We performed PK-PD and simulation analyses to evaluate standard 15 mg/kg/day amikacin in children with cancer and to determine an optimal dosing strategy. A population pharmacokinetic model was developed from clinical data collected in 34 pediatric patients and used in a simulation study to predict the population probability of various dosing regimens to achieve accepted safety- (fCmin < 10 mg/L) and efficacy-linked (fCmax/MIC ≥ 8) targets. In addition, an adaptive resistance PD (ARPD) model of Pseudomonas aeruginosa was built based on literature time-kill curve data and linked to the PK model to perform PK-ARPD simulations and compare results with the probability approach. Using the probability approach, an amikacin dose of 60 mg/kg administered once daily is expected to achieve the target fCmax/MIC in 80% of pediatric patients weighing 8-70 kg with a 97.5% probability and almost all patients were predicted to have fCmin < 10 mg/L. However, PK-ARPD simulation predicted that 60 mg/kg/day is unlikely to suppress bacterial resistance with repeated dosing. Furthermore, PK-ARPD simulation suggested that amikacin 90 mg/kg given in two divided doses (45 mg/kg BID) are expected to hit safety and efficacy targets, and associated with a lower rate of bacterial resistance. The disagreement between the two methods is due to the inability of probability approach in predicting development of drug resistance with repeated dosing. This originates from the use of PK-PD indices based on the MIC that neglects measurement errors, ignores the time-course dynamic nature of bacterial growth and killing, and incorrectly assumes the MIC to be constant during treatment.
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