrol. 14, 309–321 (1989).

    Article  CAS  Google Scholar 

  17. Kitano, H. et al. Metabolic syndrome and robustness tradeoffs. Diabetes 53 (Suppl. 3), S6–S15 (2004).

    Article  CAS  Google Scholar 

  18. US FDA. FDA Drug and Device Product Approvals [online], <http://www.fda.gov/cder/da/ddpa.htm> (1996).

  19. Nightingale, P. & Martin, P. The myth of the biotech revolution. Trends Biotechnol. 22, 564–569 (2004).

    Article  CAS  Google Scholar 

  20. Huang, S., Eichler, G., Bar-Yam, Y. & Ingber, D. Cell fates as high-dimensional attractor states of a complex gene regulatory network. Phys. Rev. Lett. 94, 128701 (2005).

    Article  Google Scholar 

  21. Morel, N. M. et al. Primer on medical genomics. Part XIV: Introduction to systems biology — a new approach to understanding disease and treatment. Mayo Clin. Proc. 79, 651–658 (2004).

    Article  CAS  Google Scholar 

  22. van der Greef, J., Stroobant, P. & van der Heijden, R. The role of analytical sciences in medical systems biology. Curr. Opin. Chem. Biol. 8, 559–565 (2004).

    Article  CAS  Google Scholar 

  23. Steuer, R., Kurths, J., Fiehn, O. & Weckwerth, W. Interpreting correlations in metabolomic networks. Biochem. Soc. Trans. 31, 1476–1478 (2003).

    Article  CAS  Google Scholar 

  24. Steuer, R., Kurths, J., Fiehn, O. & Weckwerth, W. Observing and interpreting correlations in metabolomic networks. Bioinformatics 19, 1019–1026 (2003).

    Article  CAS  Google Scholar 

  25. Clish, C. B. et al. Integrative biological analysis of the APOE*3-Leiden transgenic mouse. OMICS 1, 3–13 (2004).

    Article  Google Scholar 

  26. Oresic, M. et al. Phenotype characterization using integrated gene transcript, protein and metabolite profiling. Appl. Bioinformatics 3, 205–217 (2004).

    Article  CAS  Google Scholar 

  27. Bhalla, U. S. & Iyengar, R. Emergent properties of networks of biological signaling pathways. Science 283, 381–387 (1999).

    Article  CAS  Google Scholar 

  28. Davidov, E. et al. Methods for the differential integrative omic analysis of plasma from a transgenic disease animal model. OMICS 8, 267–288 (2004).

    Article  CAS  Google Scholar 

  29. Butcher, E. C., Berg, E. L. & Kunkel, E. J. Systems biology in drug discovery. Nature Biotechnol. 22, 1253–1259 (2004).

    Article  CAS  Google Scholar 

  30. van der Greef, J. et al. in Metabolic Profiling: Its Role in Biomarker Discovery and Gene Function Analysis (eds Harrigan, G. G. & Goodacre, R.) 170–198 (Kluwer Academic, Boston, 2003).

    Google Scholar 

  31. Stermitz, F. R., Lorenz, P., Tawara, J. N., Zenewicz, L. A. & Lewis, K. Synergy in a medicinal plant: antimicrobial action of berberine potentiated by 5′-methoxyhydnocarpin, a multidrug pump inhibitor. Proc. Natl Acad. Sci. USA 97, 1433–1437 (2000).

    Article  CAS  Google Scholar 

  32. Sengupta, S. et al. Modulating angiogenesis: the yin and the yang in gensing. Circulation 110, 1219–1225 (2004).

    Article  CAS  Google Scholar 

  33. Wang, M. et al. Metabolomics in the context of systems biology: bridging traditional Chinese medicine and molecular pharmacology. J. Phytother Res. 19, 173–182 (2005).

    Article  CAS  Google Scholar 

  34. Szegedi, A., Kohnen, R., Dienel, A. & Kieser, M. Acute treatment of moderate to severe depression with hypericum extract WS 5570 (St John's wort): randomised controlled double blind non-inferiority trial versus paroxetine. BMJ 330, 503–507 (2005).

    Article  CAS  Google Scholar 

  35. Ogawa, N., List, J. F., Habener, J. F. & Maki, T. Cure of overt diabetes in NOD mice by transient treatment with anti-lymphocyte serum and exendin-4. Diabetes 53, 1700–1705 (2004).

    Article  CAS  Google Scholar 

  36. Borisy, A. A. et al. Systematic discovery of multicomponent therapeutics. Proc. Natl Acad. Sci. USA 100, 7977–7982 (2003).

    Article  CAS  Google Scholar 

  37. Keith, C. T., Borisy, A. A. & Stockwell, B. R. Multicomponent therapeutics for networked systems. Nature Rev. Drug Discov. 4, 71–78 (2005).

    Article  CAS  Google Scholar 

  38. Henry, C. M. Systems biology. C&E News 83, 47–55 (2005).

    Google Scholar 

  39. US Department of Human and Health Services. Food and Drug Administration. Innovation or Stagnation, Challenge and Opportunity on the Critical Path to New Medicinal Products [online], <http://www.fda.gov/oc/initiatives/criticalpath/whitepaper.html> (2004).

  40. Bain & Co. Rebuilding Big Pharma's Buisness Model (Bain & Co., Boston, 2003).

  41. Kohonen, T. Self-Organizing Maps 3rd edn (Springer, Berlin, 2001).

    Book  Google Scholar 

  42. Tamayo, P. et al. Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. Proc. Natl Acad. Sci. USA 96, 2907–2912 (1999).

    Article  CAS  Google Scholar 

  43. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Stat. Soc. B. 57, 289–300 (1995).

    Google Scholar 

  44. Delsing, D. J. et al. Rosuvastatin reduces plasma lipids by inhibiting VLDL production and enhancing hepatobiliary lipid excretion in ApoE*3-Leiden mice. J. Cardiovasc. Pharmacol. 45, 53–60 (2005).

    Article  CAS  Google Scholar 

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Acknowledgements

The authors thank A. Adourian and P. Stroobant for helpful suggestions on the manuscript and for assistance with the figures.

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The authors declare that they have competing financial interests. R.N.McB. is an employee of, and a shareholder in, BG Medicine. J.v.d.G. is a member of the Research and Development Advisory Board of, and a shareholder in, BG Medicine.

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van der Greef, J., McBurney, R. Rescuing drug discovery: in vivo systems pathology and systems pharmacology. Nat Rev Drug Discov 4, 961–967 (2005). https://doi.org/10.1038/nrd1904

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