Abstract
The purpose of this study was to evaluate the effects of population size, number of samples per individual, and level of interindividual variability (IIV) on the accuracy and precision of pharmacodynamic (PD) parameter estimates. Response data were simulated from concentration input data for an inhibitory sigmoid drug efficacy (Emax) model using Nonlinear Mixed Effect Modeling, version 5 (NONMEM). Seven designs were investigated using different concentration sampling windows ranging from 0 to 3 EC50 (EC50 is the drug concentration at 50% of the Emax) units. The response data were used to estimate the PD and variability parameters in NONMEM. The accuracy and precision of parameter estimates after 100 replications were assessed using the mean and SD of percent prediction error, respectively. Four samples per individual were sufficient to provide accurate and precise estimate of almost all of the PD and variability parameters, with 100 individuals and IIV of 30%. Reduction of sample size resulted in imprecise estimates of the variability parameters; however, the PD parameter estimates were still precise. At 45% IIV, designs with 5 samples per individual behaved better than those designs with 4 samples per individual. For a moderately variable drug with a high Hill coefficient, sampling from the 0.1 to 1,1 to 2,2 to 2.5, and 2.5 to 3 EC50 window is sufficient to estimate the parameters reliably in a PD study.
Keywords: pharmacodynamics, study design, sparse sampling
Full Text
The Full Text of this article is available as a PDF (272.1 KB).
References
- 1.Braat MC, Jonkers RE, Bel EH, Boxtel CJ. Quantification of theophylline-induced eosinopenia and hypokalaemia in healthy volunteers. Clin Pharmacokinet. 1992;22:231–237. doi: 10.2165/00003088-199222030-00005. [DOI] [PubMed] [Google Scholar]
- 2.White DB, Walawander CA, Tung Y, Grasela TH. An evaluation of point and interval estimates, in population pharmacokinetics using NONMEM analysis. J Pharmacokinet Biopharm. 1991;19:87–112. doi: 10.1007/BF01062194. [DOI] [PubMed] [Google Scholar]
- 3.Beal SL, Sheiner LB. NONMEM User's Guide Parts I–VIII. San Francisco: University of California, San Francisco; 1998. [Google Scholar]
- 4.Jonsson EN, Wade JR, Karlsson MO. Comparison of some practical sampling strategies for population pharmacokinetic studies. J Pharmacokinet Biopharm. 1996;24:245–263. doi: 10.1007/BF02353491. [DOI] [PubMed] [Google Scholar]
- 5.Sun H, Ette EI, Ludden TM. On the recording of sample times and parameter estimation from repeated measures pharmacokinetic data. J Pharmacokinet Biopharm. 1996;24:637–650. doi: 10.1007/BF02353484. [DOI] [PubMed] [Google Scholar]
- 6.Hashimoto Y, Sheiner LB. Designs for population pharmacodynamics: value of pharmacokinetic data and population analysis. J Pharmacokinet Biopharm. 1991;19:333–353. doi: 10.1007/BF03036255. [DOI] [PubMed] [Google Scholar]
- 7.Girgis S, Rosenbaum S. Optimum design for pharmacodynamics. Clin Pharmacol Ther. 2000;67:163–163. [Google Scholar]
- 8.Sheiner LB, Beal SL. Some suggestions for measuring predictive performance. J Pharmacokinet Biopharm. 1981;9:503–512. doi: 10.1007/BF01060893. [DOI] [PubMed] [Google Scholar]
- 9.al-Banna MK, Kelman AW, Whiting B. Experimental design and efficient parameter estimation in population pharmacokinetics. J Pharmacokinet Biopharm. 1990;18:347–360. doi: 10.1007/BF01062273. [DOI] [PubMed] [Google Scholar]
- 10.Ette EI, Howie CA, Kelman AW, Whiting B. Experimental design and efficient parameter estimation in preclinical pharmacokinetic studies. Pharm Res. 1995;12:729–737. doi: 10.1023/A:1016267811074. [DOI] [PubMed] [Google Scholar]