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    Evaluation of an in vitro in vivo correlation for nebulizer delivery using artificial neural networks

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    Publication date
    2007
    Author
    de Matas, Marcel
    Chrystyn, Henry
    Shao, Qun
    Silkstone, Victoria L.
    Keyword
    Aerosols
    Pulmonary Drug Delivery
    Lung Bioavailability
    Neural Networks
    In Vitro/In Vivo Correlations
    IVIVC
    Peer-Reviewed
    Yes
    
    Metadata
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    Abstract
    The ability to generate predictive models linking the in vitro assessment of pharmaceutical products with in vivo performance has the potential to enable greater control of clinical quality whilst minimizing the number of in vivo studies in drug development. Artificial neural networks (ANNs) provide a means of generating predictive models correlating critical product characteristics to key performance attributes. In this regard, ANNs have been used to model historical data exploring the relative lung bioavailability of salbutamol from several different nebulizers. The generated ANN model was shown to relate urinary salbutamol excretion at 30 min postinhalation, which is the index of relative lung bioavailability of salbutamol, to specific fractions of the particle size distribution, to subject body surface area and to the methods of nebulization. This model was validated using unseen data and gave good agreement with pharmacokinetic outcomes for 17 data records. The model gave improved predictions of urinary salbutamol excretion for individual subjects compared to the published linear correlation generated using the same data. It is therefore concluded that ANN models have the potential to provide reliable estimates of pharmacokinetic performance that relate to lung deposition, for nebulized medicines in individual subjects.
    URI
    http://hdl.handle.net/10454/4161
    Version
    No full-text available in the repository
    Citation
    DeMatas, M., Chrystyn, H., Shao, O. and Silkstone, V.L. (2007). Evaluation of an in vitro in vivo correlation for nebulizer delivery using artificial neural networks. Journal of Pharmaceutical Sciences. Vol. 96, No. 12, pp. 3293-3303.
    Link to publisher’s version
    http://dx.doi.org/10.1002/jps.20965
    Type
    Article
    Collections
    Life Sciences Publications

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