Lin Ruitao, Zhou Yanhong, Yan Fangrong, Li Daniel, Yuan Ying
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX.
China Pharmaceutical University, Nanjing, People's Republic of China.
JCO Precis Oncol. 2020 Nov 16;4. doi: 10.1200/PO.20.00257. eCollection 2020.
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.
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.
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.
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.
对于免疫疗法，如检查点抑制剂和嵌合抗原受体T细胞疗法，其疗效不一定随剂量增加，最大耐受剂量可能并非治疗患者的最佳剂量。对于这些新型疗法，剂量探索试验的目标是确定能优化患者风险效益权衡的最佳生物学剂量（OBD）。
我们提出一种简单灵活的贝叶斯最优区间I/II期（BOIN12）试验设计，以找到能优化风险效益权衡的OBD。BOIN12设计通过同时考虑疗效和毒性来做出剂量递增和递减的决策，并适应性地将患者分配到能优化毒性 - 疗效权衡的剂量组。我们进行了模拟研究以评估BOIN12设计的性能。
与现有的I/II期剂量探索设计相比，BOIN12设计实施更简单，识别OBD的准确性更高，且将更多患者分配到OBD组。BOIN12设计最吸引人的特点之一是其适应性规则可以预先制成表格并包含在方案中。在试验进行过程中，临床医生可以简单地查阅决策表将患者分配到某个剂量组，而无需复杂的计算。
BOIN12设计实施简单且具有理想的操作特性。它克服了困扰现有贝叶斯I/II期剂量探索设计的计算和实施复杂性，并为优化免疫疗法和靶向疗法的剂量提供了一种有用的设计。免费提供用户友好型软件以促进BOIN12设计的应用。
