Type 1 diabetes computer model's predictive success validated through lab testing

ScienceDaily (Dec. 9, 2010) ? 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.

The findings, which also showed the platform's ability to predict critical type 1 diabetes molecular "biomarkers," 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," 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.

Development of the software, the Type 1 Diabetes PhysioLab® Platform, was funded through the peer-reviewed grant program of the American Diabetes Association.

"We've found that using this in silico (computer analysis) prediction platform can quicken the pace and effectiveness of type 1 diabetes research," he continued. "By 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."

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's 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 "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 work of the software's 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's team employed the model to investigate the possible mechanisms underlying the effectiveness of nasal insulin therapy, using the B: 9-23 peptide. "The experimental aim was to evaluate the impact of dose, frequency of administration and age at treatment on key molecular mechanisms and optimal therapeutic outcome," 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. "These results support the development and application of humanized platforms for the design of clinical trials," he said.

Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.

Story Source:

The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by La Jolla Institute for Allergy and Immunology, via EurekAlert!, a service of AAAS.

Journal Reference:

G. Fousteri, J. R. Chan, Y. Zheng, C. Whiting, A. Dave, D. Bresson, M. Croft, M. von Herrath. Virtual Optimization of Nasal Insulin Therapy Predicts Immunization Frequency to Be Crucial for Diabetes Protection. Diabetes, 2010; 59 (12): 3148 DOI: 10.2337/db10-0561

Note: If no author is given, the source is cited instead.

View the original article here

Type 1 diabetes computer model's predictive success validated through lab testing

ScienceDaily (Dec. 9, 2010) ? 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.

The findings, which also showed the platform's ability to predict critical type 1 diabetes molecular "biomarkers," 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," 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.

Development of the software, the Type 1 Diabetes PhysioLab® Platform, was funded through the peer-reviewed grant program of the American Diabetes Association.

"We've found that using this in silico (computer analysis) prediction platform can quicken the pace and effectiveness of type 1 diabetes research," he continued. "By 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."

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's 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 "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 work of the software's 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's team employed the model to investigate the possible mechanisms underlying the effectiveness of nasal insulin therapy, using the B: 9-23 peptide. "The experimental aim was to evaluate the impact of dose, frequency of administration and age at treatment on key molecular mechanisms and optimal therapeutic outcome," 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. "These results support the development and application of humanized platforms for the design of clinical trials," he said.

Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.

Story Source:

The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by La Jolla Institute for Allergy and Immunology, via EurekAlert!, a service of AAAS.

Journal Reference:

G. Fousteri, J. R. Chan, Y. Zheng, C. Whiting, A. Dave, D. Bresson, M. Croft, M. von Herrath. Virtual Optimization of Nasal Insulin Therapy Predicts Immunization Frequency to Be Crucial for Diabetes Protection. Diabetes, 2010; 59 (12): 3148 DOI: 10.2337/db10-0561

Note: If no author is given, the source is cited instead.

View the original article here

New Computer Method To Measure Mechanical Stress In Fat Cells

Main Category: Obesity / Weight Loss / Fitness
Also Included In: IT / Internet / E-mail;??Diabetes;??Dermatology
Article Date: 25 Nov 2010 - 0:00 PST window.fbAsyncInit = function() { FB.init({ appId: 'aa16a4bf93f23f07eb33109d5f1134d3', status: true, cookie: true, xfbml: true, channelUrl: 'http://www.medicalnewstoday.com/scripts/facebooklike.html'}); }; (function() { var e = document.createElement('script'); e.async = true; e.src = document.location.protocol + '//connect.facebook.net/en_US/all.js'; document.getElementById('fb-root').appendChild(e); }()); email to a friend ? printer friendly ? opinions ?

From fad diets to exercise programs, Americans continue to fight the battle of the bulge. Now they'll have help from recent Tel Aviv University research that has developed a new method to look at how fat cells - which produce the fat in our bodies - respond to mechanical loads.

This might be the key to understanding how to control the amount of fat produced by fat cells, the holy grail of weight loss researchers, says Prof. Amit Gefen of Tel Aviv University's Department of Biomedical Engineering. His research is driven by the theory that fat cells, like bone or muscle cells, are influenced by mechanical loads, defined as the amount of force or deformation placed on a particular area occupied by cells. By recreating the structure of fat cells using a newly-developed computer method, Prof. Gefen and his team of researchers can determine how much mechanical load can be tolerated by fat cells, and at what point the cells will begin to disintegrate.

The research, recently reported in the Journal of Biomechanics, has direct applications in weight loss programs, the treatment of bedsores and the management of chronic diabetes.

Bones in space, fat on the ground

According to Prof. Gefen, applying mechanical loads on tissues can affect many different cells within our bodies. For example, zero gravity affects the bone density of astronauts. When astronauts return home after a prolonged space flight, he explains, they are often confined to a wheelchair for a small period of time. The structures of their bones and muscles, which are determined by the cells that produce these structures, are weakened due to a lack of mechanical loads. This occurs because cells are deprived of "normal" mechanical stimulation, like walking.

Prof. Gefen believes that, much like bone or muscle cells, fat cells are also affected by mechanical loads. His new computer model takes slices of laser confocal microscopy images of cells and reconstructs a whole, virtual version of an individual cell, allowing researchers to evaluate how that cell will respond to different mechanical stimuli. "We use these computer models to see how cells function under mechanical loading, much like simulations in structural engineering are used to test the strength of bridges or machines," he explains.

After assembling their "virtual" fat cells, Prof. Gefen and his group found that fat cells or lipids have a point where mechanical loads can disintegrate them, as well as a point at which they are able to resist disintegration. Prof. Gefen is now trying to determine the specific load magnitudes and frequencies for fat cells, perhaps using ultrasound at a supersonic frequency to vibrate the tissue.

Not all infomercials are light-weight

Those fat-busting "ab vibrators" that you can see on infomercials are on the right track, says Prof. Gefen, but the magnitude of mechanical loads and the frequency of their application need to be scientifically determined. Such information could be crucial to the future of our health, he says, noting that diabetes and obesity rates are rising. "Any treatment that would be effective in fighting obesity would also apply immediately to diabetes," he explains.

The next step for Prof. Gefen and his fellow researchers is to pin down the mathematical equations that allow for the dissolving of lipid droplets, then predict what a fat cell will do under certain levels of force. This will lead to better information on how to use mechanical loads to control the production of fat by fat cells - whether this means applying a certain frequency of ultrasonic vibration, or simply spending more time in the gym.

Source:
George Hunka
American Friends of Tel Aviv University

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