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

• Published in: European journal of pharmaceutical sciences Vol. 35; no. 1; pp. 42 - 51
• Main Authors: Amani, Amir, York, Peter, Chrystyn, Henry, Clark, Brian J., Do, Duong Q.
• Format: Journal Article
• Language: English
• Published: Shannon Elsevier B.V 02-09-2008; Elsevier
• Subjects: Algorithms; Anti-Inflammatory Agents - administration & dosage; Anti-Inflammatory Agents - chemistry; Artificial Neural Networks; Biological and medical sciences; Budesonide - administration & dosage; Budesonide - chemistry; Data Interpretation, Statistical; Emulsions; Ethanol - chemistry; Excipients; General pharmacology; Indicators and Reagents; Medical sciences; Nanoemulsion; Nanoparticles; Neural Networks (Computer); Particle Size; Pharmaceutical technology. Pharmaceutical industry; Pharmacology. Drug treatments; Polysorbates; Reproducibility of Results; Software; Solvents; Triglycerides - administration & dosage; Triglycerides - chemistry; Artificial Neural Networks; Particle size; Nanoemulsion; Nanoemulsion,Particle size,Artificial Neural Networks; Pharmaceutical technology; Neural network; Quantitative analysis
• ISSN: 0928-0987; 1879-0720
• DOI: 10.1016/j.ejps.2008.06.002

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.