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Generally trained models to predict solubility of drugs in carbitol + water mixtures at various temperatures

dc.contributor.authorKhoubnasabjafari, M
dc.contributor.authorShayanfar, A
dc.contributor.authorMartinez, F
dc.contributor.authorAcree, WE
dc.contributor.authorJouyban, A
dc.date.accessioned2018-08-26T08:56:08Z
dc.date.available2018-08-26T08:56:08Z
dc.date.issued2016
dc.identifier.urihttp://dspace.tbzmed.ac.ir:8080/xmlui/handle/123456789/54400
dc.description.abstractThe trained versions of Jouyban-Acree and Yalkowsky models are proposed employing solubility data sets of 11 drugs in carbitol + water mixtures at various temperatures. Using these models, the solubility of a drug in the mono-solvents, the Abraham solvation parameters and log P value of the drug, its solubility in the binary solvent mixtures at various temperatures could be predicted. The mean percentage deviations for the correlated data were 137 and 20% respectively for the Yalkowsky and Jouyban-Acree models. é 2016 Elsevier B.V.
dc.language.isoEnglish
dc.relation.ispartofJournal of Molecular Liquids
dc.subjectBinary mixtures
dc.subjectMixtures
dc.subjectSolvation
dc.subjectAbraham solvation parameters
dc.subjectBinary solvent mixtures
dc.subjectCarbitol
dc.subjectCorrelated data
dc.subjectJouyban-Acree model
dc.subjectMean percentage
dc.subjectSolubility data
dc.subjectSolubility prediction
dc.subjectSolubility
dc.titleGenerally trained models to predict solubility of drugs in carbitol + water mixtures at various temperatures
dc.typeArticle
dc.citation.volume219
dc.citation.spage435
dc.citation.epage438
dc.citation.indexScopus
dc.identifier.DOIhttps://doi.org/10.1016/j.molliq.2016.03.043


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