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Tropical Journal of Pharmaceutical Research
Pharmacotherapy Group, Faculty of Pharmacy, University of Benin, Benin City, Nigeria
ISSN: 1596-5996
EISSN: 1596-9827
Vol. 13, No. 9, 2014, pp. 1374-1383
Bioline Code: pr14190
Full paper language: English
Document type: Research Article
Document available free of charge

Tropical Journal of Pharmaceutical Research, Vol. 13, No. 9, 2014, pp. 1374-1383

 en Optimisation of Ondansetron Orally Disintegrating Tablets Using Artificial Neural Networks
Aksu, Buket; Yegen, Gizem; Purisa, Sevim; Cevher, Erdal & Ozsoy, Yıldız

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 logic; Artificial neural network

 
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