PK/PD modelling related to insulin
In this thesis a dynamic PK/PD model of a physical system is derived and used to predict BG level in type 1 diabetes patients. In order to gain systematic improvements of the derived model, both residual analysis and statistical tests will be used.
 PK/PB models are important tools for drug development in the pharmaceutical industry. PK models describe how a drug moves through the body in three phases: absorption phase, distribution phase and elimination phase, while PD models describe the effects of the drug in the body.
 Usually ordinary differential equations (ODEs) with an added random error are used to describe PK/PD models but in this thesis SDEs will be applied. The advantage of using SDEs instead of ODEs is that SDEs describe both true random biological variations in the data as well as unknown or uncontrollable variation factors in an individual e.g. stress, mood, daily conditions, heart rate etc.
 The derived model is tested on real data from clinical trials where an optimal set of parameters of the model are found for each trial day for all participants using a program called CTSM. Then the results are analyzed and any deficiency affecting the performance of the predictions detected.