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Analysis of pellet properties with use of artificial neural networks
artificial neural networks
neuro-fuzzy systems
pellets
data-mining
pharmaceutical technology
computational intelligence
The objective was to prepare neural models identifying relationships between formulation characteristics and pellet properties based on algorithmic approach of crucial variables selection and neuro-fuzzy systems application. The database consisted of information about 227 pellet formulations prepared by extrusion/spheronization method, with various model drugs and excipients. Cheminformatic description of excipients and model drugs was employed for numerical description of pellet formulations. Initial numbers of neural model inputs were up to around 3000. The inputs reduction procedure based on sensitivity analysis allowed to obtain less than 40 inputs for each model. The reduced models were subjects of fuzzy logic implementation resulting in logical rules tables providing human-readable rule sets applicable in future development of pellet formulations. Neural modeling enhanced knowledge about pelletization process and provided means for future computer-guided search for the optimal formulation.
cris.lastimport.scopus | 2024-04-07T16:09:57Z | |
dc.abstract.en | The objective was to prepare neural models identifying relationships between formulation characteristics and pellet properties based on algorithmic approach of crucial variables selection and neuro-fuzzy systems application. The database consisted of information about 227 pellet formulations prepared by extrusion/spheronization method, with various model drugs and excipients. Cheminformatic description of excipients and model drugs was employed for numerical description of pellet formulations. Initial numbers of neural model inputs were up to around 3000. The inputs reduction procedure based on sensitivity analysis allowed to obtain less than 40 inputs for each model. The reduced models were subjects of fuzzy logic implementation resulting in logical rules tables providing human-readable rule sets applicable in future development of pellet formulations. Neural modeling enhanced knowledge about pelletization process and provided means for future computer-guided search for the optimal formulation. | pl |
dc.affiliation | Wydział Farmaceutyczny : Zakład Technologii Postaci Leku i Biofarmacji | pl |
dc.contributor.author | Mendyk, Aleksander - 130937 | pl |
dc.contributor.author | Kleinebudde, Peter | pl |
dc.contributor.author | Thommes, Markus | pl |
dc.contributor.author | Yoo, Angelina | pl |
dc.contributor.author | Szlęk, Jakub - 162262 | pl |
dc.contributor.author | Jachowicz, Renata - 129780 | pl |
dc.date.accessioned | 2015-01-16T11:55:34Z | |
dc.date.available | 2015-01-16T11:55:34Z | |
dc.date.issued | 2010 | pl |
dc.description.number | 3-4 | pl |
dc.description.physical | 421-429 | pl |
dc.description.volume | 41 | pl |
dc.identifier.doi | 10.1016/j.ejps.2010.07.010 | pl |
dc.identifier.eissn | 1879-0720 | pl |
dc.identifier.issn | 0928-0987 | pl |
dc.identifier.uri | http://ruj.uj.edu.pl/xmlui/handle/item/2581 | |
dc.language | eng | pl |
dc.language.container | eng | pl |
dc.rights | Dodaję tylko opis bibliograficzny | * |
dc.rights.uri | * | |
dc.subject.en | artificial neural networks | pl |
dc.subject.en | neuro-fuzzy systems | pl |
dc.subject.en | pellets | pl |
dc.subject.en | data-mining | pl |
dc.subject.en | pharmaceutical technology | pl |
dc.subject.en | computational intelligence | pl |
dc.subtype | Article | pl |
dc.title | Analysis of pellet properties with use of artificial neural networks | pl |
dc.title.journal | European Journal of Pharmaceutical Sciences | pl |
dc.type | JournalArticle | pl |
dspace.entity.type | Publication |