Zhao Wei, Vicini Paolo, Novick Steven, Anderton Judith, Davies Gareth, DAngelo Gina, O'Day Terrance, Yu Binbing, Harper Jay, Narwal Rajesh, Roskos Lorin, Yang Harry
Statistical Sciences, AstraZeneca PLC, Cambridge, UK.
Clinical Pharmacology, AstraZeneca PLC, Cambridge, UK.
Pharm Stat. 2019 Nov;18(6):688-699. doi: 10.1002/pst.1952. Epub 2019 May 29.
Linear models are generally reliable methods for analyzing tumor growth in vivo, with drug effectiveness being represented by the steepness of the regression slope. With immunotherapy, however, not all tumor growth follows a linear pattern, even after log transformation. Tumor kinetics models are mechanistic models that describe tumor proliferation and tumor killing macroscopically, through a set of differential equations. In drug combination studies, although an additional drug-drug interaction term can be added to such models, however, the drug interactions suggested by tumor kinetics models cannot be translated directly into synergistic effects. We have developed a novel statistical approach that simultaneously models tumor growth in control, monotherapy, and combination therapy groups. This approach makes it possible to test for synergistic effects directly and to compare such effects among different studies.
线性模型通常是分析体内肿瘤生长的可靠方法,药物有效性由回归斜率的陡度表示。然而,对于免疫疗法,即使经过对数转换,并非所有肿瘤生长都呈线性模式。肿瘤动力学模型是通过一组微分方程从宏观上描述肿瘤增殖和肿瘤杀伤的机制模型。在联合用药研究中,虽然可以在这类模型中添加一个额外的药物-药物相互作用项,但是肿瘤动力学模型所提示的药物相互作用并不能直接转化为协同效应。我们开发了一种新颖的统计方法,可同时对对照组、单药治疗组和联合治疗组的肿瘤生长进行建模。这种方法使得直接测试协同效应并在不同研究之间比较此类效应成为可能。