Kågedal Matts, Gibiansky Leonid, Xu Jian, Wang Xin, Samineni Divya, Chen Shang-Chiung, Lu Dan, Agarwal Priya, Wang Bei, Saad Ola, Koppada Neelima, Fine Bernard M, Jin Jin Y, Girish Sandhya, Li Chunze
Clinical Pharmacology, Genentech Inc, 1 DNA Way, South San Francisco, CA, 94080, USA.
QuantPharm LLC, 49 Flints Grove Drive, North Potomac, MD, 20878, USA.
J Pharmacokinet Pharmacodyn. 2017 Dec;44(6):537-548. doi: 10.1007/s10928-017-9544-y. Epub 2017 Sep 16.
Antibody-drug conjugates (ADCs) developed using the valine-citrulline-MMAE (vc-MMAE) platform, consist of a monoclonal antibody (mAb) covalently bound with a potent anti-mitotic toxin (MMAE) through a protease-labile vc linker. Recently, clinical data for a variety of vc-MMAE ADCs has become available. The goal of this analysis was to develop a platform model that simultaneously described antibody-conjugated MMAE (acMMAE) pharmacokinetic (PK) data from eight vc-MMAE ADCs, against different targets and tumor indications; and to assess differences and similarities of model parameters and model predictions, between different compounds. Clinical PK data of eight vc-MMAE ADCs from eight Phase I studies were pooled. A population PK platform model for the eight ADCs was developed, where the inter-compound variability (ICV) was described explicitly, using the third random effect level (ICV), and implemented using LEVEL option of NONMEM 7.3. The PK was described by a two-compartment model with time dependent clearance. Clearance and volume of distribution increased with body weight; volume was higher for males, and clearance mildly decreased with the nominal dose. Michaelis-Menten elimination had only minor effect on PK and was not included in the model. Time-dependence of clearance had no effect beyond the first dosing cycle. Clearance and central volume were similar among ADCs, with ICV of 15 and 5%, respectively. Thus, PK of acMMAE was largely comparable across different vc-MMAE ADCs. The model may be applied to predict PK-profiles of vc-MMAE ADCs under development, estimate individual exposure for the subsequent PK-pharmacodynamics (PD) analysis, and project optimal dose regimens and PK sampling times.
使用缬氨酸-瓜氨酸-MMAE（vc-MMAE）平台开发的抗体药物偶联物（ADC），由单克隆抗体（mAb）通过蛋白酶可裂解的vc连接子与强效抗有丝分裂毒素（MMAE）共价结合而成。最近，多种vc-MMAE ADC的临床数据已可获取。本分析的目的是建立一个平台模型，该模型能同时描述来自8种vc-MMAE ADC的抗体偶联MMAE（acMMAE）的药代动力学（PK）数据，这些ADC针对不同靶点和肿瘤适应症；并评估不同化合物之间模型参数和模型预测的差异与相似性。汇总了八项I期研究中八种vc-MMAE ADC的临床PK数据。开发了一个针对这八种ADC的群体PK平台模型，其中使用第三个随机效应水平（ICV）明确描述化合物间变异性（ICV），并使用NONMEM 7.3的LEVEL选项进行实施。PK由具有时间依赖性清除率的二室模型描述。清除率和分布容积随体重增加；男性的分布容积更高，清除率随标称剂量略有下降。米氏消除对PK的影响较小，未纳入模型。清除率的时间依赖性在第一个给药周期之后没有影响。不同ADC之间的清除率和中央室容积相似，ICV分别为15%和5%。因此，不同vc-MMAE ADC的acMMAE的PK在很大程度上具有可比性。该模型可用于预测正在开发的vc-MMAE ADC的PK曲线，估计后续PK-药效学（PD）分析的个体暴露量，并规划最佳给药方案和PK采样时间。
