A bioresorbable and mechanically optimized nerve guidance conduit based on a naturally derived medium chain length polyhydroxyalkanoate and poly(e-caprolactone) blend
2021-01-21
This paper reports a novel blend of a natural medium chain length polyhydroxyalkanoate (MCL-PHA) with a synthetic aliphatic polyester, poly(ε-caprolactone) (PCL), suitable for extrusion based high-throughput manufacturing.
Severe peripheral nerve injuries represent a large clinical problem with relevant challenges such as the development of successful synthetic scaffolds as substitutes to autologous nerve grafting. Numerous studies have reported the use of polyesters and type I collagen-based nerve guidance conduits (NGCs) to promote nerve regeneration through critical nerve defects while providing protection from external factors.
However, none of the commercially available hollow bioresorbable NGCs have demonstrated superior clinical outcomes to an autologous nerve graft. Hence, new materials and NGC geometries have been explored in the literature to mimic the native nerve properties and architecture. Here, we report a novel blend of a natural medium chain length polyhydroxyalkanoate (MCL-PHA) with a synthetic aliphatic polyester, poly(ε-caprolactone) (PCL), suitable for extrusion based high-throughput manufacturing. The blend was designed to combine the excellent ability of PHAs to support the growth and proliferation of mammalian cells with the good processability of PCL. The material exhibited excellent neuroregenerative properties and a good bio resorption rate, while the extruded porous tubes exhibited similar mechanical properties to the rat sciatic nerve. The NGCs were implanted to treat a 10 mm long sciatic nerve defect in rats, where significant differences were found between thin and thick wall thickness implants, and both electrophysiological and histological data, as well as the number of recovered animals, provided superior outcomes than the well-referenced synthetic Neurolac NGC.
This research work has been partially funded by the EU-project Neurimp (EU-FP7-604450).