A comparison of limited sampling strategies for prediction of Ecteinascidin 743 clearance when administered as a 24-h infusion

Purpose: Ecteinascidin 743 (ET-743) is a novel, marine-derived anticancer agent currently under clinical development for the treatment of solid tumors. The aim of this study was to develop and validate limited sampling strategies for the prediction of ET-743 clearance in phase 11 studies, using two techniques: the stepwise linear regression approach and the Bayesian estimation approach. Methods: Data from a phase I dose-finding study were used with ET-743 administered as a 24-h infusion. Plasma concentration time data from 34 patients treated with 1200, 1500 or 1800 mug/m(2) ET-743 were randomly divided into an index data set, used for the development of the strategies, and a validation data set. With the linear regression approach, clearance (obtained by non-compartmental analysis) was correlated with the ratios of dose to the observed concentrations. For the Bayesian approach a three-compartment population pharmacokinetic model was developed; optimal time-points were selected using the D-optimality algorithm. The strategies were compared by assessment of their predictive performance of CL in the validation data set. Results: The linear regression method yielded a single-point sampling schedule with no significant bias and acceptable precision (-0.03% and 21%, respectively). With the Bayesian approach, a three-sample strategy was selected which resulted in less-accurate, but unbiased, predictions (bias 13%, precision 34%). Conclusions: Optimal sampling strategies were developed and validated for estimation of ET-743 clearance. Although the linear regression approach showed slightly better predictive performance, the Bayesian approach is preferred for the current phase II studies as it is more robust and flexible and allows the description of the full pharmacokinetic profile.