TY  - JOUR
AU  - Fabris, Chiara
AU  - Lv, Dayu
AU  - Garcia-Tirado, Jose
PY  - 2018
DA  - 2018/09/11
TI  - Role of Automated Insulin Delivery (Artificial Pancreas) in Islet Transplantation: An <i>In Silico</i> Assessment
JO  - OBM Transplantation
SP  - 019
VL  - 02
IS  - 03
AB  - Human pancreatic islet transplantation is a minimally-invasive procedure that is gaining recognition for the treatment of type 1 diabetes (T1D). Selected patients with unstable T1D, hypoglycemia unawareness, history of severe hypoglycemia, and glycemic lability, not successfully stabilized with intensive insulin treatment, can be offered this alternative therapy that has been shown to provide long-term glycemic control with near-normalization of hemoglobin A1c in the absence of severe hypoglycemia. Today, downsides of pancreatic islet transplantation include the need for chronic recipient immunosuppression and the limited supply of pancreatic islets. In addition, attaining long-term insulin independence remains a challenge. In this context, stabilization of a patient’s metabolic system with islet transplantation that is augmented by automated insulin delivery (AID) technology could be of significant interest. In this chapter, we want to frame and illustrate the problem of developing a combined bio-artificial system that includes an islet graft and mechanical AID. Our discussion will propose modeling approaches potentially deployable in describing glucose homeostasis after islet transplantation and will be supported by a series of in silico studies simulating post-transplant glycemic patterns and the impact of AID control strategies. To run our analyses, we used the UVA/Padova T1D Simulator – a simulation platform accepted by FDA as a substitute to animal trials in the pre-clinical evaluation of insulin treatment strategies, appropriately modified to describe the glucose-insulin regulation system in transplanted individuals. The results presented here are a very preliminary in silico assessment of the benefits of combining islet transplantation and AID. Further research, which relies on glucose and insulin data collected from transplanted patients, will be needed to optimize modeling and control strategies.
SN  - 2577-5820
UR  - https://doi.org/10.21926/obm.transplant.1803019
DO  - 10.21926/obm.transplant.1803019
ID  - Fabris2018
ER  -