Artificial neural networks modelling the prednisolone nanoprecipitation in microfluidic reactors.
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.
Ali
Hany S M
HS
Institute of Pharmaceutical Innovation, School of Pharmacy, University of Bradford, Richmond Road, Bradford BD7 1DP, United Kingdom. hsmali@bradford.ac.uk
Blagden
Nicholas
N
York
Peter
P
Amani
Amir
A
Brook
Toni
T
D000465:Algorithms; D000893:Anti-Inflammatory Agents / Q000737:chemistry*; D001185:Artificial Intelligence; D046210:Microfluidic Analytical Techniques; D008956:Models, Chemical; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010316:Particle Size; D011239:Prednisolone / Q000737:chemistry*; D012995:Solubility
D000893:Anti-Inflammatory Agents; D011239:Prednisolone