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dc.contributor.authorNiederquell, Andreas
dc.contributor.authorWyttenbach, Nicole
dc.contributor.authorKuentz, Martin
dc.date.accessioned2018-12-13T09:55:54Z
dc.date.available2018-12-13T09:55:54Z
dc.date.issued2018-07
dc.identifier.doihttps://doi.org/10.1016/j.ijpharm.2018.05.033
dc.identifier.urihttp://hdl.handle.net/11654/26966
dc.description.abstractSolubility parameters have been applied extensively in the chemical and pharmaceutical sciences. Particularly attractive is calculation of solubility parameters based on chemical structure and recently, new in silico methods have been proposed. Thus, screening charge densities of molecular surfaces (i.e. so-called σ-profiles) are used by the conductor-like screening model for real solvents (COSMO-RS) and can be employed in a quantitative structure property relationship (QSPR) to predict solubility parameters. In the current study, it was aimed to compare both in silico methods with an experimental dataset of pharmaceutical compounds, which was complemented with own measurements by inverse gas chromatography. An initial evaluation of the total solubility parameters of reference solvents resulted in excellent predictions (observed versus predicted values) with R2 of 0.855 (COSMO-RS) and 0.945 (QSPR). The subsequent main study of pharmaceutical compounds exhibited R2 values of 0.701 (COSMO-RS) and 0.717 (QSPR). The comparatively lower prediction was to some extent due to the solid state of pharmaceuticals with known conceptual limitations of the solubility parameter and possible experimental bias. Total solubility parameters were also estimated by classical group contribution methods, which had comparatively lower prediction power. Therefore, the new in silico methods are highly promising for pharmaceutical applications.
dc.description.urihttps://www.sciencedirect.com/science/article/pii/S0378517318303417?via%3Dihub
dc.language.isoen
dc.relation.ispartofInternational Journal of Pharmaceuticals
dc.accessRightsAnonymous
dc.subjectdrugs
dc.subjectsolubility parameter
dc.subjectin silico prediction
dc.subjectconductor-like screening model
dc.subjectquantitative structure property relationship
dc.subjectinverse gas chromatography
dc.titleNew prediction methods for solubility parameters based on molecular sigma profiles using pharmaceutical materials
dc.type01 - Zeitschriftenartikel, Journalartikel oder Magazin
dc.volume546
dc.issue1-2
dc.audienceScience
fhnw.publicationStatePublished
fhnw.ReviewTypeAnonymous ex ante peer review of a complete publication
fhnw.InventedHereYes
fhnw.PublishedSwitzerlandNo
fhnw.pagination137-144
fhnw.IsStudentsWorkno
fhnw.publicationOnlineJa


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