Estimation of Cefuroxime Dosage Using Pharmacodynamic Targets, MIC Distributions, and Minimization of a Risk Function

Abstract

An approach for estimation of dosing strategies based on data-derived models and assessment of the risk associated with deviation from the treatment target is presented. The work is illustrated by establishing a dosing strategy to be used for a priori individualization on the basis of renal function for the antibiotic cefuroxime. Treatment involved exposing patients to concentrations above the minimum inhibitory concentration (MIC) for 50% of the dosing interval. The risk (penalty) function incorporated both deviations from the target and the use of excess amount of drug. Dosing strategies were estimated for a target population by minimizing the risk function. The population was characterized by a population pharmacokinetic model, and distributions of CLcr and body weight were reflective of the target group. The estimated dosing strategies were assessed by evaluating population distributions of (1) percentage of dosing interval with concentrations above MIC, (2) time of drug exposure below MIC, and (3) drug administered in excess to reach the target. These distributions were generated using wild-type MIC distributions for Escherichia coli and Streptococcus pneumoniae. The authors illustrate how benefits and risks of drug treatment can be weighed quantitatively in decision-based risk functions and subsequently used in the estimation of drug dosing.