Identification and Control of Pharmacokinetic/Physiological Systems by Closed-Loop IPFM Drug Injection

当所给药物在体内的浓度保持恒定时，疾病的药理学治疗变得更加有效。这一行动为一个研究项目提供了新的支持，该项目开发了用于体内药物浓度或某些生理参数闭环控制的离散算法。这些算法基于积分脉冲频率调制（IPFM），其中控制以小剂量药物注射的形式进行。在闭环系统运行过程中，准确快速地估计各种被控植物的参数（植物动力学与药物浓度或调节的生理参数与药物输入速率有关）的识别手段将被开发。对植物药代动力学参数的持续估计将用于调整控制器，检测药物的副作用，并形成特定药物在体内行为的数据库。一个改进的，更细粒度的非线性糖代谢模型将被开发，以适应非线性IPFM控制器先前设计的有效运行与外周室葡萄糖传感器。此外，将开发人类深度疼痛动力学模型，包括神经生理和心理因素，以及阿片类镇痛药物的药代动力学。设计一种非线性、自适应的需求镇痛控制器，用于安全有效地自我给药。首席研究员在工程和生理学方面都受过良好的训练，可以领导这项跨学科的研究。机构支持是足够的，我建议支持。

The pharmacological treatment of disease becomes more effective when the concentration of the administered drug in the body is held constant. This action provides for the renewed support of a research project in which the discrete algorithms for the closed-loop control of in vivo drug concentration or some physiological parameter has been developed. These algorithms are based on Integral Pulse Frequency Modulation (IPFM), in which control is in the form of small, bolus injections of the drug. Accurate and rapid identification means to estimate the parameters of various controlled plants (the plant dynamics relate drug concentration or regulated physiological parameter to drug input rate) while the closed-loop system is operating will be developed. Continuous estimates of the pharmacokinetic plant's parameters will be used to tune the controller, detect side-effects of the drug, and form a data base for particular drug's behavior in the body. An improved, finer grained nonlinear model of glucose metabolism will be developed in order to adapt the nonlinear IPFM controller previously designed to run effectively with a peripheral compartment glucose sensor. Futhermore, models will be developed for the dynamics of deep pain in humans, including neurophysiological and psychological factors, and the pharmacokinetics of opioid drugs for analgesia. A non-linear, adaptive, demand-analgesia controller will be designed for safe and efficient self-administration of analgesic drugs. The Principal Investigator has been well trained both in engineering and physiology to lead this interdisciplinary research. The Institutional support is adequate, I recommend support.