Validation of techniques for the prediction of carboplatin exposure: Application of Bayesian methods

Objective: Several methods have been developed for the prediction of carbop latin exposure to facilitate pharmacokinetic guided dosing. The aim of this study was to develop and validate sparse data Bayesian methods for the est imation of carboplatin exposure and to validate other commonly applied tech niques, such as the Chatelut formula, the Sorensen limited sampling model, and the Calvert formula, in which glomerular filtration rate was estimated with the Cockcroft-Gault, the Jelliffe, and the recently proposed Wright fo rmulas, Methods: Complete concentration-time curves were available for a total of 4 3 patients (45 courses) receiving carboplatin (265 or 400 mg/m(2)/day) in a 1-hour infusion for 4 consecutive days in combination with thiotepa and cy clophosphamide, A population two-compartment model was developed on an inde x set of 12 courses. The other 33 courses served as validation set. Bayesia n estimates were generated with the population parameters by use of either one or two randomly timed samples or two samples at optimal time points det ermined with the D-optimality theory. Results: The Bayesian methods provided an accurate and precise prediction o f the area under the concentration-time curve (bias <4% and precision less than or equal to 18%). The other formulas (Sorensen model, Chatelut, and Ca lvert with Jelliffe, Cockcroft-Gault, and Wright) resulted in a precision > 18%, whereas the Jelliffe formula and the Sorensen model resulted in a bias >12%. Conclusion: The applicability of a Bayesian method for the prediction of th e carboplatin exposure by use of one or two samples without the necessity f or exact timing of infusion duration and sampling was demonstrated. The Bay esian method may be very instrumental to execute pharmacokinetic guided dos ing for carboplatin.