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

通过人工智能膳食检测算法和普兰林肽实现 1 型糖尿病的全自动闭环控制

• 批准号: 10472749
• 负责人: Peter G Jacobs
• 金额: $ 56.14万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-20 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10472749
• 关键词: Adult; Algorithms; Area; Area Under Curve; Artificial Intelligence; Automobile Driving; Beta Cell; Carbohydrates; Clinic; Clinical; Clinical Research; Clinical Trials; Consumption; Cross-Over Studies; Dangerousness; Detection; Dose; Engineering; Evaluation; Exercise; FDA approved; Gastric Emptying; Gastroparesis; Glucagon; Glucose; Goals; Health; Hormones; Hour; Hybrids; Hypoglycemia; Injections; Inpatients; Insulin; Insulin Infusion Systems; Insulin, Aspart, Human; Insulin-Dependent Diabetes Mellitus; Intake; Kidney Diseases; Kinetics; Lead; Metabolic; Metabolism; Modality; Modeling; Nausea; Neuropathy; Outcome; Outcome Measure; Outpatients; Participant; Patients; Pattern; Persons; Population; Pramlintide; Production; Publishing; Pump; Randomized; Retinal Diseases; System; Testing; Time; Translating; Work; analog; arm; autoimmune pathogenesis; base; blood glucose regulation; carbohydrate metabolism; clinical efficacy; design; diabetes control; glucose metabolism; glucose monitor; glucose production; glycemic control; improved; in silico; islet amyloid polypeptide; patient population; pharmacokinetics and pharmacodynamics; predictive modeling; primary outcome; recruit; response; safety and feasibility; sensor; simulation; subcutaneous; virtual patient; wireless

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.

概括 1 型糖尿病 (T1D) 患者的 β 细胞受到自身免疫性破坏，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，通常是递送 通过胰岛素泵连续注射或多次注射皮下注射 那天。现在有商业闭环系统可用，可以自动交付快速 响应无线连续血糖监测 (CGM) 传感器而发挥胰岛素作用，该传感器通知自动化系统 控制算法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 饭后血糖控制不好。人们常常忘记宣布他们的饭菜或错误估计他们的饭菜 碳水化合物的摄入，从而导致整体餐后血糖控制不佳。混合动力的主要好处 闭环系统在不进食的过夜期间一直处于闭环状态。在正常工作情况下 β 细胞，胰淀素激素与胰岛素共同分泌，以响应膳食，帮助抑制胰高血糖素 产生并延缓胃排空，从而减少餐后血糖的升高。普兰林肽是一种 内源性激素胰淀素的类似物。我们的商业合作伙伴 Adocia 开发了一种复合配方 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），完全 带有进餐检测算法的自动化闭环系统将能够显着改善 控制 T1D 患者的餐后血糖，同时通过不进餐来最大程度地减少患者负担 向系统公告。我们课题组此前已开发出多激素闭环系统 （胰岛素和胰高血糖素），我们开发了胰岛素、普兰林肽、胰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+普兰林肽闭环模型预测中 控制（MPC）系统（目标 1）。我们还开发了一种膳食检测算法，将与 这种 MPC 控制算法可在检测到进餐后立即给予胰岛素和普兰林肽，从而消除 用户需要向系统宣布这顿饭（目标 1）。我们将使用葡萄糖的计算机模拟器 代谢以确定胰岛素和普兰林肽给药时的最佳剂量和时间 对进餐检测算法的响应（目标 1）。接下来，我们将评估最佳胰岛素和普兰林肽 目标 1 中确定的剂量疗法，并在 4 组临床膳食测试期间评估最重要的 4 种策略 对 14 名 T1D 患者进行的随机交叉研究（研究 1a，目标 2）。然后我们将评估最佳胰岛素 研究 1a 中确定的普兰林肽给药疗法，并与各种纯胰岛素混合疗法和 临床随机交叉研究中的自动化疗法（研究 1b，目标 2）。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兰林肽剂量治疗，并将其与 Tandem Diabetes Control-IQ 商业混合闭环系统。