Artificial neural networks modelling the prednisolone nanoprecipitation in microfluidic reactors

Ali, Hany S. M., Blagden, Nicholas, York, Peter, Amani, Amir and Brook, Toni (2009) Artificial neural networks modelling the prednisolone nanoprecipitation in microfluidic reactors. European Journal of Pharmaceutical Sciences, 37 (3-4). pp. 514-522. ISSN 0928-0987

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Item Type:Article
Item Status:Live Archive

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 flow rates, 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. © 2009 Elsevier B.V. All rights reserved.

Keywords:nanoparticle, prednisolone, solvent, article, artificial neural network, computer program, drug delivery system, drug solubility, flow rate, microfluidics, molecular model, particle size, precipitation, priority journal, reactor, validation study, Algorithms, Anti-Inflammatory Agents, Artificial Intelligence, Microfluidic Analytical Techniques, Models, Chemical, Models, Statistical, Neural Networks (Computer), Solubility
Subjects:G Mathematical and Computer Sciences > G730 Neural Computing
F Physical Sciences > F100 Chemistry
Divisions:College of Science > School of Pharmacy
ID Code:8757
Deposited On:22 Apr 2013 09:34

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