Evaluation of methods for estimating population pharmacokinetic parameters II. Biexponential model and experimental pharmacokinetic data
Rent the article at a discountRent now
* Final gross prices may vary according to local VAT.Get Access
Individual pharmacokinetic parameters quantify the pharmacokinetics of an individual, while population pharmacokinetic parameters quantify population mean kinetics, interindividual variability, and residual variability, including intraindividual variability and measurement error. Individual pharmacokinetics are estimated by fitting individual data to a pharmacokinetic model. Population pharmacokinetic parameters have been estimated either by fitting all individuals' data together as though there were no individual kinetic differences, the naive pooled data (NPD) approach, or by fitting each individuals' data separately and then combining the individual parameter estimates, the two stage (TS) approach. A third approach, NONMEM, takes a middle course between these. This study provides further evidence of NONMEM's validity by comparing, using simulation, the three approaches on three types of data sets corresponding to three typical types of pharmacokinetic studies. The estimates of population parameters provided by the NPD method are poorer than those provided by either of the other methods. The estimates provided by the TS method are adequate for mean values and for residual variability, but not for interindividual kinetic variability. NONMEM's estimates are as good as those of the TS method for mean parameters and for residual variability, and considerably better for interindividual variability. The latter estimates are still not acceptable in an absolute sense. This is probably due, not to an intrinsic fault of the method (as it is in the case of the TS approach), but to an insufficient number of individuals being studied.
- L. B. Sheiner and S. Beal. Evaluation of methods for estimating population pharmacokinetic parameters. I. Michaelis-Menten model; routine clinical pharmacokinetic data.J Pharmacokinet. Biopharm. 8: 553–571 (1980).
- C. M. Metzler, G. L. Elfring, and A. J. McEwen.A Users Manual for NONLIN and Associated Programs. The Upjohn Co., Kalamazoo, Mich., 1974.
- W. J. Dixon and F. J. Massey,Introduction to Statistical Analysis, 3rd ed., McGraw-Hill, New York, 1969, pp. 102–103.
- S. L. Beal. Adaptive M estimation with independent non-identically distributed data. Unpublished PhD dissertation, University of California, Los Angeles, 1974.
- S. L. Beal and L. B. Sheiner.NONMEM Users Guide, Part I. Technical Report, Division of Clinical Pharmacology, University of California, San Francisco, Calif., 1980.
- R. D. Milleret al. The pharmacokinetics ofd-Tubocurarine in man with and without renal failure.J. Pharmacol. Exp. Ther. 202:1–7 (1977).
- R. Williams. Ketoprofen bioavailability study #1, final report, Drug Studies Unit, University of California, San Francisco, Calif. (1979).
- G. Box and M. Muller. A note on the generation of random normal deviates.Ann. Math. Stat. 29:610–611 (1958).
- P. Lewis, A. Goodman, and J. Miller. A pseudorandom number generator for the system 360.IBM Systems J. 8:135–146 (1969).
- W. J. Dixon and F. J. Massey.Introduction to Statistical Analysis, 3rd ed., McGraw-Hill, New York, 1969, p. 341.
- W. J. Dixon and F. J. Massey.Introduction to Statistical Analysis, 3rd ed. McGraw-Hill, New York, 1969, p. 247.
- F. Vogel and A. G. Matulsky.Human Genetics. Springer-Verlag, New York, 1979, pp. 587–590.
- Evaluation of methods for estimating population pharmacokinetic parameters II. Biexponential model and experimental pharmacokinetic data
Journal of Pharmacokinetics and Biopharmaceutics
Volume 9, Issue 5 , pp 635-651
- Cover Date
- Print ISSN
- Online ISSN
- Kluwer Academic Publishers-Plenum Publishers
- Additional Links
- pharmacokinetic data analysis
- population pharmacokinetic parameters
- Industry Sectors