title: Virtual optimization of nasal insulin therapy predicts immunization frequency to be crucial for diabetes protection.
abstract: OBJECTIVE
  Development of antigen-specific strategies to treat or prevent type 1 diabetes has been slow and difficult because of the lack of experimental tools and defined biomarkers that account for the underlying therapeutic mechanisms.
  
  
  METHODS
  The type 1 diabetes PhysioLab platform, a large-scale mathematical model of disease pathogenesis in the nonobese diabetic (NOD) mouse, was used to investigate the possible mechanisms underlying the efficacy of nasal insulin B:9-23 peptide therapy. The experimental aim was to evaluate the impact of dose, frequency of administration, and age at treatment on Treg induction and optimal therapeutic outcome.
  
  
  RESULTS
  In virtual NOD mice, treatment efficacy was predicted to depend primarily on the immunization frequency and stage of the disease and to a lesser extent on the dose. Whereas low-frequency immunization protected from diabetes atrributed to Treg and interleukin (IL)-10 induction in the pancreas 1-2 weeks after treatment, high-frequency immunization failed. These predictions were confirmed with wet-lab approaches, where only low-frequency immunization started at an early disease stage in the NOD mouse resulted in significant protection from diabetes by inducing IL-10 and Treg.
  
  
  CONCLUSIONS
  Here, the advantage of applying computer modeling in optimizing the therapeutic efficacy of nasal insulin immunotherapy was confirmed. In silico modeling was able to streamline the experimental design and to identify the particular time frame at which biomarkers associated with protection in live NODs were induced. These results support the development and application of humanized platforms for the design of clinical trials (i.e., for the ongoing nasal insulin prevention studies).
authors.affiliation: Diabetes Center, La Jolla Institute for Allergy and Immunology, La Jolla, California, USA.
authors.forename: Georgia
authors.initials: G
authors.lastname: Fousteri
authors.forename: Jason R
authors.initials: JR
authors.lastname: Chan
authors.forename: Yanan
authors.initials: Y
authors.lastname: Zheng
authors.forename: Chan
authors.initials: C
authors.lastname: Whiting
authors.forename: Amy
authors.initials: A
authors.lastname: Dave
authors.forename: Damien
authors.initials: D
authors.lastname: Bresson
authors.forename: Michael
authors.initials: M
authors.lastname: Croft
authors.forename: Matthias
authors.initials: M
authors.lastname: von Herrath
mesh_terms: D000281:Administration, Intranasal; D000595:Amino Acid Sequence; D000818:Animals; D001327:Autoimmune Diseases / Q000276:immunology; D001786:Blood Glucose / Q000032:analysis; D003198:Computer Simulation; D003922:Diabetes Mellitus, Type 1 / Q000276:immunology / Q000517:prevention & control*; D005434:Flow Cytometry; D006801:Humans; D007114:Immunization / Q000379:methods; D007328:Insulin / Q000008:administration & dosage / Q000627:therapeutic use*; D007515:Islets of Langerhans / Q000276:immunology; D051379:Mice; D016688:Mice, Inbred NOD; D008962:Models, Theoretical; D010446:Peptide Fragments / Q000737:chemistry / Q000627:therapeutic use; D013601:T-Lymphocytes / Q000276:immunology; D050378:T-Lymphocytes, Regulatory / Q000276:immunology; D014584:User-Computer Interface
chemical_list: D001786:Blood Glucose; D007328:Insulin; D010446:Peptide Fragments