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Transport Properties of Ibuprofen Encapsulated in Cyclodextrin Nanosponge Hydrogels: A Proton HR-MAS NMR Spectroscopy Study
Published on: August 15, 2016
The difference between partitioning and distribution from a thermodynamic point of view: NSAIDs as an example
German L Perlovich1, Sergey V Kurkov, Annette Bauer-Brandl
1University of Tromsø, Institute of Pharmacy, Breivika, N-9037 Tromsø, Norway.
Abstract:
Solubility and solvation of some NSAIDs were studied in their non-ionic (aqueous buffers of pH 2.0) and ionic molecular form (pH 7.4) over a wide temperature interval. Absolute scale values for the thermodynamic terms (Gibbs energy, enthalpy and entropy) were obtained. Thermodynamic parameters of the transfer of the molecules from one buffer to the other (representing protonation/deprotonation) were derived. It has been found that the thermodynamic characteristics of solvation (hydration) of (+)- and (+/-)-IBP in the buffers show a difference, which is larger than the experimental error. This may be explained by differences in the association states of the molecules in solution. For the other NSAIDs studied, a correlation between the Gibbs energy of transfer, deltaG(tr) (pH 7.4-->pH 2.0) and the pK(a)-value, and a compensation effect between the enthalpic and entropic terms have been revealed. Thermodynamic aspects of the transfer process from the buffers to n-octanol were analysed. The two types of the transfer processes (non-dissociated molecule to octanol (partitioning), and dissociated form to octanol (distribution)) have essentially different driving forces: partitioning is enthalpy driven, whereas the transfer of the ionic form is entropy driven. The following points are discussed: (a) significance of using water-octanol systems (logP as a measure of drug lipophilicity) to describe biological membranes (lipid systems); (b) differences in thermodynamic aspects of the partitioning/distribution processes of these systems; (c) advantages of the present transfer method approach in comparison with temperature dependencies of logP to analyse the driving forces of partitioning/distribution.
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