A GROUNDBREAKING TOOL FOR TYPE 1 DIABETES TREATMENT R&D.
The T1DMS is a computer simulator of the human metabolic system based on the Meal Model of glucose-insulin dynamics. In January 2008, T1DMS became the first computer tool accepted by the FDA as a substitute for animal trials in the pre-clinical testing of certain control strategies in T1DM. TEG offers services for the development and optimization of T1D-related devices, protocols, and treatments using the T1D metabolic simulator along with the full FDA-accepted in silico patient population. Additionally, TEG distributes and supports a Distributed Version of the T1D metabolic simulator (with a smaller “sample” in silico patient population) to simplify access for the wider diabetes research community. T1DMS can be used to (i) test the safety and effectiveness of new therapies under varying conditions, (ii) compare existing treatments, and (iii) bolster R&D and product strategies.
GAIN FUNDAMENTAL INSIGHT INTO YOUR PK/PD EXPERIMENTAL DATA VIA COMPARTMENTAL MODELING.
SAAM II supports the development and statistical calibration of compartmental models in biological, metabolic, and pharmaceutical systems. Thus providing valuable information about the safety, efficacy, and limitations of various interventions during treatment development. Now available through TEG and used worldwide by more than 7,000 pharmaceutical, biomedical and scientific professionals, SAAM II is a powerful compartmental and numerical software program using models to analyze data by (i) creating systems of ordinary differential equations from the compartmental model structure, (ii) enabling the simulation of complex experimental protocols on the model, and (iii) solving the model and fitting it to experimental data using state-of-the-art mathematical and statistical techniques.
The Continuous Glucose-Error Grid Analysis (CG-EGA) is a Windows-based, statistical program designed for use by the diabetes technology industry in the evaluation of continuous glucose monitoring devices. It is a logical extension of the original error grid analysis (EGA), which was developed for assessing the clinical accuracy of patient-determined blood glucose values using either estimation or self-monitoring blood glucose systems. CG-EGA is based upon the premise that information being generated by a monitoring system should be reliable enough to result in clinically accurate decision making by the user.