在细胞治疗中优化剂量策略,使用机器学习和暴露-响应集成.
Shuqi Wang1, Yunqi Zhao2, Jia Li3
1Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Pharmaceutical statistics
|December 10, 2025
概括
这项研究引入了基于数据的细胞治疗剂量选择方法,整合了患者数据和细胞动力学,以优化治疗的有效性和安全性. 该方法在无的I/II期试验中使用随机森林建模,以确定最佳剂量,改善患者的治疗结果.
科学领域:
- 生物技术是生物技术.
- 临床试验 临床试验
- 制药指标 (Pharmacometrics) 是一个指标.
背景情况:
- 细胞治疗的有效性和安全性与细胞扩张有关,由于患者和产品的异质性而有所不同.
- 整合暴露评估对于细胞治疗开发中的知情剂量选择至关重要.
- 美国食品和药物管理局的OPTIMUS项目强调为最佳剂量确定提供全面数据.
研究的目的:
- 为最佳细胞治疗剂量选择提出无的I/II期临床试验设计.
- 将毒性,疗效,细胞动力学 (CK) 和患者/产品特征整合到基于数据的剂量确定过程中.
- 利用随机森林 (RF) 建模来指导剂量升级和确定最佳剂量 (OD).
主要方法:
- 一个无的I/II阶段设计,整合了毒性,疗效,CK和基线特征.
- 使用随机森林 (RF) 建模,提供全面的剂量升级和缩小选项数据.
- 实施基于射频估计的中间分析,以停止徒劳或有毒的剂量.
- 随机分配额外的患者以有前途的剂量进行进一步评估.
主要成果:
- 提出的基于射频的设计准确地选择了具有理想操作特征的最佳剂量 (OD).
- 该设计有效地将患者分配到潜在的治疗剂量,同时最大限度地减少对有毒剂量的暴露.
- 模拟研究表明OD选择的精度很高,算法平均选择0-3剂量进行探索.
结论:
- 基于射频的无I/II期设计提供了一种数据驱动的,高效的方法,用于最佳的细胞治疗剂量确定.
- 整合暴露数据和患者/产品特征可以提高剂量选择的精度.
- 这种方法符合监管机构对优化细胞疗法开发和患者安全的期望.
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