Interval designs have recently attracted enormous attention due to their simplicity and desirable properties. We develop a Bayesian optimal interval design for dose finding in drug-combination trials. To determine the next dose combination based on the cumulative data, we propose an allocation rule by maximizing the posterior probability that the toxicity rate of the next dose falls inside a prespecified probability interval. The entire dose-finding procedure is nonparametric (model-free), which is thus robust and also does not require the typical "nonparametric" prephase used in model-based designs for drug-combination trials. The proposed two-dimensional interval design enjoys convergence properties for large samples. We conduct simulation studies to demonstrate the finite-sample performance of the proposed method under various scenarios and further make a modication to estimate toxicity contours by parallel dose-finding paths. Simulation results show that on average the performance of the proposed design is comparable with model-based designs, but it is much easier to implement.

Late-onset toxicity is common for novel molecularly targeted agents and immunotherapy. It causes major logistic difficulty for existing adaptive phase I trial designs, which require the observance of toxicity early enough to apply dose-escalation rules for new patients. The same logistic difficulty arises when the accrual is rapid. We propose the time-to-event Bayesian optimal interval (TITE-BOIN) design to accelerate phase I trials by allowing for real-time dose assignment decisions for new patients while some enrolled patients' toxicity data are still pending. Similar to the rolling six design, the TITE-BOIN dose-escalation/deescalation rule can be tabulated before the trial begins, making it transparent and simple to implement, but is more flexible in choosing the target dose-limiting toxicity (DLT) rate and has higher accuracy to identify the MTD. Compared with the more complicated model-based time-to-event continuous reassessment method (TITE-CRM), the TITE-BOIN has comparable accuracy to identify the MTD but is simpler to implement with substantially better overdose control. As the TITE-CRM is more aggressive in dose escalation, it is less likely to underdose patients. When there are no pending data, the TITE-BOIN seamlessly reduces to the BOIN design. Numerical studies show that the TITE-BOIN design supports continuous accrual without sacrificing patient safety or the accuracy of identifying the MTD, and therefore has great potential to accelerate early-phase drug development. .

PURPOSE For immunotherapy, such as checkpoint inhibitors and chimeric antigen receptor T-cell therapy, where the efficacy does not necessarily increase with the dose, the maximum tolerated dose may not be the optimal dose for treating patients. For these novel therapies, the objective of dose-finding trials is to identify the optimal biologic dose (OBD) that optimizes patients’ risk-benefit trade-off. METHODS We propose a simple and flexible Bayesian optimal interval phase I/II (BOIN12) trial design to find the OBD that optimizes the risk-benefit trade-off. The BOIN12 design makes the decision of dose escalation and de-escalation by simultaneously taking account of efficacy and toxicity and adaptively allocates patients to the dose that optimizes the toxicity-efficacy trade-off. We performed simulation studies to evaluate the performance of the BOIN12 design. RESULTS Compared with existing phase I/II dose-finding designs, the BOIN12 design is simpler to implement, has higher accuracy to identify the OBD, and allocates more patients to the OBD. One of the most appealing features of the BOIN12 design is that its adaptation rule can be pretabulated and included in the protocol. During the trial conduct, clinicians can simply look up the decision table to allocate patients to a dose without complicated computation. CONCLUSION The BOIN12 design is simple to implement and yields desirable operating characteristics. It overcomes the computational and implementation complexity that plagues existing Bayesian phase I/II dose-finding designs and provides a useful design to optimize the dose of immunotherapy and targeted therapy. User-friendly software is freely available to facilitate the application of the BOIN12 design.

This study investigated the efficacy and safety of azacitidine maintenance in the posttransplant setting based on the encouraging phase 1/2 reports for azacitidine maintenance in patients with acute myeloid leukemia/myelodysplastic syndrome (AML/MDS). Between 2009 and 2017, a total of 187 patients aged 18 to 75 years were entered into a randomized controlled study of posttransplant azacitidine if they were in complete remission. Patients randomized to the treatment arm (n = 93) were scheduled to receive azacitidine, given as 32 mg/m2 per day subcutaneously for 5 days every 28 days for 12 cycles. The control arm (n = 94) had no intervention. Eighty-seven of the 93 patients started azacitidine maintenance. The median number of cycles received was 4; a total of 29 patients relapsed on study, and 23 patients withdrew from the study due to toxicity, patient’s preference, or logistical reasons. Median relapse-free survival (RFS) was 2.07 years in the azacitidine group vs 1.28 years in the control group (P = .19). There was also no significant difference for overall survival, with a median of 2.52 years vs 3.56 years in the azacitidine and control groups (P = .43), respectively. Cox regression analysis revealed no improvement in RFS or overall survival with the use of azacitidine as maintenance compared with the control group (hazard ratios of 0.86 [95% confidence interval, 0.59-1.3; P = .43] and 0.84 [95% confidence interval, 0.55-1.29; P = .43]). This randomized trial with azacitidine maintenance showed that a prospective trial in the posttransplant setting was feasible and safe but challenging. Although RFS was comparable between the 2 arms, we believe the strategy of maintenance therapy merits further study with a goal to reduce the risk of relapse in patients with AML/MDS. This trial was registered at www.clinicaltrials.gov as #NCT00887068.