Modeling and Simulation, Vantage Research, Chennai, India.
Modeling and Simulation, Generable, New York, New York, USA.
CPT Pharmacometrics Syst Pharmacol. 2024 Aug;13(8):1317-1326. doi: 10.1002/psp4.13161. Epub 2024 Jun 11.
Phase Ib trials are common in oncology development but often are not powered for statistical significance. Go/no-go decisions are largely driven by observed trends in response data. We applied a bootstrapping method to systematically compare tumor dynamic end points to historical control data to identify drugs with clinically meaningful efficacy. A proprietary mathematical model calibrated to phase Ib anti-PD-1 therapy trial data (KEYNOTE-001) was used to simulate thousands of phase Ib trials (n = 30) with a combination of anti-PD-1 therapy and four novel agents with varying efficacy. A redacted bootstrapping method compared these results to a simulated phase III control arm (N = 511) while adjusting for differences in trial duration and cohort size to determine the probability that the novel agent provides clinically meaningful efficacy. Receiver operating characteristic (ROC) analysis showed strong ability to separate drugs with modest (area under ROC [AUROC] = 83%), moderate (AUROC = 96%), and considerable efficacy (AUROC = 99%) from placebo in early-phase trials (n = 30). The method was shown to effectively move drugs with a range of efficacy through an in silico pipeline with an overall success rate of 93% and false-positive rate of 7.5% from phase I to phase III. This model allows for effective comparisons of tumor dynamics from early clinical trials with more mature historical control data and provides a framework to predict drug efficacy in early-phase trials. We suggest this method should be employed to improve decision making in early oncology trials.
Ib 期临床试验在肿瘤学发展中很常见，但通常没有统计学意义的功效。是/否决策主要取决于反应数据的观察趋势。我们应用了一种自举方法，系统地将肿瘤动态终点与历史对照数据进行比较，以确定具有临床意义疗效的药物。一种专有的数学模型经过 Ib 期抗 PD-1 治疗试验数据（KEYNOTE-001）校准，用于模拟数千个 Ib 期试验（n=30），其中包括抗 PD-1 治疗和四种新型药物的组合，这些药物的疗效各不相同。一种经过编辑的自举方法将这些结果与模拟的 III 期对照臂（n=511）进行了比较，同时考虑了试验持续时间和队列大小的差异，以确定新型药物具有临床意义疗效的可能性。接受者操作特征（ROC）分析显示，在早期试验（n=30）中，该方法具有较强的区分能力，能够区分疗效适中（ROC 下面积 [AUROC] = 83%）、中度（AUROC = 96%）和显著疗效（AUROC = 99%）的药物与安慰剂。该方法被证明可以有效地将具有不同疗效的药物通过一个计算管道，总体成功率为 93%，假阳性率为 7.5%，从 I 期到 III 期。该模型允许将早期临床试验中的肿瘤动力学与更成熟的历史对照数据进行有效比较，并提供了一种预测早期临床试验中药物疗效的框架。我们建议采用这种方法来改善早期肿瘤学试验中的决策制定。
