BACKGROUND: In 2008-2009, the first multinational study was completed comparing closed-loop control (artificial pancreas) to state-of-the-art open-loop therapy in adults with type 1 diabetes mellitus (T1DM). METHODS: The design of the control algorithm was done entirely in silico, i.e., using computer simulation experiments with N=300 synthetic "subjects" with T1DM instead of traditional animal trials. The clinical experiments recruited 20 adults with T1DM at the Universities of Virginia (11); Padova, Italy (6); and Montpellier, France (3). Open-loop and closed-loop admission was scheduled 3-4 weeks apart, continued for 22 h (14.5 h of which were in closed loop), and used a continuous glucose monitor and an insulin pump. The only difference between the two sessions was that insulin dosing was performed by the patient under a physician's supervision during open loop, whereas insulin dosing was performed by a control algorithm during closed loop. RESULTS: In silico design resulted in rapid (less than 6 months compared to years of animal trials) and cost-effective system development, testing, and regulatory approvals in the United States, Italy, and France. In the clinic, compared to open-loop, closed-loop control reduced nocturnal hypoglycemia (blood glucose below 3.9 mmol/liter) from 23 to 5 episodes (p<.01) and increased the amount of time spent overnight within the target range (3.9 to 7.8 mmol/liter) from 64% to 78% (p=.03). CONCLUSIONS: In silico experiments can be used as viable alternatives to animal trials for the preclinical testing of insulin treatment strategies. Compared to open-loop treatment under identical conditions, closed-loop control improves the overnight regulation of diabetes.
BACKGROUND: In 2008-2009, the first multinational study was completed comparing closed-loop control (artificial pancreas) to state-of-the-art open-loop therapy in adults with type 1 diabetes mellitus (T1DM). METHODS: The design of the control algorithm was done entirely in silico, i.e., using computer simulation experiments with N=300 synthetic "subjects" with T1DM instead of traditional animal trials. The clinical experiments recruited 20 adults with T1DM at the Universities of Virginia (11); Padova, Italy (6); and Montpellier, France (3). Open-loop and closed-loop admission was scheduled 3-4 weeks apart, continued for 22 h (14.5 h of which were in closed loop), and used a continuous glucose monitor and an insulin pump. The only difference between the two sessions was that insulin dosing was performed by the patient under a physician's supervision during open loop, whereas insulin dosing was performed by a control algorithm during closed loop. RESULTS: In silico design resulted in rapid (less than 6 months compared to years of animal trials) and cost-effective system development, testing, and regulatory approvals in the United States, Italy, and France. In the clinic, compared to open-loop, closed-loop control reduced nocturnal hypoglycemia (blood glucose below 3.9 mmol/liter) from 23 to 5 episodes (p<.01) and increased the amount of time spent overnight within the target range (3.9 to 7.8 mmol/liter) from 64% to 78% (p=.03). CONCLUSIONS: In silico experiments can be used as viable alternatives to animal trials for the preclinical testing of insulin treatment strategies. Compared to open-loop treatment under identical conditions, closed-loop control improves the overnight regulation of diabetes.
Authors: Daniela Bruttomesso; Anne Farret; Silvana Costa; Maria Cristina Marescotti; Monica Vettore; Angelo Avogaro; Antonio Tiengo; Chiara Dalla Man; Jerome Place; Andrea Facchinetti; Stefania Guerra; Lalo Magni; Giuseppe De Nicolao; Claudio Cobelli; Eric Renard; Alberto Maran Journal: J Diabetes Sci Technol Date: 2009-09-01
Authors: Firas H El-Khatib; Steven J Russell; David M Nathan; Robert G Sutherlin; Edward R Damiano Journal: Sci Transl Med Date: 2010-04-14 Impact factor: 17.956
Authors: Lalo Magni; Davide M Raimondo; Luca Bossi; Chiara Dalla Man; Giuseppe De Nicolao; Boris Kovatchev; Claudio Cobelli Journal: J Diabetes Sci Technol Date: 2007-11
Authors: William V Tamborlane; Roy W Beck; Bruce W Bode; Bruce Buckingham; H Peter Chase; Robert Clemons; Rosanna Fiallo-Scharer; Larry A Fox; Lisa K Gilliam; Irl B Hirsch; Elbert S Huang; Craig Kollman; Aaron J Kowalski; Lori Laffel; Jean M Lawrence; Joyce Lee; Nelly Mauras; Michael O'Grady; Katrina J Ruedy; Michael Tansey; Eva Tsalikian; Stuart Weinzimer; Darrell M Wilson; Howard Wolpert; Tim Wysocki; Dongyuan Xing Journal: N Engl J Med Date: 2008-09-08 Impact factor: 91.245
Authors: Stephen T Bartlett; James F Markmann; Paul Johnson; Olle Korsgren; Bernhard J Hering; David Scharp; Thomas W H Kay; Jonathan Bromberg; Jon S Odorico; Gordon C Weir; Nancy Bridges; Raja Kandaswamy; Peter Stock; Peter Friend; Mitsukazu Gotoh; David K C Cooper; Chung-Gyu Park; Phillip OʼConnell; Cherie Stabler; Shinichi Matsumoto; Barbara Ludwig; Pratik Choudhary; Boris Kovatchev; Michael R Rickels; Megan Sykes; Kathryn Wood; Kristy Kraemer; Albert Hwa; Edward Stanley; Camillo Ricordi; Mark Zimmerman; Julia Greenstein; Eduard Montanya; Timo Otonkoski Journal: Transplantation Date: 2016-02 Impact factor: 4.939
Authors: Franz Feichtner; Julia K Mader; Roland Schaller; Lukas Schaupp; Martin Ellmerer; Stefan Korsatko; Venkata R Kondepati; H Michael Heise; Malgorzata E Wilinska; Roman Hovorka; Thomas R Pieber Journal: J Diabetes Sci Technol Date: 2011-07-01
Authors: Lalantha Leelarathna; Marianna Nodale; Janet M Allen; Daniela Elleri; Kavita Kumareswaran; Ahmad Haidar; Karen Caldwell; Malgorzata E Wilinska; Carlo L Acerini; Mark L Evans; Helen R Murphy; David B Dunger; Roman Hovorka Journal: Diabetes Technol Ther Date: 2012-12-20 Impact factor: 6.118
Authors: Arianne C Van Bon; Lisanne D Jonker; Rob Koebrugge; Robin Koops; Joost B L Hoekstra; J Hans DeVries Journal: J Diabetes Sci Technol Date: 2012-09-01