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    Artificial neural networks modelling the prednisolone nanoprecipitation in microfluidic reactors

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    Publication date
    28/06/2009
    Author
    Ali, Hany S.M.
    Blagden, Nicholas
    York, Peter
    Amani, Amir
    Brook, Toni
    Keyword
    Artificial neural networks
    Crystal engineering
    Modelling
    Microfluidics
    Nanoprecipitation
    Prednisolone
    Peer-Reviewed
    Yes
    
    Metadata
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    Abstract
    This study employs artificial neural networks (ANNs) to create a model to identify relationships between variables affecting drug nanoprecipitation using microfluidic reactors. The input variables examined were saturation levels of prednisolone, solvent and antisolvent flowrates, microreactor inlet angles and internal diameters, while particle size was the single output. ANNs software was used to analyse a set of data obtained by random selection of the variables. The developed model was then assessed using a separate set of validation data and provided good agreement with the observed results. The antisolvent flow rate was found to have the dominant role on determining final particle size.
    URI
    http://hdl.handle.net/10454/4850
    Citation
    Ali, H.S.M., Blagden, N., York, P., Amani, A. and Brook, T. (2009). Artificial neural networks modelling the prednisolone nanoprecipitation in microfluidic reactors. European Journal of Pharmaceutical Sciences. Vol. 37, No. 3-4, pp. 514-522.
    Link to publisher’s version
    http://dx.doi.org/10.1016/j.ejps.2009.04.007
    Type
    Article
    Collections
    Life Sciences Publications

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