Population Pharmacokinetics

Reference work entry

Abstract

Variability in exposure to a drug leads to variability in the clinical response across a patient patient population (Rowland et al. 1985). Estimating the variability of the PK (pharmacokinetics) across a patient population requires data obtained from a large study, typically including more than 100 patients. For ethical and practical reasons, pharmacokinetic properties of a drug are difficult to study in large numbers of patients using the traditional approach.

Keywords

Mixed Effect Modeling Concentration Time Population Pharmacokinetic Intraindividual Variability Unbalanced Data 
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. Beal S, Sheiner L (1992) NONMEM user guide. University of California, San FranciscoGoogle Scholar
  2. Efron B (1981) Nonparametric estimates of standard error: the jackknife, the bootstrap and other methods. Biometrika 68:589–599CrossRefGoogle Scholar
  3. FDA (ed) (1999) Guidance for industry: population pharmacokinetics. CP1Google Scholar
  4. Grasela T, Sheiner L (1991) Pharmacostatistical modeling for observational data. J Pharmacokin Biopharm 19(3):25S–37SCrossRefGoogle Scholar
  5. Jelliffe R, Gomis P, Schumitzky A (1990) A population model of genamicin made with a new nonparametric EM algorithm. Technical report 90-4, Laboratory of Applied Pharmacokinetics. University of Southern California School of Medicine, Los AngelesGoogle Scholar
  6. Mathsoft (ed) (2002) S-Plus 6.0 for UNIX users guide. Mathsoft, SeattleGoogle Scholar
  7. Press W, Flannery B, Teukolsky S, Vetterling W (1988) Numerical recipes in C, the art of scientific computing. Cambridge University Press, Cambridge, pp 551–553Google Scholar
  8. Rowland M, Sheiner L, Steimer J (eds) (1985) Variability in drug therapy: description, estimation, and control. Raven, New YorkGoogle Scholar
  9. Sheiner L (1997) Learning versus confirming in clinical drug development. Clin Pharmacol Ther 61:275–291PubMedCrossRefGoogle Scholar
  10. Sheiner L, Beal S (1980) Evaluation of methods for estimating population pharmacokinetic parameters I Michaelis–Menten model: routine clinical pharmacokinetic data. J Pharmacokinet Biopharm 8(6):553–571PubMedCrossRefGoogle Scholar
  11. Sheiner L, Grasela T (1991) An introduction to mixed effect modeling: concepts, definitions, and justification. J Pharmacokinet Biopharm 19(3):11S–24SCrossRefGoogle Scholar
  12. Sheiner L, Rosenberg B, Barathe V (1977) Estimation of population characteristics of pharmacokinetic parameters from routine clinical data. J Pharmacokinet Biopharm 5(5):445–479PubMedCrossRefGoogle Scholar
  13. Sheiner L, Stanski D, Vozeh S et al (1979) Simultaneous modeling of pharmacokinetics and pharmacodynamics: application to d-tubocurarine. Clin Pharmacol Ther 25(3):358–371PubMedGoogle Scholar
  14. Steimer J, Mallet A, Mentre F (1985) Estimating interindividual pharmacokinetic variability. In: Rowland M et al (eds) Variability in drug therapy: description, estimation and control. Raven, New YorkGoogle Scholar
  15. Tanigawara Y, Nomura H, Kagimoto N, Okumura K, Hori R (1995) Premarketing population pharmacokinetic study of levofloxacin in normal subjects and patients with infectious diseases. Biol Pharm Bull 18(2):315–320PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Sanofi Pharma Deutschland GmbHIndustriepark HöchstFrankfurtGermany

Personalised recommendations