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For full versions of posted research articles readers are encouraged to email requests for "electronic reprints" (text file, PDF files, FAX copies) to the corresponding or lead author, who is highlighted in the posting.
___________________________________________
Wednesday, January 9, 2013
Population pharmacokinetics of multiple alcohol intake in humans
The objective of this study was to determine population-based pharmacokinetics parameters for ethanol following multiple intake and to identify the factors influencing the pharmacokinetics.
Three different solutions of alcoholic liquor (ethanol 55.39 ± 0.45 g) with different dissolved oxygen concentrations were administered, and blood alcohol concentration was determined in 59 healthy subjects using a breath analyzer. Samples (n = 2955) were collected at various time points. Population pharmacokinetic modeling was performed to describe the pharmacokinetics of ethanol. The influence of individuals' demography and dissolved oxygen concentration was investigated, and Visual Predictive Check and bootstrapping were conducted for internal evaluation. The developed model was used to perform simulations to visualize the effects of covariates on individuals. A one-compartment model with Michaelis–Menten elimination kinetics described the multiple ethanol intake data.
Population pharmacokinetic estimates of Vmax and Km were 3.256 mmol min−1 and 0.8183 mmol L−1, respectively. Vd/F was estimated to be 77.0 L, and Ka was 0.0767 min−1. Body weight, age, and the dissolved oxygen concentration were confirmed to be significant covariates. The mean estimates from the developed population pharmacokinetic model were very similar to those from 500 bootstrap samples, and Visual Predictive Check showed that approximately 94% of the observed data fit well within the 5th–95th percentile.
A one-compartment model with nonlinear elimination kinetics for multiple ethanol intake was developed and the significant covariates were determined. The robustness of the developed model was evaluated by bootstrap and Visual Predictive Check.
The final model and implanted covariates explained well the variability and underlying mechanism of ethanol PK.
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