Ambulatory artificial pancreas: merging physiology, behavior, and controldesign

描述(由申请人提供)：当代研究越来越多地关注人工胰腺(AP)的发展--一种被称为闭环系统(CLC)的工程系统。最终目标--非卧床AP--有可能对1型糖尿病患者的健康和生活产生巨大影响。我们跨学科的国际团队一直站在CLC开发的前沿，在硅胶测试平台、安全和控制算法方面创建了代表当今AP开发最先进水平的模型。通过这个项目，我们将对动态CLC的探索提升到一个新的水平，首次提出合并1型糖尿病最佳控制的三个关键方面：人类行为、生理和工程学。我们的主要目标是建立、测试和验证一个新的动态CLC系统，该系统能够根据行为和生理的实时变化进行通知并适应。我们的基本假设是：行为事件和随后的代谢反应的速率可以被划分为分层的时间尺度，这些时间尺度可以转化为模块化的工程层次结构，在每个时间尺度上具有清晰可识别和易于处理的控制目标。阶段1-构建评估算法和控制模块(主要时间尺度分钟-小时)：我们将首先描述心理行为标记物和急性行为事件(如进餐、锻炼)与生理反应的大小和对《中图法》实时适应的需求之间的关系。将设计工程工具，负责患者的安全和低血糖的预防，并在本地和远程层面上负责AP系统的“健康”。我们将开发一个框架来解决由行为挑战引发的系统转变，并将进行创新的生理学实验，以评估“黎明”现象、复杂碳水化合物餐后的葡萄糖通量以及肝脏胰高血糖素的敏感性。阶段2-判断控制成分的影响大小(主要时间尺度天-周)：我们将设计一种自适应学习算法，识别患者的生物行为模式，如进餐和锻炼时间，以及胰岛素敏感性的日变化。协调的临床研究和大规模的计算机实验将估计包含在CLC中的初始化和实时适应控制组件的影响大小。具体地说，我们将评估使用生理和行为学的效果：(I)标志物来初始化CLC和(Ii)配置文件来调整胰岛素推注和基础速率。第三阶段-长期非卧床CLC的系统验证和试验：最终的多中心试验将在患者的自然环境中验证我们的系统，为最终将其转化为临床实践做准备。推动第三阶段的主要假设是：与最先进的传感器强化开环治疗相比，闭环控制将减少低血糖的频率，并将在70-180 mg/dl的目标范围内增加时间，而不会影响HbA1c测量的平均血糖控制。 公共卫生相关性：基于闭环控制的人工胰腺有可能对1型糖尿病患者的健康和生活产生巨大影响。在过去的五年中，这项技术的发展取得了长足的进步；然而，它仍处于初级阶段，目前正在住院临床研究中心进行测试。随着从诊所到门诊试验再到经批准的可移动设备的过渡，将需要开发更多的策略来优化控制和个体化治疗，需要创造性的、受医学启发的工程设计和安全监测。

DESCRIPTION (provided by applicant): Contemporary studies focus increasingly on the development of artificial pancreas (AP) - an engineering system known as closed-loop control (CLC). The final goal - an ambulatory AP - has the potential to make a tremendous impact on the health and lives of people with type 1 diabetes. Our interdisciplinary international team has been at the forefront of CLC development, creating models, in silico testing platform, safety and control algorithms that represent the state of the art in AP development today. With this project, we bring the quest for ambulatory CLC to a new level, proposing to merge for the first time three key aspects of the optimal control in type 1 diabetes: human behavior, physiology and engineering Our primary goal is to build, test, and validate a new ambulatory CLC system that is informed by, and is adaptive to, real-time changes in behavior and physiology. Our underlying hypothesis is: the rate of behavioral events and the ensuing metabolic responses can be divided into hierarchical time scales, which can be translated into a modular engineering hierarchy with clearly identifiable and tractable control goals at each time scale. Phase 1 - Building assessment algorithms and control modules (primary time scale minutes-hours): We will first characterize the relationships of psycho-behavioral markers and acute behavioral events (e.g. meals, exercise) with the magnitude of physiological response and the need for real-time adaptation of CLC. Engineering tools will be designed responsible for the patient safety and prevention of hypoglycemia and for the 'health' of the AP system on both local and remote levels. We will develop a framework to address system transitions instigated by behavioral challenges and will conduct innovative physiological experiments to assess "dawn" phenomenon, glucose fluxes following complex carbohydrate meal, and hepatic glucagon sensitivity. Phase 2 - Judging the effect size of control components (primary time scale days-weeks): We will engineer an adaptive learning algorithm that recognizes patients' bio-behavioral patterns, such as meal and exercise timing, and diurnal variation in insulin sensitivity. Coordinated clinical studies and large-scale in silico experiments will estimate the effect size of inclusion into CLC of initialization and real-time adaptation control components. Specifically, we will assess the effect of using physiological and behavioral: (i) markers to initialize CLC and (ii) profiles to adjust insulin boluses and basal rate. Phase 3 - System validation and trial of long-term ambulatory CLC: A final multi-center trial will validate our system in patients' natural environment in preparation for its ultimate translation into clinical practice. The primary hypothesis driving Phase 3 is: compared to state-of-the-art sensor augmented open loop therapy, closed-loop control will reduce the frequency of hypoglycemia and will increase the time spent within the target range of 70-180 mg/dl, without compromising average glycemic control as measured by HbA1c. PUBLIC HEALTH RELEVANCE: The artificial pancreas based on closed-loop control, has the potential to make a tremendous impact on the health and lives of people with type 1 diabetes. The development of this technology has made significant strides over the last five years; however, it is still in infancy, currently being tested in inpatient clinical-research center setting. As the transition is made from the clinic to outpatient trials and then to approved ambulatory devices, additional strategies will need to be developed to optimize control and individualize treatment, requiring creative, medically-inspired engineering design and safety monitoring.