New prediction methods for solubility parameters based on molecular sigma profiles using pharmaceutical materials
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Autor:innen
Autor:in (Körperschaft)
Publikationsdatum
07/2018
Typ der Arbeit
Studiengang
Sammlung
Typ
01A - Beitrag in wissenschaftlicher Zeitschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
International Journal of Pharmaceuticals
Themenheft
DOI der Originalpublikation
Link
Reihe / Serie
Reihennummer
Jahrgang / Band
546
Ausgabe / Nummer
1-2
Seiten / Dauer
137-144
Patentnummer
Verlag / Herausgebende Institution
Bioinfo Publications
Verlagsort / Veranstaltungsort
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
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.
Schlagwörter
drugs, solubility parameter, in silico prediction, conductor-like screening model, quantitative structure property relationship, inverse gas chromatography
Fachgebiet (DDC)
Veranstaltung
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
ISSN
0975-3079
0975-9190
0975-9190
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
Peer-Review der ganzen Publikation
Open Access-Status
Lizenz
Zitation
NIEDERQUELL, Andreas, Nicole WYTTENBACH und Martin KUENTZ, 2018. New prediction methods for solubility parameters based on molecular sigma profiles using pharmaceutical materials. International Journal of Pharmaceuticals. Juli 2018. Bd. 546, Nr. 1-2, S. 137–144. DOI 10.1016/j.ijpharm.2018.05.033. Verfügbar unter: http://hdl.handle.net/11654/26966