Tighiouart Mourad, Rogatko André
Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA 90069, USA.
Independent Researcher, 2765-399 Monte Estoril, Portugal.
Entropy (Basel). 2024 Aug 14;26(8):687. doi: 10.3390/e26080687.
We present a Bayesian adaptive design for dose finding in oncology trials with application to a first-in-human trial. The design is based on the escalation with overdose control principle and uses an intermediate grade 2 toxicity in addition to the traditional binary indicator of dose-limiting toxicity (DLT) to guide the dose escalation and de-escalation. We model the dose-toxicity relationship using the proportional odds model. This assumption satisfies an important ethical concern when a potentially toxic drug is first introduced in the clinic; if a patient experiences grade 2 toxicity at the most, then the amount of dose escalation is lower relative to that wherein if this patient experienced a maximum of grade 1 toxicity. This results in a more careful dose escalation. The performance of the design was assessed by deriving the operating characteristics under several scenarios for the true MTD and expected proportions of grade 2 toxicities. In general, the trial design is safe and achieves acceptable efficiency of the estimated MTD for a planned sample size of twenty patients. At the time of writing this manuscript, twelve patients have been enrolled to the trial.
我们提出了一种用于肿瘤学试验剂量探索的贝叶斯自适应设计,并将其应用于一项首次人体试验。该设计基于过量控制原则下的剂量递增,并除了使用传统的剂量限制毒性(DLT)二元指标外,还采用中级2级毒性来指导剂量的递增和递减。我们使用比例优势模型对剂量-毒性关系进行建模。当一种潜在有毒药物首次引入临床时,这一假设满足了一个重要的伦理考量;如果一名患者最多经历2级毒性,那么相对于该患者最多经历1级毒性的情况,剂量递增的幅度会更低。这导致了更谨慎的剂量递增。通过推导在几种真实最大耐受剂量(MTD)和2级毒性预期比例的情况下的操作特征,对该设计的性能进行了评估。总体而言,对于计划样本量为20名患者的试验设计是安全的,并且在估计MTD方面实现了可接受的效率。在撰写本手稿时,已有12名患者入组该试验。