Mo Gary, Gibbons Frank, Schroeder Patricia, Krzyzanski Wojciech
Department of Pharmaceutical Sciences, University at Buffalo, Buffalo, New York, United States of America; DMPK Modeling and Simulation, Oncology, iMED, AstraZeneca, Waltham, Massachusetts, United States of America.
DMPK Modeling and Simulation, Oncology, iMED, AstraZeneca, Waltham, Massachusetts, United States of America.
PLoS One. 2014 Oct 21;9(10):e109747. doi: 10.1371/journal.pone.0109747. eCollection 2014.
Accurate prediction of tumor growth is critical in modeling the effects of anti-tumor agents. Popular models of tumor growth inhibition (TGI) generally offer empirical description of tumor growth. We propose a lifespan-based tumor growth inhibition (LS TGI) model that describes tumor growth in a xenograft mouse model, on the basis of cellular lifespan T. At the end of the lifespan, cells divide, and to account for tumor burden on growth, we introduce a cell division efficiency function that is negatively affected by tumor size. The LS TGI model capability to describe dynamic growth characteristics is similar to many empirical TGI models. Our model describes anti-cancer drug effect as a dose-dependent shift of proliferating tumor cells into a non-proliferating population that die after an altered lifespan TA. Sensitivity analysis indicated that all model parameters are identifiable. The model was validated through case studies of xenograft mouse tumor growth. Data from paclitaxel mediated tumor inhibition was well described by the LS TGI model, and model parameters were estimated with high precision. A study involving a protein casein kinase 2 inhibitor, AZ968, contained tumor growth data that only exhibited linear growth kinetics. The LS TGI model accurately described the linear growth data and estimated the potency of AZ968 that was very similar to the estimate from an established TGI model. In the case study of AZD1208, a pan-Pim inhibitor, the doubling time was not estimable from the control data. By fixing the parameter to the reported in vitro value of the tumor cell doubling time, the model was still able to fit the data well and estimated the remaining parameters with high precision. We have developed a mechanistic model that describes tumor growth based on cell division and has the flexibility to describe tumor data with diverse growth kinetics.
准确预测肿瘤生长对于模拟抗肿瘤药物的效果至关重要。常用的肿瘤生长抑制（TGI）模型通常只是对肿瘤生长进行经验性描述。我们提出了一种基于寿命的肿瘤生长抑制（LS TGI）模型，该模型基于细胞寿命T来描述异种移植小鼠模型中的肿瘤生长。在寿命结束时，细胞进行分裂，为了考虑肿瘤负荷对生长的影响，我们引入了一个受肿瘤大小负面影响的细胞分裂效率函数。LS TGI模型描述动态生长特征的能力与许多经验性TGI模型相似。我们的模型将抗癌药物的作用描述为增殖肿瘤细胞向非增殖群体的剂量依赖性转变，这些非增殖群体在寿命TA改变后死亡。敏感性分析表明所有模型参数都是可识别的。该模型通过异种移植小鼠肿瘤生长的案例研究进行了验证。来自紫杉醇介导的肿瘤抑制的数据被LS TGI模型很好地描述，并且模型参数得到了高精度的估计。一项涉及蛋白酪蛋白激酶2抑制剂AZ968的研究包含的肿瘤生长数据仅表现出线性生长动力学。LS TGI模型准确地描述了线性生长数据，并估计了AZ968的效力，该效力与已建立的TGI模型的估计值非常相似。在泛Pim抑制剂AZD1208的案例研究中，从对照数据无法估计倍增时间。通过将参数固定为报道的肿瘤细胞倍增时间的体外值，该模型仍然能够很好地拟合数据，并高精度地估计其余参数。我们开发了一种基于细胞分裂来描述肿瘤生长的机制模型，该模型具有描述具有不同生长动力学的肿瘤数据的灵活性。
