Development of Immunologically and Mechanically Instructive Tissue Engineering Scaffolds for Rotator Cuff Repair

Abstract

Rotator cuff injury is one of the most common upper extremity injuries that requires surgery globally. These injuries cause pain, reduced mobility, and negatively affect activities of daily life. Making matters worse is the unacceptably high rate of retears post-surgery, exacerbating the patient issues prior to surgical intervention. Clinically, the bone tendon junction or enthesis vanishes post-injury with scar tissue replacing the native biomechanical gradient that was present prior to injury. This results in a weaker junction increasing chance of retear. Tendon healing is largely an unknown process, but it is well documented that there are two large influencers in the process of tissue repair: the mechanical stimuli of tendon cells (tenocytes), and their subsequent response, and the immunological response to damaged tissue. Given the primary drivers of tissue repair, work was sought out to develop two novel technologies that could eventually work synergistically with the overarching goal of creating better treatments for rotator cuff injury. The first therapy was to incorporate pattern recognition receptor agonists into a hydrogel that could be injected into the rotator cuff as a novel immunotherapy. This therapy was not found to have histological changes in the tendon after treatment but demonstrated the ability to hasten functional recovery in gait outcomes in a rat model as well as down regulate key markers of tendon degeneration in a benchtop setting. The next therapy was a rotator cuff repair augment that used an emerging additive manufacturing technique, melt electrowriting, to generate a novel architecture with a gradient of mechanical properties that reflected those seen in a healthy human rotator cuff. This gradient scaffold would be able to mechanically “instruct” local cell populations to regenerate the tissue type(s) of interest. These architectures were then screened using parametric computational analyses to examine their initial promise in providing appropriate strain levels to bone and tendon regionally. To further the investigation of the mechanically instructive scaffold, analysis of the temporal cascade of tissue ingrowth on the scaffold was completed. Marking the first time computational models have been used to generate data on how a scaffold’s mechanics can drive tendon growth and the first time in silico models have modeled multi-tissue healing on a substrate over time. Together, these aims will provide new avenues to increase clinical outcomes in rotator cuff injury and lay foundational groundwork into screening tissue engineering scaffolds for soft tissue injury using computational methods.