Our group has developed an inductively-powered, biocompatible, micro-electromechanical systems (bioMEMs) sensor that is capable of monitoring implant strain and report these data telemetrically using an external antenna. The work-to-date has produced sensors that have been fabricated on non-biological substrates (i.e. silicon and flexible tape) which can be attached to implanted hardware in order to predict the course of fracture healing in the critically-important acute healing time phase (i.e. before radiographic data are capable of discriminating the healing pathway). We have recently designed a novel technique for printing the bioMEMs circuitry directly on bone (boneMEMs) and have leveraged our existing technology to produce a new class of sensors which we postulate can be used to directly monitor bone graft loading and incorporation in vivo. These boneMEMs sensors may represent a significant diagnostic advancement because it is estimated that over 2.2 million bone graft procedures are performed annually worldwide to repair osseous defects, and many of these procedures go on to an unsatisfactory clinical result. For example, typical nonunion rates of intercalary diaphyseal reconstructions range from 15 - 35%, demonstrating the critical need for the development of novel technologies that can predict and diagnose the course of allograft incorporation in the acute post-operative time period. We hypothesize that the boneMEMs sensor can inform on the temporal loading profile and degree of incorporation of osseous repairs using instrumented bone allografts. Accordingly, the proposed research seeks to implement our bioMEMs platform directly on allograft tissue by designing novel architectures and robust biological substrate printing protocols (Specific Aim 1), use a series of in vitro experiments to evaluate the sensor's suitability for in vivo implementation (Specific Aim 2), and perform a pilot large animal in vivo study to evaluate the sensor's ability to monitor and predict large allograft incorporation and healing (Specific Aims 2 and 3). Successful development of this technology will, for the first time, provide clinicians real-time data with regard to the loading experienced by large allografts and a means for predicting the eventual course of bone graft incorporation.
Many large bone resections that require placement of an allograft produce unacceptable clinical outcomes. Our group has developed an implantable, wireless, inductively-powered (no implantable power source) and biocompatible sensor technology platform that is implementable directly on an allograft. We hypothesize that this allograft-sensor construct is capable of monitoring and predicting the incorporation and healing of large allografts in the critically- important post-operative time period, thus providing clinicians a diagnostic means to assess whether early intervention is required.
