On the microstructurally driven heterogeneous response of brain white matter to drug infusion pressure

Abstract

Delivering therapeutic agents into the brain via convection-enhanced delivery (CED), a mechanically controlled infusion method, provides an efficient approach to bypass the blood–brain barrier and deliver drugs directly to the targeted focus in the brain. Mathematical methods based on Darcy’s law have been widely adopted to predict drug distribution in the brain to improve the accuracy and reduce the side effects of this technique. However, most of the current studies assume that the hydraulic permeability and porosity of brain tissue are homogeneous and constant during the infusion process, which is less accurate due to the deformability of the axonal structures and the extracellular matrix in brain white matter. To solve this problem, a multiscale model was established in this study, which takes into account the pressure-driven deformation of brain microstructure to quantify the change of local permeability and porosity. The simulation results were corroborated using experiments measuring hydraulic permeability in ovine brain samples. Results show that both hydraulic pressure and drug concentration in the brain would be significantly underestimated by classical Darcy’s law, thus highlighting the great importance of the present multiscale model in providing a better understanding of how drugs transport inside the brain and how brain tissue responds to the infusion pressure. This new method can assist the development of both new drugs for brain diseases and preoperative evaluation techniques for CED surgery, thus helping to improve the efficiency and precision of treatments for brain diseases.

Porosity-permeability tensor relationship of closely and randomly packed fibrous biomaterials and biological tissues: Application to the brain white matter

“…brain white matter [41, 47]. Although we have successfully unravelled the mechanisms behind the complex interactions between the microstructure and transport processes via theoretical modelling [6, 48], the practical utilisation of this relationship to gain an improved understanding of the functions of biological tissue and design capability of advanced biomaterials is still difficult due to the lack of an explicit definition of the relationship between the microstructure and transport properties. The framework developed in this study provides a solution to bridge this gap, specifically between the microstructure and the anisotropic transport property of fibrous biomaterials as well as other types of composite materials.…”

“…Fluid transport in porous media is a ubiquitous phenomenon in nature, which has attracted widespread attention and extensive research in the fields of geoscience & petroleum engineering [1], environmental engineering [2], composite material [3,4], biological & medical science [5,6] etc. The development of porous transport theories has also tremendously advanced our understanding and capability of designing biomaterials, such as the bonesubstituting and -repairing biomaterials [7,8], artificial bio-membrane [9,10], and drug carriers [11,12].…”

Prediction of initial objective response to drug-eluting beads transcatheter arterial chemoembolization for hepatocellular carcinoma using CT radiomics-based machine learning model

“…In addition, some scholars had used CT radiomics signatures based on lung cancer datasets to predict head and neck squamous cell carcinoma and renal cell carcinoma, and believe that radiomics signatures based on CT may be able to predict overall survival rates for different cancer sites (Le et al, 2023). In furthermore, scholars have developed a multiscale modelling framework to explain the microstructurally driven heterogeneity of permeability and porosity in brain tissue, aiming to better understand the importance of drug transport in the brain and the response of brain tissue to infusion pressure, and to predict the flow path and concentration distribution of drugs (Yuan et al, 2022). In the future, we can further explore the extraction of microstructural features of liver tissue and tumors from patients based on CT radiomics, to validate whether the radiomics signatures of HCC can be used to predict tumors in different organs.…”

Linking fluid-axons interactions to the macroscopic fluid transport properties of the brain

“…Solid Phase. Various constitutive models have been developed to describe the axonal mechanical behaviours, including linear elastic model [40], viscoelastic model (timeand rate-dependent response) [41], and hyperelastic model (sharp increase in stress with strain under large deformation due to the contractility generated by the molecular motors) [42]. To cover all these types of axonal mechanical behaviours, the axons were, for the first time, modelled as a visco-hyperelastic material in this study.…”