It has been estimated that pharmaceutical companies spend more than $1 billion and over 12 years of R&D to get a new medicine to patients. Successful development of new, more effective treatments for diabetes and obesity has been especially difficult since patients vary widely and the effects of diet, exercise, and drug therapies on human physiology are highly unpredictable. Any insights that can be used to better predict a patient ™s response to complex treatment regimens could thus accelerate progress in diabetes and obesity research.

The newly patented method extends the ability of the Entelos Metabolism PhysioLab platform to explore, simulate, and predict differences in fuel utilization (e.g., fat, carbohydrate, and protein metabolism) between patients, a key predictor in responses to treatment.

The Entelos Metabolism PhysioLab platform is an innovative, predictive computer model that represents the underlying physiology of metabolic disorders such as obesity and diabetes and uses simulated virtual patients to help predict responses. These virtual patients enable new therapies and interventions to be efficiently flight tested in a computer before expensive clinical testing in humans, reducing the risk and time to market for novel drugs. The best treatment approaches for specific patient types can be identified earlier, potentially leading to the development of more predictive companion diagnostics and personalized care.

SOURCE Entelos, Inc.