Clinical Pharmacology/Quantitative Pharmacology/Translational Medicine, EMD Serono (a business of Merck KGaA, Darmstadt, Germany), Billerica, MA, USA.
Clin Pharmacol Ther. 2020 Dec;108(6):1156-1170. doi: 10.1002/cpt.1953. Epub 2020 Aug 2.
Recent data from immuno-oncology clinical studies have shown the exposure-response (E-R) relationship for therapeutic monoclonal antibodies (mAbs) was often confounded by various factors due to the complex interplay of patient characteristics, disease, drug exposure, clearance, and treatment response and presented challenges in characterization and interpretation of E-R analysis. To tackle the challenges, exposure relationships for therapeutic mAbs in immuno-oncology and oncology are reviewed, and a general framework for an integrative understanding of E-R relationship is proposed. In this framework, baseline factors, drug exposure, and treatment response are envisioned to form an interconnected triangle, driving the E-R relationship and underlying three components that compose the apparent relationship: exposure-driven E-R, baseline-driven E-R, and response-driven E-R. Various strategies in data analysis and study design to decouple those components and mitigate the confounding effect are reviewed for their merits and limitations, and a potential roadmap for selection of these strategies is proposed. Specifically, exposure metrics based on a single-dose pharmacokinetic model can be used to mitigate response-driven E-R, while multivariable analysis and/or case control analysis of data obtained from multiple dose levels in a randomized study may be used to account for the baseline-driven E-R. In this context, the importance of collecting data from multiple dose levels, the role of prognostic factors and predictive factors, the potential utility of clearance at baseline and its change over time, and future directions are discussed.
最近免疫肿瘤学临床研究的数据表明,由于患者特征、疾病、药物暴露、清除率和治疗反应的复杂相互作用,治疗性单克隆抗体(mAb)的暴露-反应(E-R)关系常常受到各种因素的混淆,这给 E-R 分析的特征描述和解释带来了挑战。为了解决这些挑战,本文回顾了免疫肿瘤学和肿瘤学中治疗性 mAb 的暴露关系,并提出了一个综合理解 E-R 关系的一般框架。在这个框架中,基线因素、药物暴露和治疗反应被设想为形成一个相互关联的三角形,驱动 E-R 关系和构成明显关系的三个组成部分:暴露驱动的 E-R、基线驱动的 E-R 和反应驱动的 E-R。本文还回顾了各种数据分析和研究设计策略,以分离这些成分并减轻混杂效应,并提出了选择这些策略的潜在路线图。具体而言,基于单剂量药代动力学模型的暴露指标可用于减轻反应驱动的 E-R,而多变量分析和/或来自随机研究多个剂量水平的数据的病例对照分析可用于解释基线驱动的 E-R。在这种情况下,讨论了从多个剂量水平收集数据的重要性、预后因素和预测因素的作用、基线清除率及其随时间变化的潜在效用,以及未来的方向。