Improved transformation of morphometric measurements for a priori parameter estimation in a physiologically-based pharmacokinetic model of ethanol

Prescription of the brain's time course of exposure to experimentally administered ethanol can be achieved with intravenous infusion profiles computed from a physiologically-based pharmacokinetic (PBPK) model of alcohol distribution and elimination. Previous parameter estimation employed transformations of an individual's age, height, weight and gender inferred from the literature, with modeling errors overcome with real-time, intermittent feedback. Current research applications, such as ethanol exposures administered during fMRI scanning, require open-loop infusions, thus improved transformation of morphometric measurements.Records of human breath alcohol concentration (BrAC) clamp experiments were analyzed. Optimal, unique PBPK parameters of a model of the distribution and elimination of ethanol were determined for each record and found to be in concordance with parameter values published by other investigators. A linear transformation between the readily measurable physical characteristics or morphometrics, including gender, age, height, weight, and TBW estimates, and the model parameters were then determined in a least squares sense according to the formula θ = F(x) = Fmx where x = (age height weight TBW)T ∈ R4 and θ = (RC VP VB mmax kAT)T ∈ R5.The transformation was then evaluated with several parameter prediction performance measures. A substantial improvement in all error statistics, in relation to an earlier affine transformation that used only body weight as the relevant morphometric was obtained. Deviation from the measured response was reduced from 27 to 20%. Error in parameter estimation was reduced from 109 to 38%. Percent alcohol provided in error was reduced from 46 to 28%. Error in infusion profile estimation was reduced from 55 to 33%.The algorithm described, which optimizes individual pharmacokinetic parameter values and then subsequent extension to a priori prediction, while not unique, can be readily be adapted to other molecules and pharmacokinetic models. This includes those used for distinct purposes, such as automated control of anesthetic agents.