A model of subcutaneous pramlintide pharmacokinetics and its effect on gastric emptying: Proof-of-concept based on populational data.

Pramlintide, an amylin analog, has been coming up as an agent in type 1 diabetes dual-hormone therapies (insulin/pramlintide). Since pramlintide slows down gastric emptying, it allows for easing glucose control and reducing the burden of meal announcements. Pre-clinical in silico evaluations are a key step in the development of any closed-loop strategy. However, mathematical models are needed, and pramlintide models in the literature are scarce. This work proposes a proof-of-concept pramlintide model, describing its subcutaneous pharmacokinetics (PK) and its effect on gastric emptying (PD). The model is validated with published populational (clinical) data. The model development is divided into three stages: intravenous PK, subcutaneous PK, and PD modeling. In each stage, a set of model structures are proposed, and their performance is assessed using the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). In order to evaluate the modulation of the rate of gastric emptying, a literature meal model was used. The final pramlintide model comprises four compartments and a function that modulates gastric emptying depending on plasma pramlintide. Results show an appropriate fit for the data. Some aspects are left as open questions due to the lack of specific data (e.g., the influence of meal composition on the pramlintide effect). Moreover, further validation with individual data is necessary to propose a virtual cohort of patients.

Internal model control based module for the elimination of meal and exercise announcements in hybrid artificial pancreas systems.

BACKGROUND AND OBJECTIVES: Hybrid artificial pancreas systems outperform current insulin pump therapies in blood glucose regulation in type 1 diabetes. However, subjects still have to inform the system about meals intake and exercise to achieve reasonable control. These patient announcements may result in overburden and compromise controller performance if not provided timely and accurately. Here, a hybrid artificial pancreas is extended with an add-on module that releases subjects from meals and exercise announcements. METHODS: The add-on module consists of an internal-model controller that generates a "virtual" control action to compensate for disturbances. This "virtual" action is converted into insulin delivery, rescue carbohydrates suggestions, or insulin-on-board limitations, depending on a switching logic based on glucose measurements and predictions. The controller parameters are tuned by optimization and then related to standard parameters from the open-loop therapy. This module is implemented in a hybrid artificial pancreas system proposed by our research group for validation. This hybrid system extended with the add-on module is compared with the hybrid controller with carbohydrate counting errors (hybrid) and the hybrid controller with an alternative unannounced meal compensation module based on a meal detection algorithm (meal detector). The validation used the educational version of the UVa/Padova simulator to simulate the three controllers under two scenarios: one with only meals and another with meals and exercise. The exercise was modeled as a temporal increase of the insulin sensitivity resulting in the glucose drop usually related to an aerobic exercise. RESULTS: For the scenario with only meals, the three controllers achieved similar time in range (proposed: 85.1 [77.9,88.1]%, hybrid: 84.0 [75.9,86.4]%, meal detector: 81.9 [79.3,83.8]%, median [interquartile range]) with low time in moderate hypoglycemia. Under the scenario with meals and exercise, the proposed module reduces 4.61% the time in hypoglycemia achieved with the other controllers, suggesting an acceptable amount of rescues (27.2 [23.7, 31.0] g). CONCLUSIONS: The proposed add-on module achieved promising results: it outperformed the meal-detector-based controller, even achieving a postprandial performance as good as the hybrid controller (with carbohydrate counting errors). Also, the rescue suggestion feature of the module mitigated exercise-induced hypoglycemia with admissible rescue amounts.