Abstract
The effect of extrapolated area (%AUCextrap) on estimating mean AUCinf in a simulated single-dose clinical trial was examined. Concentration–time (C–t) profiles from 12 to 36 subjects for 1- and 2-compartment models after single dose administration were simulated with increasing right-tailed censoring. Each subject’s %AUCextrap and AUCinf was calculated using eight different methods, including noncompartmental analysis (NCA), population-based methods, and maximum likelihood (ML) accounting for censoring. Each method’s geometric mean AUCinf and percent relative error (PRE) from the true AUCinf was calculated. This was repeated 100 times and the mean PRE (MPRE) was calculated. Mean %AUCextrap ranged from 1 to ~30 % for the 1-compartment and 2 to 32 % for the 2-compartment model at the lowest and highest degree of censoring, respectively. NCA methods using all subjects to estimate the population mean AUCinf had similar or less bias (within ± 20 %) than when those subjects with >20 % %AUCextrap were removed. Using Cpred compared to Clast in the calculation of individual AUCinf resulted in no performance improvement. Linear mixed effects models to estimate λz and ML methods accounting for censoring resulted in either no improvement or increased bias when censoring was high. Population pharmacokinetic method bias was dependent on the nature of the C–t profile. When the C–t profile declined biphasically, population models had higher bias than NCA methods but were superior when the C–t profile decline in a log-linear manner. It is recommended that subjects with high %AUCextrap should not be removed from the estimation of mean AUCinf in NCA analyses.
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The author would like to thank Yaning Wang for his very thoughtful and helpful comments on this manuscript.
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Supplementary Fig. 1
Representative concentration–time profiles on a log-linear scale for the 5 pharmacokinetic models studied. Red line is the theoretical population mean concentration–time profile. Gray lines are spaghetti plots of 10 subject’s observed concentrations with random measurement error added to the true concentration. Black line is the LLOQ. Observed concentrations below the LLOQ were censored. (JPEG 82 kb)
Supplemental Fig. 2
Representative concentration–time profiles on a linear scale for the 5 pharmacokinetic models studied. Red line is the theoretical population mean concentration–time profile. Gray lines are spaghetti plots of 10 subject’s observed concentrations with random measurement error added to the true concentration. Observed concentrations below the LLOQ were censored. Since a reference line for 0 LLOQ cannot be shown on a log-linear scale, the reference line was rescaled to the minimum axis value of 0.1. (JPEG 77 kb)
Supplemental Fig. 3
Plot of MPRE as function of LLOQ and number of subjects for the 1-compartment model with additive plus proportional residual error. Each marker represents the mean of 100 simulation replications. See Table 2 for marker definitions. Blue band is the approximate range where the mean %AUCextrap was <20 %. (JPEG 44 kb)
Supplemental Fig. 4
Plot of MPRE as function of LLOQ and number of subjects for the 2-compartment model with additive plus proportional residual error. Each marker represents the mean of 100 simulation replications. See Table 2 for marker definitions. Blue band is the approximate range where the mean %AUCextrap was <20 %. (JPEG 46 kb)
Supplemental Fig. 5
Plot of MPRE as function of LLOQ and number of subjects for the 1-compartment model with beta(0.5, 0.5) residual error. Each marker represents the mean of 100 simulation replications. See Table 2 for marker definitions. Blue band is the approximate range where the mean %AUCextrap was <20 %. (JPEG 46 kb)
Supplemental Fig. 6
Representative concentration–time profiles for Simulation 3 where Clast was contaminated by uniform random error to ensure that Clast > Cprior. A total of 24 subjects were simulated. Subjects with contaminated Clast values are shown in red. Data shown from 2 to 8 time units post post-dose to highlight the effect of contamination. (JPEG 63 kb)
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Bonate, P.L. Simulation studies on the estimation of total area under the curve in the presence of right-tailed censoring. J Pharmacokinet Pharmacodyn 42, 19–32 (2015). https://doi.org/10.1007/s10928-014-9395-8
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DOI: https://doi.org/10.1007/s10928-014-9395-8