PK/PD整合的贝叶斯平台设计用于II期剂量方案优化
PK/PD-integrated Bayesian platform design for phase II dose regimen optimization
- Inserm
- Université Paris Cité（巴黎西岱大学）
- Inria
- HeKA
- Centre d’Étude des Pathologies Respiratoires (CEPR)（呼吸疾病研究中心 (CEPR)）
- UMR 1100
- Université de Tours（图尔大学）
- Service de Médecine Intensive Réanimation, CHRU de Tours（图尔大学医院重症医学科）
- Université Sorbonne Paris Nord（巴黎北索邦大学）
- IAME
- Univ Rennes（雷恩大学）
- EHESP
- Irset
- UMRS 1085
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对剂量无法充分刻画治疗方案的问题，提出整合PK/PD建模的贝叶斯II期适应性平台设计PROP，以改善方案选择与决策，模拟显示其优于基于剂量的方法。
AI中文摘要：
早期阶段剂量探索方法越来越多地联合评估毒性和疗效，但仅基于给药剂量的比较可能不足以刻画在给药时间表上不同的方案。我们开发了一种用于方案优化的贝叶斯II期适应性平台设计，该设计将药代动力学/药效学（PK/PD）建模整合到毒性、疗效、方案选择和适应性决策中。所提出的PK/PD知情方案优化平台（PROP）设计使用群体PK/PD模型来生成患者和群体水平的暴露及生物活性预测。急性和累积毒性通过由PK暴露信息驱动的离散时间事件时间模型进行分析。疗效通过暴露驱动和生物标志物驱动的事件时间模型的贝叶斯模型平均进行评估。该设计支持方案毕业、因无效或安全性而终止，以及添加未探索的方案。性能通过以流感重症监护环境为背景的模拟进行评估。在六个场景中，与基于剂量的替代方案相比，PROP总体上改善了毕业和无效性决策，减少了不适当的毕业，并支持添加有前景的方案。它还更准确地估计了方案特异性毒性和臂特异性疗效，而模型平均框架倾向于与数据生成机制一致的疗效模型。在某些场景中，基于剂量的方法在安全性停止方面表现更好，尽管对方案-毒性关系的刻画不够准确。当剂量单独无法充分刻画治疗方案时，PK/PD知情的平台设计可以改善适应性方案选择和知识生成。
英文摘要：
Early-phase dose-finding methods increasingly assess toxicity and efficacy jointly, but comparisons based only on administered dose may inadequately characterize regimens differing in schedule. We developed a Bayesian phase II adaptive platform design for regimen optimization that integrates pharmacokinetic/pharmacodynamic (PK/PD) modelling into toxicity, efficacy, regimen selection and adaptation decisions. The proposed PK/PD-informed Regimen Optimization Platform (PROP) design uses a population PK/PD model to generate patient- and population-level predictions of exposure and biological activity. Acute and cumulative toxicities are analysed using a discrete-time time-to-event model informed by PK exposure. Efficacy is evaluated through Bayesian model averaging of exposure-driven and biomarker-driven time-to-event models. The design supports regimen graduation, discontinuation for futility or safety, and addition of unexplored regimens. Performance was evaluated through simulations motivated by an influenza intensive-care setting. Across six scenarios, PROP generally improved graduation and futility decisions, reduced inappropriate graduation, and supported the addition of promising regimens compared with dose-based alternatives. It also more accurately estimated regimen-specific toxicity and arm-specific efficacy, while the model-averaging framework favored the efficacy model consistent with the data-generating mechanism. Dose-based approaches performed better for safety stopping in some scenarios, despite less accurate characterization of the regimen--toxicity relationship. PK/PD-informed platform designs can improve adaptive regimen selection and knowledge generation when dose alone cannot adequately characterize treatment regimens.
