Data and Data Science, Translational Disease Modeling Oncology, Sanofi R&D, Paris, France.
Translational Medicine & Early Development, Modeling & Simulation, Sanofi R&D, Montpellier, France.
CPT Pharmacometrics Syst Pharmacol. 2024 Jun;13(6):941-953. doi: 10.1002/psp4.13128. Epub 2024 Apr 1.
A joint modeling framework was developed using data from 75 patients of early amcenestrant phase I-II AMEERA-1-2 dose escalation and expansion cohorts. A semi-mechanistic tumor growth inhibition (TGI) model was developed. It accounts for the dynamics of sensitive and resistant tumor cells, an exposure-driven effect on tumor proliferation of sensitive cells, and a delay in the initiation of treatment effect to describe the time course of target lesion tumor size (TS) data. Individual treatment exposure overtime was introduced in the model using concentrations predicted by a population pharmacokinetic model of amcenestrant. This joint modeling framework integrated complex RECISTv1.1 criteria information, linked TS metrics to progression-free survival (PFS), and was externally evaluated using the randomized phase II trial AMEERA-3. We demonstrated that the instantaneous rate of change in TS (TS slope) was an important predictor of PFS and the developed joint model was able to predict well the PFS of amcenestrant phase II monotherapy trial using only early phase I-II data. This provides a good modeling and simulation tool to inform early development decisions.
使用来自早期 amcenestrant 阶段 I-II AMEERA-1-2 剂量递增和扩展队列的 75 名患者的数据,开发了一个联合建模框架。开发了一个半机械肿瘤生长抑制(TGI)模型。它考虑了敏感和耐药肿瘤细胞的动力学,敏感细胞增殖的暴露驱动效应,以及治疗效果开始的延迟,以描述目标病变肿瘤大小(TS)数据的时间过程。使用 amcenestrant 的群体药代动力学模型预测的浓度,在模型中引入了个体治疗暴露随时间的变化。该联合建模框架集成了复杂的 RECISTv1.1 标准信息,将 TS 指标与无进展生存期(PFS)相关联,并使用随机化的 II 期试验 AMEERA-3 进行了外部评估。我们证明了 TS 的瞬时变化率(TS 斜率)是 PFS 的重要预测因子,并且仅使用早期 I-II 数据,所开发的联合模型就能够很好地预测 amcenestrant 单药 II 期试验的 PFS。这为早期开发决策提供了一个很好的建模和模拟工具。