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Predicting oral disintegrating tablet formulations by neural network techniques
Han, Run; Yang, Yilong; Li, Xiaoshan; Ouyang, Defang
2018-07
Source PublicationASIAN JOURNAL OF PHARMACEUTICAL SCIENCES
ISSN1818-0876
Volume13Issue:4Pages:336-342
AbstractOral disintegrating tablets (ODTs) are a novel dosage form that can be dissolved on the tongue within 3 min or less especially for geriatric and pediatric patients. Current ODT formulation studies usually rely on the personal experience of pharmaceutical experts and trial-and-error in the laboratory, which is inefficient and time-consuming. The aim of current research was to establish the prediction model of ODT formulations with direct compression process by artificial neural network (ANN) and deep neural network (DNN) techniques. 145 formulation data were extracted from Web of Science. All datasets were divided into three parts: training set (105 data), validation set (20) and testing set (20). ANN and DNN were compared for the prediction of the disintegrating time. The accuracy of the ANN model have reached 85.60%, 80.00% and 75.00% on the training set, validation set and testing set respectively, whereas that of the DNN model were 85.60%, 85.00% and 80.00%, respectively. Compared with the ANN, DNN showed the better prediction for ODT formulations. It is the first time that deep neural network with the improved dataset selection algorithm is applied to formulation prediction on small data. The proposed predictive approach could evaluate the critical parameters about quality control of formulation, and guide research and process development. The implementation of this prediction model could effectively reduce drug product development timeline and material usage, and proactively facilitate the development of a robust drug product. (c) 2018 Shenyang Pharmaceutical University. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license. (http://creativecommons.org/licenses/by-nc-nd/4.0/)
KeywordOral disintegrating tablets Formulation prediction Artificial neural network Deep neural network Deep-learning
DOI10.1016/j.ajps.2018.01.003
URLView the original
Indexed BySCI
Language英语
WOS Research AreaPharmacology & Pharmacy
WOS SubjectPharmacology & Pharmacy
WOS IDWOS:000441188300005
PublisherHONG KONG ASIAMED PUBLISH HOUSE
The Source to ArticleWOS
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Citation statistics
Cited Times [WOS]:6   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Recommended Citation
GB/T 7714
Han, Run,Yang, Yilong,Li, Xiaoshan,et al. Predicting oral disintegrating tablet formulations by neural network techniques[J]. ASIAN JOURNAL OF PHARMACEUTICAL SCIENCES,2018,13(4):336-342.
APA Han, Run,Yang, Yilong,Li, Xiaoshan,&Ouyang, Defang.(2018).Predicting oral disintegrating tablet formulations by neural network techniques.ASIAN JOURNAL OF PHARMACEUTICAL SCIENCES,13(4),336-342.
MLA Han, Run,et al."Predicting oral disintegrating tablet formulations by neural network techniques".ASIAN JOURNAL OF PHARMACEUTICAL SCIENCES 13.4(2018):336-342.
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