PI: Stephen Badylak

Title: REPAIR: Regenerative Electronic Platform through Advanced Intelligent Regulation

Description: We will engineer the Regenerative Electronic Platform through Advanced Intelligent Regulation (REPAIR) Patch, which will dramatically improve the speed and functional outcome of wound healing. The technologic basis of our approach is the use of a novel array of sensors and actuators, designed and controlled through the use of computational models and embedded within an inductive cytocompatible extracellular matrix (ECM) hydrogel. The REPAIR Patch will be developed and tested in a dog model of volumetric muscle loss (VML). The REPAIR platform, which is flexible with regard to geometry, time of application, and arrangement of its various modalities will decrease by 50% the time to functional healing of VML wounds by targeting two key rate limiting steps in the default wound healing process: the immune phenotype of the wound environment and neurogenesis. The REPAIR technology fundamentally changes the current empirical, reductionist approach to wound healing.

The REPAIR approach will be driven by tissue-realistic, dynamic, agent-based computational models that are spatiotemporally accurate facsimiles of key biological processes involved in the in vivo experimental model. Initiating from in silico studies to define novel control points, and combined with in vitro studies to demonstrate feasibility through an iterative process, we will  create this control strategy by utilizing; 1) experimental data (at the mRNA and protein levels) obtained from the canine studies; 2) dynamic, data-driven models that will recapitulate principle drivers and define central sense/actuate nodes; 3) modifications of the agent-based models to account for these novel sense/actuate nodes and predict experimental outcomes; 4) modification of the REPAIR Patch to carry out the model-defined sense/actuate process and validate predictions from the agent-based models; 5) the use of the agent-based models as proxy models to train an artificial intelligence (AI) controller given the sense/actuate capabilities of the REPAIR Patch, and 6) in vivo testing of the modified REPAIR Patch under closed loop control of the AI controller. Importantly, the REPAIR Patch will be applied, removed, and re-applied repeatedly to rebuild functional tissue in layers. If successful, the REPAIR platform will improve the quality of life of severely injured warfighters and civilians alike.

Source: Defense Advanced Research Projects Agency

Term: May 1, 2022 – June 30, 2023

Amount: $257,140