Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, Maryland, USA.
J Immunother Cancer. 2021 Feb;9(2). doi: 10.1136/jitc-2020-002100.
Immune checkpoint blockade therapy has clearly shown clinical activity in patients with triple-negative breast cancer, but less than half of the patients benefit from the treatments. While a number of ongoing clinical trials are investigating different combinations of checkpoint inhibitors and chemotherapeutic agents, predictive biomarkers that identify patients most likely to benefit remains one of the major challenges. Here we present a modular quantitative systems pharmacology (QSP) platform for immuno-oncology that incorporates detailed mechanisms of immune-cancer cell interactions to make efficacy predictions and identify predictive biomarkers for treatments using atezolizumab and nab-paclitaxel.
A QSP model was developed based on published data of triple-negative breast cancer. With the model, we generated a virtual patient cohort to conduct in silico virtual clinical trials and make retrospective analyses of the pivotal IMpassion130 trial that led to the accelerated approval of atezolizumab and nab-paclitaxel for patients with programmed death-ligand 1 (PD-L1) positive triple-negative breast cancer. Available data from clinical trials were used for model calibration and validation.
With the calibrated virtual patient cohort based on clinical data from the placebo comparator arm of the IMpassion130 trial, we made efficacy predictions and identified potential predictive biomarkers for the experimental arm of the trial using the proposed QSP model. The model predictions are consistent with clinically reported efficacy endpoints and correlated immune biomarkers. We further performed a series of virtual clinical trials to compare different doses and schedules of the two drugs for simulated therapeutic optimization.
This study provides a QSP platform, which can be used to generate virtual patient cohorts and conduct virtual clinical trials. Our findings demonstrate its potential for making efficacy predictions for immunotherapies and chemotherapies, identifying predictive biomarkers, and guiding future clinical trial designs.
免疫检查点阻断疗法在三阴性乳腺癌患者中已明显显示出临床疗效，但只有不到一半的患者从中获益。虽然目前有许多临床试验正在研究不同的免疫检查点抑制剂和化疗药物联合方案，但寻找能识别最有可能获益的患者的预测性生物标志物仍然是主要挑战之一。在这里，我们提出了一个用于肿瘤免疫的模块化定量系统药理学（QSP）平台，该平台结合了详细的免疫-癌细胞相互作用机制，以进行疗效预测并确定使用阿替利珠单抗和白蛋白紫杉醇治疗的预测性生物标志物。
基于三阴性乳腺癌的已发表数据，我们开发了一个 QSP 模型。使用该模型，我们生成了一个虚拟患者队列，以进行虚拟临床试验，并对导致阿替利珠单抗和白蛋白紫杉醇加速批准用于程序性死亡配体 1（PD-L1）阳性三阴性乳腺癌患者的关键性 IMpassion130 试验进行回顾性分析。来自临床试验的可用数据用于模型校准和验证。
使用来自 IMpassion130 试验安慰剂对照臂的临床数据校准的虚拟患者队列，我们使用提出的 QSP 模型对试验的实验组进行了疗效预测并确定了潜在的预测性生物标志物。模型预测与临床报告的疗效终点和相关免疫生物标志物一致。我们进一步进行了一系列虚拟临床试验，以比较两种药物的不同剂量和方案用于模拟治疗优化。
这项研究提供了一个 QSP 平台，可用于生成虚拟患者队列和进行虚拟临床试验。我们的研究结果表明，它具有用于免疫治疗和化疗的疗效预测、识别预测性生物标志物以及指导未来临床试验设计的潜力。
