单药及联合治疗的无缝剂量优化设计
A seamless dose-optimization design for monotherapy and combination therapy
AI总结：
本文针对肿瘤药物开发中传统剂量设计不适用于新型药物、单药与联合治疗评估存在方法学差距的问题，提出一种可自适应评估单药及联合治疗的无缝剂量优化设计，该设计采用模型辅助框架，经模拟验证性能稳健，可弥合方法与临床现实的差距。
AI中文摘要：
分子靶向药物与免疫肿瘤疗法的出现从根本上改变了肿瘤药物开发，要求超越传统针对细胞毒性药物的剂量探索方法。传统药物呈现可预测的单调剂量-反应关系，而新型抗癌药物常表现出平台效应模式，更高剂量可能损害治疗获益，需识别平衡疗效与耐受性的最优生物剂量。FDA的Optimus项目强调通过平行随机队列与患者补入来进行全面剂量优化，以更好地了解多剂量水平下的药理特征。当代药物开发日益重视联合治疗与单药评估，但现有设计通常假设两种药物作用相当，与临床实践存在差异——临床中新型药物与剂量选择有限的已确立治疗方案联合使用。本文提出一种无缝剂量优化设计，可通过具备患者补入能力的自适应子试验，基于疗效与毒性结果自适应评估单药及联合治疗。该模型辅助框架采用预设的贝叶斯最优边界，无需实时模型拟合，同时可容纳单药与联合治疗的评估，并支持带策略性补入的序贯入组。模拟研究表明，该设计在当代肿瘤学相关的多种剂量-反应模式下均表现出稳健性能，弥合了方法学假设与临床现实之间的关键差距，提供了一种将单药与联合治疗评估及基于疗效-毒性的补入相结合的实用方法。
英文摘要：
The emergence of molecular-targeted agents and immune-oncology therapies has fundamentally transformed oncology drug development, necessitating evolution beyond traditional dose-finding approaches designed for cytotoxic agents. While conventional agents exhibit predictable monotonic dose-response relationships, novel anticancer agents often demonstrate plateau-effect patterns where higher doses may compromise therapeutic benefit, requiring identification of optimal biological doses that balance efficacy and tolerability. The FDA's Project Optimus initiative emphasizes comprehensive dose optimization through parallel randomized cohorts and patient backfilling to better understand pharmacological profiles across multiple dose levels. Contemporary drug development increasingly prioritizes combination therapy alongside monotherapy evaluation, yet existing designs typically assume equivalent roles for both agents, diverging from clinical practice where novel agents combine with established treatments having limited dose options. This paper proposes a seamless dose-optimization design that adaptively evaluates both monotherapy and combination therapy based on efficacy and toxicity outcomes through adaptive subtrials with patient backfilling capabilities. The model-assisted framework employs predetermined Bayesian optimal boundaries, eliminating real-time model fitting while accommodating evaluation of both monotherapy and combination therapy and enabling sequential enrollment with strategic backfilling. Simulation studies demonstrate robust performance across diverse dose-response patterns relevant to contemporary oncology. The design addresses critical gaps between methodological assumptions and clinical reality, offering a practical approach that integrates monotherapy and combination therapy evaluation with efficacy-toxicity-based backfilling.
