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

Abstract

Millions of people worldwide are affected by peripheral nerve injuries (PNI), involving billions of dollars in healthcare costs. Common outcomes for patients include paralysis and loss of sensation, often leading to lifelong pain and disability. Engineered Neural Tissue (EngNT) is being developed as an alternative to the current treatments for large-gap PNIs that show underwhelming functional recovery in many cases. EngNT repair constructs are composed of a stabilised hydrogel cylinder, surrounded by a sheath of material, to mimic the properties of nerve tissue. The technology also enables the spatial seeding of therapeutic cells in the hydrogel to promote nerve regeneration. The identification of mechanisms leading to maximal nerve regeneration and to functional recovery is a central challenge in the design of EngNT repair constructs. Using in vivo experiments in isolation is costly and time-consuming, offering a limited insight on the mechanisms underlying the performance of a given repair construct. To bridge this gap, we derive a cell-solute model and apply it to the case of EngNT repair constructs seeded with therapeutic cells which produce vascular endothelial growth factor (VEGF) under low oxygen conditions to promote vascularisation in the construct. The model comprises a set of coupled non-linear diffusion-reaction equations describing the evolving cell population along with its interactions with oxygen and VEGF fields during the first 24h after transplant into the nerve injury site. This model allows us to evaluate a wide range of repair construct designs (e.g. cell-seeding strategy, sheath material, culture conditions), the idea being that designs performing well over a short timescale could be shortlisted for in vivo trials. In particular, our results suggest that seeding cells beyond a certain density threshold is detrimental regardless of the situation considered, opening new avenues for future nerve tissue engineering.

A combined experimental and computational framework to evaluate the behavior of therapeutic cells for peripheral nerve regeneration

“…To this end, we derived a cell-solute model, which comprises a set of coupled partial differential equations describing the spatial and the temporal evolution of the CTX0E03 population and its local environment, including oxygen and glucose consumption and VEGF release. This allows us to assess the spatial gradients that will be established in repaired nerves and exploit this information in construct design (Coy et al, 2020(Coy et al, , 2021. This type of mathematical model has been widely used to enhance the design of engineered tissues and tissue culture bioreactors (Cochran et al, 2006;McMurtrey, 2015;Rutkowski & Heath, 2002), as they are computationally cost-effective, rely on a limited set of parameters, while still capturing most of the underlying biophysics.…”

Mathematical modelling with Bayesian inference to quantitatively characterize therapeutic cell behaviour in nerve tissue engineering

“…One popular class of mathematical models used in tissue engineering are cell-solute models [8,[11][12][13]. Such models consist of a set of continuous, coupled and generally nonlinear, partial differential equations describing the interplay between nutrients, the cell population(s) and the cell secretome in a given tissue.…”

Challenges and opportunities of integrating imaging and mathematical modelling to interrogate biological processes

“…Cutting-edge imaging technologies provide insight into both structure and function of tissues in health and disease, in terms of fundamental biology ( Walsh et al, 2021 ) and clinical outcomes ( Wen et al, 2019 ). Mathematical modelling provides a framework to integrate such data sets, test hypotheses and make both qualitative ( Berg et al, 2020 ) and quantitative ( Epp et al, 2020 , Pearce et al, 2016 ) predictions that would be challenging using experimental assessments in isolation, and can guide diagnostic and therapeutic strategies ( Coy et al, 2021 ).…”