Objective
We propose to develop and validate a new model of islet transplantation in individuals with type 1 diabetes, which will be used to simulate the effect of specific islet transplantation procedures on glucose metabolism, at the individual and at the population level. The designed simulation platform can be used to generate pre-clinical data associated to human islet transplantation procedures and their impact on glucose metabolism. This platform could therefore allow fast and cost-effective in-silico design of optimal transplantation procedures, post-transplant immunosuppression control algorithms, and approaches that extend graft survival, thereby improving the success rate of human islet transplantation.
Background Rationale
Individuals with unstable type 1 diabetes, hypoglycemia unawareness, severe hypoglycemic episodes, or glycemic lability, can be eligible for human pancreatic islet transplantation. Islet transplantation is an effective beta-cell replacement therapy that has the capacity to improve glycemic control, with near normalization of hemoglobin A1c, reduction of glycemic variability and, in many cases, insulin independence. Despite long-term graft survival for sustained insulin independence remains a challenge, the success rate of clinical islet transplantation has significantly improved with the advancement of techniques for islet isolation and preparation, and by optimizing post-transplant immunosuppression. Another important aspect to consider when islets are transplanted is the early postoperative period, during which maintaining normoglycemia favors the survival of the transplanted islets. We hypothesize that mathematical models can support effective islet transplantation, optimizing transplant and post-transplant procedures in a fast, cost-effective way, thereby improving the success rate of human islet transplantation.
Description of Project
Type 1 diabetes (T1D) requires lifelong insulin replacement. The task of dosing insulin represents a nearly continuous effort, in an attempt to compute the optimal insulin amount and prevent hyperglycemia while simultaneously minimizing the risk for hypoglycemia. To facilitate this task, systems for automated insulin delivery (AID) have been designed and now represent the state-of-the-art in T1D treatment. For individuals with unstable T1D, hypoglycemia unawareness, severe hypoglycemic episodes, or glycemic lability, an alternative to AID is offered by human pancreatic islet transplantation. Islet transplantation is an effective beta-cell replacement therapy that has the capacity to improve glycemic control, with near normalization of hemoglobin A1c, reduction of glycemic variability and, in many cases, insulin independence. Despite long-term graft survival for sustained insulin independence remains a challenge, the success rate of clinical islet transplantation has significantly improved with the advancement of techniques for islet isolation and preparation, and by optimizing post-transplant immunosuppression. Another important aspect to consider when islets are transplanted is the early postoperative period, during which maintaining normoglycemia favors the survival of the transplanted islets. We now propose to develop and validate a new model of islet transplantation in individuals with T1D, which will be used to simulate the effect of specific islet transplantation procedures on glucose metabolism, at the individual and at the population level. This project will pave the way to create a simulation platform that can be used to generate pre-clinical data associated to human islet transplantation procedures and their impact on glucose metabolism. This platform could therefore allow fast and cost-effective in-silico design of optimal transplantation procedures, post-transplant immunosuppression control algorithms, and approaches that extend graft survival, thereby improving the success rate of human islet transplantation.
Anticipated Outcome
We expect that this project will establish the first generation of models of islet transplantation in type 1 diabetes (T1D), which will be used to simulate the effect of specific islet transplantation procedures on glucose metabolism, at the individual and at the population level. Using large available datasets collected in individuals with T1D before and after islet transplantation, the mathematical models will be well characterized and a population of in-silico subjects will be generated to represent the heterogeneity of T1D and of the metabolic effects of islet transplantation.
Relevance to T1D
Human pancreatic islet transplantation is an effective beta-cell replacement therapy for individuals with unstable type 1 diabetes (T1D), hypoglycemia unawareness, severe hypoglycemic episodes, or glycemic lability, for whom available technological solutions have not been successful at stabilizing glycemic control. Success of islet transplantation depends on several factors, including quality, amount, and location of the transplanted islets, in addition to factors associated to immunosuppression following transplantation. This project proposes to develop mathematical models and a simulation environment to support effective islet transplantation, allowing fast and cost-effective in-silico design of optimal transplantation procedures, post-transplant immunosuppression control algorithms, and approaches that extend graft survival, thereby improving the success rate of human islet transplantation for the management of T1D.