Mathematical Institute, Niels Bohrweg 1, 2333CA, Leiden, The Netherlands.
Leiden Academic Center for Drug Research, Einsteinweg 55, 2333CC, Leiden, The Netherlands.
Pharm Res. 2020 Jul 9;37(7):137. doi: 10.1007/s11095-020-2760-y.
We have developed a 3D brain unit network model to understand the spatial-temporal distribution of a drug within the brain under different (normal and disease) conditions. Our main aim is to study the impact of disease-induced changes in drug transport processes on spatial drug distribution within the brain extracellular fluid (ECF).
The 3D brain unit network consists of multiple connected single 3D brain units in which the brain capillaries surround the brain ECF. The model includes the distribution of unbound drug within blood plasma, coupled with the distribution of drug within brain ECF and incorporates brain capillaryblood flow, passive paracellular and transcellular BBB transport, active BBB transport, brain ECF diffusion, brain ECF bulk flow, and specific and nonspecific brain tissue binding. All of these processes may change under disease conditions.
We show that the simulated disease-induced changes in brain tissue characteristics significantly affect drug concentrations within the brain ECF.
We demonstrate that the 3D brain unit network model is an excellent tool to gain understanding in the interdependencies of the factors governing spatial-temporal drug concentrations within the brain ECF. Additionally, the model helps in predicting the spatial-temporal brain ECF concentrations of existing drugs, under both normal and disease conditions.
我们开发了一个 3D 脑单位网络模型，以了解在不同（正常和疾病）条件下药物在大脑中的时空分布。我们的主要目的是研究疾病引起的药物转运过程变化对脑细胞外液（ECF）中药物空间分布的影响。
3D 脑单位网络由多个相互连接的单个 3D 脑单位组成，其中脑毛细血管围绕脑 ECF。该模型包括未结合药物在血浆中的分布，与脑 ECF 中药物的分布相结合，并纳入脑毛细血管血流、被动细胞旁和跨细胞 BBB 转运、主动 BBB 转运、脑 ECF 扩散、脑 ECF 体循环和特异性及非特异性脑组织结合。所有这些过程在疾病条件下都可能发生变化。
我们表明，模拟的疾病引起的脑组织特征变化显著影响脑 ECF 内的药物浓度。
我们证明 3D 脑单位网络模型是一种很好的工具，可以了解控制脑 ECF 内药物时空浓度的因素之间的相互依存关系。此外，该模型有助于预测正常和疾病条件下现有药物在脑 ECF 中的时空浓度。
