Artificial neural networks modelling the prednisolone nanoprecipitation in microfluidic reactors
Ali, Hany S.M. Blagden, Nicholas York, Peter Amani, Amir Brook, Toni
Ali, Hany S.M.
Blagden, Nicholas
York, Peter
Amani, Amir
Brook, Toni
Publication Date
28/06/2009
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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.
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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.
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