We performed pharmacokinetic-pharmacodynamic (PK-PD) and simulation analyses to evaluate a standard amikacin dose of 15 mg/kg once daily 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 (steady-state unbound trough plasma concentration [fC min ] of <10 mg/liter)- and efficacy (free, unbound plasma concentration-to-MIC ratio [fCmax/MIC] of ≥8)-linked 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 PKARPD simulations and compare results with those of the probability approach. Using the probability approach, an amikacin dose of 60 mg/kg administered once daily is expected to achieve the target fC max /MIC in 80% of pediatric patients weighing 8 to 70 kg with a 97.5% probability, and almost all patients were predicted to have fC min of <10 mg/liter. 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 at 90 mg/kg, given in two divided doses (45 mg/kg twice a day), is expected to hit safety and efficacy targets and is associated with a lower rate of bacterial resistance. The disagreement between the two methods is due to the inability of the probability approach to predict 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.
Bibliographical noteFunding Information:
This work was supported in part by the Coin Foundation Fellowship, supporting Ph.D. students in experimental and clinical pharmacology conducting research in infectious diseases.