Translational Research Group, Department of Pharmacokinetics Dynamics and Metabolism, Pfizer Global Research and Development, Groton, CT, 06340, USA.
Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, The State University of New York at Buffalo, 455 Kapoor Hall, Buffalo, NY, 14214, USA.
J Pharmacokinet Pharmacodyn. 2018 Apr;45(2):339-349. doi: 10.1007/s10928-018-9577-x. Epub 2018 Feb 8.
The objective of this manuscript was to establish in vitro-in vivo correlation (IVIVC) between the in vitro efficacy and in vivo efficacy of antibody drug conjugates (ADCs), using a PK/PD modeling approach. Nineteen different ADCs were used to develop IVIVC. In vitro efficacy of ADCs was evaluated using a kinetic cell cytotoxicity assay. The cytotoxicity data obtained from in vitro studies was characterized using a novel mathematical model, parameter estimates from which were used to derive an in vitro efficacy matrix for each ADC, termed as 'in vitro tumor static concentration' (TSC). TSC is a theoretical concentration at continuous exposure of which the number of cells will neither increase nor decrease, compared to the initial cell number in the experiment. The in vivo efficacy of ADCs was evaluated using tumor growth inhibition (TGI) studies performed on human tumor xenograft bearing mice. The TGI data obtained from in vivo studies was characterized using a PK/PD model, parameter estimates from which were used to derive an in vivo efficacy matrix for each ADC, termed as 'in vivo tumor static concentration' (TSC). TSC is a theoretical concentration if one were to maintain in the plasma of a tumor bearing mouse, the tumor volume will neither increase nor decrease compared to the initial tumor volume. Comparison of the TSC and TSC values from 19 ADCs provided a linear and positive IVIVC. The Spearman's rank correlation coefficient for TSC and TSC was found to be 0.82. On average TSC was found to be ~ 27 times higher than TSC. The reasonable IVIVC for ADCs suggests that in vitro efficacy data was correctly able to differentiate ADCs for their in vivo efficacy. Thus, IVIVC can be used as a tool to triage ADC molecules in the discovery stage, thereby preventing unnecessary scaling-up of ADCs and waste of time and resources. An ability to predict the concentration of ADC that is efficacious in vivo using the in vitro data can also help in optimizing the experimental design of preclinical efficacy studies. As such, the novel PK/PD modeling method presented here to establish IVIVC for ADCs holds promise, and should be evaluated further using diverse set of cell lines and anticancer agents.
本研究旨在建立抗体药物偶联物(ADC)的体外疗效与体内疗效的体外-体内相关性(IVIVC),采用 PK/PD 建模方法。本研究使用 19 种不同的 ADC 来建立 IVIVC。通过动力学细胞细胞毒性测定评估 ADC 的体外疗效。从体外研究中获得的细胞毒性数据用一种新的数学模型进行了特征描述,该模型的参数估计值用于为每个 ADC 推导出一个体外疗效矩阵,称为“体外肿瘤静态浓度”(TSC)。TSC 是指在连续暴露下,与实验中初始细胞数相比,细胞数量既不会增加也不会减少的理论浓度。通过在荷人肿瘤异种移植小鼠上进行肿瘤生长抑制(TGI)研究来评估 ADC 的体内疗效。从体内研究中获得的 TGI 数据用 PK/PD 模型进行了特征描述,该模型的参数估计值用于为每个 ADC 推导出一个体内疗效矩阵,称为“体内肿瘤静态浓度”(TSC)。TSC 是指如果将其维持在荷瘤小鼠的血浆中,与初始肿瘤体积相比,肿瘤体积既不会增加也不会减少的理论浓度。比较 19 种 ADC 的 TSC 和 TSC 值提供了线性和正的 IVIVC。TSC 和 TSC 的 Spearman 秩相关系数为 0.82。平均而言,TSC 比 TSC 高约 27 倍。对于 ADC 来说,合理的 IVIVC 表明,体外疗效数据能够正确区分 ADC 的体内疗效。因此,IVIVC 可用作在发现阶段对 ADC 分子进行分类的工具,从而避免不必要地扩大 ADC 的规模并浪费时间和资源。使用体外数据预测体内有效的 ADC 浓度也有助于优化临床前疗效研究的实验设计。因此,本研究中提出的用于建立 ADC 的新型 PK/PD 建模方法具有前景,应该使用更广泛的细胞系和抗癌药物进一步评估。