Clinical Pharmacology, Development Sciences, gRED, Genentech/Roche, South San Francisco, CA, USA.
Thomas J. Long School of Pharmacy, University of the Pacific, Stockton, CA, USA.
Br J Clin Pharmacol. 2021 Jun;87(6):2493-2501. doi: 10.1111/bcp.14662. Epub 2020 Dec 7.
Dose selection and optimization is an important topic in drug development to maximize treatment benefits for all patients. While exposure-response (E-R) analysis is a useful method to inform dose-selection strategy, in oncology, special considerations for prognostic factors are needed due to their potential to confound the E-R analysis for monoclonal antibodies. The current review focuses on 3 different approaches to mitigate the confounding effects for monoclonal antibodies in oncology: (i) Cox-proportional hazards modelling and case-matching; (ii) tumour growth inhibition-overall survival modelling; and (iii) multiple dose level study design. In the presence of confounding effects, studying multiple dose levels may be required to reveal the true E-R relationship. However, it is impractical for pivotal trials in oncology drug development programmes. Therefore, the strengths and weaknesses of the other 2 approaches are considered, and the favourable utility of tumour growth inhibition-overall survival modelling to address confounding in E-R analyses is described. In the broader scope of oncology drug development, this review discusses the downfall of the current emphasis on E-R analyses using data from single dose level trials and proposes that development programmes be designed to study more dose levels in earlier trials.
剂量选择和优化是药物开发中的一个重要课题，旨在最大限度地提高所有患者的治疗效果。虽然暴露-反应（E-R）分析是一种有用的方法，可以为剂量选择策略提供信息，但在肿瘤学中，由于预后因素有可能使单克隆抗体的 E-R 分析复杂化，因此需要特殊考虑。本综述重点介绍了 3 种不同的方法，可减轻肿瘤学中单克隆抗体的混杂效应：（i）Cox 比例风险模型和病例匹配；（ii）肿瘤生长抑制-总生存模型；和（iii）多剂量水平研究设计。在存在混杂效应的情况下，可能需要研究多个剂量水平以揭示真实的 E-R 关系。然而，对于肿瘤学药物开发计划中的关键试验来说，这是不切实际的。因此，考虑了其他 2 种方法的优缺点，并描述了肿瘤生长抑制-总生存模型在解决 E-R 分析中的混杂问题方面的有利效用。在肿瘤学药物开发的更广泛范围内，本综述讨论了当前强调使用单剂量水平试验数据进行 E-R 分析的缺陷，并提出应设计开发计划在早期试验中研究更多的剂量水平。
