Applying a systems pharmacology approach to understanding glucagon-like peptide 1 receptor signalling bias

The development of new efficacious drugs is a major challenge to the pharmaceutical industry. Despite continued improvements in production, costs continue to increase, while the number of approved drugs declines. This has been particularly evident in the development of drugs aimed at G protein-coupled receptors (GPCRs), a leading pharmaceutical target. Consequently, new approaches are required. Systems pharmacology (SP) is an emerging discipline combining mathematical and computational techniques to provide a more holistic view of pharmacology. Here we propose to, for the first time, develop and apply SP approaches to quantitatively model dose-dependent time-course data derived from GPCR signal transduction, specifically the Glucagon-like peptide 1 (GLP1) receptor. Only through producing the most quantitatively accurate models of GPCR signalling may we eventually be able to use computers to predict how drugs will react when administered to the general population. Generating quantitative models of GPCR signalling requires high quality, reproducible time-course data coupled to the ability to estimate, with absolute confidence, parameters that cannot be measured directly. SP approaches will allow us to perform these types of analyses. Biological data will be obtained using a range of robust second messenger assays for the GLP1 receptor obtained from mammalian cells. Computationally, we will utilise structural identifiability analysis to ascertain the uniqueness of the unknown model parameters, ensuring that our parameter estimation is as robust as possible. The true strength of our approach is the synergy between 'wet' experiments and 'dry' modelling, ensuring that the most appropriate experiments are performed.