<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Niederquell A</dc:creator>
  <dc:creator>Wyttenbach N</dc:creator>
  <dc:creator>Kuentz M</dc:creator>
  <dc:date>2018</dc:date>
  <dc:description xmlns:ns0="xml" ns0:lang="en">Solubility 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>
  <dc:identifier>https://sonar.ch/global/documents/62604</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ijpharm.2018.05.033</dc:relation>
  <dc:relation>info:eu-repo/semantics/altIdentifier/pmid/29772285</dc:relation>
  <dc:source>International journal of pharmaceutics. - 2018</dc:source>
  <dc:subject xmlns:ns1="xml" ns1:lang="en">Conductor-like screening model</dc:subject>
  <dc:subject xmlns:ns2="xml" ns2:lang="en">Drugs</dc:subject>
  <dc:subject xmlns:ns3="xml" ns3:lang="en">In silico prediction</dc:subject>
  <dc:subject xmlns:ns4="xml" ns4:lang="en">Inverse gas chromatography</dc:subject>
  <dc:subject xmlns:ns5="xml" ns5:lang="en">Quantitative structure property relationship</dc:subject>
  <dc:subject xmlns:ns6="xml" ns6:lang="en">Solubility parameter</dc:subject>
  <dc:subject xmlns:ns7="xml" ns7:lang="en">Chromatography, Gas</dc:subject>
  <dc:subject xmlns:ns8="xml" ns8:lang="en">Computer Simulation</dc:subject>
  <dc:subject xmlns:ns9="xml" ns9:lang="en">Models, Molecular</dc:subject>
  <dc:subject xmlns:ns10="xml" ns10:lang="en">Pharmaceutical Preparations</dc:subject>
  <dc:subject xmlns:ns11="xml" ns11:lang="en">Quantitative Structure-Activity Relationship</dc:subject>
  <dc:subject xmlns:ns12="xml" ns12:lang="en">Solubility</dc:subject>
  <dc:subject xmlns:ns13="xml" ns13:lang="en">Solvents</dc:subject>
  <dc:title xmlns:ns14="xml" ns14:lang="en">New prediction methods for solubility parameters based on molecular sigma profiles using pharmaceutical materials.</dc:title>
  <dc:type>http://purl.org/coar/resource_type/c_6501</dc:type>
</oai_dc:dc>