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Original Research Article | OPEN ACCESS

Optimisation of Ondansetron Orally Disintegrating Tablets Using Artificial Neural Networks

Buket Aksu1 , Gizem Yegen2, Sevim Purisa3, Erdal Cevher2, Yıldız Ozsoy2

1Santa Farma Drug Pharmaceuticals, Boruçiçeği Sokak, #0;i#1;li-Istanbul; 2Department of Pharmaceutical Technology, Faculty of Pharmacy; 3Department of Biostatistics, Faculty of Medicine, Istanbul University, Istanbul, Turkey.

For correspondence:-  Buket Aksu   Email: baksu@santafarma.com.tr   Tel:+902122206400

Received: 24 April 2014        Accepted: 29 July 2014        Published: 24 September 2014

Citation: Aksu B, Yegen G, Purisa S, Cevher E, Ozsoy Y. Optimisation of Ondansetron Orally Disintegrating Tablets Using Artificial Neural Networks. Trop J Pharm Res 2014; 13(9):1374-1383 doi: 10.4314/tjpr.v13i9.1

© 2014 The authors.
This is an Open Access article that uses a funding model which does not charge readers or their institutions for access and distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0) and the Budapest Open Access Initiative (http://www.budapestopenaccessinitiative.org/read), which permit unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited..

Abstract

Purpose: To investigate the impact of critical quality attributes (CQAs) and critical process parameters (CPPs) on quality target product profile (QTPP) attributes of orally disintegrating tablet (ODT) containing ondansetron (OND) using two artificial neural network (ANN) programs.
Methods: Different amounts of two different commercial superdisintegrants commonly used in ODT formulations (Ludiflash® and Parteck®) were examined as CQAs, while three different tablet-pressing forces were evaluated as CPPs for an orally disintegrating tablet (ODT) formulation. The impact of CQAs, and CPPs on the target product profile (tablet hardness, friability and disintegration time) were analysed using gene expression programming (GEP) and neuro-fuzzy logic (NFL) models.
Results: NFL model defined the relations between CQAs, CPPs and QTPP, while GEP model favoured the use of an ODT formulation with suitable QTPP features which contained 4 mg ondansetron, 21.30 mg Parteck®, and 119 mg Avicel, fabricated with a compression force of 515 psi. In this regard, the tablet formulation demonstrated the required specifications.
Conclusion: ANN programs are a useful tool for research and development (R&D) studies in the pharmaceutical industry and the use of ANNs can be beneficial in terms of raw materials, time and cost, as demonstrated for ondansetron ODT tablets.

Keywords: Ondansetron, Critical quality attributes, Critical process parameters, Quality target product profile, Gene expression programming, Neuro-fuzzy log

Impact Factor
Thompson Reuters (ISI): 0.523 (2021)
H-5 index (Google Scholar): 39 (2021)

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