Determination of factors controlling the particle size in nanoemulsions using Artificial Neural Networks.

The purpose of this study was to use Artificial Neural Networks (ANNs) in identifying factors, in addition to surfactant and internal phase content, that influence the particle size of nanoemulsions. The phase diagram and rheometric characteristics of a nanoemulsion system containing polysorbate 80, ethanol, medium chain triglycerides and normal saline loaded with budesonide were investigated. The particle size of samples of various compositions prepared using different rates and amounts of applied energy was measured. Data, divided into training, test and validation sets, were modelled by ANNs. The developed model was assessed and found to be of high quality. The model was then used to explore the effect of composition and processing factors on particle size of the nanoemulsion preparation. The study demonstrates the potential of ANNs in identifying critical parameters controlling preparation for this system, with the total amount of applied energy during preparation found to be the dominant factor in controlling the final particle size.

Modelling drug dissolution from controlled release products using genetic programming.

This study has investigated and compared genetic programming (GP) - a method of automatically generating equations that describe the cause-and-effect relationships in a system - and statistical methods for modelling two controlled release formulations--a matrix tablet and microspheres. With the improved GP models exhibiting comparable predictive power, as well as simpler equations in some cases, the results obtained indicate that GP can be considered as an effective and efficient method for modelling controlled release formulations.