Journal of Pharmacokinetics and Pharmacodynamics

, Volume 34, Issue 1, pp 103–113 | Cite as

Establishing Bioequivalence in Serial Sacrifice Designs

  • Martin J. WolfseggerEmail author

Nonclinical in vivo animal studies have to be completed before starting clinical studies of the pharmacokinetic behavior of a drug in human subjects. The classic complete data design, where each animal is sampled for analysis once per time point, is usually only applicable for large animals using the traditional two-stage approach. The first stage involves estimation of pharmacokinetic parameters for each animal separately and the second stage uses the individual parameter estimates for statistical inference. In the case of rats and mice, where blood sampling is restricted, the batch design or the serial sacrifice design may be applicable. In batch designs samples are taken more than once from each animal, but not at all time points. In serial sacrifice designs only one sample is taken from each animal. In this paper, three methods are presented to construct confidence intervals for the ratio of two AUCs assessed in a serial sacrifice design, which can be used to assess bioequivalence in this parameter. The presented methods are compared in a simulation study.


AUC bioequivalence bootstrap Fieller’s theorem Satterthwaite’s approximation serial sacrifice design sparse sampling 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bailer A.J. (1988). Testing for the equality of area under the curves when using destructive measurement techniques. J. Pharmacokinet. Biopharm. 16(3):303–309PubMedCrossRefGoogle Scholar
  2. 2.
    Tang-Liu D.D.-S., Burke P.J. (1988). The effect of azone on ocular levobunolol absorption: calculating the area under the curve and its standard error using tissue sampling compartments. Pharm. Res. 5(4):238–241PubMedCrossRefGoogle Scholar
  3. 3.
    Nedelman J.R., Gibiansky E., Lau D.T.W., (1995). Applying Bailer’s method for AUC confidence intervals to sparse sampling. Pharm. Res. 12(1):124–128PubMedCrossRefGoogle Scholar
  4. 4.
    Satterthwaite F.E., (1946). An approximate distribution of estimates of variance components. Biometrics Bull. 2:110–114CrossRefGoogle Scholar
  5. 5.
    Pai S.M., Nedelman J.R., Hajian G., Gibiansky E., Batra V.K. (1996). Performance of Bailer’s method for AUC confidence intervals from sparse non-normally distributed drug concentrations in toxicokinetic studies. Pharm. Res. 13(9):1280–1282PubMedCrossRefGoogle Scholar
  6. 6.
    Nedelman J.R., Gibiansky E. (1996). The variance of a better AUC estimator for sparse, destructive sampling in toxicokinetics. J. Pharm. Sci. 85(8):884–886PubMedCrossRefGoogle Scholar
  7. 7.
    Gagnon R.C., Peterson J.J. (1998). Estimation of confidence intervals for area under the curve from destructively obtained pharmacokinetic data. J. Pharmacokinet. Biopharm. 26(1):87–102PubMedGoogle Scholar
  8. 8.
    Yuan J. (1993). Estimation of variance for AUC in animal studies. J. Pharm. Sci. 82(7):761–763PubMedCrossRefGoogle Scholar
  9. 9.
    Wolfsegger M.J., Jaki T. (2005). Estimation of AUC from 0 to infinity in serial sacrifice designs. J. Pharmacokinet. Pharmacodyn. 32(5–6):757–766PubMedCrossRefGoogle Scholar
  10. 10.
    Heinzl H. (1996). A note on testing areas under the curve when using destructive measurement techniques. J. Pharmacokinet. Biopharm. 24(6):651–655PubMedCrossRefGoogle Scholar
  11. 11.
    Bailer A.J., Ruberg S.J. (1996). Randomization tests for assessing the equality of area under curves for studies using destructive sampling. J. Appl. Toxicol. 16(5):391–395PubMedCrossRefGoogle Scholar
  12. 12.
    Wellek S (2003) Testing Statistical Hypothesis of Equivalence. Chapman & Hall, London, pp. 29Google Scholar
  13. 13.
    Hu C., Moore K.H.P., Kim Y.H., Sale M.E., (2004). Statistical issues in a modeling approach to assessing bioequivalence or PK similarity with presence of sparsely sampled subjects. J. Pharmacokinet. Pharmacodyn. 31(4):321–339PubMedCrossRefGoogle Scholar
  14. 14.
    Fieller E.C. (1954). Some problems in interval estimation. J. Roy. Stat. Soc.: Ser. B 16:175–185Google Scholar
  15. 15.
    Efron B., Tibshirani R.J. (1993). An Introduction to the Bootstrap. Chapman & Hall, London, p. 160Google Scholar
  16. 16.
    R Development Core Team. R: A Language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, (2005).Google Scholar
  17. 17.
    Guidance for Industry. Statistical Approaches to Establishing Bioequivelence. U.S. Department of Health and Human Services Food and Drug Administration. Center for Drug Evaluation and Research (CDER), 2001. URL:; last visited on 2006-08-08.Google Scholar
  18. 18.
    Guidance for Industry. Population Pharmacokinetics. U.S. Department of Health and Human Services Food and Drug Administration. Center for Drug Evaluation and Research (CDER). Center for Biologics Evaluation and Research (CBER), 1999. URL:; last visited on 2006-08-08.Google Scholar
  19. 19.
    Van der Vaart A.W. (1998). Asymptotic Statistics. Cambridge University Press, United Kingdom p. 26Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2006

Authors and Affiliations

  1. 1.Department of BiostatisticsBaxter AGViennaAustria

Personalised recommendations