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Acta Pharmaceutica, Vol. 66 No. 2, 2016.

Original scientific paper

https://doi.org/10.1515/acph-2016-0020

Development of ANN optimized mucoadhesive buccal tablet containing flurbiprofen and lidocaine for dental pain

AMJAD HUSSAIN ; University College of Pharmacy, University of the Punjab, Lahore, Pakistan
MUHAMMAD ALI SYED ; University College of Pharmacy, University of the Punjab, Lahore, Pakistan
NASIR ABBAS ; University College of Pharmacy, University of the Punjab, Lahore, Pakistan
SANA HANIF ; University College of Pharmacy, University of the Punjab, Lahore, Pakistan
MUHAMMAD SOHAIL ARSHAD ; Department of Pharmacy, Bahauddin Zakariya University, Multan, Pakistan
NADEEM IRFAN BUKHARI ; University College of Pharmacy, University of the Punjab, Lahore, Pakistan
KHALID HUSSAIN ; University College of Pharmacy, University of the Punjab, Lahore, Pakistan
MUHAMMAD AKHLAQ ; Departement of Pharmacy, Gomal University, Dera Ismail khan, Pakistan
ZEESHAN AHMAD ; Leicester School of Pharmacy, De Montfort University, Leicester, LE1 9BH, UK

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Abstract

A novel mucoadhesive buccal tablet containing flurbiprofen (FLB) and lidocaine HCl (LID) was prepared to relieve dental pain. Tablet formulations (F1-F9) were prepared using variable quantities of mucoadhesive agents, hydroxypropyl methyl cellulose (HPMC) and sodium alginate (SA). The formulations were evaluated for their physicochemical properties, mucoadhesive strength and mucoadhesion time, swellability index and in vitro release of active agents. Release of both drugs depended on the relative ratio of HPMC:SA. However, mucoadhesive strength and mucoadhesion time were better in formulations, containing higher proportions of HPMC compared to SA. An artificial neural network (ANN) approach was applied to optimise formulations based on known effective parameters (i.e., mucoadhesive strength, mucoadhesion time and drug release), which proved valuable. This study indicates that an effective buccal tablet formulation of flurbiprofen and lidocaine can be prepared via an optimized ANN approach.

Keywords

dental pain; mucoadhesion time; mucoadhesive strength; buccal tablet; artificial neural network

Hrčak ID:

153876

URI

https://hrcak.srce.hr/153876

Publication date:

30.6.2016.

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