A Biohybrid Device for Regulating Inflammation in Sepsis

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Enter the text here that is the new abstract information for your application. This section must be no longer than 30 lines of text. Sepsis accounts for nearly 10% of total U.S. deaths, costing nearly $17 billion. Sepsis induces an acute inflammatory response. Properly-regulated inflammation allows for recognition and reaction to injury or infection, but inadequate or overly-robust inflammation can lead to multiple organ dysfunction and death. We propose that acute inflammation in sepsis may evolve too rapidly to be modulated appropriately, and suggest that therapies should focus not on abolishing inflammation, but rather on attenuating self-sustaining inflammation. Based on a combination of experiments and computational modeling, we propose a self- regulating device for patient-specific, adaptive regulation of inflammation. Our device, seeded with genetically- modified human HepG2 hepatocytes, has already been tested both in vitro and in endotoxemic and septic rats. We propose to create a novel class of biohybrid devices for regulating inflammation in sepsis using an iterative process of computational simulations combined with in vitro and in vivo studies (Aim 1), and to define the impact of the circulating inflammatory milieu on the HepG2 cells in the biohybrid device (Aim 2). We will optimize the molecular composition, timing, and duration of bioreactor-based delivery of sTNFR – driven constitutively or in an adaptive fashion as inferred from preliminary computational modeling. These studies will be carried out iteratively with computational modeling of the disease and the impact of the biohybrid devices on the disease in the context of in silico clinical trials, in order to predict the likelihood of clinical efficacy of this transformative class of medical device. Furthermore, we will define in vitro the inflammatory and stress responses of HepG2 cells subjected to the extracorporeal inflammatory milieu they will encounter in the context of our studies, and utilize these data to optimize in vivo bioreactor conditions and to revise in silico clinical trials. This approach represents a novel strategy for rational reprogramming of acute inflammation in sepsis, with potential impact on other acute inflammatory diseases.