Population Pharmacokinetic Model and Optimal Sampling Strategies for Micafungin in Critically Ill Patients Diagnosed with Invasive Candidiasis

Candida bloodstream infections are associated with high attributable mortality, where early initiation of adequate antifungal therapy is important to increase survival in critically ill patients. The exposure variability of micafungin, a first-line agent used for the treatment of invasive candidiasis, in critically ill patients is significant, potentially resulting in underexposure in a substantial portion of these patients. The objective of this study was to develop a population pharmacokinetic model including appropriate sampling strategies for assessing micafungin drug exposure in critically ill patients to support adequate area under the concentration-time curve (AUC) determination. A two-compartment pharmacokinetic model was developed using data from intensive care unit (ICU) patients (n = 19), with the following parameters: total body clearance (CL), volume of distribution of the central compartment (V1), inter-compartmental clearance (CL12), and volume of distribution of the peripheral compartment (V2). The final model was evaluated with bootstrap analysis and the goodness-of-fit plots for the population and individual predicted micafungin plasma concentrations. Optimal sampling strategies (with sampling every hour, 24 h per day) were developed with 1- and 2-point sampling schemes. Final model parameters (+/- SD) were: CL = 1.03 (0.37) (L/h/1.85 m(2)), V1 = 0.17 (0.07) (L/kg LBMc), CL12 = 1.80 (4.07) (L/h/1.85 m(2)), and V2 = 0.12 (0.06) (L/kg LBMc). Sampling strategies with acceptable accuracy and precision were developed to determine the micafungin AUC. The developed model with optimal sampling procedures provides the opportunity to achieve quick optimization of the micafungin exposure from a single blood sample using Bayesian software and may be helpful in guiding early dose decision-making.

Automated summary

This study developed a population pharmacokinetic model to assess micafungin drug exposure in critically ill patients, with optimal sampling strategies for determining AUC. The model provides the opportunity for quick optimization of micafungin exposure from a single blood sample using Bayesian software, which may assist in guiding early dose decision-making.