Amani Amir, York Peter, Chrystyn Henry, Clark Brian J, Do Duong Q
Institute of Pharmaceutical Innovation, University of Bradford, Bradford BD7 1DP, UK.
Eur J Pharm Sci. 2008 Sep 2;35(1-2):42-51. doi: 10.1016/j.ejps.2008.06.002. Epub 2008 Jun 20.
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.
本研究的目的是利用人工神经网络（ANNs）来识别除表面活性剂和内相含量之外影响纳米乳剂粒径的因素。研究了含有聚山梨酯80、乙醇、中链甘油三酯和载有布地奈德的生理盐水的纳米乳剂体系的相图和流变特性。测量了使用不同能量施加速率和量制备的各种组成样品的粒径。分为训练集、测试集和验证集的数据由人工神经网络进行建模。对所开发的模型进行评估，发现其质量很高。然后使用该模型来探索组成和加工因素对纳米乳剂制剂粒径的影响。该研究证明了人工神经网络在识别控制该体系制剂的关键参数方面的潜力，发现制备过程中施加能量的总量是控制最终粒径的主要因素。
