Anticipation of drug-drug interactions (DDIs) in the paediatric population are merely based on data generated in adults. Hence decision on avoiding certain combinations or attempts to adjust and manage the doses under combination-therapy are mainly speculative from the knowledge of what occurs in adults. However, due to developmental changes in elimination pathways from birth to adolescents, the assumption of DDIs being similar in adults and children might not be correct. This thesis firstly identifies and quantitatively compares the reported DDIs in paediatric and adult populations through a systematic literature review of DDIs reported in paediatric subjects. The study highlights the clear paucity of the data in children younger than 2 years. Therefore, the logical approach to test this hypothesis has been through modelling and simulation and incorporation of the biological knowledge on ontogeny of various enzymes and other elimination routes. The magnitude of any metabolic DDI depends on fractional importance of inhibited pathway which may not necessarily be the same in young children when compared to adults. To show this disparity between rate of ontogeny for metabolic pathways, the ontogeny pattern of CYP enzymes and renal function were analysed systematically. Bootstrap methodology was used to account for variability, and to define the age range over which a statistical difference is likely between each pair of specific pathways. A number of DDIs were simulated for virtual compounds to highlight the possibility that the magnitude of DDI can be influenced by age. Depending on the extent of contribution of metabolic pathways, neonates could be more sensitive to DDI than adults in certain scenarios or vice versa. Thus, extrapolation from adult DDI data may not be applicable across paediatric age groups. The uncertainty around the ontogeny functions based on in vitro information led us to carry out comprehensive performance verification for in vivo data on probe substrates of CYP1A2, -2C9 and 3A4 and assess the predictions of clearance (CL) by monitoring AUC. Although the evaluation showed that in most cases predictions were within two fold of observed data in adult and paediatric studies, the outcome suggests that the current ontogeny profiles result in under-prediction of CL values compared to clinical studies in infants and children and there is a need for better ontogeny models. Therefore, we derived novel ontogeny functions for CYP1A2 and CYP3A based on caffeine-theophylline and midazolam in vivo data. Age related CL data for caffeine, theophylline and midazolam were reconstructed back to intrinsic CL values per milligram of microsomal protein and best fit ontogeny models for CYP1A2 and CYP3A were derived from these data. The function for CYP1A2 describes an increase in relative intrinsic metabolic CL from birth to 3 years followed by a decrease to adult values. The function for CYP3A4 describes a continuous rise in relative intrinsic metabolic CL, reaching the adult value at about 2 years of age. The new models were validated by showing improved predictions of the systemic CL of ropivacaine (major CYP1A2 substrate; minor CYP3A4 substrate) and alfentanil (major CYP3A4 substrate) compared to those using a previous ontogeny function based on in vitro data. When implementing enzyme ontogeny functions it is important to consider potential confounding factors related to disease, anaesthesia and surgery that may affect the prediction of net in vivo CL. Finally, we demonstrated the application of paediatric physiologically-based pharmacokinetic (p-PBPK) models for calculation of sample size in paediatric clinical pharmacokinetic (PK) studies in a methodology suggested by Wang et al., based on desired precision for a PK parameter of interest. We obtained estimates of variability for CL, volume of distribution and area under the plasma concentration-time curve for 5 different drugs from (i) adult and paediatric classic clinical PK studies, and (ii) p-PBPK combined with in vitro-in vivo extrapolation. The estimates were applied to the sample size calculation proposal methodology for non-compartmental analysis. There were clear and drug dependent differences in calculated sample size based on various estimates of variability and overall, there was no consistent discrepancy in the sample size calculated according to the source of variability used for sample size calculations. The results are discussed in terms of their potential impact on the clinical PK studies in children. In general, considering the sensitivity of paediatric clinical PK studies and paucity of data in this group of patients, the use of p-PBPK models may offer an interim solution to uncovering age bands with potential higher vulnerability to DDI. However, these models require further refinements and testing before widely used in clinical practice with confidence.