title: An advisory protocol for rapid- and slow-acting insulin therapy based on a run-to-run methodology.
abstract: BACKGROUND
  Emerging technology, such as an artificial pancreatic beta-cell, is not likely to be affordable to people who live in developing nations in the next 20-30 years. However, multiple-daily injection (MDI) therapy can be improved using similar advanced control algorithms designed for continuous glucose monitoring and continuous insulin infusion pumps.
  
  
  METHODS
  A simulation study of run-to-run control was developed for MDI therapy. Rapid- and slow-acting insulins were used in the protocol, which uses pre- and postprandial glucose measurements. The key information for the synthesis of the control algorithm is the subject insulin sensitivity that is calculated for two cases: (a) when the subject's glycemia and insulin dosing information is known (sensitivity response) and (b) when there is no previous information about the subject's response to the insulin protocol. In the latter case, this information needs to be estimated recursively using online data. After the sensitivity is recalculated, the run-to-run correction scheme is updated, obtaining an adaptive MDI therapy. The robustness of the advisory algorithm was evaluated by constant random parameter variations and superimposing sinusoidal oscillations on glucose-insulin model parameters to simulate intra-individual variability of the glucoregulatory system.
  
  
  RESULTS
  Optimal glycemic control has been achieved for both cases (a and b) despite variable meals (15% variation in carbohydrate content and 15-min variation in timing) and parametric variations in the glucose-insulin model. In Case (b), no profound hypoglycemic (<60 mg/dL) or hyperglycemic (>180 mg/dL) events were observed on average during all evaluations.
  
  
  CONCLUSIONS
  This work shows that the run-to-run framework for insulin updating can be successfully extended to an adaptive MDI protocol. These results motivate the practical implementation of this scheme in portable units such as personal digital assistants or smartphones.
authors.affiliation: Faculty of Engineering, Center for Research and Graduate Studies, Autonomous University of San Luis Potosí, San Luis Potosí, México.
authors.forename: Fabiola
authors.initials: F
authors.lastname: Campos-Cornejo
authors.forename: Daniel U
authors.initials: DU
authors.lastname: Campos-Delgado
authors.forename: Diego
authors.initials: D
authors.lastname: Espinoza-Trejo
authors.forename: Howard
authors.initials: H
authors.lastname: Zisser
authors.forename: Lois
authors.initials: L
authors.lastname: Jovanovic
authors.forename: Francis J
authors.initials: FJ
authors.lastname: Doyle
authors.forename: Eyal
authors.initials: E
authors.lastname: Dassau
mesh_terms: D000465:Algorithms; D001786:Blood Glucose / Q000032:analysis; D003198:Computer Simulation; D003922:Diabetes Mellitus, Type 1 / Q000097:blood / Q000188:drug therapy* / Q000276:immunology; D006801:Humans; D007279:Injections, Subcutaneous; D007328:Insulin / Q000008:administration & dosage / Q000031:analogs & derivatives*; D000069036:Insulin Glargine; D061268:Insulin Lispro; D049528:Insulin, Long-Acting; D018448:Models, Immunological
chemical_list: D001786:Blood Glucose; D007328:Insulin; D061268:Insulin Lispro; D049528:Insulin, Long-Acting; D000069036:Insulin Glargine