Comparison of an assumption-free Bayesian approach with Optimal Sampling Schedule to a maximum a posteriori Approach for Personalizing Cyclophosphamide Dosing.

Study Objective: Variable metabolism, dose-dependent efficacy, and a narrow therapeutic target of cyclophosphamide (CY) suggest that dosing based on individual pharmacokinetics (PK) will improve efficacy and minimize toxicity. Real-time individualized CY dose adjustment was previously explored using a maximum a posteriori (MAP) approach based on a five serum-PK sampling in patients with hematologic malignancy undergoing stem cell transplantation. The MAP approach resulted in an improved toxicity profile without sacrificing efficacy. However, extensive PK sampling is costly and not generally applicable in the clinic. We hypothesize that the assumption-free Bayesian approach (AFBA) can reduce sampling requirements, while improving the accuracy of results.

Design: Retrospective analysis of previously published CY PK data from 20 patients undergoing stem cell transplantation. In that study, Bayesian estimation based on the MAP approach of individual PK parameters was accomplished to predict individualized day-2 doses of CY. Based on these data, we used the AFBA to select the optimal sampling schedule and compare the projected probability of achieving the therapeutic end points.

Measurements And Main Results: By optimizing the sampling schedule with the AFBA, an effective individualized PK characterization can be obtained with only two blood draws at 4 and 16 hours after administration on day 1. The second-day doses selected with the AFBA were significantly different than the MAP approach and averaged 37% higher probability of attaining the therapeutic targets.

Conclusions: The AFBA, based on cutting-edge statistical and mathematical tools, allows an accurate individualized dosing of CY, with simplified PK sampling. This highly accessible approach holds great promise for improving efficacy, reducing toxicities, and lowering treatment costs.