通过HR+/HER2-乳腺癌的临床数据共享平台进行疾病建模和外部模型评估
Kenta Yoshida1, René Bruno2, Pascal Chanu3
1Clinical Pharmacology, Genentech, South San Francisco, California, USA.
CPT: pharmacometrics & systems pharmacology
|July 17, 2025
概括
针对激素受体阳性 (HR+) /人体表皮生长因子受体2阴性 (HER2-) 乳腺癌,开发了一种新的瘤生长抑制-整体生存 (TGI-OS) 模型. 该模型显示了支持临床试验设计和药物开发决策的潜力.
科学领域:
- 在瘤学瘤学.
- 生物统计学 生物统计学
- 临床试验设计 临床试验设计
背景情况:
- 疾病进展的预测模型对于临床试验的设计和解释至关重要.
- 这些模型的开发需要获得合适的临床试验数据.
- 激素受体阳性 (HR+) /人体表皮生长因子受体2阴性 (HER2-) 乳腺癌是治疗发展的重要领域.
研究的目的:
- 为HR+/HER2-乳腺癌开发和合格一种瘤生长抑制-整体生存 (TGI-OS) 模型.
- 为了利用像Vivli这样的平台共享的临床试验数据进行模型开发.
- 评估模型在支持药物开发决策方面的有用性.
主要方法:
- 利用CONFIRM第3期研究的数据来开发TGI-OS模型.
- 采用纵向瘤大小数据和生存的基线预测指标.
- 通过使用PALOMA-3和SANDPIPER第三阶段研究的数据,对该模型进行了外部合格.
主要成果:
- 开发的TGI-OS模型证明了成功的内部资格,预测了治疗臂之间的生存差异.
- 外部资格显示预测治疗效应 (总生存期的危险比率) 的良好一致性,尽管对绝对总生存期的低估.
- 该模型的预测性能表明在临床试验环境中具有潜在的实用性.
结论:
- 共享临床试验数据的整合对于推进瘤学的预测建模至关重要.
- 该TGI-OS模型显示承诺作为一种工具,用于药物开发决策在HR+/HER2-乳腺癌.
- 在将预测模型应用于新研究时,建议谨慎,特别是在治疗景观演变的背景下.
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