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European Journal of Clinical Pharmacology

, Volume 70, Issue 12, pp 1465–1470 | Cite as

Adjustment of endogenous concentrations in pharmacokinetic modeling

  • Alexander BauerEmail author
  • Martin J. Wolfsegger
Pharmacokinetics and Disposition
  • 280 Downloads

Abstract

Purpose

Estimating pharmacokinetic parameters in the presence of an endogenous concentration is not straightforward as cross-reactivity in the analytical methodology prevents differentiation between endogenous and dose-related exogenous concentrations. This article proposes a novel intuitive modeling approach which adequately adjusts for the endogenous concentration.

Methods

Monte Carlo simulations were carried out based on a two-compartment population pharmacokinetic (PK) model fitted to real data following intravenous administration. A constant and a proportional error model were assumed. The performance of the novel model and the method of straightforward subtraction of the observed baseline concentration from post-dose concentrations were compared in terms of terminal half-life, area under the curve from 0 to infinity, and mean residence time.

Results

Mean bias in PK parameters was up to 4.5 times better with the novel model assuming a constant error model and up to 6.5 times better assuming a proportional error model.

Conclusions

The simulation study indicates that this novel modeling approach results in less biased and more accurate PK estimates than straightforward subtraction of the observed baseline concentration and overcomes the limitations of previously published approaches.

Keywords

Endogenous concentration Intrinsic concentration Baseline adjustment Pre-dose concentration Pharmacokinetics 

Notes

Acknowledgments

We gratefully acknowledge the editorial expertise of Karima Benamara and the helpful comments from Werner Engl.

Authors contribution

The authors made equal contributions to the development of this manuscript.

References

  1. 1.
    Howard DR (2004) Development considerations for biological data. In: Bonate PL, Howard DR (eds) Pharmacokinetics in drug development: regulatory and development paradigms. Volume 2. American Association of Pharmaceutical Scientists, Arlington, pp 359–380Google Scholar
  2. 2.
    Dansirikul C, Silber HE, Karlsson MO (2008) Approaches to handling pharmacodynamic baseline responses. J Pharmacokinet Pharmacodyn 35(3):269–283PubMedCrossRefGoogle Scholar
  3. 3.
    Sun YN, Jusko WJ (1999) Role of baseline parameters in determining indirect pharmacodynamic responses. J Pharm Sci 88(10):987–990PubMedCrossRefGoogle Scholar
  4. 4.
    Woo S, Pawaskar D, Jusko WJ (2009) Methods of utilizing baseline values for indirect response models. J Pharmacokinet Pharmacodyn 36(5):381–405PubMedCentralPubMedCrossRefGoogle Scholar
  5. 5.
    Gabrielsson J, Weiner D (2000) Pharamcokinetic and pharmacodynamic data analysis: concepts and applications, 4th edn. Swedish Pharmaceutical, StockholmGoogle Scholar
  6. 6.
    Schindel F (2000) Consideration of endogenous backgrounds in pharmacokinetic analyses: a simulation study. Eur J Clin Pharm 56:685–688CrossRefGoogle Scholar
  7. 7.
    Wichmann BA, Hill ID (1982) Algorithm AS 183: an efficient and portable pseudo-random number generator. Appl Stat 31:188–190, Remarks: 34:198 and 35:89CrossRefGoogle Scholar
  8. 8.
    R Development Core Team (2013) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, URL: http://www.R-project.org/ Google Scholar
  9. 9.
    Cook RD (1986) Bias in nonlinear regression. Biometrika 73(3):615–623CrossRefGoogle Scholar
  10. 10.
    Hayashi N, Kinoshita H, Yukawa E, Higuchi S (1998) Pharmacokinetic analysis of subcutaneous erythropoietin administration with nonlinear mixed effect model including endogenous production. Br J Clin Pharmacol 46(1):11–9PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Baxter Innovations GmbHViennaAustria

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