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Physiologically-based pharmacokinetic (PBPK) modelling is traditionally employed to predict drug concentration-time profiles in plasma and tissues using information from physiology/biology, in vitro experiments and in silico predictions. Model-based analysis of population pharmacokinetic (PK) data is rarely performed in such a mechanistic framework, as empirical compartmental models are mainly utilised for this purpose. However, the combination of traditional PBPK methodologies with parameter estimation techniques and non-linear mixed effects modelling is an approach with progressively increasing impact due to the significant advantages it offers. Therefore, the general aim of this thesis is to illustrate, explore and thus further facilitate the application of physiologically-based pharmacokinetic models in the context of population data analysis. In order to pursue this aim, this work firstly particularly focuses on the population pharmacokinetics of simvastatin (SV) and its active metabolite, simvastatin acid (SVA). The complex simvastatin pharmacokinetics and their clinical significance, due to the association with simvastatin-induced myopathy, provide an excellent case to illustrate the advantages of a mechanistically sound population model. In the current work, both conventional and physiologically-based population models were developed using clinical PK data for SV and SVA. Specifically, the developed model-based approaches successfully quantified the impact of demographics, genetic polymorphisms and drug-drug interactions (DDIs) on the SV/SVA pharmacokinetics. Therefore, they can be of significant application either in the clinic or during drug development in order to assess myopathy and DDI risk. Secondly, in this work the following advantages offered by integrated population PBPK modelling were clearly illustrated through specific applications: 1) prediction of drug concentrations at the tissue level, 2) ability to extrapolate outside the studied population and/or conditions and 3) ability to guide the design (sample size) of prospective clinical studies. Finally, in the current work, further methodological aspects related to the application of this integrated population PBPK modelling approach were explored. Of specific focus was the parameter estimation process aided by prior distributions and the derivation of the latter from different in vitro/in silico sources. In addition, specific methodology is illustrated in this work that allows the incorporation of stochastic population variability in the structural parameters of such models without neglecting the underlying physiological constraints.