A sequential Bayesian algorithm for dose individualisation of carboplatin

Carboplatin is associated with significantly less nephrotoxicity and neurotoxicity than is cisplatin. The dose-limiting toxicity of carboplatin is myelotoxicity. A number of dosing methods have been described that allow a value for the area under the concentration-time curve to be targeted on the basis of the patient's renal function. Recently a formalised analysis of the pharmacodynamic response to carboplatin revealed a therapeutic window in which the response rate was maximal and toxicity, tolerable. Optimal therapy would result from targeting this window in the individual patient. The aim of this study was to develop a Bayesian dose-individualisation method for carboplatin. The method involved (1) development of a high-performance liquid chromatography (HPLC) method to measure serum concentrations of carboplatin; (2) a pharmacokinetic study in 12 women receiving carboplatin for ovarian cancer to estimate the population pharmacokinetic values for this group of patients; (3) development of population models to describe the concentration-time course of carboplatin in serum along with associated errors; and (4) development of an algorithm that uses a sequential Bayesian design, which enables estimation of future doses of carboplatin on the basis of feedback from serum concentrations. The results of each of the stages were (1) the coefficient of variation of the assay was 6.3% within day and 8.4% between days (r2 = 0.9993), and the limit of detection was 0.25 mg/l; (2) Patients' ages ranged from 49 to 68 years, their weights varied from 46 to 85 kg, and their glomerular filtration rate ranged from 3.2 to 7.4 l/h. A geometric mean clearance (Cl) of 6.8 L/h and a steady-state volume of distribution (Vss) of 221 were estimated, which are similar to previously published data; (3) and a two-compartment model best described the data. Two error models were developed, the first describing the error associated with the assay and the second, the error of the two-compartment model, i.e. error due to individual variation in pharmacokinetics and error due to model mis-specification. Finally, (4) the development of a sequential Bayesian dose-individualisation method for carboplatin is described. To our knowledge, this is the first sequential design that has been used for dose individualisation of chemotherapy. The program is specific for carboplatin and operates independently of commercially available Bayesian software. Doses predicted by this program are being tested prospectively against conventional dosing methods.