Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Clin Trials. 2024 Jun;21(3):287-297. doi: 10.1177/17407745231214750. Epub 2023 Dec 18.
Identifying optimal doses in early-phase clinical trials is critically important. Therapies administered at doses that are either unsafe or biologically ineffective are unlikely to be successful in subsequent clinical trials or to obtain regulatory approval. Identifying appropriate doses for new agents is a complex process that involves balancing the risks and benefits of outcomes such as biological efficacy, toxicity, and patient quality of life.
While conventional phase I trials rely solely on toxicity to determine doses, phase I-II trials explicitly account for both efficacy and toxicity, which enables them to identify doses that provide the most favorable risk-benefit trade-offs. It is also important to account for patient covariates, since one-size-fits-all treatment decisions are likely to be suboptimal within subgroups determined by prognostic variables or biomarkers. Notably, the selection of estimands can influence our conclusions based on the prognostic subgroup studied. For example, assuming monotonicity of the probability of response, higher treatment doses may yield more pronounced efficacy in favorable prognosis compared to poor prognosis subgroups when the estimand is mean or median survival. Conversely, when the estimand is the 3-month survival probability, higher treatment doses produce more pronounced efficacy in poor prognosis compared to favorable prognosis subgroups.
Herein, we first describe why it is essential to consider clinical practice when designing a clinical trial and outline a stepwise process for doing this. We then review a precision phase I-II design based on utilities tailored to prognostic subgroups that characterize efficacy-toxicity risk-benefit trade-offs. The design chooses each patient's dose to optimize their expected utility and allows patients in different prognostic subgroups to have different optimal doses. We illustrate the design with a dose-finding trial of a new therapeutic agent for metastatic clear cell renal cell carcinoma.
在早期临床试验中确定最佳剂量至关重要。在不安全或生物学无效的剂量下给药的疗法不太可能在后续临床试验中成功,也不太可能获得监管部门的批准。为新药物确定合适的剂量是一个复杂的过程,需要平衡生物疗效、毒性和患者生活质量等结果的风险和益处。
虽然传统的 I 期临床试验仅依赖于毒性来确定剂量,但 I 期-II 期临床试验明确考虑了疗效和毒性,这使它们能够确定提供最佳风险-效益权衡的剂量。考虑患者协变量也很重要,因为一刀切的治疗决策在由预后变量或生物标志物确定的亚组内可能不太理想。值得注意的是,选择估计量可以根据所研究的预后亚组影响我们基于该亚组的结论。例如,假设反应概率的单调性,当估计量为平均或中位数生存时间时,与预后较差的亚组相比,较高的治疗剂量可能在预后较好的亚组中产生更明显的疗效。相反,当估计量为 3 个月生存率时,与预后较好的亚组相比,较高的治疗剂量在预后较差的亚组中产生更明显的疗效。
本文首先描述了为什么在设计临床试验时考虑临床实践至关重要,并概述了这样做的逐步过程。然后,我们回顾了一种基于适用于预后亚组的效用的精准 I 期-II 期设计,这些亚组可以描述疗效-毒性风险-效益权衡。该设计选择每个患者的剂量以优化其预期效用,并允许不同预后亚组的患者具有不同的最佳剂量。我们用一种新的治疗转移性透明细胞肾细胞癌的治疗药物的剂量发现试验来说明该设计。