Background Recurrence is a major cause of treatment failure after allogeneic transplantation for AML and MDS, and treatment options are very limited. Azacitidine is a DNA methyltransferase inhibitor with activity in myeloid disease. We hypothesized that low-dose azacitidine administered after transplant would reduce relapse rates, and conducted a study to determine a safe dose/schedule combination. Methods Forty-five high-risk patients were treated. Median age was 60 years; median number of comorbidities was three; 67% were not in remission. Using a Bayesian adaptive method to determine the best dose/schedule combination based on time to toxicity, we investigated combinations of five daily azacitidine doses: 8, 16, 24, 32 and 40 mg/m2, and four schedules: 1, 2, 3 or 4 cycles, each with 5 days of drug and 25 days of rest. Cycle 1 started on day +40. Results Reversible thrombocytopenia was the dose-limiting toxicity. The optimal combination was 32 mg/m2 given for 4 cycles. Median follow-up is 20.5 months. One-year event-free and overall survival were 58% and 77%, justifying further studies to estimate long-term clinical benefit. No dose significantly affected DNA global methylation. Conclusions Azacitidine at 32 mg/m2 given for 5 days is safe and can be administered after allogeneic transplant for at least 4 cycles to heavily pre-treated AML/MDS patients. Our trial also suggested that this treatment may prolong event-free and overall survival, and that more cycles may be associated with greater benefit.

No abstract

An outcome-adaptive Bayesian design is proposed for choosing the optimal dose pair of a chemotherapeutic agent and a biologic agent used in combination in a phase I/II clinical trial. Patient outcome is characterized as a vector of two ordinal variables accounting for toxicity and treatment efficacy. A generalization of the Aranda-Ordaz model (1983, Biometrika 68, 357–363) is used for the marginal outcome probabilities as functions of dose pair, and a Gaussian copula is assumed to obtain joint distributions. Numerical utilities of all elementary patient outcomes, allowing the possibility that efficacy is inevaluable due to severe toxicity, are obtained using an elicitation method aimed to establish consensus among the physicians planning the trial. For each successive patient cohort, a dose pair is chosen to maximize the posterior mean utility. The method is illustrated by a trial in bladder cancer, including simulation studies of the method’s sensitivity to prior parameters, the numerical utilities, correlation between the outcomes, sample size, cohort size and starting dose pair.

Traditionally, phase I clinical trial designs determine a maximum tolerated dose of an experimental cytotoxic agent based on a fixed schedule, usually one course consisting of multiple administrations, while varying the dose per administration between patients. However, in actual medical practice patients often receive several courses of treatment, and some patients may receive one or more dose reductions due to low-grade (non-dose limiting) toxicity in previous courses. As a result, the overall risk of toxicity for each patient is a function of both the schedule and the dose used at each adminstration. We propose a new paradigm for Phase I clinical trials that allows both the dose per administration and the schedule to vary, making treatment two-dimensional. We provide an outcome-adaptive Bayesian design that simultaneously optimizes both dose and schedule in terms of the overall risk of toxicity, based on time-to-toxicity outcomes. The method is illustrated with a trial of an agent hypothesized to prolong cancer remission after allogeneic bone marrow transplantation, and a simulation study in the context of this trial is presented. As a result, the overall risk of toxicity for each patient is a function of both the schedule and the dose used at each adminstration. We propose a new paradigm for Phase I clinical trials that allows both the dose per administration and the schedule to vary, making treatment two-dimensional. We provide an outcome-adaptive Bayesian design that simultaneously optimizes both dose and schedule in terms * email: tombraun@umich.edu; phone: 734-936-9844; fax: 734-763-2215 1Hosted by The Berkeley Electronic Press of the overall risk of toxicity, based on time-to-toxicity outcomes. The method is illustrated with a trial of an agent hypothesized to prolong cancer remission after allogeneic bone marrow transplantation, and a simulation study in the context of this trial is presented.