In addition, he said, the model was able to accurately identify the particular time frame at which key type 1 diabetes "biomarkers" kicked in. Biomarkers are specific cell types or proteins that tell researchers at what point a therapeutic option is working or when it is time to start treatment. In the case of the La Jolla Institute study, the model successfully predicted the onset of biomarkers indicating beta cell protection in the NOD mouse.
"The model accurately predicted that implementing a low frequency nasal insulin dosing regimen in animal models was more beneficial in controlling type 1 diabetes than a high frequency regimen," said Dr. von Herrath, noting that the software's prediction of the biomarkers was key in this process. "These results confirmed our hypotheses on the benefits of low-frequency nasal insulin dosing. But even more importantly, the advantage of applying computer modeling in optimizing the therapeutic efficacy of nasal insulin immunotherapy was confirmed."
The results were reported in the paper "Virtual Optimization of Nasal Insulin Therapy Predicts Immunization Frequency To Be Crucial for Diabetes Protection." Dr. von Herrath was senior author on the paper and La Jolla Institute scientist Georgia Fousteri, Ph.D., and Jason Chan, Ph.D., from Entelos' R&D group, were first co-authors.
The Type 1 Diabetes PhysioLab Platform is a large-scale mathematical model of disease pathogenesis based on non-obese diabetic (NOD) mice. The platform was developed with input from an independent scientific team of leading type 1 diabetes experts. The research support group of the American Diabetes Association funded the wor
|Contact: Bonnie Ward|
La Jolla Institute for Allergy and Immunology