Institute of Pharmaceutical Innovation, School of Pharmacy, University of Bradford, BD7 1DP, Bradford, UK.
Pharm Res. 2010 Jan;27(1):37-45. doi: 10.1007/s11095-009-0004-2. Epub 2009 Nov 12.
The aim of this study was to identify the dominant factors affecting the stability of nanoemulsions, using artificial neural networks (ANNs).
A nanoemulsion preparation of budesonide containing polysorbate 80, ethanol, medium chain triglycerides and saline solution was designed, and the particle size of samples with various compositions, prepared using different rates and amounts of applied ultrasonic energy, was measured 30 min and 30 days after preparation. Using ANNs, data were modelled and assessed. The derived predictive model was validated statistically and then used to determine the effect of different formulation and processing input variables on particle size growth of the nanoemulsion preparation as an indicator of the preparation stability.
The results indicated that the data can be satisfactorily modelled using ANNs, while showing a high degree of complexity between the dominant factors affecting the stability of the preparation.
The total amount of applied energy and concentration of ethanol were found to be the dominant factors controlling the particle size growth.
本研究旨在利用人工神经网络（ANNs）确定影响纳米乳稳定性的主要因素。
设计了含有泊洛沙姆 80、乙醇、中链甘油三酯和生理盐水的布地奈德纳米乳制剂，并测量了不同组成的样品在不同超声能量速率和用量下制备后 30 分钟和 30 天的粒径。使用人工神经网络对数据进行建模和评估。对所得到的预测模型进行了统计学验证，然后用于确定不同制剂和处理输入变量对纳米乳制剂粒径增长的影响，以此作为制剂稳定性的指标。
结果表明，ANNs 可以很好地对数据进行建模，同时显示出影响制剂稳定性的主要因素之间具有高度的复杂性。
发现应用能量总量和乙醇浓度是控制粒径增长的主要因素。
