Linked Life Data

18571348

Source:http://linkedlifedata.com/resource/pubmed/id/18571348

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1-2
pubmed:dateCreated
2008-8-4
pubmed:abstractText
The objective of this study was to develop artificial neural network (ANN) model suitable to predict successfully the borders of the microemulsion region in the quaternary system PEG-8 caprylic/capric glycerides (Labrasol)/cosurfactant/isopropyl myristate/water, in order to minimise experimental effort. In our preliminary investigations of phase behaviour, two cosurfactants were used, PEG-40 hydrogenated castor oil (Cremophor) RH 40) and polyglyceryl-6 isostearate (Plurol Isostearique). Microemulsion existance area in pseudo-ternary phase diagrams was determined using titration method at constant: (a) oil-to-water ratio (alpha=50%, w/w); (b) surfactant-to-cosurfactant ratio (Km) 4:6; (c) Km 5:5; or (d) Km 6:4. It was found that the phase behaviour of systems involving polyoxyethylene type of cosurfactant depends significantly on oil-to-surfactant/cosurfactant mixture mass ratio (O/SCoS) but it is Km-independent. The formation of microemulsions in Labrasol/polyglyceryl-6 isostearate based systems was a complex function of Km and O/SCoS and there was employed a Generalized Regression Neural Network (GRNN) with four layers as a predictive mathematical model, using data obtained from the phase behaviour study (the surfactant concentration in surfactant/cosurfactant mixture (S, %, w/w), the oil concentration in the mixture with tensides (O, %, w/w) as two input variables, and the water solubilization limit (W(max), %, w/w) as output data). After network training, six independent pairs of input/output data were used for network testing. The resulting GRNN was tested statistically and found to be of quality predictive power. This results confirmed that the trained GRNN could be effective in predicting the size of the microemulsion area providing valuable tool in formulation of this type of colloidal vehicles.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Sep
pubmed:issn
0378-5173
pubmed:author
pubmed:issnType
Print
pubmed:day
1
pubmed:volume
361
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
41-6
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
The application of artificial neural networks in the prediction of microemulsion phase boundaries in PEG-8 caprylic/capric glycerides based systems.
pubmed:affiliation
Department of Pharmaceutical Technology and Cosmetology, Faculty of Pharmacy, Vojvode Stepe 450, P.O. Box 146, 11221 Belgrade, Serbia. ljiki@pharmacy.bg.ac.yu
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't