Analysis of Pharmacokinetic Data

  • Ha Nguyen
  • Dhammika Amaratunga


Pharmacokinetics is the study of the time course of a drug administered to a biological organism, in particular, a human. A drug, following administration, is processed by the body in a series of stages: absorption, distribution, metabolism and elimination; these stages are often abbreviated by their initials, ADME. Since the nature, intensity, and duration of a drug’s biologic effects depend on the amount of drug available to the body and, more specifically, to the amount of drug available at the target site in the body, pharmacokinetic studies play an integral role in a drug’s development.


Bayesian Information Criterion Data Frame Median Absolute Deviation Nonlinear Mixed Effect Model Terminal Elimination Phase 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Bates, D.M. and Watts, D.G. (1988). Nonlinear Regression Analysis and Its Applications. Wiley, New York.zbMATHCrossRefGoogle Scholar
  2. Beal, S.L. and Sheiner, L.B. (1982). Estimating population kinetics. CRC Critical Reviews in Biomedical Engineering 8, 195–222.Google Scholar
  3. Chambers, J.M. and Hastie T.J. (1992). Statistical Models in S. Wadsworth.Google Scholar
  4. Davidian, M. and Giltinan, D.M. (1993). Some general estimation methods for nonlinear mixed effects models. Journal of Biopharmaceutical Statistics 3, 23–55.CrossRefGoogle Scholar
  5. Davidian, M. and Giltinan, D.M. (1995). Nonlinear Models for Repeated Measurement Data. Chapman Hall, New York.Google Scholar
  6. Ette, E. (1998). The application of S—PLUS graphics, modeling and statistical tools in population pharmacokinetics. Presented at the S-PLUS User Conference, Washington, DC.Google Scholar
  7. Lindstrom, M.J. and Bates, D.M. (1990). Nonlinear mixed effects models for repeated measures data. Biometrics 46, 673–687.MathSciNetCrossRefGoogle Scholar
  8. Pinheiro, J.C. (1998). NLME: Software for mixed effects models. http: //franz. stat. wi s c. edu/pub/NLME.Google Scholar
  9. Pinheiro, J.C. and Bates, D.M. (2000). Mixed Effects Models in S and S-PLUS. Springer-Verlag, New York.zbMATHCrossRefGoogle Scholar
  10. Sheiner, L.B., Rosenberg, B., and Melmon, K.L. (1972). Modeling of individual pharmacokinetics data for computer aided drug dosing. Computers and Biomedical Research 5, 441–459.CrossRefGoogle Scholar
  11. Steimer, J.L., Mallet, A., Golmard, J.L., and Boisvieux, J.F. (1984). Alternative approaches to estimation of population pharmacokinetic parameters: Comparison with the nonlinear mixed effects model. Drug Metabolism Reviews 15, 265–292.CrossRefGoogle Scholar
  12. Venables, W.N. and Ripley, B.D. (1997). Modern Applied Statistics with S-PLUS, 2nd Ed. Springer—Verlag, New York.Google Scholar
  13. Welling, P.G. (1986). Pharmacokinetics: Processes and Mathematics. American Chemical Society.Google Scholar
  14. Yeh, K.C. and Kwan, K.C. (1978). A comparison of numerical integrating algorithms by trapezoidal, Lagrange, and spline approximation. Journal of Pharmacokinetics and Biopharmaceutics 6, 79–89.Google Scholar

Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Ha Nguyen
    • 1
  • Dhammika Amaratunga
    • 2
  1. 1.Merck Research LaboratoriesRahwayUSA
  2. 2.The R. W. Johnson Pharmaceutical Research InstituteRaritanUSA

Personalised recommendations