Hybrid Closed-Loop Control with Ultrarapid Lispro Compared with Standard Insulin Aspart and Faster Insulin Aspart: An In Silico Study.

OBJECTIVE: There is room for improvement in the performance of closed-loop regulation algorithms during the prandial period. This in silico study evaluated the efficiency and safety of ultrarapid lispro insulin using the Diabeloop DBLG1® algorithm. METHODS: We modeled the insulin profile of URLi according to literature data and integrated it to the model used within a simulation platform built from a 60 patients' virtual cohort. We then ran the DBLG1® algorithm in silico with various meal intakes using modeled URLi, Aspart and Faster Aspart. The primary endpoints were glucose metrics (time in 70-180 mg/dL range and time below range). RESULTS: When insulin time constant values were tuned, time in 70-180 mg/dL range was 69.4 [61.1-75.6] (Aspart) vs 74.7 [65.5-81.5] (URLi). Glucose coefficient of variation was reduced from 34.1 [29.7-37.8] to 28.4 [25.7-34.6]. Time below 70 mg/dL and 54 mg/dL were significantly reduced with URLi, whether or not DBLG1 was specifically tuned to this insulin. Metrics with Faster Aspart were intermediate and did not significantly differ from URLi. CONCLUSIONS: This simulation study performed on a virtual T1D population suggests that the use of URLi within an unmodified closed-loop DBLG1 regulation algorithm is safe and, with DBLG1 being tuned to this specific insulin type, improved the regulation performances as compared with Aspart. This fact supports the use of such an insulin in clinical investigations.

Increasing the safety of unannounced meal detection for artificial pancreas closed-loop with patient's hourly meal schedule.

The daily challenge for people with type 1 diabetes is maintaining glycaemia in the "normal" range after meals, by injecting themselves the correct amount of insulin. Artificial pancreas systems were developed to adjust insulin delivery based on real-time monitoring of glycaemia and meal patient's report. Meal reporting is a heavy burden for patients as it requires carbohydrate estimation several times per day. To improve patient's quality of life and treatment, several methods aim at detecting unannounced meals. While untreated meals lead to hyperglycaemia and in the long-term to comorbidities, treating falsely detected meals can cause hypoglycaemia and coma. In this paper, we propose to customise the meal detection to the patient's hourly meal probability in order to limit false detection of unannounced meals.