Bayesian Dose Individualization in Anticancer Transplant Therapy

• Activity Number: 211
• Type: Contributed
• Date/Time: Tuesday, August 5, 2003 : 8:30 AM to 10:20 AM
• Sponsor: Biopharmaceutical Section
• Author(s): Gary L. Rosner*+ and Feng Tang and Peter Mueller
• Companies: University of Texas M.D. Anderson Cancer Center and University of Texas M.D. Anderson Cancer Center and University of Texas M.D. Anderson Cancer Center
• Keywords: Bayesian nonparametrics ; hierarchical models ; optimization ; population pharmacokinetics

Abstract

We discuss predicting the optimal dose for a leukemia patient undergoing high doses of chemotherapy followed by bone marrow transplantation. The optimal dose minimizes the expected loss, where the loss is incurred when the area under the concentration-time curve (AUC), a measure of systemic exposure, is outside the limits of a target range. We have historical pharmacokinetic (PK) data from leukemia patients who underwent similar high-dose chemotherapy. A subsequent study collected PK data on patients receiving a fixed low dose and a nonindividualized high-dose of the drug. In the third study, patients will receive the same low dose as in the second study. We fit a PK model to the drug concentrations measured after administration of the test dose to infer patient-specific parameters in the PK model. We determine the optimal dose for this patient by averaging the loss function with respect to the predictive distribution for the patient's AUC with high-dose therapy as a function of dose. We use Bayesian nonparametric models and combine information across studies and patients within studies by hierarchical modeling, borrowing strength to improve the precision of the prediction.