News — A La Jolla Institute team, led by leading type 1 diabetes researcher Matthias von Herrath, M.D., has demonstrated the effectiveness of a recently developed computer model in predicting key information about nasal insulin treatment regimens in type 1 (juvenile) diabetes. Development of the software, the Type 1 Diabetes PhysioLab庐 Platform, was funded through the peer-reviewed grant program of the American Diabetes Association.
The findings, which also showed the platform鈥檚 ability to predict critical type 1 diabetes molecular 鈥渂iomarkers,鈥� were published in the December issue of the scientific journal Diabetes, and further validate the importance of the new model as a valuable research tool in type 1 diabetes. The software is designed to enable researchers to rapidly streamline laboratory research through the evaluation of alternative scenarios for therapeutic strategies that show the most promise for working in humans.
"Since laboratory studies can cost hundreds of thousands of dollars, and early stage human clinical trials can cost $10 million dollars or more, predicting the right conditions to try is important,鈥� said Dr. von Herrath, director of the Type 1 Diabetes Research Center at the La Jolla Institute for Allergy & Immunology, where the studies were conducted.
鈥淲e鈥檝e found that using this in silico (computer analysis) prediction platform can quicken the pace and effectiveness of type 1 diabetes research,鈥� he continued. 鈥淏y allowing us to pre-test our theories in computer models, we can ensure that the more time-intensive and costly process of laboratory testing is focused on the most promising therapeutic strategies, with the greatest chance of success.鈥�
The platform, developed by Entelos Inc., a life sciences company specializing in predictive technologies, has previously been shown to successfully predict various data from published type 1 diabetes experiments. Dr. von Herrath鈥檚 team used a different approach to test the model, asking it to predict the outcome of a hypothetical experiment on nasal insulin dosing frequency in animal models that had not yet been performed. The prediction was then tested in the laboratory, where its results were confirmed.
In addition, he said, the model was able to accurately identify the particular time frame at which key type 1 diabetes 鈥渂iomarkers鈥� 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.
鈥淭he 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,鈥� said Dr. von Herrath, noting that the software鈥檚 prediction of the biomarkers was key in this process. 鈥淭hese 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.鈥�
The results were reported in the paper 鈥淰irtual Optimization of Nasal Insulin Therapy Predicts Immunization Frequency To Be Crucial for Diabetes Protection.鈥� 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鈥� R&D group, were first co-authors.
The Type 1 Diabetes PhysioLab庐 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 work of the software鈥檚 development to provide a new scientific tool for enhancing the speed and effectiveness of type 1 diabetes research.
More than 400,000 children worldwide suffer from type 1 diabetes, a chronic disease that can lead to severe complications, such as blindness, cardiovascular disease, renal disease, coma or even death.
The platform, developed over two years, simulates autoimmune processes and subsequent destruction of pancreatic beta cells from birth through frank diabetes onset (hyperglycemia). The destruction of insulin-producing beta cells in the pancreas is the underlying cause of type 1 diabetes.
Specifically, Dr. von Herrath鈥檚 team employed the model to investigate the possible mechanisms underlying the effectiveness of nasal insulin therapy, using the B: 9-23 peptide. 鈥淭he experimental aim was to evaluate the impact of dose, frequency of administration and age at treatment on key molecular mechanisms and optimal therapeutic outcome,鈥� he said.
Using parameters input by the scientific team, the model accurately predicted that less frequent doses of nasal insulin, started at an early disease stage, would protect more effectively against beta cell destruction than higher frequency doses in NOD mice.
Dr. von Herrath added that the positive results add credence to the idea of creating computer models for analyzing therapeutic interventions in human disease. 鈥淭hese results support the development and application of humanized platforms for the design of clinical trials,鈥� he said.
About La Jolla InstituteFounded in 1988, the La Jolla Institute for Allergy & Immunology is a biomedical research nonprofit focused on improving human health through increased understanding of the immune system. Its scientists carry out research seeking new knowledge leading to the prevention of disease through vaccines and the treatment and cure of infectious diseases, cancer and autoimmune diseases such as rheumatoid arthritis, type 1 (juvenile) diabetes, Crohn鈥檚 disease and asthma. La Jolla Institute鈥檚 research staff includes more than 100 Ph.D.s and M.D.s. For more information, go to www.liai.org
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