Document Detail

Evolutionary artificial neural networks as tools for predicting the internal structure of microemulsions.
MedLine Citation:
PMID:  18445365     Owner:  NLM     Status:  MEDLINE    
PURPOSE: The purpose of this study was to predict microemulsion structures by creating two artificial evolutionary neural networks (ANN) combined with a genetic algorithm. The first ANN would be able to determine the type of microemulsion from the desired composition, and the second to determine the type of microemulsion directly from a differential scanning calorimetry (DSC) curve. METHODS: The algorithms and the structures for each ANN were constructed and programmed in C++ computer language. The ANNs had a feedforward structure with one hidden level and were trained using a genetic algorithm. DSC was used to determine the microemulsion type. RESULTS: The ANNs showed very encouraging accuracy in predicting the microemulsion type from its composition and also directly from the DSC curve. The percentage success, calculated over the tested data, was over 90%. This enabled us, with satisfactory accuracy, to construct several pseudoternary diagrams that could facilitate the selection of the microemulsion composition to obtain the optimal desired drug carrier. CONCLUSIONS: The ANN constructed here, enhanced with a genetic algorithm, is an effective tool for predicting the type of microemulsion. These findings provide the basis for reducing research time and development cost for characterizing microemulsion properties. Its application would stimulate the further development of such colloidal drug delivery systems, exploit their advantages and, to a certain extent, avoid their disadvantages.
M Gasperlin; F Podlogar; R Sibanc
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Publication Detail:
Type:  Comparative Study; Journal Article    
Journal Detail:
Title:  Journal of pharmacy & pharmaceutical sciences : a publication of the Canadian Society for Pharmaceutical Sciences, Société canadienne des sciences pharmaceutiques     Volume:  11     ISSN:  1482-1826     ISO Abbreviation:  J Pharm Pharm Sci     Publication Date:  2008  
Date Detail:
Created Date:  2008-04-30     Completed Date:  2008-10-13     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9807281     Medline TA:  J Pharm Pharm Sci     Country:  Canada    
Other Details:
Languages:  eng     Pagination:  67-76     Citation Subset:  IM    
University of Ljubljana, Faculty of Pharmacy, 1000 Ljubljana, Askerceva 7, SI-Slovenia.
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MeSH Terms
Chemistry, Pharmaceutical / methods
Emulsions / chemistry*,  pharmacokinetics
Models, Genetic*
Neural Networks (Computer)*
Predictive Value of Tests
Reg. No./Substance:

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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