Department of Pharmaceutical Informatics, Academy of Pharmacy, Shenyang Pharmaceutical University, Shenyang, Liaoning, China.
BMC Med Res Methodol. 2023 Mar 2;23(1):57. doi: 10.1186/s12874-023-01867-y.
Combinations of drugs are becoming increasingly common in oncology treatment. In some cases, patients can benefit from the interaction between two drugs, although there is usually a higher risk of developing toxicity. Due to drug-drug interactions, multidrug combinations often exhibit different toxicity profiles than those of single drugs, leading to a complex trial scenario. Numerous methods have been proposed for the design of phase I drug combination trials. For example, the two-dimensional Bayesian optimal interval design for combination drug (BOINcomb) is simple to implement and has desirable performance. However, in scenarios where the lowest and starting dose is close to being toxic, the BOINcomb design may tend to allocate more patients to overly toxic doses, and select an overly toxic dose combination as the maximum tolerated dose combination.
To improve the performance of BOINcomb in the above extreme scenarios, we widen the range of variation of the boundaries by setting the self-shrinking dose escalation and de-escalation boundaries. We refer to the new design as adaptive shrinking Bayesian optimal interval design for combination drug (asBOINcomb). We conduct a simulation study to evaluate the performance of the proposed design using a real clinical trial example.
Our simulation results show that asBOINcomb is more accurate and stable than BOINcomb, especially in some extreme scenarios. Specifically, in all ten scenarios, the percentage of correct selection is higher than the BOINcomb design within 30 to 60 patients.
The proposed asBOINcomb design is transparent and simple to implement and can reduce the trial sample size while maintaining accuracy compared with the BOINcomb design.
药物联合使用在肿瘤治疗中越来越常见。在某些情况下，两种药物的相互作用可以使患者受益，尽管通常会增加产生毒性的风险。由于药物相互作用，多药物联合往往表现出与单一药物不同的毒性特征，导致试验情况复杂。已经提出了许多方法来设计 I 期药物联合试验。例如，二维贝叶斯最优区间设计（BOINcomb）用于组合药物设计简单易用，具有良好的性能。然而，在最低起始剂量接近毒性的情况下，BOINcomb 设计可能倾向于将更多的患者分配到毒性过高的剂量，从而选择毒性过高的剂量组合作为最大耐受剂量组合。
为了提高 BOINcomb 在上述极端情况下的性能，我们通过设置自收缩剂量递增和递减边界来扩大边界的变化范围。我们将新设计称为自适应收缩贝叶斯最优区间设计（asBOINcomb）。我们进行了一项模拟研究，使用真实的临床试验示例来评估所提出设计的性能。
我们的模拟结果表明，asBOINcomb 比 BOINcomb 更准确和稳定，尤其是在某些极端情况下。具体来说，在所有十个场景中，在 30 到 60 名患者内，正确选择的比例均高于 BOINcomb 设计。
与 BOINcomb 设计相比，所提出的 asBOINcomb 设计具有透明性和易于实现的优点，可以在保持准确性的同时减少试验样本量。
