Efficient sampling strategies for forecasting pharmacokinetic parameters of irinotecan (CPT-11): implication for area under the concentration-time curve monitoring.
A linear two-compartment Bayesian pharmacokinetic model was developed using a standard two-stage population method for the novel anti-cancer agent CPT-11 from 11 adult patients with refractory cancer. The accuracy and efficiency of this Bayesian model for estimating pharmacokinetic parameters including the area under the concentration-time curve (AUC) was then evaluated using two different sampling strategies in a new study cohort of 13 patients with cancer. Sampling strategies included either one, two, or three nonsteady-state feedback levels determined empirically and from optimal sampling theory (D-optimality). All 24 patients in this study received CPT-11 (60 mg/m2) as a 90-min infusion. Pharmacokinetic parameters derived from the Bayesian model combined with these limited sampling strategies were compared with those parameters obtained from the full sample data sets (n = 10) analyzed by weighted nonlinear least squares regression (reference method). The least-bias and most precise sampling times for estimating AUC were 3.5; 3.5 and 9.5; and 0.5, 3.5, and 9.5 h, respectively. At these times, only marginal improvement in precision of the AUC estimate was observed using two versus three samples. However, the precision of the estimate of clearance was not improved using two versus three samples. The sampling times derived from optimal sampling theory were 0.25, 3.5, 8.5, and 24 h and correlated closely to the actual and best empirical sampling times of 0.5, 3.5, 9.5, and 24 h. These results strongly suggest that Bayesian estimation combined with only two optimally timed samples accurately predicts the AUC of CPT-11 and should be useful for implementing adaptive control dosing for monitoring CPT-11 systemic exposure in patients with cancer.