Partial Solvation Parameters of Drugs as a New Thermodynamic Tool for Pharmaceutics

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.

From Quantum Chemistry to Prediction of Drug Solubility in Glycerides

“…Such poor predictions can not only be due to individual model failures but can also be caused by experimental issues as discussed in a recent article using partial solvation parameters to predict drug solubility in solvents. 48 In the current work, albendazole, dexamethasone, digitoxin, reserpine, saquinavir, and sulfasalazine showed high deviations for all COSMO-RS models. These compounds were found directly in the database, so their σ-profiles were not assembled from molecular fragments and were of the highest quality.…”

Synergistic Computational Modeling Approaches as Team Players in the Game of Solubility Predictions

“…In a study by Niederquell and Kuentz a dataset of 40 compounds were investigated for which the Abraham solvation descriptors were related to the solubility enhancement observed in FaSSIF compared to the corresponding buffer medium that is without the colloidal phase. 18 The solubility enhancement can be viewed as a solubilization factor and unlike other absolute solubility predictions, such an LFER approach does not need to predict solid state properties of a given drug. The study identified the positive effect of size (described as the McGowan's volume), acidity and tendency to donate protons whereas basicity, dipolarity/polarizability and excess molar refraction were negatively influencing the solubility enhancement.…”

“…Such descriptors for solubility models can be solubility parameters, 14 Abraham solvation parameters, 15,16 or partial solvation parameters. 17,18 These molecular descriptors can be used in both entirely mechanistic solubility predictions as well as theoryguided modeling to cope with complex media. 19 Table 1 also present model multiplicity as a categorization, which refers to whether a single method is applied or a combination of models is used to solve the task.…”

Machine Estimation of Drug Melting Properties and Influence on Solubility Prediction

“…Recent examples of machine learning (ML) in pharmaceutics have been reported in nanomedicine, solvate prediction of drugs, stability of solid dispersions, and development of hydrotrope formulations . It is not only modern data-driven approaches that are causing a computational revolution in pharmaceutics, there are also more applications of mechanistic modeling ranging from modern thermodynamic approaches − to physiologically based modeling of drug absorption. − Different modeling approaches can be integrated, for example, a drug absorption model can make use of either experimental input data, such as biorelevant drug solubility, or such values may be estimated computationally using only the chemical structure . Solubility estimation itself provides an example of such integrated modeling.…”