Little is known mechanistically about why implanted glucose sensors lag behind blood glucose levels in both the time to peak sensor response and the magnitude of peak sensor response. A mathematical model of glucose transport from capillaries through surrounding tissue to the sensor surface was constructed to address how different aspects of the tissue affect glucose transport to an implanted sensor. Physiologically relevant values of capsule diffusion coefficient, capsule porosity, cellular glucose consumption, capsule thickness, and subcutaneous vessel density were used as inputs to create simulated sensor traces that mimic experimental instances of time lag and concentration attenuation relative to a given blood glucose profile. Using logarithmic sensitivity analysis, each parameter was analyzed to study the effect of these variables on both lag and attenuation. Results identify capsule thickness as the strongest determinant of sensor time lag, while subcutaneous vessel density and capsule porosity had the largest effects on attenuation of glucose that reaches the sensor surface. These findings provide mechanistic insight for the rational design of sensor modifications that may alleviate the deleterious consequences of tissue effects on implanted sensor performance.

Abbreviations: (ANCOVA) analysis of covariance, (PBS) phosphate-buffered saline, (PPP) platelet-poor plasma, (SEM) scanning electron microscope Keywords: biocompatibility, biomaterials, biosensors, modeling

For implantable sensors to become a more viable option for continuous glucose monitoring strategies, they must be able to persist in vivo for periods longer than the 3-to 7-day window that is the current industry standard. Recent studies have attributed such limited performance to tissue reactions resulting from implantation. While in vivo biocompatibility studies have provided much in the way of understanding histology surrounding an implanted sensor, little is known about how each constituent of the foreign body response affects sensor function. Due to the ordered composition and geometry of implantassociated tissue reactions, their effects on sensor function may be computationally modeled and analyzed in a way that would be prohibitive using in vivo studies. This review both explains how physiologically accurate computational models of implant-associated tissue reaction can be designed and shows how they have been utilized thus far. Going forward, these in silico models of implanted sensor behavior may soon complement in vivo studies to provide valuable information for improved sensor designs.

The erroneous and unpredictable behavior of percutaneous glucose sensors just days following implantation has limited their clinical utility for diabetes management. Recent research has implicated the presence of adherent inflammatory cells as the key mitigating factor limiting sensor functionality in this period of days post-implantation. Here we present a novel in vitro platform to mimic the cell-embedded provisional matrix that forms adjacent to the sensor immediately after implantation for the focused investigation of the effects of early stage tissue response on sensor function. This biomimetic surrogate is formed by imbibing fibrin-based gels with physiological densities of inflammatory RAW 264.7 macrophages. When surrounding functional sensors, macrophage-embedded fibrin gels contribute to sensor signal declines that are similar in both shape and magnitude to those observed in previous whole blood and small animal studies. Signal decline in the presence of gels is both metabolically-mediated and sensitive to cell type and activation. Computational modeling of the experimental setup is also presented to validate the design by showing that the cellular glucose uptake parameters necessary to achieve such experimental declines align well with literature values. Together, these data suggest this in vitro provisional matrix surrogate may serve as an effective screening tool for testing the biocompatibility of future glucose sensor designs.

The field of biomaterial design has begun to focus upon methods by which materials can modulate immune response. While certain approaches appear promising, they are limited to isolated facets of inflammation. It is well documented that both bacteria and viruses have highly developed methods for evading the immune system, providing impetus for a more biomimetic approach to material design. This review presents the immune evasive tactics employed by viruses and bacteria and offers suggestions for future directions in applying these principles to biomaterial design.