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Improving the Prediction of Local Drug Distribution Profiles in the Brain with a New 2D Mathematical Model

机译:用新的二维数学模型改善对大脑中局部药物分布曲线的预测

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摘要

The development of drugs that target the brain is very challenging. A quantitative understanding is needed of the complex processes that govern the concentration–time profile of a drug (pharmacokinetics) within the brain. So far, there are no studies on predicting the drug concentration within the brain that focus not only on the transport of drugs to the brain through the blood–brain barrier (BBB), but also on drug transport and binding within the brain. Here, we develop a new model for a 2D square brain tissue unit, consisting of brain extracellular fluid (ECF) that is surrounded by the brain capillaries. We describe the change in free drug concentration within the brain ECF, by a partial differential equation (PDE). To include drug binding, we couple this PDE to two ordinary differential equations that describe the concentration–time profile of drug bound to specific as well as non-specific binding sites that we assume to be evenly distributed over the brain ECF. The model boundary conditions reflect how free drug enters and leaves the brain ECF by passing the BBB, located at the level of the brain capillaries. We study the influence of parameter values for BBB permeability, brain ECF bulk flow, drug diffusion through the brain ECF and drug binding kinetics, on the concentration–time profiles of free and bound drug.
机译:开发针对大脑的药物非常具有挑战性。需要对控制大脑中药物的浓度-时间曲线(药代动力学)的复杂过程进行定量理解。到目前为止,还没有关于预测大脑中药物浓度的研究,不仅关注通过血脑屏障(BBB)将药物运输到大脑,还关注药物在大脑中的运输和结合。在这里,我们为二维方脑组织单元开发了一个新模型,该模型由被脑毛细血管包围的脑细胞外液(ECF)组成。我们通过偏微分方程(PDE)描述了大脑ECF中游离药物浓度的变化。为了包括药物结合,我们将此PDE耦合到两个常微分方程,这些方程描述了结合至特定和非特异性结合位点的药物的浓度-时间曲线,我们假设这些结合位点均匀分布在脑电图上。模型边界条件反映了自由药物如何通过位于脑毛细血管水平的BBB进入和离开大脑ECF。我们研究了BBB通透性,脑ECF总体流量,药物通过脑ECF扩散以及药物结合动力学等参数值对游离和结合药物浓度-时间曲线的影响。

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