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Background of Pharmacologic Modeling

  • Ronald GieschkeEmail author
  • Daniel Serafin
Chapter
  • 2.9k Downloads

Abstract

The pharmaceutical industry, which emerged during the second half of the nineteenth century, operates nowadays in a sensitive environment. Providing patients with differentiated medicines that can be afforded by health care systems is proving more and more difficult. Today’s medical treatment concepts are largely determined by drugs that act on predefined targets whereas previously a more phenomenological approach was taken. Drug development is a lengthy and resource-intensive process subject to tight regulatory supervision with emphasis on the safety of medicines. Due to high attrition rates at the different stages of drug research and development (R&D), pharmaceutical productivity is declining. To support the complex process of turning a treatment concept into a marketable drug, industry specialists increasingly advocate the consistent application of mathematical modeling based on realistic models of pharmacokinetics, pharmacodynamics, and disease biology.

Keywords

Pharmaceutical Industry Clinical Trial Simulation Single Ascend Dose Multiple Ascend Dose Single Ascend Dose Study 
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.

References

  1. 1.
    Chandler AD (2005) Shaping the industrial century: the remarkable story of the modern chemical and pharmaceutical industries. Harvard University Press, CambridgeGoogle Scholar
  2. 2.
    LaMattina JL (2011) The impact of mergers on pharmaceutical R&D. Nat Rev Drug Discov 10(559):560Google Scholar
  3. 3.
    Drews J (2000) Drug discovery: a historical perspective. Science 287:1960–1964PubMedCrossRefGoogle Scholar
  4. 4.
    John V (2010) BACE: lead target for orchestrated therapy of Alzheimer’s disease. Wiley, New YorkGoogle Scholar
  5. 5.
    Talele TT, Khedkar SA, Rigby AC (2010) Successful applications of computer aided drug discovery: moving drugs from concept to the clinic. Curr Topics Med Chemistry 10:127–141CrossRefGoogle Scholar
  6. 6.
    Swinney DC, Anthony J (2011) How were new medicines discovered. Nat Rev Drug Discov 10:507–519PubMedCrossRefGoogle Scholar
  7. 7.
    Lindsay MA (2005) Finding new drug targets in the twenty-first century. Drug Discov Today 10:1683–1687PubMedCrossRefGoogle Scholar
  8. 8.
    Avorn J (2011) Learning about the safety of drugs – a half-century of evolution. N Engl J Med 365:2151–2153PubMedCrossRefGoogle Scholar
  9. 9.
    de Regt HW, Dieks D (2003) A contextual approach to scientific understanding. http://philsci-archive.pitt.edu/1354/. Accessed 13 March 2013
  10. 10.
    Box GEP, Draper NR (1987) Empirical model-building and response surfaces. Wiley, New YorkGoogle Scholar
  11. 11.
    Di Stefano JJ, Landaw EM (1984) Multiexponential, multicompartmental, and noncompartmental modeling. I. Methodological limitations and physiological interpretations. Am J Physiol - Regul Integr Comp Physiol 246:651–664Google Scholar
  12. 12.
    Sterman JD (1991) A skeptic’s guide to computer models. http://www.systems-thinking.org/ simulation/skeptics.pdf Accessed 13 March 2013
  13. 13.
    Bazzoli C, Retout S, Mentre F (2010) Design evaluation and optimisation in multiple response nonlinear mixed effect models: PFIM 3.0. Comput Methods Programs Biomed 98:55–65PubMedCrossRefGoogle Scholar
  14. 14.
    Serafin D, Skrzypek J, Grzesik M (1989) Optimization of directly cooled multi-stage adiabatic reactors I. Chem Process Eng 3:389–403Google Scholar
  15. 15.
    Serafin D, Skrzypek J, Grzesik M (1989) Optimization of directly cooled multi-stage adiabatic reactors II. Chem Process Eng 4:641–653Google Scholar
  16. 16.
    Serafin D, Skrzypek J, Grzesik M (1990) Optimization of directly cooled multi-stage adiabatic reactors III. Chem Process Eng 2:493–499Google Scholar
  17. 17.
    Garred LJ, Pretlac R (1991) Mathematical modeling of erythropoietin therapy. Am Soc Artif Intern Organs Trans 37:M457–M459Google Scholar
  18. 18.
    Ward MP, Irazoqui PP (2010) Evolving refractory major depressive disorder diagnostic and treatment paradigms: toward closed-loop therapeutics. Front Neuroeng 3:7.1-7.15Google Scholar
  19. 19.
    Meadows DH, Randers J, Meadows DL (2004) Limits to growth: The 30-year update. Chelsea Green Publishing Company, With River JunctionGoogle Scholar
  20. 20.
    Skrzypek J, Lachowska M, Serafin D (1990) Methanol synthesis from CO2 and H2: Dependence of equilibrium conversions and exit equilibrium concentration of components on the main process variables. In: Chemical Engineering Science, vol 45. Pergamon Press, Oxford, pp 89-96Google Scholar
  21. 21.
    Kaufmann WJ, Smarr LL (1993) Supercomputing and the transformation of science. Scientific American Library, New YorkGoogle Scholar
  22. 22.
    Axelrod R (2006) The Evolution of Cooperation: Revised Ed. Basic Books, New YorkGoogle Scholar
  23. 23.
    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 9:203–214PubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Pharma Research and Early DevelopmentF. Hoffmann-La Roche LtdBaselSwitzerland

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