Sanofi Bridgewater, New Jersey, US.
Harvard University, Cambridge, MA, US.
BMC Cancer. 2024 Jul 29;24(1):912. doi: 10.1186/s12885-024-12664-1.
In oncology anti-PD1 / PDL1 therapy development for solid tumors, objective response rate (ORR) is commonly used clinical endpoint for early phase study decision making, while progression free survival (PFS) and overall survival (OS) are widely used for late phase study decision making. Developing predictive models to late phase outcomes such as median PFS (mPFS) and median OS (mOS) based on early phase clinical outcome ORR could inform late phase study design optimization and probability of success (POS) evaluation. In existing literature, there are ORR / mPFS / mOS association and surrogacy investigations with limited number of included clinical trials. In this paper, without establishing surrogacy, we attempt to predict late phase survival (mPFS and mOS) based on early efficacy ORR and optimize late phase trial design for anti-PD1 / PDL1 therapy development. In order to include adequate number of eligible clinical trials, we built a comprehensive quantitative clinical trial landscape database (QLD) by combining information from different sources such as clinicaltrial.gov, publications, company press releases for relevant indications and therapies. We developed a generalizable algorithm to systematically extract structured data for scientific accuracy and completeness. Finally, more than 150 late phase clinical trials were identified for ORR / mPFS (ORR / mOS) predictive model development while existing literature included at most 50 trials. A tree-based machine learning regression model has been derived to account for ORR / mPFS (ORR / mOS) relationship heterogeneity across tumor type, stage, line of therapy, treatment class and borrow strength simultaneously when homogeneity persists. The proposed method ensures that the predictive model is robust and have explicit structure for clinical interpretation. Through cross validation, the average predictive mean square error of the proposed model is competitive to random forest and extreme gradient boosting methods and outperforms commonly used additive or interaction linear regression models. An example application of the proposed ORR / mPFS (ORR / mOS) predictive model on late phase trial POS evaluation for anti-PD1 / PDL1 combination therapy was illustrated.
在肿瘤学中，抗 PD1/PDL1 疗法在实体瘤中的开发中，客观缓解率（ORR）通常被用作早期研究决策的临床终点，而无进展生存期（PFS）和总生存期（OS）则被广泛用于晚期研究决策。基于早期临床结果 ORR 开发预测模型，可用于预测晚期结果（如中位 PFS（mPFS）和中位 OS（mOS）），从而为晚期研究设计优化和成功概率（POS）评估提供信息。在现有文献中，已有基于有限数量临床试验的 ORR/mPFS/mOS 关联和替代物研究。在本文中，我们没有建立替代物关系，而是尝试基于早期疗效 ORR 预测晚期生存（mPFS 和 mOS），并优化抗 PD1/PDL1 治疗开发的晚期试验设计。为了纳入足够数量的合格临床试验，我们通过整合来自 clinicaltrial.gov、出版物、相关适应症和疗法公司新闻稿等不同来源的信息，构建了一个全面的定量临床试验景观数据库（QLD）。我们开发了一种可推广的算法，以系统地提取结构化数据，确保科学准确性和完整性。最后，确定了 150 多项晚期临床试验，用于开发 ORR/mPFS（ORR/mOS）预测模型，而现有文献最多只包含 50 项试验。基于树的机器学习回归模型已被推导出来，以同时考虑肿瘤类型、阶段、治疗线、治疗类别和借用强度的 ORR/mPFS（ORR/mOS）关系异质性，当存在同质性时，该模型具有更强的解释能力。该方法确保预测模型稳健，并具有明确的临床解释结构。通过交叉验证，该方法的平均预测均方误差与随机森林和极端梯度提升方法相当，优于常用的加性或交互线性回归模型。通过一个抗 PD1/PDL1 联合治疗晚期试验 POS 评估的实例应用，说明了所提出的 ORR/mPFS（ORR/mOS）预测模型的应用。
