The present project has been carried out in collaboration with a multinational enterprise. The goal is to identify the parameters of an insulin glucose model for Type 1 diabetes. To accomplish this, different population approaches have been implemented to study different dynamical systems and a comparison between them has been done. Furthermore, the algorithm proposed in the study [1] and tested in a previous project [2] has also been examined. These methods allow the identification of the system parameters. The aim is to compare the methods with both artificial and real clinical data. The use of hierarchical models is widely used in pharmaceutical studies where only few observations are available. In this study, it has been proved that the use of the method tested in [2] does not contribute any significant improvement in estimating either the parameters or the model noise. The extension to the second stage has been done with Global Two Stage (GTS), which contributed by substantially improving the duration of the computation time with no loss of precision. This method has proved the effectiveness of the use of hierarchical models for the estimation of fixed-effect parameters as well as the random-effect parameters. Besides, it has been necessary to distinguish the intra and inter individual variation for several subjects from the same experiment. Real clinical data has been tested and the results analyzed to improve the identification process in the near future.

Outgoing