In this two-part Bench to Clinic narrative, recent advances in both the preclinical and clinical aspects of artificial pancreas (AP) development are described. In the preceding Bench narrative, Kudva and colleagues provide an in-depth understanding of the modified glucoregulatory physiology of type 1 diabetes that will help refine future AP algorithms. In the Clinic narrative presented here, we compare and evaluate AP technology to gain further momentum toward outpatient trials and eventual approval for widespread use. We enumerate the design objectives, variables, and challenges involved in AP development, concluding with a discussion of recent clinical advancements. Thanks to the effective integration of engineering and medicine, the dream of automated glucose regulation is nearing reality. Consistent and methodical presentation of results will accelerate this success, allowing head-to-head comparisons that will facilitate adoption of the AP as a standard therapy for type 1 diabetes.

OBJECTIVETo evaluate two widely used control algorithms for an artificial pancreas (AP) under nonideal but comparable clinical conditions.RESEARCH DESIGN AND METHODSAfter a pilot safety and feasibility study (n = 10), closed-loop control (CLC) was evaluated in a randomized, crossover trial of 20 additional adults with type 1 diabetes. Personalized model predictive control (MPC) and proportional integral derivative (PID) algorithms were compared in supervised 27.5-h CLC sessions. Challenges included overnight control after a 65-g dinner, response to a 50-g breakfast, and response to an unannounced 65-g lunch. Boluses of announced dinner and breakfast meals were given at mealtime. The primary outcome was time in glucose range 70–180 mg/dL.RESULTSMean time in range 70–180 mg/dL was greater for MPC than for PID (74.4 vs. 63.7%, P = 0.020). Mean glucose was also lower for MPC than PID during the entire trial duration (138 vs. 160 mg/dL, P = 0.012) and 5 h after the unannounced 65-g meal (181 vs. 220 mg/dL, P = 0.019). There was no significant difference in time with glucose <70 mg/dL throughout the trial period.CONCLUSIONSThis first comprehensive study to compare MPC and PID control for the AP indicates that MPC performed particularly well, achieving nearly 75% time in the target range, including the unannounced meal. Although both forms of CLC provided safe and effective glucose management, MPC performed as well or better than PID in all metrics.

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The paramount goal in the treatment of type 1 diabetes is the maintenance of normoglycemia. Continuous glucose monitoring (CGM) technologies enable frequent sensing of glucose to inform exogenous insulin delivery timing and dosages. The most commonly available CGMs are limited by the physiology of the subcutaneous space in which they reside. The very same advantages of this minimally invasive approach are disadvantages with respect to speed. Because subcutaneous blood flow is sensitive to local fluctuations (e.g., temperature, mechanical pressure), subcutaneous sensing can be slow and variable. We propose the use of a more central, physiologically stable body space for CGM: the intraperitoneal space. We compared the temporal response characteristics of simultaneously placed subcutaneous and intraperitoneal sensors during intravenous glucose tolerance tests in eight swine. Using compartmental modeling based on simultaneous intravenous sensing, blood draws, and intraarterial sensing, we found that intraperitoneal kinetics were more than twice as fast as subcutaneous kinetics (mean time constant of 5.6 min for intraperitoneal vs. 12.4 min for subcutaneous). Combined with the known faster kinetics of intraperitoneal insulin delivery over subcutaneous delivery, our findings suggest that artificial pancreas technologies may be optimized by sensing glucose and delivering insulin in the intraperitoneal space.

Treatment of type 1 diabetes mellitus could be greatly improved by applying a closed-loop control strategy to insulin delivery, also known as an artificial pancreas (AP). In this work, we outline the design of a fully implantable AP using intraperitoneal (IP) insulin delivery and glucose sensing. The design process utilizes the rapid glucose sensing and insulin action offered by the IP space to tune a PID controller with insulin feedback to provide safe and effective insulin delivery. The controller was tuned to meet robust performance and stability specifications. An anti-reset windup strategy was introduced to prevent dangerous undershoot toward hypoglycemia after a large meal disturbance. The final controller design achieved 78% of time within the tight glycemic range of 80–140 mg/dL, with no time spent in hypoglycemia. The next step is to test this controller design in an animal model to evaluate the in vivo performance.

Aims To compare intraperitoneal (IP) to subcutaneous (SC) insulin delivery in an artificial pancreas (AP). Research design and methods Ten adults with type 1 diabetes participated in a non-randomized, non-blinded sequential AP study using the same SC glucose sensing and Zone Model Predictive Control (ZMPC) algorithm adjusted for insulin clearance. On first admission, subjects underwent closed-loop control with SC delivery of a fast-acting insulin analogue for 24 hours. Following implantation of a DiaPort IP insulin delivery system, the identical 24-hour trial was performed with IP regular insulin delivery. The clinical protocol included 3 unannounced meals with 70, 40 and 70 g carbohydrate, respectively. Primary endpoint was time spent with blood glucose (BG) in the range of 80 to 140 mg/dL (4.4–7.7 mmol/L). Results Percent of time spent within the 80 to 140 mg/dL range was significantly higher for IP delivery than for SC delivery: 39.8 ± 7.6 vs 25.6 ± 13.1 (P = .03). Mean BG (mg/dL) and percent of time spent within the broader 70 to 180 mg/dL range were also significantly better for IP insulin: 151.0 ± 11.0 vs 190.0 ± 31.0 (P = .004) and 65.7 ± 9.2 vs 43.9 ± 14.7 (P = .001), respectively. Superiority of glucose control with IP insulin came from the reduced time spent in hyperglycaemia (>180 mg/dL: 32.4 ± 8.9 vs 53.5 ± 17.4, P = .014; >250 mg/dL: 5.9 ± 5.6 vs 23.0 ± 11.3, P = .0004). Higher daily doses of insulin (IU) were delivered with the IP route (43.7 ± 0.1 vs 32.3 ± 0.1, P < .001) with no increased percent time spent <70 mg/dL (IP: 2.5 ± 2.9 vs SC: 4.1 ± 5.3, P = .42). Conclusions Glycaemic regulation with fully-automated AP delivering IP insulin was superior to that with SC insulin delivery. This pilot study provides proof-of-concept for an AP system combining a ZMPC algorithm with IP insulin delivery.

OBJECTIVEArtificial pancreas (AP) systems are best positioned for optimal treatment of type 1 diabetes (T1D) and are currently being tested in outpatient clinical trials. Our consortium developed and tested a novel adaptive AP in an outpatient, single-arm, uncontrolled multicenter clinical trial lasting 12 weeks.RESEARCH DESIGN AND METHODSThirty adults with T1D completed a continuous glucose monitor (CGM)-augmented 1-week sensor-augmented pump (SAP) period. After the AP was started, basal insulin delivery settings used by the AP for initialization were adapted weekly, and carbohydrate ratios were adapted every 4 weeks by an algorithm running on a cloud-based server, with automatic data upload from devices. Adaptations were reviewed by expert study clinicians and patients. The primary end point was change in hemoglobin A1c (HbA1c). Outcomes are reported adhering to consensus recommendations on reporting of AP trials.RESULTSTwenty-nine patients completed the trial. HbA1c, 7.0 ± 0.8% at the start of AP use, improved to 6.7 ± 0.6% after 12 weeks (−0.3, 95% CI −0.5 to −0.2, P < 0.001). Compared with the SAP run-in, CGM time spent in the hypoglycemic range improved during the day from 5.0 to 1.9% (−3.1, 95% CI −4.1 to −2.1, P < 0.001) and overnight from 4.1 to 1.1% (−3.1, 95% CI −4.2 to −1.9, P < 0.001). Whereas carbohydrate ratios were adapted to a larger extent initially with minimal changes thereafter, basal insulin was adapted throughout. Approximately 10% of adaptation recommendations were manually overridden. There were no protocol-related serious adverse events.CONCLUSIONSUse of our novel adaptive AP yielded significant reductions in HbA1c and hypoglycemia.

Background: The objective of this study was to assess the safety and effectiveness of the first commercial configuration of a tubeless automated insulin delivery system, Omnipod ® 5, in children (6–13.9 years) and adults (14–70 years) with type 1 diabetes (T1D) in an outpatient setting. Materials and Methods: This was a single-arm, multicenter, prospective clinical study. Data were collected over a 14-day standard therapy (ST) phase followed by a 14-day hybrid closed-loop (HCL) phase, where participants ( n = 36) spent 72 h at each of three prespecified glucose targets (130, 140, and 150 mg/dL, 9 days total) then 5 days with free choice of glucose targets (110–150 mg/dL) using the Omnipod 5. Remote safety monitoring alerts were enabled during the HCL phase. Primary endpoints were difference in time in range (TIR) (70–180 mg/dL) between ST and HCL phases and proportion of participants reporting serious device-related adverse events. Results: Mean TIR was significantly higher among children in the free-choice period overall (64.9% ± 12.2%, P < 0.01) and when using a 110 mg/dL target (71.2% ± 10.2%, P < 0.01), a 130 mg/dL target (61.5% ± 7.7%, P < 0.01), and a 140 mg/dL target (64.8% ± 11.6%, P < 0.01), and among adults using a 130 mg/dL target (75.1% ± 11.6%, P < 0.05), compared to the ST phase (children: 51.0% ± 13.3% and adults: 65.6% ± 15.7%). There were no serious device-related adverse events reported during the HCL phase, nor were there episodes of severe hypoglycemia or diabetic ketoacidosis. Conclusion: The Omnipod 5 System was safe and effective when used at glucose targets from 110 to 150 mg/dL for 14 days at home in children and adults with T1D.