Modelling-informed cell-seeded nerve repair construct designs for treating peripheral nerve injuries.

Area of Science:

• Biomaterials Science
• Regenerative Medicine
• Computational Biology

Background:

• Peripheral nerve injuries (PNI) affect millions globally, causing significant disability and healthcare costs.
• Current treatments for large-gap PNIs often yield suboptimal functional recovery.
• Engineered Neural Tissue (EngNT) offers a promising alternative for nerve repair.

Purpose of the Study:

• To develop a computational model for predicting EngNT repair construct performance.
• To identify mechanisms driving maximal nerve regeneration and functional recovery.
• To evaluate various EngNT design parameters computationally before in vivo testing.

Main Methods:

• Derivation of a cell-solute model comprising coupled non-linear diffusion-reaction equations.
• Simulation of cell population dynamics and interactions with oxygen and vascular endothelial growth factor (VEGF) fields within the first 24 hours post-transplant.
• In silico evaluation of different EngNT repair construct designs, including cell-seeding strategies and sheath materials.

Main Results:

• The model accurately simulates the complex interactions within EngNT constructs.
• Results indicate that cell seeding beyond a critical density threshold is detrimental to nerve regeneration.
• Computational analysis allows for the rapid screening of potential EngNT designs.

Conclusions:

• Computational modeling provides a cost-effective and time-efficient approach to optimize EngNT design for peripheral nerve repair.
• The study highlights the critical role of cell density in EngNT performance.
• This approach can accelerate the development of effective treatments for peripheral nerve injuries.