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
Highlights
The manufacture of pharmaceuticals is a complicated process from formulation to the finished product
artificial neural network (ANN) methodology is very different from standard statistical analysis methods because it is based on an experimental model of the data-processing methods of a biological brain
While the task of establishing a central model is undertaken by the neural network element, the genetic algorithm, fuzzy logic and pre-trained models are used for optimisation of formulation [2]
Summary
The manufacture of pharmaceuticals is a complicated process from formulation to the finished product. To design an experimental space for the required data, computerised systems such as artificial neural network ANN, genetic algorithms (GAs), and fuzzy logic are essential. Fuzzy logic is widely used in process control because the related reliability level associated with the membership functions for a set, which is described as IF (A) (B), allows the statement of rules in plain terms. In our study, using commercially available excipients for fast disintegrating oral preparations from two different companies; Parteck® ODT (Dmannitol and croscarmellose sodium) and Ludiflash® (mannitol, crospovidone and polyvinyl acetate), OND-containing ODT formulations were developed. Thereafter, the relationships between the formulation and process parameters (disintegrant type and amount, compression pressure) and the target product properties (tablet hardness, friability and disintegration time) and the pharmaceutically acceptable ODT formulation were determined using ANN models. Parteck® was from Merck Co (Germany), and magnesium stearate was from FACI (Genoa, Italy)
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