The application of artificial neural network in dosage form development of bi-layer floating tablets of baclofen

dc.contributor.author	Patel, Rakesh P.	en
dc.contributor.author	Shah, Devarshi	en
dc.contributor.author	Patel, Manisha	en
dc.date.accessioned	2015-02-01T20:10:20Z
dc.date.available	2015-02-01T20:10:20Z
dc.date.issued	2015-02-01
dc.identifier.uri	http://hdl.handle.net/11400/5378
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://e-jst.teiath.gr/	en
dc.subject	PEO WSR N 10
dc.subject	HPMC K 4M
dc.subject	Artificial neural network (ANN)
dc.subject	Τεχνητό νευρωνικό δίκτυο (ΤΝΔ)
dc.subject	Baclofen
dc.subject	Βακλοφένη
dc.subject	Bilayer floating tablet
dc.subject	Διστοιβάδα επιπλέοντος δισκίου
dc.title	The application of artificial neural network in dosage form development of bi-layer floating tablets of baclofen	en
heal.type	journalArticle
heal.classification	Medicine
heal.classification	Pharmacy
heal.classification	Ιατρική
heal.classification	Φαρμακευτική
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	N/A-Pharmacy
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Φαρμακευτική
heal.language	en
heal.access	free
heal.publicationDate	2011
heal.bibliographicCitation	Patel, R.P., Shah, D. and Patel, M. (2011). The application of artificial neural network in dosage form development of bi-layer floating tablets of baclofen. "e-Journal of Science & Technology". [Online] 6(1): 1-18. Available from: http://e-jst.teiath.gr/	en
heal.abstract	The aim of the present investigation was to prepare & evaluate bilayer floating tablet of Baclofen. In which one layer was made up of immediate release layer to provide loading dose, while another layer was made up of floating layer of BCF to provide maintenance dose. The focus of present work was to prepare and evaluate bilayer floating tablet of Baclofen to increase residence time in stomach and there by gives prolog action. Tablets were prepared by wet granulation technique. Drug-excipients compatibility study was done by using Fourier transform infrared spectroscopy (FTIR). Optimization was carried out using artificial neural network (ANN) and multiple regression analysis using 32 factorial designs. Cumulative percentage release (CPR) at 24 hr, time required for 50% of drug release (T50%), floating lag time study of the tablet formulations were selected as dependent variables. The Content of HPMC K 4M (X1) and Content of PEO WSR N 10 (X2) were selected as independent variables. Tablets were evaluated for swelling index, in vitro buoyancy and in vitro drug release. The similarity factor (f2) was used as a base to compare dissolution profiles. Optimized batch was subjected for kinetic modeling. Different process parameters of optimized batch were also studied. From FTIR spectra it was observed that there were no any interaction between drug and excipients used. The results demonstrate that 3.5% of crosspovidone released 99% of drug in 20 minutes. It was found that HPMC K 4M with concentration 30% and PEO WSR N 10 with concentration 20% showed good sustained as well floating ability and its releases 99.39% of drug within 24 hrs. Drug release was best explained by Higuchi plot. It was seen that the process parameters have great influence on performance of bilayer floating tablet. To check the accuracy of these predictions, experimentally three formulations were prepared by random selection of causal factors as per counter plot and also validated ANN. The experimental data were compared with predicted data by paired t test, no statistically significant difference was observed. ANN showed less error compared with multiple regression analysis. These findings demonstrate that ANN provides more accurate prediction and was quite useful in the optimization of pharmaceutical formulations than the multiple regression analysis method.	en
heal.publisher	Νερατζής, Ηλίας	el
heal.publisher	Σιανούδης, Ιωάννης	el
heal.journalName	e-Journal of Science & Technology	en
heal.journalName	e-Περιοδικό Επιστήμης & Τεχνολογίας	el
heal.journalType	peer-reviewed
heal.fullTextAvailability	true