Zhou Qingyu, Guo Ping, Kruh Gary D, Vicini Paolo, Wang Xiaomin, Gallo James M
Department of Pharmaceutical Sciences, School of Pharmacy, Temple University, Philadelphia, PA 19140, USA.
Clin Cancer Res. 2007 Jul 15;13(14):4271-9. doi: 10.1158/1078-0432.CCR-07-0658.
Knowledge of drug concentrations in tumors is critical for understanding the determinants of drug accumulation in tumors. Because significant obstacles prevent making these measurements in humans, development of a predictive pharmacokinetic model would be of great value to the translation of preclinical data to the clinic. Our goal was to show how the latter could be achieved for temozolomide, an agent used in the treatment of brain tumors, using an orthotopic brain tumor model in rats.
Rats bearing i.c. tumors received 20 mg/kg i.v. of temozolomide followed by the subsequent measurement of serial plasma, cerebrospinal fluid (CSF), normal brain, and brain tumor temozolomide concentrations. The resultant data provided the framework to develop a hybrid physiologically based pharmacokinetic model for temozolomide in brain. The preclinical pharmacokinetic model was scaled to predict temozolomide concentrations in human CSF, normal brain, and brain tumor, and through a series of Monte Carlo simulations, the accumulation of temozolomide in brain tumors under conditions of altered blood-brain barrier permeability, fractional blood volume, and clinical dosing schedules was evaluated.
The developed physiologically based pharmacokinetic model afforded a mechanistic and accurate prediction of temozolomide brain disposition in rats, which through model scale-up procedures accurately predicted the CSF/plasma area under the drug concentration-time curve ratios of 0.2 reported in patients. Through a series of model simulations, it was shown that the brain tumor accumulation of temozolomide varied substantially based on changes in blood-brain barrier permeability and fractional tumor blood volume but minimally based on clinical dosing regimens.
A physiologically based pharmacokinetic modeling approach offers a means to translate preclinical to clinical characteristics of drug disposition in target tissues and, thus, a means to select appropriate drug dosing regimens for achieving optimal target tissue drug concentrations.
了解肿瘤中的药物浓度对于理解药物在肿瘤中蓄积的决定因素至关重要。由于存在重大障碍,难以在人体中进行这些测量,因此开发预测性药代动力学模型对于将临床前数据转化为临床应用具有重要价值。我们的目标是展示如何使用大鼠原位脑肿瘤模型,对用于治疗脑肿瘤的替莫唑胺实现后者。
颅内接种肿瘤的大鼠静脉注射20mg/kg替莫唑胺,随后连续测量血浆、脑脊液(CSF)、正常脑和脑肿瘤中的替莫唑胺浓度。所得数据为开发替莫唑胺在脑中的基于生理学的混合药代动力学模型提供了框架。将临床前药代动力学模型进行缩放,以预测人CSF、正常脑和脑肿瘤中的替莫唑胺浓度,并通过一系列蒙特卡洛模拟,评估在血脑屏障通透性、血容量分数和临床给药方案改变的情况下,替莫唑胺在脑肿瘤中的蓄积情况。
所开发的基于生理学的药代动力学模型对替莫唑胺在大鼠脑中的处置提供了机械且准确的预测,通过模型放大程序准确预测了患者中报道的药物浓度-时间曲线下CSF/血浆面积比为0.2。通过一系列模型模拟表明,替莫唑胺在脑肿瘤中的蓄积根据血脑屏障通透性和肿瘤血容量分数的变化有很大差异,但根据临床给药方案的差异最小。
基于生理学的药代动力学建模方法提供了一种将临床前药物处置特征转化为临床特征的手段,因此,是选择合适给药方案以实现最佳靶组织药物浓度的一种手段。