Optimization of crushing strength and disintegration time of a high-dose plant extract tablet by neural networks.

Abstract

Optimization of crushing strength and disintegration time of a high-dose plant extract tablet was reached after extensive experimentation. Effects of the processing parameters, like compression force and tooling, and also of the excipients were found to be significant. Best results for both disintegration time and crushing strength were obtained with a plant extract that was granulated by roller compaction before compression. To gain more information about the different effects, artificial neural networks (ANNs) and a conventional multivariate method (partial least squares [PLS]) were used for data analysis. The topologies of the neural networks of the feed-forward type were optimized manually and by pruning methods. All methods were tested for contemplated parameters, crushing strength, and disintegration time. In general, ANNs were found to be more successful in characterizing the effects that influence crushing strength and disintegration time than the conventional multivariate methods.

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@article{Rocksloh1999OptimizationOC, title={Optimization of crushing strength and disintegration time of a high-dose plant extract tablet by neural networks.}, author={K Rocksloh and F. Rapp and S Abu Abed and Wolfhart M{\"{u}ller and Michael Reher and G{\"{u}nther Gauglitz and Peter Christian Schmidt}, journal={Drug development and industrial pharmacy}, year={1999}, volume={25 9}, pages={1015-25} }