Application of Physicochemical Properties and Process Parameters in the Development of a Neural Network Model for Prediction of Tablet Characteristics
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The importance of in silico modeling in the pharmaceutical industry is continuously increasing. The aim of the present study was the development of a neural network model for prediction of the postcompressional properties of scored tablets based on the application of existing data sets from our previous studies. Some important process parameters and physicochemical characteristics of the powder mixtures were used as training factors to achieve the best applicability in a wide range of possible compositions. The results demonstrated that, after some pre-processing of the factors, an appropriate prediction performance could be achieved. However, because of the poor extrapolation capacity, broadening of the training data range appears necessary.
Key wordsartificial neural network mechanical properties plasticity surface characteristics tablet
The work was supported by the Project named “TÁMOP-4.2.1/B-09/1/KONV-2010-0005, –creating the Center of Excellence at the University of Szeged” is supported by the European Union and co-financed by the European Regional Fund and by the bilateral Hungarian-Slovenian Science and Technology Transfer Project SI-17/2009.
- 1.Bourquin J, Schmidli H, van Hoogevest P, Leuenberger H. Advantages of Artificial Neural Networks (ANNs) as alternative modelling technique for data sets showing non-linear relationships using data from a galenical study on a solid dosage form. Eur J Pharm Sci. 1998;7:5–16.CrossRefPubMedGoogle Scholar
- 9.Onuki Y, Kawai S, Arai H, Maedea J, Takakagi K, Takayama K. Contribution of the physicochemical properties of active pharmaceutical ingredients to tablet properties identified by ensemble artificial neural networks and Kohonen’s self organizing maps. J Pharm Sci. 2010;101:2372–81.CrossRefGoogle Scholar
- 14.Stamm A, Mathis C. Verprelssbarkeit von festen Hilf- stoffen für Direkttablettierung. Acta Pharm Technol. 1976;22:7–16.Google Scholar
- 17.Schiffmann W, Joost M, Werner R. Comparison of optimized backpropagation algorithms Proc. of ESANN’93, Brussels 1993.Google Scholar