Application of Physicochemical Properties and Process Parameters in the Development of a Neural Network Model for Predic
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Research Article Application of Physicochemical Properties and Process Parameters in the Development of a Neural Network Model for Prediction of Tablet Characteristics Tamás Sovány,1 Kitti Papós,1 Péter Kása Jr.,1 Ilija Ilič,2 Stane Srčič,2 and Klára Pintye-Hódi1,3
Received 9 October 2012; accepted 4 February 2013; published online 15 February 2013 Abstract. 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 WORDS: artificial neural network; mechanical properties; plasticity; surface characteristics; tablet.
INTRODUCTION The use of in silico modeling in the pharmaceutical industry is continuously increasing. This is due in part to the quality by design approach to new pharmaceutical product developments, which requires exact and well-supported design of experiments. However, the quality of pharmaceutical products has a multifactorial background that is influenced by many parameters. The screening of appropriate factors is time-consuming and demands considerable financial outgoings. A decrease in the number of screening experiments through the use of artificial neural network (ANN) models for getting predictions based on previous data is of a great benefit (1). These systems demonstrate considerable advances over traditional factorial design of experiment (DoE) methods, including greater flexibility or their ability to handle a large number of input factors and to model nonlinear problems, which makes them a useful complementary method and/or extension of the DoE methods during the early pharmaceutical development by screening of the appropriate factors, and in the improvement of the production process via the processing and mining of data of the routine production (2). ANN models in basics mimic the structure and function of the human brain; they are adaptive, self-organizing and fault-tolerant. These principles make them able to accommodate to different problems, and hence ANNs are able to “learn”. Thanks to these properties,
ANNs demonstrate certain ability to predict the outcomes of a given data set. Their combination with other systems, such as neurofuzzy logic, leads to the added advantage of the generation of rule sets representing the cause–effect relationships contained in the experimental data (3). In recent years, there has been increasing interest in these systems with regard to formulation (4) or process opt
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