Mukherjee Ayon, Moscovici Jonathan L, Liu Zheng
Population Health Sciences Institute, Newcastle University, Newcastle, UK.
Center for Statistics in Drug Development (CSDD), IQVIA, Montreal, Canada.
Biom J. 2025 Oct;67(5):e70072. doi: 10.1002/bimj.70072.
Phase I dose escalation trials in oncology generally aim to find the maximum tolerated dose. However, with the advent of molecular-targeted therapies and antibody drug conjugates, dose-limiting toxicities are less frequently observed, giving rise to the concept of optimal biological dose (OBD), which considers both efficacy and toxicity. The estimand framework presented in the addendum of the ICH E9(R1) guidelines strengthens the dialogue between different stakeholders by bringing in greater clarity in the clinical trial objectives and by providing alignment between the targeted estimand under consideration and the statistical analysis methods. However, there is a lack of clarity in implementing this framework in early-phase dose optimization studies. This paper aims to discuss the estimand framework for dose optimization trials in oncology, considering efficacy and toxicity through utility functions. Such trials should include pharmacokinetics data, toxicity data, and efficacy data. Based on these data, the analysis methods used to identify the optimized dose/s are also described. Focusing on optimizing the utility function to estimate the OBD, the population-level summary measure should reflect only the properties used for estimating this utility function. A detailed strategy recommendation for intercurrent events has been provided using a real-life oncology case study. Key recommendations regarding the estimand attributes include that in a seamless phase I/II dose optimization trial, the treatment attribute should start when the subject receives the first dose. We argue that such a framework brings in additional clarity to dose optimization trial objectives and strengthens the understanding of the drug under consideration, which would enable the correct dose to move to phase II of clinical development.
肿瘤学中的I期剂量递增试验通常旨在找到最大耐受剂量。然而，随着分子靶向疗法和抗体药物偶联物的出现，剂量限制性毒性的观察频率降低，从而产生了最佳生物学剂量（OBD）的概念，该概念同时考虑了疗效和毒性。国际人用药品注册技术协调会（ICH）E9（R1）指南附录中提出的估计量框架，通过使临床试验目标更加清晰，并使所考虑的目标估计量与统计分析方法保持一致，加强了不同利益相关者之间的对话。然而，在早期剂量优化研究中实施该框架仍缺乏明确性。本文旨在讨论肿瘤学剂量优化试验的估计量框架，通过效用函数考虑疗效和毒性。此类试验应包括药代动力学数据、毒性数据和疗效数据。基于这些数据，还描述了用于确定优化剂量的分析方法。专注于优化效用函数以估计OBD，总体水平的汇总指标应仅反映用于估计此效用函数的属性。通过一个实际的肿瘤学案例研究，提供了关于并发事件的详细策略建议。关于估计量属性的关键建议包括，在无缝的I/II期剂量优化试验中，治疗属性应在受试者接受第一剂时开始。我们认为，这样一个框架为剂量优化试验目标带来了额外的清晰度，并加强了对所研究药物的理解，这将使正确的剂量进入临床开发的II期。
