In Oncology, trials evaluating drug combinations are becoming more common. While combination therapies bring the potential for greater efficacy, they also create unique challenges for ensuring drug safety. In Phase-I dose escalation trials of drug combinations, model-based approaches enable efficient use of information gathered, but the models need to account for trial complexities: appropriate modeling of interactions becomes increasingly important with growing numbers of drugs being tested simultaneously in a given trial. In principle, we can use data from multiple arms testing varying combinations to jointly estimate toxicity of the drug combinations. However, such efforts have highlighted limitations when modelling drug-drug interactions in the Bayesian Logistic Regression Model (BLRM) framework used to ensure patient safety. Previous models either do not account for non-monotonicity due to antagonistic toxicity, or exhibit the fundamental flaw of exponentially overpowering the contributions of the individual drugs in the dose-response. This specifically leads to issues when drug combinations exhibit antagonistic toxicity, in which case the toxicity probability gets vanishingly small as doses get very large. We put forward additional constraints inspired by Paracelsus' intuition of "the dose makes the poison" which avoid this flaw and present an improved interaction model which is compatible with these constraints. We create instructive data scenarios that showcase the improved behavior of this more constrained drug-drug interaction model in terms of preventing further dosing at overly toxic dose combinations and more sensible dose-finding under antagonistic drug toxicity. This model is now available in the open-source OncoBayes2 R package that implements the BLRM framework for an arbitrary number of drugs and trial arms.

翻译：在肿瘤学中，评估药物组合的试验日益普遍。虽然联合疗法可能带来更高的疗效，但也对确保药物安全性提出了独特挑战。在药物组合的一期剂量递增试验中，基于模型的方法能够有效利用收集到的信息，但模型需考虑试验的复杂性：随着同一试验中同时测试的药物数量增加，对相互作用的适当建模变得愈发重要。原则上，我们可以利用来自测试多种组合的多个试验臂的数据，共同估计药物组合的毒性。然而，这种努力在用于确保患者安全性的贝叶斯逻辑回归模型（BLRM）框架中对药物-药物相互作用进行建模时，暴露了局限性。以往的模型要么未考虑由于拮抗毒性导致的非单调性，要么存在根本性缺陷，即指数级地过度放大剂量-反应中单个药物的贡献。这尤其会在药物组合表现出拮抗毒性时引发问题，此时毒性概率随剂量增大而变得极低。我们借鉴帕拉塞尔苏斯"剂量决定毒性"的直觉，提出额外的约束条件以避免这一缺陷，并提出一种与这些约束兼容的改进型相互作用模型。我们创建了有启发性的数据场景，展示了这种更受约束的药物-药物相互作用模型在防止过度毒性剂量组合下继续给药以及在拮抗药物毒性下进行更合理剂量探索方面的改进行为。该模型现已集成至开源OncoBayes2 R软件包中，该软件包支持任意数量药物和试验臂的BLRM框架。