Skip to main content

Reducing Systems Biology to Practice in Pharmaceutical Company Research; Selected Case Studies

  • Conference paper
  • First Online:
Advances in Systems Biology

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 736))

Abstract

Reviews of the productivity of the pharmaceutical industry have concluded that the current business model is unsustainable. Various remedies for this have been proposed, however, arguably these do not directly address the fundamental issue; namely, that it is the knowledge required to enable good decisions in the process of delivering a drug that is largely absent; in turn, this leads to a disconnect between our intuition of what the right drug target is and the reality of pharmacological intervention in a system such as a human disease state. As this system is highly complex, modelling will be required to elucidate emergent properties together with the data necessary to construct such models. Currently, however, both the models and data available are limited. The ultimate solution to the problem of pharmaceutical productivity may be the virtual human, however, it is likely to be many years, if at all, before this goal is realised. The current challenge is, therefore, whether systems modelling can contribute to improving productivity in the pharmaceutical industry in the interim and help to guide the optimal route to the virtual human. In this context, this chapter discusses the emergence of systems pharmacology in drug discovery from the interface of pharmacokinetic–pharmacodynamic modelling and systems biology. Examples of applications to the identification of optimal drug targets in given pathways, selecting drug modalities and defining biomarkers are discussed, together with future directions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Munos B (2009) Lessons from 60 years of pharmaceutical innovation. Nat Rev Drug Discov 8(12):959–968

    Article  CAS  PubMed  Google Scholar 

  2. Kola I, Landis J (2004) Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 3:711–716. doi:10.1038/nrd1470

    Article  CAS  PubMed  Google Scholar 

  3. 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 9:203–214. doi:10.1038/nrd3078

    Article  CAS  PubMed  Google Scholar 

  4. Jensen ON (2004) Modification-specific proteomics: characterization of post-translational modifications by mass spectrometry. Curr Opin Chem Biol 8:33–41

    Article  PubMed  Google Scholar 

  5. Kohl P, Crampin EJ, Quinn TA, Noble D (2010) Systems biology: an approach. Clin Pharmacol Ther 88(1):25–33. Epub 2010 June 9

    Article  CAS  PubMed  Google Scholar 

  6. Kohl P, Noble D (2009) Systems biology and the virtual physiological human. Mol Syst Biol 5:292. Epub 2009 July 28

    Article  PubMed  PubMed Central  Google Scholar 

  7. Cohen A (2008) Pharmacokinetic and pharmacodynamic data to be derived from early-phase drug development-designing informative human pharmacological studies. Clin Pharmacokin 47:373–381

    Article  CAS  Google Scholar 

  8. Benson N, vander Graaf PH (2011) Systems pharmacology: bridging systems biology and pharmacokinetics–pharmacodynamics (PKPD) in drug discovery and development. Pharm Res. 2011 Jul;28(7):1460–4. Epub 2011 May 11.

    Article  PubMed  Google Scholar 

  9. Swat MJ, Kiełbasa SM, Polak S, Olivier B, Bruggeman FJ, Tulloch MQ, Snoep JL, Verhoeven AJ, Westerhoff HV (2011) What it takes to understand and cure a living system: computational systems biology and a systems biology-driven pharmacokinetics–pharmacodynamics platform. Interface Focus 1:16–23

    Article  PubMed  Google Scholar 

  10. Wist AD, Berger SI, Iyengar R (2009) Systems pharmacology and genome medicine: a future perspective. Genome Med 1(1):11

    Article  PubMed  PubMed Central  Google Scholar 

  11. Ananiadou S, Kell DB, Tsujii J-i (2006) Text mining and its potential applications in systems biology. Trends Biotechnol 24(12):571–579

    Article  CAS  PubMed  Google Scholar 

  12. Löscher W, Potschka H (2005) Blood–brain barrier active efflux transporters: ATP-binding cassette gene family. NeuroRx 2(1):86–98

    Article  PubMed  PubMed Central  Google Scholar 

  13. Peletier LA, Benson N, van der Graaf PH (2010) Impact of protein binding on receptor occupancy: a two-compartment model. J Theor Biol 265(4):657–671. Epub 2010 June 2

    Article  CAS  PubMed  Google Scholar 

  14. Feng MR (2002) Assessment of blood–brain barrier penetration: in silico, in vitro and in vivo. Curr Drug Metab 3(6):647–657

    Article  CAS  PubMed  Google Scholar 

  15. Ihekwaba AE, Broomhead DS, Grimley RL, Benson N, Kell DB (2004) Sensitivity analysis of parameters controlling oscillatory signalling in the NF-kappab pathway: the roles of IKK and ikappabalpha. Syst Biol 1(1):93–103

    Article  CAS  Google Scholar 

  16. Dahari H, Shudo Emi, Cotler SJ, Layden TJ, Perelson AS (2009) Modeling hepatitis C virus kinetics: the relationship between the infected cell loss rate and the final slope of viral decay. Antivir Ther 14(3):459–464

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Dahari H, Lo A, Ribeiro RM, Perelson AS (2007) Modeling hepatitis C virus dynamics: liver regeneration and critical drug efficacy. J Theor Biol 247(2):371–381. Epub 2007a, Mar 14

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Dahari H, Ribeiro RM, Perelson AS (2007) Triphasic decline of hepatitis C virus RNA during antiviral therapy. Hepatology 46(1):16–21

    Article  CAS  PubMed  Google Scholar 

  19. Dixit NM, Layden-Almer JE, Layden TJ, Perelson AS (2004) Modelling how ribavirin improves interferon response rates in hepatitis C virus infection. Nature 432(7019):922–924

    Article  CAS  PubMed  Google Scholar 

  20. Snoeck E, Chanu P, Lavielle M, Jacqmin P, Jonsson EN, Jorga K, Goggin T, Grippo J, Jumbe NL, Frey NA (2010) Comprehensive hepatitis C viral kinetic model explaining cure. Clin Pharmacol Ther 87(6):706–713. Epub 2010 May 12

    Article  CAS  PubMed  Google Scholar 

  21. Maiwald T, Schneider A, Busch H, Sahle S, Gretz N, Weiss TS, Kummer U, Klingmüller U (2010) Combining theoretical analysis and experimental data generation reveals IRF9 as a crucial factor for accelerating interferon-induced early antiviral signalling. FEBS J 277(22):4741–4754

    Article  CAS  PubMed  Google Scholar 

  22. Igawa T, Tsunoda H, Kuramochi T, Sampei Z, Ishii S, Hattori K (2011) Engineering the variable region of therapeutic igg antibodies. MAbs. 3(3). Epub ahead of print

    Google Scholar 

  23. Meuleman P, Hesselgesser J, Paulson M, Vanwolleghem T, Desombere I, Reiser H, Leroux-Roels G (2008) Anti-CD81 antibodies can prevent a hepatitis C virus infection in vivo. Hepatology 48(6):1761–1768

    Article  CAS  PubMed  Google Scholar 

  24. Finney, A., Hucka, M., Bornstein, B.J., Keating, S.M., Shapiro, B.E., Matthews, J., Kovitz, B.L., Schilstra, M.J., Funahashi, A., Doyle, J.C., Kitano, H. (2006). “Software Infrastructure for Effective Communication and Reuse of Computational Models”. Systems Modeling in Cell Biology: From Concepts to Nuts and Bolts. MIT Press. pp. 369-378.

    Google Scholar 

  25. Rosenfeld S (2011) Mathematical descriptions of biochemical networks: stability, stochasticity, evolution. Progr Biophys Mol Biol 106(2):400–409. doi:10.1016/j.pbiomolbio.2011.03.003

    Article  Google Scholar 

  26. Valeyev NV, Hundhausen C, Umezawa Y, Kotov NV, Williams G, Clop A, Ainali C, Ouzounis C, Tsoka S, Nestle FO (2010) A systems model for immune cell interactions unravels the mechanism of inflammation in human skin. PLoS Comput Biol 6(12):e1001024

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to N. Benson .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media, LLC

About this paper

Cite this paper

Benson, N., Cucurull-Sanchez, L., Demin, O., Smirnov, S., van der Graaf, P. (2012). Reducing Systems Biology to Practice in Pharmaceutical Company Research; Selected Case Studies. In: Goryanin, I.I., Goryachev, A.B. (eds) Advances in Systems Biology. Advances in Experimental Medicine and Biology, vol 736. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7210-1_36

Download citation

Publish with us

Policies and ethics