• Emerging Model Predictive Control (MPC) algorithms are discussed within fully closed loop (FCL) systems. • Features are compared between three emerging FCL algorithms. • We propose a novel hybrid algorithm, combining benefits from three leading FCL algorithms. • We outline clinical effectiveness of FCL systems and explore future optimisations. • Real-world factors are discussed including exercise, meal and dietary variability. Model Predictive Control (MPC) is emerging within fully closed loop (FCL) systems to offer a promising advancement, by automating glucose regulation for people with Type 1 Diabetes. This article assesses the clinical effectiveness of FCL systems and explores future optimisations by comparison of recent developed systems. Evidence suggests that MPC-based FCL systems outperform hybrid closed-loop (HCL) models using Proportional-Integral-Derivative (PID) control, achieving higher time-in-range (TIR, 74.4% vs. 63.7%, P = 0.020) and better postprandial glucose regulation. However, no system has consistently surpassed the clinical TIR target (>70%), with postprandial hyperglycaemia and insulin absorption delays remaining key challenges. Three recent emerging FCL advancements include nonlinear MPC (NMPC) for dual-hormone systems, integrating glucagon to reduce hypoglycaemia, λ-Policy Iteration (λ-PI), an adaptive reinforcement learning model, and pulse-modulated artificial pancreas (PMCL) systems, which mimic natural insulin secretion. We compare features of these three emerging solutions and propose a novel hybrid model which combines benefits from these algorithms, to improve accuracy. While these innovations show promise in in-silico models, clinical validation is lacking. Key barriers include glucagon instability, CGM inaccuracies, cost, and patient adherence. Future research must prioritise long-term trials incorporating real-world factors such as exercise and dietary variability. By integrating predictive control, adaptive learning, and dual-hormone regulation, FCL systems could transform diabetes management, bridging the gap between technology and full automation.
Vaughan et al. (Sun,) studied this question.