Abstract
Using population analysis, sparsely sampled Phase 3 clinical data can be utilized to determine the pharmacokinetic characteristics of the target population. Data arising from such studies are likely to be constrained to certain sampling windows, i.e., the visiting hours at the study clinic. When the sampling window is narrow compared to the half-life of the drug, the advantage of taking more than one sample is not obvious. Study designs with one or two samples per visit have been compared with respect to (i) precision and bias of the population parameter estimates, (ii) the ability to identify the underlying pharmacokinetic model, and (iii) the estimation of individual parameter values. The first point was assessed using simulated data while the latter two were studied using a real data set. Results show: (i) Parameter estimates are more biased and imprecise when only one sample is taken compared to when two samples are obtained, this is true irrespective of the time span between the two samples. (ii) Ability to identify a more complex model is increased if two samples are taken. Specifically, the variability between occasions can be quantified. (iii) Two-sample designs are generally better with respect to prediction of individual parameter values. Even minor changes to commonly employed study designs, in this case the addition of one sample at each study occasion, can improve quality and quantity of the information obtained.
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References
L. Aarons. Sparse data analysis.Eur. J. Drug Metab. Pharmacokin. 18:97–100 (1993).
L. B. Sheiner and S. L. Beal. Evaluation of methods for estimating population pharmacokinetic parameters III. Mono exponential model: Clinical pharmacokinetic data.J. Pharmacokin. Biopharm. 11:303–319 (1983).
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).
Y. Hashimoto and L. B. Sheiner. Designs for population pharmacodynamics: Value of pharmacokinetic data and population analysis.J. Pharmacokin. Biopharm. 19:333–353 (1991).
D. Z. D'Argenio. Optimal sampling times for pharmacokinetic experiments.J. Pharmacokin. Biopharm. 9:739–756 (1981).
F. Mentré. Optimal design in population analysis including Bayesian aspects. InPAGE, First meeting, Paris, France, 1993.
A. Grahnén, M. Hammarlund, and T. Lundqvist. Implications of intraindividual variability in bioavailability studies of furosemide.Eur. J. Clin. Pharmacol. 27:595–602 (1984).
M. O. Karlsson and A. Lindberg-Freijs. Comparison of methods to calculate cyclosporine: A bioavailability from consecutive oral and intravenous doses.J. Pharmacokin. Biopharm. 18:293–311 (1990).
M. Rowland. Intra-individual variability in pharmacokinetics. In D. D. Breimer (ed.), Towards Better Safety of Drugs and Pharmaceutical Products, Elsevier, North-Holland, 1980.
J. R. Wade, A. W. Kelman, C. A. Howie, and B. Whiting. Effect of misspecification of the absorption process on subsequent parameter estimation in population analysis.J. Pharmacokin. Biopharm. 21:209–222 (1993).
S. L. Beal and L. B. Sheiner.NONMEM Users Guide—Part VII. NONMEM Project Group, University of California, San Francisco, CA, 1992.
L. B. Sheiner and S. B. Beal. Some suggestions for measuring predictive performance.J. Pharmacokin. Biopharm. 9:503–512 (1981).
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).
M. O. Karlsson and L. B. Sheiner. The importance of modelling the inter-occasion variability in population pharmacokinetic studies.J. Pharmacokin. Biopharm. 21:735–750 (1993).
A. J. Boeckmann, L. B. Sheiner, and S. L. Beal.NONMEM Users Guide—Part V. NONMEM Project Group, University of California, San Francisco, CA, 1992.
P. O. Maitre, M. Buhrer, D. Thomason, and D. R. Stanski. A three-step approach combining Bayesian regression and NONMEM population analysis: Application to midazolam.J. Pharmacokin. Biopharm. 19:377–384 (1991).
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During the course of this study E. Niclas Jonsson was paid by a grant from ASTRA AB.
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Jonsson, E.N., Wade, J.R. & Karlsson, M.O. Comparison of some practical sampling strategies for population pharmacokinetic studies. Journal of Pharmacokinetics and Biopharmaceutics 24, 245–263 (1996). https://doi.org/10.1007/BF02353491
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DOI: https://doi.org/10.1007/BF02353491