Current artificial pancreas systems (AP) operate via subcutaneous (SC) glucose sensing and SC insulin delivery. Due to slow diffusion and transport dynamics across the interstitial space, even the most sophisticated control algorithms in on-body AP systems cannot react fast enough to maintain tight glycemic control under the effect of exogenous glucose disturbances caused by ingesting meals or performing physical activity. Recent efforts made towards the development of an implantable AP have explored the utility of insulin infusion in the intraperitoneal (IP) space: a region within the abdominal cavity where the insulin-glucose kinetics are observed to be much more rapid than the SC space. In this paper, a series of canine experiments are used to determine the dynamic association between IP insulin boluses and plasma glucose levels. Data from these experiments are employed to construct a new mathematical model and to formulate a closed-loop control strategy to be deployed on an implantable AP. The potential of the proposed controller is demonstrated via in-silico experiments on an FDA-accepted benchmark cohort: the proposed design significantly outperforms a previous controller designed using artificial data (time in clinically acceptable glucose range: 97.3±1.5% vs. 90.1±5.6%). Furthermore, the robustness of the proposed closed-loop system to delays and noise in the measurement signal (for example, when glucose is sensed subcutaneously) and deleterious glycemic changes (such as sudden glucose decline due to physical activity) is investigated. The proposed model based on experimental canine data leads to the generation of more effective control algorithms and is a promising step towards fully automated and implantable artificial pancreas systems.

中文翻译：

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腹膜内胰岛素-葡萄糖动力学的新动物模型增强了闭环控制性能


目前的人工胰腺系统 (AP) 通过皮下 (SC) 葡萄糖传感和 SC 胰岛素输送进行操作。由于跨间隙空间的扩散和运输动力学缓慢，即使是体内 AP 系统中最复杂的控制算法也无法足够快地反应，以在因进食或进行体力活动引起的外源性葡萄糖扰动的影响下维持严格的血糖控制。最近为开发可植入 AP 所做的努力探索了腹膜内 (IP) 空间中胰岛素输注的效用：腹腔内的一个区域，观察到胰岛素-葡萄糖动力学比 SC 空间快得多。在本文中，使用一系列犬实验来确定腹膜内胰岛素推注与血浆葡萄糖水平之间的动态关联。这些实验的数据用于构建新的数学模型并制定要部署在植入式 AP 上的闭环控制策略。所提出的控制器的潜力通过 FDA 接受的基准队列的计算机模拟实验得到证明：所提出的设计明显优于之前使用人工数据设计的控制器（临床可接受的血糖范围内的时间：97.3±1.5% vs. 90.1±5.6） %）。此外，还研究了所提出的闭环系统对测量信号中的延迟和噪声（例如，当皮下感测到葡萄糖时）和有害的血糖变化（例如由于体力活动导致的血糖突然下降）的鲁棒性。所提出的基于实验犬数据的模型可以产生更有效的控制算法，并且是迈向全自动和可植入人工胰腺系统的有希望的一步。