Eyal Dassau1, Cesar C Palerm, Howard Zisser, Bruce A Buckingham, Lois Jovanovic, Francis J Doyle. 1. Department of Chemical Engineering, Univ. of California at Santa Barbara, California; Biomolecular Science & Engineering Program, Univ. of California at Santa Barbara, California; Sansum Diabetes Research Institute, Santa Barbara, California, USA.
Abstract
BACKGROUND: A critical step in algorithm development for an artificial beta-cell is extensive in silico testing. Computer simulations usually involve only the controller software, leaving untested the hardware elements, including the critical communication interface between the controller and the glucose sensor and insulin pump. METHODS: An in silico simulation platform has been developed that uses all of the components of the clinical system. At the core is a comprehensive in silico population model that covers the variability of principal metabolic parameters observed in vivo, to replace the human subject, with the ability to use historical clinical data. A continuous glucose monitor, in this case either the Abbott Diabetes Care (Alameda, CA) FreeStyle Navigator or the DexCom (San Diego, CA) STS7, is supplied with a glucose signal provided by the simulator. The Insulet (Bedford, MA) OmniPod insulin pump is also interfaced with the simulator to provide insulin delivery data. These hardware elements are an integral part of the system under testing, which also includes the algorithm components. RESULTS: The system is unique in that it uses the same hardware components for simulations as are required in clinical trials, allowing for full-system level verification and validation. With a detailed mathematical model, a suite of patients can be simulated to reflect various conditions. Because all hardware is used, their related limitations are automatically included. CONCLUSIONS: A complete artificial beta-cell evaluation platform was realized with the flexibility to interface various algorithms and patient models, allowing for the systematic analysis of monitoring and control algorithms. The system facilitates a variety of tests and challenges to the software and the component devices, streamlining preclinical validation trials.
BACKGROUND: A critical step in algorithm development for an artificial beta-cell is extensive in silico testing. Computer simulations usually involve only the controller software, leaving untested the hardware elements, including the critical communication interface between the controller and the glucose sensor and insulin pump. METHODS: An in silico simulation platform has been developed that uses all of the components of the clinical system. At the core is a comprehensive in silico population model that covers the variability of principal metabolic parameters observed in vivo, to replace the human subject, with the ability to use historical clinical data. A continuous glucose monitor, in this case either the Abbott Diabetes Care (Alameda, CA) FreeStyle Navigator or the DexCom (San Diego, CA) STS7, is supplied with a glucose signal provided by the simulator. The Insulet (Bedford, MA) OmniPod insulin pump is also interfaced with the simulator to provide insulin delivery data. These hardware elements are an integral part of the system under testing, which also includes the algorithm components. RESULTS: The system is unique in that it uses the same hardware components for simulations as are required in clinical trials, allowing for full-system level verification and validation. With a detailed mathematical model, a suite of patients can be simulated to reflect various conditions. Because all hardware is used, their related limitations are automatically included. CONCLUSIONS: A complete artificial beta-cell evaluation platform was realized with the flexibility to interface various algorithms and patient models, allowing for the systematic analysis of monitoring and control algorithms. The system facilitates a variety of tests and challenges to the software and the component devices, streamlining preclinical validation trials.
Authors: Ramin Bighamian; Bahram Parvinian; Christopher G Scully; George Kramer; Jin-Oh Hahn Journal: Control Eng Pract Date: 2018-03-14 Impact factor: 3.475
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