Certara Strategic Consulting, Certara, 54 Rue de Londres, 75009, Paris, France.
Clinical Pharmacology, Genentech, Inc, 1 DNA Way, South San Francisco, CA, 94080, USA.
Cancer Chemother Pharmacol. 2021 Aug;88(2):211-221. doi: 10.1007/s00280-021-04276-4. Epub 2021 Apr 27.
The time-varying clearance (CL) of the PD-L1 inhibitor atezolizumab was assessed on a population of 1519 cancer patients (primarily with non-small-cell lung cancer or metastatic urothelial carcinoma) from three clinical studies.
The first step was to identify the baseline covariates affecting atezolizumab CL without including time-varying components (stationary covariate model). Two time-varying models were then investigated: (1) a model allowing baseline covariates to vary over time (time-varying covariate model), (2) a model with empirical time-varying Emax CL function.
The final stationary covariate model included main effects of body weight, albumin levels, tumor size, anti-drug antibodies (ADA) and gender on atezolizumab CL. Both time-varying models resulted in a clear improvement of the data fit and visual predictive checks over the stationary model. The time-varying covariate model provided the best fit of the data. In this model, the main driver for change in CL over time was variations in albumin level with an increase in serum albumin (improvement in a patient's status) mirroring a decrease in CL. Time-varying ADAs had a small impact (9% increase in CL). None of the covariates impacted atezolizumab CL by more than ± 30% from median. The estimated maximum decrease in CL with time was 22% with the Emax model.
The overall impact of covariates on atezolizumab CL did not warrant any change in atezolizumab dosing recommendations. The results support the hypothesis that variation in atezolizumab CL over time is associated with patients' disease status, as shown with other checkpoint inhibitors.
评估三种临床研究中 1519 名癌症患者（主要为非小细胞肺癌或转移性尿路上皮癌患者）群体中 PD-L1 抑制剂阿特珠单抗的时变清除率（CL）。
第一步是确定影响阿特珠单抗 CL 的基线协变量，而不包括时变成分（固定协变量模型）。然后研究了两种时变模型：（1）允许基线协变量随时间变化的模型（时变协变量模型），（2）具有经验时变 Emax CL 函数的模型。
最终的固定协变量模型包括体重、白蛋白水平、肿瘤大小、抗药物抗体（ADA）和性别对阿特珠单抗 CL 的主要影响。两种时变模型均明显改善了数据拟合度和视觉预测检查结果，优于固定模型。时变协变量模型提供了数据的最佳拟合。在该模型中，CL 随时间变化的主要驱动因素是白蛋白水平的变化，血清白蛋白的增加（患者状况的改善）反映了 CL 的降低。时变 ADA 对 CL 的影响较小（CL 增加 9%）。没有任何协变量对阿特珠单抗 CL 的影响超过中位数的±30%。Emax 模型估计 CL 随时间的最大下降幅度为 22%。
协变量对阿特珠单抗 CL 的总体影响不支持对阿特珠单抗剂量建议进行任何改变。结果支持这样的假设，即阿特珠单抗 CL 随时间的变化与患者的疾病状态有关，这与其他检查点抑制剂的结果一致。
