Biodistribution of Glial Progenitors in a Three Dimensional-Printed Model of the Piglet Cerebral Ventricular System.

White matter damage persists in hypoxic-ischemic newborns even when treated with hypothermia. We have previously shown that intraventricular delivery of human glial progenitors (GPs) at the neonatal stage is capable of replacing abnormal host glia and rescuing the lifespan of dysmyelinated mice. However, such transplantation in the human brain poses significant challenges as related to high-volume ventricles and long cell migration distances. These challenges can only be studied in large animal model systems. In this study, we developed a three dimensional (3D)-printed model of the ventricular system sized to a newborn pig to investigate the parameters that can maximize a global biodistribution of injected GPs within the ventricular system, while minimizing outflow to the subarachnoid space. Bioluminescent imaging and magnetic resonance imaging were used to image the biodistribution of luciferase-transduced GPs in simple fluid containers and a custom-designed, 3D-printed model of the piglet ventricular system. Seven independent variables were investigated. The results demonstrated that a low volume (0.1 mL) of cell suspension is essential to keep cells within the ventricular system. If higher volumes (1 mL) are needed, a very slow infusion speed (0.01 mL/min) is necessary. Real-time magnetic resonance imaging demonstrated that superparamagnetic iron oxide (SPIO) labeling significantly alters the rheological properties of the GP suspension, such that, even at high speeds and high volumes, the outflow to the subarachnoid space is reduced. Several other factors, including GP species (human vs. mouse), type of catheter tip (end hole vs. side hole), catheter length (0.3 vs. 7.62 m), and cell concentration, had less effect on the overall distribution of GPs. We conclude that the use of a 3D-printed phantom model represents a robust, reproducible, and cost-saving alternative to in vivo large animal studies for determining optimal injection parameters.