Over 500,000 Americans suffer from peripheral nerve injury (PNI), and despite surgical interventions, most suffer permanent loss of motor function and sensation. Current clinical options for long nerve gap PNI include naturally- derived grafts, which provide native matrix cues to regenerate neurons but suffer from very limited supply and batch-to-batch variability, or synthetic nerve guidance conduits (NGCs), which are easy to manufacture but often fail due to lack of regenerative cues. The main challenge with using any NGC for treatment of PNI is the immense trade-off between providing the complex matrix cues necessary for optimal nerve regeneration while providing a conduit that is readily available, reproducible, and easily fabricated. To overcome this challenge, we propose an entirely new type of biomaterial: a computationally optimized, protein-engineered recombinant NGC (rNGC). This rNGC combines the reliability of synthetic NGCs with the presentation of multiple regenerative matrix cues of natural NGCs. Because current understanding of cell-matrix interactions is insufficient to enable to direct design of a fully functional rNGC, we hypothesize that the use of machine learning, computational optimization methods will allow identification of an rNGC that promotes nerve regeneration similar to the current gold standard autograft. We utilize a family of protein-engineered, elastin-like proteins (ELPs) that are reproducible, with predictable, consistent material properties, and fully chemically defined for streamlined FDA approval. Due to ELPs? modular design, they have biomechanical (i.e. matrix stiffness) and biochemical (i.e. cell-adhesive ligand) properties that are independently tunable over a broad range. While numerous studies detail the effects of individual biomechanical or biochemical matrix cues on neurite outgrowth using single-variable approaches, their combinatorial effects have been largely unexplored as insufficient knowledge exists to make accurate predictions of their interactions a priori. This fundamentally prohibits the direct design of combinatorial matrix cues. We hypothesize that optimized presentation of biomechanical and biochemical cues will create a microenvironment that better mimics the native ECM milieu, resulting in synergistic ligand cross-talk to improve nerve regeneration.
In Aim 1, we use computational optimization methods to identify the combination of ligand identities, ligand concentrations, and matrix stiffness that best enhances neurite outgrowth. We will develop and characterize a library of ELP variants with distinct cell-adhesive ligands derived from native ECM, and assess their ability to support neurite outgrowth from rat dorsal root ganglia (DRG).
In Aim 2, we will validate our in vitro optimization results in a preclinical, rat sciatic nerve injury model. A core-shell, ELP-based rNGC with an inner core matrix of the optimized ELP formulation from Aim 1 will be fabricated and evaluated for its ability to enhance therapeutic outcome. Controls include reversed nerve autograft, hollow silicone conduit, and non-optimized ELP- based rNGC. This study would represent the first use of computational optimization methods to design a reproducible, reliable, recombinant biomaterial with multiple regenerative matrix cues.
The main challenge with using nerve guidance conduits (NGCs) to bridge long peripheral nerve gap injuries is the immense trade-off between providing the complex matrix cues necessary for optimal nerve regeneration while providing a conduit that is readily available, reproducible, and easily fabricated. To address this challenge, here we utilize (1) computational optimization methods to identify the optimal biochemical and biomechanical matrix cues for nerve regeneration, and (2) advanced protein-engineering strategies to incorporate these cues into a recombinant NGC (rNGC). Our rNGC combines the reliability of synthetic NGCs with the matrix cues of naturally-derived NGCs to make an affordable, off-the-shelf rNGC that promotes nerve regeneration.
