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Application of a PK-PD Modeling and Simulation-Based Strategy for Clinical Translation of Antibody-Drug Conjugates: a Case Study with Trastuzumab Emtansine (T-DM1)

机译:PK-PD建模和基于仿真的策略在抗体-药物缀合物临床翻译中的应用:曲妥珠单抗Emtansine(T-DM1)的案例研究

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摘要

Successful clinical translation of antibody-drug conjugates (ADCs) can be challenging due to complex pharmacokinetics and differences between preclinical and clinical tumors. To facilitate this translation, we have developed a general pharmacokinetic-pharmacodynamic (PK-PD) modeling and simulation (M&S)-based strategy for ADCs. Here we present the validation of this strategy using T-DM1 as a case study. A previously developed preclinical tumor disposition model for T-DM1 (Singh and Shah, AAPSJ. 2015; 18(4):861–875) was used to develop a PK-PD model that can characterize in vivo efficacy of T-DM1 in preclinical tumor models. The preclinical data was used to estimate the efficacy parameters for T-DM1. Human PK of T-DM1 was a priori predicted using allometric scaling of monkey PK parameters. The predicted human PK, preclinically estimated efficacy parameters, and clinically observed volume and growth parameters for breast cancer were combined to develop a translated clinical PK-PD model for T-DM1. Clinical trial simulations were performed using the translated PK-PD model to predict progression-free survival (PFS) and objective response rates (ORRs) for T-DM1. The model simulated PFS rates for HER2 1+ and 3+ populations were comparable to the rates observed in three different clinical trials. The model predicted only a modest improvement in ORR with an increase in clinically approved dose of T-DM1. However, the model suggested that a fractionated dosing regimen (e.g., front loading) may provide an improvement in the efficacy. In general, the PK-PD M&S-based strategy presented here is capable of a priori predicting the clinical efficacy of ADCs, and this strategy has been now retrospectively validated for all clinically approved ADCs.
机译:由于复杂的药代动力学以及临床前和临床肿瘤之间的差异,抗体-药物偶联物(ADC)的成功临床翻译可能具有挑战性。为便于翻译,我们为ADC开发了基于通用药代动力学-药效学(PK-PD)建模和模拟(M&S)的策略。在这里,我们以T-DM1为例,对这一策略进行了验证。先前开发的T-DM1临床前肿瘤处置模型(Singh and Shah,AAPSJ。2015; 18(4):861–875)用于开发可表征T-DM1在临床前体内功效的PK-PD模型。肿瘤模型。临床前数据用于估计T-DM1的功效参数。 T-DM1的人类PK是使用猴子PK参数的异度缩放按先验预测的。结合预测的人PK,临床前估计的疗效参数以及临床观察到的乳癌体积和生长参数,可以开发出针对T-DM1的临床PK-PD翻译模型。使用翻译后的PK-PD模型进行临床试验模拟,以预测T-DM1的无进展生存期(PFS)和客观缓解率(ORR)。该模型模拟的HER2 1+和3+人群的PFS率与在三个不同临床试验中观察到的率相当。该模型预测,随着临床批准的T-DM1剂量的增加,ORR只会适度改善。但是,该模型表明分次给药方案(例如前负荷)可能会提高疗效。通常,此处介绍的基于PK-PD M&S的策略能够事先预测ADC的临床疗效,并且该策略现已针对所有临床批准的ADC进行了回顾性验证。

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