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使用动态生物标志物总生存期模型为药物开发和个性化医学中的早期临床决策提供支持,以使用检查点抑制剂。

Support to early clinical decisions in drug development and personalised medicine with checkpoint inhibitors using dynamic biomarker-overall survival models.

机构信息

Clinical Pharmacology, Genentech-Roche, Marseille, France.

Clinical Pharmacology, Genentech-Roche, Lyon, France.

出版信息

Br J Cancer. 2023 Oct;129(9):1383-1388. doi: 10.1038/s41416-023-02190-5. Epub 2023 Feb 10.

DOI:10.1038/s41416-023-02190-5
PMID:36765177
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10628227/
Abstract

Longitudinal models of biomarkers such as tumour size dynamics capture treatment efficacy and predict treatment outcome (overall survival) of a variety of anticancer therapies, including chemotherapies, targeted therapies, immunotherapies and their combinations. These pharmacological endpoints like tumour dynamic (tumour growth inhibition) metrics have been proposed as alternative endpoints to complement the classical RECIST endpoints (objective response rate, progression-free survival) to support early decisions both at the study level in drug development as well as at the patients level in personalised therapy with checkpoint inhibitors. This perspective paper presents recent developments and future directions to enable wider and robust use of model-based decision frameworks based on pharmacological endpoints.

摘要

生物标志物(如肿瘤大小动态)的纵向模型可捕获各种抗癌疗法(包括化疗、靶向治疗、免疫治疗及其组合)的治疗效果,并预测治疗结果(总生存期)。这些药理学终点(如肿瘤动态[肿瘤生长抑制]指标)已被提议作为替代终点,以补充经典的 RECIST 终点(客观缓解率、无进展生存期),以支持药物开发中的研究层面以及检查点抑制剂的个体化治疗中的患者层面的早期决策。本观点文章介绍了使基于药理学终点的基于模型的决策框架得到更广泛和稳健应用的最新进展和未来方向。

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Support to early clinical decisions in drug development and personalised medicine with checkpoint inhibitors using dynamic biomarker-overall survival models.使用动态生物标志物总生存期模型为药物开发和个性化医学中的早期临床决策提供支持,以使用检查点抑制剂。
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本文引用的文献

1
Assessing the Increased Variability in Individual Lesion Kinetics During Immunotherapy: Does It Exist, and Does It Matter?评估免疫治疗期间个体病灶动力学增加的变异性:它是否存在,以及是否重要?
JCO Precis Oncol. 2023 Feb;7:e2200368. doi: 10.1200/PO.22.00368.
2
Multistate Pharmacometric Model to Define the Impact of Second-Line Immunotherapies on the Survival Outcome of the IMpower131 Study.多州药代动力学模型以确定二线免疫疗法对IMpower131研究生存结果的影响。
Clin Pharmacol Ther. 2023 Apr;113(4):851-858. doi: 10.1002/cpt.2838. Epub 2023 Jan 29.
3
Tumor Dynamic Model-Based Decision Support for Phase Ib/II Combination Studies: A Retrospective Assessment Based on Resampling of the Phase III Study IMpower150.基于肿瘤动态模型的 Ib/II 期联合研究决策支持:基于 III 期 IMpower150 研究的重采样回顾性评估。
Clin Cancer Res. 2023 Mar 14;29(6):1047-1055. doi: 10.1158/1078-0432.CCR-22-2323.
4
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JAMA Oncol. 2022 Dec 1;8(12):1733-1735. doi: 10.1001/jamaoncol.2022.4452.
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Bayesian forecasting of tumor size metrics and overall survival.贝叶斯预测肿瘤大小指标和总生存期。
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