Enabling Fully Automated Closed Loop Control in Type 1 Diabetes Through an Artificial Intelligence Meal Detection Algorithm and Pramlintide

总结 患有1型糖尿病（T1 D）的人具有β细胞的自身免疫性破坏，导致胰岛素不足， 维持正常的葡萄糖水平，从而需要外源性胰岛素治疗， 通过胰岛素泵连续地皮下输注或通过注射多次， 白天现在有商业闭环系统可供使用，使自动交付的快速- 响应于无线连续葡萄糖监测（CGM）传感器， 控制算法，以计算并通过泵输送胰岛素。不幸的是，目前的商业闭环 系统是混合系统，需要用户向系统宣布用餐，并且这些混合系统确实 餐后血糖控制不佳。人们经常忘记宣布他们的饭菜或错误估计他们的食物。 碳水化合物摄入，从而导致总体餐后血糖控制不佳。Hybrid的主要优势 闭环系统是在不吃饭的过夜期间。处于正常工作 胰岛β细胞，胰淀素激素与胰岛素共同分泌，以帮助抑制胰高血糖素 生产和延迟胃排空，从而减少餐后葡萄糖增加。普兰林肽是一种 内源激素胰淀素的类似物。我们的商业合作伙伴，Alcancia，开发了一种共制剂， 胰岛素和普兰林肽。在这个项目中，我们将开发一种双激素（胰岛素和普兰林肽）， 具有进餐检测算法的自动闭环系统，其将使得能够实质性地改进 T1 D患者的餐后血糖控制，同时通过不需要进餐来最大限度地减少患者负担 通知系统。我们小组以前开发了一个多激素闭环系统， （胰岛素和胰高血糖素），我们已经开发了胰岛素，普兰林肽，胰高血糖素和碳水化合物的模型 将整合到新的胰岛素+普兰林肽闭环模型预测中的动力学和动力学 控制（MPC）系统（目标1）。我们还开发了一种膳食检测算法， 该MPC控制算法在检测到进餐后不久给药胰岛素和普兰林肽，从而消除了 用户需要向系统通告这顿饭（目标1）。我们将使用葡萄糖的计算机模拟器 以确定胰岛素和普兰林肽递送时的最佳给药量和时间， 响应于进餐检测算法（Aim 1）。接下来，我们将评估最佳胰岛素和普兰林肽 目标1中确定的给药治疗，并在4组临床进餐试验期间评价前4种策略 14例T1 D患者的随机交叉研究（研究1a，目标2）。然后我们将评估最佳胰岛素 和普兰林肽给药治疗，并与各种仅胰岛素混合和 在一项临床随机交叉研究（研究1b，Aim 2）中进行自动化治疗。在目标3中，我们将 评价目标2中确定的最佳胰岛素和普兰林肽给药治疗，并将其与 Tandem糖尿病控制-IQ商用混合闭环系统。

Summary People with type 1 diabetes (T1D) have autoimmune destruction of beta cells resulting in insufficient insulin to maintain normal glucose levels, thereby requiring exogenous insulin treatment, typically delivered subcutaneously either continuously infused through an insulin pump or by injection multiple times throughout the day. There are now commercial closed loop systems available that enable automated delivery of fast- acting insulin in response to wireless continuous glucose monitoring (CGM) sensors that inform an automated control algorithm to calculate and deliver insulin via a pump. Unfortunately, current commercial closed loop systems are hybrid systems that require users to announce meals to the system, and these hybrid systems do not control glucose well following meals. People oftentimes forget to announce their meals or misestimate their carbohydrate intake, thereby leading to poor overall postprandial glucose control. The primary benefit of hybrid closed loop systems has been during the overnight period when meals are not consumed. In a normal working beta cell, the hormone amylin is co-secreted with insulin in response to meals to help suppress glucagon production and delay gastric emptying, thereby reducing postprandial glucose increases. Pramlintide is an analog of the endogenous hormone amylin. Our commercial partner, Adocia, has developed a coformulation of insulin and pramlintide. In this project, we will develop a dual hormone (insulin and pramlintide), fully automated closed loop system with a meal detection algorithm that will enable substantial improvements in postprandial glycemic control for people with T1D while minimizing patient burden by not requiring a meal announcement to the system. Our group has previously developed a multi hormone closed loop system (insulin and glucagon) and we have developed models of insulin, pramlintide, glucagon, and carbohydrate kinetics and dynamics that will be integrated into a new insulin+pramlintide closed loop model predictive control (MPC) system (Aim 1). We have also developed a meal detection algorithm that will be integrated with this MPC control algorithm to dose insulin and pramlintide shortly after a meal is detected, thereby eliminating the need for the user to announce this meal to the system (Aim 1). We will use our in silico simulator of glucose metabolism to identify the optimal dosing amount and timing when insulin and pramlintide are delivered in response to the meal detection algorithm (Aim 1). Next, we will evaluate the optimal insulin and pramlintide dosing therapies identified in Aim 1 and evaluate the top 4 strategies during an in-clinic meal test in a 4-arm randomized crossover study in 14 people with T1D (Study 1a, Aim 2). We will then evaluate the optimal insulin and pramlintide dosing therapy identified in Study 1a and compare with various insulin-only hybrid and automated therapies in an in-clinic randomized crossover study (Study 1b, Aim2). Finally, in Aim 3, we will evaluate the optimal insulin and pramlintide dosing therapy determined in Aim 2 and compare it with the Tandem Diabetes Control-IQ commercial hybrid closed loop system.