Construction of best confidence intervals for half-lives

Averaging pharmacokinetic parameter estimates from experimental studies: statistical theory and application

In most experimental pharmacokinetic studies, parameter estimates are computed separately for each subject, then averaged across subjects. Average estimators for ratios and functions of parameters are often of interest; examples include half-life and clearance. For these parameters, recommendations regarding averaging using the arithmetic versus the harmonic mean have been based on computer simulations.1-3 The goal in this paper was to demonstrate that these empirically generated results can be derived using approximations for the expected values of reciprocals and ratios. We first consider estimating the reciprocal of a parameter, and predict the earlier simulation results for half-life. We additionally predict results for clearance when computed as dose divided by area under the curve. Next we consider estimating the ratio of two parameters, and predict the earlier simulation results for clearance in a first-order exponential model. As a further example, we predict results for the mean residence time in noncompartmental analysis. These approximations provide a unifying approach that can be used to determine optimal summary estimators, without the need for extensive computer simulations.

Pharmacokinetics of enrofloxacin in fingerling rainbow trout (Oncorhynchus mykiss)

The pharmacokinetics of intravenously and orally administered enrofloxacin was determined in fingerling rainbow trout (Oncorhynchus mykiss). Doses of 5 or 10 mg enrofloxacin/kg body weight were administered intravenously to 26 fish for each dose and blood was sampled over a 60-h period at 15 degrees C. Two groups of fish were treated orally with 5, 10, or 50 mg/kg (80 fish/dose at each temperature) and held at 15 degrees C or 10 degrees C during the 60-h sampling period. Following intravenous administration, the serum concentration-time data of enrofloxacin in rainbow trout were best described by a two-compartment open model for both doses of 5 and 10 mg enrofloxacin/kg. The hybrid rate constants alpha and beta did not differ between doses. The distributional phase was rapid with a half-life of 6-7 min for both doses. Overall half-lives of elimination were 24.4 h (95% CI = 20.2-30.8) and 30.4 h (24.2-41.0), respectively, for the 5- and 10-mg/kg doses. A large Vd(area) was observed following dosing of either 5 or 10 mg enrofloxacin/kg,: 3.22 and 2.56 l/kg, respectively. Whole body clearance for 5 mg/kg was 92 ml/h.kg and 58 ml/h.kg at the 10-mg/kg dose. Following oral administration, the serum concentration-time data for enrofloxacin were best described as a one-compartment open model with first-order absorption and elimination. Apparent Ka over all doses at 10 degrees C averaged 62% less than apparent Ka at 15 degrees C. Estimates of the apparent t(1/2)e over both temperatures ranged from 29.5 h (18.4-73.4) to 56.3 h (38.3-106.6). Bioavailability averaged 42% over all doses at 15 degrees C and was decreased to an average of 25% at 10 degrees C. Peak serum concentrations appeared between 6 and 8 h following dosing. A dose of 5 mg/kg/day was estimated to provide average steady-state serum concentrations at 10 degrees C that are approximately 4.5 times the highest reported MIC values for Streptococcus spp., the fish pathogen least sensitive to enrofloxacin. Owing to the long apparent half-life of elimination of enrofloxacin in fingerling trout, it would take approximately 5 to 9 days to achieve these predicted steady-state serum concentrations; this estimate is important when considering the duration of therapy in clinical trials.

On the construction of shortest confidence intervals and Bayesian highest posterior density intervals

In this note it is argued that the principal characteristic of the confidence intervals proposed by Bartoszynski & Powers (1990) is not primarily the fact that they are of minimum length but that they are Bayesian highest posterior density intervals. A simple iterative process for determining the ends of the interval is presented.