Understanding mass transport within the brain is essential to advancing knowledge of brain diseases and improving the efficacy of treatment. It remains an ongoing challenge due to the multiscale nature of brain tissue whose microenvironment can be highly altered when affected by diseases such as glioblastomas, which are particularly resistant to treatment [1, 2]. Different computational mechanical approaches have been proposed to investigate the mechanisms governing fluid and particle transport associated in drug delivery within the brain, but models often remain incomplete and lack sufficient predictive capability [3]. They do not typically take into consideration the changing microenvironment associated with brain tumors, or are impacted by simplifications such as the linearization of the mechanical behavior [4, 5]. The aim of this work is to investigate how mechanisms associated with fluid and particle transport in the brain are influenced by the presence of the growing tumor and the deformation of the surrounding tissues. The authors propose a nonlinear porous continuum approach, inspired by the works of Sciumé [6] and Urcun [7], which uses the Thermodynamically Constrained Averaging Theory (TCAT), to model brain tissue while conserving the link between the microscale and macroscale properties of the tissue. The nonlinear and deformable behavior of the solid matrix is taken into consideration through a continuum Lagrangian framework capable of resolving the model for long temporal scales consistent with tumor growth. In addition, the varying hydraulic permeability of the brain tissue is informed by patient-specific parameters obtained from MRI diffusion imaging. This modeling approach demonstrates the coupling between the solid and fluid phases and highlights the regions of increased stress and lack of mass transport which are most impacted during tumor growth.