在随机临床试验中测试抗体与药物合物的随机临床试验中,替代总生存终点是替代终点
Laura Pala1,2, Federico Merlo3, Isabella Sala3,4
1Department of Medical Oncology, Humanitas Gavazzeni, Bergamo, Italy.
Journal of the National Cancer Institute
|March 4, 2025
概括
无进展生存率 (PFS) 是固体瘤抗体-药物合物 (ADC) 试验中整体生存率 (OS) 的可靠替代品,与整体反应率 (ORR) 不同. 这一发现支持ADC加速批准.
科学领域:
- 在瘤学瘤学.
- 临床试验 临床试验
- 生物统计学 生物统计学
背景情况:
- 在抗体-药物合物 (ADC) 试验中,研究替代终点对整体存活 (OS) 的研究对于药物开发至关重要.
- 在ADC试验中,OS的试验级别代孕的整体响应率 (ORR) 或无进展生存率 (PFS) 在ADC试验中尚未先前评估.
研究的目的:
- 在ADC的随机临床试验 (RCT) 中,在试验水平上评估代用终点 (ORR,PFS) 和整体存活率 (OS) 之间的关联.
- 为了确定PFS或ORR是否是更可靠的替代品OS在先进的固体瘤.
主要方法:
- 分析涉及高级固体瘤中ADCs的RCT,报告了OS,PFS和/或ORR的数据.
- 评估ORR和OS,PFS和OS之间的试验级相关性,包括按瘤类型,先前治疗和ADC特征进行子组分析.
主要成果:
- 分析了25个RCT (26个治疗比较,11,729名患者).
- 在PFS和OS之间发现了强烈的关联 (R2=0.79),而ORR和OS之间的关联是中度的 (R2=0.47).
- PFS在各个子组中表现出一致的可靠性,而ORR与OS的关联不同,特别是在乳腺癌试验中很强.
结论:
- 无进展生存率 (PFS) 是高级固体瘤的ADCs的RCT中总生存率 (OS) 的强大的替代终点.
- 作为OS的替代品,PFS提供了比整体响应率 (ORR) 更高的可靠性.
- 这些发现支持在未来的临床试验中使用PFS加速批准ADC.
更多相关视频
13:19Quantifying Antibody-Dependent Cellular Cytotoxicity in a Tumor Spheroid Model: Application for Drug Discovery
Published on: April 26, 2024
2.5K
11:02Genetic Encoding of a Non-Canonical Amino Acid for the Generation of Antibody-Drug Conjugates Through a Fast Bioorthogonal Reaction
Published on: September 14, 2018
7.7K
相关概念视频
01:21Cancer Survival Analysis
319
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
319
01:20Comparing the Survival Analysis of Two or More Groups
117
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
117