OBJECTIVE: To develop a population pharmacokinetic model of irinotecan and its major metabolites in children with cancer and to identify covariates that predict variability in disposition. METHODS: A population pharmacokinetic model was developed using plasma concentration data from 82 patients participating in a multicenter Pediatric Oncology Group (POG) single agent phase II clinical trial. Patients between 1 and 21 years of age with solid tumors refractory to standard therapy received irinotecan, 50 mg/m(2), as a 60-min intravenous infusion for 5 consecutive days every 3 weeks. Blood samples were collected and analyzed for irinotecan and three metabolites (SN-38, SN-38G, and APC). The population model was developed with NONMEM. Clearance and volume were scaled allometrically using corrected body weight. Exponential error models were used to describe the interindividual variance in pharmacokinetic parameters, and the residual error was described with a proportional model. Significant covariate effects were identified graphically using S-PLUS and were added to the base-model. The final model was evaluated by simulating data from two other POG trials. RESULTS: The best structural model for irinotecan and its metabolites consisted of six-compartments: two compartments for irinotecan and SN-38, and one each for APC and SN-38G. Age and bilirubin were found to be significant covariates affecting SN-38 clearance. SN-38 clearance was greater in patients less than 10 years of age and lower in patients with a total serum bilirubin >0.6 mg/dL. Simulations revealed that the model was able to predict drug and metabolite exposure (AUC) for patients receiving the same or similar doses (30-65 mg/m(2)) of irinotecan. CONCLUSIONS: This population model accurately describes the pharmacokinetics of irinotecan and its primary metabolites. The model, which includes age and bilirubin as covariate effects on SN-38 clearance, is the first population model to describe the pharmacokinetics of irinotecan and its major metabolites in children.
OBJECTIVE: To develop a population pharmacokinetic model of irinotecan and its major metabolites in children with cancer and to identify covariates that predict variability in disposition. METHODS: A population pharmacokinetic model was developed using plasma concentration data from 82 patients participating in a multicenter Pediatric Oncology Group (POG) single agent phase II clinical trial. Patients between 1 and 21 years of age with solid tumors refractory to standard therapy received irinotecan, 50 mg/m(2), as a 60-min intravenous infusion for 5 consecutive days every 3 weeks. Blood samples were collected and analyzed for irinotecan and three metabolites (SN-38, SN-38G, and APC). The population model was developed with NONMEM. Clearance and volume were scaled allometrically using corrected body weight. Exponential error models were used to describe the interindividual variance in pharmacokinetic parameters, and the residual error was described with a proportional model. Significant covariate effects were identified graphically using S-PLUS and were added to the base-model. The final model was evaluated by simulating data from two other POG trials. RESULTS: The best structural model for irinotecan and its metabolites consisted of six-compartments: two compartments for irinotecan and SN-38, and one each for APC and SN-38G. Age and bilirubin were found to be significant covariates affecting SN-38 clearance. SN-38 clearance was greater in patients less than 10 years of age and lower in patients with a total serum bilirubin >0.6 mg/dL. Simulations revealed that the model was able to predict drug and metabolite exposure (AUC) for patients receiving the same or similar doses (30-65 mg/m(2)) of irinotecan. CONCLUSIONS: This population model accurately describes the pharmacokinetics of irinotecan and its primary metabolites. The model, which includes age and bilirubin as covariate effects on SN-38 clearance, is the first population model to describe the pharmacokinetics of irinotecan and its major metabolites in children.
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