Adjusting Therapy Profiles When Switching to Ultra-Rapid Lispro in an Advanced Hybrid Closed-Loop System: An in Silico Study

BACKGROUND

It has been shown that insulin acceleration by itself might not be sufficient to see clear improvements in glycemic metrics, and insulin therapy may need to be adjusted to fully leverage the extra safety margin provided by faster pharmacokinetic (PK) and pharmacodynamic (PD) profiles. The objective of this work is to explore how to perform such adjustments on a commercially available automated insulin delivery (AID) system.

METHODS

Ultra-rapid lispro (URLi) is modeled within the UVA/Padova simulation platform using data from previously published clamp studies. The Control-IQ AID algorithm is selected as it leverages carbohydrate-to-insulin ratio (CR in g/U), correction factor (CF in mg/dL/U), and basal rate (BR in U/h) daily profiles that are fully customizable. An experiment roadmap is proposed to understand how to safely modify these profiles when switching from lispro to URLi.

RESULTS

Simulations show that a 7% decrease in CR (approximately an 8% increase in prandial insulin) and a 7.5% increase in BR lead to cumulative improvements in glucose control with URLi. Comparing with baseline metrics using lispro, a clinically significant increase in time in the range of 70 to 180 mg/dL (overall: 70.2%-75.2%, P < .001; 6 am-12 am: 62.4%-68.5%, P < .001) and a reduction in time below 70 mg/dL (overall: 1.8%-1.2%, P < .001; 6 am-12 am: 1.8%-1.3%, P < .001) were observed.

CONCLUSION

Properly adjusting therapy parameters allows to fully leverage glucose control benefits provided by faster insulin analogues, opening opportunities to take another step forward into a next generation of more effective AID solutions.

Assessment of Meal Anticipation for Improving Fully Automated Insulin Delivery in Adults With Type 1 Diabetes

OBJECTIVE

Meals are a consistent challenge to glycemic control in type 1 diabetes (T1D). Our objective was to assess the glycemic impact of meal anticipation within a fully automated insulin delivery (AID) system among adults with T1D.

RESEARCH DESIGN AND METHODS

We report the results of a randomized crossover clinical trial comparing three modalities of AID systems: hybrid closed loop (HCL), full closed loop (FCL), and full closed loop with meal anticipation (FCL+). Modalities were tested during three supervised 24-h admissions, where breakfast, lunch, and dinner were consumed per participant's home schedule, at a fixed time, and with a 1.5-h delay, respectively. Primary outcome was the percent time in range 70-180 mg/dL (TIR) during the breakfast postprandial period for FCL+ versus FCL.

RESULTS

Thirty-five adults with T1D (age 44.5 ± 15.4 years; HbA1c 6.7 ± 0.9%; n = 23 women and n = 12 men) were randomly assigned. TIR for the 5-h period after breakfast was 75 ± 23%, 58 ± 21%, and 63 ± 19% for HCL, FCL, and FCL+, respectively, with no significant difference between FCL+ and FCL. For the 2 h before dinner, time below range (TBR) was similar for FCL and FCL+. For the 5-h period after dinner, TIR was similar for FCL+ and FCL (71 ± 34% vs. 72 ± 29%; P = 1.0), whereas TBR was reduced in FCL+ (median 0% [0-0%] vs. 0% [0-0.8%]; P = 0.03). Overall, 24-h control for HCL, FCL, and FCL+ was 86 ± 10%, 77 ± 11%, and 77 ± 12%, respectively.

CONCLUSIONS

Although postprandial control remained optimal with hybrid AID, both fully AID solutions offered overall TIR >70% with similar or lower exposure to hypoglycemia. Anticipation did not significantly improve postprandial control in AID systems but also did not increase hypoglycemic risk when meals were delayed.

Automated Insulin Delivery with SGLT2i Combination Therapy in Type 1 Diabetes

Background: Use of sodium-glucose cotransporter 2 inhibitors (SGLT2i) as adjunct therapy to insulin in type 1 diabetes (T1D) has been previously studied. In this study, we present data from the first free-living trial combining low-dose SGLT2i with commercial automated insulin delivery (AID) or predictive low glucose suspend (PLGS) systems. Methods: In an 8-week, randomized, controlled crossover trial, adults with T1D received 5 mg/day empagliflozin (EMPA) or no drug (NOEMPA) as adjunct to insulin therapy. Participants were also randomized to sequential orders of AID (Control-IQ) and PLGS (Basal-IQ) systems for 4 and 2 weeks, respectively. The primary endpoint was percent time-in-range (TIR) 70-180 mg/dL during daytime (7:00-23:00 h) while on AID (NCT04201496). Findings: A total of 39 subjects were enrolled, 35 were randomized, 34 (EMPA; n = 18 and NOEMPA n = 16) were analyzed according to the intention-to-treat principle, and 32 (EMPA; n = 16 and NOEMPA n = 16) completed the trial. On AID, EMPA versus NOEMPA had higher daytime TIR 81% versus 71% with a mean estimated difference of +9.9% (confidence interval [95% CI] 0.6-19.1); p = 0.04. On PLGS, the EMPA versus NOEMPA daytime TIR was 80% versus 63%, mean estimated difference of +16.5% (95% CI 7.3-25.7); p < 0.001. One subject on SGLT2i and AID had one episode of diabetic ketoacidosis with nonfunctioning insulin pump infusion site occlusion contributory. Interpretation: In an 8-week outpatient study, addition of 5 mg daily empagliflozin to commercially available AID or PLGS systems significantly improved daytime glucose control in individuals with T1D, without increased hypoglycemia risk. However, the risk of ketosis and ketoacidosis remains. Therefore, future studies with SGLT2i will need modifications to closed-loop control algorithms to enhance safety.

Using an Online Disturbance Rejection and Anticipation System to Reduce Hyperglycemia in a Fully Closed-Loop Artificial Pancreas System

INTRODUCTION

Hyperglycemia following meals is a recurring challenge for people with type 1 diabetes, and even the most advanced available automated systems currently require manual input of carbohydrate amounts. To progress toward fully automated systems, we present a novel control system that can automatically deliver priming boluses and/or anticipate eating behaviors to improve postprandial full closed-loop control.

METHODS

A model predictive control (MPC) system was enhanced by an automated bolus system reacting to early glucose rise and/or a multistage MPC (MS-MPC) framework to anticipate historical patterns. Priming was achieved by detecting large glycemic disturbances, such as meals, and delivering a fraction of the patient's total daily insulin (TDI) modulated by the disturbance's likelihood (bolus priming system [BPS]). In the anticipatory module, glycemic disturbance profiles were generated from historical data using clustering to group days with similar behaviors; the probability of each cluster is then evaluated at every controller step and informs the MS-MPC framework to anticipate each profile. We tested four configurations: MPC, MPC + BPS, MS-MPC, and MS-MPC + BPS in simulation to contrast the effect of each controller module.

RESULTS

Postprandial time in range was highest for MS-MPC + BPS: 60.73 ± 25.39%, but improved with each module: MPC + BPS: 56.95±25.83 and MS-MPC: 54.83 ± 26.00%, compared with MPC: 51.79 ± 26.12%. Exposure to hypoglycemia was maintained for all controllers (time below 70 mg/dL <0.5%), and improvement came primarily from a reduction in postprandial time above range (MS-MPC + BPS: 39.10 ± 25.32%, MPC + BPS: 42.99 ± 25.81%, MS-MPC: 45.09 ± 25.96%, MPC: 48.18 ± 26.09%).

CONCLUSIONS

The BPS and anticipatory disturbance profiles improved blood glucose control and were most efficient when combined.

Advanced hybrid artificial pancreas system improves on unannounced meal response - In silico comparison to currently available system

BACKGROUND AND OBJECTIVE

Glycemic control, especially meal-related disturbance rejection, has proven to be a major challenge for people with type 1 diabetes. In this manuscript, we introduce a novel, personalized, advanced hybrid insulin infusion system (a.k.a. artificial pancreas) based on the Model Predictive Control (MPC) methodology to adjust insulin infusion while automatically rejecting uninformed meals.

METHODS

The proposed advanced hybrid closed-loop system relies on the integration of three key elements: (i) an adaptive personalized MPC control law that modulates the control strength depending on recent past control actions, glucose measurements, and its derivative, (ii) an automatic Bolus Priming System (BPS) that commands additional insulin injections safely upon the detection of enabling metabolic conditions (e.g., an unacknowledged meal), and (iii) a new hyperglycemia mitigation system to avoid prevailing hyperglycemia. The benefits of the proposed system are demonstrated through simulations and tests using the most up-to-date Type 1 UVA/Padova simulator as preclinical stage prior to in vivo clinical tests. We used a legacy algorithm (USS Virginia), currently used in clinical care, as a benchmark controller.

RESULTS

Overall, the proposed control strategy enhanced by an automatic BPS improves glycemic control when compared with an available system. When a large meal is not announced (80g CHO), the proposed controller outperformed the legacy controller in time-in-target-range TIR (postprandial and overnight) and time-in-tight-range TTR (overall, postprandial, and overnight).

CONCLUSION

The integration of a novel BPS into an advanced control system allowed to automatically reject unannounced meals. Exhaustive simulation studies indicated the safety and feasibility of the proposed controller to be deployed in human clinical trials.

Advanced Closed-Loop Control System Improves Postprandial Glycemic Control Compared With a Hybrid Closed-Loop System Following Unannounced Meal

OBJECTIVE

Meals are a major hurdle to glycemic control in type 1 diabetes (T1D). Our objective was to test a fully automated closed-loop control (CLC) system in the absence of announcement of carbohydrate ingestion among adolescents with T1D, who are known to commonly omit meal announcement.

RESEARCH DESIGN AND METHODS

Eighteen adolescents with T1D (age 15.6 ± 1.7 years; HbA1c 7.4 ± 1.5%; 9 females/9 males) participated in a randomized crossover clinical trial comparing our legacy hybrid CLC system (Unified Safety System Virginia [USS]-Virginia) with a novel fully automated CLC system (RocketAP) during two 46-h supervised admissions (each with one announced and one unannounced dinner), following 2 weeks of data collection. Primary outcome was the percentage time-in-range 70-180 mg/dL (TIR) following the unannounced meal, with secondary outcomes related to additional continuous glucose monitoring-based metrics.

RESULTS

Both TIR and time-in-tight-range 70-140 mg/dL (TTR) were significantly higher using RocketAP than using USS-Virginia during the 6 h following the unannounced meal (83% [interquartile range 64-93] vs. 53% [40-71]; P = 0.004 and 49% [41-59] vs. 27% [22-36]; P = 0.002, respectively), primarily driven by reduced time-above-range (TAR >180 mg/dL: 17% [1.3-34] vs. 47% [28-60]), with no increase in time-below-range (TBR <70 mg/dL: 0% median for both). RocketAP also improved control following the announced meal (mean difference TBR: -0.7%, TIR: +7%, TTR: +6%), overall (TIR: +5%, TAR: -5%, TTR: +8%), and overnight (TIR: +7%, TTR: +19%, TAR: -5%). RocketAP delivered less insulin overall (78 ± 23 units vs. 85 ± 20 units, P = 0.01).

CONCLUSIONS

A new fully automated CLC system with automatic prandial dosing was proven to be safe and feasible and outperformed our legacy USS-Virginia in an adolescent population with and without meal announcement.

Impact of Accelerating Insulin on an Artificial Pancreas System Without Meal Announcement: An In Silico Examination

BACKGROUND

Controlling postprandial blood glucose without the benefit of an appropriately sized premeal insulin bolus has been challenging given the delays in absorption and action of subcutaneously injected insulin during conventional and artificial pancreas (AP) system diabetes treatment. We aim to understand the impact of accelerating insulin and increasing aggressiveness of the AP controller as potential solutions to address the postprandial hyperglycemia challenge posed by unannounced meals through a simulation study.

METHODS

Accelerated rapid-acting insulin analogue is modeled within the UVA/Padova simulation platform by uniformly reducing its pharmacokinetic time constants (α multiplier) and used with a model predictive control, where the controller's aggressiveness depends on α. Two sets of single-meal simulations were performed: (1) where we only tune the controller's aggressiveness and (2) where we also accelerate insulin absorption and action to assess postprandial glycemic control during each intervention.

RESULTS

Mean percent of time spent within the 70 to 180 mg/dL postprandial glycemic range is significantly higher in set (2) than in set (1): 79.9, 95% confidence interval [77.0, 82.7] vs 88.8 [86.8, 90.9] ([Note to typesetter: Set all unnecessary math in text format and insert appropriate spaces between operators.] P < .05) for α = 2, and 81.4 [78.6, 84.3] vs 94.1 [92.6, 95.6] (P < .05) for α = 3. A decrease in percent of time below 70 mg/dL is also detected: 0.9 [0.4, 2.2] vs 0.6 [0.2, 1.4] (P = .23) for α = 2 and 1.4 [0.7, 2.8] vs 0.4 [0.1, 1.4] (P < .05) for α = 3.

CONCLUSION

These proof-of-concept simulations suggest that an AP without prandial insulin boluses combined with significantly faster insulin analogues could match the glycemic performance obtained with an optimal hybrid AP.

Anticipation of Historical Exercise Patterns by a Novel Artificial Pancreas System Reduces Hypoglycemia During and After Moderate-Intensity Physical Activity in People with Type 1 Diabetes

Objective: Physical activity is a major challenge to glycemic control for people with type 1 diabetes. Moderate-intensity exercise often leads to steep decreases in blood glucose and hypoglycemia that closed-loop control systems have so far failed to protect against, despite improving glycemic control overall. Research Design and Methods: Fifteen adults with type 1 diabetes (42 ± 13.5 years old; hemoglobin A_1c 6.6% ± 1.0%; 10F/5M) participated in a randomized crossover clinical trial comparing two hybrid closed-loop (HCL) systems, a state-of-the-art hybrid model predictive controller and a modified system designed to anticipate and detect unannounced exercise (APEX), during two 32-h supervised admissions with 45 min of planned moderate activity, following 4 weeks of data collection. Primary outcome was the number of hypoglycemic episodes during exercise. Continuous glucose monitor (CGM)-based metrics and hypoglycemia are also reported across the entire admissions. Results: The APEX system reduced hypoglycemic episodes overall (9 vs. 33; P = 0.02), during exercise (5 vs. 13; P = 0.04), and in the 4 h following (2 vs. 11; P = 0.02). Overall CGM median percent time <70 mg/dL decreased as well (0.3% vs. 1.6%; P = 0.004). This protection was obtained with no significant increase in time >180 mg/dL (18.5% vs. 16.6%, P = 0.15). Overnight control was notable for both systems with no hypoglycemia, median percent in time 70-180 mg/dL at 100% and median percent time 70-140 mg/dL at ∼96% for both. Conclusions: A new closed-loop system capable of anticipating and detecting exercise was proven to be safe and feasible and outperformed a state-of-the-art HCL, reducing participants' exposure to hypoglycemia during and after moderate-intensity physical activity. ClinicalTrials.gov NCT03859401.

In Silico Analysis of an Exercise-Safe Artificial Pancreas With Multistage Model Predictive Control and Insulin Safety System

BACKGROUND

Maintaining glycemic equilibrium can be challenging for people living with type 1 diabetes (T1D) as many factors (eg, length, type, duration, insulin on board, stress, and training) will impact the metabolic changes triggered by physical activity potentially leading to both hypoglycemia and hyperglycemia. Therefore, and despite the noted health benefits, many individuals with T1D do not exercise as much as their healthy peers. While technology advances have improved glucose control during and immediately after exercise, it remains one of the key limitations of artificial pancreas (AP) systems, largely because stopping insulin at the onset of exercise may not be enough to prevent impending, exercise-induced hypoglycemia.

METHODS

A hybrid AP algorithm with subject-specific exercise behavior recognition and anticipatory action is designed to prevent hypoglycemic events during and after moderate-intensity exercise. Our approach relies on a number of key innovations, namely, an activity informed premeal bolus calculator, personalized exercise pattern recognition, and a multistage model predictive control (MS-MPC) strategy that can transition between reactive and anticipatory modes. This AP design was evaluated on 100 in silico subjects from the most up-to-date FDA-accepted UVA/Padova metabolic simulator, emulating an outpatient clinical trial setting. Results with a baseline controller, a regular MPC (rMPC), are also included for comparison purposes.

RESULTS

In silico experiments reveal that the proposed MS-MPC strategy markedly reduces the number of exercise-related hypoglycemic events (8 vs 68).

CONCLUSION

An anticipatory mode for insulin administration of a monohormonal AP controller reduces the occurrence of hypoglycemia during moderate-intensity exercise.

Closed-Loop Control with Unannounced Exercise for Adults with Type 1 Diabetes using the Ensemble Model Predictive Control

This paper presents an individualized Ensemble Model Predictive Control (EnMPC) algorithm for blood glucose (BG) stabilization and hypoglycemia prevention in people with type 1 diabetes (T1D) who exercise regularly. The EnMPC formulation can be regarded as a simplified multi-stage MPC allowing for the consideration of N _en scenarios gathered from the patient's recent behavior. The patient's physical activity behavior is characterized by an exercise-specific input signal derived from the deconvolution of the patient's continuous glucose monitor (CGM), accounting for known inputs such as meal, and insulin pump records. The EnMPC controller was tested in a cohort of in silico patients with representative inter-subject and intra-subject variability from the FDA-accepted UVA/Padova simulation platform. Results show a significant improvement on hypoglycemia prevention after 30 min of mild to moderate exercise in comparison to a similarly tuned baseline controller (rMPC); with a reduction in hypoglycemia occurrences (< 70 mg/dL), from 3.08% ± 3.55 with rMPC to 0.78% ± 2.04 with EnMPC (P < 0.05).

Identifiability Analysis of Three Control-Oriented Models for Use in Artificial Pancreas Systems

OBJECTIVE

Our aim is to analyze the identifiability of three commonly used control-oriented models for glucose control in patients with type 1 diabetes (T1D).

METHODS

Structural and practical identifiability analysis were performed on three published control-oriented models for glucose control in patients with type 1 diabetes (T1D): the subcutaneous oral glucose minimal model (SOGMM), the intensive control insulin-nutrition-glucose (ICING) model, and the minimal model control-oriented (MMC). Structural identifiability was addressed with a combination of the generating series (GS) approach and identifiability tableaus whereas practical identifiability was studied by means of (1) global ranking of parameters via sensitivity analysis together with the Latin hypercube sampling method (LHS) and (2) collinearity analysis among parameters. For practical identifiability and model identification, continuous glucose monitor (CGM), insulin pump, and meal records were selected from a set of patients (n = 5) on continuous subcutaneous insulin infusion (CSII) that underwent a clinical trial in an outpatient setting. The performance of the identified models was analyzed by means of the root mean square (RMS) criterion.

RESULTS

A reliable set of identifiable parameters was found for every studied model after analyzing the possible identifiability issues of the original parameter sets. According to an importance factor ([Formula: see text]), it was shown that insulin sensitivity is not the most influential parameter from the dynamical point of view, that is, is not the parameter impacting the outputs the most of the three models, contrary to what is assumed in the literature. For the test data, the models demonstrated similar performance with most RMS values around 20 mg/dl (min: 15.64 mg/dl, max: 51.32 mg/dl). However, MMC failed to identify the model for patient 4. Also, considering the three models, the MMC model showed the higher parameter variability when reidentified every 6 hours.

CONCLUSION

This study shows that both structural and practical identifiability analysis need to be considered prior to the model identification/individualization in patients with T1D. It was shown that all the studied models are able to represent the CGM data, yet their usefulness in a hypothetical artificial pancreas could be a matter of debate. In spite that the three models do not capture all the dynamics and metabolic effects as a maximal model (ie, our FDA-accepted UVa/Padova simulator), SOGMM and ICING appear to be more appealing than MMC regarding both the performance and parameter variability after reidentification. Although the model predictions of ICING are comparable to the ones of the SOGMM model, the large parameter set makes the model prone to overfitting if all parameters are identified. Moreover, the existence of a high nonlinear function like [Formula: see text] prevents the use of tools from the linear systems theory.