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

摘要 1型糖尿病(T1D)患者的β细胞受到自身免疫破坏，导致胰岛素不足 维持正常的血糖水平，因此需要外源性胰岛素治疗，通常是 通过胰岛素泵持续皮下注射或多次皮下注射 这一天。现在有商业闭环系统可用来实现快速- 对无线连续血糖监测(CGM)传感器的响应作用胰岛素 通过泵计算和输送胰岛素的控制算法。不幸的是，目前的商业闭环系统 系统是混合系统，需要用户向系统通知用餐，而这些混合系统做到了 餐后血糖控制不好。人们常常忘记宣布他们的饭菜，或者错误地估计了他们的饭菜 碳水化合物摄入，从而导致整体餐后血糖控制不佳。混合动力车的主要优势 闭合循环系统一直是在夜间不进食的时候。在正常的工作中 胰淀素是一种β细胞荷尔蒙，在进餐时与胰岛素共同分泌，以帮助抑制高血糖素 产生并延缓胃排空，从而减少餐后血糖升高。普拉林肽是一种 内源性激素胰淀素的类似物。我们的商业合作伙伴ADOCIA已经开发出一种共同配方 胰岛素和普拉林肽。在这个项目中，我们将开发一种双重激素(胰岛素和普拉林肽)，完全 具有餐食检测算法的自动闭环系统，将使 T1D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 向系统发出通知。我们的团队此前已经开发出一种多激素闭环系统 (胰岛素和高血糖素)，我们已经开发出胰岛素、普拉林肽、高血糖素和碳水化合物的模型 动力学和动力学将被整合到新的胰岛素+普拉林肽闭环预测模型中 控制(MPC)系统(目标1)。我们还开发了一个餐食检测算法，该算法将与 这种MPC控制算法在检测到餐后不久就给胰岛素和普拉林肽，从而消除了 用户需要向系统宣布这顿饭(目标1)。我们将使用我们的葡萄糖电子计算机模拟器 代谢以确定胰岛素和普拉林肽在体内的最佳给药量和时间 对膳食检测算法的反应(目标1)。接下来，我们将评估最佳的胰岛素和普拉林肽 在一项四臂门诊膳食试验中，对目标1中确定的剂量治疗进行评估，并评估排名前4位的策略 14例T1D患者的随机交叉研究(研究1a，目标2)。然后我们将评估最佳的胰岛素 和研究1a中确定的普拉林肽剂量疗法，并与各种仅胰岛素混合疗法和 临床随机交叉研究中的自动化治疗(研究1b，AIM2)。最后，在目标3中，我们将 评估目标2中确定的最佳胰岛素和普拉林肽剂量治疗，并将其与 串联糖尿病控制-IQ商业混合闭环系统。

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.