Intratumoral modeling of gefitinib pharmacokinetics and pharmacodynamics in an orthotopic mouse model of glioblastoma.
Intratumoral modeling of gefitinib pharmacokinetics and pharmacodynamics in an orthotopic mouse model of glioblastoma.
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DOI：
10.1158/0008-5472.can-13-0690
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发表时间：
2013-08-15
期刊：
影响因子：
11.2
通讯作者：
Gallo JM
中科院分区：
文献类型：
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作者：
Sharma J;Lv H;Gallo JM
与许多实体瘤一样，胶质母细胞瘤的特征在于肿瘤内生物异质性，这可能导致药物及其相关药效学（PD）反应的可变分布，因此基于全肿瘤匀浆分析的标准药代动力学（PK）方法可能不准确。为了解决肿瘤药理学的这一方面，我们分析了EGFR抑制剂吉非替尼在脑内肿瘤小鼠中的瘤内PK/PD特征，并开发了相应的数学模型。单次口服吉非替尼（50或150 mg/kg）后，根据新的脑肿瘤切片方案在选定的时间处理肿瘤，该方案产生系列样品以测量肿瘤四个不同区域中的吉非替尼浓度、磷酸化ERK和免疫组织化学（IHC）。值得注意的是，我们观察到吉非替尼瘤内浓度的变化高达3倍，但pERK水平的变化仅为该变化的一半。由于我们观察到类似程度的变化，在免疫组织化学指数称为微血管周细胞指数（MPI），在血脑屏障的渗透性的措施，我们使用MPI的混合生理为基础的PK（PBPK）模型，以解释观察到的药物分布的区域变化。随后，将PBPK模型与PD模型相关联，该模型可以解释在pERK水平中观察到的变异性。总之，我们的肿瘤切片方案能够整合吉非替尼的肿瘤内PK/PD变异性和IHC指数，然后构建预测性PBPK/PD模型。这些类型的模型为了解肿瘤异质性提供了机制基础，因为它影响抗癌药物的活性。
Like many solid tumors, glioblastomas are characterized by intratumoral biological heterogeneity that may contribute to a variable distribution of drugs and their associated pharmacodynamics (PD) responses, such that that standard pharmacokinetic (PK) approaches based on analysis of whole tumor homogenates may be inaccurate. To address this aspect of tumor pharmacology, we analyzed intratumoral PK/PD characteristics of the EGFR inhibitor gefitinib in mice with intracerebral tumors and developed corresponding mathematical models. Following a single oral dose of gefitinib (50 or 150 mg/kg), tumors were processed at selected times according to a novel brain tumor sectioning protocol that generated serial samples to measure gefitinib concentrations, phosphorylated ERK and immunohistochemistry (IHC) in four different regions of tumors. Notably, we observed up to 3-fold variations in intratumoral concentrations of gefitinib, but only up to half this variability in pERK levels. Since we observed a similar degree of variation in the immunohistochemical index termed the microvessel pericyte index (MPI), a measure of permeability in the blood-brain barrier, we used MPI in a hybrid physiologically-based PK (PBPK) model to account for regional changes in drug distribution that were observed. Subsequently, the PBPK models were linked to a PD model that could account for the variability observed in pERK levels. Together, our tumor sectioning protocol enabled integration of the intratumoral PK/PD variability of gefitinib and IHC indices followed by the construction of a predictive PBPK/PD model. These types of models offer a mechanistic basis to understand tumor heterogeneity as it impacts the activity of anticancer drugs.