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Assumption Testing in Population Pharmacokinetic Models: Illustrated with an Analysis of Moxonidine Data from Congestive Heart Failure Patients

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Abstract

Deriving a population pharmacokinetic model from real data is always associated with numerous assumptions. Violations of these assumptions, especially if undetected, may lead to inappropriate conclusions being made from the analysis. Routinely, only a few of the assumptions are explicitly stated and justified in the reporting of a population model. Here, we attempt to be exhaustive in the presentation of the assumptions made in the course of an analysis of moxonidine pharmacokinetics. The different ways that assumptions were justified, through experience, graphical examination, or additional modeling, are outlined. Models for relaxing assumptions regarding the covariate and statistical submodels, not previously reported in the area of population pharmacokinetic modeling, are also described.

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REFERENCES

  1. L. Aarons, L. P. Balant, M. Danhof, M. Gex-Fabry, U. A. Gundert-Remy, M. O. Karlsson, F. Mentré, F. Rombout, M. Rowland, J.-L. Steimer, and S. Vozeh (eds.). The Population Approach: Measuring and Managing Variability in Response, Concentration and Dose, European Commission, Brussels, 1997.

    Google Scholar 

  2. K. Fattinger, S. Vozeh, and H. R. Ha. Population pharmacokinetics of quinidine. Br. J. Clin. Pharmacol. 31:279–286 (1991).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  3. D. J. Nichols, P. A. Milligan, E. N. Jonsson, et al. Application of population analysis in phase I/II clinical drug development. In L. Aarons et al. (eds.) The Population Approach: Measuring and Managing Variability in Response, Concentration and Dose, European Commission, Brussels, 1997.

    Google Scholar 

  4. M. D. Hale. Using population pharmacokinetics for planning a randomized concentration-controlled trial with a binary response. In L. Aarons et al. (eds.) The Population Approach: Measuring and Managing Variability in Response, Concentration and Dose, European Commission, Brussels, 1997.

    Google Scholar 

  5. M. Hale, R. Gillespie, S. Gupta, B. Tuk, and N. H. G. Holford. Clinical trial simulation as a tool for increased drug development efficiency. Appl. Clin. Trials 5:35–40 (1996).

    Google Scholar 

  6. D. W. Cockroft and M. H. Gault. Prediction of creatinine clearance from serum creatinine. Nephron 16:31–41 (1976).

    Article  Google Scholar 

  7. J. W. Mandema, D. Verotta, and L. B. Sheiner. Building population pharmacokinetic-pharmacodynamic models. I. Models for covariate effects. J. Pharmacokin. Biopharm. 20:511–528 (1992).

    Article  CAS  Google Scholar 

  8. R. Bruno, N. Vivier, G. Montay, A. Le Liboux, L. K. Powe, J.-C. Delumeau, and G. R. Rhodes. Population pharmacokinetics of riluzole in patients with amyotrophic lateral sclerosis. Clin. Pharmacol. Ther. 62:518–526 (1997).

    Article  CAS  PubMed  Google Scholar 

  9. V. Schmith, J. Fiedler-Kelly, M. Abou-Donia, C. S. Huffman, and T. H. Grasela. Population pharmacodynamics of doxacurium. Clin. Pharmacol. Ther. 52:528–536 (1992).

    Article  CAS  PubMed  Google Scholar 

  10. P. Burtin, E. V. Jacz-Aigrain, P. Girard, R. Lenclen, J-F. Magny, P. Betremeiux, C. Tehiry, L. Desplanques, and P. Mussat. Population pharmacokinetics of midazolam in neonates. Clin. Pharmacol. Ther. 56:615–625 (1994).

    Article  CAS  PubMed  Google Scholar 

  11. M. O. Karlsson and L. B. Sheiner. The importance of modelling interoccasion variability in population pharmacokinetic analyses. J. Pharmacokin. Biopharm. 21:735–750 (1993).

    Article  CAS  Google Scholar 

  12. A. Mallet, F. Mentré, J. Gilles, et al. Handling covariates in population pharmacokinetics, with application to Gentamicin. Biomed. Meas. Inform. Contr. 2:138–146 (1988).

    Google Scholar 

  13. S. L. Beal and L. B. Sheiner (eds.). NONMEM Users Guides, NONMEM project group, University of California, San Fransisco, CA, 1992.

    Google Scholar 

  14. G. A. F. Seber and C. J. Wild. Nonlinear Regression, Wiley, New York, 1989.

    Book  Google Scholar 

  15. M. O. Karlsson, S. L. Beal, and L. B. Sheiner. Three new residual error models for population PK/PD analyses. J. Pharmacokin. Biopharm. 23:651–672 (1995).

    Article  CAS  Google Scholar 

  16. M. Davidian and D. M. Giltinan. Some general estimation methods for nonlinear mixed-effects models. J. Biopharm. Stat. 3:23–55 (1993).

    Article  CAS  PubMed  Google Scholar 

  17. D. Ruppert, N. Cressie, and R. J. Carroll. A transformation/weighting model estimating Michaelis-Menten parameters. Biometrics 45:637–656 (1989).

    Article  Google Scholar 

  18. A. Mallet. A maximum likelihood estimation method for random coefficient regression models. Biometrika 73:645–656 (1986).

    Article  Google Scholar 

  19. E. N. Jonsson, Janet R. Wade, G. Almqvist, and M. O. Karlsson. Discrimination between rival dosing histories. Pharm. Res. 14:984–991 (1997).

    Article  CAS  PubMed  Google Scholar 

  20. MathSoft. S-PLUS User's Manual, Version 3.4, Data Analysis Products Division, MathSoft, Seattle, 1997.

    Google Scholar 

  21. M. K. Al-Banna, A. W. Kelman, and B. Whiting. Experimental design and efficient parameter estimation in population pharmacokinetics. J. Pharmacokin. Biopharm. 18:347–360 (1990).

    Article  CAS  Google Scholar 

  22. E. I. Ette, C. A. Howie, A. W. Kelman, and B. Whiting. Experimental design and efficient parameter estimation in preclinical pharmacokinetic studies. Pharm. Res. 12:729–737 (1995).

    Article  CAS  PubMed  Google Scholar 

  23. L. B. Sheiner and S. L. Beal. Evaluation of methods for estimating population pharmacokinetic parameters. I. Michaelis-Menten model: Routine clinical pharmacokinetic data. J. Pharmacokin. Biopharm. 8:553–571 (1980).

    Article  CAS  Google Scholar 

  24. L. B. Sheiner and S. L. Beal. Evaluation of methods for estimating population pharmacokinetic parameters II. Biexponential model; experimental pharmacokinetic data. J. Pharmacokin. Biopharm. 9 (1981).

  25. L. B. Sheiner and S. L. Beal. Evaluation of methods for estimating population pharmacokinetic parameters: III. Monoexponential model; routine clinical pharmacokinetic data. J. Pharmacokin. Biopharm. 11:303–319 (1983).

    Article  CAS  Google Scholar 

  26. J. E. Bennet and J. C. Wakefield. A comparison of a Bayesian population method with two methods as implemented in commercially available software. J. Pharmacokin. Biopharm. 24:403–432 (1996).

    Article  Google Scholar 

  27. P. Girard, L. B. Sheiner, H. Kastrissios, and T. F. Blaschke. Do we need full compliance data for population pharmacokinetic analysis. J. Pharmacokin. Biopharm. 24:265–282 (1996).

    Article  CAS  Google Scholar 

  28. N. Wang and M. Davidian. A note on covariate measurement error in nonlinear mixed effects models. Biometrika 83:801–812 (1996).

    Article  Google Scholar 

  29. N. Draper and H. Smith. Applied regression analysis, 2nd ed., Wiley, 1981, pp. 122–125.

  30. J. W. Mandema. Population pharmacokinetics and pharmacodynamics. In P. G. Welling and F. L. S. Tse (eds.), Pharmacokinetics, Marcel Dekker, New York, 1995.

    Google Scholar 

  31. D. Trenk, F. Wagner, E. Jahnchen, and V. Plantiz. Pharmacokinetics of moxonidine after single and repeated doses in healthy volunteers. J. Clin. Pharmacol. 27:988–993 (1987).

    Article  CAS  PubMed  Google Scholar 

  32. W. S. Cleveland, Visualizing Data, Hobart Press, Summit, NJ, 1993.

    Google Scholar 

  33. J. W. Mandema, D. Verotta, and L. B. Sheiner. Advanced Methods of Pharmacokinetic and Pharmacodynamic Systems Analysis, Vol. 2, Chap. “Building population pharmacokinetic-pharmacodynamic models,” Plenum Press, 1993.

  34. E. J. Antal, T. H. Grasela, and R. B. Smith. An evaluation of population pharmacokinetics in therapeutic trials. Part III. Prospective data collection versus retrospective data assembly. Clin. Pharmacol. Ther. 46:552–559 (1989).

    Article  CAS  PubMed  Google Scholar 

  35. S. L. Beal. Population pharmacokinetic data and parameter estimation based on their first two statistical moments. Drug Metab. Rev. 15(1&2):173–193 (1984).

    Article  CAS  PubMed  Google Scholar 

  36. J. R. Wade, S. L. Beal, and N. C. Sambol. Interaction between structural, statistical and covariate models in population pharmacokinetic analysis. J. Pharmacokin. Biopharm. 22:165–177 (1994).

    Article  CAS  Google Scholar 

  37. E. I. Ette and T. M. Ludden. Population pharmacokinetic modelling: The importance of informative graphics. Pharm. Res. 12:1845–1855, (1995).

    Article  CAS  PubMed  Google Scholar 

  38. N. Taright, F. Mentré, and A. Mallet. Non-stationarity of kinetic parameters in multi-occasion designs. PAGE conference, Glasgow, June 1997. (Abstract on internet address http://www.gla.ac.uk/Acad/MedTer/Page97).

  39. K. E. Fattinger and D. Verotta. A nonparametric subject-specific population method for deconvolution: II. External validation. J. Pharmacokin. Biopharm. 23:611–634 (1995).

    Article  CAS  Google Scholar 

  40. M. O. Karlsson, R. E. Port, M. J. Ratain, and L. B. Sheiner. A population-model for the leukopenic effect of etoposide. Clin. Pharmacol. Ther. 57:325–334 (1995).

    Article  PubMed  Google Scholar 

  41. L. Aarons, L. P. Balant, F. Mentré, et al. Practical experience and issues in designing and performing population pharmacokinetic/pharmacodynamic studies. Eur. J. Clin. Pharmacol. 49:251–254 (1996).

    Article  CAS  PubMed  Google Scholar 

  42. W. Kirch, H. J. Hutt, and V. Plantiz. The influence of renal function on clinical pharmacokinetics of moxonidine. Clin. Pharmacokin. 15:245–253 (1988).

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. Department of Pharmacy, Uppsala University, Box 580, S-751 23, Uppsala, Sweden

    Mats O. Karlsson & E. Niclas Jonsson

  2. Eli Lilly & Co, Indianapolis, Indiana

    Curtis G. Wiltse

  3. The Swedish Medical Products Agency, Uppsala, Sweden

    Janet R. Wade

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  1. Mats O. Karlsson
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  2. E. Niclas Jonsson
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  3. Curtis G. Wiltse
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  4. Janet R. Wade
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Karlsson, M.O., Jonsson, E.N., Wiltse, C.G. et al. Assumption Testing in Population Pharmacokinetic Models: Illustrated with an Analysis of Moxonidine Data from Congestive Heart Failure Patients. J Pharmacokinet Pharmacodyn 26, 207–246 (1998). https://doi.org/10.1023/A:1020561807903

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