• Pieter Colin, Douglas J Eleveld, Johannes P van den Berg, Vereecke Hugo E M HEM Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, Postbus 30 001, Groningen, 9700 RB, The Ne, Struys Michel M R F MMRF Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, Postbus 30 001, Groningen, 9700 RB, The, Gustav Schelling, Christian C Apfel, and Cyrill Hornuss.
• Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, Postbus 30 001, Groningen, 9700 RB, The Netherlands. P.J.Colin@umcg.nl.
• Clin Pharmacokinet. 2016 Jul 1; 55 (7): 849-859.

IntroductionMonitoring of drug concentrations in breathing gas is routinely being used to individualize drug dosing for the inhalation anesthetics. For intravenous anesthetics however, no decisive evidence in favor of breath concentration monitoring has been presented up until now. At the same time, questions remain with respect to the performance of currently used plasma pharmacokinetic models implemented in target-controlled infusion systems. In this study, we investigate whether breath monitoring of propofol could improve the predictive performance of currently applied, target-controlled infusion models.MethodsBased on data from a healthy volunteer study, we developed an addition to the current state-of-the-art pharmacokinetic model for propofol, to accommodate breath concentration measurements. The potential of using this pharmacokinetic (PK) model in a Bayesian forecasting setting was studied using a simulation study. Finally, by introducing bispectral index monitor (BIS) measurements and the accompanying BIS models into our PK model, we investigated the relationship between BIS and predicted breath concentrations.Results And DiscussionWe show that the current state-of-the-art pharmacokinetic model is easily extended to reliably describe propofol kinetics in exhaled breath. Furthermore, we show that the predictive performance of the a priori model is improved by Bayesian adaptation based on the measured breath concentrations, thereby allowing further treatment individualization and a more stringent control on the targeted plasma concentrations during general anesthesia. Finally, we demonstrated concordance between currently advocated BIS models, relying on predicted effect-site concentrations, and our new approach in which BIS measurements are derived from predicted breath concentrations.

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