Valproic acid is an anti-convulscant drug that is widely used in the treatment of different types of epilepsy and since its introduction the clinical use has increased rapidly both as a sole agent and in combination therapies. The mechanism of action has been linked to blockade of voltage-dependent sodium channels and potentiation of GABAergic transmission. The most widely used route of administration of Valproic acid is oral, although it can also be given intravenously and rectally and its pharmacokinetics has been studied extensively. The aim of this work was to develop a physiologically based pharmacokinetic model for plasma and tissue/organ prediction in children and adults following intravenous and oral dosing of Valproic acid. The plasma/tissue concentration profile will be used for clinical trial simulation in Dravet syndrome, a rare form of epilepsy in children where the combination of Valproic acid, stiripentol and clobazam has shown remarkable results. A physiologically based pharmacokinetic model was developed with compartments for gut lumen, enterocyte, gut tissue, systemic blood, kidney, liver, brain, spleen, muscle and rest of body. System and drug specific parameters for the model were obtained from the literature from in vitro and in vivo experiments. The model was initially developed for adults and scaled to children using age-dependent changes in anatomical and physiological parameters and ontogeny functions for enzyme maturation assuming the same elimination pathways in adults and children. The results from the model validation showed satisfactory prediction of plasma concentration both in terms of mean prediction and variability in children and adults following intravenous and oral dosing especially after single doses. The model also adequately predicts clearance in children. Due to limited distribution of Valproic acid into tissues, the concentration in plasma is about 8-9 times higher than tissues/organs. The model could help to improve clinical outcome in the treatment of Dravet syndrome through dose optimisation.