Relationships Between Intra- Or Interindividual Variability and Biological Covariates: Application to Zidovudine Pharmacokinetics

  • France Mentré
  • Alain Mallet

Abstract

Quantification of interindividual variability and determination of the covariates contributing to this variability have proved to improve decision-making procedures and individualization of dosage regimen. Several methods have been developed to estimate the probability distribution of the pharmacokinetic parameters from measurements obtained in a sample of individuals. All these methods assume that parameters and covariates remain stationary within an individual. However, ascertaining the stationarity of the parameters or quantifying intraindividual variability can be important when chronic administration is scheduled. We propose a population approach to study the stationarity of the pharmacokinetic parameters. This method is developed around the Non-Parametric Maximum Likelihood estimation method and is based on the comparison of the likelihood of two samples of data from the same individuals at two different times under several assumptions. Some ideas to study stationarity of both individual pharmacokinetic parameters and covariates are also given. The proposed method is illustrated on a very simple simulated example which reflects continuous infusion of a drug involving various assumptions about the nonstationarity of the parameters. The approach is then applied to population analysis of zidovudine kinetics data measured on the first and 35th day of therapy in 36 patients.

Keywords

Pharmacokinetic Parameter Conditional Distribution Interindividual Variability Population Pharmacokinetic Intraindividual Variability 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    B. Whiting, A. Kelman, and J. Grevel. Population pharmacokinetics. Theory and clinical application. Clin. Pharmacokinet. 11:387–401 (1986).PubMedCrossRefGoogle Scholar
  2. 2.
    J. L. Steimer, A. Mallet, and F. Mentré. Estimating interindividual pharmacokinetic variability. In M. Rowland, L. B. Sheiner, and J. L. Steimer (eds.), Variability in Drug Therapy, Raven Press, New York, 1985, pp. 65–111.Google Scholar
  3. 3.
    L. B. Sheiner and S. L. Beal. Bayesian individualization of pharmacokinetics: simple implementation and comparison with non-Bayesian methods. J. Pharm. Sci. 71:1344–1348 (1982).PubMedCrossRefGoogle Scholar
  4. 4.
    A. Mallet. A maximum likelihood estimation method for random coefficient regression models. Biometrika 73:645–656 (1986).CrossRefGoogle Scholar
  5. 5.
    A. Mallet, F. Mentré, J. L. Steimer, and F. Lokiec. Nonparametric maximum likelihood estimation for population pharmacokinetics. An application to Cyclosporine. J. Pharmacokin. Biopharm. 16:311–327 (1988).CrossRefGoogle Scholar
  6. 6.
    A. Mallet, F. Mentré, J. Gilles, A. W. Kelman, A. N. Thomson, S. M. Bryson, and G. Whiting. Handling covariates in population pharmacokinetics with an application to gentamicin. Biomed. Meas. Infor. Contr. 2:673–683 (1988).Google Scholar
  7. 7.
    J. M. Collins and J. D. Unadkat. Clinical pharmacokinetics of zidovudine. An overview of current data. Clin. Pharmacokinet. 17:1–9 (1989).PubMedCrossRefGoogle Scholar
  8. 8.
    S. R. Gitterman, G. L. Drusano, M. J. Egorin, H. C. Standiford, and the Veterans Administration Cooperative Studies Group. Population pharmacokinetics of zidovudine. Clin. Pharmacol. Ther. 48:161–167 (1990).PubMedCrossRefGoogle Scholar
  9. 9.
    S. S. Good, D. J. Reynolds, and P. de Miranda. Simultaneous quantification of zidovudine and its glucuronide in serum by high-performance liquid chromatography. J. Chromatogr. 431:123–133 (1988).PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1991

Authors and Affiliations

  • France Mentré
    • 1
  • Alain Mallet
    • 1
  1. 1.Méthodologie Informatique et Statistique en MédecineSIM-INSERM U 194ParisFrance

Personalised recommendations