Background: A new version of the UVA/Padova Type 1 Diabetes (T1D) Simulator is presented which provides a more realistic testing scenario. The upgrades to the previous simulator, which was accepted by the Food and Drug Administration in 2013, are described. Method: Intraday variability of insulin sensitivity (S_I) has been modeled, based on clinical T1D data, accounting for both intra- and intersubject variability of daily S_I. Thus, time-varying distributions of both subject’s basal insulin infusion and insulin-to-carbohydrate ratio were calculated and made available to the user. A model of “dawn” phenomenon based on clinical T1D data has been also included. Moreover, the model of subcutaneous insulin delivery has been updated with a recently developed model of commercially available fast-acting insulin analogs. Models of both intradermal and inhaled insulin pharmacokinetics have been included. Finally, new models of error affecting continuous glucose monitoring and self-monitoring of blood glucose devices have been added. Results: One hundred in silico adults, adolescent, and children have been generated according to the above modifications. The new simulator reproduces the intraday glucose variability observed in clinical data, also describing the nocturnal glucose increase, and the simulated insulin profiles reflect real life data. Conclusions: The new modifications introduced in the T1D simulator allow to extend its domain of validity from “single-meal” to “single-day” scenarios, thus enabling a more realistic framework for in silico testing of advanced diabetes technologies including glucose sensors, new insulin molecules and artificial pancreas.

OBJECTIVE: To evaluate the feasibility of a wearable artificial pancreas system, the Diabetes Assistant (DiAs), which uses a smart phone as a closed-loop control platform. RESEARCH DESIGN AND METHODS: Twenty patients with type 1 diabetes were enrolled at the Universities of Padova, Montpellier, and Virginia and at Sansum Diabetes Research Institute. Each trial continued for 42 h. The United States studies were conducted entirely in outpatient setting (e.g., hotel or guest house); studies in Italy and France were hybrid hospital–hotel admissions. A continuous glucose monitoring/pump system (Dexcom Seven Plus/Omnipod) was placed on the subject and was connected to DiAs. The patient operated the system via the DiAs user interface in open-loop mode (first 14 h of study), switching to closed-loop for the remaining 28 h. Study personnel monitored remotely via 3G or WiFi connection to DiAs and were available on site for assistance. RESULTS: The total duration of proper system communication functioning was 807.5 h (274 h in open-loop and 533.5 h in closed-loop), which represented 97.7% of the total possible time from admission to discharge. This exceeded the predetermined primary end point of 80% system functionality. CONCLUSIONS: This study demonstrated that a contemporary smart phone is capable of running outpatient closed-loop control and introduced a prototype system (DiAs) for further investigation. Following this proof of concept, future steps should include equipping insulin pumps and sensors with wireless capabilities, as well as studies focusing on control efficacy and patient-oriented clinical outcomes.

Closed-loop control clinical research trials have been considerably accelerated by in silico trials using the Food and Drug Administration-accepted type 1 diabetes mellitus (T1DM) simulator. We have recently demonstrated that postprandial insulin sensitivity (SI) in T1DM subjects was lower at breakfast (B) than lunch (L) and dinner (D), but not significantly, because of the small population size. The goal of this study was therefore to incorporate this novel information into the University of Virginia/Padova T1DM simulator and to reproduce in silico the observed circadian variability. Twenty T1DM subjects received an identical mixed meal at B, L, and D. SI was calculated for each meal using the oral glucose minimal model. Seven SI daily patterns were identified, and their probabilities were estimated. Each in silico subject was linked to a time-varying SI profile, while random deviations of up to 40% were allowed. Simulations were compared with experimental data. The integrated area above the basal glucose curve values were 2.60 ± 0.91 (B), 1.38 ± 0.91 (L), and 1.44 ± 1.07 (D) 10(4) min · mg/dL in silico versus 2.87 ± 1.65 (B), 1.98 ± 1.56 (L), and 2.16 ± 2.00 (D) 10(4) min · mg/dL in vivo. Incremental peak glucose values were 109 ± 33 (B), 80 ± 29 (L), and 81 ± 30 (D) mg/dL in silico versus 136 ± 39 (B), 126 ± 37 (L), and 125 ± 48 (D) mg/dL in vivo. The incorporation of a time-varying SI into the simulator makes this technology suitable for running multiple-meal scenarios, thus enabling a more robust design of artificial pancreas algorithms.

Objective: Contemporary and future outpatient long-term artificial pancreas (AP) studies need to cope with the well-known large intra- and interday glucose variability occurring in type 1 diabetic (T1D) subjects. Here, we propose an adaptive model predictive control (MPC) strategy to account for it and test it in silico. Methods: A run-to-run (R2R) approach adapts the subcutaneous basal insulin delivery during the night and the carbohydrate-to-insulin ratio (CR) during the day, based on some performance indices calculated from subcutaneous continuous glucose sensor data. In particular, R2R aims, first, to reduce the percentage of time in hypoglycemia and, secondarily, to improve the percentage of time in euglycemia and average glucose. In silico simulations are performed by using the University of Virginia/Padova T1D simulator enriched by incorporating three novel features: intra- and interday variability of insulin sensitivity, different distributions of CR at breakfast, lunch, and dinner, and dawn phenomenon. Results: After about two months, using the R2R approach with a scenario characterized by a random ±30% variation of the nominal insulin sensitivity the time in range and the time in tight range are increased by 11.39% and 44.87%, respectively, and the time spent above 180 mg/dl is reduced by 48.74%. Conclusions : An adaptive MPC algorithm based on R2R shows in silico great potential to capture intra- and interday glucose variability by improving both overnight and postprandial glucose control without increasing hypoglycemia. Significance: Making an AP adaptive is key for long-term real-life outpatient studies. These good in silico results are very encouraging and worth testing in vivo.

Background: In 2008, the Food and Drug Administration (FDA) accepted our type 1 diabetes mellitus (T1DM) simulator (S2008), equipped with 100 in silico adults, 100 adolescents, and 100 children, as a substitute for preclinical trials for certain insulin treatments, including closed-loop algorithms. Hypoglycemia was well described in the simulator, but recent closed-loop trials showed a much larger frequency of hypoglycemia events in patients compared with the in silico ones. In order to better describe the distribution of glucose concentration observed in clinical trials, the simulator has recently been updated, and modifications have been accepted by the FDA (S2013). The aim of this study is to assess the validity of the S2013 simulator against clinical data and compare its performance with that of the S2008. Subjects and Methods: The database consists of 24 T1DM subjects who received dinner (70.7±3.3 g of carbohydrate) and breakfast (52.9±0.1 g of carbohydrate) in two occasions (open- and closed-loop), for a total of 96 postmeal glucose profiles. Measured plasma glucose profiles were compared with those simulated in 100 in silico adults, and the continuous glucose error grid analysis (CG-EGA) was used to assess the validity of the simulated traces. Moreover, the most common outcome metrics have been compared. Results: The frequency of hypoglycemia episodes predicted by the S2013 well reproduces that observed during clinical trials as proven by the CG-EGA. In addition, the outcome metrics provided by the S2013 are similar to those observed in clinical trials in a set of T1DM subjects. Conclusions: We demonstrated that the virtual subjects of the S2013 are representative of the T1DM population observed in a clinical trial. We conclude that the S2013 is a valid tool usable to test the robustness of closed-loop control algorithms for artificial pancreas.