Abstract
Partial solvation parameters (PSP) have much in common with the Hansen solubility parameter or with a linear solvation energy relationship (LSER), but there are advantages based on the sound thermodynamic basis. It is, therefore, surprising that PSP has so far not been harnessed in pharmaceutics for the selection of excipients or property estimation of formulations and their components. This work introduces PSP calculation for drugs, where the raw data were obtained from inverse gas chromatography. It was shown that only a few probe gases were needed to get reasonable estimates of the drug PSPs. Interestingly, an alternative calculation of LSER parameters in silico did not reflect the experimentally obtained activity coefficients for all probe gases as well, which was attributed to the complexity of the drug structures. The experimental PSPs were proven to be helpful in predicting drug solubility in various solvents and the PSP framework allowed calculation of the different surface energy contributions. A specific benefit of PSP is that parameters can be readily converted to either classical solubility or LSER parameters. Therefore, PSP is not just about a new definition of solvatochromic parameters, but the underlying thermodynamics provides a unified approach, which holds much promise for broad applications in pharmaceutics.
Highlights
The majority of drug formulations are complex multicomponent and often multiphase systems, and empirical approaches have traditionally been applied in the field of pharmaceutics
Partial solvation parameters (PSP) have much in common with the Hansen solubility parameter or with a linear solvation energy relationship (LSER), but there are advantages based on the sound thermodynamic basis
We summarize the definitions, the working equations, and the interrelations between partial solvation parameters (PSP) and LSER molecular descriptors
Summary
The majority of drug formulations are complex multicomponent and often multiphase systems, and empirical approaches have traditionally been applied in the field of pharmaceutics. PSP shares the versatility of HSP and LSER but has key advantages similar to the conductor-like screening model for real solvents (COSMO-RS) [22,23,24,25,26,27,28,29] and equation-of-state [30,31,32,33,34] approaches This similarity is important, since COSMO-RS [22,23,24,25,26,27,28,29] has been applied already for cocrystal screening and the solubility of various drugs and drug-like compounds (e.g., amides, methylxanthines, or phenolic acids), for drug solubility in ionic liquids (e.g., methotrexate or flavonoids), for excipient selection, and various other applications. Details may be found in the recent references [17,20,21], along with extensive tables with all the needed LSER descriptors
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