Predicting the optimum compositions of a parenteral nanoemulsion system loaded with azithromycin antibiotic utilizing the artificial neural network model
For the purpose of brain delivery via intravenous administration, the formulation of an azithromycin-loaded nanoemulsion system was optimized utilizing the artificial neural network (ANN) as a multivariate statistical technique. The input effective variables for nanoemulsion formulation were drug lo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Royal Society of Chemistry
2015
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| Online Access: | http://psasir.upm.edu.my/id/eprint/45930/ http://psasir.upm.edu.my/id/eprint/45930/1/Predicting%20the%20optimum%20compositions%20of%20a%20parenteral%20nanoemulsion%20system%20loaded%20with%20azithromycin%20antibiotic%20utilizing%20the%20artificial%20neural%20network%20model.pdf |
| _version_ | 1848850562267742208 |
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| author | S. Daood, Ghaidaa Basri, Hamidon Stanslas, Johnson Fard Masoumi, Hamid Reza Basri, Mahiran |
| author_facet | S. Daood, Ghaidaa Basri, Hamidon Stanslas, Johnson Fard Masoumi, Hamid Reza Basri, Mahiran |
| author_sort | S. Daood, Ghaidaa |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | For the purpose of brain delivery via intravenous administration, the formulation of an azithromycin-loaded nanoemulsion system was optimized utilizing the artificial neural network (ANN) as a multivariate statistical technique. The input effective variables for nanoemulsion formulation were drug loading, surfactant and co-surfactant content, concentration of glycerol, and concentration of vitamin E; the particle size was the output response, because size reduction will improve the stability of the nanoemulsion and the biological efficacy of the drug in vivo after parenteral administration. To achieve the optimum topologies, the ANN was trained by Incremental Back-Propagation (IBP), Batch Back-Propagation (BBP), Quick Propagation (QP), and Levenberg–Marquardt (LM) algorithms for testing data set. The topologies were confirmed by the indicator of minimized root mean squared error (RMSE) for each. Based on this indicator, BBP-5-14-1 was selected as the optimum topology to be used as a final model to predict the desirable particle size and relative importance of the effective variables of the formulation. The ANN analysis showed that the actual particle size (54.7 nm ± 0.8) of the formulated nanoemulsion was quite close to the predicted value (53.9 nm) obtained from the batch back propagation-ANN model, which supports the conclusion that the ANN model has the potential to predict a stable nanoemulsion system that could be used efficiently for the parenteral administration of azithromycin antibiotic.. |
| first_indexed | 2025-11-15T10:08:16Z |
| format | Article |
| id | upm-45930 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T10:08:16Z |
| publishDate | 2015 |
| publisher | Royal Society of Chemistry |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-459302022-05-19T04:41:21Z http://psasir.upm.edu.my/id/eprint/45930/ Predicting the optimum compositions of a parenteral nanoemulsion system loaded with azithromycin antibiotic utilizing the artificial neural network model S. Daood, Ghaidaa Basri, Hamidon Stanslas, Johnson Fard Masoumi, Hamid Reza Basri, Mahiran For the purpose of brain delivery via intravenous administration, the formulation of an azithromycin-loaded nanoemulsion system was optimized utilizing the artificial neural network (ANN) as a multivariate statistical technique. The input effective variables for nanoemulsion formulation were drug loading, surfactant and co-surfactant content, concentration of glycerol, and concentration of vitamin E; the particle size was the output response, because size reduction will improve the stability of the nanoemulsion and the biological efficacy of the drug in vivo after parenteral administration. To achieve the optimum topologies, the ANN was trained by Incremental Back-Propagation (IBP), Batch Back-Propagation (BBP), Quick Propagation (QP), and Levenberg–Marquardt (LM) algorithms for testing data set. The topologies were confirmed by the indicator of minimized root mean squared error (RMSE) for each. Based on this indicator, BBP-5-14-1 was selected as the optimum topology to be used as a final model to predict the desirable particle size and relative importance of the effective variables of the formulation. The ANN analysis showed that the actual particle size (54.7 nm ± 0.8) of the formulated nanoemulsion was quite close to the predicted value (53.9 nm) obtained from the batch back propagation-ANN model, which supports the conclusion that the ANN model has the potential to predict a stable nanoemulsion system that could be used efficiently for the parenteral administration of azithromycin antibiotic.. Royal Society of Chemistry 2015 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/45930/1/Predicting%20the%20optimum%20compositions%20of%20a%20parenteral%20nanoemulsion%20system%20loaded%20with%20azithromycin%20antibiotic%20utilizing%20the%20artificial%20neural%20network%20model.pdf S. Daood, Ghaidaa and Basri, Hamidon and Stanslas, Johnson and Fard Masoumi, Hamid Reza and Basri, Mahiran (2015) Predicting the optimum compositions of a parenteral nanoemulsion system loaded with azithromycin antibiotic utilizing the artificial neural network model. RSC Advances, 5 (101). pp. 82654-82665. ISSN 2046-2069 https://pubs.rsc.org/en/content/articlelanding/2015/ra/c5ra14913d 10.1039/c5ra14913d |
| spellingShingle | S. Daood, Ghaidaa Basri, Hamidon Stanslas, Johnson Fard Masoumi, Hamid Reza Basri, Mahiran Predicting the optimum compositions of a parenteral nanoemulsion system loaded with azithromycin antibiotic utilizing the artificial neural network model |
| title | Predicting the optimum compositions of a parenteral nanoemulsion system loaded with azithromycin antibiotic utilizing the artificial neural network model |
| title_full | Predicting the optimum compositions of a parenteral nanoemulsion system loaded with azithromycin antibiotic utilizing the artificial neural network model |
| title_fullStr | Predicting the optimum compositions of a parenteral nanoemulsion system loaded with azithromycin antibiotic utilizing the artificial neural network model |
| title_full_unstemmed | Predicting the optimum compositions of a parenteral nanoemulsion system loaded with azithromycin antibiotic utilizing the artificial neural network model |
| title_short | Predicting the optimum compositions of a parenteral nanoemulsion system loaded with azithromycin antibiotic utilizing the artificial neural network model |
| title_sort | predicting the optimum compositions of a parenteral nanoemulsion system loaded with azithromycin antibiotic utilizing the artificial neural network model |
| url | http://psasir.upm.edu.my/id/eprint/45930/ http://psasir.upm.edu.my/id/eprint/45930/ http://psasir.upm.edu.my/id/eprint/45930/ http://psasir.upm.edu.my/id/eprint/45930/1/Predicting%20the%20optimum%20compositions%20of%20a%20parenteral%20nanoemulsion%20system%20loaded%20with%20azithromycin%20antibiotic%20utilizing%20the%20artificial%20neural%20network%20model.pdf |