Application of Neural Computing in Pharmaceutical Product Development View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1991-10

AUTHORS

Ajaz S. Hussain, Xuanqiang Yu, Robert D. Johnson

ABSTRACT

Neural computing technology is capable of solving problems involving complex pattern recognition. This technology is applied here to pharmaceutical product development. The most commonly used computational algorithm, the delta back-propagation network, was utilized to recognize the complex relationship between the formulation variables and the in vitro drug release parameters for a hydrophilic matrix capsule system. This new computational technique was also compared with the response surface methodology (RSM). Artificial neural network (ANN) analysis was able to predict the response values for a series of validation experiments more precisely than RSM. ANN may offer an alternative to RSM because it allows for the development of a system that can incorporate literature and experimental data to solve common problems in the pharmaceutical industry. More... »

PAGES

1248-1252

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1023/a:1015843527138

DOI

http://dx.doi.org/10.1023/a:1015843527138

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1002628904

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/1796042


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