Analysis of pellet properties with use of artificial neural networks.

The objective was to prepare neural models identifying relationships between formulation characteristics and pellet properties based on algorithmic approach of crucial variables selection and neuro-fuzzy systems application. The database consisted of information about 227 pellet formulations prepared by extrusion/spheronization method, with various model drugs and excipients. Cheminformatic description of excipients and model drugs was employed for numerical description of pellet formulations. Initial numbers of neural model inputs were up to around 3000. The inputs reduction procedure based on sensitivity analysis allowed to obtain less than 40 inputs for each model. The reduced models were subjects of fuzzy logic implementation resulting in logical rules tables providing human-readable rule sets applicable in future development of pellet formulations. Neural modeling enhanced knowledge about pelletization process and provided means for future computer-guided search for the optimal formulation. Copyright © 2010 Elsevier B.V. All rights reserved.

Citation

Aleksander Mendyk, Peter Kleinebudde, Markus Thommes, Angelina Yoo, Jakub Szlęk, Renata Jachowicz. Analysis of pellet properties with use of artificial neural networks. European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences. 2010 Nov 20;41(3-4):421-9

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PMID: 20659554

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