Population pharmacokinetic/pharmacodynamic modelling of the hypothalamic-pituitary-gonadal axis

The present thesis deals with different aspects of population pharmacokinetic/ pharmacodynamic (PK/PD) modelling of the male hypothalamic-pituitary-gonadal (HPG) axis. The thesis consists of a summary report and five scientific research papers. An overview of the main topics covered in the thesis is provided in the summary report including PK/PD modelling in drug development, the pathological, physiological, and pharmacological aspects of the male HPG axis, and a detailed description of the methodology behind non-linear mixed-effects modelling based on stochastic differential equations (SDEs). The main objective of the work underlying this thesis was to develop mechanismbased population PK/PD models of the HPG axis. The HPG axis is a multivariate closed-loop control system consisting of regulatory hormonal feedback mechanisms. The number and complexity of the physiological mechanisms involved in such models makes them difficult to develop and are often too complex to be conveniently described by empirical models. Hence, the use of SDEs in population PK/PD modelling was used as a tool to systematically develop a mechanism-based model of the HPG axis following treatment with gonadotropin-releasing hormone (GnRH) agonist triptorelin and GnRH antagonist degarelix in a combined model. The use of SDEs in non-linear mixed-effects modelling was investigated by implementing the Extended Kalman Filter in the NONMEM software. Non-linear mixed-effects models based on SDEs extend the first-stage model of the hierarchical structure by decomposing the intra-individual variability into two types of noise, i.e. a system noise term representing unknown or incorrectly specified dynamics and a measurement noise term accounting for uncorrelated errors