PI: Stephen Badylak
Title: REPAIR: Regenerative Electronic Patch through Advanced Intelligent Regulation
Description: Volumetric muscle loss (VML) affects war fighters and civilians alike, involves loss of between 20-80% of any single muscle group, and overwhelms the natural regenerative capacity of skeletal muscle. Treatment options are limited, marginally effective, and consist primarily of muscle transfers/flaps; physical therapy and cell-based therapies have largely failed. An acellular extracellular matrix (ECM)-based approach has shown modest success in a small cohort of 13 patients. Key challenges to success include lack of adequate insight into in vivo stem/progenitor cell expansion which is regulated by appropriate spatial and temporal immunomodulatory signals, and lack of early and adequate neurogenesis at the wound site. This project will address both of these key rate-limiting steps. To overcome these challenges, an established pre-clinical model will be used to: 1) Continuously monitor the wound state, immune status, and neurogenesis using electrical and optical sensors/actuators and release of biologics via light-driven “living cell drug factories” 2) improve the outcome of the wound healing process with the help of in silico models and methods predicting the future state of the process and optimizing wound management actions. Regenerative Electronic Patch through Advanced Intelligent Regulation (REPAIR) is a 3D patch of optical and electrical sensors/actuators integrated within a thin layer of ECM hydrogel that is transiently and repeatedly placed at the wound bed. This approach will provide: 1. continuous classification of wound status using minimally invasive, confocal 3D interfaces with real-time electrical and optical sensing of biochemical and biophysical markers; 2. on-demand, local synthesis, and release of biologics using optical control of cell-based “biologics production factories” safely isolated in a biologic-permeable elastomer scaffold; 3. layer-by-layer approach of applying ECM of known and controlled composition to the optoelectronically active components; 4. RNA sequencing and protein analysis of cells and neotissue at leading edge of healing wound to further characterize the wound state and correlate this information with measured parameters such as neurogenesis and immune cell phenotype; 5. computational modeling of the wound healing processes, including model-guided selection of reduced set of biomarkers and actuation pathways to enabled closed-loop acceleration of wound healing; and lastly 6. reinforcement learning of wound management policies from the computational wound healing models. Importantly, several key components of our approach have a proven regulatory path with successful clinical translation. Several team members have extensive experience with preclinical models and experience with human clinical trials, which will accelerate clinical translation of our findings. Impact. Our approach will allow >50% restoration of innervated functional skeletal muscle mass, >70% restoration of functional deficit, recovery time < 3 months compared to > 6 months control, and rapid return to battlefield and a markedly improved quality of life. This is a 4 year project.
Term: 4 years
Amount: $22 million