Goez-Mora Jhon E, Arbelaez-Córdoba Natalia, Balcazar-Morales Norman, Rivadeneira Pablo S
Grupo GITA, Facultad de Minas, Universidad Nacional de Colombia, Medellín, Colombia.
Grupo GENMOL, Departamento de Fisiología y Bioquímica, Facultad de Medicina, Universidad de Antioquia, Medellín, Colombia.
PLoS One. 2025 Sep 4;20(9):e0330121. doi: 10.1371/journal.pone.0330121. eCollection 2025.
The development of an impulsive automated insulin delivery system (i-AiDS) for type 1 diabetes mellitus aims to provide real-time blood glucose regulation with minimal human intervention. This study presents the validation of an offset-free impulsive zone model predictive control strategy designed to cope with external disturbances such as meal intake and plant-model mismatch in a diabetic rat model. Fourteen male Wistar rats induced diabetes with streptozotocin were monitored using an continuous glucose monitoring and regulated by delivering insulin with a customized low-cost pump. After acquiring diabetes condition, the procedure for installing the devices in the rat is carried out. During the first day, manual insulin injections are made by the pump, the glucose response is recorded by the interface and an off-line parametric estimation is executed. Based on the parameters found, simulations are used for the first tuning of the controller and the estimator. During the second day, the parameters of the model and the control are tested and adjusted. Finally, on the third day, a 72-hour test of the impulsive begins in full autonomous mode. Results showed that the controller achieved an average of 83.4% of the time within the target range of 80-180 mg/dL, with no severe hypoglycemic or hyperglycemic events. The median absolute relative difference between model predictions and actual sensor data was 24.66%, indicating the presence of plant-model mismatch that was effectively handled by the controller. Peak hyperglycemic events reached 320 mg/dL, but were regulated within 50 minutes, while mild hypoglycemic events occurred in 3.62 ± 1.8 cases per subject. The study demonstrates the efficacy of the controller in managing unannounced carbohydrate intake and physiological disturbances in a real-world preclinical environment. These findings provide a foundation for future clinical trials, emphasizing the importance of in vivo validation of control strategies to refine the i-AiDS for human use. Improvements in model accuracy and dynamic parameter tuning could further enhance performance, particularly in longer experimental periods.
开发用于1型糖尿病的脉冲式自动胰岛素输送系统（i - AiDS）旨在以最少的人为干预实现实时血糖调节。本研究展示了一种无偏移脉冲区域模型预测控制策略的验证，该策略旨在应对糖尿病大鼠模型中的外部干扰，如进食和植物 - 模型不匹配。使用连续血糖监测仪对14只经链脲佐菌素诱导患糖尿病的雄性Wistar大鼠进行监测，并通过定制的低成本泵输送胰岛素进行调节。在大鼠出现糖尿病症状后，进行在大鼠身上安装设备的操作。第一天，由泵进行手动胰岛素注射，通过接口记录葡萄糖反应并执行离线参数估计。基于所获得的参数，使用模拟对控制器和估计器进行首次调整。第二天，对模型和控制的参数进行测试和调整。最后，在第三天，在完全自主模式下开始对脉冲式系统进行72小时测试。结果表明，控制器在80 - 180 mg/dL的目标范围内平均有83.4%的时间处于该范围，且未发生严重的低血糖或高血糖事件。模型预测与实际传感器数据之间的中位数绝对相对差异为24.66%，表明存在植物 - 模型不匹配，但控制器有效地处理了这一问题。高血糖峰值事件达到320 mg/dL，但在50分钟内得到调节，而轻度低血糖事件每只大鼠平均发生3.62 ± 1.8次。该研究证明了控制器在真实临床前环境中管理未宣布的碳水化合物摄入和生理干扰方面的有效性。这些发现为未来的临床试验奠定了基础，强调了对控制策略进行体内验证以优化用于人类的i - AiDS的重要性。模型准确性和动态参数调整的改进可以进一步提高性能，特别是在更长的实验周期中。
