Bridging the Gap: Dextran-Based 3D Printed Nerve Guidance Conduits for Peripheral Nerve Repair

Abstract

Injuries to peripheral nerves are common but currently lack adequate treatment options for satisfactory functional repair. The greater the severity of the peripheral nerve injury (PNI), the likelihood of patient recovery decreases, with effective repair becoming progressively more challenging for longer nerve gaps. The current gold standard treatment for PNIs is autologous nerve grafting. However, autografts are not always successful and can lead to morbidity at the donor site. Alternative treatment options are currently available in the form of nerve guidance conduits (NGCs). These are engineered tubes of natural and synthetic materials that are designed to direct nerve repair. Nonetheless, the currently clinically available NGCs have their disadvantages and are limited in efficacy in repairing nerve gaps larger than 20 mm. A dextran and polycaprolactone (PCL) 3D printed NGC fabricated using digital light processing (DLP) is presented as a potential solution to overcome the shortcomings of currently available devices. The formulation of the biomaterial is demonstrated to be tuneable, and optimisation has been achieved to make it suitable for DLP-based 3D printing. Methacrylated dextran and PCL-diol is shown to form hydrolytically degradable polymer networks when exposed to 405 nm violet-blue light, suggesting its suitability as a biomaterial ink for DLP-based 3D printing of NGCs. To optimise the dextran/PCL biomaterial ink for DLP-based 3D printing, a step-by-step optimisation framework was designed and validated by tuning the formulation of the dextran‑GMA/PCL‑IEMA biomaterial ink and the printer parameters needed to fabricate dimensionally accurate constructs. The optimisation pipeline incorporated photorheology to define gelation kinetics and crosslink density, a novel low‑volume, laser‑scanning confocal microscopy working‑curve method to determine critical exposure and depth of penetration, and systematic printer calibration with shrinkage quantification and compensation, thereby minimising trial‑and‑error and providing a generally applicable, low volume, high throughput pathway for solvent‑based photocurable biomaterial inks.

Date of Award	26 Nov 2025
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Julie Gough (Main Supervisor), Adam Reid (Co Supervisor) & Marco Domingos (Co Supervisor)