In silicoframework to inform the design of repair constructs for peripheral nerve injury repair

Abstract

Peripheral nerve injuries affect millions of people per year and cause loss of sensation and muscle control alongside chronic pain. The most severe injuries are treated through a nerve autograft; however, donor site morbidity and poor outcomes mean alternatives are required. One option is to engineer nerve replacement tissues to provide a supportive microenvironment to encourage nerve regeneration as an alternative to nerve grafts. Currently, progress is hampered due to a lack of consensus on how to arrange materials and cells in space to maximize rate of regeneration. This is compounded by a reliance on experimental testing, which precludes extensive investigations of multiple parameters due to time and cost limitations. Here, a computational framework is proposed to simulate the growth of repairing neurites, captured using a random walk approach and parameterized against literature data. The framework is applied to a specific scenario where the engineered tissue comprises a collagen hydrogel with embedded biomaterial fibres. The size and number of fibres are optimized to maximize neurite regrowth, and the robustness of model predictions is tested through sensitivity analyses. The approach provides an in silico tool to inform the design of engineered replacement tissues, with the opportunity for further development to multi-cue environments.

Perspectives on optimizing local delivery of drugs to peripheral nerves using mathematical models

“…Numerous other mathematical approaches exist for optimizing model predictions that more efficiently explore the parameter space. These include genetic algorithms utilizing the rules that govern genetic evolution and have proven effective in a variety of related fields (Laranjeira et al, 2022 ; Mirchi & Soltani, 2020 ).…”