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

  • N. BensonEmail author
  • L. Cucurull-Sanchez
  • O. Demin
  • S. Smirnov
  • P. van der Graaf
Conference paper
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 736)


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.


Virtual Human Disease Biology System Pharmacology Human Disease State Drug Discovery Project 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • N. Benson
    • 1
    Email author
  • L. Cucurull-Sanchez
    • 1
  • O. Demin
    • 2
  • S. Smirnov
    • 2
  • P. van der Graaf
    • 3
  1. 1.Modelling and simulation, Department of Pharmacokinetics, Dynamics and MetabolismPfizer Worldwide Research, Pfizer Ltd.SandwichUK
  2. 2.Institute for Systems BiologyLeninskie GoriMoscowRussia
  3. 3.Pfizer, Pharmacometrics, Global Clinical PharmacologyWalton OaksUK

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