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BACKGROUND Studies have shown repeatedly that ethanol pharmacokinetics are not linear, yet most researchers still determine ethanol elimination by linear, zero-order kinetics. The goals of the present work were to: (1) fit four nonlinear pharmacokinetic models to mean breath alcohol concentration (BrAC)-time data of 27 women and determine the best-fit model; (2) fit the determined best-fit model to individual BrAC data and estimate the pharmacokinetic parameters; and (3) compare the method of nonlinear model fitting with the classical zero-order elimination method and determine in which cases the classical approach is justified. METHODS Twenty-seven healthy white women ingested four drinks (total of 0.67 g x kg(-1)) of ethanol on two test days. Approximately 24 breath ethanol samples (for pharmacokinetic analyses) and one blood sample (for hormonal markers) were taken per day. Pharmacokinetic model evaluation was based on the coefficient of variation, the weighted residual sum of squares, and the sequence of the weighted residuals. Because hormonal changes across the menstrual cycle did not significantly influence ethanol pharmacokinetics, data from the two test days were pooled. RESULTS The best-fit model was a one-compartment open model with first-order absorption and sequential first-order elimination, followed by Michaelis-Menten elimination kinetics. Fitting this model to the individual BrAC data yielded mean ka = 0.062 hr(-1), Vd = 0.457 L x kg(-1), ke = 0.011 hr(-1), Vmax = 0.136 g x L(-1) x hr(-1), and Km = 0.096 g x L(-1). For the classical analyses, mean time to peak BrAC = 1.83 hr, disappearance rate = 0.179 g x L(-1) x hr(-1), and area under the blood ethanol-time curve (AUC) = 2.884 g x L(-1) x hr. Correlational analyses showed that more frequent drinkers eliminated ethanol significantly faster and reached significantly lower AUC than less frequent drinkers. CONCLUSIONS After multiple dose ingestion in white women, classical zero-order elimination analyses can be applied only to a limited portion of the descending BrAC-time curve. They seem justified and practical from 0.5 hr after peak BrAC until BrAC reaches 0.2 g x L(-1). To describe ethanol pharmacokinetics across the entire BrAC-time curve, however, sophisticated nonlinear model fitting is required.

This study investigated the influence of menstrual cycle and female sex steroid levels on ethanol pharmacokinetics. In a within-subjects design, 24 female volunteers each consumed 0.67 g kg -1 ethanol during the menstrual and luteal phases of their menstrual cycle. On each test day, we collected blood samples before ethanol administration to determine estradiol (E 2 ) and progesterone (P) levels and to confirm ovulation. We took 20 or more postdrink breath ethanol concentration readings and examined pharmacokinetic differences between the two phases, using classical pharmacokinetic measures, as well as Michaelis-Menten measures. Despite highly significant differences in measured E 2 as well as P levels on the 2 test days, and despite excluding subjects with anovulatory cycles from the analysis, there were no significant differences between menstrual and luteal phases for any of the pharmacokinetic variables. We found no correlation between E 2 or P levels and any of the pharmacokinetic measures. In summary, we found no evidence that the tested menstrual cycle phases or varying E 2 and progesterone levels significantly influence ethanol pharmacokinetics. Because previous studies about the topic have used few subjects and revealed controversial results, we consider our negative findings based on 24 subjects meaningful.