Virtual CNS Test Station for Computational Prediction of Species Transport in the CSF

Currently, the behavior and phenomena governing cerebrospinal drug distribution in the subarachnoid space (SAS) are unknown. There is no standardized system for testing the dynamic behavior of intrathecally (IT) delivered agents, and their distribution is not well understood. Published data measuring drug spread in animal subjects yield conflicting drug distribution profiles. Scaling distribution profiles in the cerebrospinal fluid (CSF) from animal models to human is untested, thereby data is less applicable to a clinical or research setting. Obtaining measured data from human subjects require invasive sampling of CSF and time consuming imaging procedures. The long term goal of this project is to develop a standardized system to accurately predict distribution profiles of individual drugs in subject-specific models of the CSF. Such a system would provide pharmaceutical companies a tool to develop new compounds and treatment modalities while reducing cost and development time. As a research tool this model can be utilized to increase understanding of the driving forces behind CSF circulation, and how disruption of those forces can influence development of pathological states such as hydrocephalus or syringomyelia. The immediate goals are to use a computational model to independently capture the phenomena of CSF dispersion dynamics and test novel applications. The central hypothesis is a computational model can accurately predict IT drug dispersion in a subject specific model by incorporating the pulsatile nature of CSF flow combined with spinal microanatomical obstacles. Work conducted in an idealized spinal analogue bench-top model demonstrated a cephalad bias for dye motion in vertically oriented spines with nerve roots and pulsatile CSF flow. Preliminary in silico studies of a 3D patient specific CNS with explicitly generated CSF pulsations predict a cephalad bias of IT injected species. The virtual CNS patient offers a platform to evaluate novel IT treatment strategies and will further research capabilities to understand the evolution of CNS conditions and repective treatments.