我们引入了一种新颖的算法方法来设计肿瘤药物组合的 I 期试验。我们提出的毒性自适应列表设计 (TALE) 易于实施，需要预先指定少量参数来定义管理剂量递增、降级的规则，或重新评估先前探索的剂量水平。这些规则有效地规范了剂量探索并控制了毒性的数量。 TALE 的一个关键特征是可以同时分配先前积累的数据认为安全的多剂量组合。一项数值研究表明，在确定最大耐受剂量 (MTD) 方面，TALE 具有相似的操作特征，以替代方法，如贝叶斯最优区间设计、COPULA、独立 beta 概率升级的乘积以及部分排序设计的持续重新评估方法，同时降低患者用药过量的风险。所提出的 TALE 设计在维护患者安全之间提供了有利的平衡并准确识别 MTD。为了方便 TALE 的使用，我们提供了一个用户友好的 R Shiny 应用程序和一个 R 包，用于计算相关操作特性，例如分配剧毒剂量组合的风险。

We introduce a novel algorithmic approach to design phase I trials for oncology drug combinations.Our proposed Toxicity Adaptive Lists Design (TALE) is straightforward to implement, requiring the prespecification of a small number of parameters that define rules governing dose escalation, de-escalation, or reassessment of previously explored dose levels. These rules effectively regulate dose exploration and control the number of toxicities. A key feature of TALE is the possibility of simultaneous assignment of multiple-dose combinations that are deemed safe by previously accrued data.A numerical study shows that TALE shares comparable operative characteristics, in terms of identification of the maximum tolerated dose (MTD), to alternative approaches such as the Bayesian optimal interval design, the COPULA, the product of independent beta probabilities escalation, and the continual reassessment method for partial ordering designs while reducing the risk of overdosing patients.The proposed TALE design provides a favorable balance between maintaining patient safety and accurately identifying the MTD. To facilitate the use of TALE, we provide a user-friendly R Shiny application and an R package for computing relevant operating characteristics, such as the risk of assigning highly toxic dose combinations.