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New prediction methods for solubility parameters based on molecular sigma profiles using pharmaceutical materials.
Journal article

New prediction methods for solubility parameters based on molecular sigma profiles using pharmaceutical materials.

  • Niederquell A Institute of Pharma Technology, University of Applied Sciences and Arts Northwestern Switzerland, 4132 Muttenz, Switzerland.
  • Wyttenbach N Roche Pharmaceutical Research & Early Development, Pre-Clinical CMC, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, Basel, Switzerland.
  • Kuentz M Institute of Pharma Technology, University of Applied Sciences and Arts Northwestern Switzerland, 4132 Muttenz, Switzerland. Electronic address: martin.kuentz@fhnw.ch.
  • 2018-05-18
Published in:
  • International journal of pharmaceutics. - 2018
Conductor-like screening model
Drugs
In silico prediction
Inverse gas chromatography
Quantitative structure property relationship
Solubility parameter
Chromatography, Gas
Computer Simulation
Models, Molecular
Pharmaceutical Preparations
Quantitative Structure-Activity Relationship
Solubility
Solvents
English 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.
Language
  • English
Open access status
closed
Identifiers
Persistent URL
https://sonar.ch/global/documents/62604
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