Modelling formulations using - artificial neural networks

Eur J Pharm Sci. 2011 Aug 28. [Epub ahead of print]
Modelling formulations using gene expression programming - A comparative analysis with artificial neural networks.Colbourn EA, Roskilly SJ, Rowe RC, York P.
SourceIntelligensys Ltd., Springboard Business Centre, Ellerbeck Way, Stokesley TS9 5JZ, UK; Formulating Knowledge Ltd., Springboard Business Centre, Ellerbeck Way, Stokesley TS9 5JZ, UK.

AbstractThis study has investigated the utility and potential advantages of gene expression programming (GEP) - a new development in evolutionary computing for modelling data and automatically generating equations that describe the cause-and-effect relationships in a system- to four types of pharmaceutical formulation and compared the models with those generated by neural networks, a technique now widely used in the formulation development. Both methods were capable of discovering subtle and non-linear relationships within the data, with no requirement from the user to specify the functional forms that should be used. Although the neural networks rapidly developed models with higher values for the ANOVA R(2) these were black box and provided little insight into the key relationships. However, GEP, although significantly slower at developing models, generated relatively simple equations describing the relationships that could be interpreted directly. The results indicate that GEP can be considered an effective and efficient modelling technique for formulation data.
Copyright © 2011 Elsevier B.V. All rights reserved.

PMID:21903163[PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/pubmed/21903163

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