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    ABSTRACT: 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.
    No preview · Article · Oct 2014 · European Journal of Pharmaceutical Sciences
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    ABSTRACT: To develop and evaluate a tool for the qualitative prediction of human oral bioavailability (Fhuman) from animal oral bioavailability (Fanimal) data employing ROC analysis and to identify the optimal thresholds for such predictions. A dataset of 184 compounds with known Fhuman and Fanimal in at least one species (mouse, rat, dog and non-human primates (NHP)) was employed. A binary classification model for Fhuman was built by setting a threshold for high/low Fhuman at 50%. The thresholds for high/low Fanimal were varied from 0 to 100 to generate the ROC curves. Optimal thresholds were derived from 'cost analysis' and the outcomes with respect to false negative and false positive predictions were analyzed against the BDDCS class distributions. We successfully built ROC curves for the combined dataset and per individual species. Optimal Fanimal thresholds were found to be 67% (mouse), 22% (rat), 58% (dog), 35% (NHP) and 47% (combined dataset). No significant trends were observed when sub-categorizing the outcomes by the BDDCS. Fanimal can predict high/low Fhuman with adequate sensitivity and specificity. This methodology and associated thresholds can be employed as part of decisions related to planning necessary studies during development of new drug candidates and lead selection.
    Full-text · Article · Mar 2014 · Pharmaceutical Research
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    ABSTRACT: Pharmacokinetic models range from being entirely "exploratory" and empirical, to semi-mechanistic and ultimately complex physiologically based pharmacokinetic (PBPK) models. This choice is conditional on the modelling purpose as well as the amount and quality of the available data. The main advantage of PBPK models is that they can be used to extrapolate outside the studied population and experimental conditions. The trade-off for this advantage is a complex system of differential equations with a considerable number of model parameters. When these parameters cannot be informed from in vitro or in silico experiments they are usually optimised with respect to observed clinical data. Parameter estimation in complex models is a challenging task associated with many methodological issues which are discussed here with specific recommendations. Concepts such as structural and practical identifiability are described with regards to PBPK modelling and the value of experimental design and sensitivity analyses is sketched out. Parameter estimation approaches are discussed, while we also highlight the importance of not neglecting the covariance structure between model parameters and the uncertainty and population variability that is associated with them. Finally the possibility of using model order reduction techniques and minimal semi-mechanistic models that retain the physiological-mechanistic nature only in the parts of the model which are relevant to the desired modelling purpose is emphasized. Careful attention to all the above issues allows us to successfully integrate information from in vitro or in silico experiments together with information deriving from observed clinical data and develop mechanistically sound models with clinical relevance.
    No preview · Article · Sep 2013 · British Journal of Clinical Pharmacology
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