Ebling W F, Wada D R, Stanski D R
Department of Pharmaceutics, State University of New York at Buffalo, 14260-1200.
J Pharmacokinet Biopharm. 1994 Aug;22(4):259-92. doi: 10.1007/BF02353622.
Physiologically based pharmacokinetic modeling procedures employ anatomical tissue weight, blood flow, and steady tissue/blood partition data, often obtained from different sources, to construct a system of differential equations that predict blood and tissue concentrations. Because the system of equations and the number of variables optimized is considerable, physiologic modeling frequently remains a simulation activity where fits to the data are adjusted by eye rather than with a computer-driven optimization algorithm. We propose a new approach to physiological modeling in which we characterize drug disposition in each tissue separately using constrained numerical deconvolution. This technique takes advantage of the fact that the drug concentration time course, CT(t), in a given tissue can be described as the convolution of an input function with the unit disposition function (UDFT) of the drug in the tissue, (i.e., CT(t) = (Ca(t)QT)*UDFT(t) where Ca(t) is the arterial concentration, Q tau is the tissue blood flow and * is the convolution operator). The obtained tissue until disposition function (UDF) for each tissue describes the theoretical disposition of a unit amount of drug infected into the tissue in the absence of recirculation. From the UDF, a parametric model for the intratissue disposition of each tissue can be postulated. Using as input the product of arterial concentration and blood flow, this submodel is fit separately utilizing standard nonlinear regression programs. In a separate step, the entire body is characterized by reassembly of the individuals submodels. Unlike classical physiologic modeling the fit for a given tissue is not dependent on the estimates obtained for other tissues in the model. Additionally, because this method permits examination of individual UDFs, appropriate submodel selection is driven by relevant information. This paper reports our experience with a piecewise modeling approach for thiopental disposition in the rat.
基于生理的药代动力学建模程序利用解剖组织重量、血流量以及通常从不同来源获得的稳态组织/血液分配数据,构建一个预测血液和组织浓度的微分方程组。由于方程组和优化变量的数量相当可观,生理建模常常仍是一种模拟活动,在这种活动中,对数据的拟合是通过肉眼而非计算机驱动的优化算法进行调整的。我们提出一种新的生理建模方法,即使用约束数值反卷积分别表征每个组织中的药物处置情况。该技术利用了这样一个事实,即给定组织中的药物浓度随时间变化过程CT(t),可描述为输入函数与药物在该组织中的单位处置函数(UDFT)的卷积(即CT(t) = (Ca(t)QT)*UDFT(t),其中Ca(t)是动脉浓度,Q tau是组织血流量,*是卷积算子)。所获得的每个组织的组织单位处置函数(UDF)描述了在无再循环情况下注入组织的单位剂量药物的理论处置情况。从UDF中,可以假设每个组织的组织内处置参数模型。将动脉浓度与血流量的乘积作为输入,利用标准非线性回归程序分别对该子模型进行拟合。在单独的步骤中,通过重新组合各个子模型来表征整个身体。与经典生理建模不同,给定组织的拟合不依赖于模型中其他组织的估计值。此外,由于该方法允许检查单个UDF,因此合适的子模型选择由相关信息驱动。本文报告了我们对大鼠硫喷妥钠处置的分段建模方法的经验。