人工神经网络
分子描述符
计算机科学
生物系统
脂质体
分散性
人工智能
化学
材料科学
机器学习
纳米技术
数量结构-活动关系
生物
有机化学
作者
Sameera Sansare,Tibo Duran,Hossein Mohammadiarani,Manish Kumar Goyal,Gowtham Yenduri,Antonio Costa,Xiao Ming Xu,Thomas G. O'Connor,Diane J. Burgess,Bodhisattwa Chaudhuri
标识
DOI:10.1016/j.ijpharm.2021.120713
摘要
The current study utilized an artificial neural network (ANN) to generate computational models to achieve process optimization for a previously developed continuous liposome manufacturing system. The liposome formation was based on a continuous manufacturing system with a co-axial turbulent jet in a co-flow technology. The ethanol phase with lipids and aqueous phase resulted in liposomes of homogeneous sizes. The input features of the ANN included critical material attributes (CMAs) (e.g., hydrocarbon tail length, cholesterol percent, and buffer type) and critical process parameters (CPPs) (e.g., solvent temperature and flow rate), while the ANN outputs included critical quality attributes (CQAs) of liposomes (i.e., particle size and polydispersity index (PDI)). Two common ANN architectures, multiple-input-multiple-output (MIMO) models and multiple-input–single-output (MISO) models, were evaluated in this study, where the MISO outperformed MIMO with improved accuracy. Molecular descriptors, obtained from PaDEL-Descriptor software, were used to capture the physicochemical properties of the lipids and used in training of the ANN. The combination of CMAs, CPPs, and molecular descriptors as inputs to the MISO ANN model reduced the training and testing mean relative error. Additionally, a graphic user interface (GUI) was successfully developed to assist the end-user in performing interactive simulated risk analysis and visualizing model predictions.
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