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Ongoing Debate About Models for Artificial Pancreas Systems and In Silico Studies.

It is with great pleasure that I agreed to provide clinical perspectives and commentary on the issues raised by Garry Steil, PhD regarding the best use of models to advance artificial pancreas (AP) systems. Respectful and constructive academic debate on these fundamental issues serves to refine our thinking and educate the broader audience, provided that such debate does not stifle continued medical advancement. Indeed, much of my understanding of the algorithms behind emerging AP systems was gained by reading Dr. Steil’s original debate with B. Wayne Bequette, PhD on the cases for proportional-integral-derivative (PID) control and model predictive control (MPC). This initial debate occurred in 2013 at a time when many AP systems were still laptop based, and before the publication of any 24-h home-use AP trials. Contrast this to the current environment where the first hybrid closed loop (HCL) AP system is now in routine clinical use, although numerous others are in commercial development, and we see that the landscape has evolved dramatically over the intervening 5 years. The issues raised by Dr. Steil in his commentary relate to the optimal control strategy for AP systems as well as preclinical testing of evolving controllers given the current and rapidly evolving diabetes technology landscape. The control algorithm in an AP system is the behind-thescenes ‘‘brains’’ of the system linking the glycemic measurement of the continuous glucose monitor (CGM) with the insulin delivery of the continuous subcutaneous insulin infusion (CSII) pump. The concepts behind the algorithms can be broken down into one of three main designs: PID, MPC, or fuzzy logic (FL) control, which have been fully described elsewhere. The only commercially available AP system, the Medtronic 670G HCL AP, uses a PID-based controller along with an insulin feedback component. Other systems currently undergoing clinical trials use model predictive control-based controllers developed by Kovatchev, Breton, and Cobelli, Doyle and Dassau, Hovorka, and Damiano. An FL-based system by Phillip and Atlas is also undergoing trials. Although this list is not exhaustive, and work is ongoing by numerous other groups, it serves to show that a wide array of AP algorithms are currently under development and that the majority of these are MPC in design. It is not clear why MPC appears to be favored, although it may be due to MPC’s ability to handle additional inputs beyond glucose and insulin infusion rates (e.g., insulin on board or exercise data) or MPC’s ability to consider events that are a function of time. There continues to be substantial debate about which form of controller design will be better for AP system use in type 1 diabetes (T1D) patients. In their original debate, both Dr. Steil and Dr. Bequette emphasized that there are many different forms of PID and model predictive controllers and that different designs may result in different tradeoffs of benefits and deficits for different scenarios. A major point at that time was that no head-to-head studies of different controllers had been conducted. Then in 2016, Pinsker published the results of a randomized crossover trial comparing a PID controller with an model predictive controller, both designed by the Doyle/Dassau group. They found that their model predictive controller produced significantly greater time in target range (70–180 mg/dL) than their PID controller (74.4 vs. 63.7%; P = 0.020), with lower mean glucose (138 vs. 160 mg/dL; P = 0.012), and without a statistically significant difference in time <70 mg/dL (4.6 vs. 2.9%; P = 0.329). This study subsequently concludes that ‘‘MPC performed as well or better than PID in all metrics.’’ The results from this study produced another stimulating and educational academic debate between Dr. Steil and the Pinsker/Doyle/Dassau group, wherein the authors discuss the importance of iterative testing and tuning of AP systems and the challenges in establishing the correct level of system aggressiveness across different clinical scenarios. Additional comparison of controller designs can be seen in the AP meta-analysis by Weisman et al. In this metaanalysis they analyze 27 comparisons from 24 randomized controlled studies. They report that the mean difference for percentage time in the range between AP and conventional therapy for MPC was 14.3% and that for FL was 16.49%, both a significant improvement (P < 0.0001), whereas for PID it was 6.97% and not statistically significant (P = 0.11). Hypoglycemia reduction (% <70 mg/dL) for MPC was -1.95% and that for FL was -2.45%, both significant (P £ 0.001), whereas that for PID was larger at -3.98%, although not statistically significant (P = 0.21). It could be argued that the